işsızlik

library(WDI)

NY.GDP.MKTP.CD: Nominal GSYH

SP.POP.TOTL: populasyon

data <- WDI(country = "all",indicator =c("NY.GDP.MKTP.CD","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   2.01e 3  2.02e 3
## 5 NY.GDP.MKTP.CD dbl     512    8     5833 13964732.  2.11e12  1.01e14
## 6 SP.POP.TOTL    dbl     289    4.5   6047     9609   2.86e 8  7.95e 9

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_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      215       NA            NA  NA      
##  2 iso2c          chr       0    0      215       NA            NA  NA      
##  3 iso3c          chr       0    0      215       NA            NA  NA      
##  4 year           int       0    0       24     2000          2012.  2.02e 3
##  5 NY.GDP.MKTP.CD dbl     438    8.5   4723 13964732. 319966608092.  2.54e13
##  6 SP.POP.TOTL    dbl     215    4.2   4943     9609      32268693.  1.42e 9
##  7 region         chr       0    0        7       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        5       NA            NA  NA      
## 12 lending        chr       0    0        4       NA            NA  NA
veri <- data.frame(
  Ulke = c("Turkiye", "ABD", "cin", "Almanya", "Fransa"),
  GSYH_2000 = c(100, 200, NA, 150, 180),
  GSYH_2001 = c(110, 210, 220, NA, 190),
  GSYH_2002 = c(120, 220, NA, 170, 200),
  GSYH_2022 = c(300, NA, 500, 350, 380)
)
veri <- pivot_longer(veri, cols = -Ulke, names_to = "Yil", values_to = "GSYH")
kayip_degerler <- data_ekstra %>% group_by(country) %>% 
  summarise(kayip = sum(is.na(NY.GDP.MKTP.CD)))
data <- left_join(data_ekstra, kayip_degerler) 
## Joining with `by = join_by(country)`
describe_all(data)
## # 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      215       NA  NA       NA      
##  2 iso2c          chr       0    0      215       NA  NA       NA      
##  3 iso3c          chr       0    0      215       NA  NA       NA      
##  4 year           int       0    0       24     2000   2.01e 3  2.02e 3
##  5 NY.GDP.MKTP.CD dbl     438    8.5   4723 13964732.  3.20e11  2.54e13
##  6 SP.POP.TOTL    dbl     215    4.2   4943     9609   3.23e 7  1.42e 9
##  7 region         chr       0    0        7       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        5       NA  NA       NA      
## 12 lending        chr       0    0        4       NA  NA       NA      
## 13 kayip          int       0    0       14        1   2.04e 0  2.4 e 1

Bireysel ülke seçimi ve grafik

Almanya’yı seçelim

data_Almanya <- data %>% filter(iso2c == "DE")
library(ggplot2)
ggplot(data_Almanya,aes(x= year,y=NY.GDP.MKTP.CD))+ 
geom_line(colours ="blue") +
  labs(tiltle = "GDP Değişimi (2000-2022)", x = "Yıl", y = 
"GDP") +
  theme_minimal()
## Warning in geom_line(colours = "blue"): Ignoring unknown parameters: `colours`
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).

##kesit veri almak

df_kesitveri <- data %>% filter(year==2022)
ggplot(df_kesitveri, aes(x= SP.POP.TOTL, y=NY.GDP.MKTP.CD)) + 
 geom_point() +
 geom_text(label=df_kesitveri$iso2c)
## Warning: Removed 23 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 23 rows containing missing values or values outside the scale range
## (`geom_text()`).

Dünya toplam verisi olusturma

toplam dünya nufusu, toplam dunya uretimi, her sene icin dunya kisi basi ne kadar uretim yapmis

dunya_datası <-data %>% group_by(year) %>%
  summarise(dunyauretimi = sum(NY.GDP.MKTP.CD),
            dunyanufusu = sum(SP.POP.TOTL),
            kisibasiuretim = dunyauretimi/dunyanufusu)
ggplot(dunya_datası,aes(x= year, y = kisibasiuretim, colour = "red")) +
  geom_line() +
  labs(title = "kisi basi uretim",
       y = "kisi basi uretim",
       x = "sene") +
  theme_linedraw()
## Warning: Removed 24 rows containing missing values or values outside the scale range
## (`geom_line()`).

data<- left_join(data,dunya_datası,by= "year")

ulkelerin paylari

Belirli bir ulkenin o sene icin uretimdeki payi nedir?

Belirli bir ulkenin populasyondaki payi nedir?

ulkenin senelik verimi nedir?

mutate

dat <-data%>% mutate(ulkenınuretimoranı = NY.GDP.MKTP.CD /dunyauretimi,
                        ulkeninpopulasyonorani = SP.POP.TOTL/dunyanufusu,
                        verim =dunyauretimi /dunyanufusu)
ggplot(data[985:1000,], aes(x=year, y= dunyauretimi)) +
  geom_point()
## Warning: Removed 16 rows containing missing values or values outside the scale range
## (`geom_point()`).

not