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Manipulasi data dengan library dplyr

library(readxl)
datainflowjawa <- read_excel(path = "C:/COba/jawa.xlsx")
datainflowjawa 
## # A tibble: 7 x 12
##   Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019` `2020`
##   <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 DKI Jak~ 6.30e4 7.67e4 8.45e4 9.21e4 1.00e5 1.16e5 1.12e5 1.26e5 1.43e5 1.11e5
## 2 Jawa     1.24e5 1.60e5 1.35e5 2.17e5 2.30e5 2.62e5 2.78e5 3.07e5 3.25e5 2.59e5
## 3 Jawa Ba~ 4.38e4 6.06e4 3.52e4 7.87e4 8.13e4 8.80e4 8.32e4 8.72e4 9.48e4 7.69e4
## 4 Jawa Te~ 3.51e4 4.33e4 4.22e4 6.05e4 6.52e4 7.28e4 7.70e4 8.78e4 9.08e4 8.50e4
## 5 Yogyaka~ 6.49e3 9.17e3 8.94e3 1.39e4 1.48e4 1.74e4 1.75e4 2.06e4 2.09e4 7.35e3
## 6 Jawa Ti~ 3.85e4 4.74e4 4.87e4 6.43e4 6.88e4 8.34e4 9.84e4 1.06e5 1.14e5 8.68e4
## 7 Banten   0      0      0      0      0      0      1.49e3 4.83e3 4.48e3 3.40e3
## # ... with 1 more variable: `2021` <dbl>
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## 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()
jawa2019 <- select(datainflowjawa, '2019')
jawa2019
## # A tibble: 7 x 1
##    `2019`
##     <dbl>
## 1 142940.
## 2 324624.
## 3  94846.
## 4  90751.
## 5  20899.
## 6 113651.
## 7   4477.
library(tidyverse)
jawaskip2015 <- select(datainflowjawa, -'2015')
jawaskip2015
## # A tibble: 7 x 11
##   Provinsi `2011` `2012` `2013` `2014` `2016` `2017` `2018` `2019` `2020` `2021`
##   <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 DKI Jak~ 6.30e4 7.67e4 8.45e4 9.21e4 1.16e5 1.12e5 1.26e5 1.43e5 1.11e5 8.55e4
## 2 Jawa     1.24e5 1.60e5 1.35e5 2.17e5 2.62e5 2.78e5 3.07e5 3.25e5 2.59e5 1.88e5
## 3 Jawa Ba~ 4.38e4 6.06e4 3.52e4 7.87e4 8.80e4 8.32e4 8.72e4 9.48e4 7.69e4 5.73e4
## 4 Jawa Te~ 3.51e4 4.33e4 4.22e4 6.05e4 7.28e4 7.70e4 8.78e4 9.08e4 8.50e4 6.20e4
## 5 Yogyaka~ 6.49e3 9.17e3 8.94e3 1.39e4 1.74e4 1.75e4 2.06e4 2.09e4 7.35e3 6.71e3
## 6 Jawa Ti~ 3.85e4 4.74e4 4.87e4 6.43e4 8.34e4 9.84e4 1.06e5 1.14e5 8.68e4 5.90e4
## 7 Banten   0      0      0      0      0      1.49e3 4.83e3 4.48e3 3.40e3 2.80e3
sumatera2021 <- datainflowjawa %>% select('2021')
