UIN Maulana Malik Ibrahim Malang
library(readxl)
## Warning: package 'readxl' was built under R version 4.1.3
#baca data excel
mobindexjkt <- read_excel("mobindex_jkt.xlsx")
covidjkt <- read_excel("c19data_jkt.xlsx")
Datagab <- read_excel("Datagab.xlsx")
retail <- mobindexjkt$retail_and_recreation_percent_change_from_baseline
grocery <- mobindexjkt$grocery_and_pharmacy_percent_change_from_baseline
park <- mobindexjkt$parks_percent_change_from_baseline
station <- mobindexjkt$transit_stations_percent_change_from_baseline
workplace <- mobindexjkt$workplaces_percent_change_from_baseline
residental <- mobindexjkt$residential_percent_change_from_baseline
date <- mobindexjkt$date
tanggal <- Datagab$Tanggal
dirawat <- Datagab$Dirawat
station2 <- Datagab$Station
Positif <- covidjkt$Positif
Dirawat <- covidjkt$Dirawat
Sembuh <- covidjkt$Sembuh
Meninggal <- covidjkt$Meninggal
Isolasi_Mandiri <- covidjkt$SelfIsolation
#Visualisasi Data Menggunakan Fungsi plot().
#Fungsi plot() merupakan fungsi umum yang digunakan untuk membuat pola pada R.
#korelasi kasus dirawat vs retail
cor(Dirawat,retail)
## [1] -0.3700471
#korelasi kasus dirawat vs grocery
cor(Dirawat,grocery)
## [1] 0.1195116
#korelasi kasus dirawat vs park
cor(Dirawat,park)
## [1] -0.2227841
#korelasi kasus dirawat vs station
cor(Dirawat,station)
## [1] -0.5003673
#korelasi kasus dirawat vs workplace
cor(Dirawat,workplace)
## [1] -0.07595505
#korelasi kasus dirawat vs residental
cor(Dirawat,residental)
## [1] 0.1272799
summary(covidjkt)
## Tanggal Positif Dirawat
## Min. :2021-09-01 00:00:00 Min. :851256 Min. : 510.0
## 1st Qu.:2021-09-08 06:00:00 1st Qu.:853676 1st Qu.: 708.8
## Median :2021-09-15 12:00:00 Median :855512 Median :1044.0
## Mean :2021-09-15 12:00:00 Mean :855112 Mean :1155.8
## 3rd Qu.:2021-09-22 18:00:00 3rd Qu.:856709 3rd Qu.:1521.5
## Max. :2021-09-30 00:00:00 Max. :857765 Max. :2172.0
## Sembuh Meninggal SelfIsolation
## Min. :831293 Min. :13302 Min. :1195
## 1st Qu.:835822 1st Qu.:13389 1st Qu.:1506
## Median :838851 Median :13460 Median :2158
## Mean :838165 Mean :13438 Mean :2353
## 3rd Qu.:841002 3rd Qu.:13492 3rd Qu.:2944
## Max. :842541 Max. :13519 Max. :4489
#dataframe KASUS DIRAWAT
dataku1 <- data.frame(Dirawat, retail, grocery, park, station, workplace, residental)
dataku3 <- data.frame(date, retail, grocery, park, station, workplace, residental)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.3
library(reshape2)
## Warning: package 'reshape2' was built under R version 4.1.3
dataku4 <- melt(data = dataku3, id.vars = "date")
ggplot(data = dataku4, aes(x = date, y = value, colour = variable))+
geom_point() +
geom_line() +
theme(legend.justification = "top") +
labs(title = "Grafik Google Mobility Index",
subtitle = "Propinsi DKI Jakarta Indonesia SEPT 2021",
y = "Index Moility", x = "Tanggal") +
theme(axis.text.x = element_text(angle = -90))
head(Datagab)
## # A tibble: 6 x 3
## Tanggal Dirawat Station
## <dttm> <dbl> <dbl>
## 1 2021-09-01 00:00:00 2172 -41
## 2 2021-09-02 00:00:00 2043 -42
## 3 2021-09-03 00:00:00 1914 -41
## 4 2021-09-04 00:00:00 1813 -35
## 5 2021-09-05 00:00:00 1765 -38
## 6 2021-09-06 00:00:00 1644 -41
p <- ggplot(Datagab, aes(x = tanggal))
p <- p + geom_line(aes(y = dirawat/30, colour = "Dirawat" ))
p <- p + geom_line(aes(y = station2, colour = "Station"))
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*1, name = "Station"))
p
model <- lm(Dirawat ~ station)
summary(model)
##
## Call:
## lm(formula = Dirawat ~ station)
##
## Residuals:
## Min 1Q Median 3Q Max
## -574.56 -390.81 -58.22 249.36 853.11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1746.79 952.57 -1.834 0.07734 .
