Challenge 7

abc_poll

Visualizing Multiple Dimensions

Author

Mariia Dubyk

Published

November 29, 2022

library(tidyverse)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read in data

abc_poll ⭐⭐

abc_poll<-read.csv("_data/abc_poll_2021.csv")
library(summarytools)
view(dfSummary(abc_poll))

Error in nchar(xx): invalid multibyte string, element 4

Briefly describe the data

The dataframe shows responses to the questionnaire. The respondents are adults (age 18-91) from the USA. - We can see some demographic data like education, income, age, state, etc (column 4-17). - Responses to other questions (about views or experience) are in columns 18-28. Column 2 identifies language of the respondent.

Dataset contains of 527 observations and 31 variables.

Tidy Data (as needed)

rename and delete certain columns
simplify information in some columns

#Rename and delete certain columns
abc_poll<-rename(abc_poll, language = xspanish, age =  ppage, education5 = ppeduc5, education = ppeducat, gender = ppgender, ethnicity = ppethm, household_size = pphhsize, income = ppinc7, marital_status = ppmarit5, region = ppreg4, rent = pprent, state = ppstaten, work = PPWORKA, employment = ppemploy)
abc_poll <- select(abc_poll, !contains("complete_status"))

#Simplify information in some columns
abc_poll<-abc_poll %>%
  mutate(education5=case_when(education5 == "Bachelor’s degree" ~ "bachelor",
                              education5 == "High school graduate (high school diploma or the equivalent GED)" ~ "hight_school",
                              education5 == "Master’s degree or above" ~ "master",
                              education5 == "No high school diploma or GED" ~ "no_high_school",
                              education5 == "Some college or Associate degree" ~ "college/associate"))

abc_poll<-abc_poll %>%
  mutate(education=case_when(education == "Bachelors degree or higher" ~ "bachelor",
                              education == "High school" ~ "high_school",
                              education == "Less than high school" ~ "less_high_school",
                              education == "Some college" ~ "college"))

abc_poll <- abc_poll%>%
  mutate(ethnicity = str_remove_all(ethnicity, c(", Non-Hispanic")))
abc_poll <- abc_poll%>%
  mutate(household_size = str_replace_all (household_size, c("6 or more" = "6<")))
abc_poll <- abc_poll%>%
  mutate(income=case_when(income == "Less than $10,000" ~ "1",
                             income == "$10,000 to $24,999" ~ "2",
                             income == "$25,000 to $49,999" ~ "3",
                             income == "$50,000 to $74,999" ~ "4",
                             income == "$75,000 to $99,999" ~ "5",
                             income == "$100,000 to $149,999" ~ "6",
                             income == "$150,000 or more" ~ "7"))

abc_poll <- abc_poll%>%
  mutate(marital_status = str_replace_all(marital_status, c(" " ="_", "m" = "M")))
abc_poll<-abc_poll %>%
  mutate(QPID = fct_recode(QPID, "dem" = "A Democrat",
                                "rep" = "A Republican",
                                "ind" = "An Independent",
                                "skipped" = "Skipped",
                              "other" = "Something else")) %>%
  mutate(QPID = fct_relevel(QPID, "dem", "ind", "rep","other", "skipped"))

Visualization with Multiple Dimensions

I decided to make simple bar chart to show answer to one of the questions and add gender as another dimension. It will show if there are differences between two groups. I am not sure if it is multiple dimensions because the visualization is very simple. But it shows number of responses, type of response and gender.

ggplot(abc_poll, aes(Q2, fill=gender)) + geom_bar() + ggtitle("Responses to Question2 by gender") + ggeasy::easy_center_title() + xlab("Question2") + ylab("Responses") + theme(axis.text = element_text(size = 7)) + labs(fill = "Gender")

Error in loadNamespace(x): there is no package called 'ggeasy'

As a second visualization I chose violin chart to show distribution of age variable for groups who answered Q2 differently.

library(hrbrthemes)
library(viridis)


ggplot(abc_poll, aes(x=Q2, y=age, fill=Q2, color=Q2)) +
    geom_violin(width=0.35, size=1) +
    scale_fill_viridis(discrete=TRUE) +
    scale_color_viridis(discrete=TRUE) +
    coord_flip() + xlab("") +
    ylab("Age") + ggtitle ("Distribution of age variable and Question2 responses")

