R-Data-Wrangling-Legacy

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R-Data-Wrangling-Legacy

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D-Lab's 6 hour introduction to data wrangling with R. Learn how to manipulate dataframes using the tidyverse in R.

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# Data Wrangling and Manipulation in R

## Workshop Goals

It is often said that 80% of data analysis is spent on the process of cleaning and preparing the data. This workshop will introduce tools (notably `dplyr` and `tidyr`) that makes data wrangling and manipulation much easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful stuff.

Prior experience with R is helpful. We recommend that students feel comfortable with the skills taught in [R Fundamentals](https://github.com/dlab-berkeley/R-Fundamentals). Students who complete this workshop will be well prepared for the D-Lab's advanced data wrangling offerings.

## Installation Instructions

We will use RStudio to go through the workshop materials, which requires installation of both the R language and the RStudio software.

1. [Download R](https://www.r-project.org/): Follow the links according to the operating system that you are running. Download the package, and install R onto your compute. You should install the most recent version (at least version 4.1).

2. [Download RStudio](https://www.rstudio.com/products/rstudio/download/): Install RStudio Desktop. This should be free. Do this after you have already installed R. The D-Lab strongly recommends an RStudio edition of 2022.02.0+443 "Prairie Trillium" or higher.

3. Download these workshop materials:

- Click the green "Code" button in the top right of the repository information.

- Click "Download Zip".

- Extract this file to a folder on your computer where you can easily access it (we recommend Desktop).

4. Optional: If you are familiar with `git`, you can instead clone this repository by opening a terminal and entering

5. Make sure that the following packages are installed on your computer

```
fs
here
tibble
tidyverse
```

## Run the Code

Now that you have all the required software and materials, you need to run the code:

1. Launch the RStudio software.

2. Use the file navigator to find the `R-Data-Wrangling` folder that you downloaded.

3. Open the `R-Data-Wrangling.Rproj` by double clicking to open the code in an R project.

4. Open up the file corresponding to the part of the workshop currently in focus.

5. Place your cursor on a given line and press "Command + Enter" (Mac) or "Control + Enter" (PC) to run an individual line of code.

6. The `solutions` folder contains the solutions to the challenge problems.

## Is R not working on your laptop?

If you do not have R installed and the materials loaded for your workshop by the time it starts, we *strongly* recommend using the UC Berkeley DataHub to run the materials. You can access the DataHub by clicking [![Datauhb](https://img.shields.io/badge/launch-datahub-blue)](https://datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fdlab-berkeley%2FR-Data-Wrangling&urlpath=rstudio%2F&branch=main)

The DataHub downloads this repository, along with any necessary packages, and allows you to run the materials in an RStudio instance on UC Berkeley's servers. No installation is necessary from your end--you only need an internet browser and a CalNet ID to log in. By using the DataHub, you can save your work and come back to it at any time. When you want to return to your saved work, go straight to [DataHub](https://datahub.berkeley.edu/), sign in, and click on the `advanced-data-wrangling-in-R` folder.

## About the UC Berkeley D-Lab

D-Lab works with Berkeley faculty, research staff, and students to advance data-intensive social science and humanities research. Our goal at D-Lab is to provide practical training, staff support, resources, and space to enable you to use R for your own research applications. Our services cater to all skill levels and no programming, statistical, or computer science backgrounds are necessary. We offer these services in the form of workshops, one-to-one consulting, and working groups that cover a variety of research topics, digital tools, and programming languages.

Visit the [D-Lab homepage](https://dlab.berkeley.edu/) to learn more about us. You can view our [calendar](https://dlab.berkeley.edu/events/calendar) for upcoming events, learn about how to utilize our [consulting](https://dlab.berkeley.edu/consulting) and [data](https://dlab.berkeley.edu/data) services, and check out upcoming [workshops](https://dlab.berkeley.edu/events/workshops).

## Other D-Lab R Workshops

Here are other R workshops offered by the D-Lab:

### Basic Competency
- [R Fundamentals](https://github.com/dlab-berkeley/R-Fundamentals)
- [R Data Wrangling](https://github.com/dlab-berkeley/R-Data-Wrangling)
- [R Graphics with ggplot2](https://github.com/dlab-berkeley/R-graphics)
- [R Functional Programming](https://github.com/dlab-berkeley/R-functional-programming)
- [Project Management in R](https://github.com/dlab-berkeley/efficient-reproducible-project-management-in-R)
- [Geospatial Fundamentals in R with sf](https://github.com/dlab-berkeley/Geospatial-Fundamentals-in-R-with-sf)
- [Census Data in R](https://github.com/dlab-berkeley/Census-Data-in-R)

### Intermediate/Advanced Competency
- [Advanced Data Wrangling in R](https://github.com/dlab-berkeley/advanced-data-wrangling-in-R)
- [Introduction to Machine Learning in R](https://github.com/dlab-berkeley/Machine-Learning-in-R)
- [Unsupervised Learning in R](https://github.com/dlab-berkeley/Unsupervised-Learning-in-R)
- [R Machine Learning with tidymodels](https://github.com/dlab-berkeley/Machine-Learning-with-tidymodels)
- [Introduction to Deep Learning in R](https://github.com/dlab-berkeley/Deep-Learning-in-R)
- [Fairness and Bias in Machine Learning](https://github.com/dlab-berkeley/fairML)
- [R Package Development](https://github.com/dlab-berkeley/R-package-development)

## Contributors

- [Alex Stephenson](https://github.com/asteves)
- [Rochelle Terman](https://github.com/rochelleterman)
- [Josh Quan](https://github.com/wrathofquan)
- [Lawrence Y. Trello](https://github.com/lytello)
- [Avery Richards](https://github.com/Averysaurus)
- [Drew Hart](https://github.com/drewhart)

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