PS3115 Fall 2024 Optional Lab Assignment
(Lance Skelton)
(11/26/24)
This Document
This document contains a set of tasks that you need to do to complete the optional lab assignment. The lab assignment is focused on analyzing data related to the domestic politics and foreign policies of the United States since 1945. Successfully completing this assignment will require you to implement most of the techniques we’ve covered in the lab, including various descriptive statistics, creating new variables based on existing variables, creating numeric variables based on categoric variables and vice-versa, visualizing variables on their own and in terms of other variables, and statistically analyzing bivariate relationships.
Two things are important to note about this lab assignment. First, this lab is completely optional. However, if you complete the lab, I will replace your lowest grade of the semester with the grade you get on this lab if your grade on this lab is higher than your previous lowest lab grade. If your grade on this lab assign is worse than all of your other lab assignments, I will not replace your previous lowest grade with your grade on this lab assignment. Framed differently, completing this lab assignment can potentially raise your grade in the lab but it cannot hurt your grade.
Second, this is the only RStudio/RMarkdown file associated with the optional lab. The reason for this is that everything you need to know to complete the tasks in the assignment have been covered in the previous three lab assignments. Accordingly, there are no additional documents that provide you with information about the lab assignment and/or the code needed to complete the lab assignment.
Add your Name and the Date
Add your name and date in the lines underneath this document’s title.
Set Your Working Directory
You need to set your working directory in this section.
setwd("~/Documents/OptionalLab")Loading Packages
You’ll need to load the following packages to complete the lab assignment.
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, unionlibrary("tidyverse")## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ ggplot2 3.5.1 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary("openxlsx")
library("forcats")
library("corrr")
library("janitor")##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testLoading Data Set
You need to load the data set you’ll use for the lab assignment in this section.
read.xlsx("~/Documents/OptionalLab/OptionalAssignmentData.xlsx")## year rep.ideology dem.ideology polarization president.party divided war
## 1 1945 0.3034564 -0.2646040 0.5680604 democrat 0 1
## 2 1946 0.3034564 -0.2646040 0.5680604 democrat 0 0
## 3 1947 0.2853439 -0.2373247 0.5226686 democrat 1 0
## 4 1948 0.2853439 -0.2373247 0.5226686 democrat 1 0
## 5 1949 0.2770057 -0.2755693 0.5525750 democrat 1 0
## 6 1950 0.2770057 -0.2755693 0.5525750 democrat 0 1
## 7 1951 0.2800193 -0.2623333 0.5423527 democrat 0 1
## 8 1952 0.2800193 -0.2623333 0.5423527 democrat 0 1
## 9 1953 0.2776577 -0.2533440 0.5310017 republican 0 1
## 10 1954 0.2776577 -0.2533440 