library(WDI)
library(wooldridge)
library(rmarkdown)
işsizlik<-WDI(country = "all", indicator = c("SL.UEM.TOTL.FE.ZS","SL.UEM.TOTL.MA.ZS" ))
str(işsizlik)
## 'data.frame': 16758 obs. of 6 variables:
## $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ iso2c : chr "AF" "AF" "AF" "AF" ...
## $ iso3c : chr "AFG" "AFG" "AFG" "AFG" ...
## $ year : int 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
## $ SL.UEM.TOTL.FE.ZS: num NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "label")= chr "Unemployment, female (% of female labor force) (modeled ILO estimate)"
## $ SL.UEM.TOTL.MA.ZS: num NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "label")= chr "Unemployment, male (% of male labor force) (modeled ILO estimate)"
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
library(tidyr)
library(dplyr)
library(explore)
countries <- unique(işsizlik$country)
işsizlik %>% describe_all()
## # A tibble: 6 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 country chr 0 0 266 NA NA NA
## 2 iso2c chr 0 0 266 NA NA NA
## 3 iso3c chr 0 0 262 NA NA NA
## 4 year int 0 0 63 1960 1991 2022
## 5 SL.UEM.TOTL.FE.ZS dbl 9239 55.1 6544 0.15 9.19 44.6
## 6 SL.UEM.TOTL.MA.ZS dbl 9239 55.1 6371 0.04 7.39 37.0
işsizlik2 <- WDI_data$country
işsizlik <- left_join(işsizlik, işsizlik2)
## Joining with `by = join_by(country, iso2c, iso3c)`
işsizlik3 <- işsizlik%>%filter(region!="aggregates")
işsizlik3 %>% describe_all()
## # 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 63 1960 1991 2022
## 5 SL.UEM.TOTL.FE.ZS dbl 8928 55.1 6342 0.15 9.3 44.6
## 6 SL.UEM.TOTL.MA.ZS dbl 8928 55.1 6166 0.04 7.46 37.0
## 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
işsizlik3 <- işsizlik3 %>% filter(year >= 2000)
işsizlik3 %>% describe_all()
## # 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 23 2000 2011 2022
## 5 SL.UEM.TOTL.FE.ZS dbl 691 11.7 4720 0.15 9.26 42.6
## 6 SL.UEM.TOTL.MA.ZS dbl 691 11.7 4621 0.04 7.37 37.0
## 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
boşsayi <- işsizlik3 %>% group_by(country) %>% summarise(sayi=sum(is.na(SL.UEM.TOTL.FE.ZS)))
işsizlik3 <-left_join(işsizlik3, boşsayi)
## Joining with `by = join_by(country)`
işsizlik3 <- işsizlik3 %>% filter(sayi <= 0)
işsizlik3 %>% describe_all()
## # A tibble: 13 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 country chr 0 0 226 NA NA NA
## 2 iso2c chr 0 0 226 NA NA NA
## 3 iso3c chr 0 0 226 NA NA NA
## 4 year int 0 0 23 2000 2011 2022
## 5 SL.UEM.TOTL.FE.ZS dbl 0 0 4700 0.15 9.27 42.6
## 6 SL.UEM.TOTL.MA.ZS dbl 0 0 4603 0.04 7.37 37.0
## 7 region chr 0 0 8 NA NA NA
## 8 capital chr 0 0 180 NA NA NA
## 9 longitude chr 0 0 183 NA NA NA
## 10 latitude chr 0 0 183 NA NA NA
## 11 income chr 0 0 6 NA NA NA
## 12 lending chr 0 0 5 NA NA NA
## 13 sayi int 0 0 1 0 0 0