$ continent Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Europe, Europe, Europe, Europe, Europe, Europe, Europ~ $ country Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghanis~ In our case, we are going to use the gapminder dataset, which contains data on life expectancy, population, and GDP per Capita of different countries for different years. Likewise, it is important to emphasize that the most common is to group based on one or more variables in text or categorical format since it rarely makes sense that several numerical observations have the same (although it may be the case). This is why the group_by function is rarely used alone, but will most likely be used with other dplyr functions, such as summarize or mutate. When a dataframe is grouped, it is still a single dataframe, but the operations we do will apply to each of the groups. The group_by function allows grouping the data into different groups based on one or more variables of our dataframe. There are basically two types of functions: group_by and ungroup. The grouping functions allow grouping the data in such a way that we can apply certain functions for each of the groups. Let’s get to it! dplyr grouping functions So, let’s go step by step knowing each of the functions of each group. Main functions of dplyrīasically, dplyr has 5 different groups of functions: summary, grouping, selection/filter, manipulation, and combination functions.Īlthough the value of dplyr lies in being able to combine all these functions to be able to manipulate data in a simple and clean way, in order to do that it is essential to first know the main functions that the package offers. Now that you know what the pipe operator is, let’s continue with the dplyr tutorial, gradually seeing what are the main functions of dplyr focused on different transformations. Also, the code is much cleaner if we use the pipe operator. Mtcars_ordered = arrange(mtcars_filtered, desc(mpg))Īs we can see, the result is exactly the same, but by using the pipe we have avoided having to create intermediate objects, so if we wanted to change our transformation it would be very simple. Let’s see how we would do it without using the pipe operator. This operator allows you to concatenate functions in such a way that the data is passed from one function to another without having to assign it to any variable.įor example, suppose we want to rank cars that have more than 100 horsepower by their gas mileage (mpg variable) in descending order. To do this, you simply have to run the following code: install.packages("dplyr")ĭplyr includes the %>% operator, called pipe, which is very useful and applicable to the entire tidyverse ecosystem. The first thing we will have to do is install dplyr. There is much to learn, so set’s get to it! Installation and pipe operator And you’ll even have exercises to practice. However, do you know exactly how it works, all the features it offers, and why it is so powerful? Well, in this tutorial I will explain everything you need to know about dplyr. Without a doubt, dplyr is a very powerful package, since allows you to manipulate data very easily, and it enables you to work with other languages and frameworks, such as SQL, Spark o R’s data.table.īesides, as it is part of the tidyverse universe, it is very easy to use dplyr with other packages within tidyverse, such as ggplot, which allows, for example, to make very cool graphics in a simple way and without having to create any intermediate objects.Īs you can see, dplyr is very powerful. Read the documentation ( ?join) for explanations.Dplyr is one of the main packages in the tidyverse universe, and one of the most used packages in R. Using dplyr, one can perform a left join, a right join, an inner join, a full join, a semi-join, or an anti-join. There are various flavors of the join operation. # 40 d 8 gather(x,variable,value,-a) # a variable value # 20 j C d 8 gather(x,variable,value) # variable value # 20 j C d 8 gather(x,variable,value,-a,-c) -> y y # a c variable value # 10 j 10 C 8 gather(x,variable,value,b,d) # a c variable value By default, every column is gathered One can exclude columns, or explicitly include them, using very simple syntax. Multiple columns are combined into one value column with a key column keeping track of which column each value came from. Gather takes a wide data frame and makes it long.
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