From c6ec21370047239057d0aedb16f733109377e971 Mon Sep 17 00:00:00 2001 From: George Moroz Date: Thu, 1 Aug 2024 20:48:36 +0300 Subject: [PATCH] finish 2. --- book/2_data_transformation.qmd | 131 ++- book/3_data_visualization.qmd | 1 - .../figure-html/unnamed-chunk-39-1.png | Bin 140881 -> 141068 bytes book/images/02_rstudio_pipe.png | Bin 0 -> 133678 bytes docs/2_data_transformation.html | 763 +++++++++++------- ...-chunk-29-1.png => unnamed-chunk-38-1.png} | Bin docs/5_working_with_texts.html | 2 +- .../figure-html/unnamed-chunk-39-1.png | Bin 140881 -> 141068 bytes docs/6_working_with_time.html | 2 +- docs/7_working_with_geodata.html | 48 +- docs/images/02_rstudio_pipe.png | Bin 0 -> 133678 bytes docs/search.json | 62 +- 12 files changed, 612 insertions(+), 397 deletions(-) create mode 100644 book/images/02_rstudio_pipe.png rename docs/2_data_transformation_files/figure-html/{unnamed-chunk-29-1.png => unnamed-chunk-38-1.png} (100%) create mode 100644 docs/images/02_rstudio_pipe.png diff --git a/book/2_data_transformation.qmd b/book/2_data_transformation.qmd index 474efa4..a8d076f 100644 --- a/book/2_data_transformation.qmd +++ b/book/2_data_transformation.qmd @@ -34,7 +34,11 @@ tibble(x = 1:100, ## Использование конвеера (пайпа) -В нашем коде мы часто будем использовать знаки конвеера (или пайпа): `|>` (также можно встретить популярный ранее `%>%`). Конвеер призван показывать последовательность действий. Сочетание клавиш для написание конвеера: `Ctrl/Cmd + M`. +В нашем коде мы часто будем использовать знаки конвеера (или пайпа): `|>` (также можно встретить популярный ранее `%>%`). Чтобы вставлять новый конвеер придется сделать изменение в настройках RStudio (Tools > Global Options > вкладвка Code): + +![](images/02_rstudio_pipe.png) + +Конвеер призван показывать последовательность действий. Сочетание клавиш для написание конвеера: `Ctrl/Cmd + Shift + M` (хотя RStudio позволяет горячие клавиши переназначить). ```{r} mean(sqrt(abs(sin(c(1:100, NA)))), na.rm = TRUE) @@ -115,35 +119,6 @@ zhadina |> slice_sample(prop = 0.3) ``` -## Функция `select()` - -Функция `select()` позволяет выбрать из датасета некоторый набор столбцов. - -```{r} -zhadina |> - select(word_1, type, n) -``` - -Внутри функции `select()` можно использовать функции, которые позволяют не только перечислять через запятую необходимые столбцы, но и выделять их по имени: - -```{r} -zhadina |> - select(starts_with("word")) - -zhadina |> - select(ends_with("1")) - -zhadina |> - select(matches("ord")) -``` - -Кроме того, внутри функции `select()` действует оператор двоеточие, который мы видели при создании векторов `5:10`: - -```{r} -zhadina |> - select(word_3:n) # от колонки word_3 до колонки n -``` - ## Функция `filter()` Функция `filter()` позволяет отфильтровывать строки таблицы по одному или нескольким условиям. @@ -237,6 +212,36 @@ zhadina |> arrange(word_3) ``` +## Функция `select()` + +Функция `select()` позволяет выбрать из датасета некоторый набор столбцов. + +```{r} +zhadina |> + select(word_1, type, n) +``` + +Внутри функции `select()` можно использовать функции, которые позволяют не только перечислять через запятую необходимые столбцы, но и выделять их по имени: + +```{r} +zhadina |> + select(starts_with("word")) + +zhadina |> + select(ends_with("1")) + +zhadina |> + select(matches("ord")) +``` + +Кроме того, внутри функции `select()` действует оператор двоеточие, который мы видели при создании векторов `5:10`: + +```{r} +zhadina |> + select(word_3:n) # от колонки word_3 до колонки n +``` + + ## Функция `mutate()` Функция `mutate()` позволяет создавать и изменять уже имеющиеся переменные: @@ -250,34 +255,90 @@ zhadina |> mutate(n = n^3) ``` +## Функция `relocate()` + +Функция `relocate()` позволяет перемещать переменные в таблице (по умолчанию в начало): + +```{r} +zhadina |> + relocate(type) +``` + +Если хочется перемещать в какое-то другое место, то можно использовать аргументы `.after` и `.before`: + +```{r} +zhadina |> + relocate(n, .after = word_3) + +zhadina |> + relocate(n, .before = type) +``` + ## Функция `group_by() |> summarise()` и `group_by() |> mutate()` +Все предыдущие операции работали со строками и столбцами таблицами. В этом разделе мы познакомимся с операцией группировки, которая позволяет разделять таблицу на группы и агрегировать информацию по этим группам. Функция `group_by()` разделяет на группы, но саму структуру и содержание таблицы не меняет, однако при печати выводится сообщение об имени группирующей переменной и сколько всего групп: +```{r} +zhadina |> + group_by(type) +``` + +После того как мы сгруппировали можно использовать функцию `summarise()` (или ее американский вариант `summarize()`), она возвращает по одному значению на каждую группу. Например, посчитаем среднее количество встречаемости для каждого типа: ```{r} zhadina |> group_by(type) |> summarise(mean_by_type = mean(n)) +``` + +Функций саммаризации может быть несколько: +```{r} zhadina |> group_by(type) |> summarise(mean_by_type = mean(n), median_by_type = median(n)) +``` +Кроме того, важно отметить, что группировка может происходить не по одной переменной, а по нескольким. + +```{r} zhadina |> group_by(type, word_3) |> summarise(mean_by_type = mean(n), median_by_type = median(n)) +``` + +Важно также отметить, что в функции `summarise()` с недавних пор аргумент `.by`, который является заменой функции `group_by()`: +```{r} zhadina |> - group_by(type, word_3) |> - mutate(mean_by_type = mean(n), - median_by_type = median(n)) + summarise(mean_by_type = mean(n), + median_by_type = median(n), + .by = c(type, word_3)) +``` + +Кроме того, функцию `group_by()` или аргумент `.by` можно использовать в связке с функцией `mutate()`. В таком случае саммаризирующий результат будет выведен не единожды для каждой группы, а присоединен к исходной таблице в виде новой переменной. +```{r} zhadina |> group_by(type) |> mutate(mean_by_type = mean(n), median_by_type = median(n)) + +zhadina |> + mutate(mean_by_type = mean(n), + median_by_type = median(n), + .by = type) +``` + +Не так часто, но все же бывает, что в большом длинном конвеере, который совмещает в себе много действий, группировка может мешать, так что ее можно снять функцией `ungroup()`: + +```{r} +zhadina |> + group_by(type) |> + mutate(mean_by_type = mean(n)) |> + ungroup() ``` ## Функция `across()` @@ -388,7 +449,9 @@ zhadina |> Синтаксис функций выглядит так (`A` и `B` --- датафреймы): -``` +```{r} +#| eval: false + A |> ..._join(B) ``` diff --git a/book/3_data_visualization.qmd b/book/3_data_visualization.qmd index bb29ee1..9aa0a33 100644 --- a/book/3_data_visualization.qmd +++ b/book/3_data_visualization.qmd @@ -1,6 +1,5 @@ # Визуализация данных - ## Зачем 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  • 2.1 tibble
  • 2.2 Использование конвеера (пайпа)
  • 2.3 Функции семейства slice()
    • -
  • 2.4 Функция select()
    • -
  • 2.5 Функция filter()
    • -
  • 2.6 Функции count(), distinct(), arrange()
    • +
  • 2.4 Функция filter()
    • +
  • 2.5 Функции count(), distinct(), arrange()
    • +
  • 2.6 Функция select()
  • 2.7 Функция mutate()
    • -
  • 2.8 Функция group_by() |> summarise() и group_by() |> mutate()
    • -
  • 2.9 Функция across()
    • -
  • 2.10 Функции семейства pivot_...()
    • -
  • 2.11 Функции семейства bind_...()
    • -
  • 2.12 Функции семейства ..._join()
    • +
  • 2.8 Функция relocate()
    • +
  • 2.9 Функция group_by() |> summarise() и group_by() |> mutate()
    • +
  • 2.10 Функция across()
    • +
  • 2.11 Функции семейства pivot_...()
    • +
  • 2.12 Функции семейства bind_...()
    • +
  • 2.13 Функции семейства ..._join()
    • @@ -288,15 +289,15 @@

    2.2 Использование конвеера (пайпа)

    -

    В нашем коде мы часто будем использовать знаки конвеера (или пайпа): |> (также можно встретить популярный ранее %>%). Конвеер призван показывать последовательность действий. Сочетание клавиш для написание конвеера: Ctrl/Cmd + M.

    +

    В нашем коде мы часто будем использовать знаки конвеера (или пайпа): |> (также можно встретить популярный ранее %>%). Чтобы вставлять новый конвеер придется сделать изменение в настройках RStudio (Tools > Global Options > вкладвка Code):

    +

    +

    Конвеер призван показывать последовательность действий. Сочетание клавиш для написание конвеера: Ctrl/Cmd + Shift + M (хотя RStudio позволяет горячие клавиши переназначить).

    mean(sqrt(abs(sin(c(1:100, NA)))), na.rm = TRUE)
    @@ -465,16 +468,16 @@ 

    zhadina |> 
   slice_sample(prop = 0.3)

    @@ -482,124 +485,22 @@

    -

    2.4 Функция select()

    -

    Функция select() позволяет выбрать из датасета некоторый набор столбцов.

    -
    -
    zhadina |> 
-  select(word_1, type, n)
    -
    -
    # A tibble: 26 × 3
-   word_1          type           n
-   <chr>           <chr>      <dbl>
- 1 жадина-говядина шоколадина   941
- 2 жадина-говядина шоколадина    44
- 3 жадина-говядина шоколадина   250
- 4 жадина-говядина огурец        11
- 5 жадина-говядина шоколадина  1117
- 6 жадина-говядина шоколадина    12
- 7 жадина-говядина шоколадина    29
- 8 жадина-говядина огурец       343
- 9 жадина-говядина шоколадина    10
-10 жадина-говядина другое        10
-# ℹ 16 more rows
    -
    -
    -

    Внутри функции select() можно использовать функции, которые позволяют не только перечислять через запятую необходимые столбцы, но и выделять их по имени:

    -
    -
    zhadina |> 
-  select(starts_with("word"))
    -
    -
    # A tibble: 26 × 3
-   word_1          word_2  word_3    
-   <chr>           <chr>   <chr>     
- 1 жадина-говядина большая шоколадина
- 2 жадина-говядина бука    шоколадина
- 3 жадина-говядина булка   шоколадина
- 4 жадина-говядина в *опе  огурец    
- 5 жадина-говядина в *опе  шоколадина
- 6 жадина-говядина гнилая  шоколадина
- 7 жадина-говядина дома    шоколадина
- 8 жадина-говядина зеленый огурец    
- 9 жадина-говядина злая    шоколадина
-10 жадина-говядина кислая  капуста   
-# ℹ 16 more rows
    -
    -
    zhadina |> 
-  select(ends_with("1"))
    -
    -
    # A tibble: 26 × 1
-   word_1         
-   <chr>          
- 1 жадина-говядина
- 2 жадина-говядина
- 3 жадина-говядина
- 4 жадина-говядина
- 5 жадина-говядина
- 6 жадина-говядина
- 7 жадина-говядина
- 8 жадина-говядина
- 9 жадина-говядина
-10 жадина-говядина
-# ℹ 16 more rows
    -
    -
    zhadina |> 
-  select(matches("ord"))
    -
    -
    # A tibble: 26 × 3
-   word_1          word_2  word_3    
-   <chr>           <chr>   <chr>     
- 1 жадина-говядина большая шоколадина
- 2 жадина-говядина бука    шоколадина
- 3 жадина-говядина булка   шоколадина
- 4 жадина-говядина в *опе  огурец    
- 5 жадина-говядина в *опе  шоколадина
- 6 жадина-говядина гнилая  шоколадина
- 7 жадина-говядина дома    шоколадина
- 8 жадина-говядина зеленый огурец    
- 9 жадина-говядина злая    шоколадина
-10 жадина-говядина кислая  капуста   
-# ℹ 16 more rows
    -
    -
    -

    Кроме того, внутри функции select() действует оператор двоеточие, который мы видели при создании векторов 5:10:

    -
    -
    zhadina |> 
-  select(word_3:n) # от колонки word_3 до колонки n
    -
    -
    # A tibble: 26 × 3
-   word_3     type           n
-   <chr>      <chr>      <dbl>
- 1 шоколадина шоколадина   941
- 2 шоколадина шоколадина    44
- 3 шоколадина шоколадина   250
- 4 огурец     огурец        11
- 5 шоколадина шоколадина  1117
- 6 шоколадина шоколадина    12
- 7 шоколадина шоколадина    29
- 8 огурец     огурец       343
- 9 шоколадина шоколадина    10
-10 капуста    другое        10
-# ℹ 16 more rows
    +1 жадина-говядина соленый помидор другое 17 +2 жадина-говядина никто ее не ест другое 10 +3 жадина-говядина кусок шоколадины шоколадина 113 +4 жадина-говядина кислая капуста другое 10 +5 жадина-говядина турецкий барабан барабан 3003 +6 жадина-говядина пустая шоколадина шоколадина 3774 +7 жадина-говядина простая шоколадина шоколадина 59
    -
    -

    2.5 Функция filter()

    +
    +

    2.4 Функция filter()

    Функция filter() позволяет отфильтровывать строки таблицы по одному или нескольким условиям.

    -
    zhadina |> 
-  filter(n > 100)
    +
    zhadina |> 
+  filter(n > 100)
    # A tibble: 9 × 5
   word_1          word_2   word_3     type           n
@@ -614,8 +515,8 @@
    -
    zhadina |> 
-  filter(n < 100)
    +
    zhadina |> 
+  filter(n < 100)
    # A tibble: 17 × 5
    word_1          word_2   word_3     type           n
@@ -638,8 +539,8 @@
    -
    zhadina |> 
-  filter(n <= 100) # меньше или равно
    +
    zhadina |> 
+  filter(n <= 100) # меньше или равно
    # A tibble: 17 × 5
    word_1          word_2   word_3     type           n
@@ -662,8 +563,8 @@
    -
    zhadina |> 
-  filter(n >= 100) # больше или равно
    +
    zhadina |> 
+  filter(n >= 100) # больше или равно
    # A tibble: 9 × 5
   word_1          word_2   word_3     type           n
@@ -678,8 +579,8 @@
    -
    zhadina |> 
-  filter(n != 10) # не равно
    +
    zhadina |> 
+  filter(n != 10) # не равно
    # A tibble: 23 × 5
    word_1          word_2  word_3     type           n
@@ -696,8 +597,8 @@
    -
    zhadina |> 
-  filter(n == 10) # равно (дважды знак равно)
    +
    zhadina |> 
+  filter(n == 10) # равно (дважды знак равно)
    # A tibble: 3 × 5
   word_1          word_2   word_3     type           n
@@ -706,8 +607,8 @@
    -
    zhadina |> 
-  filter(word_3 == "шоколадина")
    +
    zhadina |> 
+  filter(word_3 == "шоколадина")
    # A tibble: 13 × 5
    word_1          word_2  word_3     type           n
@@ -726,8 +627,8 @@
    -
    zhadina |> 
-  filter(word_3 != "шоколадина")
    +
    zhadina |> 
+  filter(word_3 != "шоколадина")
    # A tibble: 13 × 5
    word_1          word_2   word_3     type           n
@@ -746,8 +647,8 @@
    -
    zhadina |> 
-  filter(nchar(word_3) == 7)
    +
    zhadina |> 
+  filter(nchar(word_3) == 7)
    # A tibble: 7 × 5
   word_1          word_2   word_3  type        n
@@ -763,9 +664,9 @@ 
    
 
    Кроме того, условия можно перечислить через запятую (аналог логического “и”):
    
 
    
-
    zhadina |> 
-  filter(n > 15,
-         n < 100)
    
+
    zhadina |> 
+  filter(n > 15,
+         n < 100)
    
 
    
 
    # A tibble: 8 × 5
   word_1          word_2  word_3     type           n
@@ -782,8 +683,8 @@ 
    
 
    Логическое “или” обозначается палочкой |:
    
 
    
-
    zhadina |> 
-  filter(n < 15 | n > 100)
    
+
    zhadina |> 
+  filter(n < 15 | n > 100)
    
 
    
 
    # A tibble: 18 × 5
    word_1          word_2   word_3     type           n
@@ -809,12 +710,12 @@
    -
    -

    2.6 Функции count(), distinct(), arrange()

    +
    +

    2.5 Функции count(), distinct(), arrange()

    Функция count() очень полезна в разведочном анализе, так как позволяет посчитать сколько какое значение переменной или группы переменных встретилось в датасете. Аргумент sort позволяет дополнительно отсортировать результат от большего к меньшего.

    -
    zhadina |> 
-  count(word_3)
    +
    zhadina |> 
+  count(word_3)
    # A tibble: 9 × 2
   word_3         n
@@ -829,8 +730,8 @@ 
    zhadina |> 
-  count(word_3, sort = TRUE)
    +
    zhadina |> 
+  count(word_3, sort = TRUE)
    # A tibble: 9 × 2
   word_3         n
@@ -845,8 +746,8 @@ 
    zhadina |> 
-  count(word_1, word_2, sort = TRUE)
    +
    zhadina |> 
+  count(word_1, word_2, sort = TRUE)
    # A tibble: 21 × 3
    word_1          word_2       n
@@ -866,8 +767,8 @@ 
    
-
    zhadina |> 
-  distinct(word_3)
    
+
    zhadina |> 
+  distinct(word_3)
    
 
    
 
    # A tibble: 9 × 1
   word_3    
@@ -882,8 +783,8 @@ 
    zhadina |> 
-  distinct(word_1, word_2)
    
+
    zhadina |> 
+  distinct(word_1, word_2)
    
 
    
 
    # A tibble: 21 × 2
    word_1          word_2 
@@ -903,9 +804,9 @@ 
    Раздел 4.5):
    
 
    
-
    zhadina |> 
-  count(word_3) |> 
-  arrange(n)
    
+
    zhadina |> 
+  count(word_3) |> 
+  arrange(n)
    
 
    
 
    # A tibble: 9 × 2
   word_3         n
@@ -920,9 +821,9 @@ 
    zhadina |> 
-  count(word_3) |> 
-  arrange(desc(n))
    
+
    zhadina |> 
+  count(word_3) |> 
+  arrange(desc(n))
    
 
    
 
    # A tibble: 9 × 2
   word_3         n
@@ -937,9 +838,9 @@ 
    zhadina |> 
-  count(word_3) |> 
-  arrange(-n)
    
+
    zhadina |> 
+  count(word_3) |> 
+  arrange(-n)
    
 
    
 
    # A tibble: 9 × 2
   word_3         n
@@ -954,9 +855,9 @@ 
    zhadina |> 
-  count(word_3) |> 
-  arrange(word_3)
    
+
    zhadina |> 
+  count(word_3) |> 
+  arrange(word_3)
    
 
    
 
    # A tibble: 9 × 2
   word_3         n
@@ -973,6 +874,108 @@ 
    
+
    2.6 Функция select()
    
+
    Функция select() позволяет выбрать из датасета некоторый набор столбцов.
    
