Mini Project 1
Kai Ostaszewski
2025-09-27
question 1
data("cars")median(cars$speed)## [1] 15question 2
# install.packages("jsonlite", dependencies = TRUE)
library(jsonlite)url <- "https://min-api.cryptocompare.com/data/v2/histoday?fsym=BTC&tsym=USD&limit=100&tryConversion=true"btc_data <- fromJSON(url) btc_df <- btc_data$Data$Datamax.close <- max(btc_df$close, na.rm = TRUE)
max.close## [1] 124723btc_df## time high low open volumefrom volumeto close
## 1 1752019200 112077.2 108341.9 108955.1 19761.34 2177951433 111291.7
## 2 1752105600 116848.3 110555.5 111291.7 31905.27 3616719328 116023.3
## 3 1752192000 118890.3 115236.7 116023.3 26050.27 3060911543 117573.9
## 4 1752278400 118240.0 116954.1 117573.9 7395.67 869899617 117468.2
## 5 1752364800 119503.6 117264.6 117468.2 9553.52 1133368268 119127.7
## 6 1752451200 123220.3 118951.6 119127.7 31361.70 3784774921 119869.0
## 7 1752537600 119958.9 115709.6 119869.0 50123.19 5881848281 117777.9
## 8 1752624000 120122.2 117043.9 117777.9 27837.68 3310853210 118700.4
## 9 1752710400 121020.9 117486.0 118700.4 25218.52 3000532789 119282.7
## 10 1752796800 120911.2 116902.9 119282.7 26111.51 3096339405 118025.9
## 11 1752883200 118572.5 117339.4 118025.9 6706.12 791893577 117913.3
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## 13 1753056000 119712.2 116581.7 117328.6 18543.72 2187836281 117441.8
## 14 1753142400 120298.6 116186.2 117441.8 21981.53 2607540535 120023.5
## 15 1753228800 120167.5 117361.9 120023.5 17163.70 2029832616 118808.8
## 16 1753315200 119555.9 117214.4 118808.8 17930.71 2128340081 118397.3
## 17 1753401600 118523.3 114756.4 118397.3 47767.94 5546818212 117644.1
## 18 1753488000 118355.1 117143.7 117644.1 10959.83 1291296799 117971.3
## 19 1753574400 119808.3 117872.1 117971.3 10341.68 1228024808 119468.7
## 20 1753660800 119835.4 117403.3 119468.7 18092.60 2142740462 118059.9
## 21 1753747200 119286.0 116933.7 118059.9 17580.06 2073820445 117942.1
## 22 1753833600 118800.1 115769.5 117942.1 18335.33 2153325010 117853.3
## 23 1753920000 118927.1 115493.8 117853.3 16934.89 1991848800 115765.4
## 24 1754006400 116060.7 112682.8 115765.4 33588.86 3851719229 113263.5
## 25 1754092800 114034.0 112006.4 113263.5 12452.57 1407609649 112546.3
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## 34 1754870400 122309.7 118106.9 119311.7 25659.91 3086540255 118714.6
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## 44 1755734400 114816.8 111993.8 114291.4 19947.31 2256735255 112491.8
## 45 1755820800 117428.1 111675.1 112491.8 28375.72 3261042644 116909.8
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## 47 1755993600 115626.3 110860.7 115391.8 17025.82 1932434686 113486.7
## 48 1756080000 113648.6 109292.2 113486.7 33305.33 3708998747 110150.1
## 49 1756166400 112407.5 108701.3 110150.1 27215.82 2999152225 111805.9
## 50 1756252800 112669.1 110381.6 111805.9 21850.84 2440706984 111277.0
## 51 1756339200 113487.6 110887.4 111277.0 16865.13 1898278606 112582.2
## 52 1756425600 112646.7 107512.4 112582.2 29122.98 3185827655 108393.1
## 53 1756512000 108937.2 107381.3 108393.1 9682.99 1049906248 108833.5
## 54 1756598400 109510.2 108095.9 108833.5 9099.54 990157825 108273.7
## 55 1756684800 109913.3 107271.4 108273.7 21783.85 2365084072 109256.3
## 56 1756771200 111796.2 108420.8 109256.3 27112.92 2993969176 111245.9
## 57 1756857600 112601.7 110556.5 111245.9 19087.45 2132239040 111751.5
## 58 1756944000 112225.9 109345.2 111751.5 19577.20 2162838408 110732.3
## 59 1757030400 113400.4 110217.3 110732.3 28328.28 3163045481 110677.0
## 60 1757116800 111320.3 110019.9 110677.0 6640.60 734143439 110230.2
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## 70 1757980800 117006.6 114765.7 115401.1 16305.16 1890323068 116838.3
## 71 1758067200 117331.1 114742.8 116838.3 23702.53 2750924038 116488.2
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## 101 1760659200 109278.9 103552.1 108212.4 33208.93 3521182277 105670.5
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## 101 directquestion 3 - research project (What Makes You Happy? Analyzing different countries of Life Satisfaction)