sumatera2021
## # A tibble: 7 x 1
##    `2021`
##     <dbl>
## 1  85530.
## 2 187816.
## 3  57295.
## 4  62024.
## 5   6714.
## 6  58986.
## 7   2798.
jawagantitahun <- datainflowjawa %>% rename('2002' = '2020')
head(jawagantitahun)
## # A tibble: 6 x 12
##   Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019` `2002`
##   <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 DKI Jak~ 6.30e4 7.67e4 8.45e4 9.21e4 1.00e5 1.16e5 1.12e5 1.26e5 1.43e5 1.11e5
## 2 Jawa     1.24e5 1.60e5 1.35e5 2.17e5 2.30e5 2.62e5 2.78e5 3.07e5 3.25e5 2.59e5
## 3 Jawa Ba~ 4.38e4 6.06e4 3.52e4 7.87e4 8.13e4 8.80e4 8.32e4 8.72e4 9.48e4 7.69e4
## 4 Jawa Te~ 3.51e4 4.33e4 4.22e4 6.05e4 6.52e4 7.28e4 7.70e4 8.78e4 9.08e4 8.50e4
## 5 Yogyaka~ 6.49e3 9.17e3 8.94e3 1.39e4 1.48e4 1.74e4 1.75e4 2.06e4 2.09e4 7.35e3
## 6 Jawa Ti~ 3.85e4 4.74e4 4.87e4 6.43e4 6.88e4 8.34e4 9.84e4 1.06e5 1.14e5 8.68e4
## # ... with 1 more variable: `2021` <dbl>
jawatimur <- datainflowjawa %>%
    filter(Provinsi == 'Jawa Timur') %>%
    select('2020','2021')
jawatimur
## # A tibble: 1 x 2
##   `2020` `2021`
##    <dbl>  <dbl>
## 1 86848. 58986.
jawap1 <- datainflowjawa %>%
  filter(Provinsi == 'Jawa Timur', Provinsi=='Jawa Tengah') %>%
  select('2020','2021')
jawap1
## # A tibble: 0 x 2
## # ... with 2 variables: 2020 <dbl>, 2021 <dbl>
str(datainflowjawa)
## tibble [7 x 12] (S3: tbl_df/tbl/data.frame)
##  $ Provinsi: chr [1:7] "DKI Jakarta" "Jawa" "Jawa Barat" "Jawa Tengah" ...
##  $ 2011    : num [1:7] 62958 123917 43775 35137 6490 ...
##  $ 2012    : num [1:7] 76665 160482 60629 43298 9173 ...
##  $ 2013    : num [1:7] 84526 134998 35190 42182 8939 ...
##  $ 2014    : num [1:7] 92106 217303 78660 60476 13890 ...
##  $ 2015    : num [1:7] 100426 230141 81303 65198 14831 ...
##  $ 2016    : num [1:7] 115684 261607 88036 72782 17350 ...
##  $ 2017    : num [1:7] 112213 277609 83220 77031 17483 ...
##  $ 2018    : num [1:7] 126459 306911 87243 87829 20574 ...
##  $ 2019    : num [1:7] 142940 324624 94846 90751 20899 ...
##  $ 2020    : num [1:7] 110549 259444 76883 84970 7348 ...
##  $ 2021    : num [1:7] 85530 187816 57295 62024 6714 ...
str(datainflowjawa %>% group_by(Provinsi))
