Likert scales - horizontal bar

Likert scale counts based on Reuters 2018 Digital Report Tables

Libraries

library(tidyverse)

## ── Attaching packages ───────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.1.0       ✔ purrr   0.2.5  
## ✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
## ✔ tidyr   0.8.2       ✔ stringr 1.3.1  
## ✔ readr   1.1.1       ✔ forcats 0.3.0

## ── Conflicts ──────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

#Thinking about online news, I am concerned about what is real and what is fake on the internet.

real_fake <- read_csv("data/misinfo/concern-real-vs-fake-seg.csv")

## Parsed with column specification:
## cols(
##   segment = col_character(),
##   `Not at all concerned` = col_double(),
##   `Not very concerned` = col_double(),
##   `Somewhat concerned` = col_double(),
##   `Very concerned` = col_double(),
##   `Extremely concerned` = col_double(),
##   total_concerned = col_double(),
##   total_not_concerned = col_double()
## )

# don't use column name as key or value in `gather()`!!!

stack_real_fake <- real_fake %>% select(segment, `Not at all concerned`, `Not very concerned`, `Somewhat concerned`, `Very concerned`, `Extremely concerned`) %>% gather(concern, percent, `Not at all concerned`, `Not very concerned`, `Somewhat concerned`, `Very concerned`, `Extremely concerned`)

stack_real_fake <- stack_real_fake %>% mutate(percent_new = percent * 100)

#To what extent, if at all, are you concerned about the following: - Poor journalism (factual mistakes, dumbed down stories, misleading headlines/clickbait)

poor_J <- read_csv("data/misinfo/poor-journalism-agreement-seg.csv")

## Parsed with column specification:
## cols(
##   segment = col_character(),
##   `Strongly disagree` = col_double(),
##   `Tend to disagree` = col_double(),
##   `Neither agree nor disagree` = col_double(),
##   `Tend to agree` = col_double(),
##   `Strongly agree` = col_double(),
##   total_agree = col_double(),
##   total_disagree = col_double()
## )

stack_poor_J <- poor_J %>% select(segment, `Strongly disagree`, `Tend to disagree`, `Neither agree nor disagree`, `Tend to agree`, `Strongly agree`) %>% gather(agreement, percent, `Strongly disagree`, `Tend to disagree`, `Neither agree nor disagree`, `Tend to agree`, `Strongly agree`)

stack_poor_J <- stack_poor_J %>% mutate(percent_new = percent * 100)

fake_theme <- function(){
  list(
      theme_minimal(),
      scale_fill_manual(values=c('#253494', '#ffffcc','#a1dab4','#41b6c4','#2c7fb8'), 
                       name="Level of Concern",
                       breaks=c("Not at all concerned", "Not very concerned", "Somewhat concerned", "Very concerned", "Extremely concerned"),
                       labels=c("Not at all concerned", "Not very concerned", "Somewhat concerned", "Very concerned", "Extremely concerned")) 
    )
}

poorJ_theme <- function(){
  list(
      theme_minimal(),
      scale_fill_manual(values=c('#2c7fb8', '#253494', '#ffffcc','#a1dab4','#41b6c4'), 
                       name="Level of Agreement",
                       breaks=c("Strongly disagree", "Tend to disagree", "Neither agree nor disagree", "Tend to agree", "Strongly agree"),
                       labels=c("Strongly disagree", "Tend to disagree", "Neither agree nor disagree", "Tend to agree", "Strongly agree")) 
    )
}

fake_plot <- ggplot(stack_real_fake, aes(segment, percent_new, width=.6)) + 
  geom_bar(aes(fill = concern), stat = "identity", position = "dodge") +
  fake_theme() +
  coord_flip() +
  labs(title = "I am concerned about what is real and what is fake on the internet:", x = "Segment", y = "Percent", caption = "Source: Reuters 2018 Digital News Report") 

fake_plot

poorJ_plot <- ggplot(stack_poor_J, aes(segment, percent_new, width=.6)) + 
  geom_bar(aes(fill = agreement), stat = "identity", position = "dodge") +
  poorJ_theme() +
  coord_flip() +
  labs(title = "Concerned about Poor journalism\n(factual mistakes, dumbed down stories, misleading headlines/clickbait):", x = "Segment", y = "Percent", caption = "Source: Reuters 2018 Digital News Report") 

poorJ_plot

export:

# PDF or PNG 
# to speficiy a chart add a "plot = plot_name" parameter after the file name.
ggsave("real-v-fake.pdf", plot = fake_plot, width = 8, height = 8, units = "in", dpi = 320)
ggsave("real-v-fake.png", plot = fake_plot, width = 8, height = 8, units = "in", dpi = 320)

ggsave("poor_journalism.pdf", plot = poorJ_plot, width = 11, height = 8, units = "in", dpi = 320)
ggsave("poor_journalism.png", plot = poorJ_plot, width = 11, height = 8, units = "in", dpi = 320)