0.5310017 republican 0 0
## 11 1955 0.2772512 -0.2790979 0.5563491 republican 1 0
## 12 1956 0.2772512 -0.2790979 0.5563491 republican 1 0
## 13 1957 0.2682206 -0.2827792 0.5509998 republican 1 0
## 14 1958 0.2682206 -0.2827792 0.5509998 republican 1 0
## 15 1959 0.2667179 -0.2957098 0.5624277 republican 1 0
## 16 1960 0.2667179 -0.2957098 0.5624277 republican 1 0
## 17 1961 0.2623693 -0.2772857 0.5396550 democrat 0 0
## 18 1962 0.2623693 -0.2772857 0.5396550 democrat 0 0
## 19 1963 0.2560166 -0.2920458 0.5480624 democrat 0 0
## 20 1964 0.2560166 -0.2920458 0.5480624 democrat 0 1
## 21 1965 0.2458252 -0.3007641 0.5465893 democrat 0 1
## 22 1966 0.2458252 -0.3007641 0.5465893 democrat 0 1
## 23 1967 0.2430265 -0.2955200 0.5385465 democrat 0 1
## 24 1968 0.2430265 -0.2955200 0.5385465 democrat 0 1
## 25 1969 0.2546231 -0.3097680 0.5643911 republican 1 1
## 26 1970 0.2546231 -0.3097680 0.5643911 republican 1 1
## 27 1971 0.2544000 -0.3105483 0.5649483 republican 1 1
## 28 1972 0.2544000 -0.3105483 0.5649483 republican 1 1
## 29 1973 0.2609436 -0.3234675 0.5844111 republican 1 1
## 30 1974 0.2609436 -0.3234675 0.5844111 republican 1 0
## 31 1975 0.2650136 -0.3152891 0.5803027 republican 1 0
## 32 1976 0.2650136 -0.3152891 0.5803027 republican 1 0
## 33 1977 0.2671497 -0.3059048 0.5730544 democrat 0 0
## 34 1977 0.2671497 -0.3059048 0.5730544 democrat 0 0
## 35 1978 0.2671497 -0.3059048 0.5730544 democrat 0 0
## 36 1979 0.2947826 -0.3013238 0.5961065 democrat 0 0
## 37 1980 0.2947826 -0.3013238 0.5961065 democrat 0 0
## 38 1981 0.3065051 -0.3004008 0.6069059 republican 1 0
## 39 1982 0.3065051 -0.3004008 0.6069059 republican 1 0
## 40 1983 0.3256527 -0.3017868 0.6274395 republican 1 0
## 41 1984 0.3256527 -0.3017868 0.6274395 republican 1 0
## 42 1985 0.3337637 -0.3095292 0.6432929 republican 1 0
## 43 1986 0.3337637 -0.3095292 0.6432929 republican 1 0
## 44 1987 0.3353128 -0.3093168 0.6446296 republican 1 0
## 45 1988 0.3353128 -0.3093168 0.6446296 republican 1 0
## 46 1989 0.3396167 -0.3130377 0.6526544 republican 1 0
## 47 1990 0.3396167 -0.3130377 0.6526544 republican 1 1
## 48 1991 0.3447059 -0.3142185 0.6589244 republican 1 1
## 49 1992 0.3447059 -0.3142185 0.6589244 republican 1 0
## 50 1993 0.3709333 -0.3338893 0.7048226 democrat 0 0
## 51 1994 0.3709333 -0.3338893 0.7048226 democrat 0 0
## 52 1995 0.3965678 -0.3613702 0.7579380 democrat 1 0
## 53 1996 0.3965678 -0.3613702 0.7579380 democrat 1 0
## 54 1997 0.4015281 -0.3755660 0.7770942 democrat 1 1
## 55 1998 0.4015281 -0.3755660 0.7770942 democrat 1 1
## 56 1999 0.4040000 -0.3728310 0.7768310 democrat 1 1
## 57 2000 0.4040000 -0.3728310 0.7768310 democrat 1 1
## 58 2001 0.4096140 -0.3767710 0.7863851 republican 1 1
## 59 2002 0.4096140 -0.3767710 0.7863851 republican 1 1
## 60 2003 0.4167229 -0.3762212 0.7929441 republican 0 1
## 61 2004 0.4167229 -0.3762212 0.7929441 republican 0 0
## 62 2005 0.4227532 -0.3871626 0.8099158 republican 0 0
## 63 2006 0.4227532 -0.3871626 0.8099158 republican 0 0
## 64 2007 0.4394951 -0.3684033 0.8078984 republican 1 0
## 65 2008 0.4394951 -0.3684033 0.8078984 