Nike

The table below shows the percentage of replies to the Nike release that have the words “women”, “sexist”, and “female”. This percentage shows that people are talking about the women’s outfit much more than the male’s.

Mentions of the topic
Topic	Count	Percent
0	4784	87.3
1	695	12.7

The words for the table below include words for female body parts and bathing suits. The percentage shows that people brought it up quite a bit for one post.

Mentions of the topic
Topic	Count	Percent
0	5319	97.1
1	160	2.9

Code:

# 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")

# Counting posts about Biden

tidy_text <- mydata %>% 
  unnest_tokens(word,Full.Text) %>% 
  count(word, sort = TRUE)

# Deleting standard stop words

data("stop_words")
tidy_text <- tidy_text %>%
  anti_join(stop_words)

# Deleting custom stop words

my_stopwords <- tibble(word = c("https",
                                "t.co",
                                "rt"))
tidy_text <- tidy_text %>% 
  anti_join(my_stopwords)

head(tidy_text, 25)

searchterms <- "cunt|vagina|bathing"

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 the topic") %>%
  cols_align(align = "left") %>%
  gt_theme_538

TopicTable

# 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")

# Counting posts about Biden

tidy_text <- mydata %>% 
  unnest_tokens(word,Full.Text) %>% 
  count(word, sort = TRUE)

# Deleting standard stop words

data("stop_words")
tidy_text <- tidy_text %>%
  anti_join(stop_words)

# Deleting custom stop words

my_stopwords <- tibble(word = c("https",
                                "t.co",
                                "rt"))
tidy_text <- tidy_text %>% 
  anti_join(my_stopwords)

head(tidy_text, 25)

searchterms <- "cunt|vagina|bathing"

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)
  )

TopicTable2 <- gt(Topic) %>% 
  tab_header("Mentions of the topic") %>%
  cols_align(align = "left") %>%
  gt_theme_538

TopicTable2