R Notebook

First I chose one of the website variables (Twitter), and turned it into a factor.

pew %>%
  mutate(web1a = as_factor(web1a))

Next, I recoded the factor in order to provide verbal labels. I also ran a table to create an organized display of the numbers.

pew <- pew %>%
  mutate(Twitter = fct_recode(web1a,
                              "Yes" = "1",
                              "No" = "2",
                              NULL = "8",
                              NULL = "9"))

pew %>% 
  count(web1a)

NA
NA

The next step was to make education levels into a factor.

pew %>% 
  mutate(educ2 = as.factor(educ2))

4.Once education level had become a factor, I recoded it so the levels would have the appropriate labels. Just as for the last factor, I ran a table for an organized few of the data.

pew <- pew %>%
  mutate(education_level = fct_recode(educ2 ,
                              "Less Than HS" = "1",
                              "Some HS" = "2",
                              "HS graduate" = "3" ,
                              "Some College" = "4" ,
                              "Associates Degree" = "5" ,
                              "College Degree" = "6" ,
                              "Some Grad School" = "7",
                              "Grad Degree" = "8",
                              "Don't Know" = "98" ,
                              "Refused" = "99"))

pew %>% 
  count(educ2)

NA

Now that each factor has an individual table, I put the factors together to create a larger, more extensive table.

pew %>% 
  count(Twitter, education_level)

A graph provides a better visual presentation than just lookinh at the raw data, so I ran a graph in order for a clear, visual view of the data.

pew %>% 
  drop_na(internet_use) %>% 
  ggplot(aes(x = education_level, fill = Twitter)) +
  geom_bar() +
  coord_flip()

Next, I collapsed the education levels into two categories, instead of 8. This will create a more basic and clear form of the table, but will not be as detailed.

pew <- pew %>% 
  mutate(education_level_simple = fct_collapse(education_level,
                                             college_degree = c("Associates Degree",
                                                                "College Degree" ,
                                                                "Some Grad School" ,
                                                                "Grad Degree" ),
                                            no_degree = c("Some College", 
                                                            "HS graduate",
                                                                "Some HS" ,
                                                                "Less Than HS" ,
                                                          "Refused" ,
                                                          "Don't Know")))

pew %>% 
  count(education_level_simple)

NA

Finally, I used the above data in order to make a cleaner, simpler virsion of the data.

pew %>% 
  drop_na(education_level) %>% 
  ggplot(aes(x = education_level_simple, fill = Twitter)) +
  geom_bar() +
  coord_flip()

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