DACSS-601
library(tidyverse)
library(dplyr)
library(poliscidata)
gss %>%
select(sex,polviews,degree) %>%
head(25) %>%
tibble()
# A tibble: 25 x 3
sex polviews degree
<fct> <fct> <fct>
1 Male Moderate Bachelor deg
2 Male SlghtCons HS
3 Male SlghtCons HS
4 Female SlghtCons HS
5 Female Liberal Bachelor deg
6 Female Moderate Bachelor deg
7 Female Moderate Junior Coll
8 Female Moderate <HS
9 Female Conserv <HS
10 Female Liberal Bachelor deg
# ... with 15 more rows
I included the following graph in HW4, so I’m doing a facet wrap with it in an attempt to either tidy it up by majority or just make the existing graph easier to read.
library(poliscidata)
data(gss)
gss_refined<-gss %>%
select(polviews,sex,degree)
summary(gss_refined)
polviews sex degree
Moderate :713 Male : 886 <HS :288
Conserv :292 Female:1088 HS :976
SlghtCons:268 Junior Coll :151
Liberal :244 Bachelor deg:354
SlghtLib :208 Graduate deg:205
(Other) :149
NA's :100
library(poliscidata)
ggplot(gss_refined) + geom_point(aes(x=degree, y=polviews,color=sex)) +
labs(x="Highest Degree Awarded",y="Political Views") +
facet_grid()
data(gss)
ggplot(gss_refined) + geom_jitter(aes(x=degree, y=polviews,color=sex)) +
labs(x="Highest Degree Awarded",y="Political Views") +
facet_grid()
Note: I think the above graph is the best one this far, but I’m going to try to tweak it to make it a little more precise
1. I don’t think anything is missing from my analysis, I’m just trying to see if there’s a better way to visualize everything. There may not be a better way, but I’m going to use these homeworks to keep trying!
2. I have to do some more analysis, but right now, I can at least draw the conclusion that there is a very significant majority (or concentration of respondents) who have a high school diploma and hold moderate political views.
3. A reader without any context would at the very least need some understanding of the current political trends in the US (e.g. the rise of conservatism and the rise of far-left socialism among Gen Z/younger generations).
4. I’d like to incorporate how age plays a part in all this but I couldn’t figure out how to graph 4 different variables.
Text and figures are licensed under Creative Commons Attribution CC BY-NC 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Popiela (2022, April 27). Data Analytics and Computational Social Science: Homework 5. Retrieved from https://github.com/DACSS/dacss_course_website/posts/httprpubscomkpopiela895215/
BibTeX citation
@misc{popiela2022homework, author = {Popiela, Katie}, title = {Data Analytics and Computational Social Science: Homework 5}, url = {https://github.com/DACSS/dacss_course_website/posts/httprpubscomkpopiela895215/}, year = {2022} }