--- title: "Challenge 7" author: "Mariia Dubyk" description: "Visualizing Multiple Dimensions" date: "11/29/2022" format: html: toc: true code-copy: true code-tools: true categories: - abc_poll --- ```{r} #| label: setup #| warning: false #| message: false library(tidyverse) library(ggplot2) knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) ``` ## Read in data - abc_poll ⭐⭐ ```{r} abc_poll<-read.csv("_data/abc_poll_2021.csv") library(summarytools) view(dfSummary(abc_poll)) ``` ### Briefly describe the data The dataframe shows responses to the questionnaire. The respondents are adults (age 18-91) from the USA. - We can see some demographic data like education, income, age, state, etc (column 4-17). - Responses to other questions (about views or experience) are in columns 18-28. Column 2 identifies language of the respondent. Dataset contains of 527 observations and 31 variables. ## Tidy Data (as needed) - rename and delete certain columns - simplify information in some columns ```{r} #Rename and delete certain columns abc_poll<-rename(abc_poll, language = xspanish, age = ppage, education5 = ppeduc5, education = ppeducat, gender = ppgender, ethnicity = ppethm, household_size = pphhsize, income = ppinc7, marital_status = ppmarit5, region = ppreg4, rent = pprent, state = ppstaten, work = PPWORKA, employment = ppemploy) abc_poll <- select(abc_poll, !contains("complete_status")) #Simplify information in some columns abc_poll<-abc_poll %>% mutate(education5=case_when(education5 == "Bachelor’s degree" ~ "bachelor", education5 == "High school graduate (high school diploma or the equivalent GED)" ~ "hight_school", education5 == "Master’s degree or above" ~ "master", education5 == "No high school diploma or GED" ~ "no_high_school", education5 == "Some college or Associate degree" ~ "college/associate")) abc_poll<-abc_poll %>% mutate(education=case_when(education == "Bachelors degree or higher" ~ "bachelor", education == "High school" ~ "high_school", education == "Less than high school" ~ "less_high_school", education == "Some college" ~ "college")) abc_poll <- abc_poll%>% mutate(ethnicity = str_remove_all(ethnicity, c(", Non-Hispanic"))) abc_poll <- abc_poll%>% mutate(household_size = str_replace_all (household_size, c("6 or more" = "6<"))) abc_poll <- abc_poll%>% mutate(income=case_when(income == "Less than $10,000" ~ "1", income == "$10,000 to $24,999" ~ "2", income == "$25,000 to $49,999" ~ "3", income == "$50,000 to $74,999" ~ "4", income == "$75,000 to $99,999" ~ "5", income == "$100,000 to $149,999" ~ "6", income == "$150,000 or more" ~ "7")) abc_poll <- abc_poll%>% mutate(marital_status = str_replace_all(marital_status, c(" " ="_", "m" = "M"))) abc_poll<-abc_poll %>% mutate(QPID = fct_recode(QPID, "dem" = "A Democrat", "rep" = "A Republican", "ind" = "An Independent", "skipped" = "Skipped", "other" = "Something else")) %>% mutate(QPID = fct_relevel(QPID, "dem", "ind", "rep","other", "skipped")) ``` ## Visualization with Multiple Dimensions I decided to make simple bar chart to show answer to one of the questions and add gender as another dimension. It will show if there are differences between two groups. I am not sure if it is multiple dimensions because the visualization is very simple. But it shows number of responses, type of response and gender. ```{r} ggplot(abc_poll, aes(Q2, fill=gender)) + geom_bar() + ggtitle("Responses to Question2 by gender") + ggeasy::easy_center_title() + xlab("Question2") + ylab("Responses") + theme(axis.text = element_text(size = 7)) + labs(fill = "Gender") ``` As a second visualization I chose violin chart to show distribution of age variable for groups who answered Q2 differently. ```{r} library(hrbrthemes) library(viridis) ggplot(abc_poll, aes(x=Q2, y=age, fill=Q2, color=Q2)) + geom_violin(width=0.35, size=1) + scale_fill_viridis(discrete=TRUE) + scale_color_viridis(discrete=TRUE) + coord_flip() + xlab("") + ylab("Age") + ggtitle ("Distribution of age variable and Question2 responses") ```