Improved structure

-added directory for data
-updated the overview of protocols

.Rproj.user/shared/notebooks/paths

@@ -1,24 +1,30 @@
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+/Users/joachimgoedhart/Desktop/test-time.html="0E754777"
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+/Users/joachimgoedhart/surfdrive/Bookdown-project/DataViz-protocols/.gitignore="476F9C7E"
 /Users/joachimgoedhart/surfdrive/Bookdown-project/DataViz-protocols/01-getting-started.Rmd="7443EEB0"
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 /Users/joachimgoedhart/surfdrive/Bookdown-project/DataViz-protocols/Protocols/protocol_3.Rmd="C0BDFDF7"
+/Users/joachimgoedhart/surfdrive/Bookdown-project/DataViz-protocols/Protocols/protocol_4.R="A51D40F5"
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@@ -35,11 +41,32 @@
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 /Users/joachimgoedhart/surfdrive/Bookdown-project/Protocols-in-progress/Conversions.txt="A9DCF86C"
+/Users/joachimgoedhart/surfdrive/Bookdown-project/Protocols-in-progress/EC-FPs.csv="8662EB97"
+/Users/joachimgoedhart/surfdrive/Bookdown-project/Protocols-in-progress/EC_FPs_Cranfill.csv="FDAB563B"
+/Users/joachimgoedhart/surfdrive/Bookdown-project/Protocols-in-progress/Fitting-exp.Rmd="0DC1B273"
+/Users/joachimgoedhart/surfdrive/Bookdown-project/Protocols-in-progress/Pi-chart.Rmd="912EA351"
+/Users/joachimgoedhart/surfdrive/Bookdown-project/Protocols-in-progress/Protocol_17_Animated_plots-ECIS.Rmd="A2B4589D"
+/Users/joachimgoedhart/surfdrive/Bookdown-project/Protocols-in-progress/Protocol_18_Animated_plots-with_GIF.Rmd="E566FB86"
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04-complete-protocols.Rmd

@@ -5,7 +5,7 @@ knitr::opts_chunk$set(comment = NA, warning=FALSE)
 
 # Complete protocols {#complete-protocols}
 
-This chapter showcases several *complete* protocols for different kinds of data visualizations. Each protocol starts with the raw data and ends with a publication quality plot. The data is available from Github, just follow [this link](https://github.com/JoachimGoedhart/DataViz-protocols/tree/main/Protocols). In the same folder you can find the R markdown files for each protocol. To reproduce the data visualizations you can either take the data and follow the instructions in the chapter. Alternatively you can download the R markdown file (and the data) and run it step by step. The R markdown files can also be used as a starting point to apply the same visualization to your own data.
+This chapter showcases several *complete* protocols for different kinds of data visualizations. Each protocol starts with the raw data and ends with a publication quality plot. The data is available from Github in the '/data'  folder on [this page](https://github.com/JoachimGoedhart/DataViz-protocols/tree/main/Protocols). The github page holds a separate Rmarkdown file for each protocol. To reproduce the data visualizations you can either take the data and follow the instructions in the chapter. Alternatively you can download the Rmarkdown file (and the data) and run it step by step. The Rmarkdown files can also be used as a starting point to apply the same visualization to your own data.
 
 An overview of the data visualizations that are generated by the protocols:
 

Plotting all shapes.R

@@ -0,0 +1,24 @@
+library(tidyverse)
+
+x <- rep(0:15, 8)
+y <- rep(0:7, each=16)
+shape <- 1:128
+
+df_shapes <- data.frame(x=x, y=y, shape=shape) 
+
+ggplot(df_shapes, aes(x,y))+geom_label(aes(label=shape))
+
+
+df_label <- df_shapes %>% filter(!shape %in% c(26:32))
+
+ggplot(data=(df_shapes), aes(x,y)) +
+  geom_point(shape = as.factor(shape), size=4, fill="blue") +
+  scale_shape_manual(values = c(1:128)) +
+  geom_label(data=df_label, aes(x=x,y=y,label=shape), nudge_y = -0.4, size=2, label.size = .2) +
+  theme_minimal() +
+  theme(legend.position = "none",
+        axis.title = element_blank(),
+        axis.text = element_blank()) +
+  theme(panel.grid = element_blank()) +
+  scale_y_reverse() +
+  NULL

