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R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread)

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Data analysis can be divided into three parts

  • Extraction: First, we need to collect the data from many sources and combine them.
  • Transform: This step involves the data manipulation. Once we have consolidated all the sources of data, we can begin to clean the data.
  • Visualize: The last move is to visualize our data to check irregularity.

One of the most significant challenges faced by data scientist is the data manipulation. Data is never available in the desired format. The data scientist needs to spend at least half of his time, cleaning and manipulating the data. That is one of the most critical assignments in the job. If the data manipulation process is not complete, precise and rigorous, the model will not perform correctly.

R has a library called dplyr to help in data transformation.

The dplyr library is fundamentally created around four functions to manipulate the data and five verbs to clean the data. After that, we can use the ggplot library to analyze and visualize the data.

Merge with dplyr()

dplyr provides a nice and convenient way to combine datasets. We may have many sources of input data, and at some point, we need to combine them. A join with dplyr adds variables to the right of the original dataset. The beauty is dplyr is that it handles four types of joins similar to SQL

  • Left_join()
  • right_join()
  • inner_join()
  • full_join()

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