library(readr)
DataCovid19_DKI_Jakarta <- read_csv("LiniearAlgebra/DataCovid19 DKI Jakarta.csv")
## Rows: 31 Columns: 15
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): country_region_code, country_region, Nama Kota
## dbl (11): retail_and_recreation_percent_change_from_baseline, grocery_and_p...
## date (1): date
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
DataCovid19_DKI_Jakarta
## # A tibble: 31 x 15
## country_region_code country_region `Nama Kota` date retail_and_recreat~
## <chr> <chr> <chr> <date> <dbl>
## 1 ID Indonesia DKI JAKARTA 2022-01-01 3
## 2 ID Indonesia DKI JAKARTA 2022-01-02 11
## 3 ID Indonesia DKI JAKARTA 2022-01-03 14
## 4 ID Indonesia DKI JAKARTA 2022-01-04 11
## 5 ID Indonesia DKI JAKARTA 2022-01-05 12
## 6 ID Indonesia DKI JAKARTA 2022-01-06 10
## 7 ID Indonesia DKI JAKARTA 2022-01-07 6
## 8 ID Indonesia DKI JAKARTA 2022-01-08 8
## 9 ID Indonesia DKI JAKARTA 2022-01-09 7
## 10 ID Indonesia DKI JAKARTA 2022-01-10 7
## # ... with 21 more rows, and 10 more variables:
## # 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>, POSITIF <dbl>,
## # Dirawat <dbl>, Sembuh <dbl>, Meninggal <dbl>, `Self Isolation` <dbl>
library(ggplot2)
library(reshape2)
x<-DataCovid19_DKI_Jakarta$date
retail <- DataCovid19_DKI_Jakarta$retail_and_recreation_percent_change_from_baseline
grocery <- DataCovid19_DKI_Jakarta$grocery_and_pharmacy_percent_change_from_baseline
park <- DataCovid19_DKI_Jakarta$parks_percent_change_from_baseline
station <- DataCovid19_DKI_Jakarta$transit_stations_percent_change_from_baseline
workplace <- DataCovid19_DKI_Jakarta$workplaces_percent_change_from_baseline
residental <- DataCovid19_DKI_Jakarta$residential_percent_change_from_baseline
positif<-DataCovid19_DKI_Jakarta$POSITIF
dirawat<-DataCovid19_DKI_Jakarta$Dirawat
sembuh<-DataCovid19_DKI_Jakarta$Sembuh
meninggal<-DataCovid19_DKI_Jakarta$Meninggal
selfIsolation<-DataCovid19_DKI_Jakarta$`Self Isolation`
df <- data.frame(x, retail, grocery, park, station, workplace,residental,positif,dirawat,sembuh,meninggal,selfIsolation )
# melt the data to a long format
df2 <- melt(data = df, id.vars = "x")
# plot, using the aesthetics argument 'colour'
ggplot(data = df2, aes(x = x, y = value, colour = variable))+
geom_point() +
geom_line() +
theme(legend.justification = "top") +
labs(title = "Google Mobility Index",
subtitle = "Propinsi DKI Jakarta Indonesia 2022",
y = "Mobility", x = "Date") +
theme(axis.text.x = element_text(angle = -90))
library(ggplot2)
library(reshape2)
x<-DataCovid19_DKI_Jakarta$date
retail <- DataCovid19_DKI_Jakarta$retail_and_recreation_percent_change_from_baseline
grocery <- DataCovid19_DKI_Jakarta$grocery_and_pharmacy_percent_change_from_baseline
park <- DataCovid19_DKI_Jakarta$parks_percent_change_from_baseline
station <- DataCovid19_DKI_Jakarta$transit_stations_percent_change_from_baseline
workplace <- DataCovid19_DKI_Jakarta$workplaces_percent_change_from_baseline
residental <- DataCovid19_DKI_Jakarta$residential_percent_change_from_baseline
meninggal<-DataCovid19_DKI_Jakarta$Meninggal
selfIsolation<-DataCovid19_DKI_Jakarta$`Self Isolation`
df <- data.frame(x, retail, grocery, park, station, workplace,residental,meninggal,selfIsolation )
# melt the data to a long format
df2 <- melt(data = df, id.vars = "x")
# plot, using the aesthetics argument 'colour'
ggplot(data = df2, aes(x = x, y = value, colour = variable))+
geom_point() +
geom_line() +
theme(legend.justification = "top") +
labs(title = "Google Mobility Index",
