Pew Analysis

load required package:

library(tidyverse)

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

## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
## ✔ tibble  2.0.0     ✔ dplyr   0.7.8
## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
## ✔ readr   1.3.1     ✔ forcats 0.3.0

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

Read in the date:

pew <- read_csv("January 3-10, 2018 - Core Trends Survey/January 3-10, 2018 - Core Trends Survey - CSV.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   usr = col_character(),
##   `pial11ao@` = col_character()
## )

## See spec(...) for full column specifications.

Recode snapchate use

 pew <- pew %>%
  mutate(snapchat = as.factor(web1d))%>%
  mutate(snapchat = fct_recode(snapchat,
                               "Yes" = "1", 
                               "No" = "2", 
                               NULL = "8", 
                               NULL = "9"))

Recode education

pew <- pew %>%
  mutate(education = as.factor(educ2))%>%
  mutate(education = fct_recode(education, 
                                "Less than HS" = "1",
                                "Some HS" = "2",
                                "HS graduate" = "3", 
                                "Some college" = "4", 
                                "Associate degree" = "5",
                                "College degree" = "6",
                                "Some grad school" = "7",
                                "Grad degree" = "8",
                                NULL = "98", 
                                NULL = "99"))

pew %>%
  drop_na(education)%>%
  count (education)

pew %>%
  drop_na(snapchat) %>%
  count(snapchat)

pew%>%
  drop_na(snapchat, education) %>%
  count(education, snapchat)

pew %>% 
  drop_na(education, snapchat) %>% 
  ggplot(aes(x = education, fill = snapchat)) +
  scale_fill_viridis_d()+
  geom_bar( position = "fill") +
  coord_flip()+
  labs(title = "Snapchat use by education level")

pew <- pew %>%
  mutate(education_simple = fct_collapse(education, 
                                         college_degree = c("Associate degree",
                                                            "College degree",
                                                            "Some grad school",
                                                            "Grad degree"),
                                         no_college_degree = c("Less than HS",
                                                              "Some HS",
                                                              "HS graduate",
                                                              "Some college")))

pew%>%
  drop_na(education_simple)%>%
  count(education_simple)

pew%>%
  drop_na(education_simple, snapchat) %>%
  count(education_simple, snapchat)

pew %>% 
  drop_na(education_simple, snapchat) %>% 
  ggplot(aes(x = education_simple, fill = snapchat)) +
  scale_fill_viridis_d()+
  geom_bar( position = "fill") +
  coord_flip()+
  labs(title= "Snapchat use by education level")