Updated code examples

Author: devnich

scripts/curriculum.Rmd

@@ -148,20 +148,18 @@ mutate()
 
 Create a data frame that has the African values for `lifeExp`, `country`, and `year`.
 
-
 ### Challenge 8
 
-1. Calculate the average life expectancy in 2002 of 2 randomly selected countries for each continent.
-2. Then arrange the continent names in reverse order.
+1.  Calculate the average life expectancy in 2002 of 2 randomly selected countries for each continent.
+2.  Then arrange the continent names in reverse order.
 
 Hint: Use the dplyr functions `arrange()` and `sample_n()`. They have similar syntax to other dplyr functions.
 
-
 ## Tidyverse in the wild
 
 ![Highest single contributors to increased mortality by age group, gender, and race between 2018/2019 average and 2020 totals for younger people](../images/nri_tidyverse.png)
 
-Credit: https://twitter.com/NewRiverInvest/status/1538984422518599680
+Credit: <https://twitter.com/NewRiverInvest/status/1538984422518599680>
 
 ```{r tidyverse_example}
 

scripts/examples.R

@@ -71,34 +71,53 @@ for (year in unique(gapminder$year)) {
 }
 
 ## --- Aside: Get list of files in subdirectory ---
-dir(path = "../data", pattern = "north_america_[1-9]*.csv")
+#dir(path = "../data", pattern = "north_america_[1-9]*.csv")
+dir(path = "../data", pattern = "gapminder_gdp.*.csv")
 
 ## --- Read files using a for loop ---
 # Create an empty list to hold your input
 df_list <- list()
 
 # Get list of files to read
-file_names <- dir(path = "../data", pattern = "north_america_[1-9]*.csv")
+file_names <- dir(path = "../data", pattern = "gapminder_gdp.*.csv")
 
+# Read files into data frames
 for (f in file_names){
   df_list[[f]] <- read.csv(file = file.path("../data", f))
 }
 
+# Check data frame dimensions
+for (name in names(df_list)) {
+  print(name)
+  print(dim(df_list[[name]]))
+}
+
+# What's going on with Americas?
+for (name in names(df_list)) {
+  print(name)
+  print(dim(df_list[[name]]))
+  print(colnames(df_list[[name]]))
+}
+
+# Drop the continent column for Americas
+americas <- df_list[["gapminder_gdp_americas.csv"]]
+df_list[["gapminder_gdp_americas.csv"]] <- americas[, ! colnames(americas) %in% c("continent")]
+
+# Number of columns don't match
+df <- do.call(rbind, df_list)
+
 ## --- Read files using apply ---
 file_names <- dir(path = "../data", pattern = "north_america_[1-9]*.csv")
 df_list <- lapply(file.path("../data", file_names), read.csv)
-
-names(df_list)
-df_list[[1]]
 names(df_list) <- file_names
 
+
 ## --- Read files using apply with pipes ---
 
 ## Vectorize version with pipes
 df_list <- file.path("../data", file_names) |>
     lapply(read.csv)
 
-
 ##-------------------------------------------------
 ##  Challenges
 ##-------------------------------------------------