Compute Summary Statistics in R - Datanovia (2024)

109812781077

• Required packages
• Demo dataset
• Summary statistics of ungrouped data
• Summary statistics of grouped data
  • Group by one variable
  • Group by multiple variables
• Summarise multiple variables
  • Key R functions
  • Summarise variables
• Useful statistical summary functions
• Summary

Required packages

Load the tidyverse packages, which include dplyr:

library(tidyverse)

Demo dataset

We’ll use the R built-in iris data set, which we start by converting into a tibble data frame (tbl_df) for easier data analysis.

my_data <- as_tibble(iris)my_data

## # A tibble: 150 x 5## Sepal.Length Sepal.Width Petal.Length Petal.Width Species## <dbl> <dbl> <dbl> <dbl> <fct> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## # ... with 144 more rows

Summary statistics of ungrouped data

Compute the mean of Sepal.Length and Petal.Length as well as the number of observations using the function n():

my_data %>% summarise( count = n(), mean_sep = mean(Sepal.Length, na.rm = TRUE), mean_pet = mean(Petal.Length, na.rm = TRUE) )

## # A tibble: 1 x 3## count mean_sep mean_pet## <int> <dbl> <dbl>## 1 150 5.84 3.76

Note that, we used the additional argument na.rm to remove NAs, before computing means.

Summary statistics of grouped data

Key R functions: group_by() and summarise()

my_data %>% group_by(Species) %>% summarise( count = n(), mean_sep = mean(Sepal.Length), mean_pet = mean(Petal.Length) )

## # A tibble: 3 x 4## Species count mean_sep mean_pet## <fct> <int> <dbl> <dbl>## 1 setosa 50 5.01 1.46## 2 versicolor 50 5.94 4.26## 3 virginica 50 6.59 5.55

# ToothGrowth demo data setshead(ToothGrowth)

## len supp dose## 1 4.2 VC 0.5## 2 11.5 VC 0.5## 3 7.3 VC 0.5## 4 5.8 VC 0.5## 5 6.4 VC 0.5## 6 10.0 VC 0.5

# SummarizeToothGrowth %>%group_by(supp, dose) %>% summarise( n = n(), mean = mean(len), sd = sd(len) )

## # A tibble: 6 x 5## # Groups: supp [?]## supp dose n mean sd## <fct> <dbl> <int> <dbl> <dbl>## 1 OJ 0.5 10 13.2 4.46## 2 OJ 1 10 22.7 3.91## 3 OJ 2 10 26.1 2.66## 4 VC 0.5 10 7.98 2.75## 5 VC 1 10 16.8 2.52## 6 VC 2 10 26.1 4.80

Summarise multiple variables

summarise_all(.tbl, .funs, ...)summarise_if(.tbl, .predicate, .funs, ...)summarise_at(.tbl, .vars, .funs, ...)

my_data %>% group_by(Species) %>% summarise_all(mean)

## # A tibble: 3 x 5## Species Sepal.Length Sepal.Width Petal.Length Petal.Width## <fct> <dbl> <dbl> <dbl> <dbl>## 1 setosa 5.01 3.43 1.46 0.246## 2 versicolor 5.94 2.77 4.26 1.33 ## 3 virginica 6.59 2.97 5.55 2.03

my_data %>% group_by(Species) %>% summarise_at(c("Sepal.Length", "Sepal.Width"), mean, na.rm = TRUE)

my_data %>% group_by(Species) %>% summarise_if(is.numeric, mean, na.rm = TRUE)

Useful statistical summary functions

This section presents some R functions for computing statistical summaries.

Measure of location:

• mean(x): sum of x divided by the length
• median(x): 50% of x is above and 50% is below

Measure of variation:

• sd(x): standard deviation
• IQR(x): interquartile range (robust equivalent of sd when outliers are present in the data)
• mad(x): median absolute deviation (robust equivalent of sd when outliers are present in the data)

Measure of rank:

• min(x): minimum value of x
• max(x): maximum value of x
• quantile(x, 0.25): 25% of x is below this value

Measure of position:

• first(x): equivalent to x[1]
• nth(x, 2): equivalent to n<-2; x[n]
• last(x): equivalent to x[length(x)]

Counts:

• n(x): the number of element in x
• sum(!is.na(x)): count non-missing values
• n_distinct(x): count the number of unique value

Counts and proportions of logical values:

• sum(x > 10): count the number of elements where x > 10
• mean(y == 0): proportion of elements where y = 0

Summary

In this tutorial, we describe how to easily compute statistical summaries using the R functions summarise() and group_by() [in dplyr package].

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