Gapminder Revisited + GDP Evolution over time!
2017-10-31T21:28:43-05:00
Gapminder Revisited
Gapminder data frame contains just 6 columns from the larger data in Gapminder World. In this part, we shall join a few dataframes with more data than the ‘gapminder’ package. Specifically, let’s look at data on
- Life expectancy at birth (life_expectancy_years.csv)
- GDP per capita in constant 2010 US$ (https://data.worldbank.org/indicator/NY.GDP.PCAP.KD)
- Female fertility: The number of babies per woman (https://data.worldbank.org/indicator/SP.DYN.TFRT.IN)
- Primary school enrollment as % of children attending primary school (https://data.worldbank.org/indicator/SE.PRM.NENR)
- Mortality rate, for under 5, per 1000 live births (https://data.worldbank.org/indicator/SH.DYN.MORT)
- HIV prevalence (adults_with_hiv_percent_age_15_49.csv): The estimated number of people living with HIV per 100 population of age group 15-49.
library(gapminder)
skim(gapminder)| Name | gapminder |
| Number of rows | 1704 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| factor | 2 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| country | 0 | 1 | FALSE | 142 | Afg: 12, Alb: 12, Alg: 12, Ang: 12 |
| continent | 0 | 1 | FALSE | 5 | Afr: 624, Asi: 396, Eur: 360, Ame: 300 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| year | 0 | 1 | 1.98e+03 | 1.73e+01 | 1952.0 | 1.97e+03 | 1.98e+03 | 1.99e+03 | 2.01e+03 | ▇▅▅▅▇ |
| lifeExp | 0 | 1 | 5.95e+01 | 1.29e+01 | 23.6 | 4.82e+01 | 6.07e+01 | 7.08e+01 | 8.26e+01 | ▁▆▇▇▇ |
| pop | 0 | 1 | 2.96e+07 | 1.06e+08 | 60011.0 | 2.79e+06 | 7.02e+06 | 1.96e+07 | 1.32e+09 | ▇▁▁▁▁ |
| gdpPercap | 0 | 1 | 7.22e+03 | 9.86e+03 | 241.2 | 1.20e+03 | 3.53e+03 | 9.33e+03 | 1.14e+05 | ▇▁▁▁▁ |
# load gapminder HIV data
hiv <- read_csv(here::here("data","adults_with_hiv_percent_age_15_49.csv"))
life_expectancy <- read_csv(here::here("data","life_expectancy_years.csv"))
# get World bank data using wbstats
indicators <- c("SP.DYN.TFRT.IN","SE.PRM.NENR", "SH.DYN.MORT", "NY.GDP.PCAP.KD")
library(wbstats)
worldbank_data <- wb_data(country="countries_only", #countries only- no aggregates like Latin America, Europe, etc.
indicator = indicators,
start_date = 1960,
end_date = 2016)
# get a dataframe of information regarding countries, indicators, sources, regions, indicator topics, lending types, income levels, from the World Bank API
countries <- wbstats::wb_cachelist$countriesLeft Join makes the most sense among the different types of join operations like outer joins - left, right, and full. This is because a left join operation (regardless of there being a match) preserves the original observations, especially when one looks up additional data from another table. Since, we are working with 3 dataframes, while mapping the year and date column with different start/end time frames, it is essential to preserve the original observations in each dataframe.
