vize projesi

wdı bir Turkey ve chad

library(WDI)

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyr)

library(ggplot2)

göstergeler <- c(
  "SE.PRM.UNER.MA",  # Okul dışı çocuklar, ilkokul, erkek
  "SL.TLF.TOTL.IN",      # iş gücü toplamı
  "SE.XPD.TOTL.GD.ZS",      # Eğitime yönelik devlet harcamaları, toplam (% GSYİH)
  "SE.ADT.1524.LT.ZS" # Okuryazarlık oranı, genç toplam (% 15-24 yaş arası)
)

veri <- WDI(
  country = c("TR", "TD"),  
  indicator = göstergeler,      
  start = 2000,                
  end = 2023                   
)

colnames(veri)[colnames(veri) == "country"] <- "Ulke"

head(veri)

  Ulke iso2c iso3c year SE.PRM.UNER.MA SL.TLF.TOTL.IN SE.XPD.TOTL.GD.ZS
1 Chad    TD   TCD 2000         280428        3012805           2.64671
2 Chad    TD   TCD 2001         285275        3080214           2.42614
3 Chad    TD   TCD 2002         260733        3158872                NA
4 Chad    TD   TCD 2003         260258        3273492                NA
5 Chad    TD   TCD 2004             NA        3419195           1.59295
6 Chad    TD   TCD 2005             NA        3541960           1.69477
  SE.ADT.1524.LT.ZS
1                38
2                NA
3                NA
4                NA
5                42
6                NA

sum(is.na(veri))

[1] 72

veri_temiz <- veri %>% drop_na()

names(veri)[names(veri) == "country"] <- "Ülke"
names(veri)[names(veri) == "year"] <- "Yıl"
names(veri)[names(veri) == "SE.PRM.UNER.MA"] <- "Okul dışı çocuklar, ilkokul, erkek"
names(veri)[names(veri) == "SL.TLF.TOTL.IN"] <- " iş gücü toplamı"
names(veri)[names(veri) == "SE.XPD.TOTL.GD.ZS"] <- "Eğitime yönelik devlet harcamaları, toplam (% GSYİH)"
names(veri)[names(veri) == "SE.ADT.1524.LT.ZS"] <- "Okuryazarlık oranı, genç toplam (% 15-24 yaş arası"

str(veri_temiz)

'data.frame':   9 obs. of  8 variables:
 $ Ulke             : chr  "Chad" "Chad" "Chad" "Chad" ...
 $ iso2c            : chr  "TD" "TD" "TD" "TD" ...
 $ iso3c            : chr  "TCD" "TCD" "TCD" "TCD" ...
 $ year             : int  2000 2015 2016 2019 2022 2014 2015 2016 2017
 $ SE.PRM.UNER.MA   : num  280428 147226 249834 299709 300130 ...
  ..- attr(*, "label")= chr "Children out of school, primary, male"
 $ SL.TLF.TOTL.IN   : num  3012805 4641540 4749824 5188027 5881552 ...
  ..- attr(*, "label")= chr "Labor force, total"
 $ SE.XPD.TOTL.GD.ZS: num  2.65 2.34 2.39 2.37 2.54 ...
  ..- attr(*, "label")= chr "Government expenditure on education, total (% of GDP)"
 $ SE.ADT.1524.LT.ZS: num  38 38.8 31 45.1 36.1 ...
  ..- attr(*, "label")= chr "Literacy rate, youth total (% of people ages 15-24)"

summary(veri_temiz)

     Ulke              iso2c              iso3c                year     
 Length:9           Length:9           Length:9           Min.   :2000  
 Class :character   Class :character   Class :character   1st Qu.:2015  
 Mode  :character   Mode  :character   Mode  :character   Median :2016  
                                                          Mean   :2015  
                                                          3rd Qu.:2017  
                                                          Max.   :2022  
 SE.PRM.UNER.MA   SL.TLF.TOTL.IN     SE.XPD.TOTL.GD.ZS SE.ADT.1524.LT.ZS
 Min.   :  8934   Min.   : 3012805   Min.   :2.343     Min.   : 31.00   
 1st Qu.: 37939   1st Qu.: 4749824   1st Qu.:2.388     1st Qu.: 38.00   
 Median :147226   Median : 5881552   Median :2.647     Median : 45.13   
 Mean   :155582   Mean   :16399569   Mean   :3.329     Mean   : 65.22   
 3rd Qu.:280428   3rd Qu.:30550902   3rd Qu.:4.324     3rd Qu.: 99.00   
 Max.   :300130   Max.   :32535361   Max.   :4.628     Max.   :100.00