library(WDI)
library(wbstats)
library(tidyverse)
## āā Attaching packages āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā tidyverse 1.3.2 āā
## ā ggplot2 3.3.6 ā purrr 0.3.4
## ā tibble 3.1.8 ā dplyr 1.0.9
## ā tidyr 1.2.0 ā stringr 1.4.0
## ā readr 2.1.2 ā forcats 0.5.1
## āā Conflicts āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā tidyverse_conflicts() āā
## ā dplyr::filter() masks stats::filter()
## ā dplyr::lag() masks stats::lag()
gdp_data<- wb_data(country = "MX", indicator = "NY.GDP.PCAP.CD", start_date = 1973, end_date = 2022)
summary(gdp_data)
## iso2c iso3c country date
## Length:49 Length:49 Length:49 Min. :1973
## Class :character Class :character Class :character 1st Qu.:1985
## Mode :character Mode :character Mode :character Median :1997
## Mean :1997
## 3rd Qu.:2009
## Max. :2021
## NY.GDP.PCAP.CD unit obs_status footnote
## Min. : 981.5 Length:49 Length:49 Length:49
## 1st Qu.: 2569.2 Class :character Class :character Class :character
## Median : 5650.0 Mode :character Mode :character Mode :character
## Mean : 5751.7
## 3rd Qu.: 9068.3
## Max. :10928.9
## last_updated
## Min. :2022-09-16
## 1st Qu.:2022-09-16
## Median :2022-09-16
## Mean :2022-09-16
## 3rd Qu.:2022-09-16
## Max. :2022-09-16
head(gdp_data)
## # A tibble: 6 Ć 9
## iso2c iso3c country date NY.GDP.PCAP.CD unit obs_status footnote last_updaā¦Ā¹
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <date>
## 1 MX MEX Mexico 1973 981. <NA> <NA> <NA> 2022-09-16
## 2 MX MEX Mexico 1974 1242. <NA> <NA> <NA> 2022-09-16
## 3 MX MEX Mexico 1975 1476. <NA> <NA> <NA> 2022-09-16
## 4 MX MEX Mexico 1976 1454. <NA> <NA> <NA> 2022-09-16
## 5 MX MEX Mexico 1977 1301. <NA> <NA> <NA> 2022-09-16
## 6 MX MEX Mexico 1978 1589. <NA> <NA> <NA> 2022-09-16
## # ⦠with abbreviated variable name ¹ālast_updated
tail(gdp_data)
## # A tibble: 6 Ć 9
## iso2c iso3c country date NY.GDP.PCAP.CD unit obs_status footnote last_updaā¦Ā¹
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <date>
## 1 MX MEX Mexico 2016 8745. <NA> <NA> <NA> 2022-09-16
## 2 MX MEX Mexico 2017 9288. <NA> <NA> <NA> 2022-09-16
## 3 MX MEX Mexico 2018 9687. <NA> <NA> <NA> 2022-09-16
## 4 MX MEX Mexico 2019 9950. <NA> <NA> <NA> 2022-09-16
## 5 MX MEX Mexico 2020 8432. <NA> <NA> <NA> 2022-09-16
## 6 MX MEX Mexico 2021 9926. <NA> <NA> <NA> 2022-09-16
## # ⦠with abbreviated variable name ¹ālast_updated
ggplot(gdp_data, aes(x = date, y = NY.GDP.PCAP.CD)) +
geom_point()
ggplot(gdp_data, aes(x = date, y = NY.GDP.PCAP.CD)) +
geom_col()
ggplot(gdp_data, aes(x = date, y = NY.GDP.PCAP.CD)) +
geom_col(fill = "red") +
geom_point(color = "blue")
more_gdp_data<- wb_data(country = c("NG", "HT", "KE"),
indicator = "NY.GDP.PCAP.CD",
start_date = 1981, end_date = 2015)
ggplot(more_gdp_data, aes(x=date,y=NY.GDP.PCAP.CD,color=country,shape=country))+
geom_point()
Este codigo nos ayuda a extraer datos del banco mundial sobre muchos paises ya que ellos cuentan con muchisimas bases de datos. En este codigo se puede observar como el GDP de Mexico ha cambiado durante los aƱos en una grafica y como el GDP de diferentes paises ha cambiado en una misma grafica.