Voters of 2020 Analysis
Kathryn Agnas
5/11/2020
library(tidyverse)## ── Attaching packages ────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.0 ✓ purrr 0.3.3
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Vote <- read_csv("~/Downloads/Voter.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## weight_18_24_2018 = col_logical(),
## izip_2019 = col_character(),
## housevote_other_2019 = col_character(),
## senatevote_other_2019 = col_character(),
## senatevote2_other_2019 = col_character(),
## SenCand1Name_2019 = col_character(),
## SenCand1Party_2019 = col_character(),
## SenCand2Name_2019 = col_character(),
## SenCand2Party_2019 = col_character(),
## SenCand3Name_2019 = col_character(),
## SenCand3Party_2019 = col_character(),
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## SenCand1Party2_2019 = col_character(),
## SenCand2Name2_2019 = col_character(),
## SenCand2Party2_2019 = col_character(),
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## SenCand3Party2_2019 = col_character(),
## governorvote_other_2019 = col_character(),
## GovCand1Name_2019 = col_character(),
## GovCand1Party_2019 = col_character()
## # ... with 108 more columns
## )## See spec(...) for full column specifications.## Warning: 800 parsing failures.
## row col expected actual file
## 2033 weight_18_24_2018 1/0/T/F/TRUE/FALSE .917710168467982 '~/Downloads/Voter.csv'
## 2828 weight_18_24_2018 1/0/T/F/TRUE/FALSE 1.41022291345592 '~/Downloads/Voter.csv'
## 4511 weight_18_24_2018 1/0/T/F/TRUE/FALSE 1.77501243840922 '~/Downloads/Voter.csv'
## 7264 weight_18_24_2018 1/0/T/F/TRUE/FALSE 1.29486870319614 '~/Downloads/Voter.csv'
## 7277 weight_18_24_2018 1/0/T/F/TRUE/FALSE 1.44972719707603 '~/Downloads/Voter.csv'
## .... ................. .................. ................ .......................
## See problems(...) for more details.head(Vote)## # A tibble: 6 x 1,282
## weight_2016 weight_2017 weight_panel_20… weight_latino_2… weight_18_24_20…
## <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 0.358 0.438 0.503 NA NA
## 2 0.563 0.366 0.389 NA NA
## 3 0.552 0.550 0.684 NA NA
## 4 0.208 NA NA NA NA
## 5 0.334 0.346 0.322 NA NA
## 6 0.207 0.148 0.594 NA NA
## # … with 1,277 more variables: weight_overall_2018 <dbl>, weight_2019 <dbl>,
## # weight1_2018 <dbl>, weight1_2019 <dbl>, weight2_2019 <dbl>,
## # weight3_2019 <dbl>, cassfullcd <dbl>, vote2020_2019 <dbl>,
## # trumpapp_2019 <dbl>, fav_trump_2019 <dbl>, fav_obama_2019 <dbl>,
## # fav_hrc_2019 <dbl>, fav_sanders_2019 <dbl>, fav_putin_2019 <dbl>,
## # fav_schumer_2019 <dbl>, fav_pelosi_2019 <dbl>, fav_comey_2019 <dbl>,
## # fav_mueller_2019 <dbl>, fav_mcconnell_2019 <dbl>, fav_kavanaugh_2019 <dbl>,
## # fav_biden_2019 <dbl>, fav_warren_2019 <dbl>, fav_harris_2019 <dbl>,
## # fav_gillibrand_2019 <dbl>, fav_patrick_2019 <dbl>, fav_booker_2019 <dbl>,
## # fav_garcetti_2019 <dbl>, fav_klobuchar_2019 <dbl>, fav_gorsuch_2019 <dbl>,
## # fav_kasich_2019 <dbl>, fav_haley_2019 <dbl>, fav_bloomberg_2019 <dbl>,
## # fav_holder_2019 <dbl>, fav_avenatti_2019 <dbl>, fav_castro_2019 <dbl>,
## # fav_landrieu_2019 <dbl>, fav_orourke_2019 <dbl>,
## # fav_hickenlooper_2019 <dbl>, fav_pence_2019 <dbl>, add_confirm_2019 <dbl>,
## # izip_2019 <chr>, votereg_2019 <dbl>, votereg_f_2019 <dbl>,
## # regzip_2019 <dbl>, region_2019 <dbl>, turnout18post_2019 <dbl>,
## # tsmart_G2018_2019 <dbl>, tsmart_G2018_vote_type_2019 <dbl>,
## # tsmart_P2018_2019 <dbl>, tsmart_P2018_party_2019 <dbl>,
## # tsmart_P2018_vote_type_2019 <dbl>, housevote_2019 <dbl>,
## # housevote_other_2019 <chr>, senatevote_2019 <dbl>,
## # senatevote_other_2019 <chr>, senatevote2_2019 <dbl>,
## # senatevote2_other_2019 <chr>, SenCand1Name_2019 <chr>,
## # SenCand1Party_2019 <chr>, SenCand2Name_2019 <chr>,
## # SenCand2Party_2019 <chr>, SenCand3Name_2019 <chr>,
## # SenCand3Party_2019 <chr>, SenCand1Name2_2019 <chr>,
## # SenCand1Party2_2019 <chr>, SenCand2Name2_2019 <chr>,
## # SenCand2Party2_2019 <chr>, SenCand3Name2_2019 <chr>,
## # SenCand3Party2_2019 <chr>, governorvote_2019 <dbl>,
## # governorvote_other_2019 <chr>, GovCand1Name_2019 <chr>,
## # GovCand1Party_2019 <chr>, GovCand2Name_2019 <chr>,
## # GovCand2Party_2019 <chr>, GovCand3Name_2019 <chr>,
## # GovCand3Party_2019 <chr>, inst_court_2019 <dbl>, inst_media_2019 <dbl>,
## # inst_congress_2019 <dbl>, inst_justice_2019 <dbl>, inst_FBI_2019 <dbl>,
## # inst_military_2019 <dbl>, inst_church_2019 <dbl>, inst_business_2019 <dbl>,
## # Democrats_2019 <dbl>, Republicans_2019 <dbl>, Men_2019 <dbl>,
## # Women_2019 <dbl>, wm_2019 <dbl>, ww_2019 <dbl>, bm_2019 <dbl>,
## # bw_2019 <dbl>, hm_2019 <dbl>, hw_2019 <dbl>, rwm_2019 <dbl>,
## # rww_2019 <dbl>, rbm_2019 <dbl>, rbw_2019 <dbl>, pwm_2019 <dbl>, …How do those who would vote for either Donald Trump or the Democratic Candidate in the 2020 election differ in their perspectives on women, abortion, healthcare laws?
Data Preparation
Vote <- Vote %>%
rename(imiss_t_2019, abortion_2019 = imiss_t_2019, abortidentity_baseline, abort_identity = abortidentity_baseline, abortview3_baseline, Legalize_Abortion = abortview3_baseline)Vote <-Vote %>%
mutate(vote2020_2019 =
ifelse(vote2020_2019==1,"Donald Trump",
ifelse(vote2020_2019==2,"The Democratic candidate",
ifelse(vote2020_2019==3, "I would not vote",
ifelse(vote2020_2019==4, "Not sure", NA)))),
abortion_2019 =
ifelse(abortion_2019==1,"Very Important",
ifelse(abortion_2019==2,"Somewhat Important",
ifelse(abortion_2019==3,"Not very Important",
ifelse(abortion_2019==4,"Unimportant", NA)))),
abortion_2019= factor(abortion_2019,
levels=c("Unimportant", "Not very Important", "Somewhat Important", "Very Important")),
abort_identity =
ifelse(abort_identity=="1", "Pro-life",
ifelse(abort_identity=="2", "Pro-choice", NA)),
abort_identity= factor(abort_identity,
levels=c("Pro-life","Pro-choice")),
healthcarelaw1_2019 =
ifelse(healthcarelaw1_2019=="1","Very important",
ifelse(healthcarelaw1_2019=="2","Somewhat important",
ifelse(healthcarelaw1_2019=="3","Not too important",
ifelse(healthcarelaw1_2019=="4","Not at all important", NA)))),
healthcarelaw2_2019 =
ifelse(healthcarelaw2_2019=="1","Very important",
ifelse(healthcarelaw2_2019=="2","Somewhat important",
ifelse(healthcarelaw2_2019=="3","Not too important",
ifelse(healthcarelaw2_2019=="4", "Not at all important", NA)))),
Legalize_Abortion =
ifelse(Legalize_Abortion=="1","Legal in all cases",
ifelse(Legalize_Abortion=="2","Legal in some cases and illegal in others",