+
    
+
    zhadina |> 
+  select(word_1, type, n)
    
+
    
+
    # A tibble: 26 × 3
+   word_1          type           n
+   <chr>           <chr>      <dbl>
+ 1 жадина-говядина шоколадина   941
+ 2 жадина-говядина шоколадина    44
+ 3 жадина-говядина шоколадина   250
+ 4 жадина-говядина огурец        11
+ 5 жадина-говядина шоколадина  1117
+ 6 жадина-говядина шоколадина    12
+ 7 жадина-говядина шоколадина    29
+ 8 жадина-говядина огурец       343
+ 9 жадина-говядина шоколадина    10
+10 жадина-говядина другое        10
+# ℹ 16 more rows
    
+
    
+
    
+
    Внутри функции select() можно использовать функции, которые позволяют не только перечислять через запятую необходимые столбцы, но и выделять их по имени:
    
+
    
+
    zhadina |> 
+  select(starts_with("word"))
    
+
    
+
    # A tibble: 26 × 3
+   word_1          word_2  word_3    
+   <chr>           <chr>   <chr>     
+ 1 жадина-говядина большая шоколадина
+ 2 жадина-говядина бука    шоколадина
+ 3 жадина-говядина булка   шоколадина
+ 4 жадина-говядина в *опе  огурец    
+ 5 жадина-говядина в *опе  шоколадина
+ 6 жадина-говядина гнилая  шоколадина
+ 7 жадина-говядина дома    шоколадина
+ 8 жадина-говядина зеленый огурец    
+ 9 жадина-говядина злая    шоколадина
+10 жадина-говядина кислая  капуста   
+# ℹ 16 more rows
    
+
    
+
    zhadina |> 
+  select(ends_with("1"))
    
+
    
+
    # A tibble: 26 × 1
+   word_1         
+   <chr>          
+ 1 жадина-говядина
+ 2 жадина-говядина
+ 3 жадина-говядина
+ 4 жадина-говядина
+ 5 жадина-говядина
+ 6 жадина-говядина
+ 7 жадина-говядина
+ 8 жадина-говядина
+ 9 жадина-говядина
+10 жадина-говядина
+# ℹ 16 more rows
    
+
    
+
    zhadina |> 
+  select(matches("ord"))
    
+
    
+
    # A tibble: 26 × 3
+   word_1          word_2  word_3    
+   <chr>           <chr>   <chr>     
+ 1 жадина-говядина большая шоколадина
+ 2 жадина-говядина бука    шоколадина
+ 3 жадина-говядина булка   шоколадина
+ 4 жадина-говядина в *опе  огурец    
+ 5 жадина-говядина в *опе  шоколадина
+ 6 жадина-говядина гнилая  шоколадина
+ 7 жадина-говядина дома    шоколадина
+ 8 жадина-говядина зеленый огурец    
+ 9 жадина-говядина злая    шоколадина
+10 жадина-говядина кислая  капуста   
+# ℹ 16 more rows
    
+
    
+
    
+
    Кроме того, внутри функции select() действует оператор двоеточие, который мы видели при создании векторов 5:10:
    
+
    
+
    zhadina |> 
+  select(word_3:n) # от колонки word_3 до колонки n
    
+
    
+
    # A tibble: 26 × 3
+   word_3     type           n
+   <chr>      <chr>      <dbl>
+ 1 шоколадина шоколадина   941
+ 2 шоколадина шоколадина    44
+ 3 шоколадина шоколадина   250
+ 4 огурец     огурец        11
+ 5 шоколадина шоколадина  1117
+ 6 шоколадина шоколадина    12
+ 7 шоколадина шоколадина    29
+ 8 огурец     огурец       343
+ 9 шоколадина шоколадина    10
+10 капуста    другое        10
+# ℹ 16 more rows
    
+
    
+
    
+

    2.7 Функция mutate()

    Функция mutate() позволяет создавать и изменять уже имеющиеся переменные:

    @@ -1016,12 +1019,98 @@

    -
    -

    2.8 Функция group_by() |> summarise() и group_by() |> mutate()

    +
    +

    2.8 Функция relocate()

    +

    Функция relocate() позволяет перемещать переменные в таблице (по умолчанию в начало):

    zhadina |> 
-  group_by(type) |> 
-  summarise(mean_by_type = mean(n))
    + relocate(type)
    +
    +
    # A tibble: 26 × 5
+   type       word_1          word_2  word_3         n
+   <chr>      <chr>           <chr>   <chr>      <dbl>
+ 1 шоколадина жадина-говядина большая шоколадина   941
+ 2 шоколадина жадина-говядина бука    шоколадина    44
+ 3 шоколадина жадина-говядина булка   шоколадина   250
+ 4 огурец     жадина-говядина в *опе  огурец        11
+ 5 шоколадина жадина-говядина в *опе  шоколадина  1117
+ 6 шоколадина жадина-говядина гнилая  шоколадина    12
+ 7 шоколадина жадина-говядина дома    шоколадина    29
+ 8 огурец     жадина-говядина зеленый огурец       343
+ 9 шоколадина жадина-говядина злая    шоколадина    10
+10 другое     жадина-говядина кислая  капуста       10
+# ℹ 16 more rows
    +
    + +

    Если хочется перемещать в какое-то другое место, то можно использовать аргументы .after и .before:

    +
    +
    zhadina |> 
+  relocate(n, .after =  word_3)
    +
    +
    # A tibble: 26 × 5
+   word_1          word_2  word_3         n type      
+   <chr>           <chr>   <chr>      <dbl> <chr>     
+ 1 жадина-говядина большая шоколадина   941 шоколадина
+ 2 жадина-говядина бука    шоколадина    44 шоколадина
+ 3 жадина-говядина булка   шоколадина   250 шоколадина
+ 4 жадина-говядина в *опе  огурец        11 огурец    
+ 5 жадина-говядина в *опе  шоколадина  1117 шоколадина
+ 6 жадина-говядина гнилая  шоколадина    12 шоколадина
+ 7 жадина-говядина дома    шоколадина    29 шоколадина
+ 8 жадина-говядина зеленый огурец       343 огурец    
+ 9 жадина-говядина злая    шоколадина    10 шоколадина
+10 жадина-говядина кислая  капуста       10 другое    
+# ℹ 16 more rows
    +
    +
    zhadina |> 
+  relocate(n, .before =  type)
    +
    +
    # A tibble: 26 × 5
+   word_1          word_2  word_3         n type      
+   <chr>           <chr>   <chr>      <dbl> <chr>     
+ 1 жадина-говядина большая шоколадина   941 шоколадина
+ 2 жадина-говядина бука    шоколадина    44 шоколадина
+ 3 жадина-говядина булка   шоколадина   250 шоколадина
+ 4 жадина-говядина в *опе  огурец        11 огурец    
+ 5 жадина-говядина в *опе  шоколадина  1117 шоколадина
+ 6 жадина-говядина гнилая  шоколадина    12 шоколадина
+ 7 жадина-говядина дома    шоколадина    29 шоколадина
+ 8 жадина-говядина зеленый огурец       343 огурец    
+ 9 жадина-говядина злая    шоколадина    10 шоколадина
+10 жадина-говядина кислая  капуста       10 другое    
+# ℹ 16 more rows
    +
    +
    +
    +
    +

    2.9 Функция group_by() |> summarise() и group_by() |> mutate()

    +

    Все предыдущие операции работали со строками и столбцами таблицами. В этом разделе мы познакомимся с операцией группировки, которая позволяет разделять таблицу на группы и агрегировать информацию по этим группам. Функция group_by() разделяет на группы, но саму структуру и содержание таблицы не меняет, однако при печати выводится сообщение об имени группирующей переменной и сколько всего групп:

    +
    +
    zhadina |> 
+  group_by(type)
    +
    +
    # A tibble: 26 × 5
+# Groups:   type [4]
+   word_1          word_2  word_3     type           n
+   <chr>           <chr>   <chr>      <chr>      <dbl>
+ 1 жадина-говядина большая шоколадина шоколадина   941
+ 2 жадина-говядина бука    шоколадина шоколадина    44
+ 3 жадина-говядина булка   шоколадина шоколадина   250
+ 4 жадина-говядина в *опе  огурец     огурец        11
+ 5 жадина-говядина в *опе  шоколадина шоколадина  1117
+ 6 жадина-говядина гнилая  шоколадина шоколадина    12
+ 7 жадина-говядина дома    шоколадина шоколадина    29
+ 8 жадина-говядина зеленый огурец     огурец       343
+ 9 жадина-говядина злая    шоколадина шоколадина    10
+10 жадина-говядина кислая  капуста    другое        10
+# ℹ 16 more rows
    +
    +
    +

    После того как мы сгруппировали можно использовать функцию summarise() (или ее американский вариант summarize()), она возвращает по одному значению на каждую группу. Например, посчитаем среднее количество встречаемости для каждого типа:

    +
    +
    zhadina |> 
+  group_by(type) |> 
+  summarise(mean_by_type = mean(n))
    # A tibble: 4 × 2
   type       mean_by_type
@@ -1031,10 +1120,13 @@ 
    zhadina |> 
-  group_by(type) |> 
-  summarise(mean_by_type = mean(n),
-            median_by_type = median(n))
    +
    +

    Функций саммаризации может быть несколько:

    +
    +
    zhadina |> 
+  group_by(type) |> 
+  summarise(mean_by_type = mean(n),
+            median_by_type = median(n))
    # A tibble: 4 × 3
   type       mean_by_type median_by_type
@@ -1044,10 +1136,13 @@ 
    zhadina |> 
-  group_by(type, word_3) |> 
-  summarise(mean_by_type = mean(n),
-            median_by_type = median(n))
    +
    +

    Кроме того, важно отметить, что группировка может происходить не по одной переменной, а по нескольким.

    +
    +
    zhadina |> 
+  group_by(type, word_3) |> 
+  summarise(mean_by_type = mean(n),
+            median_by_type = median(n))
    `summarise()` has grouped output by 'type'. You can override using the
 `.groups` argument.
    @@ -1067,34 +1162,57 @@ 

    zhadina |> 
-  group_by(type, word_3) |> 
-  mutate(mean_by_type = mean(n),
-         median_by_type = median(n))

    +
    +

    Важно также отметить, что в функции summarise() с недавних пор аргумент .by, который является заменой функции group_by():

    +
    +
    zhadina |> 
+  summarise(mean_by_type = mean(n),
+            median_by_type = median(n),
+            .by = c(type, word_3))
    +
    +
    # A tibble: 9 × 4
+  type       word_3     mean_by_type median_by_type
+  <chr>      <chr>             <dbl>          <dbl>
+1 шоколадина шоколадина         509.             50
+2 огурец     огурец            9088             343
+3 другое     капуста             10              10
+4 другое     поросятина          42              42
+5 другое     не ест              10              10
+6 шоколадина шоколадины         113             113
+7 барабан    барабан            760              13
+8 другое     помидор             17              17
+9 другое     таракан             13              13
    +
    +
    +

    Кроме того, функцию group_by() или аргумент .by можно использовать в связке с функцией mutate(). В таком случае саммаризирующий результат будет выведен не единожды для каждой группы, а присоединен к исходной таблице в виде новой переменной.

    +
    +
    zhadina |> 
+  group_by(type) |> 
+  mutate(mean_by_type = mean(n),
+         median_by_type = median(n))
    # A tibble: 26 × 7
-# Groups:   type, word_3 [9]
+# Groups:   type [4]
    word_1          word_2  word_3     type         n mean_by_type median_by_type
    <chr>           <chr>   <chr>      <chr>    <dbl>        <dbl>          <dbl>
- 1 жадина-говядина большая шоколадина шоколад…   941         509.             50
- 2 жадина-говядина бука    шоколадина шоколад…    44         509.             50
- 3 жадина-говядина булка   шоколадина шоколад…   250         509.             50
- 4 жадина-говядина в *опе  огурец     огурец      11        9088             343
- 5 жадина-говядина в *опе  шоколадина шоколад…  1117         509.             50
- 6 жадина-говядина гнилая  шоколадина шоколад…    12         509.             50
- 7 жадина-говядина дома    шоколадина шоколад…    29         509.             50
- 8 жадина-говядина зеленый огурец     огурец     343        9088             343
- 9 жадина-говядина злая    шоколадина шоколад…    10         509.             50
-10 жадина-говядина кислая  капуста    другое      10          10              10
+ 1 жадина-говядина большая шоколадина шоколад…   941        481.            54.5
+ 2 жадина-говядина бука    шоколадина шоколад…    44        481.            54.5
+ 3 жадина-говядина булка   шоколадина шоколад…   250        481.            54.5
+ 4 жадина-говядина в *опе  огурец     огурец      11       9088            343  
+ 5 жадина-говядина в *опе  шоколадина шоколад…  1117        481.            54.5
+ 6 жадина-говядина гнилая  шоколадина шоколад…    12        481.            54.5
+ 7 жадина-говядина дома    шоколадина шоколад…    29        481.            54.5
+ 8 жадина-говядина зеленый огурец     огурец     343       9088            343  
+ 9 жадина-говядина злая    шоколадина шоколад…    10        481.            54.5
+10 жадина-говядина кислая  капуста    другое      10         18.4           13  
 # ℹ 16 more rows
    -
    zhadina |> 
-  group_by(type) |> 
-  mutate(mean_by_type = mean(n),
-         median_by_type = median(n))
    +
    zhadina |> 
+  mutate(mean_by_type = mean(n),
+         median_by_type = median(n),
+         .by = type)
    # A tibble: 26 × 7
-# Groups:   type [4]
    word_1          word_2  word_3     type         n mean_by_type median_by_type
    <chr>           <chr>   <chr>      <chr>    <dbl>        <dbl>          <dbl>
  1 жадина-говядина большая шоколадина шоколад…   941        481.            54.5
@@ -1110,13 +1228,36 @@ 
    
+
    zhadina |> 
+  group_by(type) |> 
+  mutate(mean_by_type = mean(n)) |> 
+  ungroup()
    
+
    
+
    # A tibble: 26 × 6
+   word_1          word_2  word_3     type           n mean_by_type
+   <chr>           <chr>   <chr>      <chr>      <dbl>        <dbl>
+ 1 жадина-говядина большая шоколадина шоколадина   941        481. 
+ 2 жадина-говядина бука    шоколадина шоколадина    44        481. 
+ 3 жадина-говядина булка   шоколадина шоколадина   250        481. 
+ 4 жадина-говядина в *опе  огурец     огурец        11       9088  
+ 5 жадина-говядина в *опе  шоколадина шоколадина  1117        481. 
+ 6 жадина-говядина гнилая  шоколадина шоколадина    12        481. 
+ 7 жадина-говядина дома    шоколадина шоколадина    29        481. 
+ 8 жадина-говядина зеленый огурец     огурец       343       9088  
+ 9 жадина-говядина злая    шоколадина шоколадина    10        481. 
+10 жадина-говядина кислая  капуста    другое        10         18.4
+# ℹ 16 more rows
    
+
    
+
    -
    -

    2.9 Функция across()

    +
    +

    2.10 Функция across()

    Функция across() позволяет применять одно и то же изменение к группе колонок, которые выбираются набором функций сходных с операциями для функции select(). Важно отметить, что трансформация обычно описывается функцией, и имя функции обычно пишут без круглых скобок.

    -
    zhadina |> 
-  mutate(across(starts_with("word"), toupper))
    +
    zhadina |> 
+  mutate(across(starts_with("word"), toupper))
    # A tibble: 26 × 5
    word_1          word_2  word_3     type           n
@@ -1133,8 +1274,8 @@
    -
    zhadina |> 
-  mutate(across(where(is.character), toupper))
    +
    zhadina |> 
+  mutate(across(where(is.character), toupper))
    # A tibble: 26 × 5
    word_1          word_2  word_3     type           n
@@ -1154,11 +1295,11 @@ 
    
 
    Функция может так же работать в связке с функцией group_by() |> summarize() и group_by() |> mutate():
    
 
    
-
    set.seed(42)
-zhadina |> 
-  mutate(new_numeric_value = rnorm(26)) |> 
-  group_by(type) |> 
-  summarise(across(where(is.double), mean))
    
+
    set.seed(42)
+zhadina |> 
+  mutate(new_numeric_value = rnorm(26)) |> 
+  group_by(type) |> 
+  summarise(across(where(is.double), mean))
    
 
    
 
    # A tibble: 4 × 3
   type            n new_numeric_value
@@ -1168,12 +1309,12 @@ 
    
 
    
-
    set.seed(42)
-zhadina |> 
-  mutate(new_numeric_value = rnorm(26)) |> 
-  group_by(type) |> 
-  summarise(n = mean(n),
-            new_numeric_value = mean(new_numeric_value))
    
+
    set.seed(42)
+zhadina |> 
+  mutate(new_numeric_value = rnorm(26)) |> 
+  group_by(type) |> 
+  summarise(n = mean(n),
+            new_numeric_value = mean(new_numeric_value))
    
 
    
 
    # A tibble: 4 × 3
   type            n new_numeric_value
@@ -1183,10 +1324,10 @@ 
    
 
    
-
    zhadina |> 
-  mutate(new_numeric_value = rnorm(26)) |> 
-  group_by(type) |> 
-  mutate(across(where(is.double), mean))
    
+
    zhadina |> 
+  mutate(new_numeric_value = rnorm(26)) |> 
+  group_by(type) |> 
+  mutate(across(where(is.double), mean))
    
 
    
 
    # A tibble: 26 × 6
 # Groups:   type [4]
@@ -1206,8 +1347,8 @@
    -
    -

    2.10 Функции семейства pivot_...()

    +
    +

    2.11 Функции семейства pivot_...()

    Основная концепция пакет tidyverse строится вокруг понятия опрятный формат (tidy data). Опрятные данные означает приведение табличных данных к такому формату, в котором:

    • каждая переменная имеет собственный столбец;
      • @@ -1217,7 +1358,7 @@

      данных количества носителей разных языков Индии:

      -
      india_langauges_wide <- read_csv("https://raw.githubusercontent.com/agricolamz/daR4hs/main/data/w2_india_languages.csv")
      +
      india_langauges_wide <- read_csv("https://raw.githubusercontent.com/agricolamz/daR4hs/main/data/w2_india_languages.csv")
      Rows: 12 Columns: 5
 ── Column specification ────────────────────────────────────────────────────────
@@ -1228,7 +1369,7 @@ 
      india_langauges_wide
      +
      india_langauges_wide
      # A tibble: 12 × 5
    language    n_L1_sp  n_L2_sp  n_L3_sp  n_all_sp
@@ -1249,8 +1390,8 @@ 
      
-
      india_langauges_wide |> 
-  pivot_longer(names_to = "L", values_to = "value", n_L1_sp:n_all_sp)
      
+
      india_langauges_wide |> 
+  pivot_longer(names_to = "L", values_to = "value", n_L1_sp:n_all_sp)
      
 
      
 
      # A tibble: 48 × 3
    language L            value
@@ -1270,12 +1411,12 @@ 
      
-
      india_langauges_wide |> 
-  pivot_longer(names_to = "L", values_to = "value", starts_with("n_")) |> 
-  filter(L != "n_all_sp") |> 
-  group_by(L) |> 
-  summarise(sum = sum(value),
-            mean = mean(value))
      
+
      india_langauges_wide |> 
+  pivot_longer(names_to = "L", values_to = "value", starts_with("n_")) |> 
+  filter(L != "n_all_sp") |> 
+  group_by(L) |> 
+  summarise(sum = sum(value),
+            mean = mean(value))
      
 
      
 
      # A tibble: 3 × 3
   L             sum      mean
@@ -1287,9 +1428,9 @@ 
      
-
      india_langauges_wide |> 
-  pivot_longer(names_to = "L", values_to = "value", starts_with("n_")) |> 
-  pivot_wider(names_from = "L", values_from = "value")
      
+
      india_langauges_wide |> 
+  pivot_longer(names_to = "L", values_to = "value", starts_with("n_")) |> 
+  pivot_wider(names_from = "L", values_from = "value")
      
 
      
 
      # A tibble: 12 × 5
    language    n_L1_sp  n_L2_sp  n_L3_sp  n_all_sp
@@ -1309,18 +1450,18 @@ 
      
-
      2.11 Функции семейства bind_...()
      
+
      
+
      2.12 Функции семейства bind_...()
      
 
      Функции bind_rows() и bind_cols() позволяют соединять несколько таблиц. Функция bind_rows() присоединяет таблицу снизу, при этом она должна иметь столько же переменных и тип переменных должен совпадать с основной таблицей.
      
 
      
-
      zhadina_new <- tibble(word_1 = "жадина-говядина",
-                      word_2 = "пупецкий",
-                      word_3 = "барабан",
-                      type = "барабан",
-                      n = 0)
-
-zhadina |> 
-  bind_rows(zhadina_new)
      
+
      zhadina_new <- tibble(word_1 = "жадина-говядина",
+                      word_2 = "пупецкий",
+                      word_3 = "барабан",
+                      type = "барабан",
+                      n = 0)
+
+zhadina |> 
+  bind_rows(zhadina_new)
      
 
      
 
      # A tibble: 27 × 5
    word_1          word_2  word_3     type           n
@@ -1340,11 +1481,11 @@ 
      
-
      zhadina_additional_columns <- tibble(new_var1 = rnorm(26),
-                                     new_var2 = letters)
-
-zhadina |> 
-  bind_cols(zhadina_additional_columns)
      
+
      zhadina_additional_columns <- tibble(new_var1 = rnorm(26),
+                                     new_var2 = letters)
+
+zhadina |> 
+  bind_cols(zhadina_additional_columns)
      
 
      
 
      # A tibble: 26 × 7
    word_1          word_2  word_3     type           n new_var1 new_var2
@@ -1363,12 +1504,14 @@ 
      
-
      2.12 Функции семейства ..._join()
      
+
      
+
      2.13 Функции семейства ..._join()
      
 
      Иногда возникает ситуация, когда нужно соединить две таблицы, на основании одного или нескольких общих столбцов. Так бывает, если нужно что-то включить или исключить из анализа. Примером включения может послужить случай, когда для построения карты хочется присоединить расшифровку аббревиатур или для анализа нужны еще какие-то данные, например, размер популяции. Примером исключения может послужить случаи, когда при анализе текстов возникает задача исключения из анализа служебных слов. Эту задачу решают функции семейства ..._join().
      
