R Packages for Data Science
R Packages for Data Science, you’ll learn about the tidyverse library in this lesson, which is a collection of R tools that you can use to manipulate your datasets.
You’ll also discover how to use some of the dplyr package’s key functions to select and filter data.
R Packages for Data Science
An R package is a collection of code, data, documentation, and tests that is easy to share.
The enormous number of packages available in R is one of the reasons for its popularity.
There’s a good probability that someone else has previously solved a problem identical to yours, and you may take advantage of utilizing those r packages.
The tidyverse library, which is a collection of fundamental R programs for data research, will be used extensively in this post.
The tidyverse library’s core contains packages that you’re likely to utilize in your daily data analysis.
There are four sections to the tidyverse library:
1. Data Wrangling
Dplyr and tidyr are two packages in the Data Wrangling and Transformation category.
The pipe operator may be used to mix several functions, which is the package’s primary advantage.
This package does it all, from filtering to grouping data.
2. Data Import and Management
The readr package belongs to the Data Import and Management group. This package handles the problem of converting a flat file, such as a.csv, to a tibble.
“purrr” is a package from the Functional Programming group. This package calculates the mean value for each column and offers statistics for the dataset.
4. Data Visualization
The ggplot2 package is part of the Data Visualization and Exploration group.
ggplot2 is popular among data scientists for creating charts and visualizations like box plots, density plots, violin plots, tile plots, and time series plots.
The tidyverse’s dplyr package includes methods for performing some of the most popular actions when working with data.
The following are the five most important dplyr functions:
The select() function chooses variables based on their names.
The filter() function filters observations based on their values.
The summarise() function computes summary statistics.
The arrange() function rearranges the rows.
The modify() function creates new variables.
We provided a full explanation for the dplyr package in one of our older posts, which you can read by following the link below.
You learned in this tutorial that the tidyverse packages, such as dplyr, tidyr, readr, purr, and ggplot2, provide a plethora of capabilities for data analysis.
Selecting, filtering, summarizing, organizing, and modifying are some of the most frequent operations you’ll do when working with data, and you can even combine functions using the pipe operator to generate more powerful operations.