## grouped_df [7 x 12] (S3: grouped_df/tbl_df/tbl/data.frame)
##  $ Provinsi: chr [1:7] "DKI Jakarta" "Jawa" "Jawa Barat" "Jawa Tengah" ...
##  $ 2011    : num [1:7] 62958 123917 43775 35137 6490 ...
##  $ 2012    : num [1:7] 76665 160482 60629 43298 9173 ...
##  $ 2013    : num [1:7] 84526 134998 35190 42182 8939 ...
##  $ 2014    : num [1:7] 92106 217303 78660 60476 13890 ...
##  $ 2015    : num [1:7] 100426 230141 81303 65198 14831 ...
##  $ 2016    : num [1:7] 115684 261607 88036 72782 17350 ...
##  $ 2017    : num [1:7] 112213 277609 83220 77031 17483 ...
##  $ 2018    : num [1:7] 126459 306911 87243 87829 20574 ...
##  $ 2019    : num [1:7] 142940 324624 94846 90751 20899 ...
##  $ 2020    : num [1:7] 110549 259444 76883 84970 7348 ...
##  $ 2021    : num [1:7] 85530 187816 57295 62024 6714 ...
##  - attr(*, "groups")= tibble [7 x 2] (S3: tbl_df/tbl/data.frame)
##   ..$ Provinsi: chr [1:7] "Banten" "DKI Jakarta" "Jawa" "Jawa Barat" ...
##   ..$ .rows   : list<int> [1:7] 
##   .. ..$ : int 7
##   .. ..$ : int 1
##   .. ..$ : int 2
##   .. ..$ : int 3
##   .. ..$ : int 4
##   .. ..$ : int 6
##   .. ..$ : int 5
##   .. ..@ ptype: int(0) 
##   ..- attr(*, ".drop")= logi TRUE
jawab4 <- datainflowjawa %>%
    group_by(Provinsi)
jawab4
## # A tibble: 7 x 12
## # Groups:   Provinsi [7]
##   Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019` `2020`
##   <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 DKI Jak~ 6.30e4 7.67e4 8.45e4 9.21e4 1.00e5 1.16e5 1.12e5 1.26e5 1.43e5 1.11e5
## 2 Jawa     1.24e5 1.60e5 1.35e5 2.17e5 2.30e5 2.62e5 2.78e5 3.07e5 3.25e5 2.59e5
## 3 Jawa Ba~ 4.38e4 6.06e4 3.52e4 7.87e4 8.13e4 8.80e4 8.32e4 8.72e4 9.48e4 7.69e4
## 4 Jawa Te~ 3.51e4 4.33e4 4.22e4 6.05e4 6.52e4 7.28e4 7.70e4 8.78e4 9.08e4 8.50e4
## 5 Yogyaka~ 6.49e3 9.17e3 8.94e3 1.39e4 1.48e4 1.74e4 1.75e4 2.06e4 2.09e4 7.35e3
## 6 Jawa Ti~ 3.85e4 4.74e4 4.87e4 6.43e4 6.88e4 8.34e4 9.84e4 1.06e5 1.14e5 8.68e4
## 7 Banten   0      0      0      0      0      0      1.49e3 4.83e3 4.48e3 3.40e3
## # ... with 1 more variable: `2021` <dbl>
datainflowjawa %>%
    filter(Provinsi == 'Yogyakarta') %>%
    count('2020', sort = TRUE)
## # A tibble: 1 x 2
##   `"2020"`     n
##   <chr>    <int>
## 1 2020         1
jawap3 <- datainflowjawa %>%
    mutate('2020' = datainflowjawa$'2021'/2)
jawap3
## # A tibble: 7 x 12
##   Provinsi `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019` `2020`
##   <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 DKI Jak~ 6.30e4 7.67e4 8.45e4 9.21e4 1.00e5 1.16e5 1.12e5 1.26e5 1.43e5 42765.
## 2 Jawa     1.24e5 1.60e5 1.35e5 2.17e5 2.30e5 2.62e5 2.78e5 3.07e5 3.25e5 93908.
## 3 Jawa Ba~ 4.38e4 6.06e4 3.52e4 7.87e4 8.13e4 8.80e4 8.32e4 8.72e4 9.48e4 28647.
## 4 Jawa Te~ 3.51e4 4.33e4 4.22e4 6.05e4 6.52e4 7.28e4 7.70e4 8.78e4 9.08e4 31012.
## 5 Yogyaka~ 6.49e3 9.17e3 8.94e3 1.39e4 1.48e4 1.74e4 1.75e4 2.06e4 2.09e4  3357.
## 6 Jawa Ti~ 3.85e4 4.74e4 4.87e4 6.43e4 6.88e4 8.34e4 9.84e4 1.06e5 1.14e5 29493.
## 7 Banten   0      0      0      0      0      0      1.49e3 4.83e3 4.48e3  1399.
## # ... with 1 more variable: `2021` <dbl>
ggplot(data = datainflowjawa, mapping = aes(x = Provinsi, y = `2021`)) +
  geom_bar(stat="identity")+
  geom_col(fill="blue")