# tidying HIV data - hiv and life_expectancy dataframes - using pivot_longer() + remving NA values
hiv1 <- hiv %>%
pivot_longer(2:34, names_to = "year", values_to = "Percentage_HIV_Cases_Age_15_49") %>%
drop_na()
skim(hiv1)| Name | hiv1 |
| Number of rows | 3301 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country | 0 | 1 | 3 | 24 | 0 | 154 | 0 |
| year | 0 | 1 | 4 | 4 | 0 | 31 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Percentage_HIV_Cases_Age_15_49 | 0 | 1 | 1.74 | 4.09 | 0.01 | 0.1 | 0.3 | 1.2 | 26.5 | ▇▁▁▁▁ |
life_expectancy_1 <- life_expectancy %>%
pivot_longer(2:302, names_to = "year", values_to = "Life_Expectancy") %>%
drop_na()
skim(life_expectancy_1)| Name | life_expectancy_1 |
| Number of rows | 55528 |
| Number of columns | 3 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| country | 0 | 1 | 3 | 30 | 0 | 187 | 0 |
| year | 0 | 1 | 4 | 4 | 0 | 301 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Life_Expectancy | 0 | 1 | 53 | 21.7 | 1.01 | 32.3 | 48.7 | 74.2 | 94.8 | ▁▇▂▅▅ |
#Removing NA values in worldbank_data
worldbank_data_1 <- worldbank_data %>%
drop_na()
skim(worldbank_data_1)| Name | worldbank_data_1 |
| Number of rows | 3715 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| iso2c | 0 | 1 | 2 | 2 | 0 | 177 | 0 |
| iso3c | 0 | 1 | 3 | 3 | 0 | 177 | 0 |
| country | 0 | 1 | 4 | 30 | 0 | 177 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| date | 0 | 1 | 1998.57 | 12.65 | 1970.00 | 1989.00 | 2001.0 | 2009.00 | 2.02e+03 | ▂▃▃▆▇ |
| NY.GDP.PCAP.KD | 0 | 1 | 12893.37 | 18110.77 | 164.46 | 1514.19 | 4313.9 | 17178.37 | 1.16e+05 | ▇▁▁▁▁ |
| SE.PRM.NENR | 0 | 1 | 85.08 | 17.09 | 10.05 | 81.05 | 91.7 | 96.43 | 1.00e+02 | ▁▁▁▂▇ |
| SH.DYN.MORT | 0 | 1 | 52.96 | 59.80 | 2.20 | 10.90 | 26.7 | 76.35 | 3.70e+02 | ▇▂▁▁▁ |
| SP.DYN.TFRT.IN | 0 | 1 | 3.35 | 1.82 | 1.08 | 1.83 | 2.7 | 4.82 | 8.45e+00 | ▇▃▂▂▁ |
#Left Join of life_expectancy_1 (tidied life_expectancy) and hiv1 (tidied hiv) dataframes
join_1 <- left_join(life_expectancy_1, hiv1, by = c("country"="country", "year"="year"))
# Converting datatype of column (year) from character type to numeric type
join_1$year = as.numeric(join_1$year)#Left Join of join_1 (left join of hiv1 and life_expectancy_1 dataframes) and worldbank_data dataframes
join_2 <- left_join(join_1, worldbank_data_1, by = c("country"="country", "year"="date"))#Left Join of join_1 (left join of hiv1 and life_expectancy_1 dataframes) and worldbank_data dataframes
join_3 <- left_join(join_2, countries, "country"="country") %>%
drop_na()
join_3## # A tibble: 1,083 × 25
## country year Life_Expectancy Percentage_HIV_Case… iso2c iso3c NY.GDP.PCAP.KD
## <chr> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
## 1 Algeria 1990 71.7 0.06 DZ DZA 3572.
## 2 Algeria 1991 72.2 0.06 DZ DZA 3444.
## 3 Algeria 1992 72.5 0.06 DZ DZA 3424.
## 4 Algeria 1993 72.7 0.06 DZ DZA 3279.
## 5 Algeria 1994 72.8 0.06 DZ DZA 3183.
## 6 Algeria 1995 72.9 0.06 DZ DZA 3241.
## 7 Algeria 1996 73.3 0.06 DZ DZA 3315.
## 8 Algeria 1997 73.2 0.06 DZ DZA 3298.
## 9 Algeria 1999 73.9 0.06 DZ DZA 3474.
## 10 Algeria 2000 74 0.06 DZ DZA 3558.
## # … with 1,073 more rows, and 18 more variables: SE.PRM.NENR <dbl>,
## # SH.DYN.MORT <dbl>, SP.DYN.TFRT.IN <dbl>, capital_city <chr>,
## # longitude <dbl>, latitude <dbl>, region_iso3c <chr>, region_iso2c <chr>,
## # region <chr>, admin_region_iso3c <chr>, admin_region_iso2c <chr>,
## # admin_region <chr>, income_level_iso3c <chr>, income_level_iso2c <chr>,
## # income_level <chr>, lending_type_iso3c <chr>, lending_type_iso2c <chr>,
## # lending_type <chr>What is the relationship between HIV prevalence and life expectancy?
Based on the scatterplot with a smoothing line, it seems like region plays a role in the relationship between HIV prevalence and life expectancy. HIV Prevalence is depicted through the percentage of HIV cases in the age group 15-49 years. In the case of Sub-Saharan Africa, there is a definite pattern of a negative slope which implies that as HIV prevalence increases, there is a decrease in Life Expectancy. In Latin America too, there is a negative slope implying an inverse relationship between HIV prevalence and Life Expectancy. However, there is more variability in the plot for Sub-Saharan Africa as compared to Latin America.