 
      Синтаксис функций выглядит так (A и B — датафреймы):
      
-
      A |> 
-  ..._join(B)
      
+
      
+
      A |> 
+  ..._join(B)
      
+
      
 
      Вместо многоточия может стоять одна из следующих функций:
      
 
        
 
      • inner_join() — возвращает только те наблюдения в А, которым есть соответствие в B
        • 
@@ -1383,23 +1526,23 @@ 
        
 
        
 
        
-
        
+
        
 
        
 
        
 
      
 
      
 
      В качесиве примера создадим новую таблицу на основе датасета продожений Жадины-говядины:
      
 
      
-
      zhadina |> 
-  count(type) |>  
-  rename(our_new_var1 = n) |> 
-  mutate(our_new_var2 = c("совсем редкое", 
-                          "редкое", 
-                          "второй по популярности",
-                          "самый частый тип")) ->
-  type_new_var
-
-type_new_var
      
+
      zhadina |> 
+  count(type) |>  
+  rename(our_new_var1 = n) |> 
+  mutate(our_new_var2 = c("совсем редкое", 
+                          "редкое", 
+                          "второй по популярности",
+                          "самый частый тип")) ->
+  type_new_var
+
+type_new_var
      
 
      
 
      # A tibble: 4 × 3
   type       our_new_var1 our_new_var2          
@@ -1411,8 +1554,8 @@ 
      
-
      zhadina |> 
-  left_join(type_new_var)
      
+
      zhadina |> 
+  left_join(type_new_var)
      
 
      
 
      Joining with `by = join_by(type)`
      
 
      
@@ -1435,12 +1578,12 @@ 
      
-
      type_new_var |> 
-  slice(-1) ->
-  type_new_var2
-
-zhadina |> 
-  left_join(type_new_var2)
      
+
      type_new_var |> 
+  slice(-1) ->
+  type_new_var2
+
+zhadina |> 
+  left_join(type_new_var2)
      
 
      
 
      Joining with `by = join_by(type)`
      
 
      
@@ -1463,14 +1606,14 @@ 
      
-
      tibble(type = "карниз", 
-       our_new_var1 = 0, 
-       our_new_var2 = "такого не бывает") |> 
-  bind_rows(type_new_var)->
-  type_new_var3
-
-zhadina |> 
-  left_join(type_new_var3) 
      
+
      tibble(type = "карниз", 
+       our_new_var1 = 0, 
+       our_new_var2 = "такого не бывает") |> 
+  bind_rows(type_new_var)->
+  type_new_var3
+
+zhadina |> 
+  left_join(type_new_var3) 
      
 
      
 
      Joining with `by = join_by(type)`
      
 
      
@@ -1490,8 +1633,8 @@ 
      zhadina |> 
-  full_join(type_new_var3)
      
+
      zhadina |> 
+  full_join(type_new_var3)
      
 
      
 
      Joining with `by = join_by(type)`
      
 
      
@@ -1514,8 +1657,8 @@ 
      
-
      zhadina |> 
-  anti_join(tibble(type = "шоколадина"))
      
+
      zhadina |> 
+  anti_join(tibble(type = "шоколадина"))
      
 
      
 
      Joining with `by = join_by(type)`
      
 
      
@@ -1539,10 +1682,10 @@ 
      
-
      type_new_var3 |> 
-  rename(new_type = type) ->
-  type_new_var4
-type_new_var4
      
+
      type_new_var3 |> 
+  rename(new_type = type) ->
+  type_new_var4
+type_new_var4
      
 
      
 
      # A tibble: 5 × 3
   new_type   our_new_var1 our_new_var2          
@@ -1556,8 +1699,8 @@ 
      
-
      zhadina |> 
-  left_join(type_new_var4, by = c("type" = "new_type"))
      
+
      zhadina |> 
+  left_join(type_new_var4, by = c("type" = "new_type"))
      
 
      
 
      # A tibble: 26 × 7
    word_1          word_2  word_3     type           n our_new_var1 our_new_var2
diff --git a/docs/2_data_transformation_files/figure-html/unnamed-chunk-29-1.png b/docs/2_data_transformation_files/figure-html/unnamed-chunk-38-1.png
similarity index 100%
rename from docs/2_data_transformation_files/figure-html/unnamed-chunk-29-1.png
rename to docs/2_data_transformation_files/figure-html/unnamed-chunk-38-1.png
diff --git a/docs/5_working_with_texts.html b/docs/5_working_with_texts.html
index b569c47..f282177 100644
--- a/docs/5_working_with_texts.html
+++ b/docs/5_working_with_texts.html
@@ -792,7 +792,7 @@ 
      Раздел 2.12), мы можем повторить наш анализ:
      
+
      Так что используя функцию из семейства ..._join() (см. Раздел 2.13), мы можем повторить наш анализ:
      
 
      
 
      tidy_alice |> 
   count(chapter_title, word, sort = TRUE) |> 
diff --git a/docs/5_working_with_texts_files/figure-html/unnamed-chunk-39-1.png b/docs/5_working_with_texts_files/figure-html/unnamed-chunk-39-1.png
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diff --git a/docs/6_working_with_time.html b/docs/6_working_with_time.html
index d71a0e1..6ec712f 100644
--- a/docs/6_working_with_time.html
+++ b/docs/6_working_with_time.html
@@ -280,7 +280,7 @@ 
      6  
      now()
      
 
      
-
      [1] "2024-08-01 12:45:29 MSK"
      
+
      [1] "2024-08-01 20:45:31 MSK"
      
 
      
 
      
 
      Как видно из этих функций в R можно работать как с датами, так и со временем. В качестве иллюстрации мы будем использовать датасет flights из пакета nycflights13, в котором содержатся данные полетов из Нью Йорка в 2013 году.
      
diff --git a/docs/7_working_with_geodata.html b/docs/7_working_with_geodata.html
index bd525e0..6258188 100644
--- a/docs/7_working_with_geodata.html
+++ b/docs/7_working_with_geodata.html
@@ -346,8 +346,8 @@ 
        addCircles(lng = ~lon, # обратите внимание на особый синтаксис с тильдой
              lat = ~lat)
      
 
      
-
      
-
+
      
+
 
      
 
      
 
      
@@ -361,8 +361,8 @@ 
      
   addTiles() |> 
   addGeoJSON(geojson = moscow_districts)
      
 
      
-
      
-
+
      
+
 
      
 
      
 
      Необходимо зазумиться, так как при отображении полигонов зум не происходит автоматически, этого можно добиться при помощи функции setView():
      
@@ -372,8 +372,8 @@ 
      
   addGeoJSON(geojson = moscow_districts) |> 
   setView(zoom = 8, lng = 37.35, lat = 55.65)
      
 
      
-
      
-
+
      
+

      Кроме того, .geojson и .shp можно прочитать функцией st_read() из пакета sf.

      @@ -388,8 +388,8 @@

      addCircles(lng = ~lon, lat = ~lat)

      -
      - +
      +

      Функция addCircles() имеет массу аргументов, которые отвечают за отображение:

      @@ -437,8 +437,8 @@

      values = ~collapsed_cause, title = "")
      -
      - +
      +

      Вообще цветовая схема не очень сочетается с подложкой, так что можно поменять подложку при помощи функции addProviderTiles() (галлерею подложек можно посмотреть вот здесь):

      @@ -457,8 +457,8 @@

      values = ~collapsed_cause, title = "")
      -
      - +
      +

    @@ -507,12 +507,12 @@ 

    sync(m_2014, m_2015)
    -
    - +
    +
    -
    - +
    +
    @@ -551,8 +551,8 @@

    addCircles(lng = ~longitude, lat = ~latitude)

    -
    - +
    +

    Полигоны

    @@ -568,8 +568,8 @@

    lat = ~latitude, label = ~name)
    -
    - +
    +

    К сожалению, leaflet не такой удобный, как, скажем, ggplot2, поэтому для того, чтобы нарисовать много отдельных линий или полигонов, нужно использовать цикл:

