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✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library("dslabs")Loading data
help(gapminder)starting httpd help server ... donedata("gapminder")
summary(gapminder) country year infant_mortality life_expectancy
Albania : 57 Min. :1960 Min. : 1.50 Min. :13.20
Algeria : 57 1st Qu.:1974 1st Qu.: 16.00 1st Qu.:57.50
Angola : 57 Median :1988 Median : 41.50 Median :67.54
Antigua and Barbuda: 57 Mean :1988 Mean : 55.31 Mean :64.81
Argentina : 57 3rd Qu.:2002 3rd Qu.: 85.10 3rd Qu.:73.00
Armenia : 57 Max. :2016 Max. :276.90 Max. :83.90
(Other) :10203 NA's :1453
fertility population gdp continent
Min. :0.840 Min. :3.124e+04 Min. :4.040e+07 Africa :2907
1st Qu.:2.200 1st Qu.:1.333e+06 1st Qu.:1.846e+09 Americas:2052
Median :3.750 Median :5.009e+06 Median :7.794e+09 Asia :2679
Mean :4.084 Mean :2.701e+07 Mean :1.480e+11 Europe :2223
3rd Qu.:6.000 3rd Qu.:1.523e+07 3rd Qu.:5.540e+10 Oceania : 684
Max. :9.220 Max. :1.376e+09 Max. :1.174e+13
NA's :187 NA's :185 NA's :2972
region
Western Asia :1026
Eastern Africa : 912
Western Africa : 912
Caribbean : 741
South America : 684
Southern Europe: 684
(Other) :5586 class(gapminder)[1] "data.frame"head(gapminder) country year infant_mortality life_expectancy fertility
1 Albania 1960 115.40 62.87 6.19
2 Algeria 1960 148.20 47.50 7.65
3 Angola 1960 208.00 35.98 7.32
4 Antigua and Barbuda 1960 NA 62.97 4.43
5 Argentina 1960 59.87 65.39 3.11
6 Armenia 1960 NA 66.86 4.55
population gdp continent region
1 1636054 NA Europe Southern Europe
2 11124892 13828152297 Africa Northern Africa
3 5270844 NA Africa Middle Africa
4 54681 NA Americas Caribbean
5 20619075 108322326649 Americas South America
6 1867396 NA Asia Western AsiaSubsetting data from African countries
africadata <- gapminder %>%
filter(continent=="Africa")
str(africadata)'data.frame': 2907 obs. of 9 variables:
$ country : Factor w/ 185 levels "Albania","Algeria",..: 2 3 18 22 26 27 29 31 32 33 ...
$ year : int 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ...
$ infant_mortality: num 148 208 187 116 161 ...
$ life_expectancy : num 47.5 36 38.3 50.3 35.2 ...
$ fertility : num 7.65 7.32 6.28 6.62 6.29 6.95 5.65 6.89 5.84 6.25 ...
$ population : num 11124892 5270844 2431620 524029 4829291 ...
$ gdp : num 1.38e+10 NA 6.22e+08 1.24e+08 5.97e+08 ...
$ continent : Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
$ region : Factor w/ 22 levels "Australia and New Zealand",..: 11 10 20 17 20 5 10 20 10 10 ...summary(africadata) country year infant_mortality life_expectancy
Algeria : 57 Min. :1960 Min. : 11.40 Min. :13.20
Angola : 57 1st Qu.:1974 1st Qu.: 62.20 1st Qu.:48.23
Benin : 57 Median :1988 Median : 93.40 Median :53.98
Botswana : 57 Mean :1988 Mean : 95.12 Mean :54.38
Burkina Faso: 57 3rd Qu.:2002 3rd Qu.:124.70 3rd Qu.:60.10
Burundi : 57 Max. :2016 Max. :237.40 Max. :77.60
(Other) :2565 NA's :226
fertility population gdp continent
Min. :1.500 Min. : 41538 Min. :4.659e+07 Africa :2907
1st Qu.:5.160 1st Qu.: 1605232 1st Qu.:8.373e+08 Americas: 0
Median :6.160 Median : 5570982 Median :2.448e+09 Asia : 0
Mean :5.851 Mean : 12235961 Mean :9.346e+09 Europe : 0
3rd Qu.:6.860 3rd Qu.: 13888152 3rd Qu.:6.552e+09 Oceania : 0
Max. :8.450 Max. :182201962 Max. :1.935e+11
NA's :51 NA's :51 NA's :637
region
Eastern Africa :912
Western Africa :912
Middle Africa :456
Northern Africa :342
Southern Africa :285
Australia and New Zealand: 0
(Other) : 0 Creating new objects from ‘africadata’– ‘imle’ containing infant mortality and life expectancy, and ‘ple’ containing population size and life expectancy
imle <- africadata %>%
select(c(infant_mortality, life_expectancy))
str(imle)'data.frame': 2907 obs. of 2 variables:
$ infant_mortality: num 148 208 187 116 161 ...
