R Advanced

To calculate the percentage by the group in R, you need to combine various dplyr functions such as  group_by(), summarise(), mutate(), and ungroup(). Percentage by group means calculating the percentage of a variable within each group defined by another variable in a dataset. Here is the core concept behind it: First, you must divide your data … Read more

In R, you can calculate the sum by group using the base aggregate(), dplyr’s group_by() with summarise(), or the data table package. What do we mean when we say sum by group? Well, it means dividing the dataset into meaningful groups based on categorical variables and then calculating the summation of the values within each … Read more

What does it mean when we say “mean by group”? It means grouping the data based on the values of single or multiple columns and then calculating the mean (average) of those values, but doing so separately for each group. Here are three ways to calculate the mean by group for single or multiple columns … Read more

Whether you want to summarize the categorical data, identify patterns and trends, or calculate percentages and proportions, you must quickly analyze how many observations fall into each category. For that, you can create a count column that tells the number of observations that fall in each variable. To count the number of rows by group … Read more

DataFrames are like tables that contain rows and columns. Each column can have a different data type. Here are six ways to remove a single column from a data frame in R: By assigning NULL to a specific column Removing column by subsetting Deleting by column index Using subset() Using dplyr Using data.table Here is … Read more

Dates in R are stored as the number of days since 1970-01-01, so converting a Date object to a Numeric gives those day counts. But what about POSIXct? Well, those are stored as seconds since the same epoch, so converting them would give a much larger number. Here are three ways to convert Date to … Read more

NA values are missing values. They are somehow absent from a data frame. Before creating a model based on a data frame, we need to clean the data frame from missing values, and it depends on different scenarios. Here are four different ways for different scenarios to remove NA values from a data frame: Use … Read more

NULL represents a null object, and sometimes, it’s logical for the project to filter it out from either a list or data frame. Here are three ways to remove NULL values from a list in R: Using Filter() with Negate() and is.null() Using sapply() or lapply() with Subsetting Using purrr::discard() (Tidyverse Approach) For example, if … Read more

The basename() is a base R function that extracts the last component (or the ‘base name’) of a file or directory path and returns the name of the file or directory. So, if you have a path like “/Users/krunallathiya/Desktop/Code/pypro/data.pdf,“ basename() would return “data.pdf.“ The opposite function of basename() is dirname(), which gives the directory part of the path. Syntax basename(path) … Read more

For meaningful and accurate analysis, you may want to remove rows with too many missing or incorrect values from the data frame or dataset to improve the quality of your data. The best and most efficient way to remove rows from a data frame is using “negative indexing”. It is a base R approach that … Read more