Using the split-apply-combine technique with dplyr usually involves taking a data frame, forming subsets with the group_by() function, applying a summary function to to the groups, and collecting the results into a single data frame.Ī simple example would be to answer the following questions about our subset of mpg: This dataset contains the fuel efficiency and other interesting properties of 234 cars. We will be working with a subset of the mpg dataset, which is automatically loaded with the tidyverse package in R. Let’s go through a simple example where using these two properties can help with performing complex statistical summaries with dplyr. sum(age >= 30) # 4 mean(age >= 30) # 0.5714286 How Can This Help When Using dplyr? We see from the output below that 4 people in our survey were 30 years or older and that this represents 57% of the total respondents. the mean of a logical vector returns the proportion of TRUE values.the sum of a logical vector returns the number of TRUE values.To answer our question above, we can use the following properties of logical vectors in R: Anytime we use R’s comparison operators ( >, >=, = 30 # FALSE TRUE FALSE TRUE TRUE TRUE FALSE Two Important Operations on Logical Vectors in R age = operator to find where values stored in the age vector are greater than or equal to the value 30. This data is stored in the age vector below. Imagine we have data from a survey we recently conducted where 7 people responded and provided their age. Special Properties of Logical Vectors in R This post will introduce the power of using logical vectors within your dplyr code to create complex data summaries with ease.

A common data analysis technique, known as split-apply-combine, involves creating statistical summaries by groups within a data frame. Hadley Wickham’s dplyr package is an incredibly powerful R package for data analysis.

Sum using dplyr summarize