R dplyr

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

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

The dplyr filter() function in R subsets a data frame and retains all rows that satisfy the conditions. In other words, you can select the data frame rows based on conditions. To retain rows, they should produce the output to TRUE, and if they return NA, they will be dropped from the data frame. Syntax … Read more

The dplyr::distinct() function in R removes duplicate rows from a data frame or tibble and keeps unique rows. You can provide additional arguments, like columns, to check for duplicates in those specific columns. Syntax distinct(.data, …, .keep_all = FALSE) Parameters Argument Description .data It is an input data frame or tibble from which to remove duplicate … Read more