data wrangling and management in r
Clicks: 93
ID: 200793
2017
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
27.6
/100
93 views
8 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #4 of 4 articles by views in writing history, constructing religion
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
This tutorial explores how scholars can organize 'tidy' data, understand R packages to manipulate data, and conduct basic data analysis. By the end of this lesson, you will:
1. Know how to organize data to be “tidy” and why this is important.
2. Understand the dplyr package and use it to manipulate and wrangle with data.
3. Become acquainted with the pipe operator in R and observe how it can assist you in creating more readable code.
4. Learn to work through some basic examples of data manipulation to gain a foundation in exploratory data analysis.
| Reference Key |
siddiqui2017thedata
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Nabeel Siddiqui |
| Journal | writing history, constructing religion |
| Year | 2017 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.