How to Group and Summarize Data in R

How to Group and Summarize Data in R?, Grouping and summarising data are two of the most frequent actions you’ll conduct in data analysis.

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Fortunately, you can easily organize and summarise data using the R dplyr library.

The dplyr package must initially be loaded before you may use any of its functions:

Let’s install the dplyr package (if not already installed)

install.packages('dplyr')
library(dplyr)

The built-in R dataset mtcars will be used to demonstrate numerous examples of how to group and summarise data using the functions in dplyr.

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Obtain mtcars dataset in rows and columns.

dim(mtcars)
[1] 32 11

Now we can view the 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

We’ll group and summarise data using the following fundamental syntax.

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

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

Example 1: Search for Mean & Median by Group

The mean and median, as well as other measures of central tendency by group, can be calculated using the code below.

cylinder #find mean mpg

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

To find median mpg by cylinder

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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 Spread Measures by Group

The standard deviation, interquartile range, and median absolute deviation may all be calculated for each group using the following code.

by cylinder, find sd, IQR, and mad

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: Discover Count by Group

The R code below demonstrates how to find the count and the unique count by the group.

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To find unique row count and row count by the 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: Calculate Percentile by Group

The 90th percentile of data for mpg by cylinder group can be determined using the following code.

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For each cylinder group, find the 90th percentile of the mpg.

mtcars %>%
  group_by(cyl) %>%
  summarize(quant90 = quantile(mpg, probs = .9))

cyl quant90
  <dbl>   <dbl>
1     4    32.4
2     6    21.2
3     8    18.3