Data Sembuh Positif Covid-19 dan Google Mobility Index di Jakarta
pada Bulan Desember 2020
library(readxl)
datainflowjakarta <- read_excel(path = "datainflowjakarta.xlsx")
datainflowjakarta
## # A tibble: 31 x 12
## Tanggal POSITIF Dirawat Sembuh Meninggal `Self Isolation`
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-12-31 00:00:00 183735 5821 164881 3287 9746
## 2 2020-12-30 00:00:00 181713 5654 162911 3266 9882
## 3 2020-12-29 00:00:00 179660 5059 161337 3246 10018
## 4 2020-12-28 00:00:00 177604 4581 159878 3226 9919
## 5 2020-12-27 00:00:00 175926 4452 158615 3204 9655
## 6 2020-12-26 00:00:00 173929 4510 156798 3182 9439
## 7 2020-12-25 00:00:00 171871 5742 154242 3167 8720
## 8 2020-12-24 00:00:00 169775 5305 152491 3146 8833
## 9 2020-12-23 00:00:00 167842 4947 151122 3130 8643
## 10 2020-12-22 00:00:00 165888 4462 149691 3115 8620
## # ... with 21 more rows, and 6 more variables:
## # retail_and_recreation_percent_change_from_baseline <dbl>,
## # grocery_and_pharmacy_percent_change_from_baseline <dbl>,
## # parks_percent_change_from_baseline <dbl>,
## # transit_stations_percent_change_from_baseline <dbl>,
## # workplaces_percent_change_from_baseline <dbl>,
## # residential_percent_change_from_baseline <dbl>
summary(datainflowjakarta)
## Tanggal POSITIF Dirawat Sembuh
## Min. :2020-12-01 00:00:00 Min. :137919 Min. :1861 Min. :125102
## 1st Qu.:2020-12-08 12:00:00 1st Qu.:147220 1st Qu.:2297 1st Qu.:132783
## Median :2020-12-16 00:00:00 Median :156343 Median :3705 Median :141365
## Mean :2020-12-16 00:00:00 Mean :158331 Mean :3725 Mean :142770
## 3rd Qu.:2020-12-23 12:00:00 3rd Qu.:168809 3rd Qu.:4944 3rd Qu.:151807
## Max. :2020-12-31 00:00:00 Max. :183735 Max. :5821 Max. :164881
## Meninggal Self Isolation
## Min. :2689 Min. : 7673
## 1st Qu.:2851 1st Qu.: 8232
## Median :3010 Median : 8720
## Mean :2997 Mean : 8840
## 3rd Qu.:3138 3rd Qu.: 9456
## Max. :3287 Max. :10018
## retail_and_recreation_percent_change_from_baseline
## Min. :-36.00
## 1st Qu.:-28.50
## Median :-27.00
## Mean :-27.48
## 3rd Qu.:-25.00
## Max. :-23.00
## grocery_and_pharmacy_percent_change_from_baseline
## Min. :-17.000
## 1st Qu.: -8.500
## Median : -6.000
## Mean : -6.548
## 3rd Qu.: -3.500
## Max. : 1.000
## parks_percent_change_from_baseline
## Min. :-59.00
## 1st Qu.:-49.50
## Median :-47.00
## Mean :-47.52
## 3rd Qu.:-45.50
## Max. :-37.00
## transit_stations_percent_change_from_baseline
## Min. :-53.00
## 1st Qu.:-39.00
## Median :-36.00
## Mean :-36.39
## 3rd Qu.:-33.50
## Max. :-27.00
## workplaces_percent_change_from_baseline
## Min. :-66.00
## 1st Qu.:-32.50
## Median :-30.00
## Mean :-30.77
## 3rd Qu.:-27.50
## Max. :-10.00
## residential_percent_change_from_baseline
## Min. : 6.00
## 1st Qu.:10.50
## Median :12.00
## Mean :11.87
## 3rd Qu.:13.00
## Max. :22.00
pairs(datainflowjakarta)
pairs(datainflowjakarta, lower.panel=NULL)
plot(datainflowjakarta$Sembuh ~ datainflowjakarta$Tanggal, data = datainflowjakarta)
plot(datainflowjakarta$Sembuh ~ datainflowjakarta$
