Pendahuluan

Latihan penggunaan RMarkdown menggunakan built-in dataset yang ada di R, yaitu data swiss. Data tersebut berisi standarisasi ukuran kesuburan dan indikator sosial ekonomi untuk masing-masing 47 provinsi berbahasa Perancis di Swiss pada sekitar tahun 1888.

Deskripsi Data

Swiss, pada tahun 1888, memasuki periode yang dikenal sebagai transisi demografi; yaitu, kesuburannya mulai turun dari tingkat tinggi yang biasa terjadi di negara-negara terbelakang. Data yang dikumpulkan adalah untuk 47 “provinsi” berbahasa Perancis pada sekitar tahun 1888.

summary(swiss)
##    Fertility      Agriculture     Examination      Education    
##  Min.   :35.00   Min.   : 1.20   Min.   : 3.00   Min.   : 1.00  
##  1st Qu.:64.70   1st Qu.:35.90   1st Qu.:12.00   1st Qu.: 6.00  
##  Median :70.40   Median :54.10   Median :16.00   Median : 8.00  
##  Mean   :70.14   Mean   :50.66   Mean   :16.49   Mean   :10.98  
##  3rd Qu.:78.45   3rd Qu.:67.65   3rd Qu.:22.00   3rd Qu.:12.00  
##  Max.   :92.50   Max.   :89.70   Max.   :37.00   Max.   :53.00  
##     Catholic       Infant.Mortality
##  Min.   :  2.150   Min.   :10.80   
##  1st Qu.:  5.195   1st Qu.:18.15   
##  Median : 15.140   Median :20.00   
##  Mean   : 41.144   Mean   :19.94   
##  3rd Qu.: 93.125   3rd Qu.:21.70   
##  Max.   :100.000   Max.   :26.60

Visualisasi Data Dari Swiss Dataset

plot(swiss$Fertility)

plot(swiss$Infant.Mortality)

library(corrplot)
## corrplot 0.92 loaded
#Correlation between fertility and infant mortality
cor.test(swiss$Fertility, swiss$Infant.Mortality)
## 
##  Pearson's product-moment correlation
## 
## data:  swiss$Fertility and swiss$Infant.Mortality
## t = 3.0737, df = 45, p-value = 0.003585
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1469699 0.6285366
## sample estimates:
##      cor 
## 0.416556
#Correlation between agriculture and fertility
cor.test(swiss$Agriculture, swiss$Fertility)
## 
##  Pearson's product-moment correlation
## 
## data:  swiss$Agriculture and swiss$Fertility
## t = 2.5316, df = 45, p-value = 0.01492
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.07334947 0.58130587
## sample estimates:
##       cor 
## 0.3530792
#plot
plot(swiss$Fertility, swiss$Infant.Mortality)

plot(swiss$Agriculture, swiss$Fertility)

#ggplot
library(ggplot2)

ggplot(swiss, aes(x=Fertility, y=Infant.Mortality))+
  geom_line(color="indianred3",
            linewidth=0.7)+
  geom_smooth()+
  labs(title="Infant Mortality by Fertility",
       x="Infant Mortality",
       y="Fertility")
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

ggplot(swiss, aes(x=Agriculture, y=Fertility))+
  geom_line(color="indianred3",
            linewidth=0.7)+
  geom_smooth()+
  labs(title="Fertility by Agriculture",
       x="Fertility",
       y="Agriculture")
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

#GGCorrPlot
library(ggcorrplot)

df <- dplyr::select_if(swiss, is.numeric)
r <- cor(df, use="complete.obs")
round(r,2)
##                  Fertility Agriculture Examination Education Catholic
## Fertility             1.00        0.35       -0.65     -0.66     0.46
## Agriculture           0.35        1.00       -0.69     -0.64     0.40
## Examination          -0.65       -0.69        1.00      0.70    -0.57
## Education            -0.66       -0.64        0.70      1.00    -0.15
## Catholic              0.46        0.40       -0.57     -0.15     1.00
## Infant.Mortality      0.42       -0.06       -0.11     -0.10     0.18
##                  Infant.Mortality
## Fertility                    0.42
## Agriculture                 -0.06
## Examination                 -0.11
## Education                   -0.10
## Catholic                     0.18
## Infant.Mortality             1.00
ggcorrplot(r,
           hc.order= TRUE,
           type="lower",
           lab= TRUE)

#Linear Model
fertility_lm <- lm(Fertility~Agriculture + Examination + Education + Catholic + Infant.Mortality,data=swiss)
summary(fertility_lm)
## 
## Call:
## lm(formula = Fertility ~ Agriculture + Examination + Education + 
##     Catholic + Infant.Mortality, data = swiss)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15.2743  -5.2617   0.5032   4.1198  15.3213 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      66.91518   10.70604   6.250 1.91e-07 ***
## Agriculture      -0.17211    0.07030  -2.448  0.01873 *  
## Examination      -0.25801    0.25388  -1.016  0.31546    
## Education        -0.87094    0.18303  -4.758 2.43e-05 ***
## Catholic          0.10412    0.03526   2.953  0.00519 ** 
## Infant.Mortality  1.07705    0.38172   2.822  0.00734 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.165 on 41 degrees of freedom
## Multiple R-squared:  0.7067, Adjusted R-squared:  0.671 
## F-statistic: 19.76 on 5 and 41 DF,  p-value: 5.594e-10

Kesimpulan

Dari hasil analisis data di atas dapat diketahui beberapa variabel yang signifikan mempengaruhi tingkat fertilitas di Swiss (tingkat alpha 5%), yaitu Agriculture, Catholic, dan Infant Mortality.

Daftar Pustaka

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Mosteller, F. and Tukey, J. W. (1977) Data Analysis and Regression: A Second Course in Statistics. Addison-Wesley, Reading Mass.