    @@ -594,8 +594,8 @@

    moscow_map
    -
    - +
    +

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zFJh%)(uX~!V-rO;Y$7;%eS%!Zt5>h`GdJ=7L}ZAHl}N!crW-R1mW<-x!vaAywY4Y& zZ0lxNfL3k#=Pt&f5ryE+>-1?Qq1zg)DCc2  Трансформация данных" ] @@ -154,7 +154,7 @@ "href": "2_data_transformation.html#использование-конвеера-пайпа", "title": "2  Трансформация данных", "section": "2.2 Использование конвеера (пайпа)", - "text": "2.2 Использование конвеера (пайпа)\nВ нашем коде мы часто будем использовать знаки конвеера (или пайпа): |> (также можно встретить популярный ранее %>%). Конвеер призван показывать последовательность действий. Сочетание клавиш для написание конвеера: Ctrl/Cmd + M.\n\nmean(sqrt(abs(sin(c(1:100, NA)))), na.rm = TRUE)\n\n[1] 0.7654264\n\nc(1:100, NA) |>\n sin() |> \n abs() |> \n sqrt() |> \n mean(na.rm = TRUE)\n\n[1] 0.7654264\n\n\nВыражения с конвеерами можно запускать частями и смотреть, что на каком этапе трансформации данных получилось. Это полезно при поиске ошибок.", + "text": "2.2 Использование конвеера (пайпа)\nВ нашем коде мы часто будем использовать знаки конвеера (или пайпа): |> (также можно встретить популярный ранее %>%). Чтобы вставлять новый конвеер придется сделать изменение в настройках RStudio (Tools > Global Options > вкладвка Code):\n\nКонвеер призван показывать последовательность действий. Сочетание клавиш для написание конвеера: Ctrl/Cmd + Shift + M (хотя RStudio позволяет горячие клавиши переназначить).\n\nmean(sqrt(abs(sin(c(1:100, NA)))), na.rm = TRUE)\n\n[1] 0.7654264\n\nc(1:100, NA) |>\n sin() |> \n abs() |> \n sqrt() |> \n mean(na.rm = TRUE)\n\n[1] 0.7654264\n\n\nВыражения с конвеерами можно запускать частями и смотреть, что на каком этапе трансформации данных получилось. Это полезно при поиске ошибок.", "crumbs": [ "2  Трансформация данных" ] @@ -164,37 +164,37 @@ "href": "2_data_transformation.html#функции-семейства-slice", "title": "2  Трансформация данных", "section": "2.3 Функции семейства slice()", - "text": "2.3 Функции семейства slice()\nДальше мы посмотрим на фрагмент данных из “Исследования дразнилки”Жадина-говядина” издания N+1, где исследовались социолингвистические аспекты влияющие на тенденцию к тому или иному продолжению. Переменные word_1, word_2 и word_3 соотвествуют разным вариантам начала, переменная type описывает классификацию, которую варианту дали исследователи, а переменная n отвечает за количество этих вариантов в данных.\n\nzhadina <- read_csv(\"https://raw.githubusercontent.com/agricolamz/daR4hs/main/data/w2_zhadina.csv\")\nzhadina\n\n# A tibble: 26 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе огурец огурец 11\n 5 жадина-говядина в *опе шоколадина шоколадина 1117\n 6 жадина-говядина гнилая шоколадина шоколадина 12\n 7 жадина-говядина дома шоколадина шоколадина 29\n 8 жадина-говядина зеленый огурец огурец 343\n 9 жадина-говядина злая шоколадина шоколадина 10\n10 жадина-говядина кислая капуста другое 10\n# ℹ 16 more rows\n\n\nПервые функции tidyverse, которые будут нас интересовать, — это функции семейства slice(). Функция slice() позволяет фильтровать нужные строчки датасета по индексу:\n\nzhadina |> \n slice(c(6, 25, 3))\n\n# A tibble: 3 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина гнилая шоколадина шоколадина 12\n2 жадина-говядина турецкий барабан барабан 3003\n3 жадина-говядина булка шоколадина шоколадина 250\n\nzhadina |> \n slice(6:25)\n\n# A tibble: 20 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина гнилая шоколадина шоколадина 12\n 2 жадина-говядина дома шоколадина шоколадина 29\n 3 жадина-говядина зеленый огурец огурец 343\n 4 жадина-говядина злая шоколадина шоколадина 10\n 5 жадина-говядина кислая капуста другое 10\n 6 жадина-говядина кошка поросятина другое 42\n 7 жадина-говядина кошка шоколадина шоколадина 18\n 8 жадина-говядина никто ее не ест другое 10\n 9 жадина-говядина кусок шоколадины шоколадина 113\n10 жадина-говядина на пузе барабан барабан 14\n11 жадина-говядина немецкий барабан барабан 12\n12 жадина-говядина *опа шоколадина шоколадина 299\n13 жадина-говядина простая шоколадина шоколадина 59\n14 жадина-говядина пустая шоколадина шоколадина 3774\n15 жадина-говядина соленый барабан барабан 11\n16 жадина-говядина соленый огурец огурец 26910\n17 жадина-говядина соленый помидор другое 17\n18 жадина-говядина сухая шоколадина шоколадина 16\n19 жадина-говядина тупая шоколадина шоколадина 50\n20 жадина-говядина турецкий барабан барабан 3003\n\n\nСтоит обратить внимание, что результат работы функции выводится в консоль, чтобы сохранить результат работы, следует сделать операцию приписывания одним из следующих способов (первый наиболее распространенный):\n\nnew_zhadina <- zhadina |> \n slice(6:25)\n\nzhadina |> \n slice(6:25) ->\n new_zhadina\n\nКроме того, есть функции slice_head() и slice_tail(), которые выдают первое/последнее наблюдение. Аргумент n позволяет донастроить, сколько первых/последних наблюдений хочется вывести:\n\nzhadina |> \n slice_head()\n\n# A tibble: 1 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина большая шоколадина шоколадина 941\n\nzhadina |> \n slice_tail()\n\n# A tibble: 1 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина турецкий таракан другое 13\n\nzhadina |> \n slice_tail(n = 6)\n\n# A tibble: 6 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина соленый огурец огурец 26910\n2 жадина-говядина соленый помидор другое 17\n3 жадина-говядина сухая шоколадина шоколадина 16\n4 жадина-говядина тупая шоколадина шоколадина 50\n5 жадина-говядина турецкий барабан барабан 3003\n6 жадина-говядина турецкий таракан другое 13\n\n\nСтоит отметить, что совпадение имени аргумента и названия столбца в нашем датасете (n) никак не влияет на работу функции. Также полезными могут быть функции slice_max() и slice_min(), которые выдают максимальное/минимальное значение переменной. У этой функции тоже есть аргумент n:\n\nzhadina |> \n slice_max(n)\n\n# A tibble: 1 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина соленый огурец огурец 26910\n\nzhadina |> \n slice_min(n)\n\n# A tibble: 3 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина злая шоколадина шоколадина 10\n2 жадина-говядина кислая капуста другое 10\n3 жадина-говядина никто ее не ест другое 10\n\nzhadina |> \n slice_max(n, n = 3)\n\n# A tibble: 3 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина соленый огурец огурец 26910\n2 жадина-говядина пустая шоколадина шоколадина 3774\n3 жадина-говядина турецкий барабан барабан 3003\n\n\nФункция slice_sample() позволяет вывести выборку из некоторого количества строчек (аргумент n) или долю строчек (аргумент prop):\n\nzhadina |> \n slice_sample(n = 10)\n\n# A tibble: 10 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина в *опе шоколадина шоколадина 1117\n 2 жадина-говядина пустая шоколадина шоколадина 3774\n 3 жадина-говядина дома шоколадина шоколадина 29\n 4 жадина-говядина соленый барабан барабан 11\n 5 жадина-говядина на пузе барабан барабан 14\n 6 жадина-говядина гнилая шоколадина шоколадина 12\n 7 жадина-говядина сухая шоколадина шоколадина 16\n 8 жадина-говядина кислая капуста другое 10\n 9 жадина-говядина большая шоколадина шоколадина 941\n10 жадина-говядина немецкий барабан барабан 12\n\nzhadina |> \n slice_sample(prop = 0.3)\n\n# A tibble: 7 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина простая шоколадина шоколадина 59\n2 жадина-говядина большая шоколадина шоколадина 941\n3 жадина-говядина тупая шоколадина шоколадина 50\n4 жадина-говядина турецкий таракан другое 13\n5 жадина-говядина соленый огурец огурец 26910\n6 жадина-говядина никто ее не ест другое 10\n7 жадина-говядина кислая капуста другое 10", + "text": "2.3 Функции семейства slice()\nДальше мы посмотрим на фрагмент данных из “Исследования дразнилки”Жадина-говядина” издания N+1, где исследовались социолингвистические аспекты влияющие на тенденцию к тому или иному продолжению. Переменные word_1, word_2 и word_3 соотвествуют разным вариантам начала, переменная type описывает классификацию, которую варианту дали исследователи, а переменная n отвечает за количество этих вариантов в данных.\n\nzhadina <- read_csv(\"https://raw.githubusercontent.com/agricolamz/daR4hs/main/data/w2_zhadina.csv\")\nzhadina\n\n# A tibble: 26 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе огурец огурец 11\n 5 жадина-говядина в *опе шоколадина шоколадина 1117\n 6 жадина-говядина гнилая шоколадина шоколадина 12\n 7 жадина-говядина дома шоколадина шоколадина 29\n 8 жадина-говядина зеленый огурец огурец 343\n 9 жадина-говядина злая шоколадина шоколадина 10\n10 жадина-говядина кислая капуста другое 10\n# ℹ 16 more rows\n\n\nПервые функции tidyverse, которые будут нас интересовать, — это функции семейства slice(). Функция slice() позволяет фильтровать нужные строчки датасета по индексу:\n\nzhadina |> \n slice(c(6, 25, 3))\n\n# A tibble: 3 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина гнилая шоколадина шоколадина 12\n2 жадина-говядина турецкий барабан барабан 3003\n3 жадина-говядина булка шоколадина шоколадина 250\n\nzhadina |> \n slice(6:25)\n\n# A tibble: 20 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина гнилая шоколадина шоколадина 12\n 2 жадина-говядина дома шоколадина шоколадина 29\n 3 жадина-говядина зеленый огурец огурец 343\n 4 жадина-говядина злая шоколадина шоколадина 10\n 5 жадина-говядина кислая капуста другое 10\n 6 жадина-говядина кошка поросятина другое 42\n 7 жадина-говядина кошка шоколадина шоколадина 18\n 8 жадина-говядина никто ее не ест другое 10\n 9 жадина-говядина кусок шоколадины шоколадина 113\n10 жадина-говядина на пузе барабан барабан 14\n11 жадина-говядина немецкий барабан барабан 12\n12 жадина-говядина *опа шоколадина шоколадина 299\n13 жадина-говядина простая шоколадина шоколадина 59\n14 жадина-говядина пустая шоколадина шоколадина 3774\n15 жадина-говядина соленый барабан барабан 11\n16 жадина-говядина соленый огурец огурец 26910\n17 жадина-говядина соленый помидор другое 17\n18 жадина-говядина сухая шоколадина шоколадина 16\n19 жадина-говядина тупая шоколадина шоколадина 50\n20 жадина-говядина турецкий барабан барабан 3003\n\n\nСтоит обратить внимание, что результат работы функции выводится в консоль, чтобы сохранить результат работы, следует сделать операцию приписывания одним из следующих способов (первый наиболее распространенный):\n\nnew_zhadina <- zhadina |> \n slice(6:25)\n\nzhadina |> \n slice(6:25) ->\n new_zhadina\n\nКроме того, есть функции slice_head() и slice_tail(), которые выдают первое/последнее наблюдение. Аргумент n позволяет донастроить, сколько первых/последних наблюдений хочется вывести:\n\nzhadina |> \n slice_head()\n\n# A tibble: 1 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина большая шоколадина шоколадина 941\n\nzhadina |> \n slice_tail()\n\n# A tibble: 1 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина турецкий таракан другое 13\n\nzhadina |> \n slice_tail(n = 6)\n\n# A tibble: 6 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина соленый огурец огурец 26910\n2 жадина-говядина соленый помидор другое 17\n3 жадина-говядина сухая шоколадина шоколадина 16\n4 жадина-говядина тупая шоколадина шоколадина 50\n5 жадина-говядина турецкий барабан барабан 3003\n6 жадина-говядина турецкий таракан другое 13\n\n\nСтоит отметить, что совпадение имени аргумента и названия столбца в нашем датасете (n) никак не влияет на работу функции. Также полезными могут быть функции slice_max() и slice_min(), которые выдают максимальное/минимальное значение переменной. У этой функции тоже есть аргумент n:\n\nzhadina |> \n slice_max(n)\n\n# A tibble: 1 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина соленый огурец огурец 26910\n\nzhadina |> \n slice_min(n)\n\n# A tibble: 3 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина злая шоколадина шоколадина 10\n2 жадина-говядина кислая капуста другое 10\n3 жадина-говядина никто ее не ест другое 10\n\nzhadina |> \n slice_max(n, n = 3)\n\n# A tibble: 3 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина соленый огурец огурец 26910\n2 жадина-говядина пустая шоколадина шоколадина 3774\n3 жадина-говядина турецкий барабан барабан 3003\n\n\nФункция slice_sample() позволяет вывести выборку из некоторого количества строчек (аргумент n) или долю строчек (аргумент prop):\n\nzhadina |> \n slice_sample(n = 10)\n\n# A tibble: 10 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина никто ее не ест другое 10\n 2 жадина-говядина соленый помидор другое 17\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе шоколадина шоколадина 1117\n 5 жадина-говядина большая шоколадина шоколадина 941\n 6 жадина-говядина пустая шоколадина шоколадина 3774\n 7 жадина-говядина немецкий барабан барабан 12\n 8 жадина-говядина кусок шоколадины шоколадина 113\n 9 жадина-говядина простая шоколадина шоколадина 59\n10 жадина-говядина злая шоколадина шоколадина 10\n\nzhadina |> \n slice_sample(prop = 0.3)\n\n# A tibble: 7 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина соленый помидор другое 17\n2 жадина-говядина никто ее не ест другое 10\n3 жадина-говядина кусок шоколадины шоколадина 113\n4 жадина-говядина кислая капуста другое 10\n5 жадина-говядина турецкий барабан барабан 3003\n6 жадина-говядина пустая шоколадина шоколадина 3774\n7 жадина-говядина простая шоколадина шоколадина 59", "crumbs": [ "2  Трансформация данных" ] }, { - "objectID": "2_data_transformation.html#функция-select", - "href": "2_data_transformation.html#функция-select", + "objectID": "2_data_transformation.html#функция-filter", + "href": "2_data_transformation.html#функция-filter", "title": "2  Трансформация данных", - "section": "2.4 Функция select()", - "text": "2.4 Функция select()\nФункция select() позволяет выбрать из датасета некоторый набор столбцов.\n\nzhadina |> \n select(word_1, type, n)\n\n# A tibble: 26 × 3\n word_1 type n\n <chr> <chr> <dbl>\n 1 жадина-говядина шоколадина 941\n 2 жадина-говядина шоколадина 44\n 3 жадина-говядина шоколадина 250\n 4 жадина-говядина огурец 11\n 5 жадина-говядина шоколадина 1117\n 6 жадина-говядина шоколадина 12\n 7 жадина-говядина шоколадина 29\n 8 жадина-говядина огурец 343\n 9 жадина-говядина шоколадина 10\n10 жадина-говядина другое 10\n# ℹ 16 more rows\n\n\nВнутри функции select() можно использовать функции, которые позволяют не только перечислять через запятую необходимые столбцы, но и выделять их по имени:\n\nzhadina |> \n select(starts_with(\"word\"))\n\n# A tibble: 26 × 3\n word_1 word_2 word_3 \n <chr> <chr> <chr> \n 1 жадина-говядина большая шоколадина\n 2 жадина-говядина бука шоколадина\n 3 жадина-говядина булка шоколадина\n 4 жадина-говядина в *опе огурец \n 5 жадина-говядина в *опе шоколадина\n 6 жадина-говядина гнилая шоколадина\n 7 жадина-говядина дома шоколадина\n 8 жадина-говядина зеленый огурец \n 9 жадина-говядина злая шоколадина\n10 жадина-говядина кислая капуста \n# ℹ 16 more rows\n\nzhadina |> \n select(ends_with(\"1\"))\n\n# A tibble: 26 × 1\n word_1 \n <chr> \n 1 жадина-говядина\n 2 жадина-говядина\n 3 жадина-говядина\n 4 жадина-говядина\n 5 жадина-говядина\n 6 жадина-говядина\n 7 жадина-говядина\n 8 жадина-говядина\n 9 жадина-говядина\n10 жадина-говядина\n# ℹ 16 more rows\n\nzhadina |> \n select(matches(\"ord\"))\n\n# A tibble: 26 × 3\n word_1 word_2 word_3 \n <chr> <chr> <chr> \n 1 жадина-говядина большая шоколадина\n 2 жадина-говядина бука шоколадина\n 3 жадина-говядина булка шоколадина\n 4 жадина-говядина в *опе огурец \n 5 жадина-говядина в *опе шоколадина\n 6 жадина-говядина гнилая шоколадина\n 7 жадина-говядина дома шоколадина\n 8 жадина-говядина зеленый огурец \n 9 жадина-говядина злая шоколадина\n10 жадина-говядина кислая капуста \n# ℹ 16 more rows\n\n\nКроме того, внутри функции select() действует оператор двоеточие, который мы видели при создании векторов 5:10:\n\nzhadina |> \n select(word_3:n) # от колонки word_3 до колонки n\n\n# A tibble: 26 × 3\n word_3 type n\n <chr> <chr> <dbl>\n 1 шоколадина шоколадина 941\n 2 шоколадина шоколадина 44\n 3 шоколадина шоколадина 250\n 4 огурец огурец 11\n 5 шоколадина шоколадина 1117\n 6 шоколадина шоколадина 12\n 7 шоколадина шоколадина 29\n 8 огурец огурец 343\n 9 шоколадина шоколадина 10\n10 капуста другое 10\n# ℹ 16 more rows", + "section": "2.4 Функция filter()", + "text": "2.4 Функция filter()\nФункция filter() позволяет отфильтровывать строки таблицы по одному или нескольким условиям.\n\nzhadina |> \n filter(n > 100)\n\n# A tibble: 9 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина большая шоколадина шоколадина 941\n2 жадина-говядина булка шоколадина шоколадина 250\n3 жадина-говядина в *опе шоколадина шоколадина 1117\n4 жадина-говядина зеленый огурец огурец 343\n5 жадина-говядина кусок шоколадины шоколадина 113\n6 жадина-говядина *опа шоколадина шоколадина 299\n7 жадина-говядина пустая шоколадина шоколадина 3774\n8 жадина-говядина соленый огурец огурец 26910\n9 жадина-говядина турецкий барабан барабан 3003\n\nzhadina |> \n filter(n < 100)\n\n# A tibble: 17 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина бука шоколадина шоколадина 44\n 2 жадина-говядина в *опе огурец огурец 11\n 3 жадина-говядина гнилая шоколадина шоколадина 12\n 4 жадина-говядина дома шоколадина шоколадина 29\n 5 жадина-говядина злая шоколадина шоколадина 10\n 6 жадина-говядина кислая капуста другое 10\n 7 жадина-говядина кошка поросятина другое 42\n 8 жадина-говядина кошка шоколадина шоколадина 18\n 9 жадина-говядина никто ее не ест другое 10\n10 жадина-говядина на пузе барабан барабан 14\n11 жадина-говядина немецкий барабан барабан 12\n12 жадина-говядина простая шоколадина шоколадина 59\n13 жадина-говядина соленый барабан барабан 11\n14 жадина-говядина соленый помидор другое 17\n15 жадина-говядина сухая шоколадина шоколадина 16\n16 жадина-говядина тупая шоколадина шоколадина 50\n17 жадина-говядина турецкий таракан другое 13\n\nzhadina |> \n filter(n <= 100) # меньше или равно\n\n# A tibble: 17 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина бука шоколадина шоколадина 44\n 2 жадина-говядина в *опе огурец огурец 11\n 3 жадина-говядина гнилая шоколадина шоколадина 12\n 4 жадина-говядина дома шоколадина шоколадина 29\n 5 жадина-говядина злая шоколадина шоколадина 10\n 6 жадина-говядина кислая капуста другое 10\n 7 жадина-говядина кошка поросятина другое 42\n 8 жадина-говядина кошка шоколадина шоколадина 18\n 9 жадина-говядина никто ее не ест другое 10\n10 жадина-говядина на пузе барабан барабан 14\n11 жадина-говядина немецкий барабан барабан 12\n12 жадина-говядина простая шоколадина шоколадина 59\n13 жадина-говядина соленый барабан барабан 11\n14 жадина-говядина соленый помидор другое 17\n15 жадина-говядина сухая шоколадина шоколадина 16\n16 жадина-говядина тупая шоколадина шоколадина 50\n17 жадина-говядина турецкий таракан другое 13\n\nzhadina |> \n filter(n >= 100) # больше или равно\n\n# A tibble: 9 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина большая шоколадина шоколадина 941\n2 жадина-говядина булка шоколадина шоколадина 250\n3 жадина-говядина в *опе шоколадина шоколадина 1117\n4 жадина-говядина зеленый огурец огурец 343\n5 жадина-говядина кусок шоколадины шоколадина 113\n6 жадина-говядина *опа шоколадина шоколадина 299\n7 жадина-говядина пустая шоколадина шоколадина 3774\n8 жадина-говядина соленый огурец огурец 26910\n9 жадина-говядина турецкий барабан барабан 