dplyr’s groupby() function lets you group a dataframe by one or more variables and compute summary statistics on the other variables in a dataframe using summarize function. Sometimes you might want to compute some summary statistics like mean/median or some other thing on multiple columns. Naive approach is to compute summary statistics by manually doing […]
tidyverse 101
dplyr groupby() and summarize(): Group By One or More Variables
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’s groupby() function is the at the core of Hadley Wickham’ Split-Apply-Combine paradigm useful for most common data […]
dplyr filter(): Filter/Select Rows based on conditions
dplyr, R package that is at core of tidyverse suite of packages, provides a great set of tools to manipulate datasets in the tabular form. dplyr has a set of useful functions for “data munging”, including select(), mutate(), summarise(), and arrange() and filter(). And in this tidyverse tutorial, we will learn how to use dplyr’s […]
dplyr arrange(): Sort/Reorder by One or More Variables
dplyr, R package part of tidyverse suite of packages, 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(), summarise(), and arrange(). And in this tidyverse tutorial, we will learn how to use dplyr’s arrange() function to sort a data […]
dplyr mutate(): Create New Variables with mutate
dplyr, R package part of tidyverse suite of packages, 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(), summarise(), and arrange(). And in this tidyverse tutorial, a part of tidyverse 101 series, we will learn how to use […]