imyks

WDI Paketi ve Veri indirme

GDP ve POP veriler

GDP: NY.GDP.MKTP.CD

POP: SP.POP.TOTL

Elektriğe erişim: EG.ELC.ACCS.ZS

Yakıt enerjisi: EG.USE.COMM.FO.ZS

library(WDI)

library(tidyverse)

proje <- WDI(country = ("all"), indicator = c("gsyh" = "NY.GDP.MKTP.CD","pop" = "SP.POP.TOTL", "elektrik" = "EG.ELC.ACCS.ZS", "yakıt" = "EG.USE.COMM.FO.ZS"), start = 1999, end = 2020)

library(explore)

describe_all(proje)

## # A tibble: 8 × 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       22     1999    2.01e 3  2.02e 3
## 5 gsyh     dbl     234    4     5581 13687141.   1.96e12  8.78e13
## 6 pop      dbl      22    0.4   5784     9609    2.80e 8  7.82e 9
## 7 elektrik dbl     152    2.6   3623        0.8  7.98e 1  1   e 2
## 8 yakıt    dbl    2754   47.1   2881        0    6.57e 1  1   e 2

proje2 <- WDI_data$country

proje <- left_join(proje,proje2)

## Joining with `by = join_by(country, iso2c, iso3c)`

proje <- proje %>%  filter(income != "Aggregates")

describe_all(proje)

## # A tibble: 14 × 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       22     1999    2.01e 3  2.02e 3
##  5 gsyh      dbl     212    4.5   4519 13687141.   2.99e11  2.14e13
##  6 pop       dbl       0    0     4727     9609    3.17e 7  1.41e 9
##  7 elektrik  dbl     112    2.4   2651        0.8  8.04e 1  1   e 2
##  8 yakıt     dbl    2436   51.5   2146        0    6.46e 1  1   e 2
##  9 region    chr       0    0        7       NA   NA       NA      
## 10 capital   chr       0    0      210       NA   NA       NA      
## 11 longitude chr       0    0      210       NA   NA       NA      
## 12 latitude  chr       0    0      210       NA   NA       NA      
## 13 income    chr       0    0        5       NA   NA       NA      
## 14 lending   chr       0    0        4       NA   NA       NA

none <- proje %>% group_by(country) %>% summarise(sayi = sum(is.na(elektrik)))

proje <- left_join(proje, none)

## Joining with `by = join_by(country)`

proje <- proje %>% filter(sayi == 0)

describe_all(proje)

## # A tibble: 15 × 8
##    variable  type     na na_pct unique         min     mean      max
##    <chr>     <chr> <int>  <dbl>  <int>       <dbl>    <dbl>    <dbl>
##  1 country   chr       0    0      169       NA    NA       NA      
##  2 iso2c     chr       0    0      169       NA    NA       NA      
##  3 iso3c     chr       0    0      169       NA    NA       NA      
##  4 year      int       0    0       22     1999     2.01e 3  2.02e 3
##  5 gsyh      dbl     143    3.8   3576 47562845.    3.21e11  2.14e13
##  6 pop       dbl       0    0     3715    10233     2.81e 7  1.40e 9
##  7 elektrik  dbl       0    0     1924        2.07  8.30e 1  1   e 2
##  8 yakıt     dbl    1869   50.3   1745        0     6.51e 1  1   e 2
##  9 region    chr       0    0        7       NA    NA       NA      
## 10 capital   chr       0    0      164       NA    NA       NA      
## 11 longitude chr       0    0      164       NA    NA       NA      
## 12 latitude  chr       0    0      164       NA    NA       NA      
## 13 income    chr       0    0        5       NA    NA       NA      
## 14 lending   chr       0    0        4       NA    NA       NA      
## 15 sayi      int       0    0        1        0     0        0

none2 <- proje %>% group_by(country) %>% summarise(sayi2= sum(is.na(gsyh)))

proje <- left_join(proje, none2)

