Jurusan : Teknik Informatika
library(readxl)
## Warning: package 'readxl' was built under R version 4.1.2
datainflowsulampua <- read_excel(path = "inflowsulampua.xlsx")
datainflowsulampua
## # A tibble: 11 x 12
## Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Sulampua 25056. 31011. 63774. 4.16e4 4.03e4 45737. 44126. 52672. 60202.
## 2 Sulawesi Utara 5671. 6635. 21646. 7.37e3 6.29e3 7266. 7044. 7781. 7809.
## 3 Sulawesi Teng~ 1563. 1885. 1520. 3.00e3 2.59e3 2665. 2806. 3701. 4042.
## 4 Sulawesi Sela~ 10593. 13702. 17770. 1.94e4 1.96e4 21043. 18803. 21894. 24749.
## 5 Sulawesi Teng~ 659. 964. 6093. 2.26e3 2.38e3 3491. 3618. 3632. 4390.
## 6 Sulawesi Barat 0 0 0 0 4.92e1 536. 746. 606. 542.
## 7 Gorontalo 0 0 0 0 0 0 0 1088. 1983.
## 8 Maluku Utara 586. 633. 10273. 1.01e3 1.01e3 1259. 1339. 1530. 1924.
## 9 Maluku 1273. 1147. 4341. 1.78e3 1.79e3 2367. 2484. 3210. 4056.
## 10 Papua 4710. 6047. 2131. 6.79e3 6.10e3 6291. 6353. 8076. 9259.
## 11 Papua Barat 0 0 0 1.17e1 5.18e2 818. 933. 1153. 1448.
## # ... 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 'purrr' was built under R version 4.1.2
## Warning: package 'dplyr' was built under R version 4.1.2
## Warning: package 'forcats' was built under R version 4.1.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
sulampua2018 <- select(datainflowsulampua, '2018')
sulampua2018
## # A tibble: 11 x 1
## `2018`
## <dbl>
## 1 52672.
## 2 7781.
## 3 3701.
## 4 21894.
## 5 3632.
## 6 606.
## 7 1088.
## 8 1530.
## 9 3210.
## 10 8076.
## 11 1153.
library(tidyverse)
sulampuanon2018 <- select(datainflowsulampua, -'2018')
sulampuanon2018
## # A tibble: 11 x 11
## Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2019` `2020`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Sulampua 25056. 31011. 63774. 4.16e4 4.03e4 45737. 44126. 60202. 52812.
## 2 Sulawesi Utara 5671. 6635. 21646. 7.37e3 6.29e3 7266. 7044. 7809. 6324.
## 3 Sulawesi Teng~ 1563. 1885. 1520. 3.00e3 2.59e3 2665. 2806. 4042. 3052.
## 4 Sulawesi Sela~ 10593. 13702. 17770. 1.94e4 1.96e4 21043. 18803. 24749. 21551.
## 5 Sulawesi Teng~ 659. 964. 6093. 2.26e3 2.38e3 3491. 3618. 4390. 3353.
## 6 Sulawesi Barat 0 0 0 0 4.92e1 536. 746. 542. 329.
## 7 Gorontalo 0 0 0 0 0 0 0 1983. 2227.
## 8 Maluku Utara 586. 633. 10273. 1.01e3 1.01e3 1259. 1339. 1924. 1876.
## 9 Maluku 1273. 1147. 4341. 1.78e3 1.79e3 2367. 2484. 4056. 2909.
## 10 Papua 4710. 6047. 2131. 6.79e3 6.10e3 6291. 6353. 9259. 9556.
## 11 Papua Barat 0 0 0 1.17e1 5.18e2 818. 933. 1448. 1635.
## # ... with 1 more variable: `2021` <dbl>
sulampua2019 <- datainflowsulampua %>% select('2019')
sulampua2019
## # A tibble: 11 x 1
## `2019`
## <dbl>
## 1 60202.
## 2 7809.
## 3 4042.
## 4 24749.
## 5 4390.
## 6 542.
## 7 1983.
## 8 1924.
## 9 4056.
## 10 9259.
## 11 1448.
library(dplyr)
sulampuatahun <- datainflowsulampua %>% rename('2022' = '2018')
head(sulampuatahun)
## # A tibble: 6 x 12
## Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2022` `2019` `2020`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Sulampua 25056. 31011. 63774. 41607. 4.03e4 45737. 44126. 52672. 60202. 52812.
