Understanding the map Function in Tidyverse Language

Introduction to the tidyverse Ecosystem

The tidyverse is a collection of R packages designed for data science. It provides a consistent set of tools for data manipulation, modeling, and visualization. The tidyverse ecosystem is built around three main components: dplyr for data manipulation, tidyr for data transformation, and broom for statistical analysis.

In this article, we will focus on the map function in the tidyverse language, specifically how it can be used to apply functions to each element of a list or vector.

The Problem Statement

We are given a list of regression models, regression_list, which contains multiple linear models (lm objects) fit to different datasets. We want to analyze these models together and extract various statistics from them using the glance() function from the broom package.

Individual Model Analysis

Let’s first analyze an individual model using the tidy() function from the tidyr package, which converts a model object into a tidy data frame:

# Load necessary libraries
library(tidyverse)

# Define a sample dataset and regression model
df_final <- data.frame(x = rnorm(100), y = rnorm(100))
model1 <- lm(y ~ x, data = df_final)

# Analyze the individual model using tidy()
tidy(model1) %>% as_tibble() %>% glance()

This code will output a summary of the individual model’s statistics.

Applying Map Function to Multiple Models

Now, let’s apply the map function to multiple models in regression_list. However, when we do this, R throws an error because the lm object cannot be directly converted to a data frame:

# Load necessary libraries
library(purrr)

# Define regression list
regression_list <- list(
  lm(y ~ x, data = df_final),
  lm(y ~ x + z, data = df_final),
  lm(y ~ x + z + w, data = df_final)
)

# Apply map function to each model in the list
result <- regression_list %>% 
  map(~ glance(lm(.x, data = df_final)))

# Error output
Error in as.data.frame.default(value, stringsAsFactors = FALSE) :
  cannot coerce class ‘"lm"’ to a data.frame

The error message indicates that we need to convert the lm object to a data frame before applying the glance() function.

Using map and tidyr for Data Frame Conversion

To resolve this issue, we can use the as_tibble() function from the tidyr package to convert the lm object to a data frame. We also need to specify the data argument in the lm() function:

# Load necessary libraries
library(purrr)
library(broom)
library(tidyr)

# Define regression list
regression_list <- list(
  lm(y ~ x, data = df_final),
  lm(y ~ x + z, data = df_final),
  lm(y ~ x + z + w, data = df_final)
)

# Apply map function to each model in the list
result <- regression_list %>% 
  map(~ glance(as_tibble(lm(.x, data = df_final))))

# Output
result

By using as_tibble() and specifying the data argument in the lm() function, we can convert the lm object to a data frame and apply the glance() function successfully.

Using map_df for Data Frame Concatenation

If you want to concatenate the results of each model into a single data frame, you can use the map_df() function instead of map(). Here’s an example:

# Load necessary libraries
library(purrr)
library(broom)
library(tidyr)

# Define regression list
regression_list <- list(
  lm(y ~ x, data = df_final),
  lm(y ~ x + z, data = df_final),
  lm(y ~ x + z + w, data = df_final)
)

# Apply map_df function to each model in the list
result <- regression_list %>% 
  map_df(~ glance(as_tibble(lm(.x, data = df_final))))

# Output
result

By using map_df(), we can concatenate the results of each model into a single data frame.

Conclusion

In this article, we explored how to use the map function in the tidyverse language to apply functions to multiple models in a list. We discussed the importance of converting the lm object to a data frame and demonstrated how to do so using tidyr’s as_tibble() function. Additionally, we covered the difference between map and map_df and how to use them for data manipulation.

Last modified on 2023-06-26