#Scatterplot for Life Expectancy vs. HIV prevalence
ggplot(join_3, aes(x = Percentage_HIV_Cases_Age_15_49, y = Life_Expectancy)) +
geom_point(size = 0.3) +
geom_smooth(method="lm") +
facet_wrap(~ region) +
labs(title = "Relationship between HIV Prevalence and Life Expectancy",
x = "HIV Prevalence",
y = "Life Expectancy")
What is the relationship between fertility rate and GDP per capita?
Based on the scatterplot with a smoothing line, it seems like facetting by region solidifies the belief that all regions depict an inverse relationship between fertility rate and GDP per capita. In the case of Sub-Saharan Africa in particular, as the fertility rate increases, the GDP per capita decreases. The inverse relationship between the two variables demonstrates the connection between fertility choices and economic considerations. In general, developing or low-income countries tend to have higher levels of fertility than their developed counterparts for several reasons such as infant mortality, lack of access to contraceptives, generally lower levels of female education, etc.
ggplot(join_3, aes(x = SP.DYN.TFRT.IN, y = NY.GDP.PCAP.KD)) +
geom_point(size = 0.5) +
geom_smooth(method="lm") +
facet_wrap(~ region) +
labs(title = "Relationship between Fertility Rate and GDP per capita",
x = "Fertility Rate",
y = "GDP per capita")+
NULL
Which regions have the most observations with missing HIV data?
Region ‘Sub-Saharan Africa’ has the most observations with missing HIV data. This is followed by Europe & Central Asia with the 2nd most observations with missing HIV data.
#Tidying hiv dataframe
tidy_hiv <- hiv %>%
pivot_longer(cols=2:34, names_to="year", values_to = "Percentage_HIV_Cases_Age_15_49")
#Left joining tidy_hiv and countries dataframes
joined_hiv_countries <- left_join(tidy_hiv, countries, "country"= "country")
joined_hiv_countries## # A tibble: 5,082 × 20
## country year Percentage_HIV_… iso3c iso2c capital_city longitude latitude
## <chr> <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 Afghanistan 1979 NA AFG AF Kabul 69.2 34.5
## 2 Afghanistan 1980 NA AFG AF Kabul 69.2 34.5
## 3 Afghanistan 1981 NA AFG AF Kabul 69.2 34.5
## 4 Afghanistan 1982 NA AFG AF Kabul 69.2 34.5
## 5 Afghanistan 1983 NA AFG AF Kabul 69.2 34.5
## 6 Afghanistan 1984 NA AFG AF Kabul 69.2 34.5
## 7 Afghanistan 1985 NA AFG AF Kabul 69.2 34.5
## 8 Afghanistan 1986 NA AFG AF Kabul 69.2 34.5
## 9 Afghanistan 1987 NA AFG AF Kabul 69.2 34.5
## 10 Afghanistan 1988 NA AFG AF Kabul 69.2 34.5
## # … with 5,072 more rows, and 12 more variables: region_iso3c <chr>,
## # region_iso2c <chr>, region <chr>, admin_region_iso3c <chr>,
## # admin_region_iso2c <chr>, admin_region <chr>, income_level_iso3c <chr>,
## # income_level_iso2c <chr>, income_level <chr>, lending_type_iso3c <chr>,
## # lending_type_iso2c <chr>, lending_type <chr># Determining NA values in joined_hiv_countries dataframe
joined_hiv_countries %>%
filter(!is.na(region)) %>%
group_by(region) %>%
summarise(missing_hiv_values=sum(is.na(Percentage_HIV_Cases_Age_15_49))) %>%
mutate(
region=fct_reorder(region,-missing_hiv_values)) %>%
# Plotting Bar Chart of Region Specific Missing HIV Data in Descending Order
ggplot(aes(x=missing_hiv_values,y=region))+
geom_col(fill = "coral")+
labs(title="Bar Chart of Region Specific Missing HIV Data in Descending Order",
x= "Missing NA values in HIV data",
y= "Region"
)
How has mortality rate for under 5 changed by region? In each region, the top 5 countries that have seen the greatest improvement, as well as those 5 countries where mortality rates have had the least improvement or even deterioration, have been determined.