## 2 Sulawes~ 5671. 6635. 21646. 7374. 6.29e3 7266. 7044. 7781. 7809. 6324.
## 3 Sulawes~ 1563. 1885. 1520. 3000. 2.59e3 2665. 2806. 3701. 4042. 3052.
## 4 Sulawes~ 10593. 13702. 17770. 19384. 1.96e4 21043. 18803. 21894. 24749. 21551.
## 5 Sulawes~ 659. 964. 6093. 2256. 2.38e3 3491. 3618. 3632. 4390. 3353.
## 6 Sulawes~ 0 0 0 0 4.92e1 536. 746. 606. 542. 329.
## # ... with 1 more variable: `2021` <dbl>
library(dplyr)
sulampuasulut <- datainflowsulampua %>%
filter(Provinsi == 'Sulawesi Utara') %>%
select('2018','2019')
sulampuasulut
## # A tibble: 1 x 2
## `2018` `2019`
## <dbl> <dbl>
## 1 7781. 7809.
library(dplyr)
sulampuaup1 <- datainflowsulampua %>%
filter(Provinsi == 'Sulawesi Utara', Provinsi == 'Sulawesi Tengah') %>%
select('2018','2019')
sulampuaup1
## # A tibble: 0 x 2
## # ... with 2 variables: 2018 <dbl>, 2019 <dbl>
str(datainflowsulampua)
## tibble [11 x 12] (S3: tbl_df/tbl/data.frame)
## $ Provinsi: chr [1:11] "Sulampua" "Sulawesi Utara" "Sulawesi Tengah" "Sulawesi Selatan" ...
## $ 2011 : num [1:11] 25056 5671 1563 10593 659 ...
## $ 2012 : num [1:11] 31011 6635 1885 13702 964 ...
## $ 2013 : num [1:11] 63774 21646 1520 17770 6093 ...
## $ 2014 : num [1:11] 41607 7374 3000 19384 2256 ...
## $ 2015 : num [1:11] 40309 6286 2593 19583 2385 ...
## $ 2016 : num [1:11] 45737 7266 2665 21043 3491 ...
## $ 2017 : num [1:11] 44126 7044 2806 18803 3618 ...
## $ 2018 : num [1:11] 52672 7781 3701 21894 3632 ...
## $ 2019 : num [1:11] 60202 7809 4042 24749 4390 ...
## $ 2020 : num [1:11] 52812 6324 3052 21551 3353 ...
## $ 2021 : num [1:11] 45714 4671 2453 18335 3270 ...
str(datainflowsulampua %>% group_by(Provinsi))
## grouped_df [11 x 12] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Provinsi: chr [1:11] "Sulampua" "Sulawesi Utara" "Sulawesi Tengah" "Sulawesi Selatan" ...
