# 3 Scottish National Party Nicola Sturgeon 3.0 35 -21 # 2 Labour Party Jeremy Corbyn 40.0 262 30 # 1 Conservative Party Theresa May 42.4 317 -13 Uk2017 %>% mutate( leader_new = case_when( leader = "Theresa May" ~ "Boris Johnson", TRUE ~ as.character(leader)) ) # party leader votes seats seats_change Furthermore, they all return a new data frame that you will need to save in a new object or overwrite the existing object with your data frame.Īs the dplyr package is part of the tidyverse, the first thing we do is to call the tidyverse. In other words, you can not use these functions on other types of data in R. summarize() allows you to collapse many values to a single summary.Īll these functions rely on data frames. mutate() allows you to create new variables based on the values of old variables. arrange() allows you to reorder the rows. filter() allows you to pick observations by their values. 7 select() allows you to pick variables by their names. Select(), filter(), arrange(), rename(), mutate() and summarize(). The package provides some basic functions making it easy to work with data frames. The syntax is inspired by SQL and if you want to learn SQL at some point, you will have an advantage from having used the dplyr package. This is part of the tidyverse package so you do not need to install any new packages if you have already installed tidyverse. Noteworthy, there are multiple packages we can use to manipulate data frames, but the best is without a doubt dplyr (Hadley Wickham & Francois, 2016). In this chapter, we show different ways of working with data frames with a focus on how to change and create new variables. There are multiple ways to manage data in R and in particular different ways to create and change variables in a data frame.
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