ifelse(Legalize_Abortion=="3","Illegal in all cases", NA))),
Legalize_Abortion= factor(Legalize_Abortion, levels=c("Legal in all cases", "Legal in some cases and illegal in others", "Illegal in all cases")),
favorhealth_2019 =
ifelse(favorhealth_2019==1,"Strongly favor",
ifelse(favorhealth_2019==2,"Somewhat favor",
ifelse(favorhealth_2019==3,"Somewhat oppose",
ifelse(favorhealth_2019==4,"Strongly oppose", NA)))),
favorhealth_2019= factor(favorhealth_2019,
levels=c("Strongly oppose","Somewhat oppose", "Somewhat favor", "Strongly favor")),
Women_2019 = ifelse(Women_2019>100,NA, Women_2019)) %>%
filter(vote2020_2019 %in% c("Donald Trump","The Democratic candidate")) T-Test on Voter Candidates for 2020’s feelings on Women
Actual Hypothesis / Averages for both groups
Vote %>%
group_by(vote2020_2019) %>%
summarize(Women_2019 = mean(Women_2019, na.rm=TRUE))## # A tibble: 2 x 2
## vote2020_2019 Women_2019
## <chr> <dbl>
## 1 Donald Trump 73.0
## 2 The Democratic candidate 81.9This reveals that those who would vote for Donald Trump have weaker and negative feelings on Women, whereas those would vote for the Democratic candidate have stronger and positive feelings on Women.
Population Distribution // Bar chart
Below is a visualization of the previous table
Vote %>%
filter(vote2020_2019 %in% c("Donald Trump","The Democratic candidate")) %>%
ggplot()+
geom_histogram(aes(x=Women_2019, fill=vote2020_2019))+
facet_wrap(~vote2020_2019)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 305 rows containing non-finite values (stat_bin).
From the histograms, it shows that those who would vote the Democratic candidate are more than likely than those who would vote for Donald Trump to give a score of 100 when asked about their feelings towards women. The histogram on Voters for Donald Trump reveals that they tend to give somewhat positive ratings since it leans on the higher end of scale. However, the score 50 out of 100 is very common for voters of Donald Trump, and their scores for 100 is not as high as the Republicans.
Null Hypothesis
Vote%>%
summarize(Women_2019 = mean(Women_2019,na.rm=TRUE))## # A tibble: 1 x 1
## Women_2019
## <dbl>
## 1 77.9If a person’s political vote (Donald Trump and the Democratic candidate) makes no difference in their feeling towards women, then we should find that the group-wise averages are .78 for members of both groups.
Sampling Prep
Donald_vote <- Vote %>%
filter(vote2020_2019=="Donald Trump")
Donald_vote <-
replicate(10000,
sample(Donald_vote$Women_2019,40) %>%
mean(na.rm=TRUE)) %>%
data.frame()%>%
rename("mean"=1)Dem_vote <- Vote %>%
filter(vote2020_2019=="The Democratic candidate")
Dem_vote <-
replicate(10000,
sample(Dem_vote$Women_2019,40) %>%
mean(na.rm=TRUE)) %>%
data.frame()%>%
rename("mean"=1)Sampling Distributions
ggplot()+
geom_histogram(data=Donald_vote, aes(x=mean),fill="red")+ geom_histogram(data=Dem_vote, aes(x=mean),fill="blue")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Both sampling disturbutions overlap a little in between the 70s-80s. However, we see that those who would vote for the Democratic candidate lean more on the higher 80s and those who would vote for Donald Trump lean more on the lower 70s. In conclusion, both sampling distributions are normally distrubuted.
