Data Reduction with R: Collapsing Rows by Year and Summing Counts

In this article, we will explore the process of reducing a dataframe in R by collapsing rows based on specific values and summing up corresponding columns. We’ll delve into two approaches: one using the popular dplyr package and another utilizing base R functions.

Overview of the Problem

We have a dataframe df containing information about badges, with each row representing a specific badge. The dataframe includes three columns:

• badge_name: the name of the badge
• year.month: the date in the format YYYY-MM
• count: the number of times the badge is worn

Our goal is to create a new dataframe that summarizes the counts by year, effectively collapsing rows with the same year value.

Approach 1: Using dplyr

The dplyr package provides an efficient and elegant way to perform data manipulation tasks. In this approach, we’ll use the group_by, summarise, and mutate functions to achieve our goal.

Step 1: Load the necessary libraries

library(dplyr)

Step 2: Modify the year column using mutate

We’ll create a new variable year by extracting the first four characters from the year.month column using the substr function. This will allow us to distinguish between rows with the same year value.

df %>% 
  mutate(year = substr(year.month, 1, 4))

Step 3: Group by badge_name and year, then summarize counts

Next, we’ll group the dataframe by badge_name and year, and then calculate the sum of the count column for each group using the summarise function.

df %>% 
  mutate(year = substr(year.month, 1, 4)) %>% 
  group_by(badge_name, year) %>% 
  summarise(count = sum(count))

This will produce a new dataframe with the desired output:

Example Usage

To demonstrate the usage of this code, let’s assume we have a dataframe df containing the provided data:

df <- data.frame(
  badge_name = c("Teacher", "Student", "Teacher", "Student", "Teacher", "Student", "Teacher", "Student", "Teacher", "Student"),
  year.month = c("2009-1", "2009-1", "2009-2", "2009-2", "2009-3", "2009-3", "2009-4", "2009-4", "2009-5", "2009-5"),
  count = c(2161, 1933, 2193, 2243, 2163, 2197, 2205, 2725, 3004, 2904)
)

Running the code:

library(dplyr)

df %>% 
  mutate(year = substr(year.month, 1, 4)) %>% 
  group_by(badge_name, year) %>% 
  summarise(count = sum(count))

Produces the desired output.

Approach 2: Using Base R

While dplyr provides an elegant solution, we can also achieve this using base R functions. In this approach, we’ll use the substr function to extract the year from each row and then calculate the sum of the counts for each group.

Step 1: Extract the year column

df$year <- substr(df$year.month, 1, 4)

Step 2: Calculate the sum of counts for each group

We’ll use the tapply function to calculate the sum of the count column for each group of rows with the same badge_name and year. We’ll also create a new dataframe using data.frame to store the results.

df$year <- substr(df$year.month, 1, 4)

with(df, tapply(count, df[,c('badge_name', 'year')], sum))

Example Usage

To demonstrate the usage of this code, let’s assume we have a dataframe df containing the provided data:

df <- data.frame(
  badge_name = c("Teacher", "Student", "Teacher", "Student", "Teacher", "Student", "Teacher", "Student", "Teacher", "Student"),
  year.month = c("2009-1", "2009-1", "2009-2", "2009-2", "2009-3", "2009-3", "2009-4", "2009-4", "2009-5", "2009-5"),
  count = c(2161, 1933, 2193, 2243, 2163, 2197, 2205, 2725, 3004, 2904)
)

Running the code:

df$year <- substr(df$year.month, 1, 4)

with(df, tapply(count, df[,c('badge_name', 'year')], sum))

Produces the desired output.

Conclusion

In this article, we demonstrated two approaches to reducing a dataframe in R by collapsing rows based on specific values and summing up corresponding columns. The dplyr package provides an efficient and elegant solution using the group_by, summarise, and mutate functions, while base R functions can also achieve this result. By choosing the right approach and understanding the underlying concepts, you can effectively manipulate your data to extract insights and gain a deeper understanding of your dataset.

Last modified on 2024-07-06