Insightful Analytics
Krunal Lathiya 
If you want to find the maximum value within a specific subset of your data, you must find the maximum value within each group. First, group your data based on the values of one or more categorical variables (columns). The second step is identifying the maximum value of a specific numeric variable (numeric column) for … Read more
The dplyr summarise()(or summarize()) function aggregates data into a single summary value for each group or the entire dataset if ungrouped. It collapses multiple rows into a concise statistical summary, such as the mean, sum, and count. Developers often use summarize() with group_by(), which splits the data into groups based on one or more categorical variables (Columns … Read more
Counting conditionally in the data frame means counting the number of rows that meet specific conditions defined by the user. It is the process of determining the number of rows based on certain conditions evaluated to TRUE. You can count several rows conditionally in a data frame using base R functions, dplyr methods, or data.table … Read more
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
