UIN Maulana Malik Ibrahim Malang
library(readxl)
datainflowsumatera <- read_excel(path = "sumatera.xlsx")
datainflowsumatera
## # A tibble: 10 x 10
## 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 4570 4710 5775 5514 5799 7509
## 2 Sumatera Utara 23238 25981 18120 3050 30254 34427 35617 41769 47112
## 3 Sumatera Barat 9385 11192 14056 1410 13309 14078 15312 15058 14750
## 4 Riau 3012 4447 8933 6360 7156 8211 8553 10730 10915
## 5 Kep. Riau 1426 2236 3378 2560 3218 4317 4412 5134 6077
## 6 Jambi 1868 2138 3047 5170 4978 4398 4404 5657 6486
## 7 Sumatera Sela~ 7820 9126 8647 10000 10797 12752 13075 14267 14812
## 8 Bengkulu 1153 1201 2378 3260 2791 2889 3620 4150 5789
## 9 Lampung 7690 6969 3474 9450 8160 9373 12078 13415 17046
## 10 Kep. Bangka B~ 0 0 0 1370 1177 1544 1164 1517 3265
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.6 v purrr 0.3.4
## v tibble 3.1.7 v dplyr 1.0.9
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
sumatera2011 <- select(datainflowsumatera, '2011')
sumatera2011
## # A tibble: 10 x 1
## `2011`
## <dbl>
## 1 2308
## 2 23238
## 3 9385
## 4 3012
## 5 1426
## 6 1868
## 7 7820
## 8 1153
## 9 7690
## 10 0
library(tidyverse)
sumateranon2011 <- select(datainflowsumatera, -'2011')
sumateranon2011
## # A tibble: 10 x 9
## Propinsi `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aceh 2620 36337 4570 4710 5775 5514 5799 7509
## 2 Sumatera Utara 25981 18120 3050 30254 34427 35617 41769 47112
## 3 Sumatera Barat 11192 14056 1410 13309 14078 15312 15058 14750
## 4 Riau 4447 8933 6360 7156 8211 8553 10730 10915
## 5 Kep. Riau 2236 3378 2560 3218 4317 4412 5134 6077
## 6 Jambi 2138 3047 5170 4978 4398 4404 5657 6486
## 7 Sumatera Selatan 9126 8647 10000 10797 12752 13075 14267 14812
## 8 Bengkulu 1201 2378 3260 2791 2889 3620 4150 5789
## 9 Lampung 6969 3474 9450 8160 9373 12078 13415 17046
## 10 Kep. Bangka Belitung 0 0 1370 1177 1544 1164 1517 3265
sumatera2012 <- datainflowsumatera %>% select('2012')
sumatera2012
## # A tibble: 10 x 1
## `2012`
## <dbl>
## 1 2620
## 2 25981
## 3 11192
## 4 4447
## 5 2236
## 6 2138
## 7 9126
## 8 1201
## 9 6969
## 10 0
library(dplyr)
sumateratahun <- datainflowsumatera %>% rename('2010' = '2011')
head(sumateratahun)
## # A tibble: 6 x 10
## Propinsi `2010` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aceh 2308 2620 36337 4570 4710 5775 5514 5799 7509
## 2 Sumatera Utara 23238 25981 18120 3050 30254 34427 35617 41769 47112
## 3 Sumatera Barat 9385 11192 14056 1410 13309 14078 15312 15058 14750
## 4 Riau 3012 4447 8933 6360 7156 8211 8553 10730 10915
## 5 Kep. Riau 1426 2236 3378 2560 3218 4317 4412 5134 6077
## 6 Jambi 1868 2138 3047 5170 4978 4398 4404 5657 6486
library(dplyr)
sumateraaceh <- datainflowsumatera %>%
filter(Propinsi == 'Aceh') %>%
select('2011','2012')
sumateraaceh
## # A tibble: 1 x 2
## `2011` `2012`
## <dbl> <dbl>
## 1 2308 2620
library(dplyr)
sumateraup1 <- datainflowsumatera %>%
filter(Propinsi == 'Aceh', Propinsi == 'Bengkulu') %>%
select('2011','2012')
sumateraup1
## # A tibble: 0 x 2
## # ... with 2 variables: 2011 <dbl>, 2012 <dbl>
str(datainflowsumatera)
