In this article, we will discuss how to calculate summary statistics by the group in the R programming language.
What is summary statistics in R?
Summary Statistics by Group in R Programming Language are numerical or graphical representations that provide a concise and informative overview of a dataset. They help you understand the central tendencies, dispersion, and shape of your data. R offers various functions and tools to compute and visualize summary statistics. Some common summary statistics in R include.
Summary statistics will return the following from the given data
- Min – Minimum value in the given data
- 1st Quartile – first quartile in the data
- Median – Median of the data
- Mean – Mean of the data
- 3rd Quartile – third quartile in the data
- Max – Maximum value in the given data
Let’s create the dataframe
# create dataframe with 4 columnsdata=data.frame(name=c("ojaswi","bobby","rohith","gnanesh","sireesha"),
subjects=c("java","java","python","cpp","python"),
age=c(21,23,21,20,19),
id=c(1,2,3,4,5))
# displaydata |
Output:
name subjects age id
1 ojaswi java 21 1
2 bobby java 23 2
3 rohith python 21 3
4 gnanesh cpp 20 4
5 sireesha python 19 5
Method 1: Using tapply() Function
In this method to calculate the summary statistics by group, the user needs to simply call the inbuilt tapply() function with the summary argument of this function passed with the given data for which the summary statistics is to be calculated, and under this method, user will take a summary function as the third parameter in the R language.
Syntax:
tapply(data$column_name, data$group_column, summary) Parameters:
- data is the input dataframe
- column_name is the column to be summarized
- group_column is the column to be grouped
- summary is to get the summary data
We are going to display a summary by grouping subjects with age using the tapply() function with the summary argument in the R language.
# create dataframe with 4 columnsdata=data.frame(name=c("ojaswi","bobby","rohith","gnanesh","sireesha"),
subjects=c("java","java","python","cpp","python"),
age=c(21,23,21,20,19),
id=c(1,2,3,4,5))
# display summary by grouping subjects with agetapply(data$age, data$subjects, summary)
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Output:
$cpp
Min. 1st Qu. Median Mean 3rd Qu. Max.
20 20 20 20 20 20
$java
Min. 1st Qu. Median Mean 3rd Qu. Max.
21.0 21.5 22.0 22.0 22.5 23.0
$python
Min. 1st Qu. Median Mean 3rd Qu. Max.
19.0 19.5 20.0 20.0 20.5 21.0
Method 2: Using purrr Package
In this method Summary Statistics by Group the user has to first install and import the purr package, then the user has to follow the below syntax to calculate the summary statistics by a group of the given data in the R language.
install.package('purr')
library('purr')
Syntax:
data %>% split(.$group_column) %>%map(summary) where,
- data is the input dataframe
- group_column is the column to be grouped
- summary is the function to get summary
We are displaying a summary by grouping subjects with the help of the purr package in the R language.
# load the librarylibrary("purrr")
# create dataframe with 4 columnsdata=data.frame(name=c("ojaswi","bobby","rohith","gnanesh","sireesha"),
subjects=c("java","java","python","cpp","python"),
age=c(21,23,21,20,19),
id=c(1,2,3,4,5))
# display summary by grouping subjectsdata %>% split(.$subjects) %>%map(summary)
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Output:
$cpp
name subjects age id
Length:1 Length:1 Min. :20 Min. :4
Class :character Class :character 1st Qu.:20 1st Qu.:4
Mode :character Mode :character Median :20 Median :4
Mean :20 Mean :4
3rd Qu.:20 3rd Qu.:4
Max. :20 Max. :4
$java
name subjects age id
Length:2 Length:2 Min. :21.0 Min. :1.00
Class :character Class :character 1st Qu.:21.5 1st Qu.:1.25
Mode :character Mode :character Median :22.0 Median :1.50
Mean :22.0 Mean :1.50
3rd Qu.:22.5 3rd Qu.:1.75
Max. :23.0 Max. :2.00
$python
name subjects age id
Length:2 Length:2 Min. :19.0 Min. :3.0
Class :character Class :character 1st Qu.:19.5 1st Qu.:3.5
Mode :character Mode :character Median :20.0 Median :4.0
Mean :20.0 Mean :4.0
3rd Qu.:20.5 3rd Qu.:4.5
Max. :21.0 Max. :5.0
Method 3: Using dplyr Package
In this approach Summary Statistics by Groupthe user has to install and import the dplyr package in the working R console and then follow the below syntax with group_by and summarize() function to get summary by group in the R language.
install.package('dplyr')
library('dplyr')
Syntax:
data %>% group_by(group_column) %>% summarize(min = min(column),
q1 = quantile(column, 0.25),
median = median(column),
mean = mean(column),
q3 = quantile(column, 0.75),
max = max(column))
Parameters:
- min(column) – to get the minimum of the column
- max(column) – to get the maximum of the column
- median(column) – to get the median of the column
- mean(column) – to get the mean of the column
- quantile(column, 0.25) – to get the first quartile of the column
- quantile(column, 0.75) – to get the third quartile of the column
We are displaying a summary by grouping subjects with age columns using the dplyr package in the R programming language.
# load the librarylibrary("dplyr")
# create dataframe with 4 columnsdata=data.frame(name=c("ojaswi","bobby","rohith","gnanesh","sireesha"),
subjects=c("java","java","python","cpp","python"),
age=c(21,23,21,20,19),
id=c(1,2,3,4,5))
# display summary by grouping subjects with age columndata %>% group_by(subjects) %>% summarize(min = min(age),
q1 = quantile(age, 0.25),
median = median(age),
mean = mean(age),
q3 = quantile(age, 0.75),
max = max(age))
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Output:
# A tibble: 3 × 7
subjects min q1 median mean q3 max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 cpp 20 20 20 20 20 20
2 java 21 21.5 22 22 22.5 23
3 python 19 19.5 20 20 20.5 21