Plotting_all_point_shapes.R

@@ -0,0 +1,21 @@
+library(tidyverse)
+
+x <- rep(0:15, 8)
+y <- rep(0:7, each=16)
+shape <- 0:127
+
+df_shapes <- data.frame(x=x, y=y, shape=shape) %>%
+  filter(!shape %in% c(26:31)) #Shapes 26-31 are not defined, so we'll remove those
+
+ggplot(data=df_shapes, aes(x,y)) +
+  geom_point(aes(shape = as.factor(shape)), size=4, fill="blue") +
+  scale_shape_manual(values = df_shapes$shape) +
+  geom_label(aes(label=shape), nudge_y = -0.4, size=2, label.size = .2) +
+  theme_minimal() +
+  theme(legend.position = "none",
+        axis.title = element_blank(),
+        axis.text = element_blank(),
+        panel.grid = element_blank()) +
+  scale_y_reverse() +
+  NULL
+

Protocols/protocol_1.Rmd

@@ -18,7 +18,7 @@ The data was downloaded in CSV format (by clicking on the button in the lower ri
 We read the data from the CSV by using the `read_csv()` function. This function is part of the tidy verse and loads the data as a tibble. It also guesses type of data for each column. To hide that information, we use `show_col_types = FALSE` here.
 
 ```{r }
-df_raw <- read_csv("FPbase_Spectra_4FPs.csv", show_col_types = FALSE)
+df_raw <- read_csv("data/FPbase_Spectra_4FPs.csv", show_col_types = FALSE)
 ```
 
 Let's briefly look at what we have loaded:

Protocols/protocol_10.Rmd

@@ -21,7 +21,7 @@ After clicking on 'Publish' a link is generated that can be read as a csv, and a
 ```{r}
 # df_sheet <- read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSc-nI1-s_u-XkNXEn_u2l6wkBafxJMHQ_Cd3kStrnToh7kawqjQU3y2l_1riLigKRkIqlNOqPrgkdW/pub?output=csv", na.strings = "")
 
-df_sheet <-  read.csv("20220421_Resultaat-metingen.csv", na.strings = "")
+df_sheet <-  read.csv("data/20220421_Resultaat-metingen.csv", na.strings = "")
 
 head(df_sheet)
 ```

Protocols/protocol_11.Rmd

@@ -18,7 +18,7 @@ After clicking on 'Publish' a link is generated that can be read as a csv. Any e
 ```{r}
 # df_sheet <- read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSc-nI1-s_u-XkNXEn_u2l6wkBafxJMHQ_Cd3kStrnToh7kawqjQU3y2l_1riLigKRkIqlNOqPrgkdW/pub?output=csv", na.strings = "")
 
-df_sheet <-  read.csv("20220421_Resultaat-metingen.csv", na.strings = "")
+df_sheet <-  read.csv("data/20220421_Resultaat-metingen.csv", na.strings = "")
 head(df_sheet)
 ```
 

Protocols/protocol_2.Rmd

@@ -25,7 +25,7 @@ Confidence_level <-  0.95
 The data is stored in an excel sheet and we read it, skipping the first 6 lines which contain comments:
 
 ```{r }
-df_raw <- readxl::read_excel('figure5.xlsx', skip=6)
+df_raw <- readxl::read_excel('data/figure5.xlsx', skip=6)
 ```
 
 Let's look at the data:

