In this tutorial I give you a quick introduction to the pipe. The pipe has a lot of advantages but here I simply talk about how it works and why your code gets more organised because of it
Here is the link to the datacamp article about the pipe: https://www.datacamp.com/community/tutorials/pipe-r-tutorial
PS. I made a small mistake in the video. You actually need the data.table package to read in the data with fread. But everything according to the pipe still makes sense.
setwd("YOURPATHTOYOURWORKINGDIRECTORY") library(tidyverse) data <- fread("batting_players_2018.csv") data <- as_tibble(data) #Let's filter out the Leauge Average filter(data, Name != "League Average per 600 PA") #unique Teams without the pipe unique(data$Tm) #average age with the pipe data$Tm %>% unique() #filter for the American League data %>% filter(Lg == "AL") %>% #select Name, Tm, AB, H, BA columsn select(Name, Tm, AB, H, BA) %>% #calculate BA again mutate(avg = H / AB) %>% #select second and third column with the . operator .[,2:3] #how the code would look without the pipe mutate(select(filter(data, Lg == "AL"), Name, Tm, AB, H, BA), avg = H / AB)[, 2:3]