#Minimum Year and Maximum Year determination
mortality <- join_3 %>%
group_by(country) %>%
summarise(minimum_year=min(year), maximum_year=max(year))
mortality## # A tibble: 92 × 3
## country minimum_year maximum_year
## <chr> <dbl> <dbl>
## 1 Algeria 1990 2008
## 2 Angola 1998 2011
## 3 Argentina 1991 2011
## 4 Armenia 2002 2011
## 5 Azerbaijan 1991 2011
## 6 Bangladesh 1990 2010
## 7 Belarus 1995 2011
## 8 Belize 1999 2011
## 9 Benin 1984 2011
## 10 Bhutan 1998 2011
## # … with 82 more rows# Dataframe with original mortality rates
join_5 <- left_join(join_3, mortality, "country"="country") %>% #Left Joining join_3 and mortality dataframes
select(country, year, minimum_year, maximum_year, SH.DYN.MORT, region) %>% # Selecting required columns
mutate(
original_mortality = ifelse(year == minimum_year, SH.DYN.MORT, 0)) %>% #Determining original mortality rates
select(!year) %>%
filter(!original_mortality == 0)%>%
select(!SH.DYN.MORT)
join_5## # A tibble: 92 × 5
## country minimum_year maximum_year region original_mortal…
## <chr> <dbl> <dbl> <chr> <dbl>
## 1 Algeria 1990 2008 Middle East & North Africa 49.5
## 2 Angola 1998 2011 Sub-Saharan Africa 214.
## 3 Argentina 1991 2011 Latin America & Caribbean 28.1
## 4 Armenia 2002 2011 Europe & Central Asia 27.8
## 5 Azerbaijan 1991 2011 Europe & Central Asia 95.3
## 6 Bangladesh 1990 2010 South Asia 144.
## 7 Belarus 1995 2011 Europe & Central Asia 15.6
## 8 Belize 1999 2011 Latin America & Caribbean 24.4
## 9 Benin 1984 2011 Sub-Saharan Africa 199.
## 10 Bhutan 1998 2011 South Asia 86.1
## # … with 82 more rows# Dataframe with final mortality rates
join_6 <- left_join(join_3, mortality, "country"="country") %>% #Left Joining join_3 and mortality dataframes
select(country, year, minimum_year, maximum_year, SH.DYN.MORT, region) %>% # Selecting required columns
mutate(
final_mortality = ifelse(year == maximum_year, SH.DYN.MORT, 0)) %>% #Determining final mortality rates
select(!year) %>%
filter(!final_mortality == 0)%>%
select(!SH.DYN.MORT)
join_6## # A tibble: 92 × 5
## country minimum_year maximum_year region final_mortality
## <chr> <dbl> <dbl> <chr> <dbl>
## 1 Algeria 1990 2008 Middle East & North Africa 29.5
## 2 Angola 1998 2011 Sub-Saharan Africa 112.
## 3 Argentina 1991 2011 Latin America & Caribbean 13.9
## 4 Armenia 2002 2011 Europe & Central Asia 17.6
## 5 Azerbaijan 1991 2011 Europe & Central Asia 35
## 6 Bangladesh 1990 2010 South Asia 48.7
## 7 Belarus 1995 2011 Europe & Central Asia 5.1
## 8 Belize 1999 2011 Latin America & Caribbean 18.3
## 9 Benin 1984 2011 Sub-Saharan Africa 109.
## 10 Bhutan 1998 2011 South Asia 39.9
## # … with 82 more rows# Joining aforementioned join_5 and join_6 dataframes along with Improvement in Mortality Rates calculation
join_7 <- left_join(join_5, join_6, "country"="country") %>% #Left Joining join_5 and join_6
mutate(
mortality_improvement = (final_mortality - original_mortality)/original_mortality) %>%
arrange(desc(mortality_improvement))
join_7## # A tibble: 92 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 South Af… 1991 2005 Sub-Sah… 56 79.1