3003\n\nzhadina |> \n filter(n != 10) # не равно\n\n# A tibble: 23 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе огурец огурец 11\n 5 жадина-говядина в *опе шоколадина шоколадина 1117\n 6 жадина-говядина гнилая шоколадина шоколадина 12\n 7 жадина-говядина дома шоколадина шоколадина 29\n 8 жадина-говядина зеленый огурец огурец 343\n 9 жадина-говядина кошка поросятина другое 42\n10 жадина-говядина кошка шоколадина шоколадина 18\n# ℹ 13 more rows\n\nzhadina |> \n filter(n == 10) # равно (дважды знак равно)\n\n# A tibble: 3 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина злая шоколадина шоколадина 10\n2 жадина-говядина кислая капуста другое 10\n3 жадина-говядина никто ее не ест другое 10\n\nzhadina |> \n filter(word_3 == \"шоколадина\")\n\n# A tibble: 13 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе шоколадина шоколадина 1117\n 5 жадина-говядина гнилая шоколадина шоколадина 12\n 6 жадина-говядина дома шоколадина шоколадина 29\n 7 жадина-говядина злая шоколадина шоколадина 10\n 8 жадина-говядина кошка шоколадина шоколадина 18\n 9 жадина-говядина *опа шоколадина шоколадина 299\n10 жадина-говядина простая шоколадина шоколадина 59\n11 жадина-говядина пустая шоколадина шоколадина 3774\n12 жадина-говядина сухая шоколадина шоколадина 16\n13 жадина-говядина тупая шоколадина шоколадина 50\n\nzhadina |> \n filter(word_3 != \"шоколадина\")\n\n# A tibble: 13 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина в *опе огурец огурец 11\n 2 жадина-говядина зеленый огурец огурец 343\n 3 жадина-говядина кислая капуста другое 10\n 4 жадина-говядина кошка поросятина другое 42\n 5 жадина-говядина никто ее не ест другое 10\n 6 жадина-говядина кусок шоколадины шоколадина 113\n 7 жадина-говядина на пузе барабан барабан 14\n 8 жадина-говядина немецкий барабан барабан 12\n 9 жадина-говядина соленый барабан барабан 11\n10 жадина-говядина соленый огурец огурец 26910\n11 жадина-говядина соленый помидор другое 17\n12 жадина-говядина турецкий барабан барабан 3003\n13 жадина-говядина турецкий таракан другое 13\n\nzhadina |> \n filter(nchar(word_3) == 7)\n\n# A tibble: 7 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина кислая капуста другое 10\n2 жадина-говядина на пузе барабан барабан 14\n3 жадина-говядина немецкий барабан барабан 12\n4 жадина-говядина соленый барабан барабан 11\n5 жадина-говядина соленый помидор другое 17\n6 жадина-говядина турецкий барабан барабан 3003\n7 жадина-говядина турецкий таракан другое 13\n\n\nКроме того, условия можно перечислить через запятую (аналог логического “и”):\n\nzhadina |> \n filter(n > 15,\n n < 100)\n\n# A tibble: 8 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина бука шоколадина шоколадина 44\n2 жадина-говядина дома шоколадина шоколадина 29\n3 жадина-говядина кошка поросятина другое 42\n4 жадина-говядина кошка шоколадина шоколадина 18\n5 жадина-говядина простая шоколадина шоколадина 59\n6 жадина-говядина соленый помидор другое 17\n7 жадина-говядина сухая шоколадина шоколадина 16\n8 жадина-говядина тупая шоколадина шоколадина 50\n\n\nЛогическое “или” обозначается палочкой |:\n\nzhadina |> \n filter(n < 15 | n > 100)\n\n# A tibble: 18 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина булка шоколадина шоколадина 250\n 3 жадина-говядина в *опе огурец огурец 11\n 4 жадина-говядина в *опе шоколадина шоколадина 1117\n 5 жадина-говядина гнилая шоколадина шоколадина 12\n 6 жадина-говядина зеленый огурец огурец 343\n 7 жадина-говядина злая шоколадина шоколадина 10\n 8 жадина-говядина кислая капуста другое 10\n 9 жадина-говядина никто ее не ест другое 10\n10 жадина-говядина кусок шоколадины шоколадина 113\n11 жадина-говядина на пузе барабан барабан 14\n12 жадина-говядина немецкий барабан барабан 12\n13 жадина-говядина *опа шоколадина шоколадина 299\n14 жадина-говядина пустая шоколадина шоколадина 3774\n15 жадина-говядина соленый барабан барабан 11\n16 жадина-говядина соленый огурец огурец 26910\n17 жадина-говядина турецкий барабан барабан 3003\n18 жадина-говядина турецкий таракан другое 13", "crumbs": [ "2  Трансформация данных" ] }, { - "objectID": "2_data_transformation.html#функция-filter", - "href": "2_data_transformation.html#функция-filter", + "objectID": "2_data_transformation.html#функции-count-distinct-arrange", + "href": "2_data_transformation.html#функции-count-distinct-arrange", "title": "2  Трансформация данных", - "section": "2.5 Функция filter()", - "text": "2.5 Функция filter()\nФункция filter() позволяет отфильтровывать строки таблицы по одному или нескольким условиям.\n\nzhadina |> \n filter(n > 100)\n\n# A tibble: 9 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина большая шоколадина шоколадина 941\n2 жадина-говядина булка шоколадина шоколадина 250\n3 жадина-говядина в *опе шоколадина шоколадина 1117\n4 жадина-говядина зеленый огурец огурец 343\n5 жадина-говядина кусок шоколадины шоколадина 113\n6 жадина-говядина *опа шоколадина шоколадина 299\n7 жадина-говядина пустая шоколадина шоколадина 3774\n8 жадина-говядина соленый огурец огурец 26910\n9 жадина-говядина турецкий барабан барабан 3003\n\nzhadina |> \n filter(n < 100)\n\n# A tibble: 17 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина бука шоколадина шоколадина 44\n 2 жадина-говядина в *опе огурец огурец 11\n 3 жадина-говядина гнилая шоколадина шоколадина 12\n 4 жадина-говядина дома шоколадина шоколадина 29\n 5 жадина-говядина злая шоколадина шоколадина 10\n 6 жадина-говядина кислая капуста другое 10\n 7 жадина-говядина кошка поросятина другое 42\n 8 жадина-говядина кошка шоколадина шоколадина 18\n 9 жадина-говядина никто ее не ест другое 10\n10 жадина-говядина на пузе барабан барабан 14\n11 жадина-говядина немецкий барабан барабан 12\n12 жадина-говядина простая шоколадина шоколадина 59\n13 жадина-говядина соленый барабан барабан 11\n14 жадина-говядина соленый помидор другое 17\n15 жадина-говядина сухая шоколадина шоколадина 16\n16 жадина-говядина тупая шоколадина шоколадина 50\n17 жадина-говядина турецкий таракан другое 13\n\nzhadina |> \n filter(n <= 100) # меньше или равно\n\n# A tibble: 17 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина бука шоколадина шоколадина 44\n 2 жадина-говядина в *опе огурец огурец 11\n 3 жадина-говядина гнилая шоколадина шоколадина 12\n 4 жадина-говядина дома шоколадина шоколадина 29\n 5 жадина-говядина злая шоколадина шоколадина 10\n 6 жадина-говядина кислая капуста другое 10\n 7 жадина-говядина кошка поросятина другое 42\n 8 жадина-говядина кошка шоколадина шоколадина 18\n 9 жадина-говядина никто ее не ест другое 10\n10 жадина-говядина на пузе барабан барабан 14\n11 жадина-говядина немецкий барабан барабан 12\n12 жадина-говядина простая шоколадина шоколадина 59\n13 жадина-говядина соленый барабан барабан 11\n14 жадина-говядина соленый помидор другое 17\n15 жадина-говядина сухая шоколадина шоколадина 16\n16 жадина-говядина тупая шоколадина шоколадина 50\n17 жадина-говядина турецкий таракан другое 13\n\nzhadina |> \n filter(n >= 100) # больше или равно\n\n# A tibble: 9 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина большая шоколадина шоколадина 941\n2 жадина-говядина булка шоколадина шоколадина 250\n3 жадина-говядина в *опе шоколадина шоколадина 1117\n4 жадина-говядина зеленый огурец огурец 343\n5 жадина-говядина кусок шоколадины шоколадина 113\n6 жадина-говядина *опа шоколадина шоколадина 299\n7 жадина-говядина пустая шоколадина шоколадина 3774\n8 жадина-говядина соленый огурец огурец 26910\n9 жадина-говядина турецкий барабан барабан 3003\n\nzhadina |> \n filter(n != 10) # не равно\n\n# A tibble: 23 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе огурец огурец 11\n 5 жадина-говядина в *опе шоколадина шоколадина 1117\n 6 жадина-говядина гнилая шоколадина шоколадина 12\n 7 жадина-говядина дома шоколадина шоколадина 29\n 8 жадина-говядина зеленый огурец огурец 343\n 9 жадина-говядина кошка поросятина другое 42\n10 жадина-говядина кошка шоколадина шоколадина 18\n# ℹ 13 more rows\n\nzhadina |> \n filter(n == 10) # равно (дважды знак равно)\n\n# A tibble: 3 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина злая шоколадина шоколадина 10\n2 жадина-говядина кислая капуста другое 10\n3 жадина-говядина никто ее не ест другое 10\n\nzhadina |> \n filter(word_3 == \"шоколадина\")\n\n# A tibble: 13 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе шоколадина шоколадина 1117\n 5 жадина-говядина гнилая шоколадина шоколадина 12\n 6 жадина-говядина дома шоколадина шоколадина 29\n 7 жадина-говядина злая шоколадина шоколадина 10\n 8 жадина-говядина кошка шоколадина шоколадина 18\n 9 жадина-говядина *опа шоколадина шоколадина 299\n10 жадина-говядина простая шоколадина шоколадина 59\n11 жадина-говядина пустая шоколадина шоколадина 3774\n12 жадина-говядина сухая шоколадина шоколадина 16\n13 жадина-говядина тупая шоколадина шоколадина 50\n\nzhadina |> \n filter(word_3 != \"шоколадина\")\n\n# A tibble: 13 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина в *опе огурец огурец 11\n 2 жадина-говядина зеленый огурец огурец 343\n 3 жадина-говядина кислая капуста другое 10\n 4 жадина-говядина кошка поросятина другое 42\n 5 жадина-говядина никто ее не ест другое 10\n 6 жадина-говядина кусок шоколадины шоколадина 113\n 7 жадина-говядина на пузе барабан барабан 14\n 8 жадина-говядина немецкий барабан барабан 12\n 9 жадина-говядина соленый барабан барабан 11\n10 жадина-говядина соленый огурец огурец 26910\n11 жадина-говядина соленый помидор другое 17\n12 жадина-говядина турецкий барабан барабан 3003\n13 жадина-говядина турецкий таракан другое 13\n\nzhadina |> \n filter(nchar(word_3) == 7)\n\n# A tibble: 7 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина кислая капуста другое 10\n2 жадина-говядина на пузе барабан барабан 14\n3 жадина-говядина немецкий барабан барабан 12\n4 жадина-говядина соленый барабан барабан 11\n5 жадина-говядина соленый помидор другое 17\n6 жадина-говядина турецкий барабан барабан 3003\n7 жадина-говядина турецкий таракан другое 13\n\n\nКроме того, условия можно перечислить через запятую (аналог логического “и”):\n\nzhadina |> \n filter(n > 15,\n n < 100)\n\n# A tibble: 8 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n1 жадина-говядина бука шоколадина шоколадина 44\n2 жадина-говядина дома шоколадина шоколадина 29\n3 жадина-говядина кошка поросятина другое 42\n4 жадина-говядина кошка шоколадина шоколадина 18\n5 жадина-говядина простая шоколадина шоколадина 59\n6 жадина-говядина соленый помидор другое 17\n7 жадина-говядина сухая шоколадина шоколадина 16\n8 жадина-говядина тупая шоколадина шоколадина 50\n\n\nЛогическое “или” обозначается палочкой |:\n\nzhadina |> \n filter(n < 15 | n > 100)\n\n# A tibble: 18 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина булка шоколадина шоколадина 250\n 3 жадина-говядина в *опе огурец огурец 11\n 4 жадина-говядина в *опе шоколадина шоколадина 1117\n 5 жадина-говядина гнилая шоколадина шоколадина 12\n 6 жадина-говядина зеленый огурец огурец 343\n 7 жадина-говядина злая шоколадина шоколадина 10\n 8 жадина-говядина кислая капуста другое 10\n 9 жадина-говядина никто ее не ест другое 10\n10 жадина-говядина кусок шоколадины шоколадина 113\n11 жадина-говядина на пузе барабан барабан 14\n12 жадина-говядина немецкий барабан барабан 12\n13 жадина-говядина *опа шоколадина шоколадина 299\n14 жадина-говядина пустая шоколадина шоколадина 3774\n15 жадина-говядина соленый барабан барабан 11\n16 жадина-говядина соленый огурец огурец 26910\n17 жадина-говядина турецкий барабан барабан 3003\n18 жадина-говядина турецкий таракан другое 13", + "section": "2.5 Функции count(), distinct(), arrange()", + "text": "2.5 Функции count(), distinct(), arrange()\nФункция count() очень полезна в разведочном анализе, так как позволяет посчитать сколько какое значение переменной или группы переменных встретилось в датасете. Аргумент sort позволяет дополнительно отсортировать результат от большего к меньшего.\n\nzhadina |> \n count(word_3)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 барабан 4\n2 капуста 1\n3 не ест 1\n4 огурец 3\n5 помидор 1\n6 поросятина 1\n7 таракан 1\n8 шоколадина 13\n9 шоколадины 1\n\nzhadina |> \n count(word_3, sort = TRUE)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 шоколадина 13\n2 барабан 4\n3 огурец 3\n4 капуста 1\n5 не ест 1\n6 помидор 1\n7 поросятина 1\n8 таракан 1\n9 шоколадины 1\n\nzhadina |> \n count(word_1, word_2, sort = TRUE)\n\n# A tibble: 21 × 3\n word_1 word_2 n\n <chr> <chr> <int>\n 1 жадина-говядина соленый 3\n 2 жадина-говядина в *опе 2\n 3 жадина-говядина кошка 2\n 4 жадина-говядина турецкий 2\n 5 жадина-говядина *опа 1\n 6 жадина-говядина большая 1\n 7 жадина-говядина бука 1\n 8 жадина-говядина булка 1\n 9 жадина-говядина гнилая 1\n10 жадина-говядина дома 1\n# ℹ 11 more rows\n\n\nФункция distinct() позволяет вывести уникальное значение переменной или все уникальные комбинации значений нескольких переменных, встретившихся в датасете:\n\nzhadina |> \n distinct(word_3)\n\n# A tibble: 9 × 1\n word_3 \n <chr> \n1 шоколадина\n2 огурец \n3 капуста \n4 поросятина\n5 не ест \n6 шоколадины\n7 барабан \n8 помидор \n9 таракан \n\nzhadina |> \n distinct(word_1, word_2)\n\n# A tibble: 21 × 2\n word_1 word_2 \n <chr> <chr> \n 1 жадина-говядина большая\n 2 жадина-говядина бука \n 3 жадина-говядина булка \n 4 жадина-говядина в *опе \n 5 жадина-говядина гнилая \n 6 жадина-говядина дома \n 7 жадина-говядина зеленый\n 8 жадина-говядина злая \n 9 жадина-говядина кислая \n10 жадина-говядина кошка \n# ℹ 11 more rows\n\n\nФункция arrange() позволяет отсортировать одну или несколько переменных от меньшего к большему (если нужно наоборот — используйте функцию desc()). Числовые переменные сортируются по значениям, а строковые по алфавиту (с учетом особенностей локали, см. раздел Раздел 4.5):\n\nzhadina |> \n count(word_3) |> \n arrange(n)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 капуста 1\n2 не ест 1\n3 помидор 1\n4 поросятина 1\n5 таракан 1\n6 шоколадины 1\n7 огурец 3\n8 барабан 4\n9 шоколадина 13\n\nzhadina |> \n count(word_3) |> \n arrange(desc(n))\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 шоколадина 13\n2 барабан 4\n3 огурец 3\n4 капуста 1\n5 не ест 1\n6 помидор 1\n7 поросятина 1\n8 таракан 1\n9 шоколадины 1\n\nzhadina |> \n count(word_3) |> \n arrange(-n)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 шоколадина 13\n2 барабан 4\n3 огурец 3\n4 капуста 1\n5 не ест 1\n6 помидор 1\n7 поросятина 1\n8 таракан 1\n9 шоколадины 1\n\nzhadina |> \n count(word_3) |> \n arrange(word_3)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 барабан 4\n2 капуста 1\n3 не ест 1\n4 огурец 3\n5 помидор 1\n6 поросятина 1\n7 таракан 1\n8 шоколадина 13\n9 шоколадины 1", "crumbs": [ "2  Трансформация данных" ] }, { - "objectID": "2_data_transformation.html#функции-count-distinct-arrange", - "href": "2_data_transformation.html#функции-count-distinct-arrange", + "objectID": "2_data_transformation.html#функция-select", + "href": "2_data_transformation.html#функция-select", "title": "2  Трансформация данных", - "section": "2.6 Функции count(), distinct(), arrange()", - "text": "2.6 Функции count(), distinct(), arrange()\nФункция count() очень полезна в разведочном анализе, так как позволяет посчитать сколько какое значение переменной или группы переменных встретилось в датасете. Аргумент sort позволяет дополнительно отсортировать результат от большего к меньшего.\n\nzhadina |> \n count(word_3)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 барабан 4\n2 капуста 1\n3 не ест 1\n4 огурец 3\n5 помидор 1\n6 поросятина 1\n7 таракан 1\n8 шоколадина 13\n9 шоколадины 1\n\nzhadina |> \n count(word_3, sort = TRUE)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 шоколадина 13\n2 барабан 4\n3 огурец 3\n4 капуста 1\n5 не ест 1\n6 помидор 1\n7 поросятина 1\n8 таракан 1\n9 шоколадины 1\n\nzhadina |> \n count(word_1, word_2, sort = TRUE)\n\n# A tibble: 21 × 3\n word_1 word_2 n\n <chr> <chr> <int>\n 1 жадина-говядина соленый 3\n 2 жадина-говядина в *опе 2\n 3 жадина-говядина кошка 2\n 4 жадина-говядина турецкий 2\n 5 жадина-говядина *опа 1\n 6 жадина-говядина большая 1\n 7 жадина-говядина бука 1\n 8 жадина-говядина булка 1\n 9 жадина-говядина гнилая 1\n10 жадина-говядина дома 1\n# ℹ 11 more rows\n\n\nФункция distinct() позволяет вывести уникальное значение переменной или все уникальные комбинации значений нескольких переменных, встретившихся в датасете:\n\nzhadina |> \n distinct(word_3)\n\n# A tibble: 9 × 1\n word_3 \n <chr> \n1 шоколадина\n2 огурец \n3 капуста \n4 поросятина\n5 не ест \n6 шоколадины\n7 барабан \n8 помидор \n9 таракан \n\nzhadina |> \n distinct(word_1, word_2)\n\n# A tibble: 21 × 2\n word_1 word_2 \n <chr> <chr> \n 1 жадина-говядина большая\n 2 жадина-говядина бука \n 3 жадина-говядина булка \n 4 жадина-говядина в *опе \n 5 жадина-говядина гнилая \n 6 жадина-говядина дома \n 7 жадина-говядина зеленый\n 8 жадина-говядина злая \n 9 жадина-говядина кислая \n10 жадина-говядина кошка \n# ℹ 11 more rows\n\n\nФункция arrange() позволяет отсортировать одну или несколько переменных от меньшего к большему (если нужно наоборот — используйте функцию desc()). Числовые переменные сортируются по значениям, а строковые по алфавиту (с учетом особенностей локали, см. раздел Раздел 4.5):\n\nzhadina |> \n count(word_3) |> \n arrange(n)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 капуста 1\n2 не ест 1\n3 помидор 1\n4 поросятина 1\n5 таракан 1\n6 шоколадины 1\n7 огурец 3\n8 барабан 4\n9 шоколадина 13\n\nzhadina |> \n count(word_3) |> \n arrange(desc(n))\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 шоколадина 13\n2 барабан 4\n3 огурец 3\n4 капуста 1\n5 не ест 1\n6 помидор 1\n7 поросятина 1\n8 таракан 1\n9 шоколадины 1\n\nzhadina |> \n count(word_3) |> \n arrange(-n)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 шоколадина 13\n2 барабан 4\n3 огурец 3\n4 капуста 1\n5 не ест 1\n6 помидор 1\n7 поросятина 1\n8 таракан 1\n9 шоколадины 1\n\nzhadina |> \n count(word_3) |> \n arrange(word_3)\n\n# A tibble: 9 × 2\n word_3 n\n <chr> <int>\n1 барабан 4\n2 капуста 1\n3 не ест 1\n4 огурец 3\n5 помидор 1\n6 поросятина 1\n7 таракан 1\n8 шоколадина 13\n9 шоколадины 1", + "section": "2.6 Функция select()", + "text": "2.6 Функция select()\nФункция select() позволяет выбрать из датасета некоторый набор столбцов.