## Joining with `by = join_by(country)`

proje <- proje %>% filter(sayi2 == 0)

describe_all(proje)

## # A tibble: 16 × 8
##    variable  type     na na_pct unique          min     mean      max
##    <chr>     <chr> <int>  <dbl>  <int>        <dbl>    <dbl>    <dbl>
##  1 country   chr       0    0      152        NA    NA       NA      
##  2 iso2c     chr       0    0      152        NA    NA       NA      
##  3 iso3c     chr       0    0      152        NA    NA       NA      
##  4 year      int       0    0       22      1999     2.01e 3  2.02e 3
##  5 gsyh      dbl       0    0     3344 114326300     3.42e11  2.14e13
##  6 pop       dbl       0    0     3342     26404     3.09e 7  1.40e 9
##  7 elektrik  dbl       0    0     1765         2.07  8.18e 1  1   e 2
##  8 yakıt     dbl    1562   46.7   1686         0     6.48e 1  1   e 2
##  9 region    chr       0    0        7        NA    NA       NA      
## 10 capital   chr       0    0      149        NA    NA       NA      
## 11 longitude chr       0    0      152        NA    NA       NA      
## 12 latitude  chr       0    0      152        NA    NA       NA      
## 13 income    chr       0    0        4        NA    NA       NA      
## 14 lending   chr       0    0        4        NA    NA       NA      
## 15 sayi      int       0    0        1         0     0        0      
## 16 sayi2     int       0    0        1         0     0        0

none3 <- proje %>% group_by(country) %>% summarise(sayi3= sum(is.na(yakıt)))

proje <- left_join(proje, none3)

## Joining with `by = join_by(country)`

proje <- proje %>% filter(sayi3 == 5)

describe_all(proje)

## # A tibble: 17 × 8
##    variable  type     na na_pct unique          min     mean      max
##    <chr>     <chr> <int>  <dbl>  <int>        <dbl>    <dbl>    <dbl>
##  1 country   chr       0    0       32         NA   NA       NA      
##  2 iso2c     chr       0    0       32         NA   NA       NA      
##  3 iso3c     chr       0    0       32         NA   NA       NA      
##  4 year      int       0    0       22       1999    2.01e 3  2.02e 3
##  5 gsyh      dbl       0    0      704 5686579748.   1.30e12  2.14e13
##  6 pop       dbl       0    0      703     277381    3.58e 7  3.32e 8
##  7 elektrik  dbl       0    0       60         96.7  9.99e 1  1   e 2
##  8 yakıt     dbl     160   22.7    545         10.2  7.32e 1  9.81e 1
##  9 region    chr       0    0        5         NA   NA       NA      
## 10 capital   chr       0    0       32         NA   NA       NA      
## 11 longitude chr       0    0       32         NA   NA       NA      
## 12 latitude  chr       0    0       32         NA   NA       NA      
## 13 income    chr       0    0        2         NA   NA       NA      
## 14 lending   chr       0    0        2         NA   NA       NA      
## 15 sayi      int       0    0        1          0    0        0      
## 16 sayi2     int       0    0        1          0    0        0      
## 17 sayi3     int       0    0        1          5    5   e 0  5   e 0

ULKE Analizi

BELGIUM,FRANCE

BELGIUM

proje_BE <- proje %>% filter(country == "Belgium")

library(ggplot2)

ggplot(proje_BE, aes(x=year, y=gsyh)) + geom_area(colour = "yellow")

FRANCE

proje_FR <- proje %>%  filter(country == "France")

proje_FR <- proje_FR %>% mutate( GSYH_kisibasi = gsyh / pop)

ggplot(proje_FR, aes(x= year, y= GSYH_kisibasi)) + geom_area()

2008 analizi

proje_2008 <- proje %>% filter(year==2008)

proje_2008 <- proje_2008 %>% mutate(kisibasi = gsyh/pop)

ggplot(proje_2008, aes(pop)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.