## 2 Gabon 1997 1997 Sub-Sah… 87.2 87.2
## 3 Haiti 1997 1997 Latin A… 115. 115.
## 4 South Su… 2011 2011 Sub-Sah… 102. 102.
## 5 Sudan 2011 2011 Sub-Sah… 73.4 73.4
## 6 Lesotho 1984 2011 Sub-Sah… 101. 96.1
## 7 Zimbabwe 1998 2003 Sub-Sah… 95.6 90.6
## 8 Sao Tome… 2009 2010 Sub-Sah… 47.9 45
## 9 Uzbekist… 2007 2008 Europe … 41.3 38.5
## 10 Ethiopia 2009 2011 Sub-Sah… 86.7 77.6
## # … with 82 more rows, and 1 more variable: mortality_improvement <dbl># Determining Change in Mortality Rate for Under 5 in each region
mortality_improvement_by_region <- join_7 %>%
group_by(region) %>%
summarise(mean_mortality_improvement_rate = 100 * mean(mortality_improvement))
mortality_improvement_by_region## # A tibble: 6 × 2
## region mean_mortality_improvement_rate
## <chr> <dbl>
## 1 East Asia & Pacific -40.0
## 2 Europe & Central Asia -48.1
## 3 Latin America & Caribbean -48.9
## 4 Middle East & North Africa -56.5
## 5 South Asia -48.0
## 6 Sub-Saharan Africa -32.0# Plot for Region-Specific Change in Mortality Rate for Under 5
ggplot(mortality_improvement_by_region, aes(x = mean_mortality_improvement_rate, y = fct_reorder(region, -mean_mortality_improvement_rate))) +
geom_col(fill = "steelblue")+
labs(
title= "Region-Specific Change in Mortality Rate for Under 5",
x= "Change in Mortality Rate for Under 5",
y="Region"
)
#Sub-Saharan Africa
join_7 %>% #The Top 5 countries with the greatest improvement in mortality rates
filter(region == "Sub-Saharan Africa") %>%
slice_max(order_by = mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 South Africa 1991 2005 Sub-S… 56 79.1
## 2 Gabon 1997 1997 Sub-S… 87.2 87.2
## 3 South Sudan 2011 2011 Sub-S… 102. 102.
## 4 Sudan 2011 2011 Sub-S… 73.4 73.4
## 5 Lesotho 1984 2011 Sub-S… 101. 96.1
## # … with 1 more variable: mortality_improvement <dbl>join_7 %>% #The Top 5 countries with the least improvement in mortality rates
filter(region == "Sub-Saharan Africa") %>%
slice_max(order_by = -mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Senegal 1981 2011 Sub-Sah… 198. 63
## 2 Niger 1990 2011 Sub-Sah… 329. 115.
## 3 Eritrea 1992 2011 Sub-Sah… 138. 53.2
## 4 Mozambique 1990 2011 Sub-Sah… 243. 100.
## 5 Tanzania 1990 2010 Sub-Sah… 165. 71.9
## # … with 1 more variable: mortality_improvement <dbl>#South Asia
join_7 %>% #The Top 5 countries with the greatest improvement in mortality rates
filter(region == "South Asia") %>%
slice_max(order_by = mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Pakistan 2002 2011 South Asia 102. 85
## 2 Sri Lanka 2001 2011 South Asia 16 11.2
## 3 Nepal 1999 2011 South Asia 85.8 44.2
## 4 India 1990 2009 South Asia 126. 61.4
## 5 Bhutan 1998 2011 South Asia 86.1 39.9
## # … with 1 more variable: mortality_improvement <dbl>join_7 %>% #The Top 5 countries with the least improvement in mortality rates
filter(region == "South Asia") %>%
slice_max(order_by = -mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Maldives 1997 2008 South Asia 52.8 16
## 2 Bangladesh 1990 2010 South Asia 144. 48.7
## 3 Bhutan 1998 2011 South Asia 86.1 39.9
## 4 India 1990 2009 South Asia 126. 61.4
## 5 Nepal 1999 2011 South Asia 85.8 44.2
## # … with 1 more variable: mortality_improvement <dbl>#Latin America & Caribbean
join_7 %>% #The Top 5 countries with the greatest improvement in mortality rates
filter(region == "Latin America & Caribbean") %>%
slice_max(order_by = mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Haiti 1997 1997 Latin… 115. 115.
