library(readxl)
datainflowsumatera <- read_excel(path = "sumatera.xlsx")
datainflowsumatera
## # A tibble: 10 x 12
## Propinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aceh 2308. 2620. 36337. 4.57e3 4710. 5775. 5514. 5799. 7509.
## 2 Sumatera Utara 23238. 25981. 18120. 3.05e4 30254. 34427. 35617. 41769. 47112.
## 3 Sumatera Barat 9385. 11192. 14056. 1.41e4 13309. 14078. 15312. 15058. 14750.
## 4 Riau 3012. 4447. 8933. 6.36e3 7156. 8211. 8553. 10730. 10915.
## 5 Kep. Riau 1426. 2236. 3378. 2.56e3 3218. 4317. 4412. 5134. 6077.
## 6 Jambi 1868. 2138. 3047. 5.17e3 4978. 4398. 4404. 5657. 6486.
## 7 Sumatera Sela~ 7820. 9126. 8647. 1.00e4 10797. 12752. 13075. 14267. 14812.
## 8 Bengkulu 1153. 1201. 2378. 3.26e3 2791. 2889. 3620. 4150. 5789.
## 9 Lampung 7690. 6969. 3474. 9.45e3 8160. 9373. 12078. 13415. 17046.
## 10 Kep. Bangka B~ 0 0 0 1.37e1 1177. 1544. 1164. 1517. 3265.
## # ... with 2 more variables: `2020` <dbl>, `2021` <dbl>
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.1.2
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## Warning: package 'ggplot2' was built under R version 4.1.2
## Warning: package 'tibble' was built under R version 4.1.2
## Warning: package 'tidyr' was built under R version 4.1.2
## Warning: package 'readr' was built under R version 4.1.2
## Warning: package 'dplyr' was built under R version 4.1.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
datalongersumater <- datainflowsumatera %>%
pivot_longer(!Propinsi, names_to = "Tahun", values_to = "Kasus")
datalongersumater
## # A tibble: 110 x 3
## Propinsi Tahun Kasus
## <chr> <chr> <dbl>
## 1 Aceh 2011 2308.
## 2 Aceh 2012 2620.
## 3 Aceh 2013 36337.
## 4 Aceh 2014 4567.
## 5 Aceh 2015 4710.
## 6 Aceh 2016 5775.
## 7 Aceh 2017 5514.
## 8 Aceh 2018 5799.
## 9 Aceh 2019 7509.
## 10 Aceh 2020 6641.
## # ... with 100 more rows
library(dplyr)
sumateraup2 <- select(datalongersumater, Propinsi, Kasus)
sumateraup2
## # A tibble: 110 x 2
## Propinsi Kasus
## <chr> <dbl>
## 1 Aceh 2308.
## 2 Aceh 2620.
## 3 Aceh 36337.
## 4 Aceh 4567.
## 5 Aceh 4710.
## 6 Aceh 5775.
## 7 Aceh 5514.
## 8 Aceh 5799.
## 9 Aceh 7509.
## 10 Aceh 6641.
## # ... with 100 more rows
library(dplyr)
sumateraup4 <- datalongersumater %>%
filter(Propinsi == 'Aceh') %>%
select('Propinsi', 'Tahun', 'Kasus')
sumateraup4
## # A tibble: 11 x 3
## Propinsi Tahun Kasus
## <chr> <chr> <dbl>
## 1 Aceh 2011 2308.
## 2 Aceh 2012 2620.
## 3 Aceh 2013 36337.
## 4 Aceh 2014 4567.
## 5 Aceh 2015 4710.
## 6 Aceh 2016 5775.
## 7 Aceh 2017 5514.
## 8 Aceh 2018 5799.
## 9 Aceh 2019 7509.
## 10 Aceh 2020 6641.
## 11 Aceh 2021 3702.
sumateraup5 <- datalongersumater %>%
filter(Propinsi == 'Aceh', Tahun == '2011') %>%
select('Propinsi', 'Tahun', 'Kasus')
sumateraup5
## # A tibble: 1 x 3
## Propinsi Tahun Kasus
## <chr> <chr> <dbl>
## 1 Aceh 2011 2308.
ggplot(data = datalongersumater, mapping = aes(x = Tahun, y = Kasus)) +
geom_point() +
facet_wrap( ~ Propinsi) +
theme(axis.text.x = element_text(angle = 90))
ggplot(data = datalongersumater, mapping = aes(x = Propinsi, y = Kasus)) +
geom_point() +
facet_wrap( ~ Tahun) +
theme(axis.text.x = element_text(angle = 90))
