R Advanced

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

Here are four ways to append rows to a data frame in R: Using dplyr::bind_rows() (Efficient Way) Using rbind() Using nrow() Using dplyr::add_row() Method 1: Using dplyr::bind_rows() The most efficient way to append a single or multiple rows to a data frame is to use the “dplyr::bind_rows()” function. This function accepts a data frame and the … 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

The is.na() function checks for missing values (NA) in the R object. It returns TRUE for NA values and FALSE otherwise. The valid object can be anything like a data frame, matrix, list, or vector. It is extremely helpful in data cleaning and preparation, as it helps identify and handle missing values in a dataset. … Read more

Overview The easiest way to calculate the natural log of a number or vector in R is to use the built-in log() function. The natural log uses “e” as its base, whereas log10 uses 10. The “e” is Euler’s number, whose value is approximately 2.718. In R, the formula for the natural log is ln(x) … Read more