## 2 Suriname 2005 2011 Latin… 26.5 22.5
## 3 Dominican Republic 1999 2011 Latin… 42 33.7
## 4 Belize 1999 2011 Latin… 24.4 18.3
## 5 Costa Rica 1990 2011 Latin… 16.9 10.2
## # … with 1 more variable: mortality_improvement <dbl>join_7 %>% #The Top 5 countries with the least improvement in mortality rates
filter(region == "Latin America & Caribbean") %>%
slice_max(order_by = -mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Ecuador 1979 2011 Latin Am… 96.1 17.5
## 2 Honduras 1979 2008 Latin Am… 99.3 25.5
## 3 Peru 1993 2011 Latin Am… 67.1 18.9
## 4 Nicaragua 1986 2010 Latin Am… 78.7 23.9
## 5 Colombia 1984 2011 Latin Am… 45.1 17.8
## # … with 1 more variable: mortality_improvement <dbl>#Europe & Central Asia
join_7 %>% #The Top 5 countries with the greatest improvement in mortality rates
filter(region == "Europe & Central Asia") %>%
slice_max(order_by = mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Uzbekistan 2007 2008 Europe … 41.3 38.5
## 2 Serbia 2005 2011 Europe … 8.9 7.4
## 3 Ukraine 2002 2011 Europe … 16.7 11.2
## 4 Armenia 2002 2011 Europe … 27.8 17.6
## 5 Bulgaria 1996 2011 Europe … 19.2 10.4
## # … with 1 more variable: mortality_improvement <dbl>join_7 %>% #The Top 5 countries with the least improvement in mortality rates
filter(region == "Europe & Central Asia") %>%
slice_max(order_by = -mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Turkey 1990 2011 Europe … 73.9 17
## 2 Georgia 1995 2011 Europe … 45.2 13
## 3 Belarus 1995 2011 Europe … 15.6 5.1
## 4 Azerbaijan 1991 2011 Europe … 95.3 35
## 5 Kazakhstan 2000 2011 Europe … 42.2 18.3
## # … with 1 more variable: mortality_improvement <dbl>#Middle East & North Africa
join_7 %>% #The Top 5 countries with the greatest improvement in mortality rates
filter(region == "Middle East & North Africa") %>%
slice_max(order_by = mortality_improvement, n = 5)## # A tibble: 3 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Algeria 1990 2008 Middle Eas… 49.5 29.5
## 2 Morocco 1990 2011 Middle Eas… 79.1 30.4
## 3 Tunisia 1990 2011 Middle Eas… 55.3 18
## # … with 1 more variable: mortality_improvement <dbl>join_7 %>% #The Top 5 countries with the least improvement in mortality rates
filter(region == "Middle East & North Africa") %>%
slice_max(order_by = -mortality_improvement, n = 5)## # A tibble: 3 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Tunisia 1990 2011 Middle Eas… 55.3 18
## 2 Morocco 1990 2011 Middle Eas… 79.1 30.4
## 3 Algeria 1990 2008 Middle Eas… 49.5 29.5
## # … with 1 more variable: mortality_improvement <dbl>#East Asia & Pacific
join_7 %>% #The Top 5 countries with the greatest improvement in mortality rates
filter(region == "East Asia & Pacific") %>%
slice_max(order_by = mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Fiji 1992 2011 East A… 26.9 23.6
## 2 Thailand 2006 2009 East A… 16.4 14.2
## 3 Myanmar 2000 2010 East A… 89 63.4
## 4 Vietnam 1998 2011 East A… 32.8 22.6
## 5 Philippines 1992 2009 East A… 50.4 32.1
## # … with 1 more variable: mortality_improvement <dbl>join_7 %>% #The Top 5 countries with the least improvement in mortality rates
filter(region == "East Asia & Pacific") %>%
slice_max(order_by = -mortality_improvement, n = 5)## # A tibble: 5 × 7
## country minimum_year maximum_year region original_mortal… final_mortality
## <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Mongolia 1995 2011 East A… 88.1 27.9
## 2 Cambodia 1997 2011 East A… 120. 40.6
## 3 Indonesia 1990 2011 East A… 84 32.6
## 4 Malaysia 1994 2011 East A… 13.9 8
## 5 Philippines 1992 2009 East A… 50.4 32.1
## # … with 1 more variable: mortality_improvement <dbl>Is there a relationship between primary school enrollment and fertility rate?
Yes, there seems to be a strong inverse relationship between primary school enrollment and fertility rate. We would desire to see a distribution of values like in Europe&Central Asia, where there is a very high percentage of people completing primary education and the higher this percentage, the lower the mortality rate. We can observe a similar pattern for Latin America & Caribbean. In South Asia, the countries have very diverse percentages of population having completed primary education, as does Sub-Saharan Africa. In the latter and in East Asia&Pacific, we also observe an inverse trend: we seem to find an increased mortality rate for countries with higher percentage of population having completed primary education.
