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Data Manipulation has a significant role in predictive modeling. Data manipulation is also called data exploration. Data manipulation is to improve data accuracy and data precision. Data manipulation is a crucial step when it comes to data modeling.

In this blog post you can find how to conduct data Manipulation In R – Programming

Data Manipulation process is used in Data Analysis and this question is most commonly asked in Data Analyst Entry Level Job Interviews

However, we must understand the data before conducting the data manipulation steps. The following are some points that need to be considered in data manipulation.

  • Using Inbuilt functions in R to manipulate data.
  • Using CRAN packages available in R to manipulate the data.
  • Using ML Algorithms.

Data Manipulation Using Ply Package

Dplyr Package is a powerful R – Package that transfers and summarizes the data with rows and columns.

The five main data manipulation commands are used in  dplyr

  1. Filter – filters the data based on a condition.
  2. Select –used to select columns of interest from a data set.
  3. Arrange –used to arrange data set values in ascending or descending order.
  4. Mutate – used to create new variables from existing variables.
  5. Summarize (with group by) – used to perform analysis by commonly used operations such as min, max, mean, count, etc.


  1. Filter – Filter in R can be used to select the subset of rows in a data frame. the first argument is the Tibble (or data frame). The subsequent arguments refer to variables within that data frame, selecting rows where the expression is TRUE.
  2. Select – Select column with select () – when you are interested in a few or columns when you are working with a large data set with many columns this select allows you to zoom in on this usually work on numerical variable positions. ( – ) . This can be used to hide the columns
  3. Arrange – Arrange is used for re-order rows. It refers a data frame, and a set of column names to order the rows. If you provide more than one column name, each additional column is used to break ties in the values of the preceding columns.
  4. Mutate – In order to add a new column in a data set Mutate is useful. and mutate is also used to select the set of existing columns and add new columns that function as existing columns.
  5. Summaries  – Summaries are used to find the insights of the data set.
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