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Pivot data uang kartal inflow di jawa

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()
datalongerjawa <- datainflowjawa %>% 
  pivot_longer(!Provinsi, names_to = "Tahun", values_to = "Kasus")
datalongerjawa 
## # A tibble: 77 x 3
##    Provinsi    Tahun   Kasus
##    <chr>       <chr>   <dbl>
##  1 DKI Jakarta 2011   62958.
##  2 DKI Jakarta 2012   76665.
##  3 DKI Jakarta 2013   84526.
##  4 DKI Jakarta 2014   92106.
##  5 DKI Jakarta 2015  100426.
##  6 DKI Jakarta 2016  115684.
##  7 DKI Jakarta 2017  112213.
##  8 DKI Jakarta 2018  126459.
##  9 DKI Jakarta 2019  142940.
## 10 DKI Jakarta 2020  110549.
## # ... with 67 more rows
library(dplyr)
Jawab1 <- select(datalongerjawa, Provinsi, Kasus)
Jawab1
## # A tibble: 77 x 2
##    Provinsi      Kasus
##    <chr>         <dbl>
##  1 DKI Jakarta  62958.
##  2 DKI Jakarta  76665.
##  3 DKI Jakarta  84526.
##  4 DKI Jakarta  92106.
##  5 DKI Jakarta 100426.
##  6 DKI Jakarta 115684.
##  7 DKI Jakarta 112213.
##  8 DKI Jakarta 126459.
##  9 DKI Jakarta 142940.
## 10 DKI Jakarta 110549.
## # ... with 67 more rows
jawab2 <- datalongerjawa  %>%
    filter(Provinsi == 'Yogyakarta') %>%
    select('Provinsi', 'Tahun', 'Kasus')
jawab2 
## # A tibble: 11 x 3
##    Provinsi   Tahun  Kasus
##    <chr>      <chr>  <dbl>
##  1 Yogyakarta 2011   6490.
##  2 Yogyakarta 2012   9173.
##  3 Yogyakarta 2013   8939.
##  4 Yogyakarta 2014  13890.
##  5 Yogyakarta 2015  14831.
##  6 Yogyakarta 2016  17350.
##  7 Yogyakarta 2017  17483.
##  8 Yogyakarta 2018  20574.
##  9 Yogyakarta 2019  20899.
## 10 Yogyakarta 2020   7348.
## 11 Yogyakarta 2021   6714.
jawab3 <- datalongerjawa  %>%
    filter(Provinsi == 'Yogyakarta', Tahun=='2021') %>%
    select('Provinsi', 'Tahun', 'Kasus')
jawab3 
## # A tibble: 1 x 3
##   Provinsi   Tahun Kasus
##   <chr>      <chr> <dbl>
## 1 Yogyakarta 2021  6714.
ggplot(data = datalongerjawa, mapping = aes(x = Tahun, y = Kasus)) +
  geom_point() +
  facet_wrap( ~ Provinsi) +
  theme(axis.text.x = element_text(angle = 45, size = 8))