$ life_expectancy : num 47.5 36 38.3 50.3 35.2 ...summary(imle) infant_mortality life_expectancy
Min. : 11.40 Min. :13.20
1st Qu.: 62.20 1st Qu.:48.23
Median : 93.40 Median :53.98
Mean : 95.12 Mean :54.38
3rd Qu.:124.70 3rd Qu.:60.10
Max. :237.40 Max. :77.60
NA's :226 ple <- africadata %>%
select(c(population, life_expectancy))
str(ple)'data.frame': 2907 obs. of 2 variables:
$ population : num 11124892 5270844 2431620 524029 4829291 ...
$ life_expectancy: num 47.5 36 38.3 50.3 35.2 ...summary(ple) population life_expectancy
Min. : 41538 Min. :13.20
1st Qu.: 1605232 1st Qu.:48.23
Median : 5570982 Median :53.98
Mean : 12235961 Mean :54.38
3rd Qu.: 13888152 3rd Qu.:60.10
Max. :182201962 Max. :77.60
NA's :51 Data Visualization
imle %>% ggplot(aes(x=infant_mortality, y=life_expectancy)) +
geom_point() + labs(x = "Infant Mortality (per 1,000 births)", y = "Life Expectancy (years)")
ple %>% ggplot(aes(x= log(population), y = life_expectancy)) +
geom_point() + labs(x="Log Population", y = "Life Expectancy (years)")Warning: Removed 51 rows containing missing values (`geom_point()`).
Next, let’s find out which years are missing data on infant mortality
with(subset(africadata, is.na(infant_mortality)), year) [1] 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 1961 1961 1961 1961 1961
[16] 1961 1961 1961 1961 1961 1961 1961 1961 1961 1961 1961 1961 1962 1962 1962
[31] 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 1962 1963 1963
[46] 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1963 1964
[61] 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1964 1965
[76] 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 1965 1966 1966
[91] 1966 1966 1966 1966 1966 1966 1966 1966 1966 1966 1966 1967 1967 1967 1967
[106] 1967 1967 1967 1967 1967 1967 1967 1968 1968 1968 1968 1968 1968 1968 1968
[121] 1968 1968 1968 1969 1969 1969 1969 1969 1969 1969 1970 1970 1970 1970 1970
[136] 1971 1971 1971 1971 1971 1971 1972 1972 1972 1972 1972 1972 1973 1973 1973
[151] 1973 1973 1973 1974 1974 1974 1974 1974 1975 1975 1975 1975 1975 1976 1976
[166] 1976 1977 1977 1977 1978 1978 1979 1979 1980 1981 2016 2016 2016 2016 2016
[181] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
[196] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
[211] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
[226] 2016Subsetting data from the year 2000
africa2000 <- africadata %>%
filter(year == "2000")
str(africa2000)'data.frame': 51 obs. of 9 variables:
$ country : Factor w/ 185 levels "Albania","Algeria",..: 2 3 18 22 26 27 29 31 32 33 ...
$ year : int 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
$ infant_mortality: num 33.9 128.3 89.3 52.4 96.2 ...
$ life_expectancy : num 73.3 52.3 57.2 47.6 52.6 46.7 54.3 68.4 45.3 51.5 ...
$ fertility : num 2.51 6.84 5.98 3.41 6.59 7.06 5.62 3.7 5.45 7.35 ...
$ population : num 31183658 15058638 6949366 1736579 11607944 ...
$ gdp : num 5.48e+10 9.13e+09 2.25e+09 5.63e+09 2.61e+09 ...
