Computing Correlation for Each Subject Using mutate()

Introduction

The problem at hand involves computing correlation between a subject’s stock index and their investment amount for each period. The goal is to create a new column, “corr”, that contains the correlation for all periods between index and invest for each subject.

This task requires using mutate() from the dplyr package in R. However, it seems that the initial code attempt does not achieve the desired result.

Problem Analysis

The current approach attempts to compute correlation directly within mutate(), but this is not intended for such computations. Instead, we need to use other functions like group_by() and summarize() to split the analysis by groups and then merge back the resulting correlation into the original dataset using inner_join().

Solution Overview

To solve this problem, we will follow these steps:

1. Define the analysis groups using group_by().
2. Compute correlation by groups using summarize().
3. Merge back correlation into the original dataset using inner_join().

Step 1: Define Analysis Groups

First, we need to define the groups for which we want to compute the correlation. In this case, it’s each unique subject (represented by the “id” column).

library(dplyr)

# Create a sample dataframe
df = data.frame(id = c(1, 1, 1, 1, 2, 2, 3),
                index = c(102, 104, 106, 108, 102, 104, 102),
                invest = c(42, 13, 72, 18, 52, 42, 25))

# Define the analysis groups
df_grp = group_by(df, id)

Step 2: Compute Correlation by Groups

Now that we have defined our groups, we can compute the correlation between the stock index and investment amount for each period.

# Compute correlation by groups
corr = df_grp %>% 
  summarize(corr = cor(index, invest))

Step 3: Merge Back Correlation into Original Dataset

Finally, we need to merge back the resulting correlation into our original dataset. This can be achieved using inner_join().

# Merge back correlation into original dataset
df = inner_join(df, corr, by = "id")

Complete Code Example

Here is the complete code example that combines all steps:

library(dplyr)

# Create a sample dataframe
df = data.frame(id = c(1, 1, 1, 1, 2, 2, 3),
                index = c(102, 104, 106, 108, 102, 104, 102),
                invest = c(42, 13, 72, 18, 52, 42, 25))

# Define the analysis groups
df_grp = group_by(df, id)

# Compute correlation by groups
corr = df_grp %>% 
  summarize(corr = cor(index, invest))

# Merge back correlation into original dataset
df = inner_join(df, corr, by = "id")

# Print the final result
print(df)

This code will produce a new column called “corr” that contains the correlation for all periods between index and invest for each subject.

Conclusion

Computing correlation for each subject using mutate() involves using other functions like group_by(), summarize(), and inner_join(). By following these steps, you can achieve your desired result.

Last modified on 2024-09-20