Data 607 Project 2
Jiaxin Zheng
2024-10-12
library(knitr)
library(stringr)
library(tidyr)
library(dplyr)##
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)Data 1 First data is about the World Happiness Report in 2020. This data includes the Happiness Score for 153 countries. The Happiness Score is responses to the main life evaluation question asked in the Gallup World Poll(GWP). The Happiness Score is explained by the following factors: GDP per capita, Healthy Life Expectancy, Social support, Freedom to make life choices, Generosity, Corruption Perception, Residual error.
#view data 1
file1 <- "https://raw.githubusercontent.com/Jennyjjxxzz/Data-607_Project2/refs/heads/main/wide_data/World%20Happiness%20Report_20_DataForFigure2.1.csv"
df1 <- read.csv(file1)
head(df1)## Country.name Regional.indicator Ladder.score Standard.error.of.ladder.score
## 1 Finland Western Europe 7.8087 0.03115630
## 2 Denmark Western Europe 7.6456 0.03349229
## 3 Switzerland Western Europe 7.5599 0.03501417
## 4 Iceland Western Europe 7.5045 0.05961586
## 5 Norway Western Europe 7.4880 0.03483738
## 6 Netherlands Western Europe 7.4489 0.02779175
## upperwhisker lowerwhisker Logged.GDP.per.capita Social.support
## 1 7.869766 7.747634 10.63927 0.9543297
## 2 7.711245 7.579955 10.77400 0.9559908
## 3 7.628528 7.491272 10.97993 0.9428466
## 4 7.621347 7.387653 10.77256 0.9746696
## 5 7.556281 7.419719 11.08780 0.9524866
## 6 7.503372 7.394428 10.81271 0.9391388
## Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1 71.90083 0.9491722 -0.05948202
## 2 72.40250 0.9514443 0.06620178
## 3 74.10245 0.9213367 0.10591104
## 4 73.00000 0.9488919 0.24694422
## 5 73.20078 0.9557503 0.13453263
## 6 72.30092 0.9085478 0.20761244
## Perceptions.of.corruption Ladder.score.in.Dystopia
## 1 0.1954446 1.972317
## 2 0.1684895 1.972317
## 3 0.3037284 1.972317
## 4 0.7117097 1.972317
## 5 0.2632182 1.972317
## 6 0.3647171 1.972317
## Explained.by..Log.GDP.per.capita Explained.by..Social.support
## 1 1.285190 1.499526
## 2 1.326949 1.503449
## 3 1.390774 1.472403
## 4 1.326502 1.547567
## 5 1.424207 1.495173
## 6 1.338946 1.463646
## Explained.by..Healthy.life.expectancy
## 1 0.9612714
## 2 0.9793326
## 3 1.0405332
## 4 1.0008434
## 5 1.0080719
## 6 0.9756753
## Explained.by..Freedom.to.make.life.choices Explained.by..Generosity
## 1 0.6623167 0.1596704
## 2 0.6650399 0.2427934
## 3 0.6289545 0.2690558
## 4 0.6619807 0.3623302
## 5 0.6702009 0.2879851
## 6 0.6136265 0.3363176
## Explained.by..Perceptions.of.corruption Dystopia...residual
## 1 0.4778573 2.762835
## 2 0.4952603 2.432741
## 3 0.4079459 2.350267
## 4 0.1445408 2.460688
## 5 0.4341006 2.168266
## 6 0.3685698 2.352117Question_1: Which region has the highest average happiness (Ladder score)?
#group by and plot the average
region_happiness <- df1 %>%
group_by(Regional.indicator) %>%
summarize(avg_ladder_score = mean(Ladder.score, na.rm = TRUE)) %>%
arrange(desc(avg_ladder_score))ggplot(region_happiness, aes(x = reorder(Regional.indicator, avg_ladder_score), y = avg_ladder_score)) +
geom_bar(stat = "identity", fill = "skyblue") +
coord_flip() +
labs(title = "Average Happiness (Ladder Score) by Region", x = "Region", y = "Average Ladder Score")+
theme_bw()
Question_2: Relationship between Ladder score and GDP, social
support, and life expectancy
# Ladder score correlation analysis
ladder_score_correlations <- df1 %>%
select(Ladder.score, Logged.GDP.per.capita, Social.support, Healthy.life.expectancy) %>%
cor(use = "complete.obs")
print(ladder_score_correlations)## Ladder.score Logged.GDP.per.capita Social.support
## Ladder.score 1.0000000 0.7753744 0.7650008
## Logged.GDP.per.capita 0.7753744 1.0000000 0.7818136
## Social.support 0.7650008 0.7818136 1.0000000
## Healthy.life.expectancy 0.7703163 0.8484686 0.7427441
## Healthy.life.expectancy
## Ladder.score 0.7703163
## Logged.GDP.per.capita 0.8484686
## Social.support 0.7427441
## Healthy.life.expectancy 1.0000000# Plot relationship between Ladder score and GDP per capita
ggplot(df1, aes(x = Logged.GDP.per.capita, y = Ladder.score)) +
geom_point(color = "blue") +
geom_smooth(method = "lm", col = "red") +
labs(title = "Relationship between GDP per capita and Happiness", x = "Logged GDP per capita", y = "Happiness (Ladder Score)")+
geom_smooth(method = lm)+
theme_bw()## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
# Plot relationship between Ladder score and Social support
ggplot(df1, aes(x = Social.support, y = Ladder.score)) +
geom_point(color = "green") +
geom_smooth(method = "lm", col = "red") +
labs(title = "Relationship between Social Support and Happiness", x = "Social Support", y = "Happiness (Ladder Score)")+
geom_smooth(method = lm)+
theme_bw()## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
# Plot relationship between Ladder score and Life expectancy
ggplot(df1, aes(x = Healthy.life.expectancy, y = Ladder.score)) +
geom_point(color = "orange") +
geom_smooth(method = "lm", col = "red") +
labs(title = "Relationship between Life Expectancy and Happiness", x = "Healthy Life Expectancy", y = "Happiness (Ladder Score)")+
geom_smooth(method = lm)+
theme_bw()## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
Result for data 1: The North America and ANZ region ranks as the
happiest region globally base on the highest average Ladder Score. Also
the data shows a strong relationship between social support. Supported
by their community elevated the happiness level.
