Recoding Variables (Final Report)
Sakif Shadman
1. Introduction
In this assignment, I will show how different political parties see immigrants in the US. So I will be using the following variables:
Independent variable: partyreg_baseline, which breaks into two groups: Republicans and Democrats.
Categorical dependent variables: immi_naturalize_2019, immi_makedifficult_2019 and immi_muslim_2019.
Continuous dependent variables: ft_immig_2017.
2. Description of Variables & Data Preparation
I am interested in knowing how Republicans and Democrats differ their view points about the immigrants. I will be comparing them based on:
immi_naturalize_2019: Whether they support the illegal immigrants in the US to become U.S. citizen in a legal way or not?
immi_makedifficult_2019: Do they think it should be easier or harder for foreigners to immigrate to the US legally than it is currently?
immi_muslim_2019: How do they favor or oppose temporarily banning Muslims immigrants from other countries from entering the United States?
ft_immig_2017: How they feel towards immigrants?
The following steps will be showing the different political party views on the immigrant.
(a) Import Data
Loading the necessary packages. Importing data into R and named it Voter_Data.
library(readr)
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionVoter_Data = read_csv("/Users/sakif/Desktop/Data 333/Voter Data 2019.csv")##
## ── 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(),
## SenCand1Name2_2019 = col_character(),
## SenCand1Party2_2019 = col_character(),
## SenCand2Name2_2019 = col_character(),
## SenCand2Party2_2019 = col_character(),
## SenCand3Name2_2019 = col_character(),
## SenCand3Party2_2019 = col_character(),
## governorvote_other_2019 = col_character(),
## GovCand1Name_2019 = col_character(),
## GovCand1Party_2019 = col_character()
## # ... with 108 more columns
## )
## ℹ Use `spec()` for the full column specifications.## Warning: 800 parsing failures.
## row col expected actual file
## 2033 weight_18_24_2018 1/0/T/F/TRUE/FALSE .917710168467982 '/Users/sakif/Desktop/Data 333/Voter Data 2019.csv'
## 2828 weight_18_24_2018 1/0/T/F/TRUE/FALSE 1.41022291345592 '/Users/sakif/Desktop/Data 333/Voter Data 2019.csv'
## 4511 weight_18_24_2018 1/0/T/F/TRUE/FALSE 1.77501243840922 '/Users/sakif/Desktop/Data 333/Voter Data 2019.csv'
## 7264 weight_18_24_2018 1/0/T/F/TRUE/FALSE 1.29486870319614 '/Users/sakif/Desktop/Data 333/Voter Data 2019.csv'
## 7277 weight_18_24_2018 1/0/T/F/TRUE/FALSE 1.44972719707603 '/Users/sakif/Desktop/Data 333/Voter Data 2019.csv'
## .... ................. .................. ................ ...................................................
## See problems(...) for more details.Voter_Data## # A tibble: 9,548 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
## 7 0.456 0.378 NA NA NA
## 8 1.05 0.993 0.965 NA NA
## 9 0.478 1.03 NA NA NA
## 10 0.417 0.377 0.516 NA NA
## # … with 9,538 more rows, and 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>, …(b) Recoding Data
Recode Voter_Data from their numeric form to their labeled form. Store this prepared data in a new object named Immigrants_Data.
Immigrants_Data = Voter_Data %>%
mutate(Political_Party = ifelse(partyreg_baseline == 1, "Democrat",
ifelse(partyreg_baseline == 2, "Republican", NA)),
Immigrants_Naturalize = ifelse(immi_naturalize_2019 == 1, "Favor",
ifelse(immi_naturalize_2019 == 2, "Oppose",
ifelse(immi_naturalize_2019 == 8, "Don't know", NA))),
Immigrants_Hardness = ifelse(immi_makedifficult_2019 == 1, "Much easier",
ifelse(immi_makedifficult_2019 == 2, "Slightly easier",
ifelse(immi_makedifficult_2019 == 3, "No change",
ifelse(immi_makedifficult_2019 == 4, "Slightly harder",
ifelse(immi_makedifficult_2019 == 5, "Much harder",
ifelse(immi_makedifficult_2019 == 8, "Don't know", NA)))))),
Immigrants_Hardness = factor(Immigrants_Hardness, levels = c("Much easier",
"Slightly easier",
"No change",
"Slightly harder",
"Much harder",
"Don't know")),
Banning_Muslims = ifelse(immi_muslim_2019 == 1, "Strongly favor",
ifelse(immi_muslim_2019 == 2, "Somewhat favor",
ifelse(immi_muslim_2019 == 3, "Somewhat oppose",
ifelse(immi_muslim_2019 == 4, "Strongly oppose",
ifelse(immi_muslim_2019 == 8, "Don't know", NA))))),
Banning_Muslims = factor(Banning_Muslims, levels = c("Strongly favor",
"Somewhat favor",
"Somewhat oppose",
"Strongly oppose",
"Don't know")),
Feeling_About_Immigrants = ifelse(ft_immig_2017 > 100, NA, ft_immig_2017)) %>%
select(Political_Party, Immigrants_Naturalize, Immigrants_Hardness, Banning_Muslims, Feeling_About_Immigrants) %>%
filter(!is.na(Political_Party), !is.na(Immigrants_Naturalize), !is.na(Immigrants_Hardness), !is.na(Banning_Muslims), !is.na(Feeling_About_Immigrants))
Immigrants_Data## # A tibble: 2,017 x 5
## Political_Party Immigrants_Natu… Immigrants_Hard… Banning_Muslims
## <chr> <chr> <fct> <fct>
## 1 Democrat Favor No change Strongly oppose
## 2 Democrat Favor Much easier Strongly oppose
## 3 Democrat Favor Much easier Strongly oppose
## 4 Republican Oppose Slightly harder Strongly favor
## 5 Democrat Favor No change Strongly oppose
## 6 Republican Oppose Much harder Somewhat oppose
## 7 Republican Oppose Much harder Somewhat oppose
## 8 Republican Oppose Slightly harder Somewhat favor
## 9 Republican Oppose Much harder Strongly favor
## 10 Democrat Favor Slightly easier Strongly oppose
## # … with 2,007 more rows, and 1 more variable: Feeling_About_Immigrants <dbl>3. Analysis: Political_Party (IV) x Immigrants_Naturalize (DV)
Analysing Political_Party and Immigrants_Naturalize from different views.
