The group_by() function in R is used to group rows by column values in the DataFrame, It is similar to GROUP BY clause in SQL. R dplyr groupby is used to collect identical data into groups on DataFrame and perform aggregate functions on the grouped data.
The general operation here is a combination of splitting a data frame into separate pieces defined by a variable or group of variables (group_by()), and then applying a summary function across those subsets (summarize()).
For example, in the air pollution dataset, you might want to know what the average annual level of PM2.5 is. So the stratum is the year, and that is something we can derive from the date variable. In conjunction with the group_by() function we often use the summarize() function (or summarise() for some parts of the world).
Example of how to use the group_by() function in R
library(dplyr)
chicago <- read.csv("chicago_air_pollution.csv")
# Create a year variable
chicago <- mutate(chicago, year = as.POSIXlt(date)$year + 1900)
# Group the data by year
years <- group_by(chicago, year)
# Calculate the average PM2.5 level for each year
average_pm2.5 <- summarize(years, pm25 = mean(pm25, na.rm = TRUE))
# Print the average PM2.5 level for each year
print(average_pm2.5)
This code will print a data frame with one row for each year, and the average PM2.5 level for that year.
The group_by() function is a powerful tool for analyzing data in R. It can be used to group data by any variable, and then apply summary functions to the grouped data. This can be used to answer a wide variety of questions about the data.