\n\nzhadina |> \n select(word_1, type, n)\n\n# A tibble: 26 × 3\n word_1 type n\n <chr> <chr> <dbl>\n 1 жадина-говядина шоколадина 941\n 2 жадина-говядина шоколадина 44\n 3 жадина-говядина шоколадина 250\n 4 жадина-говядина огурец 11\n 5 жадина-говядина шоколадина 1117\n 6 жадина-говядина шоколадина 12\n 7 жадина-говядина шоколадина 29\n 8 жадина-говядина огурец 343\n 9 жадина-говядина шоколадина 10\n10 жадина-говядина другое 10\n# ℹ 16 more rows\n\n\nВнутри функции select() можно использовать функции, которые позволяют не только перечислять через запятую необходимые столбцы, но и выделять их по имени:\n\nzhadina |> \n select(starts_with(\"word\"))\n\n# A tibble: 26 × 3\n word_1 word_2 word_3 \n <chr> <chr> <chr> \n 1 жадина-говядина большая шоколадина\n 2 жадина-говядина бука шоколадина\n 3 жадина-говядина булка шоколадина\n 4 жадина-говядина в *опе огурец \n 5 жадина-говядина в *опе шоколадина\n 6 жадина-говядина гнилая шоколадина\n 7 жадина-говядина дома шоколадина\n 8 жадина-говядина зеленый огурец \n 9 жадина-говядина злая шоколадина\n10 жадина-говядина кислая капуста \n# ℹ 16 more rows\n\nzhadina |> \n select(ends_with(\"1\"))\n\n# A tibble: 26 × 1\n word_1 \n <chr> \n 1 жадина-говядина\n 2 жадина-говядина\n 3 жадина-говядина\n 4 жадина-говядина\n 5 жадина-говядина\n 6 жадина-говядина\n 7 жадина-говядина\n 8 жадина-говядина\n 9 жадина-говядина\n10 жадина-говядина\n# ℹ 16 more rows\n\nzhadina |> \n select(matches(\"ord\"))\n\n# A tibble: 26 × 3\n word_1 word_2 word_3 \n <chr> <chr> <chr> \n 1 жадина-говядина большая шоколадина\n 2 жадина-говядина бука шоколадина\n 3 жадина-говядина булка шоколадина\n 4 жадина-говядина в *опе огурец \n 5 жадина-говядина в *опе шоколадина\n 6 жадина-говядина гнилая шоколадина\n 7 жадина-говядина дома шоколадина\n 8 жадина-говядина зеленый огурец \n 9 жадина-говядина злая шоколадина\n10 жадина-говядина кислая капуста \n# ℹ 16 more rows\n\n\nКроме того, внутри функции select() действует оператор двоеточие, который мы видели при создании векторов 5:10:\n\nzhadina |> \n select(word_3:n) # от колонки word_3 до колонки n\n\n# A tibble: 26 × 3\n word_3 type n\n <chr> <chr> <dbl>\n 1 шоколадина шоколадина 941\n 2 шоколадина шоколадина 44\n 3 шоколадина шоколадина 250\n 4 огурец огурец 11\n 5 шоколадина шоколадина 1117\n 6 шоколадина шоколадина 12\n 7 шоколадина шоколадина 29\n 8 огурец огурец 343\n 9 шоколадина шоколадина 10\n10 капуста другое 10\n# ℹ 16 more rows", "crumbs": [ "2  Трансформация данных" ] @@ -209,12 +209,22 @@ "2  Трансформация данных" ] }, + { + "objectID": "2_data_transformation.html#функция-relocate", + "href": "2_data_transformation.html#функция-relocate", + "title": "2  Трансформация данных", + "section": "2.8 Функция relocate()", + "text": "2.8 Функция relocate()\nФункция relocate() позволяет перемещать переменные в таблице (по умолчанию в начало):\n\nzhadina |> \n relocate(type)\n\n# A tibble: 26 × 5\n type word_1 word_2 word_3 n\n <chr> <chr> <chr> <chr> <dbl>\n 1 шоколадина жадина-говядина большая шоколадина 941\n 2 шоколадина жадина-говядина бука шоколадина 44\n 3 шоколадина жадина-говядина булка шоколадина 250\n 4 огурец жадина-говядина в *опе огурец 11\n 5 шоколадина жадина-говядина в *опе шоколадина 1117\n 6 шоколадина жадина-говядина гнилая шоколадина 12\n 7 шоколадина жадина-говядина дома шоколадина 29\n 8 огурец жадина-говядина зеленый огурец 343\n 9 шоколадина жадина-говядина злая шоколадина 10\n10 другое жадина-говядина кислая капуста 10\n# ℹ 16 more rows\n\n\nЕсли хочется перемещать в какое-то другое место, то можно использовать аргументы .after и .before:\n\nzhadina |> \n relocate(n, .after = word_3)\n\n# A tibble: 26 × 5\n word_1 word_2 word_3 n type \n <chr> <chr> <chr> <dbl> <chr> \n 1 жадина-говядина большая шоколадина 941 шоколадина\n 2 жадина-говядина бука шоколадина 44 шоколадина\n 3 жадина-говядина булка шоколадина 250 шоколадина\n 4 жадина-говядина в *опе огурец 11 огурец \n 5 жадина-говядина в *опе шоколадина 1117 шоколадина\n 6 жадина-говядина гнилая шоколадина 12 шоколадина\n 7 жадина-говядина дома шоколадина 29 шоколадина\n 8 жадина-говядина зеленый огурец 343 огурец \n 9 жадина-говядина злая шоколадина 10 шоколадина\n10 жадина-говядина кислая капуста 10 другое \n# ℹ 16 more rows\n\nzhadina |> \n relocate(n, .before = type)\n\n# A tibble: 26 × 5\n word_1 word_2 word_3 n type \n <chr> <chr> <chr> <dbl> <chr> \n 1 жадина-говядина большая шоколадина 941 шоколадина\n 2 жадина-говядина бука шоколадина 44 шоколадина\n 3 жадина-говядина булка шоколадина 250 шоколадина\n 4 жадина-говядина в *опе огурец 11 огурец \n 5 жадина-говядина в *опе шоколадина 1117 шоколадина\n 6 жадина-говядина гнилая шоколадина 12 шоколадина\n 7 жадина-говядина дома шоколадина 29 шоколадина\n 8 жадина-говядина зеленый огурец 343 огурец \n 9 жадина-говядина злая шоколадина 10 шоколадина\n10 жадина-говядина кислая капуста 10 другое \n# ℹ 16 more rows", + "crumbs": [ + "2  Трансформация данных" + ] + }, { "objectID": "2_data_transformation.html#функция-group_by-summarise-и-group_by-mutate", "href": "2_data_transformation.html#функция-group_by-summarise-и-group_by-mutate", "title": "2  Трансформация данных", - "section": "2.8 Функция group_by() |> summarise() и group_by() |> mutate()", - "text": "2.8 Функция group_by() |> summarise() и group_by() |> mutate()\n\nzhadina |> \n group_by(type) |> \n summarise(mean_by_type = mean(n))\n\n# A tibble: 4 × 2\n type mean_by_type\n <chr> <dbl>\n1 барабан 760 \n2 другое 18.4\n3 огурец 9088 \n4 шоколадина 481. \n\nzhadina |> \n group_by(type) |> \n summarise(mean_by_type = mean(n),\n median_by_type = median(n))\n\n# A tibble: 4 × 3\n type mean_by_type median_by_type\n <chr> <dbl> <dbl>\n1 барабан 760 13 \n2 другое 18.4 13 \n3 огурец 9088 343 \n4 шоколадина 481. 54.5\n\nzhadina |> \n group_by(type, word_3) |> \n summarise(mean_by_type = mean(n),\n median_by_type = median(n))\n\n`summarise()` has grouped output by 'type'. You can override using the\n`.groups` argument.\n\n\n# A tibble: 9 × 4\n# Groups: type [4]\n type word_3 mean_by_type median_by_type\n <chr> <chr> <dbl> <dbl>\n1 барабан барабан 760 13\n2 другое капуста 10 10\n3 другое не ест 10 10\n4 другое помидор 17 17\n5 другое поросятина 42 42\n6 другое таракан 13 13\n7 огурец огурец 9088 343\n8 шоколадина шоколадина 509. 50\n9 шоколадина шоколадины 113 113\n\nzhadina |> \n group_by(type, word_3) |> \n mutate(mean_by_type = mean(n),\n median_by_type = median(n))\n\n# A tibble: 26 × 7\n# Groups: type, word_3 [9]\n word_1 word_2 word_3 type n mean_by_type median_by_type\n <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>\n 1 жадина-говядина большая шоколадина шоколад… 941 509. 50\n 2 жадина-говядина бука шоколадина шоколад… 44 509. 50\n 3 жадина-говядина булка шоколадина шоколад… 250 509. 50\n 4 жадина-говядина в *опе огурец огурец 11 9088 343\n 5 жадина-говядина в *опе шоколадина шоколад… 1117 509. 50\n 6 жадина-говядина гнилая шоколадина шоколад… 12 509. 50\n 7 жадина-говядина дома шоколадина шоколад… 29 509. 50\n 8 жадина-говядина зеленый огурец огурец 343 9088 343\n 9 жадина-говядина злая шоколадина шоколад… 10 509. 50\n10 жадина-говядина кислая капуста другое 10 10 10\n# ℹ 16 more rows\n\nzhadina |> \n group_by(type) |> \n mutate(mean_by_type = mean(n),\n median_by_type = median(n))\n\n# A tibble: 26 × 7\n# Groups: type [4]\n word_1 word_2 word_3 type n mean_by_type median_by_type\n <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>\n 1 жадина-говядина большая шоколадина шоколад… 941 481. 54.5\n 2 жадина-говядина бука шоколадина шоколад… 44 481. 54.5\n 3 жадина-говядина булка шоколадина шоколад… 250 481. 54.5\n 4 жадина-говядина в *опе огурец огурец 11 9088 343 \n 5 жадина-говядина в *опе шоколадина шоколад… 1117 481. 54.5\n 6 жадина-говядина гнилая шоколадина шоколад… 12 481. 54.5\n 7 жадина-говядина дома шоколадина шоколад… 29 481. 54.5\n 8 жадина-говядина зеленый огурец огурец 343 9088 343 \n 9 жадина-говядина злая шоколадина шоколад… 10 481. 54.5\n10 жадина-говядина кислая капуста другое 10 18.4 13 \n# ℹ 16 more rows", + "section": "2.9 Функция group_by() |> summarise() и group_by() |> mutate()", + "text": "2.9 Функция group_by() |> summarise() и group_by() |> mutate()\nВсе предыдущие операции работали со строками и столбцами таблицами. В этом разделе мы познакомимся с операцией группировки, которая позволяет разделять таблицу на группы и агрегировать информацию по этим группам. Функция group_by() разделяет на группы, но саму структуру и содержание таблицы не меняет, однако при печати выводится сообщение об имени группирующей переменной и сколько всего групп:\n\nzhadina |> \n group_by(type)\n\n# A tibble: 26 × 5\n# Groups: type [4]\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе огурец огурец 11\n 5 жадина-говядина в *опе шоколадина шоколадина 1117\n 6 жадина-говядина гнилая шоколадина шоколадина 12\n 7 жадина-говядина дома шоколадина шоколадина 29\n 8 жадина-говядина зеленый огурец огурец 343\n 9 жадина-говядина злая шоколадина шоколадина 10\n10 жадина-говядина кислая капуста другое 10\n# ℹ 16 more rows\n\n\nПосле того как мы сгруппировали можно использовать функцию summarise() (или ее американский вариант summarize()), она возвращает по одному значению на каждую группу. Например, посчитаем среднее количество встречаемости для каждого типа:\n\nzhadina |> \n group_by(type) |> \n summarise(mean_by_type = mean(n))\n\n# A tibble: 4 × 2\n type mean_by_type\n <chr> <dbl>\n1 барабан 760 \n2 другое 18.4\n3 огурец 9088 \n4 шоколадина 481. \n\n\nФункций саммаризации может быть несколько:\n\nzhadina |> \n group_by(type) |> \n summarise(mean_by_type = mean(n),\n median_by_type = median(n))\n\n# A tibble: 4 × 3\n type mean_by_type median_by_type\n <chr> <dbl> <dbl>\n1 барабан 760 13 \n2 другое 18.4 13 \n3 огурец 9088 343 \n4 шоколадина 481. 54.5\n\n\nКроме того, важно отметить, что группировка может происходить не по одной переменной, а по нескольким.\n\nzhadina |> \n group_by(type, word_3) |> \n summarise(mean_by_type = mean(n),\n median_by_type = median(n))\n\n`summarise()` has grouped output by 'type'. You can override using the\n`.groups` argument.\n\n\n# A tibble: 9 × 4\n# Groups: type [4]\n type word_3 mean_by_type median_by_type\n <chr> <chr> <dbl> <dbl>\n1 барабан барабан 760 13\n2 другое капуста 10 10\n3 другое не ест 10 10\n4 другое помидор 17 17\n5 другое поросятина 42 42\n6 другое таракан 13 13\n7 огурец огурец 9088 343\n8 шоколадина шоколадина 509. 50\n9 шоколадина шоколадины 113 113\n\n\nВажно также отметить, что в функции summarise() с недавних пор аргумент .by, который является заменой функции group_by():\n\nzhadina |> \n summarise(mean_by_type = mean(n),\n median_by_type = median(n),\n .by = c(type, word_3))\n\n# A tibble: 9 × 4\n type word_3 mean_by_type median_by_type\n <chr> <chr> <dbl> <dbl>\n1 шоколадина шоколадина 509. 50\n2 огурец огурец 9088 343\n3 другое капуста 10 10\n4 другое поросятина 42 42\n5 другое не ест 10 10\n6 шоколадина шоколадины 113 113\n7 барабан барабан 760 13\n8 другое помидор 17 17\n9 другое таракан 13 13\n\n\nКроме того, функцию group_by() или аргумент .by можно использовать в связке с функцией mutate(). В таком случае саммаризирующий результат будет выведен не единожды для каждой группы, а присоединен к исходной таблице в виде новой переменной.\n\nzhadina |> \n group_by(type) |> \n mutate(mean_by_type = mean(n),\n median_by_type = median(n))\n\n# A tibble: 26 × 7\n# Groups: type [4]\n word_1 word_2 word_3 type n mean_by_type median_by_type\n <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>\n 1 жадина-говядина большая шоколадина шоколад… 941 481. 54.5\n 2 жадина-говядина бука шоколадина шоколад… 44 481. 54.5\n 3 жадина-говядина булка шоколадина шоколад… 250 481. 54.5\n 4 жадина-говядина в *опе огурец огурец 11 9088 343 \n 5 жадина-говядина в *опе шоколадина шоколад… 1117 481. 54.5\n 6 жадина-говядина гнилая шоколадина шоколад… 12 481. 54.5\n 7 жадина-говядина дома шоколадина шоколад… 29 481. 54.5\n 8 жадина-говядина зеленый огурец огурец 343 9088 343 \n 9 жадина-говядина злая шоколадина шоколад… 10 481. 54.5\n10 жадина-говядина кислая капуста другое 10 18.4 13 \n# ℹ 16 more rows\n\nzhadina |> \n mutate(mean_by_type = mean(n),\n median_by_type = median(n),\n .by = type)\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n mean_by_type median_by_type\n <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>\n 1 жадина-говядина большая шоколадина шоколад… 941 481. 54.5\n 2 жадина-говядина бука шоколадина шоколад… 44 481. 54.5\n 3 жадина-говядина булка шоколадина шоколад… 250 481. 54.5\n 4 жадина-говядина в *опе огурец огурец 11 9088 343 \n 5 жадина-говядина в *опе шоколадина шоколад… 1117 481. 54.5\n 6 жадина-говядина гнилая шоколадина шоколад… 12 481. 54.5\n 7 жадина-говядина дома шоколадина шоколад… 29 481. 54.5\n 8 жадина-говядина зеленый огурец огурец 343 9088 343 \n 9 жадина-говядина злая шоколадина шоколад… 10 481. 54.5\n10 жадина-говядина кислая капуста другое 10 18.4 13 \n# ℹ 16 more rows\n\n\nНе так часто, но все же бывает, что в большом длинном конвеере, который совмещает в себе много действий, группировка может мешать, так что ее можно снять функцией ungroup():\n\nzhadina |> \n group_by(type) |> \n mutate(mean_by_type = mean(n)) |> \n ungroup()\n\n# A tibble: 26 × 6\n word_1 word_2 word_3 type n mean_by_type\n <chr> <chr> <chr> <chr> <dbl> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941 481. \n 2 жадина-говядина бука шоколадина шоколадина 44 481. \n 3 жадина-говядина булка шоколадина шоколадина 250 481. \n 4 жадина-говядина в *опе огурец огурец 11 9088 \n 5 жадина-говядина в *опе шоколадина шоколадина 1117 481. \n 6 жадина-говядина гнилая шоколадина шоколадина 12 481. \n 7 жадина-говядина дома шоколадина шоколадина 29 481. \n 8 жадина-говядина зеленый огурец огурец 343 9088 \n 9 жадина-говядина злая шоколадина шоколадина 10 481. \n10 жадина-говядина кислая капуста другое 10 18.4\n# ℹ 16 more rows", "crumbs": [ "2  Трансформация данных" ] @@ -223,8 +233,8 @@ "objectID": "2_data_transformation.html#функция-across", "href": "2_data_transformation.html#функция-across", "title": "2  Трансформация данных", - "section": "2.9 Функция across()", - "text": "2.9 Функция across()\nФункция across() позволяет применять одно и то же изменение к группе колонок, которые выбираются набором функций сходных с операциями для функции select(). Важно отметить, что трансформация обычно описывается функцией, и имя функции обычно пишут без круглых скобок.\n\nzhadina |> \n mutate(across(starts_with(\"word\"), toupper))\n\n# A tibble: 26 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 ЖАДИНА-ГОВЯДИНА БОЛЬШАЯ ШОКОЛАДИНА шоколадина 941\n 2 ЖАДИНА-ГОВЯДИНА БУКА ШОКОЛАДИНА шоколадина 44\n 3 ЖАДИНА-ГОВЯДИНА БУЛКА ШОКОЛАДИНА шоколадина 250\n 4 ЖАДИНА-ГОВЯДИНА В *ОПЕ ОГУРЕЦ огурец 11\n 5 ЖАДИНА-ГОВЯДИНА В *ОПЕ ШОКОЛАДИНА шоколадина 1117\n 6 ЖАДИНА-ГОВЯДИНА ГНИЛАЯ ШОКОЛАДИНА шоколадина 12\n 7 ЖАДИНА-ГОВЯДИНА ДОМА ШОКОЛАДИНА шоколадина 29\n 8 ЖАДИНА-ГОВЯДИНА ЗЕЛЕНЫЙ ОГУРЕЦ огурец 343\n 9 ЖАДИНА-ГОВЯДИНА ЗЛАЯ ШОКОЛАДИНА шоколадина 10\n10 ЖАДИНА-ГОВЯДИНА КИСЛАЯ КАПУСТА другое 10\n# ℹ 16 more rows\n\nzhadina |> \n mutate(across(where(is.character), toupper))\n\n# A tibble: 26 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 ЖАДИНА-ГОВЯДИНА БОЛЬШАЯ ШОКОЛАДИНА ШОКОЛАДИНА 941\n 2 ЖАДИНА-ГОВЯДИНА БУКА ШОКОЛАДИНА ШОКОЛАДИНА 44\n 3 ЖАДИНА-ГОВЯДИНА БУЛКА ШОКОЛАДИНА ШОКОЛАДИНА 250\n 4 ЖАДИНА-ГОВЯДИНА В *ОПЕ ОГУРЕЦ ОГУРЕЦ 11\n 5 ЖАДИНА-ГОВЯДИНА В *ОПЕ ШОКОЛАДИНА ШОКОЛАДИНА 1117\n 6 ЖАДИНА-ГОВЯДИНА ГНИЛАЯ ШОКОЛАДИНА ШОКОЛАДИНА 12\n 7 ЖАДИНА-ГОВЯДИНА ДОМА ШОКОЛАДИНА ШОКОЛАДИНА 29\n 8 ЖАДИНА-ГОВЯДИНА ЗЕЛЕНЫЙ ОГУРЕЦ ОГУРЕЦ 343\n 9 ЖАДИНА-ГОВЯДИНА ЗЛАЯ ШОКОЛАДИНА ШОКОЛАДИНА 10\n10 ЖАДИНА-ГОВЯДИНА КИСЛАЯ КАПУСТА ДРУГОЕ 10\n# ℹ 16 more rows\n\n\nФункция может так же работать в связке с функцией group_by() |> summarize() и group_by() |> mutate():\n\nset.seed(42)\nzhadina |> \n mutate(new_numeric_value = rnorm(26)) |> \n group_by(type) |> \n summarise(across(where(is.double), mean))\n\n# A tibble: 4 × 3\n type n new_numeric_value\n <chr> <dbl> <dbl>\n1 барабан 760 0.929 \n2 другое 18.4 -0.472 \n3 огурец 9088 0.0772\n4 шоколадина 481. 0.190 \n\nset.seed(42)\nzhadina |> \n mutate(new_numeric_value = rnorm(26)) |> \n group_by(type) |> \n summarise(n = mean(n),\n new_numeric_value = mean(new_numeric_value))\n\n# A tibble: 4 × 3\n type n new_numeric_value\n <chr> <dbl> <dbl>\n1 барабан 760 0.929 \n2 другое 18.4 -0.472 \n3 огурец 9088 0.0772\n4 шоколадина 481. 0.190 \n\nzhadina |> \n mutate(new_numeric_value = rnorm(26)) |> \n group_by(type) |> \n mutate(across(where(is.double), mean))\n\n# A tibble: 26 × 6\n# Groups: type [4]\n word_1 word_2 word_3 type n new_numeric_value\n <chr> <chr> <chr> <chr> <dbl> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 481. -0.0562\n 2 жадина-говядина бука шоколадина шоколадина 481. -0.0562\n 3 жадина-говядина булка шоколадина шоколадина 481. -0.0562\n 4 жадина-говядина в *опе огурец огурец 9088 -0.687 \n 5 жадина-говядина в *опе шоколадина шоколадина 481. -0.0562\n 6 жадина-говядина гнилая шоколадина шоколадина 481. -0.0562\n 7 жадина-говядина дома шоколадина шоколадина 481. -0.0562\n 8 жадина-говядина зеленый огурец огурец 9088 -0.687 \n 9 жадина-говядина злая шоколадина шоколадина 481. -0.0562\n10 жадина-говядина кислая капуста другое 18.4 -0.851 \n# ℹ 16 more rows", + "section": "2.10 Функция across()", + "text": "2.10 Функция across()\nФункция across() позволяет применять одно и то же изменение к группе колонок, которые выбираются набором функций сходных с операциями для функции select(). Важно отметить, что трансформация обычно описывается функцией, и имя функции обычно пишут без круглых скобок.\n\nzhadina |> \n mutate(across(starts_with(\"word\"), toupper))\n\n# A tibble: 26 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 ЖАДИНА-ГОВЯДИНА БОЛЬШАЯ ШОКОЛАДИНА шоколадина 941\n 2 ЖАДИНА-ГОВЯДИНА БУКА ШОКОЛАДИНА шоколадина 44\n 3 ЖАДИНА-ГОВЯДИНА БУЛКА ШОКОЛАДИНА шоколадина 250\n 4 ЖАДИНА-ГОВЯДИНА В *ОПЕ ОГУРЕЦ огурец 11\n 5 ЖАДИНА-ГОВЯДИНА В *ОПЕ ШОКОЛАДИНА шоколадина 1117\n 6 ЖАДИНА-ГОВЯДИНА ГНИЛАЯ ШОКОЛАДИНА шоколадина 12\n 7 ЖАДИНА-ГОВЯДИНА ДОМА ШОКОЛАДИНА шоколадина 29\n 8 ЖАДИНА-ГОВЯДИНА ЗЕЛЕНЫЙ ОГУРЕЦ огурец 343\n 9 ЖАДИНА-ГОВЯДИНА ЗЛАЯ ШОКОЛАДИНА шоколадина 10\n10 ЖАДИНА-ГОВЯДИНА КИСЛАЯ КАПУСТА другое 10\n# ℹ 16 more rows\n\nzhadina |> \n mutate(across(where(is.character), toupper))\n\n# A tibble: 26 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 ЖАДИНА-ГОВЯДИНА БОЛЬШАЯ ШОКОЛАДИНА ШОКОЛАДИНА 941\n 2 ЖАДИНА-ГОВЯДИНА БУКА ШОКОЛАДИНА ШОКОЛАДИНА 44\n 3 ЖАДИНА-ГОВЯДИНА БУЛКА ШОКОЛАДИНА ШОКОЛАДИНА 250\n 4 ЖАДИНА-ГОВЯДИНА В *ОПЕ ОГУРЕЦ ОГУРЕЦ 11\n 5 ЖАДИНА-ГОВЯДИНА В *ОПЕ ШОКОЛАДИНА ШОКОЛАДИНА 1117\n 6 ЖАДИНА-ГОВЯДИНА ГНИЛАЯ ШОКОЛАДИНА ШОКОЛАДИНА 12\n 7 ЖАДИНА-ГОВЯДИНА ДОМА ШОКОЛАДИНА ШОКОЛАДИНА 29\n 8 ЖАДИНА-ГОВЯДИНА ЗЕЛЕНЫЙ ОГУРЕЦ ОГУРЕЦ 343\n 9 ЖАДИНА-ГОВЯДИНА ЗЛАЯ ШОКОЛАДИНА ШОКОЛАДИНА 10\n10 ЖАДИНА-ГОВЯДИНА КИСЛАЯ КАПУСТА ДРУГОЕ 10\n# ℹ 16 more rows\n\n\nФункция может так же работать в связке с функцией group_by() |> summarize() и group_by() |> mutate():\n\nset.seed(42)\nzhadina |> \n mutate(new_numeric_value = rnorm(26)) |> \n group_by(type) |> \n summarise(across(where(is.double), mean))\n\n# A tibble: 4 × 3\n type n new_numeric_value\n <chr> <dbl> <dbl>\n1 барабан 760 0.929 \n2 другое 18.4 -0.472 \n3 огурец 9088 0.0772\n4 шоколадина 481. 0.190 \n\nset.seed(42)\nzhadina |> \n mutate(new_numeric_value = rnorm(26)) |> \n group_by(type) |> \n summarise(n = mean(n),\n new_numeric_value = mean(new_numeric_value))\n\n# A tibble: 4 × 3\n type n new_numeric_value\n <chr> <dbl> <dbl>\n1 барабан 760 0.929 \n2 другое 18.4 -0.472 \n3 огурец 9088 0.0772\n4 шоколадина 481. 0.190 \n\nzhadina |> \n mutate(new_numeric_value = rnorm(26)) |> \n group_by(type) |> \n mutate(across(where(is.double), mean))\n\n# A tibble: 26 × 6\n# Groups: type [4]\n word_1 word_2 word_3 type n new_numeric_value\n <chr> <chr> <chr> <chr> <dbl> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 481. -0.0562\n 2 жадина-говядина бука шоколадина шоколадина 481. -0.0562\n 3 жадина-говядина булка шоколадина шоколадина 481. -0.0562\n 4 жадина-говядина в *опе огурец огурец 9088 -0.687 \n 5 жадина-говядина в *опе шоколадина шоколадина 481. -0.0562\n 6 жадина-говядина гнилая шоколадина шоколадина 481. -0.0562\n 7 жадина-говядина дома шоколадина шоколадина 481. -0.0562\n 8 жадина-говядина зеленый огурец огурец 9088 -0.687 \n 9 жадина-говядина злая шоколадина шоколадина 481. -0.0562\n10 жадина-говядина кислая капуста другое 18.4 -0.851 \n# ℹ 16 more rows", "crumbs": [ "2  Трансформация данных" ] @@ -233,8 +243,8 @@ "objectID": "2_data_transformation.html#функции-семейства-pivot_...", "href": "2_data_transformation.html#функции-семейства-pivot_...", "title": "2  Трансформация данных", - "section": "2.10 Функции семейства pivot_...