四則演算

3+3
## [1] 6
3-3
## [1] 0
3*3
## [1] 9
3/3
## [1] 1

#データ読み込み

library(readxl)
asia <- read_excel("asia.xlsx")
head(asia)
## # A tibble: 6 × 10
##   countrycode country    year  rgdpe    pop  ctfp percapitaGDP  oecd    G7  asia
##   <chr>       <chr>     <dbl>  <dbl>  <dbl> <dbl>        <dbl> <dbl> <dbl> <dbl>
## 1 CHN         China      2019 2.01e7 1.43e3 0.400       13988.     0     0     1
## 2 JPN         Japan      2019 5.03e6 1.27e2 0.635       39637.     1     1     1
## 3 KOR         Republic…  2019 2.09e6 5.12e1 0.604       40819.     1     0     1
## 4 PHL         Philippi…  2019 8.87e5 1.08e2 0.507        8205.     0     0     1
## 5 SGP         Singapore  2019 5.14e5 5.80e0 0.691       88619.     0     0     1
## 6 THA         Thailand   2019 1.22e6 6.96e1 0.455       17558.     0     0     1

#棒グラフ（人口と生産量）

barplot(asia$pop, names.arg=asia$countrycode)

barplot(asia$ctfp,names.arg = asia$countrycode)

#散布図（人口とGDP）

plot(asia$pop,asia$rgdpe,log = "xy",xlab="Population",ylab="GDP")

ols<-lm(asia$rgdpe ~ asia$pop)
summary(ols)
## 
## Call:
## lm(formula = asia$rgdpe ~ asia$pop)
## 
## Residuals:
##        1        2        3        4        5        6        7 
##   -78765  2386665   461636 -1503740  -506974  -653091  -105731 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   943660     572257   1.649     0.16    
## asia$pop       13385       1047  12.785 5.21e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1332000 on 5 degrees of freedom
## Multiple R-squared:  0.9703, Adjusted R-squared:  0.9644 
## F-statistic: 163.5 on 1 and 5 DF,  p-value: 5.208e-05
plot(asia$pop,asia$rgdpe,xlab="Population",ylab="GDP")
abline(ols)

# 回帰分析

model <- lm(asia$rgdpe ~ asia$pop)
summary(model)
## 
## Call:
## lm(formula = asia$rgdpe ~ asia$pop)
## 
## Residuals:
##        1        2        3        4        5        6        7 
##   -78765  2386665   461636 -1503740  -506974  -653091  -105731 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   943660     572257   1.649     0.16    
## asia$pop       13385       1047  12.785 5.21e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1332000 on 5 degrees of freedom
## Multiple R-squared:  0.9703, Adjusted R-squared:  0.9644 
## F-statistic: 163.5 on 1 and 5 DF,  p-value: 5.208e-05
library(modelsummary)
## `modelsummary` 2.0.0 now uses `tinytable` as its default table-drawing
##   backend. Learn more at: https://vincentarelbundock.github.io/tinytable/
## 
## Revert to `kableExtra` for one session:
## 
##   options(modelsummary_factory_default = 'kableExtra')
##   options(modelsummary_factory_latex = 'kableExtra')
##   options(modelsummary_factory_html = 'kableExtra')
## 
## Silence this message forever:
## 
##   config_modelsummary(startup_message = FALSE)
library(ggplot2)
library(flextable)
modelsummary(model, stars = TRUE)
tinytable_46paqhuwz81na0xssb08
(1)
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
(Intercept) 943659.767
(572257.202)
asia$pop 13384.984***
(1046.895)
Num.Obs. 7
R2 0.970
R2 Adj. 0.964
AIC 220.9
BIC 220.8
Log.Lik. -107.470
RMSE 1125766.22 
modelplot(model)