republican 1 0
## 66 2009 0.4564590 -0.3488792 0.8053383 democrat 0 0
## 67 2010 0.4564590 -0.3488792 0.8053383 democrat 0 0
## 68 2011 0.4675224 -0.3932000 0.8607224 democrat 1 0
## 69 2012 0.4675224 -0.3932000 0.8607224 democrat 1 0
## 70 2013 0.4820167 -0.3842892 0.8663059 democrat 1 0
## 71 2014 0.4820167 -0.3842892 0.8663059 democrat 1 0
## 72 2015 0.4802709 -0.3945895 0.8748604 democrat 1 0
## 73 2016 0.4802709 -0.3945895 0.8748604 democrat 1 0
## 74 2017 0.4891440 -0.3888150 0.8779590 republican 0 0
## 75 2018 0.4891440 -0.3888150 0.8779590 republican 0 0
## 76 2019 0.5003077 -0.3710958 0.8714035 republican 1 0
## 77 2020 0.5003077 -0.3710958 0.8714035 republican 1 0
## 78 2021 0.5025291 -0.3792069 0.8817360 democrat 0 0
## 79 2022 0.5025291 -0.3792069 0.8817360 democrat 0 0
## 80 2023 0.5081354 -0.3841050 0.8922404 democrat 1 0
## dissimilar.foreign.policy defense.burden gdp econ.percent.growth
## 1 0.1938571 36.775460 2.598616e+12 NA
## 2 0.6026000 19.388044 2.479307e+12 -4.59126024
## 3 0.6086000 6.286465 2.430213e+12 -1.98013267
## 4 0.6302000 4.689680 2.494227e+12 2.63409485
## 5 0.6210000 5.858731 2.466941e+12 -1.09397212
## 6 0.6641667 6.063156 2.562489e+12 3.87312329
## 7 0.7608333 13.020397 2.729119e+12 6.50268392
## 8 0.7723333 18.013726 2.823505e+12 3.45846067
## 9 0.7825000 17.644699 2.986132e+12 5.75976837
## 10 0.7143333 15.123231 3.001100e+12 0.50125209
## 11 0.7178333 13.842830 3.104892e+12 3.45846067
## 12 0.6866667 13.753200 3.218704e+12 3.66558465
## 13 0.6675000 14.219132 3.316728e+12 3.04545340
## 14 0.6673333 14.538489 3.313413e+12 -0.09995002
## 15 0.6716667 13.900462 3.543018e+12 6.92954782
## 16 0.5685000 13.317682 3.600162e+12 1.61286854
## 17 0.5538333 13.453161 3.747088e+12 4.08107742
## 18 0.5056667 13.909405 3.962911e+12 5.75976837
## 19 0.4833333 13.248999 4.149463e+12 4.70744110
## 20 0.4716667 12.243739 4.388462e+12 5.75976837
## 21 0.4783333 11.727453 4.627325e+12 5.44296451
## 22 0.4635000 14.443071 4.888956e+12 5.65406147
## 23 0.4856667 15.432332 5.103767e+12 4.39378949
## 24 0.4736667 15.770737 5.333346e+12 4.49823549
## 25 0.4836667 15.378360 5.512283e+12 3.35505392
## 26 0.4791667 14.592528 5.551004e+12 0.70245573
## 27 0.4580000 13.635375 5.714340e+12 2.94245945
## 28 0.4566667 13.491920 5.977359e+12 4.60278599
## 29 0.4576667 13.061301 6.227524e+12 4.18521055
## 30 0.4511667 13.836012 6.436461e+12 3.35505392
## 31 0.4453333 14.604198 6.455800e+12 0.30045045
## 32 0.4425000 14.168532 6.652408e+12 3.04545340
## 33 0.4488333 14.990232 6.972535e+12 4.81220091
## 34 0.4488333 14.990232 6.972535e+12 0.00000000
## 35 0.4775000 15.605642 7.235351e+12 3.76930208
## 36 0.4895000 16.648605 7.583531e+12 4.81220091
## 37 0.5010000 19.466644 7.636802e+12 0.70245573
## 38 0.5000000 22.469538 7.806673e+12 2.22437845
## 39 0.5131667 26.361232 7.698141e+12 -1.39024557
## 40 0.5101667 27.905687 8.028349e+12 4.28944788
## 41 0.5175000 28.654218 8.524794e+12 6.18365465
## 42 0.5175000 28.357958 8.899356e+12 4.39378949
## 43 0.5175000 29.712286 9.197934e+12 3.35505392
## 44 0.5050000 29.371509 9.592474e+12 4.28944788