()", - "text": "2.10 Функции семейства pivot_...()\nОсновная концепция пакет tidyverse строится вокруг понятия опрятный формат (tidy data). Опрятные данные означает приведение табличных данных к такому формату, в котором:\n\nкаждая переменная имеет собственный столбец;\nкаждый наблюдение имеет собственную строку;\nкаждое значение имеет свою собственную ячейку.\n\nТакое представление данных делает любые трансформации и визуализацию делом нескольких строк.\nОднако данный подход не дает четких инструкций по поводу того, что делать с наблюдениями похожего типа, которые сделаны по поводу одной единицы. Рассмотрим на примере данных количества носителей разных языков Индии:\n\nindia_langauges_wide <- read_csv(\"https://raw.githubusercontent.com/agricolamz/daR4hs/main/data/w2_india_languages.csv\")\n\nRows: 12 Columns: 5\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \",\"\nchr (1): language\ndbl (4): n_L1_sp, n_L2_sp, n_L3_sp, n_all_sp\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n\nindia_langauges_wide\n\n# A tibble: 12 × 5\n language n_L1_sp n_L2_sp n_L3_sp n_all_sp\n <chr> <dbl> <dbl> <dbl> <dbl>\n 1 Hindi 422048642 98207180 31160696 551416518\n 2 English 226449 86125221 38993066 125344736\n 3 Bengali 83369769 6637222 1108088 91115079\n 4 Telugu 74002856 9723626 1266019 84992501\n 5 Marathi 71936894 9546414 2701498 84184806\n 6 Tamil 60793814 4992253 956335 66742402\n 7 Urdu 51536111 6535489 1007912 59079512\n 8 Kannada 37924011 11455287 1396428 50775726\n 9 Gujarati 46091617 3476355 703989 50271961\n10 Odia 33017446 3272151 319525 36609122\n11 Malayalam 33066392 499188 195885 33761465\n12 Sanskrit 14135 1234931 3742223 4991289\n\n\nСейчас данные в так называемом широком формате. Хинди в датасете встречается единожды, но значения сколько носителей выучили его как первый/второй/третий/все вместе записаны в виде отдельных столбцов.\n\nindia_langauges_wide |> \n pivot_longer(names_to = \"L\", values_to = \"value\", n_L1_sp:n_all_sp)\n\n# A tibble: 48 × 3\n language L value\n <chr> <chr> <dbl>\n 1 Hindi n_L1_sp 422048642\n 2 Hindi n_L2_sp 98207180\n 3 Hindi n_L3_sp 31160696\n 4 Hindi n_all_sp 551416518\n 5 English n_L1_sp 226449\n 6 English n_L2_sp 86125221\n 7 English n_L3_sp 38993066\n 8 English n_all_sp 125344736\n 9 Bengali n_L1_sp 83369769\n10 Bengali n_L2_sp 6637222\n# ℹ 38 more rows\n\n\nТеперь данные представлены в длинном формате, который удобен для визуализации и каких-то трансформаций. Хинди встречается в датасете четырежды, однако информация про то какая группа носителей имеется в виду записана в переменную L. Более того, информация в столбце n_all_sp изначальна была лишней: эта инфомрация уже содержится как сумма всех остальных столбцов. Мы можем отфильтровать значения n_all_sp и получить их снова:\n\nindia_langauges_wide |> \n pivot_longer(names_to = \"L\", values_to = \"value\", starts_with(\"n_\")) |> \n filter(L != \"n_all_sp\") |> \n group_by(L) |> \n summarise(sum = sum(value),\n mean = mean(value))\n\n# A tibble: 3 × 3\n L sum mean\n <chr> <dbl> <dbl>\n1 n_L1_sp 914028136 76169011.\n2 n_L2_sp 241705317 20142110.\n3 n_L3_sp 83551664 6962639.\n\n\nФункция pivot_wider() позволяет вновь перевести данные в широкий формат, который удобен для представления данных людям:\n\nindia_langauges_wide |> \n pivot_longer(names_to = \"L\", values_to = \"value\", starts_with(\"n_\")) |> \n pivot_wider(names_from = \"L\", values_from = \"value\")\n\n# A tibble: 12 × 5\n language n_L1_sp n_L2_sp n_L3_sp n_all_sp\n <chr> <dbl> <dbl> <dbl> <dbl>\n 1 Hindi 422048642 98207180 31160696 551416518\n 2 English 226449 86125221 38993066 125344736\n 3 Bengali 83369769 6637222 1108088 91115079\n 4 Telugu 74002856 9723626 1266019 84992501\n 5 Marathi 71936894 9546414 2701498 84184806\n 6 Tamil 60793814 4992253 956335 66742402\n 7 Urdu 51536111 6535489 1007912 59079512\n 8 Kannada 37924011 11455287 1396428 50775726\n 9 Gujarati 46091617 3476355 703989 50271961\n10 Odia 33017446 3272151 319525 36609122\n11 Malayalam 33066392 499188 195885 33761465\n12 Sanskrit 14135 1234931 3742223 4991289", + "section": "2.11 Функции семейства pivot_...()", + "text": "2.11 Функции семейства pivot_...()\nОсновная концепция пакет tidyverse строится вокруг понятия опрятный формат (tidy data). Опрятные данные означает приведение табличных данных к такому формату, в котором:\n\nкаждая переменная имеет собственный столбец;\nкаждый наблюдение имеет собственную строку;\nкаждое значение имеет свою собственную ячейку.\n\nТакое представление данных делает любые трансформации и визуализацию делом нескольких строк.\nОднако данный подход не дает четких инструкций по поводу того, что делать с наблюдениями похожего типа, которые сделаны по поводу одной единицы. Рассмотрим на примере данных количества носителей разных языков Индии:\n\nindia_langauges_wide <- read_csv(\"https://raw.githubusercontent.com/agricolamz/daR4hs/main/data/w2_india_languages.csv\")\n\nRows: 12 Columns: 5\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \",\"\nchr (1): language\ndbl (4): n_L1_sp, n_L2_sp, n_L3_sp, n_all_sp\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n\nindia_langauges_wide\n\n# A tibble: 12 × 5\n language n_L1_sp n_L2_sp n_L3_sp n_all_sp\n <chr> <dbl> <dbl> <dbl> <dbl>\n 1 Hindi 422048642 98207180 31160696 551416518\n 2 English 226449 86125221 38993066 125344736\n 3 Bengali 83369769 6637222 1108088 91115079\n 4 Telugu 74002856 9723626 1266019 84992501\n 5 Marathi 71936894 9546414 2701498 84184806\n 6 Tamil 60793814 4992253 956335 66742402\n 7 Urdu 51536111 6535489 1007912 59079512\n 8 Kannada 37924011 11455287 1396428 50775726\n 9 Gujarati 46091617 3476355 703989 50271961\n10 Odia 33017446 3272151 319525 36609122\n11 Malayalam 33066392 499188 195885 33761465\n12 Sanskrit 14135 1234931 3742223 4991289\n\n\nСейчас данные в так называемом широком формате. Хинди в датасете встречается единожды, но значения сколько носителей выучили его как первый/второй/третий/все вместе записаны в виде отдельных столбцов.\n\nindia_langauges_wide |> \n pivot_longer(names_to = \"L\", values_to = \"value\", n_L1_sp:n_all_sp)\n\n# A tibble: 48 × 3\n language L value\n <chr> <chr> <dbl>\n 1 Hindi n_L1_sp 422048642\n 2 Hindi n_L2_sp 98207180\n 3 Hindi n_L3_sp 31160696\n 4 Hindi n_all_sp 551416518\n 5 English n_L1_sp 226449\n 6 English n_L2_sp 86125221\n 7 English n_L3_sp 38993066\n 8 English n_all_sp 125344736\n 9 Bengali n_L1_sp 83369769\n10 Bengali n_L2_sp 6637222\n# ℹ 38 more rows\n\n\nТеперь данные представлены в длинном формате, который удобен для визуализации и каких-то трансформаций. Хинди встречается в датасете четырежды, однако информация про то какая группа носителей имеется в виду записана в переменную L. Более того, информация в столбце n_all_sp изначальна была лишней: эта инфомрация уже содержится как сумма всех остальных столбцов. Мы можем отфильтровать значения n_all_sp и получить их снова:\n\nindia_langauges_wide |> \n pivot_longer(names_to = \"L\", values_to = \"value\", starts_with(\"n_\")) |> \n filter(L != \"n_all_sp\") |> \n group_by(L) |> \n summarise(sum = sum(value),\n mean = mean(value))\n\n# A tibble: 3 × 3\n L sum mean\n <chr> <dbl> <dbl>\n1 n_L1_sp 914028136 76169011.\n2 n_L2_sp 241705317 20142110.\n3 n_L3_sp 83551664 6962639.\n\n\nФункция pivot_wider() позволяет вновь перевести данные в широкий формат, который удобен для представления данных людям:\n\nindia_langauges_wide |> \n pivot_longer(names_to = \"L\", values_to = \"value\", starts_with(\"n_\")) |> \n pivot_wider(names_from = \"L\", values_from = \"value\")\n\n# A tibble: 12 × 5\n language n_L1_sp n_L2_sp n_L3_sp n_all_sp\n <chr> <dbl> <dbl> <dbl> <dbl>\n 1 Hindi 422048642 98207180 31160696 551416518\n 2 English 226449 86125221 38993066 125344736\n 3 Bengali 83369769 6637222 1108088 91115079\n 4 Telugu 74002856 9723626 1266019 84992501\n 5 Marathi 71936894 9546414 2701498 84184806\n 6 Tamil 60793814 4992253 956335 66742402\n 7 Urdu 51536111 6535489 1007912 59079512\n 8 Kannada 37924011 11455287 1396428 50775726\n 9 Gujarati 46091617 3476355 703989 50271961\n10 Odia 33017446 3272151 319525 36609122\n11 Malayalam 33066392 499188 195885 33761465\n12 Sanskrit 14135 1234931 3742223 4991289", "crumbs": [ "2  Трансформация данных" ] @@ -243,8 +253,8 @@ "objectID": "2_data_transformation.html#функции-семейства-bind_...", "href": "2_data_transformation.html#функции-семейства-bind_...", "title": "2  Трансформация данных", - "section": "2.11 Функции семейства bind_...()", - "text": "2.11 Функции семейства bind_...()\nФункции bind_rows() и bind_cols() позволяют соединять несколько таблиц. Функция bind_rows() присоединяет таблицу снизу, при этом она должна иметь столько же переменных и тип переменных должен совпадать с основной таблицей.\n\nzhadina_new <- tibble(word_1 = \"жадина-говядина\",\n word_2 = \"пупецкий\",\n word_3 = \"барабан\",\n type = \"барабан\",\n n = 0)\n\nzhadina |> \n bind_rows(zhadina_new)\n\n# A tibble: 27 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе огурец огурец 11\n 5 жадина-говядина в *опе шоколадина шоколадина 1117\n 6 жадина-говядина гнилая шоколадина шоколадина 12\n 7 жадина-говядина дома шоколадина шоколадина 29\n 8 жадина-говядина зеленый огурец огурец 343\n 9 жадина-говядина злая шоколадина шоколадина 10\n10 жадина-говядина кислая капуста другое 10\n# ℹ 17 more rows\n\n\nФункция bind_cols() присоединяет таблицу справа, при этом она должна иметь столько же строчек, сколько есть в с основной таблице:\n\nzhadina_additional_columns <- tibble(new_var1 = rnorm(26),\n new_var2 = letters)\n\nzhadina |> \n bind_cols(zhadina_additional_columns)\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n new_var1 new_var2\n <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 1.58 a \n 2 жадина-говядина бука шоколадина шоколадина 44 0.643 b \n 3 жадина-говядина булка шоколадина шоколадина 250 0.0898 c \n 4 жадина-говядина в *опе огурец огурец 11 0.277 d \n 5 жадина-говядина в *опе шоколадина шоколадина 1117 0.679 e \n 6 жадина-говядина гнилая шоколадина шоколадина 12 0.0898 f \n 7 жадина-говядина дома шоколадина шоколадина 29 -2.99 g \n 8 жадина-говядина зеленый огурец огурец 343 0.285 h \n 9 жадина-говядина злая шоколадина шоколадина 10 -0.367 i \n10 жадина-говядина кислая капуста другое 10 0.185 j \n# ℹ 16 more rows", + "section": "2.12 Функции семейства bind_...()", + "text": "2.12 Функции семейства bind_...()\nФункции bind_rows() и bind_cols() позволяют соединять несколько таблиц. Функция bind_rows() присоединяет таблицу снизу, при этом она должна иметь столько же переменных и тип переменных должен совпадать с основной таблицей.\n\nzhadina_new <- tibble(word_1 = \"жадина-говядина\",\n word_2 = \"пупецкий\",\n word_3 = \"барабан\",\n type = \"барабан\",\n n = 0)\n\nzhadina |> \n bind_rows(zhadina_new)\n\n# A tibble: 27 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина большая шоколадина шоколадина 941\n 2 жадина-говядина бука шоколадина шоколадина 44\n 3 жадина-говядина булка шоколадина шоколадина 250\n 4 жадина-говядина в *опе огурец огурец 11\n 5 жадина-говядина в *опе шоколадина шоколадина 1117\n 6 жадина-говядина гнилая шоколадина шоколадина 12\n 7 жадина-говядина дома шоколадина шоколадина 29\n 8 жадина-говядина зеленый огурец огурец 343\n 9 жадина-говядина злая шоколадина шоколадина 10\n10 жадина-говядина кислая капуста другое 10\n# ℹ 17 more rows\n\n\nФункция bind_cols() присоединяет таблицу справа, при этом она должна иметь столько же строчек, сколько есть в с основной таблице:\n\nzhadina_additional_columns <- tibble(new_var1 = rnorm(26),\n new_var2 = letters)\n\nzhadina |> \n bind_cols(zhadina_additional_columns)\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n new_var1 new_var2\n <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 1.58 a \n 2 жадина-говядина бука шоколадина шоколадина 44 0.643 b \n 3 жадина-говядина булка шоколадина шоколадина 250 0.0898 c \n 4 жадина-говядина в *опе огурец огурец 11 0.277 d \n 5 жадина-говядина в *опе шоколадина шоколадина 1117 0.679 e \n 6 жадина-говядина гнилая шоколадина шоколадина 12 0.0898 f \n 7 жадина-говядина дома шоколадина шоколадина 29 -2.99 g \n 8 жадина-говядина зеленый огурец огурец 343 0.285 h \n 9 жадина-говядина злая шоколадина шоколадина 10 -0.367 i \n10 жадина-говядина кислая капуста другое 10 0.185 j \n# ℹ 16 more rows", "crumbs": [ "2  Трансформация данных" ] @@ -253,8 +263,8 @@ "objectID": "2_data_transformation.html#sec-joins", "href": "2_data_transformation.html#sec-joins", "title": "2  Трансформация данных", - "section": "2.12 Функции семейства ..._join()", - "text": "2.12 Функции семейства ..._join()\nИногда возникает ситуация, когда нужно соединить две таблицы, на основании одного или нескольких общих столбцов. Так бывает, если нужно что-то включить или исключить из анализа. Примером включения может послужить случай, когда для построения карты хочется присоединить расшифровку аббревиатур или для анализа нужны еще какие-то данные, например, размер популяции. Примером исключения может послужить случаи, когда при анализе текстов возникает задача исключения из анализа служебных слов. Эту задачу решают функции семейства ..._join().\nСинтаксис функций выглядит так (A и B — датафреймы):\nA |> \n ..._join(B)\nВместо многоточия может стоять одна из следующих функций:\n\ninner_join() — возвращает только те наблюдения в А, которым есть соответствие в B\nfull_join() — возвращает все наблюдения из А и из B\nleft_join() — возвращает все наблюдения из А, а если им нет соответствия в B появляется NA. Наблюдения из B, которым нет соответствия в А, исключаются.\nright_join() — возвращает все наблюдения из B, а если им нет соответствия в A появляется NA. Наблюдения из A, которым нет соответствия в B, исключаются.\nanti_join() — возвращает все наблюдения из А, которым нет соответствия в B.\nsemi_join() — возвращает все наблюдения из А, которым есть соответствия в B, но при этом игнорирует дополнительные столбцы из B.\n\nВсе эти объединений можно проиллюстрировать при помощи диаграмм Венна:\n\n\n\n\n\n\n\n\n\nВ качесиве примера создадим новую таблицу на основе датасета продожений Жадины-говядины:\n\nzhadina |> \n count(type) |> \n rename(our_new_var1 = n) |> \n mutate(our_new_var2 = c(\"совсем редкое\", \n \"редкое\", \n \"второй по популярности\",\n \"самый частый тип\")) ->\n type_new_var\n\ntype_new_var\n\n# A tibble: 4 × 3\n type our_new_var1 our_new_var2 \n <chr> <int> <chr> \n1 барабан 4 совсем редкое \n2 другое 5 редкое \n3 огурец 3 второй по популярности\n4 шоколадина 14 самый частый тип \n\n\n\nzhadina |> \n left_join(type_new_var)\n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <int> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 16 more rows\n\n\nМы видим, что таблицы соединились, а также появилось сообщение о том, что соединение произошло по переменной type. Если мы исключим одно наблюдение из нашей таблицы type_new_var и соединим, то на месте пропусков появятся NA:\n\ntype_new_var |> \n slice(-1) ->\n type_new_var2\n\nzhadina |> \n left_join(type_new_var2)\n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <int> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 16 more rows\n\n\nЕсли же мы присоединим к таблице type_new_var строчку, которой нет в нашем исходном датасете продолжений Жадины-говядины, мы получи разный результат при присоединении разными функциями. При присоединении функцией left_join() новой строчки не будет (26 строчек), а если мы будем присоединять при помощи full_join(), то она будет (27 строчек).\n\ntibble(type = \"карниз\", \n our_new_var1 = 0, \n our_new_var2 = \"такого не бывает\") |> \n bind_rows(type_new_var)->\n type_new_var3\n\nzhadina |> \n left_join(type_new_var3) \n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 16 more rows\n\nzhadina |> \n full_join(type_new_var3)\n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 27 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 17 more rows\n\n\nЕсли мы хотим исключить наблюдения с типом шакаладина, то мы можем использовать функцию anti_join (я бы в такой задачи использовал простой фильтр, но мы на курсе еще встретим случаи, когда быстрее написать anti_joint()).\n\nzhadina |> \n anti_join(tibble(type = \"шоколадина\"))\n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 12 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина в *опе огурец огурец 11\n 2 жадина-говядина зеленый огурец огурец 343\n 3 жадина-говядина кислая капуста другое 10\n 4 жадина-говядина кошка поросятина другое 42\n 5 жадина-говядина никто ее не ест другое 10\n 6 жадина-говядина на пузе барабан барабан 14\n 7 жадина-говядина немецкий барабан барабан 12\n 8 жадина-говядина соленый барабан барабан 11\n 9 жадина-говядина соленый огурец огурец 26910\n10 жадина-говядина соленый помидор другое 17\n11 жадина-говядина турецкий барабан барабан 3003\n12 жадина-говядина турецкий таракан другое 13\n\n\nПоследнее, что важно обсудить в данном разделе — что если переменные в разных таблицах по разному называются? В таком случае можно не переименовывать переменные, а эксплицитно указать изменения в аргументе by:\n\ntype_new_var3 |> \n rename(new_type = type) ->\n type_new_var4\ntype_new_var4\n\n# A tibble: 5 × 3\n new_type our_new_var1 our_new_var2 \n <chr> <dbl> <chr> \n1 карниз 0 такого не бывает \n2 барабан 4 совсем редкое \n3 другое 5 редкое \n4 огурец 3 второй по популярности\n5 шоколадина 14 самый частый тип \n\n\nКак видно, в переменной type_new_var4 больше нет переменной type.\n\nzhadina |> \n left_join(type_new_var4, by = c(\"type\" = \"new_type\"))\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 16 more rows", + "section": "2.13 Функции семейства ..._join()", + "text": "2.13 Функции семейства ..._join()\nИногда возникает ситуация, когда нужно соединить две таблицы, на основании одного или нескольких общих столбцов. Так бывает, если нужно что-то включить или исключить из анализа. Примером включения может послужить случай, когда для построения карты хочется присоединить расшифровку аббревиатур или для анализа нужны еще какие-то данные, например, размер популяции. Примером исключения может послужить случаи, когда при анализе текстов возникает задача исключения из анализа служебных слов. Эту задачу решают функции семейства ..._join().\nСинтаксис функций выглядит так (A и B — датафреймы):\n\nA |> \n ..._join(B)\n\nВместо многоточия может стоять одна из следующих функций:\n\ninner_join() — возвращает только те наблюдения в А, которым есть соответствие в B\nfull_join() — возвращает все наблюдения из А и из B\nleft_join() — возвращает все наблюдения из А, а если им нет соответствия в B появляется NA. Наблюдения из B, которым нет соответствия в А, исключаются.\nright_join() — возвращает все наблюдения из B, а если им нет соответствия в A появляется NA. Наблюдения из A, которым нет соответствия в B, исключаются.