Load gapminder dataset and tidyverse packages
library(gapminder)
library(tidyverse)## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
class(gapminder)## [1] "tbl_df" "tbl" "data.frame"
It is a data.frame, more specifically it is a tibble.
ncol(gapminder)## [1] 6
nrow(gapminder)## [1] 1704
dim(gapminder)## [1] 1704 6
It has 6 columns and 1704 rows. These can also been seen from str():
str(gapminder)## Classes 'tbl_df', 'tbl' and 'data.frame': 1704 obs. of 6 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
If you wanted a visual summary of all the info for you to read, str() or summary() are very useful. If you wanted just a 1 peice of info to use as an input into another function, then functions like ncol() are more useful. This also showed the data type of each variable.
Alternatively can get the data type of each variable individually:
typeof(gapminder$continent)## [1] "integer"
Country, continent are factors. Year, Pop are int. lifeExp, gdpPercap are num.
unique(gapminder$country)## [1] Afghanistan Albania
## [3] Algeria Angola
## [5] Argentina Australia
## [7] Austria Bahrain
## [9] Bangladesh Belgium
## [11] Benin Bolivia
## [13] Bosnia and Herzegovina Botswana
## [15] Brazil Bulgaria
## [17] Burkina Faso Burundi
## [19] Cambodia Cameroon
## [21] Canada Central African Republic
## [23] Chad Chile
## [25] China Colombia
## [27] Comoros Congo, Dem. Rep.
## [29] Congo, Rep. Costa Rica
## [31] Cote d'Ivoire Croatia
## [33] Cuba Czech Republic
## [35] Denmark Djibouti
## [37] Dominican Republic Ecuador
## [39] Egypt El Salvador
## [41] Equatorial Guinea Eritrea
## [43] Ethiopia Finland
## [45] France Gabon
## [47] Gambia Germany
## [49] Ghana Greece
## [51] Guatemala Guinea
## [53] Guinea-Bissau Haiti
## [55] Honduras Hong Kong, China
## [57] Hungary Iceland
## [59] India Indonesia
## [61] Iran Iraq
## [63] Ireland Israel
## [65] Italy Jamaica
## [67] Japan Jordan
## [69] Kenya Korea, Dem. Rep.
## [71] Korea, Rep. Kuwait
## [73] Lebanon Lesotho
## [75] Liberia Libya
## [77] Madagascar Malawi
## [79] Malaysia Mali
## [81] Mauritania Mauritius
## [83] Mexico Mongolia
## [85] Montenegro Morocco
## [87] Mozambique Myanmar
## [89] Namibia Nepal
## [91] Netherlands New Zealand
## [93] Nicaragua Niger
## [95] Nigeria Norway
## [97] Oman Pakistan
## [99] Panama Paraguay
## [101] Peru Philippines
## [103] Poland Portugal
## [105] Puerto Rico Reunion
## [107] Romania Rwanda
## [109] Sao Tome and Principe Saudi Arabia
## [111] Senegal Serbia
## [113] Sierra Leone Singapore
## [115] Slovak Republic Slovenia
## [117] Somalia South Africa
## [119] Spain Sri Lanka
## [121] Sudan Swaziland
## [123] Sweden Switzerland
## [125] Syria Taiwan
## [127] Tanzania Thailand
## [129] Togo Trinidad and Tobago
## [131] Tunisia Turkey
## [133] Uganda United Kingdom
## [135] United States Uruguay
## [137] Venezuela Vietnam
## [139] West Bank and Gaza Yemen, Rep.
## [141] Zambia Zimbabwe
## 142 Levels: Afghanistan Albania Algeria Angola Argentina ... Zimbabwe
This shows all possible values for country (all countries included in dataset)
table(gapminder$country)##
