Insightful Analytics
Krunal Lathiya 
What do you mean by counting unique values by group? Well, it means you divide the dataset into subsets based on the values of one or more categorical variables (columns). Within each subset, you determine the number of distinct (unique) values in a specific column. Here are three ways to count unique values by group: Using … Read more
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
Here are three ways to calculate the mean by group for single or multiple columns in the R data frame: Using base R’s aggregate() Using dplyr’s group_by() and summarize() Using data.table package What does it mean when we say “mean by group”? It means grouping the data based on the values of single or multiple … Read more
To count the number of rows by group in R, you can either use the dplyr::count(), dplyr::group_by() with tally(), or dplyr::group_by() with summarize(n = n()) functions, each suited for different use cases. By a single group Counting observations by a single group variable means grouping the dataset based on a single column. For example, you … Read more
The group_by() function from the dplyr package allows us to group data frames by one or more variables (columns), enabling subsequent operations to be performed on these groups. For example, calculating the total sales for each category or counting the number of items in each category. You cannot use the group_by() function alone because it … Read more
The dplyr::slice() function subsets rows by their position or index within a data frame. If you want to select specific rows from a data frame, use the slice() function and pass the index of the specific rows, and it will return a data frame containing those rows. In the above figure, we selected rows 3 … Read more
R vectors are atomic, meaning they contain homogeneous data types. They are contiguous in memory. Here are some of the best approaches for vector initialization and appending values in the future: Using type-specific functions The most efficient and quickest way to create an empty vector is to use type-specific functions like numeric(0), which will return … Read more
Here are six ways to remove a single column from a data frame in R: By assigning NULL to a specific column Removing a column by subsetting Deleting by column index Using subset() Using dplyr Using data.table Here is the data frame for the practical: df <- data.frame( Title = c( “Inception”, “The Dark Knight”, … 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
