The Complete Guide: How to Group & Summarize Data in R

by Tutor Aspire January 17, 2023

Two of the most common tasks that you’ll perform in data analysis are grouping and summarizing data.

Fortunately the dplyr package in R allows you to quickly group and summarize data.

This tutorial provides a quick guide to getting started with dplyr.

Install & Load the dplyr Package

Before you can use the functions in the dplyr package, you must first load the package:

#install dplyr (if not already installed)
install.packages('dplyr')

#load dplyr 
library(dplyr)

Next, we’ll illustrate several examples of how to use the functions in dplyr to group and summarize data using the built-in R dataset called mtcars:

#obtain rows and columns of mtcars
dim(mtcars)

[1] 32 11

#view first six rows of mtcars
head(mtcars)

                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

The basic syntax that we’ll use to group and summarize data is as follows:

data %>%
  group_by(col_name) %>%
  summarize(summary_name = summary_function)

Note: The functions summarize() and summarise() are equivalent.

Example 1: Find Mean & Median by Group

The following code shows how to calculate measures of central tendency by group including the mean and the median:

#find mean mpg by cylinder
mtcars %>%
  group_by(cyl) %>%
  summarize(mean_mpg = mean(mpg, na.rm = TRUE))

# A tibble: 3 x 2
    cyl mean_mpg
      
1     4     26.7
2     6     19.7
3     8     15.1

#find median mpg by cylinder
mtcars %>%
  group_by(cyl) %>%
  summarize(median_mpg = median(mpg, na.rm = TRUE))

# A tibble: 3 x 2
    cyl median_mpg
        
1     4       26  
2     6       19.7
3     8       15.2

Example 2: Find Measures of Spread by Group

The following code shows how to calculate measures of dispersion by group including the standard deviation, interquartile range, and median absolute deviation:

#find sd, IQR, and mad by cylinder
mtcars %>%
  group_by(cyl) %>%
  summarize(sd_mpg = sd(mpg, na.rm = TRUE),
            iqr_mpg = IQR(mpg, na.rm = TRUE),
            mad_mpg = mad(mpg, na.rm = TRUE))

# A tibble: 3 x 4
    cyl sd_mpg iqr_mpg mad_mpg
          
1     4   4.51    7.60    6.52
2     6   1.45    2.35    1.93
3     8   2.56    1.85    1.56

Example 3: Find Count by Group

The following code shows how to find the count and the unique count by group in R:

#find row count and unique row count by cylinder
mtcars %>%
  group_by(cyl) %>%
  summarize(count_mpg = n(),
            u_count_mpg = n_distinct(mpg))

# A tibble: 3 x 3
    cyl count_mpg u_count_mpg
              
1     4        11           9
2     6         7           6
3     8        14          12

Example 4: Find Percentile by Group

The following code shows how to find the 90th percentile of values for mpg by cylinder group:

#find 90th percentile of mpg for each cylinder group
mtcars %>%
  group_by(cyl) %>%
  summarize(quant90 = quantile(mpg, probs = .9))

# A tibble: 3 x 2
    cyl quant90
     
1     4    32.4
2     6    21.2
3     8    18.3

Additional Resources

You can find the complete documentation for the dplyr package along with helpful visualize cheat sheets here.

Other useful functions that you can use along with group_by() and summarize() include functions for filtering data frame rows and arranging rows in certain orders.