Regresi Linear Berganda Covid 19 DKI Jakarta Bulan Nopember 2021 Kasus Positif
Author : Setiyaris
Tanggal : 2022-03-19
Dosen Pengampu : Prof. Dr. Suhartono, Mkom
UIN Maulana Malik Ibrahim Malang
library(readxl)
#baca data excel
mobindexjkt <- read_excel("mobindex_jkt.xlsx")## Warning in strptime(x, format, tz = tz): unable to identify current timezone 'C':
## please set environment variable 'TZ'covidjkt <- read_excel("c19data_jkt.xlsx")
Datagab <- read_excel("Datagab.xlsx")
datagab2 <- read_excel("datagab2.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_baselinedate <- mobindexjkt$date
tanggal <- Datagab$Tanggal
positif <- Datagab$Positif
park <- Datagab$ParkPositif <- covidjkt$Positif
Dirawat <- covidjkt$Dirawat
Sembuh <- covidjkt$Sembuh
Meninggal <- covidjkt$Meninggal
Isolasi_Mandiri <- covidjkt$SelfIsolationPositif <- datagab2$positif
Retail <- datagab2$retail_and_recreation_percent_change_from_baseline
Grocery <- datagab2$grocery_and_pharmacy_percent_change_from_baseline
Park <- datagab2$parks_percent_change_from_baseline
Station <- datagab2$transit_stations_percent_change_from_baseline
Workplace <- datagab2$workplaces_percent_change_from_baseline
Residental <- datagab2$residential_percent_change_from_baseline#Visualisasi Data Menggunakan Fungsi plot(). #Fungsi plot() merupakan fungsi umum yang digunakan untuk membuat pola pada R.
#korelasi kasus Positif vs retail
cor(Positif,retail)## [1] 0.25192#korelasi kasus Positif vs grocery
cor(Positif,grocery)## [1] 0.02819681#korelasi kasus Positif vs park
cor(Positif,park)## [1] 0.3573231#korelasi kasus Positif vs station
cor(Positif,station)## [1] 0.3369683#korelasi kasus Positif vs workplace
cor(Positif,workplace)## [1] 0.2926955#korelasi kasus Positif vs residental
cor(Positif,residental)## [1] -0.1997964summary(covidjkt)## Tanggal Positif Dirawat
## Min. :2021-11-01 00:00:00 Min. :861623 Min. :168.0
## 1st Qu.:2021-11-08 06:00:00 1st Qu.:862300 1st Qu.:182.2
## Median :2021-11-15 12:00:00 Median :862865 Median :198.0
## Mean :2021-11-15 12:00:00 Mean :862897 Mean :204.5
## 3rd Qu.:2021-11-22 18:00:00 3rd Qu.:863534 3rd Qu.:211.2
## Max. :2021-11-30 00:00:00 Max. :863947 Max. :273.0
## Sembuh Meninggal SelfIsolation
## Min. :847066 Min. :13562 Min. :252.0
## 1st Qu.:847922 1st Qu.:13566 1st Qu.:294.5
## Median :848657 Median :13569 Median :462.0
## Mean :848654 Mean :13570 Mean :468.7
## 3rd Qu.:849480 3rd Qu.:13574 3rd Qu.:642.8
## Max. :849929 Max. :13576 Max. :802.0#dataframe KASUS POSITIF
dataku <- data.frame(date, retail, grocery, park, station, workplace, residental)library(ggplot2)
library(reshape2)
dataku1 <- melt(data = dataku, id.vars = "date")
ggplot(data = dataku1, aes(x = date, y = value, colour = variable))+
geom_point() +
geom_line() +
theme(legend.justification = "top") +
labs(title = "Grafik Mobility Index",
subtitle = "Propinsi DKI Jakarta Indonesia November 2021",
y = "Index Mobility", x = "Tanggal") +
theme(axis.text.x = element_text(angle = -90))
head(datagab2)## # A tibble: 6 x 8
## date positif retail_and_recr~ grocery_and_pha~ parks_percent_c~
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2021-11-01 00:00:00 861623 -16 5 -33
## 2 2021-11-02 00:00:00 861700 -12 12 -29
## 3 2021-11-03 00:00:00 861832 -12 9 -26
## 4 2021-11-04 00:00:00 861943 -12 8 -25
## 5 2021-11-05 00:00:00 862061 -6 12 -15
## 6 2021-11-06 00:00:00 862130 -7 14 -16
## # ... with 3 more variables:
## # transit_stations_percent_change_from_baseline <dbl>,
## # workplaces_percent_change_from_baseline <dbl>,
## # residential_percent_change_from_baseline <dbl>p <- ggplot(datagab2, aes(x = tanggal))
p <- p + geom_line(aes(y = Positif/10000, colour = "Positif"))
p <- p + geom_line(aes(y = Park, colour = "park"))
p <- p + geom_line(aes(y = Retail, colour = "retail"))