(a) Crosstab
table(Immigrants_Data$Immigrants_Naturalize, Immigrants_Data$Political_Party) %>%
prop.table(2)##
## Democrat Republican
## Don't know 0.07383774 0.13260870
## Favor 0.79398359 0.30760870
## Oppose 0.13217867 0.55978261Interpretation:
It’s clearly showing from the crosstab that 79% Democrats are favoring immigrants in contrast, only 30% Republican are in favor. 13% Democrats are opposing immigrants meanwhile, almost 56% Republicans are opposing immigrants.
(b) Visualization: Stacked barchart
Immigrants_Data %>%
group_by(Political_Party, Immigrants_Naturalize) %>%
summarize(n=n()) %>%
mutate(Percent = n/sum(n)) %>%
ggplot()+
geom_col(aes(x = Political_Party, y = Percent, fill = Immigrants_Naturalize))## `summarise()` regrouping output by 'Political_Party' (override with `.groups` argument)
Interpretation:
From the visualization, it’s clearly showing that most Democrats are favoring immigrants where on the other side most Republican opposing immigrants.
(c) Statistical Test: Chi-square test
options(scipen = 999)
chisq.test(Immigrants_Data$Immigrants_Naturalize, Immigrants_Data$Political_Party)##
## Pearson's Chi-squared test
##
## data: Immigrants_Data$Immigrants_Naturalize and Immigrants_Data$Political_Party
## X-squared = 503.66, df = 2, p-value < 0.00000000000000022Interpretation:
So, there is a statistically significant relationship between Political_Party, and what they think about providing a legal way for illegal immigrants already in the US.
4. Analysis: Political_Party (IV) x Immigrants_Hardness (DV)
Analysing Political_Party and Immigrants_Hardness from different views.
(a) Crosstab
table(Immigrants_Data$Immigrants_Hardness, Immigrants_Data$Political_Party) %>%
prop.table(2)##
## Democrat Republican
## Much easier 0.19781222 0.05543478
## Slightly easier 0.24521422 0.15434783
## No change 0.25068368 0.24456522
## Slightly harder 0.11394713 0.20217391
## Much harder 0.13947129 0.31847826
## Don't know 0.05287147 0.02500000Interpretation:
The crosstab about making immigration prrocess easier or harder, almost 44% Democrats wants it to be easier but from Republican it’s only 21%. Also about 25% both wants no change. But only 25% Democrats wants it to be harder where 52% Republicants wants it to be harder.
(b) Visualization: Stacked barchart
Immigrants_Data %>%
group_by(Political_Party, Immigrants_Hardness) %>%
summarize(n=n()) %>%
mutate(Percent = n/sum(n)) %>%
ggplot()+
geom_col(aes(x = Political_Party, y = Percent, fill = Immigrants_Hardness))## `summarise()` regrouping output by 'Political_Party' (override with `.groups` argument)
Interpretation:
From the visualization, it’s clearly showing that most Democrats want this process to be easier where most Republicants want it to be harder.
(c) Statistical Test: Chi-square test
options(scipen = 999)
chisq.test(Immigrants_Data$Immigrants_Hardness, Immigrants_Data$Political_Party)##
## Pearson's Chi-squared test
##
## data: Immigrants_Data$Immigrants_Hardness and Immigrants_Data$Political_Party
## X-squared = 204.14, df = 5, p-value < 0.00000000000000022Interpretation:
So, there is a statistically significant relationship between Political_Party, and their thinking on immigration process for immigrants.
5. Analysis: Political_Party (IV) x Banning_Muslims (DV)
Analysing Political_Party and Banning_Muslims from different views.