retail_and_recreation_percent_change_from_baseline+datainflowjakarta$
grocery_and_pharmacy_percent_change_from_baseline+datainflowjakarta$
parks_percent_change_from_baseline+datainflowjakarta$
transit_stations_percent_change_from_baseline+datainflowjakarta$
workplaces_percent_change_from_baseline+datainflowjakarta$
residential_percent_change_from_baseline, data = datainflowjakarta)
cor(datainflowjakarta$Sembuh,datainflowjakarta$
retail_and_recreation_percent_change_from_baseline)
## [1] 0.4534586
cor(datainflowjakarta$Sembuh,datainflowjakarta$
grocery_and_pharmacy_percent_change_from_baseline)
## [1] 0.3076185
cor(datainflowjakarta$Sembuh,datainflowjakarta$
parks_percent_change_from_baseline)
## [1] -0.05979146
cor(datainflowjakarta$Sembuh,datainflowjakarta$
transit_stations_percent_change_from_baseline)
## [1] 0.1504269
cor(datainflowjakarta$Sembuh,datainflowjakarta$
workplaces_percent_change_from_baseline)
## [1] 0.2328853
cor(datainflowjakarta$Sembuh,datainflowjakarta$
residential_percent_change_from_baseline)
## [1] -0.2366697
model <- lm(datainflowjakarta$Sembuh ~ datainflowjakarta$Tanggal, data = datainflowjakarta)
summary(model)
##
## Call:
## lm(formula = datainflowjakarta$Sembuh ~ datainflowjakarta$Tanggal,
## data = datainflowjakarta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1643.9 -1046.9 -347.6 1194.4 2411.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.430e+07 4.720e+05 -51.48 <2e-16 ***
## datainflowjakarta$Tanggal 1.520e-02 2.935e-04 51.79 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1263 on 29 degrees of freedom
## Multiple R-squared: 0.9893, Adjusted R-squared: 0.9889
## F-statistic: 2682 on 1 and 29 DF, p-value: < 2.2e-16
anova(model)
## Analysis of Variance Table
##
## Response: datainflowjakarta$Sembuh
## Df Sum Sq Mean Sq F value Pr(>F)
## datainflowjakarta$Tanggal 1 4277268385 4277268385 2681.9 < 2.2e-16 ***
## Residuals 29 46251260 1594871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(datainflowjakarta$Sembuh ~ datainflowjakarta$
Tanggal,
data = datainflowjakarta, col = "Salmon", pch = 20, cex = 1.5,
main = "Data Covid Pasien Sembuh di DKI Jakarta dan Google Mobility Index")
abline(model)
plot(cooks.distance(model), pch = 16, col = "Magenta")
plot(model)
AIC(model)
## [1] 534.6582
BIC(model)
## [1] 538.9601
head(predict(model), n = 11)
## 1 2 3 4 5 6 7 8
## 162469.3 161156.0 159842.7 158529.5 157216.2 155902.9 154589.6 153276.3
## 9 10 11
## 151963.1 150649.8 149336.5
plot(head(predict(model), n = 10))
head(resid(model), n = 11)
## 1 2 3 4 5 6 7
## 2411.6996 1754.9798 1494.2601 1348.5403 1398.8206 895.1008 -347.6190
## 8 9 10 11
## -785.3387 -841.0585 -958.7782 -1028.4980
coef(model)
## (Intercept) datainflowjakarta$Tanggal
## -2.43e+07 1.52e-02
datainflowjakarta$residuals <- model$residuals
datainflowjakarta$predicted <- model$fitted.values
datainflowjakarta
## # A tibble: 31 x 14
## Tanggal POSITIF Dirawat Sembuh Meninggal `Self Isolation`
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-12-31 00:00:00 183735 5821 164881 3287 9746
