The cleaning and transformation of data belong to the most time consuming parts of any economic analysis. Many graphical or statistical functions in R require specifically formatted data to work properly. Although the standard functions of R can be used to prepare your data for further analysis, some people find them a bit laborious for daily applications. Therefore, alternatives have been developed, which make data transformation in R easier and also faster. One of these alternatives is the dplyr package of the tidyverse. It has gained great popularity among R users and being familiar with its syntax can be considered a standard skill in R.1 In the following I give an introduction to some main functions of dplyr, which I also use extensively at work.