Insightful Analytics
Krunal Lathiya 
Calculating Standard Deviation (SD) by group in a Data Frame essentially means finding the SD of a specific numeric column separately for each unique categorical variable (column) or group. It allows us to see how the data variability differs across the groups. Here are three ways to calculate SD by group: Using dplyr Using data.table … Read more
When we say summarise multiple columns, it means aggregate the input data by applying summary functions (sum, mean, max, etc.) to multiple numeric columns simultaneously. The below image describes visually: If grouping is required, you can group by a specific categorical column and get the statistics for each group. The dplyr package provides the summarise() … Read more
Cumulative sum by group means for each group, we calculate the running sum of values in the specific column that increases with each row within that group. It is the following two-step process: First, divide the data into subgroups based on single or multiple grouping variables (categorical variables). Within each subgroup, calculate the sum of … Read more
The type.convert() is a built-in R function that intelligently converts a vector, factor, or data frame column into the most appropriate data type (integer, numeric, logical, complex, or factor) based on its content. Please note that we don’t need to specify which data type to convert to; this function automatically determines, which is the main … Read more
The most common and efficient way to find the minimum value by group is to use the dplyr package. It provides group_by(), summarise(), and filter() methods to extract the smallest value based on our requirement. Finding the minimum value by group means getting the smallest value within each group in our data frame. For example, … Read more
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
