R Advanced

To calculate the sample variance (measurement of spreading) in R, you should use the built-in var() function. It calculates the sample variance (using n−1 for unbiased estimation, where n is the sample size). By default, it does not calculate the population variance. Variance measures how spread out a set of numbers is around the mean … Read more

The tryCatch() function acts as a mechanism for handling errors and other conditions (like warnings or messages) that arise during code execution in R. It allows you to define custom behaviors when an error occurs instead of stopping the execution of the script. The tryCatch() function lets you define custom handlers for four conditions. Each … Read more

The grep() function in R  searches for matches to a pattern within a character vector. It returns indices or values of elements that match the pattern. It is part of a family of functions that includes grepl(), regexpr(), gregexpr(), sub(), and gsub(). Syntax grep( pattern, char, ignore.case = FALSE, value = FALSE, invert = FALSE, perl … Read more

Whether you are reading or writing files via programs in the file system, it is necessary to check if that file already exists. If you don’t put a check in place, it can lead to either a program crash or an error. Checking if the file exists The most efficient way to check if the … Read more

The zip() function creates a new zip archive file. You must ensure that the zip tool is available in your system to work with zip() functions. For example, Windows and Mac systems have built-in zip tools, but Linux does not; you need to install one separately. Then, you can use this zip() function to create an … Read more

When working with file systems, checking the directory or file existence is always better before commencing the operations. For a directory To create a directory, if it does not exist, use the dir.exists() function. After checking its existence, if it does not exist, you can use the dir.create() function to create a new directory with a specified … Read more

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 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