makeup-shades.Rmd

---
title: "Plotting foundations according to shade"
description: |
  Graphs and analysis using the #TidyTuesday data set for week 14 of 2021
    (30/3/2021): "Makeup Shades"
author:
  - name: Ronan Harrington
    url: https://github.com/rnnh/
date: 04-06-2021
repository_url: https://github.com/rnnh/TidyTuesday/
preview: makeup-shades_files/figure-html5/figure1-1.png
output:
  distill::distill_article:
    self_contained: false
    toc: true
---

## Setup and data preparation

Loading the `R` libraries and [data set](https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-03-30/readme.md).

```{r, code_folding=TRUE, include=TRUE}
# Loading libraries
library(tidyverse)
library(tidytuesdayR)
library(viridis)
library(tidytext)
library(forcats)
library(ggridges)

# Loading data set
tt <- tt_load("2021-03-30")
```

Wrangling data for visualisation.

```{r, code_folding=TRUE, include=TRUE}
# Selecting the 14 brands with the most foundations in the data set as
# "top_brands"
top_brands <- tt$allShades %>%
  select(brand) %>%
  count(brand) %>%
  slice_max(order_by = n, n = 14)

# Selecting foundation names broken into individual words and lightness values 
# rounded to the nearest significant digit as "simplified_names"
simplified_names <- tt$allShades %>%
  mutate(rounded = signif(lightness, digits = 1)) %>%
  filter(!is.na(name)) %>%
  filter(rounded %in% c(0.2, 0.4, 0.6, 0.8, 1.0)) %>%
  select(name, rounded) %>%
  unnest_tokens(word, name) %>%
  count(rounded, word, sort = T)

# Counting the total number of words per rounded lightness value
total_words <- simplified_names %>%
  group_by(rounded) %>%
  summarise(total = sum(n))

# Added word count totals and tf-idf values to "simplified_names", and changing
# "rounded" to a factor variable with informative levels
simplified_names <- left_join(simplified_names, total_words, by = "rounded")
simplified_names <- simplified_names %>%
  bind_tf_idf(word, rounded, n)
simplified_names$rounded <- as.factor(simplified_names$rounded)
table(simplified_names$rounded)
levels(simplified_names$rounded) <- c("Lightness: 0.2, n = 28",
                                      "Lightness: 0.4, n = 148",
                                      "Lightness: 0.6, n = 221",
                                      "Lightness: 0.8, n = 217",
                                      "Lightness: 1.0, n = 50")
simplified_names
```

## Plotting foundations according to lightness

In this plot, each point represents a single foundation from the 14 most
represented brands in the data set. The colour of each point corresponds to
the dominant shade of each foundation. These points are arranged according to
the lightness of each foundation.

```{r figure1, code_folding=TRUE, include=TRUE, fig.height=8, fig.width=8}
# Plotting all the foundations from "top_brands" according to lightness
tt$allShades %>%
  filter(brand %in% top_brands$brand) %>%
  ggplot(aes(lightness, brand, colour = hex)) +
  geom_jitter() +
  scale_colour_identity() +
  xlim(0, 1) +
  theme_classic() +
  geom_vline(xintercept = 0.25, linetype = "dashed") +
  geom_vline(xintercept = 0.50, linetype = "dashed") +
  geom_vline(xintercept = 0.75, linetype = "dashed") +
  labs(y = "", x = "Lightness",
       title = "Foundations from different brands plotted according to lightness",
       subtitle = "Each point represents the dominant colour of each foundation")
```

## Plotting distributions of foundation lightness

In this plot, the distributions of foundations from the brands in the previous
graph are plotted according to lightness. Across all these brands, lighter
shades are more represented than darker shades.

```{r figure2, code_folding=TRUE, include=TRUE, fig.height=14, fig.width=8}
# Plotting the distribution of foundations from "top_brands" according to
# lightness
tt$allShades %>%
  filter(brand %in% top_brands$brand) %>%
  ggplot(aes(lightness, brand, fill = brand, group = brand)) +
  geom_density_ridges_gradient() +
  scale_fill_viridis(discrete = TRUE) +
  xlim(0, 1) +
  theme_ridges() +
  geom_vline(xintercept = 0.25, linetype = "dashed") +
  geom_vline(xintercept = 0.50, linetype = "dashed") +
  geom_vline(xintercept = 0.75, linetype = "dashed") +
  theme(legend.position = "none") +
  labs(y = "Brands", x = "Lightness",
       title = "Foundation shade distributions",
       subtitle = "Distribution of foundations from different brands according to lightness")
```

## Plotting keywords associated with foundations of different shades

In this section, keywords associated with foundations of different shades are
plotted. This is done by...

- taking all the available foundation names as a corpus
- splitting that corpus into different documents based on rounded lightness values
- calculating [tf-idf](https://www.tidytextmining.com/tfidf.html) to find
significant words used to describe foundations according to their shade

From this plot, we can see that the darkest ("Lightness: 0.2") and lightest
("Lightness: 0.8") foundations are associated with more descriptive, unique
keywords than the intermediate shades.

```{r figure3, code_folding=TRUE, include=TRUE, fig.height=8, fig.width=7}
simplified_names %>%
  group_by(rounded) %>%
  slice_max(n = 5, order_by = tf_idf) %>%
  ungroup() %>%
  ggplot(aes(tf_idf, fct_reorder(word, tf_idf), fill = rounded)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~rounded, ncol = 2, scales = "free") +
  theme_classic() +
  labs(x = "Term frequency-inverse document freqeuncy (tf-idf)", y = "Keywords",
       title = "Keywords associated with foundations of different lightnesses")
```