Challenge 10

purrr

First steps

scatter

First, I’ll define the function I used for Challenge 9. The function creates a scatter plot with arguments of a dataframe, two variables, and a plot title.

Code

# Function to plot a scatter plot
scatter <- function(data, x, y, title) {
  # Convert x and y to symbols
  x <- substitute(x)
  y <- substitute(y)
  
  # Create the scatter plot using ggplot2
  plot <- ggplot(data, aes(x = !!x, y = !!y)) +
    geom_point() +
    labs(title = title, x = deparse(x), y = deparse(y)) +
    theme_minimal()
  
  # Print the scatter plot
  print(plot)
}

Define dataframe

Next, we need to define a dataframe, df, to use.

Code

# define a dataframe
set.seed(489)  # set seed
x <- rnorm(100)
y <- rnorm(100)
df <- data.frame(x = x, y = y)

Examples

Now, we’ll use my scatter function with purrr. purrr will apply scatter to different subsets of df.

Example 1

Code

# Apply scatter function to each subset of data
df_list <- split(df, df$x > 0)  # Split data frame based on x > 0
scatter_list <- map(df_list, ~scatter(.x, x, y, title = "Scatter Plot"))

In the first example, split(df, df$x > 0) splits df into 2 subsets based on whether x is greater than 0. The resulting list df_list contains two dataframes. Then, map() applies scatter to each dataframe in the list, creating a list of scatterplots: scatter_list.

Example 2

Code

# Apply scatter function to multiple data frames
df2 <- data.frame(x = rnorm(100), y = rnorm(100))
df_list <- list(df, df2)
scatter_list <- map(df_list, ~scatter(.x, x, y, title = "Scatter Plot"))

In the second example, we create another data frame df2. We then combine df and df2 into a list df_list, and map() applies scatter to each dataframe in the list, again generating a list of scatter plots scatter_list.