## Afghanistan Albania Algeria
## 12 12 12
## Angola Argentina Australia
## 12 12 12
## Austria Bahrain Bangladesh
## 12 12 12
## Belgium Benin Bolivia
## 12 12 12
## Bosnia and Herzegovina Botswana Brazil
## 12 12 12
## Bulgaria Burkina Faso Burundi
## 12 12 12
## Cambodia Cameroon Canada
## 12 12 12
## Central African Republic Chad Chile
## 12 12 12
## China Colombia Comoros
## 12 12 12
## Congo, Dem. Rep. Congo, Rep. Costa Rica
## 12 12 12
## Cote d'Ivoire Croatia Cuba
## 12 12 12
## Czech Republic Denmark Djibouti
## 12 12 12
## Dominican Republic Ecuador Egypt
## 12 12 12
## El Salvador Equatorial Guinea Eritrea
## 12 12 12
## Ethiopia Finland France
## 12 12 12
## Gabon Gambia Germany
## 12 12 12
## Ghana Greece Guatemala
## 12 12 12
## Guinea Guinea-Bissau Haiti
## 12 12 12
## Honduras Hong Kong, China Hungary
## 12 12 12
## Iceland India Indonesia
## 12 12 12
## Iran Iraq Ireland
## 12 12 12
## Israel Italy Jamaica
## 12 12 12
## Japan Jordan Kenya
## 12 12 12
## Korea, Dem. Rep. Korea, Rep. Kuwait
## 12 12 12
## Lebanon Lesotho Liberia
## 12 12 12
## Libya Madagascar Malawi
## 12 12 12
## Malaysia Mali Mauritania
## 12 12 12
## Mauritius Mexico Mongolia
## 12 12 12
## Montenegro Morocco Mozambique
## 12 12 12
## Myanmar Namibia Nepal
## 12 12 12
## Netherlands New Zealand Nicaragua
## 12 12 12
## Niger Nigeria Norway
## 12 12 12
## Oman Pakistan Panama
## 12 12 12
## Paraguay Peru Philippines
## 12 12 12
## Poland Portugal Puerto Rico
## 12 12 12
## Reunion Romania Rwanda
## 12 12 12
## Sao Tome and Principe Saudi Arabia Senegal
## 12 12 12
## Serbia Sierra Leone Singapore
## 12 12 12
## Slovak Republic Slovenia Somalia
## 12 12 12
## South Africa Spain Sri Lanka
## 12 12 12
## Sudan Swaziland Sweden
## 12 12 12
## Switzerland Syria Taiwan
## 12 12 12
## Tanzania Thailand Togo
## 12 12 12
## Trinidad and Tobago Tunisia Turkey
## 12 12 12
## Uganda United Kingdom United States
## 12 12 12
## Uruguay Venezuela Vietnam
## 12 12 12
## West Bank and Gaza Yemen, Rep. Zambia
## 12 12 12
## Zimbabwe
## 12
barplot(table(gapminder$country))
These shows that each country occurs exactly 12 times. Graph is very ugly though (too many countries to see visually), it's easier to see this from the table above.
nrow(table(gapminder$country))## [1] 142
There are 142 countries present.
filter(gapminder, country == "Canada") %>%
select(country, year)## # A tibble: 12 x 2
## country year
## <fctr> <int>
## 1 Canada 1952
## 2 Canada 1957
## 3 Canada 1962
## 4 Canada 1967
## 5 Canada 1972
## 6 Canada 1977
## 7 Canada 1982
## 8 Canada 1987
## 9 Canada 1992
## 10 Canada 1997
## 11 Canada 2002
## 12 Canada 2007
This shows that for each of the 12 entries present for each country, each one corresponds to a different year (with 5 year intervals).
mean(gapminder$pop)## [1] 29601212
median(gapminder$pop)## [1] 7023596
range(gapminder$pop)## [1] 60011 1318683096
The mean pop size is 29 million and the median is 7 million. The range is from 60,000 to 1.3 billion.
filter(gapminder, pop == 1318683096)## # A tibble: 1 x 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 China Asia 2007 72.961 1318683096 4959.115
filter(gapminder, pop == 60011)## # A tibble: 1 x 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Sao Tome and Principe Africa 1952 46.471 60011 879.5836
As suspected, China has the biggest pop! The smallest is Sao Tome and Principe, probably why I've never heard of them.
gapminder %>%
filter(year == 1997) %>%
arrange(desc(pop)) %>%
select(country, pop) %>%
head(10)## # A tibble: 10 x 2
## country pop
## <fctr> <int>
## 1 China 1230075000
## 2 India 959000000
## 3 United States 272911760
## 4 Indonesia 199278000
## 5 Brazil 168546719
## 6 Pakistan 135564834
## 7 Japan 125956499
## 8 Bangladesh 123315288
## 9 Nigeria 106207839
## 10 Mexico 95895146
Here are the top 10 countries with largest population in 1997 in descending order.