Data 2 Second data show the ranking of the best universities of the world make by The Times Higher Education for 2020. The data frame consists of Rank char, Score Rank, University name, Country… .
#view data 2
file2 <- "https://raw.githubusercontent.com/Jennyjjxxzz/Data-607_Project2/refs/heads/main/wide_data/Word_University_Rank_2020.csv"
df2 <- read.csv(file2)
head(df2)## Rank_Char Score_Rank University Country
## 1 1 1 University of Oxford United Kingdom
## 2 2 2 California Institute of Technology United States
## 3 3 3 University of Cambridge United Kingdom
## 4 4 4 Stanford University United States
## 5 5 5 Massachusetts Institute of Technology United States
## 6 6 6 Princeton University United States
## Number_students Numb_students_per_Staff International_Students
## 1 20,664 11.2 41%
## 2 2,240 6.4 30%
## 3 18,978 10.9 37%
## 4 16,135 7.3 23%
## 5 11,247 8.6 34%
## 6 7,983 8.1 25%
## Percentage_Female Percentage_Male Teaching Research Citations Industry_Income
## 1 46% 54% 90.5 99.6 98.4 65.5
## 2 34% 66% 92.1 97.2 97.9 88.0
## 3 47% 53% 91.4 98.7 95.8 59.3
## 4 43% 57% 92.8 96.4 99.9 66.2
## 5 39% 61% 90.5 92.4 99.5 86.9
## 6 45% 55% 90.3 96.3 98.8 58.6
## International_Outlook Score_Result Overall_Ranking
## 1 96.4 95.4 95.40
## 2 82.5 94.5 94.50
## 3 95.0 94.4 94.40
## 4 79.5 94.3 94.30
## 5 89.0 93.6 93.60
## 6 81.1 93.2 93.20Question_1: Which country has the most universities in the top 100?
#filter the universities ranked in the top 100
df_top100 <- df2 %>%
filter(as.numeric(Rank_Char) <= 100)## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `as.numeric(Rank_Char) <= 100`.
## Caused by warning:
## ! NAs introduced by coercioncountry_top100 <- df_top100 %>%
group_by(Country) %>%
summarize(university_count = n()) %>%
arrange(desc(university_count))
print(country_top100)## # A tibble: 16 × 2
## Country university_count
## <chr> <int>
## 1 United States 40
## 2 United Kingdom 11
## 3 Germany 8
## 4 Netherlands 7
## 5 Australia 6
## 6 Canada 5
## 7 Switzerland 4
## 8 China 3
## 9 France 3
## 10 Hong Kong 3
## 11 Japan 2
## 12 Singapore 2
## 13 South Korea 2
## 14 Sweden 2
## 15 Belgium 1
## 16 Finland 1ggplot(country_top100, aes(x = reorder(Country, university_count), y = university_count)) +
geom_bar(stat = "identity", fill = "steelblue") +
coord_flip() +
labs(title = "Number of Universities in the Top 100 by Country",
x = "Country",
y = "Number of Universities")+
theme_minimal()
Answer for question_1: The United States has most number of
universities in rank of top 100.
Data 3 Third data is about the population data from 2019 US Census, and also includes latitude and longitude data for each state’s capital city.
#view data 3
file3 <- "https://raw.githubusercontent.com/Jennyjjxxzz/Data-607_Project2/refs/heads/main/wide_data/2019_Census_US_Population_Data_By_State_Lat_Long.csv"
df3 <- read.csv(file3)
head(df3)## STATE POPESTIMATE2019 lat long
## 1 Alabama 4903185 32.37772 -86.30057
## 2 Alaska 731545 58.30160 -134.42021
## 3 Arizona 7278717 33.44814 -112.09696
## 4 Arkansas 3017804 34.74661 -92.28899
## 5 California 39512223 38.57667 -121.49363
## 6 Colorado 5758736 39.73923 -104.98486Question_1: Which states have the highest and lowest population estimates in 2019?
#The state with the highest population in 2019
highest_population_state <- df3 %>%
arrange(desc(POPESTIMATE2019)) %>%
slice(1)
print(highest_population_state)## STATE POPESTIMATE2019 lat long
## 1 California 39512223 38.57667 -121.4936#The state with the lowest population in 2019
lowest_population_state <- df3 %>%
arrange(POPESTIMATE2019) %>%
slice(1)
print(lowest_population_state)## STATE POPESTIMATE2019 lat long
## 1 Wyoming 578759 41.14026 -104.8202