## 45 0.5050000 28.838107 1.003400e+13 4.60278599
## 46 0.4408333 29.683449 1.020603e+13 1.71453223
## 47 0.4205000 28.390565 1.047487e+13 2.63409485
## 48 0.4253750 26.045611 1.033958e+13 -1.29158650
## 49 0.4228750 27.197552 1.082631e+13 4.70744110
## 50 0.4115000 27.598776 1.105606e+13 2.12220516
## 51 0.4092500 26.256769 1.144988e+13 3.56197088
## 52 0.4120000 23.951835 1.188146e+13 3.76930208
## 53 0.4113750 22.827254 1.244077e+13 4.70744110
## 54 0.4113750 21.880486 1.292262e+13 3.87312329
## 55 0.4113750 21.004693 1.361237e+13 5.33757425
## 56 0.4190000 21.227090 1.405501e+13 3.25175053
## 57 0.4176250 21.204272 1.458479e+13 3.76930208
## 58 0.4176250 22.036619 1.493905e+13 2.42903179
## 59 0.4150000 23.946380 1.486455e+13 -0.49875208
## 60 0.4150000 26.781481 1.542484e+13 3.76930208
## 61 0.4143750 29.174999 1.594235e+13 3.35505392
## 62 0.4143750 30.424167 1.659297e+13 4.08107742
## 63 0.4122500 31.386634 1.694510e+13 2.12220516
## 64 0.4122500 32.707278 1.720120e+13 1.51130646
## 65 0.4101250 36.248086 1.732203e+13 0.70245573
## 66 0.4108750 39.305352 1.718400e+13 -0.79680852
## 67 0.4108750 40.850397 1.730471e+13 0.70245573
## 68 0.4086250 39.581246 1.768964e+13 2.22437845
## 69 0.4085000 37.197760 1.795698e+13 1.51130646
## 70 NA 33.003619 1.854090e+13 3.25175053
## 71 NA 31.179844 1.897228e+13 2.32665395
## 72 NA 32.001425 1.901026e+13 0.20020013
## 73 NA NA NA NA
## 74 NA NA NA NA
## 75 NA NA NA NA
## 76 NA NA NA NA
## 77 NA NA NA NA
## 78 NA NA NA NA
## 79 NA NA NA NA
## 80 NA NA NA NA
## foreign.aid.percent
## 1 NA
## 2 0.33141427
## 3 0.05735751
## 4 0.08298984
## 5 0.06938981
## 6 0.03875136
## 7 0.03881649
## 8 0.06438465
## 9 0.08059470
## 10 0.06453518
## 11 0.07254750
## 12 0.08859813
## 13 0.09819885
## 14 0.07581042
## 15 0.13043124
## 16 0.08427406
## 17 0.10678153
## 18 0.11600428
## 19 0.12899344
## 20 0.08203374
## 21 0.10527884
## 22 0.10401764
## 23 0.13340083
## 24 0.09536614
## 25 0.09510187
## 26 0.08979995
## 27 0.05861356
## 28 0.05508882
## 29 0.10186727
## 30 0.09875081
## 31 0.09533907
## 32 0.04348905
## 33 0.03097931
## 34 0.09296907
## 35 0.10572214
## 36 0.11437631
## 37 0.10819735
## 38 0.08442568
## 39 0.09219788
## 40 0.08815590
## 41 0.08150589
## 42 0.09121489
## 43 0.06693787
## 44 0.05567689
## 45 0.06125100
## 46 0.06176950
## 47 0.06776508
## 48 0.07166684
## 49 0.06330642
## 50 0.05656860
## 51 0.05238960
## 52 0.05741737
## 53 0.04290701
## 54 0.03531344
## 55 0.03655715
## 56 0.03464265
## 57 0.02128918
## 58 0.02782833
## 59 0.03452385
## 60 0.02824071
## 61 0.05054988
## 62 0.04782374
## 63 0.04055921
## 64 0.04460680
## 65 0.04625167
## 66 0.05282596
## 67 0.05763035
## 68 0.05667333
## 69 0.06475394
## 70 0.06210703
## 71 0.06297460
## 72 0.06005571
## 73 NA
## 74 NA
## 75 NA
## 76 NA
## 77 NA
## 78 NA
## 79 NA
## 80 NAIdentify the Variables in the data set
You need to identify the names of the variables in the data set for the assignment in this section. You should consult the codebook in the “OptionalLabMaterials” folder for information about what the variables are measuring/capturing.
