dplyr, is a R package provides that provides a great set of tools to manipulate datasets in the tabular form. dplyr has a set of core functions for “data munging”,including select(),mutate(), filter(), groupby() & summarise(), and arrange().
Dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges: mutate adds new variables that are functions of existing variables; select picks variables based on their names. Filter picks cases based on their values. Selecting columns and filtering rows We’re going to learn some of the most common dplyr functions: select , filter , mutate , groupby , and summarize. To select columns of a data frame, use select. The first argument to this function is the data frame (metadata), and the subsequent arguments are the columns to keep. When aggregating data, it is not uncommon to need to combine datasets containing identical non-key variables in varying states of completeness. There are various ways to accomplish this task. One possibility an coalescing join, a join in which missing values in x are filled with matching values from y.
dplyr’s groupby() function is the at the core of Hadley Wickham’ Split-Apply-Combine paradigm useful for most common data analysis.
Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together.
Check out the original paper introducing the strategy by Hadley Wickham and it is a must read.
Group By operation is at the heart of this useful data analysis strategy. And in this tidyverse tutorial, we will learn how to use dplyr’s groupby() and summarise() functions to group the data frame by one or more variables and compute one or more summary statistics using summarise() function.
First we will start with how to group a dataframe by a single variable and compute one summary level statistics. , And then we will learn how to compute multiple summary values.
Let us get started by loading tidyverse, suite of R packages from RStudio.
We will use our favorite fantastic Penguins dataset to illustrate groupby and summary() functions. Let us load the data from cmdlinetips.com’ github page.
Let us first use groupby() on a single variable in our dataframe. When we use groupby() function, in this example on a single variable, under the hood it splits the dataframe into multiple smaller dataframes such that there is a smaller dataframe for each value of the variable we used with groupby.
For example, when we use groupby() function on sex variable with two values Male and Female, groupby() function splits the original dataframe into two smaller dataframes one for “Male and the other for “Female”.
Then when we use summarize() function it computes some summary statistics on each smaller dataframe and gives us a new dataframe.
In our example, we have got mean bill length for each values of sex.
We can also use groupby() on single variable and do computation on multiple variables. In this example, we groupby() species variable and compute two summary statistics, mean flipper length and body mass.
We can also use groupby() on multiple variables and use summarize() on multiple varaibles. In the example below, we groupby() on species and sex and compute two summary stats for each combination of species and sex values.
Our resulting tibble has 6 rows corresponding to the six combinations of species and sex values.