ilkodev

İLK ÖDEV

DÜNYA BANKASINDAN VERİ İNDİRME

library(WDI)

data <- WDI(indicator = c("SE.PRM.UNER.FE", "SE.PRM.UNER.MA"))

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   3.5.1     ✔ 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

dataTR <- data %>% filter(country=="Turkiye")

dataTR <- dataTR %>% na.omit("SE.PRM.UNER.FE")

dataTR <- dataTR %>% na.omit("SE.PRM.UNER.MA")

KESİT VERİ

data2005 <- data %>% filter(year==2005)

data2005 <- data2005 %>% na.omit(SE.PRM.UNER.FE)

data2005 <- data2005 %>% na.omit(SE.PRM.UNER.MA)

ZAMAN SERİSİ

library(ggplot2)

library(dplyr)

ggplot(dataTR, aes(year,SE.PRM.UNER.FE)) + geom_line()

ggplot(dataTR, aes(year,SE.PRM.UNER.MA)) + geom_line()

GRAFİK KARŞILAŞTIRMASI

ggplot(dataTR, aes(SE.PRM.UNER.FE,SE.PRM.UNER.MA)) + geom_line()

REGRESYON

model <- lm(SE.PRM.UNER.FE ~ SE.PRM.UNER.MA, data = dataTR)

summary(model)

## 
## Call:
## lm(formula = SE.PRM.UNER.FE ~ SE.PRM.UNER.MA, data = dataTR)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43285 -22123  -7559  23630  52854 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -8.643e+03  1.861e+04  -0.464    0.659    
## SE.PRM.UNER.MA  1.522e+00  8.394e-02  18.129 1.81e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36370 on 6 degrees of freedom
## Multiple R-squared:  0.9821, Adjusted R-squared:  0.9791 
## F-statistic: 328.7 on 1 and 6 DF,  p-value: 1.813e-06