Insightful Analytics
Krunal Lathiya 
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
NA values are missing values. They are somehow absent from a data frame. Before creating a model based on a data frame, we need to clean the data frame from missing values, and it depends on different scenarios. Here are four different ways for different scenarios to remove NA values from a data frame: Use … Read more
The append() function in R concatenates values to a vector or list at a specified position. It returns a new object without modifying the original. You can add a single or multiple elements to a vector or list. Syntax append(obj, values, after = length(x)) Parameters Name Value obj It is an original vector or list. … Read more
NULL represents a null object, and sometimes, it’s logical for the project to filter it out from either a list or data frame. Here are three ways to remove NULL values from a list in R: Using Filter() with Negate() and is.null() Using sapply() or lapply() with Subsetting Using purrr::discard() (Tidyverse Approach) For example, if … Read more
In a real-life dataset, the last row may contain metadata, summaries, footnotes, or unwanted rows that are not needed for data analysis. That’s why we need to clean the data. Data cleaning requires removing unnecessary rows from the data frame for data accuracy and integrity. We have already seen how to remove the first row … Read more
When we attempt to remove the first row of a data frame, we are essentially selecting all the rows except for the first one. This means we are excluding the first row and retrieving all other rows in the data frame. DataFrames are like tables with rows and columns. To remove rows, you can use … 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
