Insightful Analytics
Krunal Lathiya 
To create a grouped boxplot in R, we can use the ggplot2 library’s aes() and geom_boxplot() functions. The aes() function maps the continuous and categorical variables to visual properties of a plot. The geom_boxplot() function creates a boxplot. There is a difference between a standard boxplot and a grouped boxplot. A standard boxplot provides a … Read more
For data exploration, quickly inspecting the data, verifying the structure, or debugging large datasets, we need some kind of tool that helps prevent excessive data flooding in the console. That’s where head() and tail() generic functions come into play. head() function The head() function displays the first n parts of the vector, matrix, table, or … Read more
The %in% operator checks if elements of one vector are present in another vector. It returns a logical vector of the same length as the left-side argument. Each element in the output is either TRUE or FALSE, depending on the value matching. If the value matches, it returns TRUE; otherwise, it returns FALSE. If you apply … Read more
The %in% operator checks whether the element is present. And NOT IN (%!in%) operator does the exact opposite. The %!in% is a custom operator that checks if the elements of one vector are not present in another vector. It returns a logical vector of the same length as the left-hand vector, with TRUE for those … Read more
For understanding the central tendency of your input dataset, you need to calculate the basic summaries like mean, median, and mode. It gives you the data distribution, which tells you whether it is symmetrical or skewed. Mean Mean means the arithmetic average of a number in mathematics. An average is the sum of the total … 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 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
Finding the minimum value by group means getting the smallest value within each group in our data frame. For example, if I have a column that I defined as a “group” and another column called “value” that contains different numeric values, then I will extract the minimum value from the value column for each distinct … Read more