T-Test
t.test(Women_2019~vote2020_2019, data=Vote) ##
## Welch Two Sample t-test
##
## data: Women_2019 by vote2020_2019
## t = -16.006, df = 4588.9, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -9.954328 -7.781945
## sample estimates:
## mean in group Donald Trump mean in group The Democratic candidate
## 73.03526 81.90339options(scipen =9999)The results show that there is a statistical significance between those who will vote for Donald Trump vs those who will vote for the Democratic candidate in 2020 in their feelings towards Women. This is indicated by the p value which is less than 0.05.The null hypothesis is rejected because there is a difference in the mean value between the two groups.
Chi-Squared Test on the Voter Candidates for 2020’s take on the Importance of Abortion
% of Respondents by Vote for Candidate
table(Vote$vote2020_2019)%>%
prop.table()%>%
round(2)##
## Donald Trump The Democratic candidate
## 0.45 0.55We see that the majority of the people voting for the Democratic candidate are at 55%, whereas the people voting for Donald Trump are at 45%.
% of Respondents by Importance of Abortion
table(Vote$abortion_2019)%>%
prop.table()%>%
round(2)##
## Unimportant Not very Important Somewhat Important Very Important
## 0.11 0.18 0.31 0.41We see that the majority of the voters feel that Abortion is Very Important.
Null Hypothesis
Donald Trump .45 * .11 = .05 Unimportant .45 * .18 = .08 Not very Important .45 * .31 = .14 Somewhat Important .45 * .41 = .18 Very Important
.55 * .11 = .06 Unimportant .55 * .18 = .10 Not very Important .55 * .31 = .17 Somewhat Important .55 * .41 = .23 Very Important
Cross-tabulation of actual observations
table(Vote$vote2020_2019, Vote$abortion_2019) %>%
prop.table(1)##
## Unimportant Not very Important Somewhat Important
## Donald Trump 0.11902028 0.18522771 0.27745886
## The Democratic candidate 0.09820322 0.17317224 0.33178439
##
## Very Important
## Donald Trump 0.41829315
## The Democratic candidate 0.39684015The actual observations reveal that both of voters of the two candidates strongly believe that the topic of Abortion is “Very Important”. Voters for Donald Trump have the highest percentange in their answers for “Very Important” at 42%. Voters for the Democratic candidate have the highest percentage in their answers for “Very Important” at 40%.This reveals that both groups have strong opinions on the topic of Abortion.
If there is no relationship betweent the two variables, we still see that both of the groups strongly view the topic of Abortion as “Very Important”, with voters of Donald Trump are at 18% and voters of the Democratic candidate are at 23%.
Both of the observations demonstrates how both percentages in the categories are closely simular.This demonstrates how the topic of Abortion is a very sensitive topic among the the groups.
Chi-Squared Test
chisq.test(Vote$abortion_2019, Vote$vote2020_2019)##
## Pearson's Chi-squared test
##
## data: Vote$abortion_2019 and Vote$vote2020_2019
## X-squared = 22.491, df = 3, p-value = 0.00005155options(scipen =9999)Since the p-value is less than .05, we reject the null hypothesis, there’s no difference between the means, and our actual observations are significant.
Extract the Observed Values Table from my chi-squared test output
chisq.test(Vote$vote2020_2019, Vote$abortion_2019)[7]## $expected
## Vote$abortion_2019
## Vote$vote2020_2019 Unimportant Not very Important Somewhat Important
## Donald Trump 280.9389 466.5912 803.4494
## The Democratic candidate 347.0611 576.4088 992.5506
## Vote$abortion_2019
## Vote$vote2020_2019 Very Important
## Donald Trump 1062.021
## The Democratic candidate 1311.979chisq.test(Vote$vote2020_2019, Vote$abortion_2019)[6]## $observed
## Vote$abortion_2019
## Vote$vote2020_2019 Unimportant Not very Important Somewhat Important
## Donald Trump 311 484 725
## The Democratic candidate 317 559 1071
## Vote$abortion_2019
## Vote$vote2020_2019 Very Important
## Donald Trump 1093
## The Democratic candidate 1281The Expected table and the Observed table reflect that both groups have strong opinions on the topic of abortion being Very Important, since they both have the highest number for “Very Important” in both tables. There is no major difference.
