Our first table reveals the concerns of practicality of the kits. To find this, keywords like, ” impractical,” “uncomfortable,” and,” revealing,” were used.
## word n
## 1 nike 3561
## 2 citiusmag 2527
## 3 women 1323
## 4 wear 644
## 5 athletes 523
## 6 track 517
## 7 female 432
## 8 field 407
## 9 team 391
## 10 olympics 390
## 11 shorts 373
## 12 kits 315
## 13 kit 313
## 14 run 313
## 15 2024 289
## 16 women's 283
## 17 women’s 277
## 18 design 257
## 19 paris 249
## 20 u.s 241
## 21 worn 241
## 22 designed 235
## 23 breaking 230
## 24 outfit 226
## 25 xpwonbrwsv 226| Mentions of Practicality | ||
| Topic | Count | Percent |
|---|---|---|
| 0 | 5205 | 95 |
| 1 | 274 | 5 |
Our second table represents concerns regarding equality as clearly the female’s kit has more concerns than the male. To find this, I searched words like,“equality,” “sexist,” and “misogynistic,” to find these results.
| Mentions of Equality | ||
| Topic | Count | Percent |
|---|---|---|
| 0 | 5259 | 96 |
| 1 | 220 | 4 |
# Load packages
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("tidytext")) install.packages("tidytext")
if (!require("plotly")) install.packages("plotly")
if (!require("gtExtras")) install.packages("gtExtras")
library(tidyverse)
library(tidytext)
library(gtExtras)
library(plotly)
library(lubridate)
# Read the data
mydata <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/NikeUniforms.csv")
tidy_text <- mydata %>%
unnest_tokens(word,Full.Text) %>%
count(word, sort = TRUE)
data("stop_words")
tidy_text <- tidy_text %>%
anti_join(stop_words)
my_stopwords <- tibble(word = c("https",
"t.co",
"rt"))
tidy_text <- tidy_text %>%
anti_join(my_stopwords)
head(tidy_text, 25)
searchterms <- "wedgies|chafe|impractical|uncomfortable|discomfort|exposed|expose|revealing"
mydata$Topic <- ifelse(grepl(searchterms,
mydata$Full.Text,
ignore.case = TRUE),1,0)
Topic <- mydata %>%
group_by(Topic) %>%
summarize(
Count = n(),
Percent = round(n() / nrow(mydata) * 100, 1)
)
TopicTable <- gt(Topic) %>%
tab_header("Mentions of Practicality") %>%
cols_align(align = "left") %>%
gt_theme_538
TopicTable
searchterms <- "sexist|sexism|sexualized|equality|misogyny|misogynistic|misogynist"
mydata$Topic <- ifelse(grepl(searchterms,
mydata$Full.Text,
ignore.case = TRUE),1,0)
Topic <- mydata %>%
group_by(Topic) %>%
summarize(
Count = n(),
Percent = round(n() / nrow(mydata) * 100, 1)
)
TopicTable <- gt(Topic) %>%
tab_header("Mentions of Equality") %>%
cols_align(align = "left") %>%
gt_theme_538
TopicTable