Lab 12

Two themes that were discovered while conducting this code were coverage and uniforms supplied for the women in track in field. There are currently 3,223 people who mentioned “uniforms on response to the new drop by Nike resulting to around 58%. The term”coverage” was determined by the weight and height of the athletes comparing the amount of clothing would cover each woman.

Women uniforms
Topic	Count	Percent
0	2256	41.2
1	3223	58.8

Women coverage in uniforms
height	weight
58	115
59	117
60	120
61	123
62	126
63	129
64	132
65	135
66	139
67	142
68	146
69	150
70	154
71	159
72	164

# 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 Nike

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 <- "Nike|Female|uniforms|sexist"

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("Women uniforms") %>%
  cols_align(align = "left") %>%
  gt_theme_538

#########

searchterms <- "coverage|track|color|small|men"

mydata$coverage <- ifelse(grepl(searchterms,
                             mydata$Full.Text,
                             ignore.case = TRUE),1,0)
coverage <- mydata %>%
  group_by(coverage) %>%
  summarize(
    Count = n(),
    Percent = round(n() / nrow(mydata) * 100, 1)
  )

CoverageTable <- gt(women) %>% 
  tab_header("Women coverage in uniforms") %>%
  cols_align(align = "left") %>%
  gt_theme_538

CoverageTable