Protocols/protocol_3.Rmd

@@ -18,7 +18,7 @@ library(tidyverse)
 The data comes from an excel file:
 
 ```{r }
-df_raw <- readxl::read_excel("Data_Arts_Circularity.xlsx")
+df_raw <- readxl::read_excel("data/Data_Arts_Circularity.xlsx")
 head(df_raw)
 ```
 

Protocols/protocol_4.Rmd

@@ -21,13 +21,13 @@ library(tidyverse)
 The measured data is read from an excel sheet. Note that this is the raw data that is stored by the software that operates the plate reader:
 
 ```{r }
-df_raw <- readxl::read_excel("DualLuc_example_data.xlsx", sheet = "Results")
+df_raw <- readxl::read_excel("data/DualLuc_example_data.xlsx", sheet = "Results")
 ```
 
 The experimental conditions for each well are stored in a separate CSV file, generated by the experimentalist that did the experiment:
 
 ```{r }
-df_design <- read.csv("Tidy_design.csv")
+df_design <- read.csv("data/Tidy_design.csv")
 head(df_design)
 ```
 You can see that the design file is tidy. In contrast the excel file with data is far from tidy. In the excel sheet, two 'rectangles' of cells define the data for the firefly & renilla reads.

Protocols/protocol_6.Rmd

@@ -19,7 +19,7 @@ A CSV file with the data is loaded. Since the file is a couple of Megabytes, we
 
 ```{r }
 library(data.table)
-df_S1P <- data.table::fread("ERK_repsonse_to_S1P.csv") 
+df_S1P <- data.table::fread("data/ERK_repsonse_to_S1P.csv") 
 head(df_S1P)
 ```
 

Protocols/protocol_7.Rmd

@@ -17,7 +17,7 @@ output: html_document
 
 ```{r }
 require(tidyverse)
-df1 <- read.csv("Rac_S1P.csv")
+df1 <- read.csv("data/Rac_S1P.csv")
 head(df1)
 ```
 

Protocols/protocol_8.Rmd

@@ -1,4 +1,3 @@
-
 ---
 author: "Joachim Goedhart"
 date: "`r Sys.Date()`"
@@ -18,7 +17,7 @@ library(tidyverse)
 A good example of data wrangling related to 96-wells data is given in [protocol 4](#protocol-4). Here we start from the tidy data that was the result of data tiyding in the section [Data in 96-wells format](#Data-in-96-wells-format):
 
 ```{r }
-df_8 <- read.csv("df_tidy_wells.csv")
+df_8 <- read.csv("data/df_tidy_wells.csv")
 head(df_8)
 ```
 

Protocols/protocol_9.Rmd

@@ -17,7 +17,7 @@ library(tidyverse)
 Read the data:
 
 ```{r }
-df_DRC <- read.csv("DRC_Histamine.csv", stringsAsFactors = TRUE)
+df_DRC <- read.csv("data/DRC_Histamine.csv", stringsAsFactors = TRUE)
 head(df_DRC)
 ```
 
@@ -117,13 +117,13 @@ To plot the fitted data, we use `augment()` function that is part of the `{broom
 ```{r }
 library(broom)
 
-p + geom_line(data = augment(fit), aes(x=Concentration,y=.fitted), color="black", size=1)
+p + geom_line(data = augment(fit), aes(x=Concentration, y=.fitted), color="black", size=1)
 ```
 
 Instead of `geom_lin()` for plotting the fit, we can also use `geom_smooth()`:
 
 ```{r}
-p <- p + geom_smooth(data = augment(fit), aes(x=Concentration,y=.fitted), color="black", size=1)
+p <- p + geom_smooth(data = augment(fit), aes(x=Concentration, y=.fitted), color="black", size=1)
 p
 ```
 

docs/404.html

@@ -23,7 +23,7 @@
 <meta name="author" content="Joachim Goedhart" />
 
 
-<meta name="date" content="2022-09-22" />
+<meta name="date" content="2022-10-25" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1" />
   <meta name="apple-mobile-web-app-capable" content="yes" />