## tibble [10 x 10] (S3: tbl_df/tbl/data.frame)
## $ Propinsi: chr [1:10] "Aceh" "Sumatera Utara" "Sumatera Barat" "Riau" ...
## $ 2011 : num [1:10] 2308 23238 9385 3012 1426 ...
## $ 2012 : num [1:10] 2620 25981 11192 4447 2236 ...
## $ 2013 : num [1:10] 36337 18120 14056 8933 3378 ...
## $ 2014 : num [1:10] 4570 3050 1410 6360 2560 5170 10000 3260 9450 1370
## $ 2015 : num [1:10] 4710 30254 13309 7156 3218 ...
## $ 2016 : num [1:10] 5775 34427 14078 8211 4317 ...
## $ 2017 : num [1:10] 5514 35617 15312 8553 4412 ...
## $ 2018 : num [1:10] 5799 41769 15058 10730 5134 ...
## $ 2019 : num [1:10] 7509 47112 14750 10915 6077 ...
str(datainflowsumatera %>% group_by(Propinsi))
## grouped_df [10 x 10] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Propinsi: chr [1:10] "Aceh" "Sumatera Utara" "Sumatera Barat" "Riau" ...
## $ 2011 : num [1:10] 2308 23238 9385 3012 1426 ...
## $ 2012 : num [1:10] 2620 25981 11192 4447 2236 ...
## $ 2013 : num [1:10] 36337 18120 14056 8933 3378 ...
## $ 2014 : num [1:10] 4570 3050 1410 6360 2560 5170 10000 3260 9450 1370
## $ 2015 : num [1:10] 4710 30254 13309 7156 3218 ...
## $ 2016 : num [1:10] 5775 34427 14078 8211 4317 ...
## $ 2017 : num [1:10] 5514 35617 15312 8553 4412 ...
## $ 2018 : num [1:10] 5799 41769 15058 10730 5134 ...
## $ 2019 : num [1:10] 7509 47112 14750 10915 6077 ...
## - attr(*, "groups")= tibble [10 x 2] (S3: tbl_df/tbl/data.frame)
## ..$ Propinsi: chr [1:10] "Aceh" "Bengkulu" "Jambi" "Kep. Bangka Belitung" ...
## ..$ .rows : list<int> [1:10]
## .. ..$ : int 1
## .. ..$ : int 8
## .. ..$ : int 6
## .. ..$ : int 10
## .. ..$ : int 5
## .. ..$ : int 9
## .. ..$ : int 4
## .. ..$ : int 3
## .. ..$ : int 7
## .. ..$ : int 2
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUE
sumateraup3 <- datainflowsumatera %>%
group_by(Propinsi)
sumateraup3
## # A tibble: 10 x 10
## # Groups: Propinsi [10]
## 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 4570 4710 5775 5514 5799 7509
## 2 Sumatera Utara 23238 25981 18120 3050 30254 34427 35617 41769 47112
## 3 Sumatera Barat 9385 11192 14056 1410 13309 14078 15312 15058 14750
## 4 Riau 3012 4447 8933 6360 7156 8211 8553 10730 10915
## 5 Kep. Riau 1426 2236 3378 2560 3218 4317 4412 5134 6077
## 6 Jambi 1868 2138 3047 5170 4978 4398 4404 5657 6486
## 7 Sumatera Sela~ 7820 9126 8647 10000 10797 12752 13075 14267 14812
## 8 Bengkulu 1153 1201 2378 3260 2791 2889 3620 4150 5789
## 9 Lampung 7690 6969 3474 9450 8160 9373 12078 13415 17046
## 10 Kep. Bangka B~ 0 0 0 1370 1177 1544 1164 1517 3265
datainflowsumatera %>%
filter(Propinsi == 'Aceh') %>%
count('2011', sort = TRUE)
## # A tibble: 1 x 2
## `"2011"` n
## <chr> <int>
## 1 2011 1
sumateraacehup1 <- datainflowsumatera %>%
mutate('2010' = datainflowsumatera$'2011'/2)
sumateraacehup1
## # A tibble: 10 x 11
## 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 4570 4710 5775 5514 5799 7509
## 2 Sumatera Utara 23238 25981 18120 3050 30254 34427 35617 41769 47112
## 3 Sumatera Barat 9385 11192 14056 1410 13309 14078 15312 15058 14750
## 4 Riau 3012 4447 8933 6360 7156 8211 8553 10730 10915
## 5 Kep. Riau 1426 2236 3378 2560 3218 4317 4412 5134 6077
## 6 Jambi 1868 2138 3047 5170 4978 4398 4404 5657 6486
## 7 Sumatera Sela~ 7820 9126 8647 10000 10797 12752 13075 14267 14812
## 8 Bengkulu 1153 1201 2378 3260 2791 2889 3620 4150 5789
## 9 Lampung 7690 6969 3474 9450 8160 9373 12078 13415 17046
## 10 Kep. Bangka B~ 0 0 0 1370 1177 1544 1164 1517 3265
## # ... with 1 more variable: `2010` <dbl>
ggplot(data = datainflowsumatera, mapping = aes(x = Propinsi, y = `2011`)) +
geom_point()
