r_resources.Rmd

```{r project_r, include=FALSE}
knitr::opts_chunk$set(echo=TRUE, eval=FALSE)
```

# R Resources {-}

## Outside resources {-}

- [Lisa Lendway's COMP/STAT 112 website](https://ds112-lendway.netlify.app/) (with code examples and videos)
- [RStudio cheat sheets](https://www.rstudio.com/resources/cheatsheets/)
- [ggplot2 reference](http://ggplot2.tidyverse.org/reference/)
- [R Programming Wikibook](http://en.wikibooks.org/wiki/R_Programming)
- Debugging in R
    - [Article](https://support.rstudio.com/hc/en-us/articles/205612627-Debugging-with-RStudio)
    - [Video](https://vimeo.com/99375765)
- [Colors in R](http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf)
- [Data import tutorial](https://www.datacamp.com/community/tutorials/r-data-import-tutorial)

Free online textbooks

- [R for Data Science](https://r4ds.had.co.nz/)
- [Exploratory Data Analysis with R](https://bookdown.org/rdpeng/exdata/)

<br><br>

## Example code {-}

**Creating new variables**

`case_when()` from the `dplyr` package is a very versatile function for creating new variables based on existing variables. This can be useful for creating categorical or quantitative variables and for creating indices from multiple variables.

```{r}
# Turn quant_var into a Low/Med/High version
data <- data %>%
    mutate(cat_var = case_when(
            quant_var < 10 ~ "Low",
            quant_var >= 10 & quant_var <= 20 ~ "Med",
            quant_var > 20 ~ "High"
        )
    )

# Turn cat_var (A, B, C categories) into another categorical variable
# (collapse A and B into one category)
data <- data %>%
    mutate(new_cat_var = case_when(
            cat_var %in% c("A", "B") ~ "A or B"
            cat_var=="C" ~ "C"
        )
    )

# Turn a categorical variable (x1) encoded as a numerical 0/1/2 variable into a different quantitative variable
# Doing this for multiple variables allows you to create an index
data <- data %>%
    mutate(x1_score = case_when(
            x1==0 ~ 10,
            x1==1 ~ 20,
            x1==2 ~ 50
        )
    )

# Add together multiple variables with mutate
data <- data %>%
    mutate(index = x1_score + x2_score + x3_score)
```