1. Do countries with higher social support report higher life satisfaction?
# install.packages("RKaggle")
library(RKaggle)# install.packages("tidyr")
library(tidyr)library(readr)# install.packages("httr")
library(httr)
# install.packages("readxl")
library(readxl)happiness <- read.csv("~/Downloads/archive 2/2019.csv") happiness_2 <- read.csv("~/Downloads/archive (1)/World-happiness-report-updated_2024.csv")colnames(happiness) <- c("Overall Rank", "Country/Region", "Life Satisfaction Score", "GDP per Capita", "Social Support", "Life Expectancy", "Freedom of Choice", "Generosity", "Perception of Corruption")# install.packages("dplyr")
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, unionglimpse(happiness)## Rows: 156
## Columns: 9
## $ `Overall Rank` <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, …
## $ `Country/Region` <chr> "Finland", "Denmark", "Norway", "Iceland", …
## $ `Life Satisfaction Score` <dbl> 7.769, 7.600, 7.554, 7.494, 7.488, 7.480, 7…
## $ `GDP per Capita` <dbl> 1.340, 1.383, 1.488, 1.380, 1.396, 1.452, 1…
## $ `Social Support` <dbl> 1.587, 1.573, 1.582, 1.624, 1.522, 1.526, 1…
## $ `Life Expectancy` <dbl> 0.986, 0.996, 1.028, 1.026, 0.999, 1.052, 1…
## $ `Freedom of Choice` <dbl> 0.596, 0.592, 0.603, 0.591, 0.557, 0.572, 0…
## $ Generosity <dbl> 0.153, 0.252, 0.271, 0.354, 0.322, 0.263, 0…
## $ `Perception of Corruption` <dbl> 0.393, 0.410, 0.341, 0.118, 0.298, 0.343, 0…summary(happiness)## Overall Rank Country/Region Life Satisfaction Score GDP per Capita
## Min. : 1.00 Length:156 Min. :2.853 Min. :0.0000
## 1st Qu.: 39.75 Class :character 1st Qu.:4.545 1st Qu.:0.6028
## Median : 78.50 Mode :character Median :5.380 Median :0.9600
## Mean : 78.50 Mean :5.407 Mean :0.9051
## 3rd Qu.:117.25 3rd Qu.:6.184 3rd Qu.:1.2325
## Max. :156.00 Max. :7.769 Max. :1.6840
## Social Support Life Expectancy Freedom of Choice Generosity
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.056 1st Qu.:0.5477 1st Qu.:0.3080 1st Qu.:0.1087
## Median :1.272 Median :0.7890 Median :0.4170 Median :0.1775
## Mean :1.209 Mean :0.7252 Mean :0.3926 Mean :0.1848
## 3rd Qu.:1.452 3rd Qu.:0.8818 3rd Qu.:0.5072 3rd Qu.:0.2482
## Max. :1.624 Max. :1.1410 Max. :0.6310 Max. :0.5660
## Perception of Corruption
## Min. :0.0000
## 1st Qu.:0.0470
## Median :0.0855
## Mean :0.1106
## 3rd Qu.:0.1412
## Max. :0.4530plot(happiness$"Social Support", happiness$`Life Satisfaction Score`,
main="Social Support vs. Life Satisfaction",
xlab="Social Support",
ylab="Life Satisfaction (Score)",
pch=16)
## Based on the plot chart, as social support increases, life
satisfaction increases.
2. How does GDP per Capita affect overall happiness?
library(ggplot2)ggplot(happiness, aes(x = `GDP per Capita`, y = `Life Satisfaction Score`)) +
geom_point() +
ggtitle("Happiness Score vs. GDP per Capita") +
xlab("GDP per Capita") +
ylab("Happiness Score")
ggplot(happiness, aes(x = `GDP per Capita`, y = `Life Satisfaction Score`)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red") +
ggtitle("Happiness Score vs. GDP per Capita") +
xlab("GDP per Capita") +
ylab("Happiness Score")## `geom_smooth()` using formula = 'y ~ x'
## Based on this, as GDP per capita increases, so does the Happiness
score.
3. Which regions/countries have declined in happiness over the last decade? Which have improved?
library(dplyr)colnames(happiness_2) <- c("Country", "Year", "Life_Satisfaction_Score", "GDP_per _Capita", "Social_Support", "Life_Expectancy_at_Birth", "Freedom_to_make_Life_Choices", "Generosity", "Perception_of_Corruption", "Positive_Affect", "Negative_Affect")happiness_decade_2 <- happiness_2 %>%
filter(Year >= 2013 & Year <= 2023)happiness_periods <- happiness_decade_2 %>%
mutate(period = ifelse(Year <= 2017, "Early", "Late")) %>%
group_by(Country, period) %>%
summarise(
avg_life_satisfaction = mean(Life_Satisfaction_Score, na.rm = TRUE),
avg_gdp = mean(`GDP_per _Capita`, na.rm = TRUE),
avg_social_support = mean(Social_Support, na.rm = TRUE),
avg_life_expectancy = mean(Life_Expectancy_at_Birth, na.rm = TRUE),
avg_freedom = mean(Freedom_to_make_Life_Choices, na.rm = TRUE),
avg_generosity = mean(Generosity, na.rm = TRUE),
avg_corruption = mean(Perception_of_Corruption, na.rm = TRUE),
avg_positive_affect = mean(Positive_Affect, na.rm = TRUE),
avg_negative_affect = mean(Negative_Affect, na.rm = TRUE),
.groups = "drop"
)happiness_change_factors_2 <- happiness_periods %>%
pivot_wider(
names_from = period,
values_from = c(
avg_life_satisfaction, avg_gdp, avg_social_support, avg_life_expectancy,
avg_freedom, avg_generosity, avg_corruption, avg_positive_affect, avg_negative_affect
)
) %>%
mutate(
change_life_satisfaction = avg_life_satisfaction_Late - avg_life_satisfaction_Early,
change_gdp = avg_gdp_Late - avg_gdp_Early,
change_social_support = avg_social_support_Late - avg_social_support_Early,
change_life_expectancy = avg_life_expectancy_Late - avg_life_expectancy_Early,
change_freedom = avg_freedom_Late - avg_freedom_Early,
change_generosity = avg_generosity_Late - avg_generosity_Early,
change_corruption = avg_corruption_Late - avg_corruption_Early,
change_positive_affect = avg_positive_affect_Late - avg_positive_affect_Early,
change_negative_affect = avg_negative_affect_Late - avg_negative_affect_Early
)happiness_change_factors_2 <- happiness_change_factors_2 %>%
mutate(across(where(is.numeric), ~ ifelse(is.nan(.), NA, .))) %>%
mutate(across(where(is.numeric), ~ replace_na(., mean(., na.rm = TRUE))))happiness_change_factors_2 <- happiness_change_factors_2 %>%
mutate(across(where(is.numeric), ~ round(., 3)))happiness_change_factors_2 <- happiness_change_factors_2 %>%
rename(
`Change in Life Satisfaction` = change_life_satisfaction,
`Change in GDP per Capita` = change_gdp,
`Change in Social Support` = change_social_support,
`Change in Life Expectancy` = change_life_expectancy,
`Change in Freedom` = change_freedom,
`Change in Generosity` = change_generosity,
`Change in Corruption` = change_corruption,
`Change in Positive Affect` = change_positive_affect,
`Change in Negative Affect` = change_negative_affect
)top_improvers_2 <- happiness_change_factors_2 %>%
arrange(desc(`Change in Life Satisfaction`)) %>%
slice_head(n = 10) %>%
select(Country, `Change in Life Satisfaction`)top_decliners_2 <- happiness_change_factors_2 %>%
arrange(`Change in Life Satisfaction`) %>%
slice_head(n = 10) %>%
select(Country, `Change in Life Satisfaction`)top_improvers_2## # A tibble: 10 × 2
## Country `Change in Life Satisfaction`
## <chr> <dbl>
## 1 Guinea 1.15
## 2 Armenia 1.04
## 3 Serbia 0.96
## 4 Ivory Coast 0.926
## 5 Congo (Brazzaville) 0.903
## 6 Burundi 0.87
## 7 Benin 0.86
## 8 Kosovo 0.844
## 9 Niger 0.736
## 10 Bulgaria 0.72top_decliners_2## # A tibble: 10 × 2
## Country `Change in Life Satisfaction`
## <chr> <dbl>
## 1 Lebanon -1.84
## 2 Afghanistan -1.47
## 3 Sierra Leone -1.05
## 4 Congo (Kinshasa) -1.03
## 5 Jordan -0.907
## 6 Zambia -0.762
## 7 Zimbabwe -0.741
## 8 Pakistan -0.604
## 9 Turkmenistan -0.57
## 10 Colombia -0.512improved_countries <- top_improvers_2$Country
improved_countries## [1] "Guinea" "Armenia" "Serbia"
