Using R for Data Wrangling, Analysis, and Visualization: Home

1. R and RStudio Installation
2. More about RStudio - interface, projects, working directory
3. R package installation
4. Objects, functions, and comments
5. Vectors and data types
6. Missing data

Installation for PC

1. Download R from https://cran.r-project.org/bin/windows/base/
2. Download RStudio at https://www.rstudio.com/products/rstudio/download/#download

Installation for Mac

1. Check your version number and make sure to download the correct version of R/RStudio.

2. Download R from https://cran.r-project.org/bin/macosx/, and select the .pkg link based on your macOS version.

3. Download RStudio at https://www.rstudio.com/products/rstudio/download/#download

R & RStudio Installation Guidance

https://rpubs.com/Earlien/guide-to-installing-R

Course Materials

• Before we Start
• Introduction to R
• PowerPoint slides for this lesson

Recording

• Recording of Workshop 1

1. Data frames and tibbles
2. Factors
3. Dates
4. Data wrangling with dplyr

About the Dataset

The data used for this lesson are in the figshare repository at: https://figshare.com/articles/SAFI_Survey_Results/6262019

This lesson uses SAFI_clean.csv. The direct download link for this file is: https://ndownloader.figshare.com/files/11492171

Course Materials

• Starting with data
• Data wrangling with dplyr

Extra Materials

• Cheatsheet for dplyr
• Factors
• PowerPoint for R session 2

Recording

• Recording of Workshop 2

1. Reformat data with tidyr
2. Export data
3. About ggplot
4. Building your plots iteratively
5. Boxplots
6. Bar charts
7. Faceting

## Not run, but can be used to load in data from previous lesson!
interviews_plotting <- interviews %>%
  ## pivot wider by items_owned
  separate_rows(items_owned, sep = ";") %>%
  ## if there were no items listed, changing NA to no_listed_items
  replace_na(list(items_owned = "no_listed_items")) %>%
  mutate(items_owned_logical = TRUE) %>%
  pivot_wider(names_from = items_owned, 
              values_from = items_owned_logical, 
              values_fill = list(items_owned_logical = FALSE)) %>%
  ## pivot wider by months_lack_food
  separate_rows(months_lack_food, sep = ";") %>%
  mutate(months_lack_food_logical = TRUE) %>%
  pivot_wider(names_from = months_lack_food, 
              values_from = months_lack_food_logical, 
              values_fill = list(months_lack_food_logical = FALSE)) %>%
  ## add some summary columns
  mutate(number_months_lack_food = rowSums(select(., Jan:May))) %>%
  mutate(number_items = rowSums(select(., bicycle:car)))

Course Materials

• Reshaping data with the package tidyr 
• Data visualization with the package ggplot2

Extra Materials

• RStudio cheatsheets
• Tidy data

Recording

• Recording of Workshop 3