Insightful Analytics
Krunal Lathiya 
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
The names() function in R gets or sets the names of objects such as vectors, lists, or data frames. If you are using it to fetch the name, it will return a character vector containing the names assigned to its elements or columns. Syntax names(obj) # To get names names(obj) <- value # To set names Parameters Name Value … 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
When you convert a list to a matrix, you are essentially taking the elements of a list and rearranging them into a two-dimensional structure with rows and columns. To convert a list to the matrix in R, use the matrix() function and pass the unlist() function as an argument. The unlist() function flattens a list into a … 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 multidimensional data in R, you should explore Arrays. Arrays are designed to store data in more than two dimensions and represent complex data structures. The as.array() function attempts to coerce the input object into an array structure. It handles various input types, including vectors, lists, data frames, etc. Syntax as.array(input_obj, … 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