subtitle = "Propinsi DKI Jakarta Indonesia 2022",
y = "Mobility", x = "Date") +
theme(axis.text.x = element_text(angle = -90))
# menghitung kernel density
dens <- density(DataCovid19_DKI_Jakarta$Sembuh)
# histogram
hist(DataCovid19_DKI_Jakarta$Sembuh, freq=FALSE, col="steelblue")
# tambahkan density plot
polygon(dens, border="red")
boxplot(DataCovid19_DKI_Jakarta$date~DataCovid19_DKI_Jakarta$Meninggal,
# ubah warna outline menjadi steelblue
border = "steelblue",
# ubah warna box berdasarkan grup
col= c("#999999", "#E69F00", "#56B4E9"))
summary(DataCovid19_DKI_Jakarta)
## country_region_code country_region Nama Kota date
## Length:31 Length:31 Length:31 Min. :2022-01-01
## Class :character Class :character Class :character 1st Qu.:2022-01-08
## Mode :character Mode :character Mode :character Median :2022-01-16
## Mean :2022-01-16
## 3rd Qu.:2022-01-23
## Max. :2022-01-31
## retail_and_recreation_percent_change_from_baseline
## Min. : 1.000
## 1st Qu.: 6.000
## Median : 7.000
## Mean : 7.452
## 3rd Qu.: 9.000
## Max. :14.000
## grocery_and_pharmacy_percent_change_from_baseline
## Min. :20.00
## 1st Qu.:25.00
## Median :26.00
## Mean :27.55
## 3rd Qu.:29.50
## Max. :36.00
## parks_percent_change_from_baseline
## Min. :-1.00
## 1st Qu.: 5.50
## Median : 9.00
## Mean :10.94
## 3rd Qu.:14.00
## Max. :46.00
## transit_stations_percent_change_from_baseline
## Min. :-17.00
## 1st Qu.:-15.00
## Median :-14.00
## Mean :-12.65
## 3rd Qu.:-10.00
## Max. : -4.00
## workplaces_percent_change_from_baseline
## Min. :-49.000
## 1st Qu.: -4.000
## Median : 0.000
## Mean : -1.677
## 3rd Qu.: 3.000
## Max. : 8.000
## residential_percent_change_from_baseline POSITIF Dirawat
## Min. :3.000 Min. :865415 Min. : 116
## 1st Qu.:4.500 1st Qu.:867106 1st Qu.: 278
## Median :5.000 Median :870929 Median : 781
## Mean :5.258 Mean :876297 Mean :1635
## 3rd Qu.:6.000 3rd Qu.:880304 3rd Qu.:2126
## Max. :7.000 Max. :913355 Max. :6809
## Sembuh Meninggal Self Isolation
## Min. :851280 Min. :13588 Min. : 342
## 1st Qu.:851783 1st Qu.:13589 1st Qu.: 1456
## Median :853522 Median :13591 Median : 3035
## Mean :855268 Mean :13598 Mean : 5749
## 3rd Qu.:856934 3rd Qu.:13597 3rd Qu.: 7646
## Max. :867519 Max. :13666 Max. :25361
model <- lm(DataCovid19_DKI_Jakarta$retail_and_recreation_percent_change_from_baseline ~ DataCovid19_DKI_Jakarta$`Self Isolation`)
summary(model)
##
## Call:
## lm(formula = DataCovid19_DKI_Jakarta$retail_and_recreation_percent_change_from_baseline ~
## DataCovid19_DKI_Jakarta$`Self Isolation`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1103 -1.5923 -0.3509 1.0961 7.2772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.645e+00 7.241e-01 9.177 4.47e-10
## DataCovid19_DKI_Jakarta$`Self Isolation` 1.403e-04 8.421e-05 1.666 0.106
##
## (Intercept) ***
## DataCovid19_DKI_Jakarta$`Self Isolation`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.998 on 29 degrees of freedom
## Multiple R-squared: 0.08735, Adjusted R-squared: 0.05588
## F-statistic: 2.776 on 1 and 29 DF, p-value: 0.1065
anova(model)
## Analysis of Variance Table
##
## Response: DataCovid19_DKI_Jakarta$retail_and_recreation_percent_change_from_baseline
## Df Sum Sq Mean Sq F value Pr(>F)
## DataCovid19_DKI_Jakarta$`Self Isolation` 1 24.954 24.9536 2.7756 0.1065
## Residuals 29 260.724 8.9905
plot(model)
head(predict(model), n = 11)
## 1 2 3 4 5 6 7 8
## 6.693091 6.705157 6.722834 6.733075 6.755522 6.779232 6.809676 6.832544
## 9 10 11
## 6.866074 6.898622 6.937063
head(resid(model), n = 11)
