Create copy of plotting data

episodes/04-tidyr.Rmd

@@ -58,7 +58,7 @@
 
 This graphic visually represents the three rules that define a "tidy" dataset:
 
 ![](fig/tidy-data-wickham.png)
 *R for Data Science*, Wickham H and Grolemund G ([https://r4ds.had.co.nz/index.html](https://r4ds.had.co.nz/index.html))
 © Wickham, Grolemund 2017
 This image is licenced under Attribution-NonCommercial-NoDerivs 3.0 United States (CC-BY-NC-ND 3.0 US)
@@ -128,7 +128,7 @@
 think! The gif below shows how these two formats relate to each other, and
 gives you an idea of how we can use R to shift from one format to the other.
 
 ![](fig/tidyr-pivot_wider_longer.gif)
 Long and wide dataframe layouts mainly affect readability. You may find that
 visually you may prefer the "wide" format, since you can see more of the data on
 the screen. However, all of the R functions we have used thus far expect for
@@ -435,7 +435,7 @@
 this data frame to our `data_output` directory.
 
 ```{r, purl=FALSE, eval=FALSE}
-write_csv (interviews_plotting, file = "data_output/interviews_plotting.csv")
+write_csv(interviews_plotting, file = "data_output/interviews_plotting.csv")
 ```
 
 ```{r, purl=FALSE, eval=TRUE, echo=FALSE}

episodes/05-ggplot2.Rmd

@@ -11,9 +11,16 @@ source("data/download_data.R")
 
 :::: instructor
 
-- This lesson is a broad overview of ggplot2 and focuses on (1) getting familiar
-  with the layering system of ggplot2, (2) using the argument `group` in the
-  `aes()` function, (3) basic customization of the plots.
+- This episode is a broad overview of ggplot2 and focuses on (1) getting
+  familiar with the layering system of ggplot2, (2) using the argument `group`
+  in the `aes()` function, (3) basic customization of the plots.
+- The episode depends on data created in the Data Wrangling with tidyr
+  episode. If you did not get to or through all of the tidyr episode,
+  you can have the learners access the data by either downloading it or
+  quickly creating it using the tidyr code below. You will probably want to
+  copy the code into the Etherpad.
+- If you did skip the tidyr episode, you might want to go over the exporting
+  data section in that episode.
 
 ::::::::::::
 
@@ -50,10 +57,21 @@ interviews_plotting <- read_csv("data_output/interviews_plotting.csv")
 ```
 
 If you were unable to complete the previous lesson or did not save the data,
-then you can create it now.
+then you can create it now. Either download it using `read_csv()` (Option 1)
+or create it with the **dplyr** and **tidyr** code (Option 2).
+
+::: tab
+
+### Option 1: Download the data
 
 ```{r, purl=FALSE, eval=FALSE}
-## Not run, but can be used to load in data from previous lesson!
+interviews_plotting <- read_csv("https://raw.githubusercontent.com/datacarpentry/r-socialsci/main/episodes/data/interviews_plotting.csv")
+```
+
+### Option 2: Create the data
+
+```{r, purl=FALSE, eval=FALSE}
+## Can be used to load in data from previous lesson!
 interviews_plotting <- interviews %>%
   ## pivot wider by items_owned
   separate_rows(items_owned, sep = ";") %>%
@@ -74,6 +92,8 @@ interviews_plotting <- interviews %>%
   mutate(number_items = rowSums(select(., bicycle:car)))
 ```
 
+:::
+
 ## Plotting with **`ggplot2`**
 
 **`ggplot2`** is a plotting package that makes it simple to create complex plots

episodes/data/download_data.R

@@ -8,16 +8,44 @@ if (!dir.exists("data"))
 if (! file.exists("data/SAFI_clean.csv")) {
   download.file("https://ndownloader.figshare.com/files/11492171",
                 "data/SAFI_clean.csv", mode = "wb")
-  
+
   # Clean data
   df <- read.csv("data/SAFI_clean.csv",
                  stringsAsFactors = FALSE)
-  
+
   # Remove white space
   df$respondent_wall_type <- trimws(df$respondent_wall_type, which = "both")
   # Replace duplicate ids
   df[[2, 1]] <- 2
   df[[53, 1]] <- 53
-  
+
   write.csv(df, "data/SAFI_clean.csv", row.names = FALSE)
 }
+
+# Plotting data -----------------------------------------------------------
+
+# Create plotting data for ggplot episode
+library(tidyr)
+library(dplyr)
+
+if (! file.exists("data/interviews_plotting.csv")) {
+  # Copy code from ggplot episode to create data
+  interviews_plotting <- df %>%
+    separate_rows(items_owned, sep = ";") %>%
+    replace_na(list(items_owned = "no_listed_items")) %>%
+    mutate(items_owned_logical = TRUE) %>%
+    pivot_wider(names_from = items_owned,
+                values_from = items_owned_logical,
+                values_fill = list(items_owned_logical = FALSE)) %>%
+    separate_rows(months_lack_food, sep = ";") %>%
+    mutate(months_lack_food_logical = TRUE) %>%
+    pivot_wider(names_from = months_lack_food,
+                values_from = months_lack_food_logical,
+                values_fill = list(months_lack_food_logical = FALSE)) %>%
+    mutate(number_months_lack_food = rowSums(select(., Jan:May))) %>%
+    mutate(number_items = rowSums(select(., bicycle:car)))
+
+  write.csv(interviews_plotting, "data/interviews_plotting.csv", row.names = FALSE)
+}
+
+