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Finalize lesson 12 slides, add country profile examples
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Original file line number | Diff line number | Diff line change |
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--- | ||
title: "Using World Bank Data for Country Profile" | ||
author: "Ryan Safner" | ||
date: "11/30/2019" | ||
output: | ||
html_document: | ||
df_print: paged | ||
#theme: | ||
toc: true | ||
toc_depth: 3 | ||
toc_float: true | ||
code_folding: show | ||
highlight: tango | ||
--- | ||
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```{r setup, include=FALSE} | ||
knitr::opts_chunk$set(echo = TRUE) | ||
library(tidyverse) | ||
``` | ||
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Let's use the `wbstats` package for `R`: | ||
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```{r} | ||
# install.packages("wbstats") # install for first use | ||
library(wbstats) # load package | ||
``` | ||
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Search for indicators | ||
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https://data.worldbank.org/indicator | ||
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- NY.GDP.PCAP.CD GDP per capita (current USD) | ||
- NY.GDP.PCAP.KD.ZG (GDP per capita growth rate) | ||
- SL.UEM.TOTL.ZS (total unemployment rate) | ||
- SL.TLF.CACT.FE.ZS female labor force participation rate | ||
- SL.TLF.CACT.MA.ZS male labor force participation rate | ||
- FP.CPI.TOTL.ZG inflation (CPI) | ||
- GC.DOD.TOTL.GD.ZS debt-percent-of-gdp | ||
- SP.POP.TOTL total population | ||
- SP.POP.GROW pop growth | ||
- SI.POV.GINI Gini | ||
- SP.DYN.LE00.IN total life expectancy at birth | ||
- SP.DYN.IMRT.IN infant mortality rate total per 1000 births | ||
- SI.POV.DDAY poverty headcount ratio 1.90 per day % of pop | ||
- NE.TRD.GNFS.ZS trade % of gdp | ||
- DT.ODA.OATL.CD net official aid received (current USD) | ||
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```{r} | ||
# example of searching manually in the system (vs online) | ||
wbsearch(pattern = "unemployment") | ||
``` | ||
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Figure out what country code is in World Bank (e.g. "US" for United States) | ||
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```{r} | ||
usa<-wb(country = c("US"), | ||
indicator = c("NY.GDP.PCAP.CD", "NY.GDP.PCAP.KD.ZG", "SL.UEM.TOTL.ZS", "FP.CPI.TOTL.ZG", "GC.DOD.TOTL.GD.ZS", "SP.POP.TOTL", "SP.POP.GROW", "SI.POV.GINI", "SP.DYN.LE00.IN", "SP.DYN.IMRT.IN", "SI.POV.DDAY", "NE.TRD.GNFS.ZS"), | ||
startdate = 1970, enddate = 2019, | ||
return_wide = TRUE) %>% | ||
mutate(date = as.numeric(date)) %>% | ||
rename("Year" = date, | ||
"GDP_per_Capita"= NY.GDP.PCAP.CD, | ||
"GDP_growth" = NY.GDP.PCAP.KD.ZG, | ||
"Unemployment" = SL.UEM.TOTL.ZS, | ||
"Inflation" = FP.CPI.TOTL.ZG, | ||
"Debt_pct_GDP" = GC.DOD.TOTL.GD.ZS, | ||
"Population" = SP.POP.TOTL, | ||
"Pop_growth" = SP.POP.GROW, | ||
"Gini" = SI.POV.GINI, | ||
"Life_Exp" = SP.DYN.LE00.IN, | ||
"Infant_mortality" = SP.DYN.IMRT.IN, | ||
"Poverty_pct" = SI.POV.DDAY, | ||
"Trade_pct_GDP" = NE.TRD.GNFS.ZS) | ||
``` | ||
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```{r} | ||
usa | ||
``` | ||
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```{r, fig.retina=3} | ||
ggplot(data = usa)+ | ||
aes(x = Year, | ||
y = GDP_per_Capita)+ | ||
#geom_point()+ | ||
geom_path(size=2, color = "blue")+ | ||
scale_x_continuous(breaks=seq(1970,2020,10))+ | ||
scale_y_continuous(breaks=seq(10000,60000,10000), | ||
labels=scales::dollar)+ | ||
theme_classic(base_family = "Fira Sans Condensed", base_size=18)+ | ||
labs(x = "Year", | ||
y = "GDP per Capita (Current USD)") | ||
``` | ||
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```{r} | ||
ggplot(data = usa)+ | ||
aes(x = Year, | ||
y = Unemployment)+ | ||
#geom_point()+ | ||
geom_path(size=2, color = "blue")+ | ||
scale_x_continuous(breaks=seq(1970,2020,10))+ | ||
scale_y_continuous(labels=function(x){paste(x,"%")})+ | ||
theme_classic(base_family = "Fira Sans Condensed", base_size=18)+ | ||
labs(x = "Year", | ||
y = "Unemployment Rate (%)") | ||
``` | ||
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```{r} | ||
ggplot(data = usa)+ | ||
aes(x = Year, | ||
y = GDP_growth)+ | ||
#geom_point()+ | ||
geom_path(size=2, color = "blue")+ | ||
geom_hline(yintercept=0, size =1)+ | ||
scale_x_continuous(breaks=seq(1970,2020,10))+ | ||
scale_y_continuous(labels=function(x){paste(x,"%")})+ | ||
theme_classic(base_family = "Fira Sans Condensed", base_size=18)+ | ||
labs(x = "Year", | ||
y = "GDP per Capita Growth") | ||
``` | ||
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