In this article, we will discuss how to get a summary of the dataset in the R programming language using Dplyr package. To get the summary of a dataset summarize() function of this module is used. This function basically gives the summary based on some required action for a group or ungrouped data, which in turn helps summarize the dataset.
Syntax: summarize(action)
The dataset in use: bestsellers3
Here, action can be any operation to be performed on grouped data, it can be frequency count, mean, average, etc.
Example: Summarize the dataset using summarize()
library(dplyr)
data<-read.csv("bestsellers.csv")
data %>% group_by(Genre) %>%
summarize(n())
Output:
# A tibble: 2 x 2 Genre `n()` <fct> <int> 1 Fiction 82 2 Non Fiction 117
Summarize ungrouped dataset
It is also possible to summarize ungrouped data. There are three possible functions that can be used for this.
- summarize_all().
- summarize_at().
- summarize_if().
summarize_all():
summarize_all() function summarizes all the columns based on the action to be performed.
Syntax: summarize_all(action)
library(dplyr)
data<-read.csv("bestsellers.csv")
data %>% group_by(Genre) %>%
summarize_all(mean)
Output:
summarize_at():
summarize_at() function is used to apply a required action to some specific columns and generate a summary based on that
Syntax: summarize_at(vector_of_columns,action)
library(dplyr)
data<-read.csv("bestsellers.csv")
data %>% group_by(Genre) %>%
summarize_at(c('User.Rating','Price'),mean)
Output:
summarize_if():
summarize_if() function is used to get dataset summary if a certain condition is specified.
Syntax: summarize_if(condition, action)
library(dplyr)
data<-read.csv("bestsellers.csv")
data %>% group_by(Genre) %>%
summarize_if(is.numeric, mean)
Output:
