Stat291:Lab1
Valant Huang
2025-02-17
R Markdown
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── 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(countrycode)
population <- read_csv("/Users/twinklerein/Downloads/pop.csv")## Rows: 197 Columns: 302
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (302): country, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 18...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.life_expectancy <- read_csv("/Users/twinklerein/Downloads/lex.csv")## Rows: 196 Columns: 302
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (301): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.income <- read_csv("/Users/twinklerein/Downloads/mincpcap_cppp.csv")## Rows: 195 Columns: 302
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (301): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.babies_per_woman <- read_csv("/Users/twinklerein/Downloads/children_per_woman_total_fertility.csv")## Rows: 197 Columns: 302
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (301): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.child_mortality <- read_csv("/Users/twinklerein/Downloads/child_mortality_0_5_year_olds_dying_per_1000_born.csv")## Rows: 197 Columns: 302
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (301): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.co2_emissions <- read_csv("/Users/twinklerein/Downloads/co2_pcap_cons.csv")## Rows: 194 Columns: 224
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## Delimiter: ","
## chr (8): country, 2003, 2004, 2005, 2006, 2011, 2012, 2013
## dbl (216): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.gdp_per_capita <- read_csv("/Users/twinklerein/Downloads/gdp_pcap.csv")## Rows: 195 Columns: 302
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## Delimiter: ","
## chr (199): country, 1901, 1903, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 19...
## dbl (103): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.health_spending <- read_csv("/Users/twinklerein/Downloads/total_health_spending_per_person_us.csv")## Rows: 190 Columns: 17
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## Delimiter: ","
## chr (1): country
## dbl (16): 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, ...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.population_density <- read_csv("/Users/twinklerein/Downloads/population_density_per_square_km.csv")## Rows: 236 Columns: 152
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## Delimiter: ","
## chr (152): country, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 19...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.basic_water <- read_csv("/Users/twinklerein/Downloads/at_least_basic_water_source_overall_access_percent.csv")## Rows: 215 Columns: 24
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## Delimiter: ","
## chr (1): country
## dbl (23): 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, ...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.murders <- read_csv("/Users/twinklerein/Downloads/murder_total_deaths.csv")## Rows: 204 Columns: 31
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (31): country, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 199...
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.population <- population %>% select(country, `2010`) %>% rename(population = `2010`)
life_expectancy <- life_expectancy %>% select(country, `2010`) %>% rename(lifeexp = `2010`)
income <- income %>% select(country, `2010`) %>% rename(income = `2010`)
babies_per_woman <- babies_per_woman %>% select(country, `2010`) %>% rename(chdperwoman = `2010`)
child_mortality <- child_mortality %>% select(country, `2010`) %>% rename(childmort = `2010`)
co2_emissions <- co2_emissions %>% select(country, `2010`) %>% rename(co2 = `2010`)
gdp_per_capita <- gdp_per_capita %>% select(country, `2010`) %>% rename(gdpcapita = `2010`)
health_spending <- health_spending %>% select(country, `2010`) %>% rename(healthspend = `2010`)
population_density <- population_density %>% select(country, `2010`) %>% rename(popdensity = `2010`)
basic_water <- basic_water %>% select(country, `2010`) %>% rename(water = `2010`)
murders <- murders %>% select(country, `2010`) %>% rename(murder = `2010`)
text.to.num <- function(x) {
xx <- as.character(x)
res <- rep(0, length(x))
for (i in 1:length(x)) {
if (is.na(xx[i])) {
res[i] <- NA
next
}
if (grepl("M", xx[i], ignore.case = TRUE)) {
res[i] <- as.numeric(gsub("M", "", xx[i], ignore.case = TRUE)) * 1e6
} else if (grepl("k", xx[i], ignore.case = TRUE)) {
res[i] <- as.numeric(gsub("k", "", xx[i], ignore.case = TRUE)) * 1e3
} else {
res[i] <- as.numeric(xx[i])
}
}
res
}
population$population <- text.to.num(population$population)## Warning in text.to.num(population$population): NAs introduced by coercion
## Warning in text.to.num(population$population): NAs introduced by coercionlife_expectancy$lifeexp <- text.to.num(life_expectancy$lifeexp)
income$income <- text.to.num(income$income)
babies_per_woman$chdperwoman <- text.to.num(babies_per_woman$chdperwoman)
child_mortality$childmort <- text.to.num(child_mortality$childmort)
co2_emissions$co2 <- text.to.num(co2_emissions$co2)
gdp_per_capita$gdpcapita <- text.to.num(gdp_per_capita$gdpcapita)
health_spending$healthspend <- text.to.num(health_spending$healthspend)
population_density$popdensity <- text.to.num(population_density$popdensity)
basic_water$water <- text.to.num(basic_water$water)
murders$murder <- text.to.num(murders$murder)
alldata <- list(population, life_expectancy, income, babies_per_woman, child_mortality,
co2_emissions, gdp_per_capita, health_spending, population_density,
basic_water, murders) %>%
reduce(function(x, y) merge(x, y, by = "country", all = TRUE))
alldata$continent <- countrycode(sourcevar = alldata$country,
origin = "country.name",
destination = "continent")## Warning: Some values were not matched unambiguously: Kosovoalldata <- alldata %>% drop_na()
head(alldata)## country population lifeexp income chdperwoman childmort co2
## 1 Afghanistan 28300000 60.5 4.50 6.20 88.00 0.29
## 2 Albania 2930000 78.1 9.77 1.65 13.30 2.26
## 3 Algeria 36200000 74.5 9.08 2.88 27.40 3.28
## 4 Andorra 80700 81.8 66.50 1.28 4.18 6.12
## 5 Angola 23300000 60.2 6.29 6.19 120.00 1.24
## 6 Antigua and Barbuda 85300 75.9 18.40 1.79 9.59 5.96
## gdpcapita healthspend popdensity water murder continent
## 1 2030 37.7 43.6 48.8 4130.00 Asia
## 2 10700 241.0 107.0 91.4 65.90 Europe
## 3 11000 178.0 15.2 92.3 530.00 Africa
## 4 60500 3100.0 172.0 100.0 0.50 Europe
## 5 7690 123.0 18.7 50.4 824.00 Africa
## 6 20100 690.0 194.0 98.4 5.05 Americassummary(alldata)## country population lifeexp income
## Length:177 Min. : 10000 Min. :32.50 Min. : 1.69
## Class :character 1st Qu.: 1710000 1st Qu.:64.00 1st Qu.: 4.91
## Mode :character Median : 7300000 Median :72.30 Median : 11.20
## Mean : 22512891 Mean :70.35 Mean : 19.85
## 3rd Qu.: 22500000 3rd Qu.:76.50 3rd Qu.: 23.70
## Max. :311000000 Max. :83.30 Max. :120.00
## chdperwoman childmort co2 gdpcapita
## Min. :0.930 Min. : 2.62 Min. : 0.030 Min. : 804
## 1st Qu.:1.790 1st Qu.: 8.53 1st Qu.: 0.678 1st Qu.: 3770
## Median :2.410 Median : 19.90 Median : 2.620 Median : 11300
## Mean :2.984 Mean : 37.90 Mean : 5.053 Mean : 18203
## 3rd Qu.:4.130 3rd Qu.: 59.90 3rd Qu.: 7.430 3rd Qu.: 24500
## Max. :7.640 Max. :209.00 Max. :26.400 Max. :112000
## healthspend popdensity water murder
## Min. : 11.9 Min. : 1.74 Min. : 34.00 Min. : 0.5
## 1st Qu.: 76.9 1st Qu.: 30.20 1st Qu.: 73.50 1st Qu.: 51.8
## Median : 320.0 Median : 75.00 Median : 93.00 Median : 264.0
## Mean : 998.0 Mean : 178.95 Mean : 84.69 Mean : 1804.9
## 3rd Qu.: 864.0 3rd Qu.: 162.00 3rd Qu.: 98.60 3rd Qu.: 797.0
## Max. :8360.0 Max. :7430.00 Max. :100.00 Max. :62900.0
## continent
## Length:177
## Class :character
## Mode :character
##
##
## #Task 1: Scatterplot of Life Expectancy vs. Child Mortality We begin by plotting a scatterplot of Life Expectancy against Child Mortality to examine the relationship between these two variables. The plot shows a negative correlation: countries with higher child mortality tend to have lower life expectancy.
ggplot(alldata, aes(x = childmort, y = lifeexp)) +
geom_point() +
labs(title = "Life Expectancy vs. Child Mortality",
x = "Child Mortality",
y = "Life Expectancy")
Task 2: Life Expectancy vs. Child Mortality (Colored by Continent and Sized by Population)
We further analyze the relationship between Life Expectancy and Child Mortality by adding continent (color) and population (size) to the scatterplot. Key observations: - African countries have higher child mortality and lower life expectancy. - European and American countries have lower child mortality and higher life expectancy. - Larger countries (bigger points) are more concentrated in certain regions.
Task 3: Histogram of Life Expectancy
We examine the distribution of Life Expectancy using a histogram. The distribution is right-skewed, indicating that most countries have higher life expectancy, while a few countries (mainly in Africa) have significantly lower life expectancy.
Task 4: Boxplot of Life Expectancy
A boxplot of Life Expectancy reveals the median, quartiles, and potential outliers. Key observations: - The median life expectancy is around 72 years. - Several outliers exist, primarily countries with very low life expectancy.
Task 5: Boxplot of Life Expectancy by Continent
We compare Life Expectancy across continents using a grouped boxplot. Key observations: - Europe and Americas have the highest life expectancy with little variability. - Africa has the lowest life expectancy and the most variability. - Asia and Oceania fall in between.
Task 6: Barplot of Number of Countries by Continent
We visualize the number of countries in each continent. Key observations: - Africa has the most countries in the dataset. - Asia has the second-highest number of countries. - Oceania has the fewest countries.
Task 7: Indicator for Children per Woman
We create an indicator variable child_indicator to
classify countries based on whether the average number of children per
woman is less than or equal to 2. Key observations: -
Most European and American countries
have low fertility rates (<=2). - Many African and
some Asian countries have high fertility rates
(>2).
Task 8: Barplot of Fertility Indicator by Continent
We visualize the distribution of the child_indicator
across continents. Key observations: - Europe and
Americas have a high percentage of countries with low
fertility rates (<=2). - Africa has a high
percentage of countries with high fertility rates (>2).
