What is the summarize() method in R?
The summarize() function is used in the R program to summarize the DataFrame into just one value or vector.
This summarization is done through grouping observations by using categorical values at first, using the groupby() function.
The dplyr package is used to get the summary of the dataset. The summarize() function offers the summary that is based on the action done on grouped or ungrouped data.
Summarize grouped data
The operations that can be performed on grouped data are average, factor, count, mean, etc.
# Load librarylibrary(dplyr)data <- PlantGrowth# summarizesummarize(data, mean(weight,na.rm=TRUE))
In the example above, we use the summarize() function to obtain the mean weight of all the plant species in the PlantGrowth dataset.
Summarize ungrouped data
We can also summarize ungrouped data. This can be done by using three functions.
summarize_all()summarize_at()summazrize_if()
1. The summarize_all() method
This function summarizes all the columns of data based on the action which is to be performed.
Syntax
summarize_all(action)
Parameters
action: The function to apply on DataFrame columns. It can be either lambda or use funs().
Code
In the code snippet below, we load the data variable. In the variable sample, we are loading the top six observations to process. The sample %>% summarize_all(mean) will show the mean of the six observations in the result.
# Load dplyr librarylibrary(dplyr, warn.conflicts = FALSE)# Main codedata <- mtcars# Loading starting 6 observationssample <- head(data)# Caculating mean value.sample %>% summarize_all(mean)
2. The summarize_at() method
It performs the action on the specific column and generates the summary based on that action.
Syntax
summarize_at(vector_of_columns, action)
Parameters
vector_of_columns: The list of column names or character vector of column names.action: The function to apply on DataFrame columns. It can be either lambda or usefuns().
Code
In the code snippet below, we load the data variable. In the variable sample, we are loading the top six observations to process. The sample %>% group_by(hp) %>% summarize_at(c('cyl','mpg'),mean) will show the mean of the 'cyl' and 'mpg' observations in the result, grouping with hp (dataset feature/column name).
# Load dplyr librarylibrary(dplyr, warn.conflicts = FALSE)# Main codedata<-mtcarssample <- head(data)sample %>% group_by(hp) %>%summarize_at(c('cyl','mpg'),mean)
3. The summarize_if() method
In this function, we specify a condition and the summary will be generated if the condition is satisfied.
Syntax
summarize_if(.predicate, .action)
Parameters
predicate: A predicate function to apply to logical values or DataFrame columns.action: The function to apply on DataFrame columns. It can be either lambda or usefuns().
A predicate function in R returns only True/False.
Code
In the code snippet below, we use the predicate function is.numeric and mean as an action.
# Laod dplyr librarrylibrary(dplyr, warn.conflicts = FALSE)# Main codedata<-mtcarsz<- head(data)z %>% group_by(hp) %>%summarize_if(is.numeric, mean)
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