Insightful Analytics
Krunal Lathiya 
Here are three ways to remove the first row of a data frame in R: Using negative indexing Using dplyr::slice() Using tail() Method 1: Using negative indexing Negative indexing is a method for excluding specific rows based on your requirements. Since we need to remove the first row, we can use df[-1, ], where -1 … Read more
The basename() is a base R function that extracts the last component (or the ‘base name’) of a file or directory path and returns the name of the file or directory. So, if you have a path like “/Users/krunallathiya/Desktop/Code/pypro/data.pdf,“ basename() would return “data.pdf.“ The opposite function of basename() is dirname(), which gives the directory part of the path. Syntax basename(path) … Read more
To grow the list, you can add an element (numeric value, character vectors, other lists, data frames, or functions) at whichever position you like. You can add a single element, multiple elements, or a list as an element. The final way depends on the user’s intention. Here are five ways to append an element to … Read more
In R, unique() and subsetting with !duplicated() are efficient ways to remove duplicates. Duplicate elements in a vector refer to those elements that appear more than once. Duplicates can skew the data analysis and lead to inaccurate results. Removing them leads to more reliable insights. Method 1: Using unique() The unique() function is a one-step, … Read more
The best and most efficient way to remove rows from a data frame is using “negative indexing”. It is a base R approach that does not require any packages. However, there are different approaches you should be aware of, depending on the situation you are encountering. Here are five ways: Using negative indexing (For single … Read more
Transposing means switching rows to columns and columns to rows. It is a common operation in the matrix. The above figure shows that column names from the original data frame have become row names, and the first column values of the original data frame have become column names. Everything has been switched. Here are two … Read more
Here are the two most prominent ways to select rows by single or multiple conditions in R: Subsetting with [ ] Using dplyr::filter() Method 1: Subsetting with [ ] Subsetting with square brackets ([ ]) is a basic method for selecting rows where a column meets a specific condition. For example, df[df$column_name > value, ]. Here is … Read more
The length() function sets or gets the length of vectors or any other R object. vec <- 1:5 cat(“The length of vector is:”, length(vec), “\n”) # Output: The length of vector is: 5 If you apply the length() function on a vector, it will return the number of elements of the vector. For empty vectors, … Read more
Here are five ways to round numbers in R: Using round() Using signif() Using ceiling() Using floor() Using trunc() Rounding is a process of approximating a number to a shorter, more straightforward, and interpretable value while retaining its closeness to the original value. Here are some basic different types of rounding we use in our … Read more
Here are the five ways to add single or multiple columns to a data frame in R: Using $ operator Using square ([ ]) notation Using cbind() Using tidyverse::add_column() Using dplyr::mutate() Let’s understand how this works behind the scenes. In the Data Frame, each column represents a vector. To add a column, we just need to … Read more
