Analytic Resources

Data Wrangling

Data management, cleaning, and/or wrangling is an extremely important part of data analysis. If fact, you need to do data management procedures before you conduct any analyses and will, actually, spend most of your time doing data cleaning.

There are a number of packages to help you get your data the the shape you need it to analyze. Below are just a couple of resources to help you get started and to get your data ready for analysis!

RStudio: What is data wrangling? Intro, Motivation, Outline, Setup -- Pt. 1 Data Wrangling Introduction (8:25)

RStudio: Tidy Data and tidyr -- Pt 2 Intro to Data Wrangling with R and the Tidyverse (17:35)

RStudio: What is data wrangling? Intro, Motivation, Outline, Setup -- P3 (19:33)

Hefin Rhys: Working with tibbles in R (18:44)

dplyr is a powerful R-package to transform and summarize tabular data with rows and columns.

Below is information regarding the dyplr package

A Grammar of Data ManipulationA fast, consistent tool for working with data frame like objects, both in memory and out of memory.

R Dplyr tutorial - DataScience Made SimpleSelect Random Samples in R with Dplyr – (sample_n() and sample_frac()) Sample_n() and Sample_frac () are the functions used to

Data Manipulation in R - Find all its concepts at a single place! - DataFlairIn R Data Manipulation tutorial, learn about data structures, methods of subsetting, its applications, adding calculated fields to data, sample() command and merge() function in R.

R Data Reshaping - 4 Major Functions to Organise your Data! - DataFlairIn R data reshaping tutorial, learn the need of reshaping R package, how to reshape data in R by joining rows & columns in data frame and merging data frames in R.

The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

https://www.tidyverse.org/

10 Must-Know Tidyverse Functions: #1 - relocate()relocate() is like arrange() for columns. It keeps all of the columns, but provides much more flexibility for reordering. Notice how all of the columns are returned.

10 Must-Know Tidyverse Functions: #2 - across()The across() function was released in dplyr 1.0.0. It's a new tidyverse function that extends group_by and summarize for multiple column and function summaries.

10 Must-Know Tidyverse Functions: #3 - Pivot Wider and LongerPivoting wider is essential for making summary tables that go into reports and help humans understand key information.

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