Voter Candidates of 2020’s take on their Abortion identity and view on the whether Abortion should be Illegal or Legal
How would the votes for Donald Trump and “the Democratic candidate” in 2020 distribute across categories on Abortion identity?
table(Vote$abort_identity, Vote$vote2020_2019) %>%
prop.table(2) %>%
round(2)##
## Donald Trump The Democratic candidate
## Pro-life 0.74 0.13
## Pro-choice 0.26 0.87We see that those would vote for Donald Trump are more than likely to identify as “Pro-life” for their Aborition identity at 74%. On the other hand, those who would vote for the Democratic candidate are more than likely to identify as “Pro-choice” for their Abortion identity at 87%.
Stacked Bar Chart
Below is a stacked bar chart, visualizing the information in the above table
Vote %>%
filter(!is.na(abort_identity),!is.na(vote2020_2019)) %>%
group_by(abort_identity, vote2020_2019) %>%
summarize(n=n()) %>%
mutate(percent=n/sum(n)) %>%
ggplot () +
geom_col(aes(x=abort_identity, y=percent, fill=vote2020_2019)) + theme_minimal()
The stacked bar chart clearly depicts how those who would for Donald Trump are more likely to be “Pro-life” and those who would vote for the Democratic candidate are more likely to be “Pro-life”.
Average view on Abortion
table(Vote$Legalize_Abortion, Vote$vote2020_2019) %>%
prop.table(1) %>%
round(2)##
## Donald Trump
## Legal in all cases 0.19
## Legal in some cases and illegal in others 0.58
## Illegal in all cases 0.84
##
## The Democratic candidate
## Legal in all cases 0.81
## Legal in some cases and illegal in others 0.42
## Illegal in all cases 0.16We see that those would vote for Donald Trump strongly believe that Abortion should be “Illegal in all cases” at 84%. On the other hand, those who would vote for the Democratic candidate strongly beleive that Abortion should “Legal in all cases” at 81%.
Stacked Bar Chart
Vote %>%
filter(!is.na(Legalize_Abortion),!is.na(vote2020_2019)) %>%
group_by(Legalize_Abortion, vote2020_2019) %>%
summarize(n=n()) %>%
mutate(percent=n/sum(n)) %>%
ggplot () +
geom_col(aes(x=Legalize_Abortion, y=percent, fill=vote2020_2019)) + theme_minimal()
The stacked bar chart reveals that those who would vote for Donald Trump lean more on “Illegal in all cases” and that those who would vote for the Democratic candidate lean more on “Legal in all cases” on the view on Abortion.
Average feelings toward women in each category on whether abortion should be legal or not
Vote%>%
group_by(Legalize_Abortion)%>%
summarize(Women_2019 = mean(Women_2019, na.rm=TRUE))## Warning: Factor `Legalize_Abortion` contains implicit NA, consider using
## `forcats::fct_explicit_na`## # A tibble: 4 x 2
## Legalize_Abortion Women_2019
## <fct> <dbl>
## 1 Legal in all cases 81.5
## 2 Legal in some cases and illegal in others 76.5
## 3 Illegal in all cases 74.8
## 4 <NA> 76.5From the averages, it reveals that those who have negative and weaker feelings on Women believe that Abortion should be Illegal in all cases. In constrast, we see those who think that Abortion should be Legal have positive and stronger feelings on Women.
Histogram of previous table
Below are histograms comparing the distrubution of feelings towards women for those in various categories of satisfaction in health.
Vote%>%
filter(!is.na(Legalize_Abortion),!is.na(abort_identity))%>%
ggplot()+
geom_histogram(aes(x=Women_2019, fill=Legalize_Abortion))+
facet_wrap(~Legalize_Abortion)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 120 rows containing non-finite values (stat_bin).
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## - attr(*, "validate")= logi TRUEFrom the historgrams, we see that the feelings towards Women go down in order of the histograms from left to right based on the Legalization of Abortion. We see that higher ratings on Women stem from a belief that Abortion should be Legal in all cases, possibly due to respect for Women and that Women should have the right to decide what to do with their own bodies. However, lower ratings on Women stem from a belief that Abortion should be Illegal in all cases, possibly due to religious and patriarchal reasons.