ggplot(join_3, aes(x = SE.PRM.NENR, y = SP.DYN.TFRT.IN)) +
geom_point() +
geom_smooth(method="lm") +
labs(title = "Relationship between Primary School Enrollment and Fertility Rate",
x = "Primary School Enrollment",
y = "Fertility Rate")
# GDP components over time and among countries!
UN_GDP_data <- read_excel(here::here("data", "Download-GDPconstant-USD-countries.xls"), # Excel filename
sheet="Download-GDPconstant-USD-countr", # Sheet name
skip=2) # Number of rows to skip# Tidying data - Making data long and expressing all figures in billions
tidy_GDP_data_1 <- UN_GDP_data %>%
pivot_longer(4:51, names_to = "year", values_to = "indicator_data") %>%
mutate(indicator_data = indicator_data / 1000000000)# Renaming indicators into shorter counterparts
tidy_GDP_data_1 <- tidy_GDP_data_1
tidy_GDP_data_1$IndicatorName[tidy_GDP_data_1$IndicatorName == "Exports of goods and services"] <- "Exports"
tidy_GDP_data_1$IndicatorName[tidy_GDP_data_1$IndicatorName == "General government final consumption expenditure"] <- "Government expenditure"
tidy_GDP_data_1$IndicatorName[tidy_GDP_data_1$IndicatorName == "Household consumption expenditure (including Non-profit institutions serving households)"] <- "Household expenditure"
tidy_GDP_data_1$IndicatorName[tidy_GDP_data_1$IndicatorName == "Imports of goods and services"] <- "Imports"
glimpse(tidy_GDP_data_1)## Rows: 176,880
## Columns: 5
## $ CountryID <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4…
## $ Country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanist…
## $ IndicatorName <chr> "Final consumption expenditure", "Final consumption exp…
## $ year <chr> "1970", "1971", "1972", "1973", "1974", "1975", "1976",…
## $ indicator_data <dbl> 5.56, 5.33, 5.20, 5.75, 6.15, 6.32, 6.37, 6.90, 7.09, 6…# Let us compare GDP components for these 3 countries
country_list <- c("United States","India", "Germany")# Required Indicators names for graph
list_indicator_name = c("Gross capital formation","Exports", "Government expenditure", "Household expenditure", "Imports")
tidy_GDP_data_1 %>%
filter(Country%in% country_list) %>% #Filtering by aforementioned country_list
filter(IndicatorName %in% list_indicator_name ) %>% #Filtering by list_indicator_name
group_by(IndicatorName) %>%
# Line plot for GDP over time graph
ggplot(aes(x = year, y = indicator_data, color = IndicatorName, group = IndicatorName)) +
geom_line(aes(x = year, y = indicator_data, color = IndicatorName)) +
facet_wrap(~ Country) +
theme_bw() +
theme(legend.position="right") +
scale_x_discrete(breaks = seq(1970, 2017, 10)) +
scale_color_discrete("Components of GDP", breaks = c("Gross capital formation","Exports", "Government expenditure", "Household expenditure", "Imports")) +
labs(title = "GDP Components over time", subtitle = "In constant 2010 USD", x = "", y = "Billion US$") +
theme(plot.title = element_text(face="bold")) 
Secondly, GDP is the sum of Household Expenditure (Consumption C), Gross Capital Formation (business investment I), Government Expenditure (G) and Net Exports (exports - imports). Even though there is an indicator Gross Domestic Product (GDP) in your dataframe, let’s calculate it given its components discussed above.
#converting to tidy wide format
tidy_GDP_data_2 <- tidy_GDP_data_1%>%
pivot_wider(
names_from = IndicatorName,
values_from = indicator_data)
glimpse(tidy_GDP_data_2)## Rows: 10,560
## Columns: 20
## $ CountryID <dbl> …
## $ Country <chr> …
## $ year <chr> …
## $ `Final consumption expenditure` <dbl> …
## $ `Household expenditure` <dbl> …
## $ `Government expenditure` <dbl> …
## $ `Gross capital formation` <dbl> …
## $ `Gross fixed capital formation (including Acquisitions less disposals of valuables)` <dbl> …
## $ Exports <dbl> …
## $ Imports <dbl> …
## $ `Gross Domestic Product (GDP)` <dbl> …
## $ `Agriculture, hunting, forestry, fishing (ISIC A-B)` <dbl> …
## $ `Mining, Manufacturing, Utilities (ISIC C-E)` <dbl> …
## $ `Manufacturing (ISIC D)` <dbl> …
## $ `Construction (ISIC F)` <dbl> …
## $ `Wholesale, retail trade, restaurants and hotels (ISIC G-H)` <dbl> …
## $ `Transport, storage and communication (ISIC I)` <dbl> …
## $ `Other Activities (ISIC J-P)` <dbl> …
## $ `Total Value Added` <dbl> …
## $ `Changes in inventories` <dbl> …# Using GDP formula to calculate GDP using GDP components - the sum of Household Expenditure (Consumption *C*), Gross Capital Formation (business investment *I*), Government Expenditure (G) and Net Exports (exports - imports)
tidy_GDP_data_2 <- tidy_GDP_data_2 %>%
