Dünya bankası verılerını yüklemek

WDI Paketi yüklemek

library(WDI)

FP.CPI.TOTL.ZG

SP.POP.TOTL

data <- WDI(country = "all", indicator =c("FP.CPI.TOTL.ZG","SP.POP.TOTL"), start = 2000)

explore paketi

library(explore)
describe_all(data)
## # A tibble: 6 × 8
##   variable       type     na na_pct unique    min         mean         max
##   <chr>          <chr> <int>  <dbl>  <int>  <dbl>        <dbl>       <dbl>
## 1 country        chr       0    0      266   NA          NA            NA 
## 2 iso2c          chr       0    0      266   NA          NA            NA 
## 3 iso3c          chr       0    0      262   NA          NA            NA 
## 4 year           int       0    0       24 2000        2012.         2023 
## 5 FP.CPI.TOTL.ZG dbl    1165   18.2   4612  -16.9         6.01        557.
## 6 SP.POP.TOTL    dbl     289    4.5   6047 9609   285615032.   7950946801

EKSTRA BILGI VERISI

ekstra_veri <- WDI_data$country
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.0     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
data_ekstra <- left_join(data, ekstra_veri)
## Joining with `by = join_by(country, iso2c, iso3c)`
data_ekstra <- data_ekstra %>% filter(income !="aggregates")
data_ekstra %>% describe()
## # A tibble: 12 × 8
##    variable       type     na na_pct unique    min         mean         max
##    <chr>          <chr> <int>  <dbl>  <int>  <dbl>        <dbl>       <dbl>
##  1 country        chr       0    0      257   NA          NA            NA 
##  2 iso2c          chr       0    0      257   NA          NA            NA 
##  3 iso3c          chr       0    0      257   NA          NA            NA 
##  4 year           int       0    0       24 2000        2012.         2023 
##  5 FP.CPI.TOTL.ZG dbl    1133   18.4   4523  -16.9         6.07        557.
##  6 SP.POP.TOTL    dbl     257    4.2   5886 9609   260875760.   7950946801 
##  7 region         chr       0    0        8   NA          NA            NA 
##  8 capital        chr       0    0      210   NA          NA            NA 
##  9 longitude      chr       0    0      210   NA          NA            NA 
## 10 latitude       chr       0    0      210   NA          NA            NA 
## 11 income         chr       0    0        6   NA          NA            NA 
## 12 lending        chr       0    0        5   NA          NA            NA
veri <- data.frame()