Lab 12

Due to the Nike being in a large scandle, their Twitter feed was filled with opions and complaints,

The first chart is showing how much discussion their was over the words ” sexist “, ’’ suit” and a name of a female bodypart.

The second graph displays the large mass of discussion over the words ” female” suit” and once again another female bodypart.

Overall, the stats for these used words are huge and did not look good for the Nike company.

Mentions of the topic
Topic Count Percent
0 4741 86.5
1 738 13.5
Mentions of the topic
Topic Count Percent
0 5035 91.9
1 444 8.1

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 <- "sexist|vagina|suit"

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 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 <- "female|bathing|uniform"

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