(a) Crosstab
table(Immigrants_Data$Banning_Muslims, Immigrants_Data$Political_Party) %>%
prop.table(2)##
## Democrat Republican
## Strongly favor 0.10300820 0.43260870
## Somewhat favor 0.10300820 0.27065217
## Somewhat oppose 0.20510483 0.11413043
## Strongly oppose 0.50501367 0.09347826
## Don't know 0.08386509 0.08913043Interpretation:
The crosstab about banning muslims immigrant showing that, only 20% of Democrats favor it where as 70% Republicans want to ban Muslims. Also 70% Democrats oppose it, where from Republicans it’s only 20%.
(b) Visualization: Stacked barchart
Immigrants_Data %>%
group_by(Political_Party, Banning_Muslims) %>%
summarize(n=n()) %>%
mutate(Percent = n/sum(n)) %>%
ggplot()+
geom_col(aes(x = Political_Party, y = Percent, fill = Banning_Muslims))## `summarise()` regrouping output by 'Political_Party' (override with `.groups` argument)
Interpretation:
From the visualization, it’s clearing showing that most Democrats want to oppose Muslims banning where most Republicans in favor of banning Muslims.
(c) Statistical Test: Chi-square test
options(scipen = 999)
chisq.test(Immigrants_Data$Banning_Muslims, Immigrants_Data$Political_Party)##
## Pearson's Chi-squared test
##
## data: Immigrants_Data$Banning_Muslims and Immigrants_Data$Political_Party
## X-squared = 585.46, df = 4, p-value < 0.00000000000000022Interpretation:
So, there is a statistically significant relationship between Political_Party, and their thinking on banning Muslim immigrants.
6. Analysis: Political_Party (IV) x Feeling_About_Immigrants (DV)
Analysing Political_Party and Feeling_About_Immigrants from different views.
(a) Table comparing means
Immigrants_Data %>%
group_by(Political_Party) %>%
summarise(Average_Feeling_About_Immigrants = mean(Feeling_About_Immigrants))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Political_Party Average_Feeling_About_Immigrants
## <chr> <dbl>
## 1 Democrat 70.0
## 2 Republican 52.7Interpretation:
From the table, it’s cleary showing Democrats average feeling toward immigrants are 18% more than Republicans.
(b) Visualization: Bar chart comparing means.
Immigrants_Data %>%
group_by(Political_Party) %>%
summarise(Average_Feeling_About_Immigrants = mean(Feeling_About_Immigrants)) %>%
ggplot()+
geom_col(aes(x = Political_Party, y = Average_Feeling_About_Immigrants, fill = Political_Party))## `summarise()` ungrouping output (override with `.groups` argument)
Interpretation:
From the visualization, it’s clearing showing that Democrats feel more favorable towards immigrtant compared to Republicans.
(c) Visualization: Histogram comparing population distributions
Immigrants_Data %>%
ggplot()+
geom_histogram(aes(x = Feeling_About_Immigrants)) +
facet_wrap(~Political_Party)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Interpretation:
Democrat has more density in the range of (50-100) where Republican has more density in the range of (0-50) when asked about their feeling towards immigrants. Moreover, Republican gave score of 50 than Democrat but Democrat gave score of 80, 90 and 100 more than Republic.
(d) Visualization: Histogram comparing sampling distributions
Democrat_Feeling_About_Immigrants = Immigrants_Data %>%
filter(Political_Party == "Democrat")
Republican_Feeling_About_Immigrants = Immigrants_Data %>%
filter(Political_Party == "Republican")
Democrat_Sample_Dist = replicate(10000, sample(Democrat_Feeling_About_Immigrants$Feeling_About_Immigrants, 40) %>%
mean(na.rm = TRUE)) %>%
data.frame() %>%
rename("mean" = 1)
Republican_Sample_Dist = replicate(10000, sample(Republican_Feeling_About_Immigrants$Feeling_About_Immigrants, 40) %>%
mean(na.rm = TRUE)) %>%
data.frame() %>%
rename("mean" = 1)
ggplot()+
geom_histogram(data = Democrat_Sample_Dist, aes(x = mean), fill = "red") +
geom_histogram(data = Republican_Sample_Dist, 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`.
Interpretation:
It’s clearly showing here that, Republicans range of feeling toward immigrants are (40-65) where for Democrates it’s (65-80).
(e) Statistical Test: T-test
options(scipen = 999)
t.test(Feeling_About_Immigrants~Political_Party, data = Immigrants_Data )##
## Welch Two Sample t-test
##
## data: Feeling_About_Immigrants by Political_Party
## t = 15.007, df = 1867.8, p-value < 0.00000000000000022
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 15.02043 19.53651
## sample estimates:
## mean in group Democrat mean in group Republican
## 70.00456 52.72609Interpretation:
There is a statistically significant difference between Republican & Democrates in their rating (0-100) of feeling towards immigrants.
7. Conclusions
After analysing both polital parties based on 4 different variables we end up with the conclusion that, “Democrats attitude/behavior is more positive and favorable towards immigrants”. Since Democrats are favoring more to provide legal way for immigrants, Democrats want the process of immigratition should be easier, and they oppose banning of Muslims. Moreover, Democrats’ feeling toward immigrants is much more than Republicans.