## 2 2020-12-30 00:00:00 181713 5654 162911 3266 9882
## 3 2020-12-29 00:00:00 179660 5059 161337 3246 10018
## 4 2020-12-28 00:00:00 177604 4581 159878 3226 9919
## 5 2020-12-27 00:00:00 175926 4452 158615 3204 9655
## 6 2020-12-26 00:00:00 173929 4510 156798 3182 9439
## 7 2020-12-25 00:00:00 171871 5742 154242 3167 8720
## 8 2020-12-24 00:00:00 169775 5305 152491 3146 8833
## 9 2020-12-23 00:00:00 167842 4947 151122 3130 8643
## 10 2020-12-22 00:00:00 165888 4462 149691 3115 8620
## # ... with 21 more rows, and 8 more variables:
## # retail_and_recreation_percent_change_from_baseline <dbl>,
## # grocery_and_pharmacy_percent_change_from_baseline <dbl>,
## # parks_percent_change_from_baseline <dbl>,
## # transit_stations_percent_change_from_baseline <dbl>,
## # workplaces_percent_change_from_baseline <dbl>,
## # residential_percent_change_from_baseline <dbl>, residuals <dbl>, ...
scatter.smooth(x=datainflowjakarta$Tanggal, y=datainflowjakarta$Sembuh,
main="Tanggal ~ Sembuh")
boxplot(datainflowjakarta$Sembuh, main="Sembuh",
boxplot.stats(datainflowjakarta$Sembuh)$out)
plot(density(datainflowjakarta$Sembuh), main="Google Mobility Index: Sembuh",
ylab="Frequency")
coefs <- coef(model)
plot(Sembuh ~ Tanggal, data = datainflowjakarta)
abline(coefs)
text(x = 12, y = 10, paste('expression = ', round(coefs[1], 2), '+',
round(coefs[2], 2), '*Sembuh'))
cor.test(datainflowjakarta$
retail_and_recreation_percent_change_from_baseline,
datainflowjakarta$Sembuh)
##
## Pearson's product-moment correlation
##
## data: datainflowjakarta$retail_and_recreation_percent_change_from_baseline and datainflowjakarta$Sembuh
## t = 2.7398, df = 29, p-value = 0.01041
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1180936 0.6959711
## sample estimates:
## cor
## 0.4534586
cor.test(datainflowjakarta$
grocery_and_pharmacy_percent_change_from_baseline,
datainflowjakarta$Sembuh)
##
## Pearson's product-moment correlation
##
## data: datainflowjakarta$grocery_and_pharmacy_percent_change_from_baseline and datainflowjakarta$Sembuh
## t = 1.741, df = 29, p-value = 0.09229
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05243742 0.59689599
## sample estimates:
## cor
## 0.3076185
cor.test(datainflowjakarta$
parks_percent_change_from_baseline,
datainflowjakarta$Sembuh)
##
## Pearson's product-moment correlation
##
## data: datainflowjakarta$parks_percent_change_from_baseline and datainflowjakarta$Sembuh
## t = -0.32256, df = 29, p-value = 0.7493
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4055396 0.3009242
## sample estimates:
## cor
## -0.05979146
cor.test(datainflowjakarta$
transit_stations_percent_change_from_baseline,
datainflowjakarta$Sembuh)
##
## Pearson's product-moment correlation
##
## data: datainflowjakarta$transit_stations_percent_change_from_baseline and datainflowjakarta$Sembuh
## t = 0.8194, df = 29, p-value = 0.4192
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2153943 0.4792233
## sample estimates:
## cor
## 0.1504269
cor.test(datainflowjakarta$
workplaces_percent_change_from_baseline,
datainflowjakarta$Sembuh)
##
## Pearson's product-moment correlation
##
## data: datainflowjakarta$workplaces_percent_change_from_baseline and datainflowjakarta$Sembuh
## t = 1.2896, df = 29, p-value = 0.2074
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1323788 0.5424612
## sample estimates:
## cor
## 0.2328853
cor.test(datainflowjakarta$
residential_percent_change_from_baseline,
datainflowjakarta$Sembuh)
##
## Pearson's product-moment correlation
##
## data: datainflowjakarta$residential_percent_change_from_baseline and datainflowjakarta$Sembuh
## t = -1.3118, df = 29, p-value = 0.1999
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5452817 0.1284417
## sample estimates:
## cor
## -0.2366697