R Advanced

A vector is a data structure that holds the same type of data. When working with real-time data, it may contain missing data, and it can be represented by NA (not available). For a proper data analysis, we need to exclude NA values from the vector. Now, you may have one question in your mind. … Read more

No matter how much technology has improved over the years for storing and sharing data, one format is still widely used in the industry: Excelsheet, which has file extensions like “.xls” or “.xlsx.“ Here are three ways to import Excel files in R: Using the readxl package (Efficient way) Using xlsx package RStudio’s Import Dataset tool For this tutorial’s … Read more

Most real-world data resides in external sources, including CSVs, Excels, Texts, or Databases. To bring back the data from these external sources, we need a bridge or function that we can use to analyze these data into our R ecosystem and further manipulate it to get the required results. That’s where a base function like … Read more

When you are working with large datasets, you often come across names that are in inconsistent formats or capitalization. To solve this problem, we need to standardize the format to improve data quality and consistency. One way to standardize is by converting the first letter of each word to uppercase. The most efficient way to … Read more

In the real world, data is not static. You need to keep updating it or adding new data to an existing data set. As new data becomes available, you need to add it to the existing data frame to maintain an up-to-date record. Here are four ways to append rows to a data frame in R: … Read more

Dimensions are the structure of the data. A matrix has two dimensions: a row and a column, while an array can have multiple dimensions. Dimension labels are names that we can assign to a dimension. For example, for a matrix, row names are labels, and column names are also. The dimnames() function in R is … Read more

If you are working with a data frame where you need to perform mathematical operations like addition and subtraction on a column, that column is of type character, but the data is numbers. What will you do? This is where you need to convert that specific column to numeric. Here are four ways to convert … Read more

When you are working with real-time projects, you often come across raw datasets where columns have names like (“v1”, “var2”, and “cust_id”). These names seem very cryptic and are not descriptive. That’s why we must rename them to descriptive names (e.g., “age”, “income”, “customer_ID”), which makes the data much easier to understand at a glance. Here … Read more

If you want to put data validation in progress, you must identify the file type or “MIME Type” before processing. To identify the file type, we might need to check its extension. Here are two ways to get an extension of a file in R: Using the tools::file_ext() Using regular expression Method 1: Using the … Read more

To check if a file is empty in R, you can use either the “file.info()’s size attribute” or the “file.size()” function. We must check if a file is empty to save our resources from reading or processing. It helps us put a data validation that will prevent unexpected results or errors. Based on its emptiness, … Read more