## 1 2 3 4 5 6 7
## -3.6930912 4.2948435 7.2771664 4.2669249 5.2444778 3.2207681 -0.8096758
## 8 9 10 11
## 1.1674562 0.1339259 0.1013776 -5.9370631
DataCovid19_DKI_Jakarta$residuals<-model$residuals
DataCovid19_DKI_Jakarta$predicted<- model$fitted.values
DataCovid19_DKI_Jakarta
## # A tibble: 31 x 17
## country_region_code country_region `Nama Kota` date retail_and_recreat~
## <chr> <chr> <chr> <date> <dbl>
## 1 ID Indonesia DKI JAKARTA 2022-01-01 3
## 2 ID Indonesia DKI JAKARTA 2022-01-02 11
## 3 ID Indonesia DKI JAKARTA 2022-01-03 14
## 4 ID Indonesia DKI JAKARTA 2022-01-04 11
## 5 ID Indonesia DKI JAKARTA 2022-01-05 12
## 6 ID Indonesia DKI JAKARTA 2022-01-06 10
## 7 ID Indonesia DKI JAKARTA 2022-01-07 6
## 8 ID Indonesia DKI JAKARTA 2022-01-08 8
## 9 ID Indonesia DKI JAKARTA 2022-01-09 7
## 10 ID Indonesia DKI JAKARTA 2022-01-10 7
## # ... with 21 more rows, and 12 more variables:
## # 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>, POSITIF <dbl>,
## # Dirawat <dbl>, Sembuh <dbl>, Meninggal <dbl>, `Self Isolation` <dbl>, ...
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v purrr 0.3.4 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
df<-DataCovid19_DKI_Jakarta
df
## # A tibble: 31 x 17
## country_region_code country_region `Nama Kota` date retail_and_recreat~
## <chr> <chr> <chr> <date> <dbl>
## 1 ID Indonesia DKI JAKARTA 2022-01-01 3
## 2 ID Indonesia DKI JAKARTA 2022-01-02 11
## 3 ID Indonesia DKI JAKARTA 2022-01-03 14
## 4 ID Indonesia DKI JAKARTA 2022-01-04 11
## 5 ID Indonesia DKI JAKARTA 2022-01-05 12
## 6 ID Indonesia DKI JAKARTA 2022-01-06 10
## 7 ID Indonesia DKI JAKARTA 2022-01-07 6
## 8 ID Indonesia DKI JAKARTA 2022-01-08 8
## 9 ID Indonesia DKI JAKARTA 2022-01-09 7
## 10 ID Indonesia DKI JAKARTA 2022-01-10 7
## # ... with 21 more rows, and 12 more variables:
## # 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>, POSITIF <dbl>,
## # Dirawat <dbl>, Sembuh <dbl>, Meninggal <dbl>, `Self Isolation` <dbl>, ...
df%>%select(4,11,16,17)
## # A tibble: 31 x 4
## date POSITIF residuals predicted
## <date> <dbl> <dbl> <dbl>
## 1 2022-01-01 865415 -3.69 6.69
## 2 2022-01-02 865518 4.29 6.71
## 3 2022-01-03 865690 7.28 6.72
## 4 2022-01-04 865805 4.27 6.73
## 5 2022-01-05 866064 5.24 6.76
## 6 2022-01-06 866331 3.22 6.78
## 7 2022-01-07 866631 -0.810 6.81
## 8 2022-01-08 866909 1.17 6.83
## 9 2022-01-09 867302 0.134 6.87
## 10 2022-01-10 867662 0.101 6.90
## # ... with 21 more rows
library(tidyverse)
my_data <- as_tibble(DataCovid19_DKI_Jakarta)
my_data
## # A tibble: 31 x 17
## country_region_code country_region `Nama Kota` date retail_and_recreat~
## <chr> <chr> <chr> <date> <dbl>
## 1 ID Indonesia DKI JAKARTA 2022-01-01 3
## 2 ID Indonesia DKI JAKARTA 2022-01-02 11
## 3 ID Indonesia DKI JAKARTA 2022-01-03 14
## 4 ID Indonesia DKI JAKARTA 2022-01-04 11
## 5 ID Indonesia DKI JAKARTA 2022-01-05 12
## 6 ID Indonesia DKI JAKARTA 2022-01-06 10
## 7 ID Indonesia DKI JAKARTA 2022-01-07 6
## 8 ID Indonesia DKI JAKARTA 2022-01-08 8
## 9 ID Indonesia DKI JAKARTA 2022-01-09 7
## 10 ID Indonesia DKI JAKARTA 2022-01-10 7
## # ... with 21 more rows, and 12 more variables:
## # 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>, POSITIF <dbl>,
## # Dirawat <dbl>, Sembuh <dbl>, Meninggal <dbl>, `Self Isolation` <dbl>, ...
my_data%>%select(date)
## # A tibble: 31 x 1
## date
## <date>
## 1 2022-01-01
## 2 2022-01-02
## 3 2022-01-03
## 4 2022-01-04
## 5 2022-01-05
## 6 2022-01-06
## 7 2022-01-07
## 8 2022-01-08
## 9 2022-01-09
## 10 2022-01-10
## # ... with 21 more rows