Average Feelings on Healthcare Laws
As you may know, the Affordable Care Act required nearly all Americans to have health insurance, or else pay a fine. Congress passed a law repealing this requirement starting in January 2019. Do you favor or oppose requiring all Americans have health insurance?
table(Vote$favorhealth_2019, Vote$vote2020_2019) %>%
prop.table(1) %>%
round(2)##
## Donald Trump The Democratic candidate
## Strongly oppose 0.87 0.13
## Somewhat oppose 0.58 0.42
## Somewhat favor 0.26 0.74
## Strongly favor 0.13 0.87From the averages, we definitely see a major difference between the two groups as we see those who would vote Donald Trump have the highest percentage for “Strongly oppose” at 87%. Meanwhile, those who would vote for the Democratic candidate have the highest percentage for “Strongly favor” at 87%.
Data visualization of previous table
Below is a stacked bar chart, visualizing the previous table.
Vote %>%
filter(!is.na(vote2020_2019),!is.na(favorhealth_2019)) %>%
group_by(favorhealth_2019, vote2020_2019) %>%
summarize(n=n()) %>%
mutate(percent=n/sum(n)) %>%
ggplot () +
geom_col(aes(x=favorhealth_2019, y=percent, fill=vote2020_2019)) + theme_minimal()
This chart visualizes how those who would vote Donald Trump score higher in “Strongly oppose”, but decreases in the other categories to favor. Those who would vote for the Democratic candidate score higher in “Strongly favor”, but decreases in the other categories to oppose. Both group go in different directions as shown in the chart.
This reveals that voters of Donald Trump are more against of the law requiring all Americans having health insurance, whereas voters of the Democratic candidate are more in favor of the law requiring all Americans having health insurance.
The 2010 health care law provided protections for people with pre-existing health conditions. How important is it to you that the following provisions remain the law?
table(Vote$healthcarelaw1_2019, Vote$vote2020_2019) %>%
prop.table(1) %>%
round(2)##
## Donald Trump The Democratic candidate
## Not at all important 0.92 0.08
## Not too important 0.86 0.14
## Somewhat important 0.72 0.28
## Very important 0.30 0.70From the averages, we definitely see a major difference between the two groups as we see those who would vote Donald Trump have the highest percentage for “Not at all important” at 92%. Meanwhile, those who would vote for the Democratic candidate have the highest percentage for “Very important” at 70%.
Data visualization of previous table
Vote %>%
filter(!is.na(vote2020_2019),!is.na(healthcarelaw1_2019)) %>%
group_by(healthcarelaw1_2019, vote2020_2019) %>%
summarize(n=n()) %>%
mutate(percent=n/sum(n)) %>%
ggplot () +
geom_col(aes(x=healthcarelaw1_2019, y=percent, fill=vote2020_2019)) + theme_minimal()
This chart visualizes how those who would vote Donald Trump score higher in “Not at all important”, but decreases in the other categories on the way to “Very important”. Those who would vote for the Democratic candidate score higher in “Very important”, but decreases in the other categories on the way to “Not at all important”. Both group go in different directions as shown in the chart.
This reveals that voters of Donald Trump view the protections of people with pre-existing health conditions as irrelevant, whereas voters of the Democratic candidate view the protection of people with pre-existing health conditions as relevant.
The 2010 health care law provided protections for people with pre-existing health conditions. How important is it to you that the following provisions remain the law? The provision that prohibits health insurance companies from denying coverage because of a person’s medical history.
table(Vote$healthcarelaw2_2019, Vote$vote2020_2019) %>%
prop.table(1) %>%
round(2)##
## Donald Trump The Democratic candidate
## Not at all important 0.88 0.12
## Not too important 0.85 0.15
## Somewhat important 0.78 0.22
## Very important 0.34 0.66From the averages, we definitely see a major difference between the two groups as we see those who would vote Donald Trump have the highest percentage for “Not at all important” at 88%. Meanwhile, those who would vote for the Democratic candidate have the highest percentage for “Very important” at 66%.
Data Visualization of previous table
Vote %>%
filter(!is.na(vote2020_2019),!is.na(healthcarelaw2_2019)) %>%
group_by(healthcarelaw2_2019, vote2020_2019) %>%
summarize(n=n()) %>%
mutate(percent=n/sum(n)) %>%
ggplot () +
geom_col(aes(x=healthcarelaw2_2019, y=percent, fill=vote2020_2019)) + theme_minimal()