## [4] "Ivory Coast" "Congo (Brazzaville)" "Burundi"
## [7] "Benin" "Kosovo" "Niger"
## [10] "Bulgaria"declined_countries <- top_decliners_2$Country
declined_countries## [1] "Lebanon" "Afghanistan" "Sierra Leone" "Congo (Kinshasa)"
## [5] "Jordan" "Zambia" "Zimbabwe" "Pakistan"
## [9] "Turkmenistan" "Colombia"happiness_improved_trends <- happiness_decade_2 %>%
filter(Country %in% improved_countries) ggplot(happiness_improved_trends, aes(x = Year, y = Life_Satisfaction_Score, color = Country, group = Country)) +
geom_line(size = 1.0) +
geom_point(size = 2) +
scale_x_continuous(
breaks = seq(2013, 2023, 1), # Show every year from 2013 to 2023
labels = seq(2013, 2023, 1)
) +
labs(
title = "Top 10 Countries That Improved the Most",
subtitle = "Changes in Life Satisfaction Score for countries with the largest improvement",
x = "Year",
y = "Life Satisfaction Score",
color = "Country"
) +
theme_minimal(base_size = 13) +
theme(
legend.position = "right",
plot.title = element_text(face = "bold", size = 15, hjust = 0.5),
plot.subtitle = element_text(size = 11, hjust = 0.5)
)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
happiness_decline_trends <- happiness_decade_2 %>%
filter(Country %in% declined_countries)ggplot(happiness_decline_trends, aes(x = Year, y = Life_Satisfaction_Score, color = Country, group = Country)) +
geom_line(size = 1.0) +
geom_point(size = 2) +
scale_x_continuous(
breaks = seq(2013, 2023, 1), # Show every year from 2013 to 2023
labels = seq(2013, 2023, 1)
) +
labs(
title = "Top 10 Countries That Declined the Most",
subtitle = "Changes in Life Satisfaction Score for countries with largest decline",
x = "Year",
y = "Life Satisfaction Score",
color = "Country"
) +
theme_minimal(base_size = 13) +
theme(
legend.position = "right",
plot.title = element_text(face = "bold", size = 15, hjust = 0.5),
plot.subtitle = element_text(size = 11, hjust = 0.5)
)
4. Based on the previous question, what were some of the factors that improved happiness? What were factors that decreased happiness?
# install.packages("corrr")
library(corrr)library(dplyr)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.2
## ✔ lubridate 1.9.4 ✔ tibble 3.3.0
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ purrr::flatten() masks jsonlite::flatten()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorshappiness_correlations <- happiness_change_factors_2 %>%
select(`Change in Life Satisfaction`,
`Change in GDP per Capita`, `Change in Social Support`,
`Change in Life Expectancy`, `Change in Freedom`,
`Change in Generosity`, `Change in Corruption`,
`Change in Positive Affect`, `Change in Negative Affect`) %>%
correlate() %>%
focus(`Change in Life Satisfaction`) %>%
arrange(desc(`Change in Life Satisfaction`))## Correlation computed with
## • Method: 'pearson'
## • Missing treated using: 'pairwise.complete.obs'happiness_correlations## # A tibble: 8 × 2
## term `Change in Life Satisfaction`
## <chr> <dbl>
## 1 Change in Positive Affect 0.637
## 2 Change in Freedom 0.327
## 3 Change in Social Support 0.326
## 4 Change in Generosity 0.273
## 5 Change in GDP per Capita 0.194
## 6 Change in Life Expectancy 0.0541
## 7 Change in Corruption -0.180
## 8 Change in Negative Affect -0.297library(tibble)improving_factors <- happiness_correlations %>%
filter(`Change in Life Satisfaction` > 0) %>%
arrange(desc(`Change in Life Satisfaction`))declining_factors <- happiness_correlations %>%
filter(`Change in Life Satisfaction` < 0) %>%
arrange(desc(`Change in Life Satisfaction`))improving_factors## # A tibble: 6 × 2
## term `Change in Life Satisfaction`
## <chr> <dbl>
## 1 Change in Positive Affect 0.637
## 2 Change in Freedom 0.327
## 3 Change in Social Support 0.326
## 4 Change in Generosity 0.273
## 5 Change in GDP per Capita 0.194
## 6 Change in Life Expectancy 0.0541declining_factors## # A tibble: 2 × 2
## term `Change in Life Satisfaction`
## <chr> <dbl>
## 1 Change in Corruption -0.180
## 2 Change in Negative Affect -0.297