$ continent : Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
$ region : Factor w/ 22 levels "Australia and New Zealand",..: 11 10 20 17 20 5 10 20 10 10 ...summary(africa2000) country year infant_mortality life_expectancy
Algeria : 1 Min. :2000 Min. : 12.30 Min. :37.60
Angola : 1 1st Qu.:2000 1st Qu.: 60.80 1st Qu.:51.75
Benin : 1 Median :2000 Median : 80.30 Median :54.30
Botswana : 1 Mean :2000 Mean : 78.93 Mean :56.36
Burkina Faso: 1 3rd Qu.:2000 3rd Qu.:103.30 3rd Qu.:60.00
Burundi : 1 Max. :2000 Max. :143.30 Max. :75.00
(Other) :45
fertility population gdp continent
Min. :1.990 Min. : 81154 Min. :2.019e+08 Africa :51
1st Qu.:4.150 1st Qu.: 2304687 1st Qu.:1.274e+09 Americas: 0
Median :5.550 Median : 8799165 Median :3.238e+09 Asia : 0
Mean :5.156 Mean : 15659800 Mean :1.155e+10 Europe : 0
3rd Qu.:5.960 3rd Qu.: 17391242 3rd Qu.:8.654e+09 Oceania : 0
Max. :7.730 Max. :122876723 Max. :1.329e+11
region
Eastern Africa :16
Western Africa :16
Middle Africa : 8
Northern Africa : 6
Southern Africa : 5
Australia and New Zealand: 0
(Other) : 0 Plotting data from African Countries in 2000
africa2000 %>% ggplot(aes(x=infant_mortality, y=life_expectancy)) +
geom_point() + labs(x = "Infant Mortality (per 1,000 births)", y = "Life
Expectancy (years)", title = "Infant Mortality Rate and Life Expectancy
in African Countries -- 2000")
africa2000 %>% ggplot(aes(x= log(population), y = life_expectancy)) +
geom_point() + labs(x="Log Population", y = "Life Expectancy (years)",
title = "Population Size and Life Expectancy in African Countries --
2000")
Fitting a simple model
fit1<-lm(life_expectancy ~ infant_mortality, data = africa2000)
fit2<-lm(life_expectancy ~ population, data = africa2000)summary(fit1)
Call:
lm(formula = life_expectancy ~ infant_mortality, data = africa2000)
Residuals:
Min 1Q Median 3Q Max
-22.6651 -3.7087 0.9914 4.0408 8.6817
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.29331 2.42611 29.386 < 2e-16 ***
infant_mortality -0.18916 0.02869 -6.594 2.83e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.221 on 49 degrees of freedom
Multiple R-squared: 0.4701, Adjusted R-squared: 0.4593
F-statistic: 43.48 on 1 and 49 DF, p-value: 2.826e-08summary(fit2)
Call:
lm(formula = life_expectancy ~ population, data = africa2000)
Residuals:
Min 1Q Median 3Q Max
-18.429 -4.602 -2.568 3.800 18.802
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.593e+01 1.468e+00 38.097 <2e-16 ***
population 2.756e-08 5.459e-08 0.505 0.616
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.524 on 49 degrees of freedom
Multiple R-squared: 0.005176, Adjusted R-squared: -0.01513
F-statistic: 0.2549 on 1 and 49 DF, p-value: 0.6159Conclusions
There is statistically significant evidence supporting an association between life expectancy and infant mortality. Life expectancy decreases by 2.49 years for every unit increase in infant mortality (p < 0.05). The data does not show statistically significant evidence of association between life expectancy and population size (p = 0.62).
This section has been added by Kailin (Kai) Chen.
# How Much Does Each Country Contribute to Continent's Overall GDP?
## Setting Up Initial Data
region_and_gdp <- africadata %>% filter(year == 1983) %>% group_by(region, gdp) %>%
select(country, region, gdp)
## Creating a New Variable for Continent's GDP
region_and_gdp$total_gdp <- sum(region_and_gdp$gdp, na.rm = TRUE)
# Percentage Contributed to Each
region_and_gdp <- region_and_gdp %>%
mutate(percentage = gdp/total_gdp * 100)
# Graphing African Countries' Contributions to Continent's GDP
ggplot(region_and_gdp, aes(x = percentage, y = country, group = region, fill = region)) + geom_col() + labs(x = "Percentage Contributed to Continent's GDP", y = "Nation", title = "Each Nation's Contribution to Continent's GDP in 1983")Warning: Removed 7 rows containing missing values (`position_stack()`).
library(broom)Warning: package 'broom' was built under R version 4.2.2# Creating a Model
population_factors_model <- lm(population ~ fertility + life_expectancy + infant_mortality, data = gapminder %>% filter(continent == "Asia"))
# Evaluating Model
pop_factors_model_table <- tidy(summary(population_factors_model))According to this model, fertility (p < 0.001) and life expectancy (p < 0.001) appear to significantly impact population in Asia since the null hypothesis that they do not is rejected. However, the null hypothesis that infant mortality does not affect population, in contrast, isn’t rejected.
# Investigating Fertility Across Time
ggplot(gapminder %>% filter(continent == "Europe"), aes(x = year, y = fertility, group = region, color = region)) + geom_point() + labs(x = "Year", y = "Fertility Rate", title = "Fertility Over Time in Europe")Warning: Removed 39 rows containing missing values (`geom_point()`).
# Investigating Australia's GDP Across Time
ggplot(gapminder %>% filter(country == "Australia"), aes(x = year, y = gdp, color = year)) + geom_point() + geom_line() + labs(x = "Year", y = "GDP", title = "GDP Over Time for Australia")Warning: Removed 5 rows containing missing values (`geom_point()`).Warning: Removed 5 rows containing missing values (`geom_line()`).