Analysis 2 - Normalization by Country & Word Placement (Horizontal)

Word 1

w1 <- df %>% filter(w1_perc!=0)
library(ggplot2)
p<-ggplot(w1, aes(x=factor(Category, levels=c("Geography & Language", 
                          "Race & Ethnicity", "Otherness","Negative",
                          "Neutral", "Positive", "Culture",
                          "Others & Unsure")), 
   y = w1_perc, fill=Country)) + geom_bar(stat="identity", position=position_dodge())
p + labs(title="Word 1") + 
  xlab("Category") + ylab("Normalized Percentage") + 
  coord_flip()

Word 2

w2 <- df %>% filter(w2_perc!=0)

library(ggplot2)
p<-ggplot(w2, aes(x=factor(Category, levels=c("Geography & Language", 
                                              "Race & Ethnicity",
                                              "Otherness","Negative",
                                              "Neutral", "Positive",
                                              "Culture",
                                              "Others & Unsure")), 
   y = w2_perc, fill=Country)) + geom_bar(stat="identity", position=position_dodge())

p + labs(title="Word 2") + 
  xlab("Category") + ylab("Normalized Percentage") + 
  coord_flip()

Word 3

w3 <- df %>% filter(w3_perc!=0)

library(ggplot2)
p<-ggplot(w3, aes(x=factor(Category, levels=c("Geography & Language", 
                                              "Race & Ethnicity",
                                              "Otherness","Negative",
                                              "Neutral", "Positive",
                                              "Culture",
                                              "Others & Unsure")), 
   y = w3_perc, fill=Country)) + geom_bar(stat="identity", position=position_dodge())

p + labs(title="Word 3") + 
  xlab("Category") + ylab("Normalized Percentage") + 
  coord_flip()

Word 4

w4 <- df %>% filter(w4_perc!=0)

library(ggplot2)
p<-ggplot(w4, aes(x=factor(Category, levels=c("Geography & Language", 
                                              "Race & Ethnicity",
                                              "Otherness","Negative",
                                              "Neutral", "Positive",
                                              "Culture",
                                              "Others & Unsure")), 
   y = w4_perc, fill=Country)) + geom_bar(stat="identity", position=position_dodge())

p + labs(title="Word 4") + 
  xlab("Category") + ylab("Normalized Percentage") + 
  coord_flip()

Word 5

w5 <- df %>% filter(w5_perc!=0)

library(ggplot2)
p<-ggplot(w3, aes(x=factor(Category, levels=c("Geography & Language", 
                                              "Race & Ethnicity",
                                              "Otherness","Negative",
                                              "Neutral", "Positive",
                                              "Culture",
                                              "Others & Unsure")), 
   y = w3_perc, fill=Country)) + geom_bar(stat="identity", position=position_dodge())

p + labs(title="Word 5") + 
  xlab("Category") + ylab("Normalized Percentage") + 
  coord_flip()