p <- p + geom_line(aes(y = Grocery, colour = "grocery"))
p <- p + geom_line(aes(y = Station, colour = "station"))
p <- p + geom_line(aes(y = Workplace, colour = "workplace"))
p <- p + geom_line(aes(y = Residental, colour = "residental"))
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*0.1, name = "mobility"))
p
head(Datagab)## # A tibble: 6 x 3
## Tanggal Positif Park
## <dttm> <dbl> <dbl>
## 1 2021-11-01 00:00:00 861623 -33
## 2 2021-11-02 00:00:00 861700 -29
## 3 2021-11-03 00:00:00 861832 -26
## 4 2021-11-04 00:00:00 861943 -25
## 5 2021-11-05 00:00:00 862061 -15
## 6 2021-11-06 00:00:00 862130 -16p <- ggplot(Datagab, aes(x = tanggal))
p <- p + geom_line(aes(y = Positif/10000, colour = "Positif"))
p <- p + geom_line(aes(y = Park, colour = "Park"))
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*0.1, name = "Park"))
p
model <- lm(positif ~ park)
summary(model)##
## Call:
## lm(formula = positif ~ park)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1238.86 -442.64 -10.47 534.06 1233.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 864074.59 595.05 1452.093 <2e-16 ***
## park 51.65 25.51 2.024 0.0526 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 686.9 on 28 degrees of freedom
## Multiple R-squared: 0.1277, Adjusted R-squared: 0.09653
## F-statistic: 4.098 on 1 and 28 DF, p-value: 0.05256anova(model)## Analysis of Variance Table
##
## Response: positif
## Df Sum Sq Mean Sq F value Pr(>F)
## park 1 1933470 1933470 4.0983 0.05256 .
## Residuals 28 13209646 471773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(covidjkt$Positif ~ covidjkt$Tanggal, data = covidjkt, col = "dodgerblue", pch = 20, cex = 1.5, main = "Kasus Positif Covid 19")
abline(model)
plot(cooks.distance(model), pch = 16, col = "blue")
plot(model)
AIC(model)## [1] 480.9941BIC(model)## [1] 485.1977head(predict(model), n = 30)## 1 2 3 4 5 6 7 8
## 862370.2 862576.8 862731.7 862783.4 863299.9 863248.2 862266.9 862938.3
## 9 10 11 12 13 14 15 16
## 862731.7 862783.4 863041.6 862938.3 862525.1 863041.6 862835.0 862886.7
## 17 18 19 20 21 22 23 24
## 862990.0 863144.9 863403.2 862835.0 862990.0 862990.0 862783.4 862990.0
## 25 26 27 28 29 30
## 863144.9 862938.3 862886.7 862628.4 863196.6 862990.0plot(head(predict(model), n = 30))
head(resid(model), n = 30)## 1 2 3 4 5 6
## -747.18295 -876.77787 -899.72406 -840.37279 -1238.86010 -1118.21137
## 7 8 9 10 11 12
## -19.88549 -662.31898 -361.72406 -318.37279 -469.61645 -295.31898
## 13 14 15 16 17 18
## 171.87086 -252.61645 -10.02152 17.32975 44.03228 -10.91391
## 19 20 21 22 23 24
## -172.15756 509.97848 466.03228 492.03228 767.62721 643.03228
## 25 26 27 28 29 30
## 542.08609 818.68102 924.32975 1233.57340 706.43736 957.03228coef(model)## (Intercept) park
## 864074.59106 51.64873Datagab$residuals <- model$residuals
Datagab$predicted <- model$fitted.values
Datagab## # A tibble: 30 x 5
## Tanggal Positif Park residuals predicted
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2021-11-01 00:00:00 861623 -33 -747. 862370.
## 2 2021-11-02 00:00:00 861700 -29 -877. 862577.
## 3 2021-11-03 00:00:00 861832 -26 -900. 862732.
## 4 2021-11-04 00:00:00 861943 -25 -840. 862783.
## 5 2021-11-05 00:00:00 862061 -15 -1239. 863300.
## 6 2021-11-06 00:00:00 862130 -16 -1118. 863248.
## 7 2021-11-07 00:00:00 862247 -35 -19.9 862267.
## 8 2021-11-08 00:00:00 862276 -22 -662. 862938.
## 9 2021-11-09 00:00:00 862370 -26 -362. 862732.
## 10 2021-11-10 00:00:00 862465 -25 -318. 862783.
## # ... with 20 more rowsscatter.smooth(x=Datagab$Tanggal, y=Datagab$Positif, main="Tanggal ~ Positif")
boxplot(Datagab$Positif, main="Positif", boxplot.stats(Datagab$Positif)$out)
require(fuzzyreg)## Loading required package: fuzzyregdata(fuzzydat)
f = fuzzylm(positif ~ park, data = Datagab)plot(f, res = 20, col = "lightblue", main = "PLRLS")