mutate(calculated_GDP = tidy_GDP_data_2$`Household expenditure`+ tidy_GDP_data_2$`Gross capital formation`+ tidy_GDP_data_2$`Government expenditure`+ tidy_GDP_data_2$`Exports`- tidy_GDP_data_2$`Imports`)
#Calculating % difference between calculated GDP and UN_GDP_data GDP Figures
tidy_GDP_data_2 <- tidy_GDP_data_2 %>%
mutate(percentage_difference_calculatedGDP_columnGDP = (((calculated_GDP / tidy_GDP_data_2$`Gross Domestic Product (GDP)`)-1) * 100))
tidy_GDP_data_2## # A tibble: 10,560 × 22
## CountryID Country year `Final consumpt… `Household expe… `Government exp…
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 4 Afghanistan 1970 5.56 5.07 0.372
## 2 4 Afghanistan 1971 5.33 4.84 0.382
## 3 4 Afghanistan 1972 5.20 4.70 0.402
## 4 4 Afghanistan 1973 5.75 5.21 0.421
## 5 4 Afghanistan 1974 6.15 5.59 0.431
## 6 4 Afghanistan 1975 6.32 5.65 0.598
## 7 4 Afghanistan 1976 6.37 5.68 0.627
## 8 4 Afghanistan 1977 6.90 6.15 0.676
## 9 4 Afghanistan 1978 7.09 6.30 0.725
## 10 4 Afghanistan 1979 6.92 6.15 0.708
## # … with 10,550 more rows, and 16 more variables:
## # Gross capital formation <dbl>,
## # Gross fixed capital formation (including Acquisitions less disposals of valuables) <dbl>,
## # Exports <dbl>, Imports <dbl>, Gross Domestic Product (GDP) <dbl>,
## # Agriculture, hunting, forestry, fishing (ISIC A-B) <dbl>,
## # Mining, Manufacturing, Utilities (ISIC C-E) <dbl>,
## # Manufacturing (ISIC D) <dbl>, Construction (ISIC F) <dbl>, …# Plotting a graph, utilizing Percentage Difference of Calculated GDP and UN_GDP_data GDP Figures to compare this difference across Germany, India, and United States
tidy_GDP_data_2 %>%
filter(Country %in% country_list) %>%
ggplot(aes(x=year, y=percentage_difference_calculatedGDP_columnGDP)) +
geom_line(group = 1, color = "black", size = 0.8) + geom_line(group = 1,color = "black",y=0, size = 0.8) +
facet_wrap(~ Country) +
theme_bw() +
scale_x_discrete(breaks = seq(1970, 2017, 10)) +
geom_ribbon(aes(ymin = 0, ymax = pmin(0, percentage_difference_calculatedGDP_columnGDP), group=1), alpha=0.8, fill = "blue") +
geom_ribbon(aes(ymin = percentage_difference_calculatedGDP_columnGDP, ymax = pmin(0, percentage_difference_calculatedGDP_columnGDP), group=1), alpha=0.8, fill = "yellow") +
labs(title = "Percentage Difference of Calculated GDP and UN_GDP_data GDP Figures",
subtitle = "Yellow: Calculated GDP > UN_GDP_data GDP
Blue: Calculated GDP < UN_GDP_data GDP",
x = "Time period (1970-2017)",
y = "Percentage Difference",
)+
NULL
What is the % difference between calculated GDP and the GDP figure included in the dataframe? In the case of India, the period 1970-1990, the graph depicts a yellow region wherein calculated GDP is higher than the UN_GDP_data GDP figures. However, post 1990 (excluding 2007 and 2010), the graph depicts a blue region wherein calculated GDP is lower than the UN_GDP_data GDP figures. In 2007, the % difference between calculated GDP and the GDP figure is 2.1309% and in 2010, the % difference between calculated GDP and the GDP figure is 1.0660%
In the case of Germany, the graph depicts a yellow region throughout from 1970 to 2017 wherein calculated GDP is higher than the UN_GDP_data GDP figures.The % difference between calculated GDP and the GDP figure was maximum in 1972 at 3.56%. In the United States, the graph depicts a mostly yellow region i.e.calculated GDP is higher than the UN_GDP_data GDP figures, excluding the time period from 2005-2008, 2010, and 2016-2017 which depicted a blue region i.e. calculated GDP is lower than the UN_GDP_data GDP figures.