ridgeplot.Rmd

---
title: "ridgeplot"
output:
  pdf_document: default
  html_document: default
date: "2022-10-14"
urlcolor: blue
linkcolor: blue
---

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

## Load and/or install packages.

In RStudio Cloud projects, I have already installed these packages. This
code block checks if a package is installed. If installed, it will load
it (library), if not it will install the package and load it. This
ensures that if you download this code to your local machine, it should
still run.

```{r setup, message = FALSE, warning = FALSE}
## If a package is installed, it will be loaded. If any 
## are not, the missing package(s) will be installed 
## from CRAN and then loaded.

## First specify the packages of interest
packages = c("tidyverse",
             "googlesheets4",
             "viridis",
             "ggridges")

## Now load or install&load all
package.check <- lapply(
  packages,
  FUN = function(x) {
    if (!require(x, character.only = TRUE)) {
      install.packages(x, dependencies = TRUE)
      library(x, character.only = TRUE)
    }
  }
)

```

## Load in the data from Google Sheets

This section of code loads the required packages (using the library
function), then loads in your dataset.

The data are also reshaped from "wide" to "long". This is the preferred
format for functions/packages within the "tidyverse", which the
ridgeline visualization package (ggridges) relies on.

```{r loadData, tidy = TRUE}
# Paste your link next to the variable "sheetLink <-" below. 
# Make sure your link is in "quotation marks"
sheetLink <- "https://docs.google.com/spreadsheets/d/1B_bFfEVHuo5Vpkaa_FnlyFu0WLL-Gm8ldBhL8gxQ720/edit#gid=0"

gs4_deauth()
vocab.data <- read_sheet(sheetLink)

```

## Tidy data into format needed for ggridges plot.

```{r tidyData}
vocab.data <- vocab.data %>% 
  gather(key="text", value="value") %>%
  mutate(text = gsub("\\.", " ",text)) %>%
  mutate(value = round(as.numeric(value),0))
```

## Make the ridgeline plot.

See the [ggridges package homepage](https://wilkelab.org/ggridges/),
[vignette](https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html),
and [reference
manual](https://wilkelab.org/ggridges/reference/index.html) to look into
changing parameters to best visualize your data. In this plot, I am
using the settings from the tutorial as a starting point.

```{r ridgelinePlot}
vocab.data %>%
  mutate(text = fct_reorder(text, value)) %>%
  ggplot( aes(y=text, x=value,  fill=text)) +
    geom_density_ridges(alpha=0.6, bandwidth=4) +
    scale_fill_viridis(discrete=TRUE) +
    scale_color_viridis(discrete=TRUE) +
    theme_ridges() +
    theme(
      legend.position="none",
      panel.spacing = unit(0.1, "lines"),
      strip.text.x = element_text(size = 8)
    ) +
    xlab("") +
    ylab("Assigned Probability (%)")
```