OptionalData <- read.xlsx("~/Documents/OptionalLab/OptionalAssignmentData.xlsx")
names(OptionalData)## [1] "year" "rep.ideology"
## [3] "dem.ideology" "polarization"
## [5] "president.party" "divided"
## [7] "war" "dissimilar.foreign.policy"
## [9] "defense.burden" "gdp"
## [11] "econ.percent.growth" "foreign.aid.percent"Descriptive Statistics
You need to report a set of descriptive statistics in this section. First, identify the means of variables that identify 1) how much of its available economic resources the United States spends on its military; 2) how much of its available economic resources the United States spends on foreign aid; and 3) annual economic growth.
mean(OptionalData$defense.burden, na.rm = TRUE)## [1] 21.33192mean(OptionalData$foreign.aid.percent, na.rm = TRUE)## [1] 0.07465675mean(OptionalData$econ.percent.growth, na.rm = TRUE)## [1] 2.866975Second, you need to identify the median of variables that identify: 1) whether the United States had divided government and 2) whether the United States was involved in an interstate war.
median(OptionalData$divided, na.rm = TRUE)## [1] 1median(OptionalData$divided, na.rm = TRUE)## [1] 1Third, you need to identify the minimum, maximum, and range of variables that identify 1) the average ideology of Republican members of the House; 2) the average ideology of Democratic members of the House; and 3) the average polarization of the two parties in the House.
min(OptionalData$rep.ideology, na.rm = TRUE)## [1] 0.2430265max(OptionalData$rep.ideology, na.rm = TRUE)## [1] 0.5081354range(OptionalData$rep.ideology, na.rm = TRUE)## [1] 0.2430265 0.5081354min(OptionalData$dem.ideology, na.rm = TRUE)## [1] -0.3945895max(OptionalData$dem.ideology, na.rm = TRUE)## [1] -0.2373247range(OptionalData$dem.ideology, na.rm = TRUE)## [1] -0.3945895 -0.2373247min(OptionalData$polarization, na.rm = TRUE)## [1] 0.5226686max(OptionalData$polarization, na.rm = TRUE)## [1] 0.8922404range(OptionalData$polarization, na.rm = TRUE)## [1] 0.5226686 0.8922404Visualizing Variables
You need to create a set of graphs in this section. First, create a graph that plots the total number of years the United States was involved in an interstate war and the number of years that the United States was not involved in an interstate war. When making this graph, make sure to identify the total number of years the United States was and was not involved in an interstate war.
OptionalData <- OptionalData %>%
mutate(war.label = ifelse(war == 1, "war", "no war"))
ggplot(data = OptionalData, mapping = aes(x = war.label)) +
geom_bar() +
geom_text(stat = 'count', aes(label = ..count..), vjust = -0.5) + # Display counts on bars
guides(fill = guide_legend("Number of Years the U.S. was and was not Involved in war")) +
theme_void() + scale_x_discrete(labels = c("War", "No War"))## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Second, create a single graph that plots the percentage of years that
the United States was involved in an interstate war and the percentage
of years that the United States was not involved in an interstate war.
This graph should identify the percentage of total years the United
States was and was not involved in a war on the graph.