\nanti_join() — возвращает все наблюдения из А, которым нет соответствия в B.\nsemi_join() — возвращает все наблюдения из А, которым есть соответствия в B, но при этом игнорирует дополнительные столбцы из B.\n\nВсе эти объединений можно проиллюстрировать при помощи диаграмм Венна:\n\n\n\n\n\n\n\n\n\nВ качесиве примера создадим новую таблицу на основе датасета продожений Жадины-говядины:\n\nzhadina |> \n count(type) |> \n rename(our_new_var1 = n) |> \n mutate(our_new_var2 = c(\"совсем редкое\", \n \"редкое\", \n \"второй по популярности\",\n \"самый частый тип\")) ->\n type_new_var\n\ntype_new_var\n\n# A tibble: 4 × 3\n type our_new_var1 our_new_var2 \n <chr> <int> <chr> \n1 барабан 4 совсем редкое \n2 другое 5 редкое \n3 огурец 3 второй по популярности\n4 шоколадина 14 самый частый тип \n\n\n\nzhadina |> \n left_join(type_new_var)\n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <int> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 16 more rows\n\n\nМы видим, что таблицы соединились, а также появилось сообщение о том, что соединение произошло по переменной type. Если мы исключим одно наблюдение из нашей таблицы type_new_var и соединим, то на месте пропусков появятся NA:\n\ntype_new_var |> \n slice(-1) ->\n type_new_var2\n\nzhadina |> \n left_join(type_new_var2)\n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <int> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 16 more rows\n\n\nЕсли же мы присоединим к таблице type_new_var строчку, которой нет в нашем исходном датасете продолжений Жадины-говядины, мы получи разный результат при присоединении разными функциями. При присоединении функцией left_join() новой строчки не будет (26 строчек), а если мы будем присоединять при помощи full_join(), то она будет (27 строчек).\n\ntibble(type = \"карниз\", \n our_new_var1 = 0, \n our_new_var2 = \"такого не бывает\") |> \n bind_rows(type_new_var)->\n type_new_var3\n\nzhadina |> \n left_join(type_new_var3) \n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 16 more rows\n\nzhadina |> \n full_join(type_new_var3)\n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 27 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 17 more rows\n\n\nЕсли мы хотим исключить наблюдения с типом шакаладина, то мы можем использовать функцию anti_join (я бы в такой задачи использовал простой фильтр, но мы на курсе еще встретим случаи, когда быстрее написать anti_joint()).\n\nzhadina |> \n anti_join(tibble(type = \"шоколадина\"))\n\nJoining with `by = join_by(type)`\n\n\n# A tibble: 12 × 5\n word_1 word_2 word_3 type n\n <chr> <chr> <chr> <chr> <dbl>\n 1 жадина-говядина в *опе огурец огурец 11\n 2 жадина-говядина зеленый огурец огурец 343\n 3 жадина-говядина кислая капуста другое 10\n 4 жадина-говядина кошка поросятина другое 42\n 5 жадина-говядина никто ее не ест другое 10\n 6 жадина-говядина на пузе барабан барабан 14\n 7 жадина-говядина немецкий барабан барабан 12\n 8 жадина-говядина соленый барабан барабан 11\n 9 жадина-говядина соленый огурец огурец 26910\n10 жадина-говядина соленый помидор другое 17\n11 жадина-говядина турецкий барабан барабан 3003\n12 жадина-говядина турецкий таракан другое 13\n\n\nПоследнее, что важно обсудить в данном разделе — что если переменные в разных таблицах по разному называются? В таком случае можно не переименовывать переменные, а эксплицитно указать изменения в аргументе by:\n\ntype_new_var3 |> \n rename(new_type = type) ->\n type_new_var4\ntype_new_var4\n\n# A tibble: 5 × 3\n new_type our_new_var1 our_new_var2 \n <chr> <dbl> <chr> \n1 карниз 0 такого не бывает \n2 барабан 4 совсем редкое \n3 другое 5 редкое \n4 огурец 3 второй по популярности\n5 шоколадина 14 самый частый тип \n\n\nКак видно, в переменной type_new_var4 больше нет переменной type.\n\nzhadina |> \n left_join(type_new_var4, by = c(\"type\" = \"new_type\"))\n\n# A tibble: 26 × 7\n word_1 word_2 word_3 type n our_new_var1 our_new_var2\n <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> \n 1 жадина-говядина большая шоколадина шоколадина 941 14 самый часты…\n 2 жадина-говядина бука шоколадина шоколадина 44 14 самый часты…\n 3 жадина-говядина булка шоколадина шоколадина 250 14 самый часты…\n 4 жадина-говядина в *опе огурец огурец 11 3 второй по п…\n 5 жадина-говядина в *опе шоколадина шоколадина 1117 14 самый часты…\n 6 жадина-говядина гнилая шоколадина шоколадина 12 14 самый часты…\n 7 жадина-говядина дома шоколадина шоколадина 29 14 самый часты…\n 8 жадина-говядина зеленый огурец огурец 343 3 второй по п…\n 9 жадина-говядина злая шоколадина шоколадина 10 14 самый часты…\n10 жадина-говядина кислая капуста другое 10 5 редкое \n# ℹ 16 more rows", "crumbs": [ "2  Трансформация данных" ] @@ -504,7 +514,7 @@ "href": "5_working_with_texts.html#библиотека-tidytext", "title": "5  Работа с текстами", "section": "5.3 Библиотека tidytext", - "text": "5.3 Библиотека tidytext\nСейчас скачанные тексты записывались в таблицу, где одна строка содержала один абзац. Однако для анализа текста нужно уметь работать с отдельными словами в нем. Для этого тексты нужно привести в tidy формат. С этим отлично справляется пакет tidytext (онлайн книга доступна здесь). Основное “оружие” пакета tidytext функция unnest_tokens(), которая переводит текст в tidy формат. В аргумент output подается вектор с именем будущей переменной, а аргумент input принимает переменную с текстом.\n\nlibrary(tidytext)\nalices_adventures_in_wonderland |> \n tibble(text = _) |> \n unnest_tokens(output = \"word\", input = text)\n\n# A tibble: 29,891 × 1\n word \n <chr> \n 1 the \n 2 project \n 3 gutenberg \n 4 ebook \n 5 of \n 6 alice's \n 7 adventures\n 8 in \n 9 wonderland\n10 this \n# ℹ 29,881 more rows\n\n\nПо умолчанию функция unnest_tokens() удаляет знаки препинания и приводит слова к нижнему регистру. Давайте для удобства удалим все вплоть до оглавления и создадим датафрейм и переменную, из которой будет понятно, где какая глава книги:\n\nalices_adventures_in_wonderland |> \n tibble(text = _) |> \n slice(54:3405) |> \n mutate(chapter = str_extract(text, \"CHAPTER .{1,4}\\\\.\"),\n chapter_title = ifelse(str_detect(text, \"CHAPTER .{1,4}\\\\.\"), lead(text), NA)) |> \n fill(chapter, chapter_title) |> \n filter(text != chapter) |> \n mutate(chapter = str_remove(chapter, \"CHAPTER \"),\n chapter = str_remove(chapter, \"\\\\.\"),\n chapter = as.numeric(as.roman(chapter)),\n chapter_title = fct_reorder(chapter_title, chapter)) ->\n alice_cleaned\n\nalice_cleaned\n\n# A tibble: 3,340 × 3\n text chapter chapter_title\n <chr> <dbl> <fct> \n 1 \"Down the Rabbit-Hole\" 1 Down the Rab…\n 2 \"\" 1 Down the Rab…\n 3 \"\" 1 Down the Rab…\n 4 \"Alice was beginning to get very tired of sitting by h… 1 Down the Rab…\n 5 \"bank, and of having nothing to do: once or twice she … 1 Down the Rab…\n 6 \"the book her sister was reading, but it had no pictur… 1 Down the Rab…\n 7 \"conversations in it, “and what is the use of a book,”… 1 Down the Rab…\n 8 \"“without pictures or conversations?”\" 1 Down the Rab…\n 9 \"\" 1 Down the Rab…\n10 \"So she was considering in her own mind (as well as sh… 1 Down the Rab…\n# ℹ 3,330 more rows\n\n\nВ приведенном выше коде интерес представляет функция fill(), которая заполнила пропущенные значения NA значениями выше. В остальном мы использовали несложные регулярные выражения из Раздел 4.8. Кроме того, мы воспользовались встроенными функциями as.roman() и as.numeric(), чтобы преобразовать римские номера глав в арабские. Теперь мы готовы анализировать частотность слов. Создадим переменную tidy_alice и посчитаем слова в каждой из глав:\n\nalice_cleaned |> \n unnest_tokens(output = \"word\", input = text) ->\n tidy_alice\n\ntidy_alice |> \n count(chapter_title, word, sort = TRUE)\n\n# A tibble: 7,526 × 3\n chapter_title word n\n <fct> <chr> <int>\n 1 Who Stole the Tarts? the 183\n 2 The Queen’s Croquet-Ground the 168\n 3 Alice’s Evidence the 168\n 4 A Mad Tea-Party the 158\n 5 The Lobster Quadrille the 158\n 6 Pig and Pepper the 150\n 7 The Mock Turtle’s Story the 144\n 8 The Rabbit Sends in a Little Bill the 128\n 9 The Rabbit Sends in a Little Bill and 107\n10 A Caucus-Race and a Long Tale the 101\n# ℹ 7,516 more rows\n\n\nВ основном, конечно, слова бессмысленные, но давайте посмотрим первый десяток самых частотных слов в каждой из глав:\n\ntidy_alice |> \n count(chapter_title, word, sort = TRUE) |> \n group_by(chapter_title) |> \n slice(1:10) |> \n ggplot(aes(n, word))+\n geom_col()+\n facet_wrap(~chapter_title, scales = \"free\")+\n labs(x = NULL, y = NULL)\n\n\n\n\n\n\n\n\nХорошие знатоки “Алисы в Зазеркалье”, конечно, многое понимают из распределения местоимений you и, наверное, догадываются, почему это слово становится первым, однако в большинстве случаев служебные слова неинформативны при анализе текстов. Поэтому для некоторых языков составили списки стоп-слов — служебных слов, которые никак не приближают к пониманию текста. Отдельный список уже встроен в пакет tidytext в переменную stop_words (см. также следующий раздел):\n\nstop_words\n\n# A tibble: 1,149 × 2\n word lexicon\n <chr> <chr> \n 1 a SMART \n 2 a's SMART \n 3 able SMART \n 4 about SMART \n 5 above SMART \n 6 according SMART \n 7 accordingly SMART \n 8 across SMART \n 9 actually SMART \n10 after SMART \n# ℹ 1,139 more rows\n\n\nТак что используя функцию из семейства ..._join() (см. Раздел 2.12), мы можем повторить наш анализ:\n\ntidy_alice |> \n count(chapter_title, word, sort = TRUE) |> \n anti_join(stop_words) |> \n group_by(chapter_title) |> \n slice(1:10) |> \n ggplot(aes(n, word))+\n geom_col()+\n facet_wrap(~chapter_title, scales = \"free\")+\n labs(x = NULL, y = NULL)\n\nJoining with `by = join_by(word)`\n\n\n\n\n\n\n\n\n\nПолучившийся результат уже значительно интереснее, однако алфавитный порядок слов в каждом фасете немного мешает. Для того, чтобы победить это, в пакете tidytext есть несколько функций:\n\nreorder_within() — функция которая позволяет указать группировку, внутри которой нужно упорядочить единицы;\nscale_y_reordered()/scale_x_reordered() — функция, которая маскирует работу reorder_within() при отображении на графике.\n\n\ntidy_alice |> \n count(chapter_title, word, sort = TRUE) |> \n anti_join(stop_words) |> \n group_by(chapter_title) |> \n slice(1:10) |> \n mutate(word = reorder_within(word, within = chapter_title, by = n)) |> \n ggplot(aes(n, word))+\n geom_col()+\n facet_wrap(~chapter_title, scales = \"free\")+\n scale_y_reordered()+\n labs(x = NULL, y = NULL)\n\nJoining with `by = join_by(word)`\n\n\n\n\n\n\n\n\n\nВажно отметить, что разделение на слова не единственная цель функции unnest_tokens(), важным бывает анализировать сочетания слов: биграммы, триграммы и т. д.\n\nalice_cleaned |> \n unnest_tokens(output = \"ngram\", input = text, token = \"ngrams\", n = 2)\n\n# A tibble: 25,159 × 3\n chapter chapter_title ngram \n <dbl> <fct> <chr> \n 1 1 Down the Rabbit-Hole down the \n 2 1 Down the Rabbit-Hole the rabbit \n 3 1 Down the Rabbit-Hole rabbit hole \n 4 1 Down the Rabbit-Hole <NA> \n 5 1 Down the Rabbit-Hole <NA> \n 6 1 Down the Rabbit-Hole alice was \n 7 1 Down the Rabbit-Hole was beginning\n 8 1 Down the Rabbit-Hole beginning to \n 9 1 Down the Rabbit-Hole to get \n10 1 Down the Rabbit-Hole get very \n# ℹ 25,149 more rows\n\n\nВ связи с этим можно повторить наш анализ с биграммами:\n\nalice_cleaned |> \n unnest_tokens(output = \"ngram\", input = text, token = \"ngrams\", n = 2) |> \n na.omit() |> \n separate(ngram, into = c(\"ngram_1\", \"ngram_2\"), sep = \" \") |> \n anti_join(tibble(ngram_1 = stop_words$word)) |> \n anti_join(tibble(ngram_2 = stop_words$word)) |> \n mutate(ngram = str_c(ngram_1, \" \", ngram_2)) |> \n count(chapter_title, ngram, sort = TRUE) |>\n group_by(chapter_title) |> \n slice(1:10) |> \n mutate(ngram = reorder_within(ngram, within = chapter_title, by = n)) |> \n ggplot(aes(n, ngram))+\n geom_col()+\n facet_wrap(~chapter_title, scales = \"free\")+\n scale_y_reordered() + \n labs(x = NULL, y = NULL)\n\nJoining with `by = join_by(ngram_1)`\nJoining with `by = join_by(ngram_2)`", + "text": "5.3 Библиотека tidytext\nСейчас скачанные тексты записывались в таблицу, где одна строка содержала один абзац. Однако для анализа текста нужно уметь работать с отдельными словами в нем. Для этого тексты нужно привести в tidy формат. С этим отлично справляется пакет tidytext (онлайн книга доступна здесь). Основное “оружие” пакета tidytext функция unnest_tokens(), которая переводит текст в tidy формат. В аргумент output подается вектор с именем будущей переменной, а аргумент input принимает переменную с текстом.\n\nlibrary(tidytext)\nalices_adventures_in_wonderland |> \n tibble(text = _) |> \n unnest_tokens(output = \"word\", input = text)\n\n# A tibble: 29,891 × 1\n word \n <chr> \n 1 the \n 2 project \n 3 gutenberg \n 4 ebook \n 5 of \n 6 alice's \n 7 adventures\n 8 in \n 9 wonderland\n10 this \n# ℹ 29,881 more rows\n\n\nПо умолчанию функция unnest_tokens() удаляет знаки препинания и приводит слова к нижнему регистру. Давайте для удобства удалим все вплоть до оглавления и создадим датафрейм и переменную, из которой будет понятно, где какая глава книги:\n\nalices_adventures_in_wonderland |> \n tibble(text = _) |> \n slice(54:3405) |> \n mutate(chapter = str_extract(text, \"CHAPTER .{1,4}\\\\.\"),\n chapter_title = ifelse(str_detect(text, \"CHAPTER .{1,4}\\\\.\"), lead(text), NA)) |> \n fill(chapter, chapter_title) |> \n filter(text != chapter) |> \n mutate(chapter = str_remove(chapter, \"CHAPTER \"),\n chapter = str_remove(chapter, \"\\\\.\"),\n chapter = as.numeric(as.roman(chapter)),\n chapter_title = fct_reorder(chapter_title, chapter)) ->\n alice_cleaned\n\nalice_cleaned\n\n# A tibble: 3,340 × 3\n text chapter chapter_title\n <chr> <dbl> <fct> \n 1 \"Down the Rabbit-Hole\" 1 Down the Rab…\n 2 \"\" 1 Down the Rab…\n 3 \"\" 1 Down the Rab…\n 4 \"Alice was beginning to get very tired of sitting by h… 1 Down the Rab…\n 5 \"bank, and of having nothing to do: once or twice she … 1 Down the Rab…\n 6 \"the book her sister was reading, but it had no pictur… 1 Down the Rab…\n 7 \"conversations in it, “and what is the use of a book,”… 1 Down the Rab…\n 8 \"“without pictures or conversations?”\" 1 Down the Rab…\n 9 \"\" 1 Down the Rab…\n10 \"So she was considering in her own mind (as well as sh… 1 Down the Rab…\n# ℹ 3,330 more rows\n\n\nВ приведенном выше коде интерес представляет функция fill(), которая заполнила пропущенные значения NA значениями выше. В остальном мы использовали несложные регулярные выражения из Раздел 4.8. Кроме того, мы воспользовались встроенными функциями as.roman() и as.numeric(), чтобы преобразовать римские номера глав в арабские. Теперь мы готовы анализировать частотность слов. Создадим переменную tidy_alice и посчитаем слова в каждой из глав:\n\nalice_cleaned |> \n unnest_tokens(output = \"word\", input = text) ->\n tidy_alice\n\ntidy_alice |> \n count(chapter_title, word, sort = TRUE)\n\n# A tibble: 7,526 × 3\n chapter_title word n\n <fct> <chr> <int>\n 1 Who Stole the Tarts? the 183\n 2 The Queen’s Croquet-Ground the 168\n 3 Alice’s Evidence the 168\n 4 A Mad Tea-Party the 158\n 5 The Lobster Quadrille the 158\n 6 Pig and Pepper the 150\n 7 The Mock Turtle’s Story the 144\n 8 The Rabbit Sends in a Little Bill the 128\n 9 The Rabbit Sends in a Little Bill and 107\n10 A Caucus-Race and a Long Tale the 101\n# ℹ 7,516 more rows\n\n\nВ основном, конечно, слова бессмысленные, но давайте посмотрим первый десяток самых частотных слов в каждой из глав:\n\ntidy_alice |> \n count(chapter_title, word, sort = TRUE) |> \n group_by(chapter_title) |> \n slice(1:10) |> \n ggplot(aes(n, word))+\n geom_col()+\n facet_wrap(~chapter_title, scales = \"free\")+\n labs(x = NULL, y = NULL)\n\n\n\n\n\n\n\n\nХорошие знатоки “Алисы в Зазеркалье”, конечно, многое понимают из распределения местоимений you и, наверное, догадываются, почему это слово становится первым, однако в большинстве случаев служебные слова неинформативны при анализе текстов. Поэтому для некоторых языков составили списки стоп-слов — служебных слов, которые никак не приближают к пониманию текста. Отдельный список уже встроен в пакет tidytext в переменную stop_words (см. также следующий раздел):\n\nstop_words\n\n# A tibble: 1,149 × 2\n word lexicon\n <chr> <chr> \n 1 a SMART \n 2 a's SMART \n 3 able SMART \n 4 about SMART \n 5 above SMART \n 6 according SMART \n 7 accordingly SMART \n 8 across SMART \n 9 actually SMART \n10 after SMART \n# ℹ 1,139 more rows\n\n\nТак что используя функцию из семейства ..._join() (см. Раздел 2.13), мы можем повторить наш анализ:\n\ntidy_alice |> \n count(chapter_title, word, sort = TRUE) |> \n anti_join(stop_words) |> \n group_by(chapter_title) |> \n slice(1:10) |> \n ggplot(aes(n, word))+\n geom_col()+\n facet_wrap(~chapter_title, scales = \"free\")+\n labs(x = NULL, y = NULL)\n\nJoining with `by = join_by(word)`\n\n\n\n\n\n\n\n\n\nПолучившийся результат уже значительно интереснее, однако алфавитный порядок слов в каждом фасете немного мешает. Для того, чтобы победить это, в пакете tidytext есть несколько функций:\n\nreorder_within() — функция которая позволяет указать группировку, внутри которой нужно упорядочить единицы;\nscale_y_reordered()/scale_x_reordered() — функция, которая маскирует работу reorder_within() при отображении на графике.\n\n\ntidy_alice |> \n count(chapter_title, word, sort = TRUE) |> \n anti_join(stop_words) |> \n group_by(chapter_title) |> \n slice(1:10) |> \n mutate(word = reorder_within(word, within = chapter_title, by = n)) |> \n ggplot(aes(n, word))+\n geom_col()+\n facet_wrap(~chapter_title, scales = \"free\")+\n scale_y_reordered()+\n labs(x = NULL, y = NULL)\n\nJoining with `by = join_by(word)`\n\n\n\n\n\n\n\n\n\nВажно отметить, что разделение на слова не единственная цель функции unnest_tokens(), важным бывает анализировать сочетания слов: биграммы, триграммы и т. д.\n\nalice_cleaned |> \n unnest_tokens(output = \"ngram\", input = text, token = \"ngrams\", n = 2)\n\n# A tibble: 25,159 × 3\n chapter chapter_title ngram \n <dbl> <fct> <chr> \n 1 1 Down the Rabbit-Hole down the \n 2 1 Down the Rabbit-Hole the rabbit \n 3 1 Down the Rabbit-Hole rabbit hole \n 4 1 Down the Rabbit-Hole <NA> \n 5 1 Down the Rabbit-Hole <NA> \n 6 1 Down the Rabbit-Hole alice was \n 7 1 Down the Rabbit-Hole was beginning\n 8 1 Down the Rabbit-Hole beginning to \n 9 1 Down the Rabbit-Hole to get \n10 1 Down the Rabbit-Hole get very \n# ℹ 25,149 more rows\n\n\nВ связи с этим можно повторить наш анализ с биграммами:\n\nalice_cleaned |> \n unnest_tokens(output = \"ngram\", input = text, token = \"ngrams\", n = 2) |> \n na.omit() |> \n separate(ngram, into = c(\"ngram_1\", \"ngram_2\"), sep = \" \") |> \n anti_join(tibble(ngram_1 = stop_words$word)) |> \n anti_join(tibble(ngram_2 = stop_words$word)) |> \n mutate(ngram = str_c(ngram_1, \" \", ngram_2)) |> \n count(chapter_title, ngram, sort = TRUE) |>\n group_by(chapter_title) |> \n slice(1:10) |> \n mutate(ngram = reorder_within(ngram, within = chapter_title, by = n)) |> \n ggplot(aes(n, ngram))+\n geom_col()+\n facet_wrap(~chapter_title, scales = \"free\")+\n scale_y_reordered() + \n labs(x = NULL, y = NULL)\n\nJoining with `by = join_by(ngram_1)`\nJoining with `by = join_by(ngram_2)`", "crumbs": [ "5  Работа с текстами" ]