We can also graph this as a barplot below, looking at the top 30 countries in Europe:
gapminder %>%
filter(year == 1997) %>%
filter(continent == "Europe") %>%
arrange(desc(pop)) %>%
head(30) %>%
ggplot(aes(x=country, y=pop)) +
geom_point(colour = "red", size = 2) +
theme(axis.text.x=element_text(angle=45,hjust=1))
Looking at all populations:
hist(gapminder$pop)
The vast majority of the pop's are small (less than 200 million) with very few larger outliers. Therefore there is a strong right skew.
Can also use ggplot to make a nicer version of the histogram:
ggplot(gapminder, aes(log10(gapminder$pop))) +
geom_histogram(bins = 25, fill = "blue") +
ggtitle("Count histogram for population size") +
geom_vline(xintercept = mean(log10(gapminder$pop)), colour = 2, size = 2 )
Here we are looking at a histogram of the counts for log10 of population size. Mean size is marked with the red line.
gapminder %>%
filter(country %in% c("Canada", "United States", "Mexico")) %>%
ggplot(aes(x = year, y = pop, colour = country)) +
geom_point(aes(size = lifeExp)) +
geom_smooth(lwd = 1, se = FALSE, method = "lm") +
theme_bw() 
Here is the population size of 3 north american countries, as it changed over the years. Life expectancy is indicated by the size of the points.
Looking at the population of countries in 2007 relative to Canada, and how this effects lifeExp. Coloured based on continent and Canada data point is labelled.
pop_canada = filter(gapminder, year == 2007, country == "Canada") %>%
select(pop)
pop_canada <- as.numeric(pop_canada)
canada_only <- filter(gapminder, year == 2007, country == "Canada")
rel_pop = 5
gapminder %>%
filter(year == 2007) %>%
mutate(rel_pop = pop / pop_canada) %>%
ggplot(aes(x=log10(rel_pop), y=lifeExp, colour = continent)) +
geom_point(size = 3) +
geom_text(data=canada_only, label = "Canada", colour = "black", size = 4,
hjust = 2, vjust=-1)
For some reason when I tried to knit the document it gave an error that object 'rel_pop' could not be found. Adding the line rel_pop = 5 to my code fixed this problem...
filter(gapminder, country == c("Rwanda", "Afghanistan"))## # A tibble: 12 x 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1957 30.332 9240934 820.8530
## 2 Afghanistan Asia 1967 34.020 11537966 836.1971
## 3 Afghanistan Asia 1977 38.438 14880372 786.1134
## 4 Afghanistan Asia 1987 40.822 13867957 852.3959
## 5 Afghanistan Asia 1997 41.763 22227415 635.3414
## 6 Afghanistan Asia 2007 43.828 31889923 974.5803
## 7 Rwanda Africa 1952 40.000 2534927 493.3239
## 8 Rwanda Africa 1962 43.000 3051242 597.4731
## 9 Rwanda Africa 1972 44.600 3992121 590.5807
## 10 Rwanda Africa 1982 46.218 5507565 881.5706
## 11 Rwanda Africa 1992 23.599 7290203 737.0686
## 12 Rwanda Africa 2002 43.413 7852401 785.6538
This did not quite work. It alternated between Rwanda and Afganistan as the country while it did the filtering. Therefore only half of the entries for each country were found (6 each instead of 12 each). If you look at the years you can see how it alternates.
Correct way:
filter(gapminder, country %in% c("Rwanda", "Afghanistan"))## # A tibble: 24 x 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134
## 7 Afghanistan Asia 1982 39.854 12881816 978.0114
## 8 Afghanistan Asia 1987 40.822 13867957 852.3959
## 9 Afghanistan Asia 1992 41.674 16317921 649.3414
## 10 Afghanistan Asia 1997 41.763 22227415 635.3414
## # ... with 14 more rows
This gets all of intended data.
It definetely took me a lot of time as I had to keep going back to the sites dplyr-end-single-table, cm005 notes, dplyr-intro, and googling things as they came up. But it was good as I learned a lot as I experimented with different functions.
I found using pipe easy and helpful. Trying to add a data label for a single data point (canada) was tough.