## $ 2011 : num [1:11] 25056 5671 1563 10593 659 ...
## $ 2012 : num [1:11] 31011 6635 1885 13702 964 ...
## $ 2013 : num [1:11] 63774 21646 1520 17770 6093 ...
## $ 2014 : num [1:11] 41607 7374 3000 19384 2256 ...
## $ 2015 : num [1:11] 40309 6286 2593 19583 2385 ...
## $ 2016 : num [1:11] 45737 7266 2665 21043 3491 ...
## $ 2017 : num [1:11] 44126 7044 2806 18803 3618 ...
## $ 2018 : num [1:11] 52672 7781 3701 21894 3632 ...
## $ 2019 : num [1:11] 60202 7809 4042 24749 4390 ...
## $ 2020 : num [1:11] 52812 6324 3052 21551 3353 ...
## $ 2021 : num [1:11] 45714 4671 2453 18335 3270 ...
## - attr(*, "groups")= tibble [11 x 2] (S3: tbl_df/tbl/data.frame)
## ..$ Provinsi: chr [1:11] "Gorontalo" "Maluku" "Maluku Utara" "Papua" ...
## ..$ .rows : list<int> [1:11]
## .. ..$ : int 7
## .. ..$ : int 9
## .. ..$ : int 8
## .. ..$ : int 10
## .. ..$ : int 11
## .. ..$ : int 1
## .. ..$ : int 6
## .. ..$ : int 4
## .. ..$ : int 3
## .. ..$ : int 5
## .. ..$ : int 2
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUE
sulampuaup3 <- datainflowsulampua %>%
group_by(Provinsi)
sulampuaup3
## # A tibble: 11 x 12
## # Groups: Provinsi [11]
## Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Sulampua 25056. 31011. 63774. 4.16e4 4.03e4 45737. 44126. 52672. 60202.
## 2 Sulawesi Utara 5671. 6635. 21646. 7.37e3 6.29e3 7266. 7044. 7781. 7809.
## 3 Sulawesi Teng~ 1563. 1885. 1520. 3.00e3 2.59e3 2665. 2806. 3701. 4042.
## 4 Sulawesi Sela~ 10593. 13702. 17770. 1.94e4 1.96e4 21043. 18803. 21894. 24749.
## 5 Sulawesi Teng~ 659. 964. 6093. 2.26e3 2.38e3 3491. 3618. 3632. 4390.
## 6 Sulawesi Barat 0 0 0 0 4.92e1 536. 746. 606. 542.
## 7 Gorontalo 0 0 0 0 0 0 0 1088. 1983.
## 8 Maluku Utara 586. 633. 10273. 1.01e3 1.01e3 1259. 1339. 1530. 1924.
## 9 Maluku 1273. 1147. 4341. 1.78e3 1.79e3 2367. 2484. 3210. 4056.
## 10 Papua 4710. 6047. 2131. 6.79e3 6.10e3 6291. 6353. 8076. 9259.
## 11 Papua Barat 0 0 0 1.17e1 5.18e2 818. 933. 1153. 1448.
## # ... with 2 more variables: `2020` <dbl>, `2021` <dbl>
datainflowsulampua %>%
filter(Provinsi == 'Sulawesi Utara') %>%
count('2018', sort = TRUE)
## # A tibble: 1 x 2
## `"2018"` n
## <chr> <int>
## 1 2018 1
sulampuasulutup1 <- datainflowsulampua %>%
mutate('2016' = datainflowsulampua$'2017'/2)
sulampuasulutup1
## # A tibble: 11 x 12
## Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Sulampua 25056. 31011. 63774. 4.16e4 4.03e4 22063. 44126. 52672. 60202.
## 2 Sulawesi Utara 5671. 6635. 21646. 7.37e3 6.29e3 3522. 7044. 7781. 7809.
## 3 Sulawesi Teng~ 1563. 1885. 1520. 3.00e3 2.59e3 1403. 2806. 3701. 4042.
## 4 Sulawesi Sela~ 10593. 13702. 17770. 1.94e4 1.96e4 9402. 18803. 21894. 24749.
## 5 Sulawesi Teng~ 659. 964. 6093. 2.26e3 2.38e3 1809. 3618. 3632. 4390.
## 6 Sulawesi Barat 0 0 0 0 4.92e1 373. 746. 606. 542.
## 7 Gorontalo 0 0 0 0 0 0 0 1088. 1983.
## 8 Maluku Utara 586. 633. 10273. 1.01e3 1.01e3 669. 1339. 1530. 1924.
## 9 Maluku 1273. 1147. 4341. 1.78e3 1.79e3 1242. 2484. 3210. 4056.
## 10 Papua 4710. 6047. 2131. 6.79e3 6.10e3 3176. 6353. 8076. 9259.
## 11 Papua Barat 0 0 0 1.17e1 5.18e2 467. 933. 1153. 1448.
## # ... with 2 more variables: `2020` <dbl>, `2021` <dbl>
ggplot(data = datainflowsulampua, mapping = aes(x = Provinsi, y = `2018`)) +
geom_point() +
theme(axis.text.x = element_text(angle = 90))