## station -77.33 25.29 -3.058 0.00486 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 440.5 on 28 degrees of freedom
## Multiple R-squared: 0.2504, Adjusted R-squared: 0.2236
## F-statistic: 9.352 on 1 and 28 DF, p-value: 0.004864
anova(model)
## Analysis of Variance Table
##
## Response: Dirawat
## Df Sum Sq Mean Sq F value Pr(>F)
## station 1 1814883 1814883 9.3516 0.004864 **
## Residuals 28 5433994 194071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(covidjkt$Dirawat ~ covidjkt$Tanggal, data = covidjkt, col = "dodgerblue", pch = 20, cex = 1.5, main = "Kasus Dirawat Covid 19")
abline(model)
plot(cooks.distance(model), pch = 16, col = "blue", main = "Cook Distance")
plot(model)
AIC(model)
## [1] 454.3459
BIC(model)
## [1] 458.5495
head(predict(model), n = 30)
## 1 2 3 4 5 6 7 8
## 1423.8902 1501.2239 1423.8902 959.8880 1191.8891 1423.8902 1346.5565 1346.5565
## 9 10 11 12 13 14 15 16
## 1269.2228 1269.2228 805.2206 1037.2217 1269.2228 1655.8913 1191.8891 1269.2228
## 17 18 19 20 21 22 23 24
## 1191.8891 727.8869 1037.2217 1191.8891 1191.8891 1269.2228 1191.8891 1114.5554
## 25 26 27 28 29 30
## 495.8858 727.8869 1114.5554 1114.5554 1037.2217 882.5543
plot(head(predict(model), n = 29))
head(resid(model), n = 30)
## 1 2 3 4 5 6 7
## 748.10984 541.77614 490.10984 853.11204 573.11094 220.10984 250.44354
## 8 9 10 11 12 13 14
## 189.44354 208.77724 184.77724 612.77944 364.77834 -42.22276 -555.89126
## 15 16 17 18 19 20 21
## -125.88906 -247.22276 -191.88906 246.11314 -74.22166 -327.88906 -396.88906
## 22 23 24 25 26 27 28
## -495.22276 -504.88906 -482.55536 95.11424 -120.88686 -562.55536 -574.55536
## 29 30
## -503.22166 -372.55426
coef(model)
## (Intercept) station
## -1746.7915 -77.3337
Datagab$residuals <- model$residuals
Datagab$predicted <- model$fitted.values
Datagab
## # A tibble: 30 x 5
## Tanggal Dirawat Station residuals predicted
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2021-09-01 00:00:00 2172 -41 748. 1424.
## 2 2021-09-02 00:00:00 2043 -42 542. 1501.
## 3 2021-09-03 00:00:00 1914 -41 490. 1424.
## 4 2021-09-04 00:00:00 1813 -35 853. 960.
## 5 2021-09-05 00:00:00 1765 -38 573. 1192.
## 6 2021-09-06 00:00:00 1644 -41 220. 1424.
## 7 2021-09-07 00:00:00 1597 -40 250. 1347.
## 8 2021-09-08 00:00:00 1536 -40 189. 1347.
## 9 2021-09-09 00:00:00 1478 -39 209. 1269.
## 10 2021-09-10 00:00:00 1454 -39 185. 1269.
## # ... with 20 more rows
scatter.smooth(x=Datagab$Tanggal, y=Datagab$Dirawat, main="Tanggal ~ Dirawat")
boxplot(Datagab$Dirawat, main="Dirawat", boxplot.stats(Datagab$Dirawat)$out)
require(fuzzyreg)
## Loading required package: fuzzyreg
## Warning: package 'fuzzyreg' was built under R version 4.1.3
## Loading required package: fuzzyreg
## Warning: package 'fuzzyreg' was built under R version 4.1.3
data(fuzzydat)
f = fuzzylm(Dirawat ~ station, data = Datagab)
plot(f, res = 20, col = "#4bdb57", main = "Estimasi Kasus Dirawat")