OptionalData %>% count(war.label) -> OptionalData2
head(OptionalData2)## war.label n
## 1 no war 56
## 2 war 24ggplot(OptionalData2, aes(x="",y=n,fill = war.label)) + geom_bar(stat = "identity",width=1,color="white") +coord_polar("y",start = 0) + guides(fill=guide_legend("Years with and Without War"))+theme_void()
Third, you need to graph the distributions of variables that identify 1)
how much of its available economic resources the United States spends on
its military and 2) how much of its available economic resources the
United States spends on foreign aid. Both of these distributions should
be filled in with the color “red.”
ggplot(OptionalData, # data argument
aes(x = foreign.aid.percent)) + # mapping and aesthetics arguments
geom_histogram(fill = "red", bins = 60) + # specifies bins
labs(x = "Foreign Aid Percentage", y = "Count") +
theme(panel.grid.major = element_blank(), # getting rid of grid lines and background color
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line()) ## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_bin()`).
ggplot(OptionalData, # data argument
aes(x = defense.burden)) + # mapping and aesthetics arguments
geom_histogram(fill = "red", # geom argument, specifies color
bins = 60) + # specifies bins
labs(x = "Defense Budget", # changing labels
y = "Count") +
theme(panel.grid.major = element_blank(), # getting rid of grid lines and background color
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line())## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_bin()`).
Finally, all of the graphs in this section should look “clean” and
professional. Specifically, they should 1) not have extraneous lines,
colors, and/or components in the background and 2) have descriptive axis
labels, not the names of the variables. Details about these points are
provided in previous lab assignments.
Visualizing Bivariate Relationships
You need to create a set of graphs that allow you to visualize bivariate relationships.
First, create a set of graphs that visualize 1) the average ideology of Republican members of the House over the years in the data set; 2) the average ideology of Democratic members of the House over the years in the data set; and 3) how much of its available economic resources the United States spends on foreign aid over annual economic growth.
ggplot(OptionalData, aes(x=year, y=rep.ideology)) + geom_point() + labs(title = "Republican Ideology Over the Years",x = "Year",y = "Republican Ideology")
ggplot(OptionalData, aes(x=year, y=dem.ideology)) + geom_point() + labs(title = "Democrat Ideology Over the Years",x = "Year",y = "Democrat Ideology")
ggplot(OptionalData, aes(x=econ.percent.growth, y=foreign.aid.percent)) + geom_point() + labs(title = "Foreign Aid Spending Percentage Over Annual Economic Growth Percentage",x = "Economic Growth",y = "Foreign Aid Spending")## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
Second, create a set of graphs that visualize 1) how much of its
available economic resources the United States spends on foreign aid
depending on whether or not the United States has a divided government;
2) annual economic growth depending on whether the president is a member
of the Democratic Party or the Republican Party; and 3) how much of its
available economic resources the United States spends on foreign aid
depending on whether or not the United States is involved in an
interstate war.
OptionalData <- OptionalData %>%
mutate(DividedGov = ifelse(divided == 1, 1, 0))
ggplot(OptionalData,aes(x=as.factor(DividedGov),y=foreign.aid.percent)) + geom_point(position = "jitter")## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(OptionalData,aes(x=as.factor(president.party),y=econ.percent.growth)) + geom_point(position = "jitter")## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(OptionalData,aes(x=as.factor(war),y=foreign.aid.percent)) + geom_point(position = "jitter")## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
Third, create a contingency table between variables that identify
whether or not the United States is involved in an interstate war and
whether or not the United States has divided government.
OptionalData %>% mutate(Divided.label=NA) %>% mutate(Divided.label=replace(Divided.label,divided==1,"Divided")) %>% mutate(Divided.label=replace(Divided.label,divided==0,"Unified")) ->OptionalData
OptionalData %>% tabyl(war.label, Divided.label) %>% adorn_title(row_name = "War Status",col_name = "Divided Government")## Divided Government
## War Status Divided Unified
## no war 35 21
## war 13 11Statistically Analyzing Bivariate Relationships
You need to statistically analyze a set of bivariate relationships in this section.
