Load the required library:
library(WDI)
library(dplyr)
library(tidyr)
library(DT)
library(ggplot2)
library(ggthemes)
library(ggrepel)
library(gganimate)And see, if need, the info of your session:
devtools::session_info()## setting value
## version R version 3.4.3 (2017-11-30)
## system i686, linux-gnu
## ui X11
## language en_US
## collate en_US.UTF-8
## tz Portugal
## date 2018-11-13
##
## package * version date source
## assertthat 0.2.0 2017-04-11 CRAN (R 3.4.3)
## backports 1.1.2 2017-12-13 CRAN (R 3.4.3)
## base * 3.4.3 2017-12-29 local
## bindr 0.1.1 2018-03-13 cran (@0.1.1)
## bindrcpp 0.2.2 2018-03-29 cran (@0.2.2)
## colorspace 1.3-2 2016-12-14 CRAN (R 3.4.3)
## compiler 3.4.3 2017-12-29 local
## crayon 1.3.4 2017-09-16 CRAN (R 3.4.3)
## datasets * 3.4.3 2017-12-29 local
## devtools 1.13.4 2017-11-09 CRAN (R 3.4.3)
## digest 0.6.18 2018-10-10 cran (@0.6.18)
## dplyr * 0.7.8 2018-11-10 cran (@0.7.8)
## DT * 0.5 2018-11-05 CRAN (R 3.4.3)
## evaluate 0.10.1 2017-06-24 CRAN (R 3.4.3)
## farver 1.0.0.9999 2018-11-09 Github (thomasp85/farver@5439336)
## gganimate * 0.9.9.9999 2018-11-09 Github (thomasp85/gganimate@cc23618)
## ggplot2 * 3.1.0.9000 2018-11-12 Github (tidyverse/ggplot2@f5a88a7)
## ggrepel * 0.8.0 2018-05-09 CRAN (R 3.4.3)
## ggthemes * 3.5.0 2018-05-07 cran (@3.5.0)
## gifski 0.8.6 2018-09-28 cran (@0.8.6)
## glue 1.3.0 2018-07-17 CRAN (R 3.4.3)
## graphics * 3.4.3 2017-12-29 local
## grDevices * 3.4.3 2017-12-29 local
## grid 3.4.3 2017-12-29 local
## gtable 0.2.0 2016-02-26 CRAN (R 3.4.3)
## hms 0.4.2 2018-03-10 cran (@0.4.2)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.4.3)
## htmlwidgets 1.3 2018-09-30 cran (@1.3)
## knitr 1.20 2018-02-20 cran (@1.20)
## lazyeval 0.2.1 2017-10-29 CRAN (R 3.4.3)
## magrittr 1.5 2014-11-22 CRAN (R 3.4.3)
## memoise 1.1.0 2017-04-21 CRAN (R 3.4.3)
## methods * 3.4.3 2017-12-29 local
## munsell 0.5.0 2018-06-12 CRAN (R 3.4.3)
## pillar 1.2.3 2018-05-25 cran (@1.2.3)
## pkgconfig 2.0.2 2018-08-16 cran (@2.0.2)
## plyr 1.8.4 2016-06-08 CRAN (R 3.4.3)
## png 0.1-7 2013-12-03 CRAN (R 3.4.3)
## prettydoc 0.2.1 2018-01-16 CRAN (R 3.4.3)
## prettyunits 1.0.2 2015-07-13 cran (@1.0.2)
## progress 1.2.0 2018-06-14 CRAN (R 3.4.3)
## purrr 0.2.5 2018-05-29 cran (@0.2.5)
## R6 2.3.0 2018-10-04 cran (@2.3.0)
## Rcpp 1.0.0 2018-11-07 cran (@1.0.0)
## RJSONIO * 1.3-0 2014-07-28 CRAN (R 3.4.3)
## rlang 0.3.0.1 2018-10-25 cran (@0.3.0.1)
## rmarkdown 1.10 2018-06-11 cran (@1.10)
## rprojroot 1.3-2 2018-01-03 cran (@1.3-2)
## scales 1.0.0 2018-08-09 CRAN (R 3.4.3)
## stats * 3.4.3 2017-12-29 local
## stringi 1.2.4 2018-07-20 cran (@1.2.4)
## stringr 1.3.1 2018-05-10 cran (@1.3.1)
## tibble 1.4.2 2018-01-22 cran (@1.4.2)
## tidyr * 0.8.2 2018-10-28 cran (@0.8.2)
## tidyselect 0.2.5 2018-10-11 cran (@0.2.5)
## tools 3.4.3 2017-12-29 local
## tweenr 0.1.5.9999 2018-11-09 Github (thomasp85/tweenr@48ea2fd)
## utils * 3.4.3 2017-12-29 local
## WDI * 2.5 2018-04-10 CRAN (R 3.4.3)
## withr 2.1.2.9000 2018-11-06 Github (jimhester/withr@be57595)
## yaml 2.2.0 2018-07-25 cran (@2.2.0)Search and browse data.worldbank.org
We can search the whole database, for example about mortality:
WDIsearch('mortality')Usually, it is much convienient if we export the results as a table to view:
WDIsearch('mortality') %>% datatable()Yes, we are looking for one of the most famous index WD has compiled: How many child loose their within first year. It is a strong indicator of how good the health system is.
url <- "https://www.google.com/search?q=SP.DYN.IMRT.IN+world+bank"
browseURL(url)Mortality rate, infant (per 1,000 live births)
wb_ind <- "SP.DYN.IMRT.IN"
wb_data <- WDI(country = c("GR", "PT", "ES"), indicator = "SP.DYN.IMRT.IN", start = 1960)
wb_data %>% datatable()wb_data <- wb_data %>%
rename(value = SP.DYN.IMRT.IN) %>%
select(iso2c, country, year, value)plotting the data with animation:
wb_data %>%
arrange(country, year) %>%
ggplot(aes(x = year, y = value, group = country)) +
geom_line(aes(colour = country), size = 1.25) +
geom_segment(aes(xend = 2013, yend = value, colour = country), linetype = 2) +
geom_point(aes(colour = country), size = 4) +
scale_fill_brewer(palette="Spectral") +
geom_text(aes(x = 2013, label = iso2c, colour = country), hjust = 0, nudge_x = 0.1,
size = 6, fontface = "bold") +
transition_reveal(country, year) +
theme_wsj() +
labs(title = "Infant mortality", subtitle = "number of losses per 1000 live births") +
xlab("Year") + ylab("mortality") +
ylim(c(0, 90)) +
scale_color_discrete(guide = FALSE) +
theme(text = element_text(size = 14)) 
Education
WDIsearch('education') %>% datatable()wb_data <- WDI(country = c("GR", "PT", "ES"), indicator = c("SE.TER.ENRL", "SE.TER.GRAD"), start = 1960)
wb_data <- wb_data %>%
rename(enrl = SE.TER.ENRL, grad = SE.TER.GRAD) %>%
mutate(perc = 100 * grad / enrl) %>%
gather(wdi, value, -iso2c, -country, -year)
wb_data %>%
filter(wdi != 'perc') %>%
ggplot(aes(x = year, y = value, colour = wdi)) +
geom_line() +
facet_grid(~country)## Warning: Removed 2 rows containing missing values (geom_path).
plotting
wb_data %>%
filter(wdi == 'perc') %>%
ggplot(aes(x = year, y = value, colour = country)) +
geom_line(size = 2) +
theme_dark()