First, identify the bivariate correlation between variables that identify 1) how much of its available economic resources the United States spends on its military and how much of its available economic resources the United States spends on foreign aid; 2) how much of its available economic resources the United States spends on its military and how dissimilar the United States’ foreign policy preferences are from its neighbors and other major powers; and 3) polarization and economic growth.
OptionalData %>% select(defense.burden,foreign.aid.percent)%>% correlate()## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'## # A tibble: 2 × 3
## term defense.burden foreign.aid.percent
## <chr> <dbl> <dbl>
## 1 defense.burden NA -0.269
## 2 foreign.aid.percent -0.269 NAOptionalData %>% select(defense.burden,dissimilar.foreign.policy)%>% correlate()## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'## # A tibble: 2 × 3
## term defense.burden dissimilar.foreign.policy
## <chr> <dbl> <dbl>
## 1 defense.burden NA -0.558
## 2 dissimilar.foreign.policy -0.558 NAOptionalData %>% select(polarization,econ.percent.growth)%>% correlate()## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'## # A tibble: 2 × 3
## term polarization econ.percent.growth
## <chr> <dbl> <dbl>
## 1 polarization NA -0.151
## 2 econ.percent.growth -0.151 NASecond, identify whether we observe systematically different patterns of 1) economic growth depending on whether the president is a democrat or a republican; 2) how dissimilar the United States’ foreign policy preferences are from its neighbors and other major powers depending on whether the president is a democrat or a republican; and 3) how much of its available economic resources the United States spends on its military depending on whether polarization is greater than average in the United States.
t.test(econ.percent.growth ~ president.party, data = OptionalData)##
## Welch Two Sample t-test
##
## data: econ.percent.growth by president.party
## t = 0.27867, df = 65.061, p-value = 0.7814
## alternative hypothesis: true difference in means between group democrat and group republican is not equal to 0
## 95 percent confidence interval:
## -0.9259384 1.2262493
## sample estimates:
## mean in group democrat mean in group republican
## 2.943111 2.792955t.test(dissimilar.foreign.policy ~ president.party, data = OptionalData)##
## Welch Two Sample t-test
##
## data: dissimilar.foreign.policy by president.party
## t = -0.75003, df = 65.278, p-value = 0.4559
## alternative hypothesis: true difference in means between group democrat and group republican is not equal to 0
## 95 percent confidence interval:
## -0.07305188 0.03316048
## sample estimates:
## mean in group democrat mean in group republican
## 0.4871909 0.5071366OptionalData %>%
mutate(polarization = ifelse(polarization > 0.67103, "Above.Mean", "Below.Mean")) %>%
mutate(polarization = factor(polarization, levels = c("Below.Mean", "Above.Mean"))) -> OptionalData
t.test(defense.burden ~ polarization, data = OptionalData)##
## Welch Two Sample t-test
##
## data: defense.burden by polarization
## t = -6.7849, df = 48.578, p-value = 1.487e-08
## alternative hypothesis: true difference in means between group Below.Mean and group Above.Mean is not equal to 0
## 95 percent confidence interval:
## -15.000907 -8.144169
## sample estimates:
## mean in group Below.Mean mean in group Above.Mean
## 17.63513 29.20767Third, identify whether the following things are related to one another: 1) whether the United States is involved in a war and whether the president is a democrat or a republican and 2) whether the United States has divided government and whether the United States is involved in an interstate war.
chisq.test(OptionalData$war.label,OptionalData$president.party)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: OptionalData$war.label and OptionalData$president.party
## X-squared = 0.059524, df = 1, p-value = 0.8073chisq.test(OptionalData$Divided.label, OptionalData$war.label)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: OptionalData$Divided.label and OptionalData$war.label
## X-squared = 0.20089, df = 1, p-value = 0.654Publish Document
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