Dosen Pengampu : Prof. Dr. Suhartono, M.Kom
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Fakultas : Sains dan Teknologi
Jurusan : Teknik Informatika
Kelas : (C) Linear Algebra
NIM : 210605110035
Pivot Table adalah ringkasan data yang dikemas dalam tabel interaktif agar memudahkan dan membantu kamu untuk membuat laporan dan menganalisisnya dengan melihat perbandingan data yang kamu miliki.
Singkatnya, gunanya pivot table adalah untuk merangkum, mengelompokkan, mengeksplorasi, mempresentasikan, menghitung, dan menganalisa data.
library(readxl)
## Warning: package 'readxl' was built under R version 4.1.2
InflowBaliNusra <- read_excel(path = "C:/Users/ASUS/Documents/excel_algebra/InflowBaliNusra2.xlsx")
InflowBaliNusra
## # A tibble: 4 x 12
## Keterangan `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Bali Nusra 10322. 14613. 17512. 20807. 23008. 30965. 30797. 33866. 38116.
## 2 Bali 6394. 8202. 5066. 11590. 13072. 17914. 16962. 18610. 21422.
## 3 Nusa Tenggara ~ 1803. 3676. 7024. 5704. 6285. 8842. 8383. 9140. 9614.
## 4 Nusa Tenggara ~ 2125. 2735. 5422. 3512. 3651. 4210. 5452. 6116. 7080.
## # ... with 2 more variables: `2020` <dbl>, `2021` <dbl>
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.1.3
## -- 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 4.1.3
## Warning: package 'readr' was built under R version 4.1.3
## Warning: package 'purrr' was built under R version 4.1.3
## Warning: package 'dplyr' was built under R version 4.1.3
## Warning: package 'forcats' was built under R version 4.1.3
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
pivot_longer() “lengthens” data, increasing the number of rows and decreasing the number of columns. The inverse transformation is pivot_wider().
dataLength <- InflowBaliNusra %>%
pivot_longer(!Keterangan, names_to = "Tahun", values_to = "Kasus")
dataLength
## # A tibble: 44 x 3
## Keterangan Tahun Kasus
## <chr> <chr> <dbl>
## 1 Bali Nusra 2011 10322.
## 2 Bali Nusra 2012 14613.
## 3 Bali Nusra 2013 17512.
## 4 Bali Nusra 2014 20807.
## 5 Bali Nusra 2015 23008.
## 6 Bali Nusra 2016 30965.
## 7 Bali Nusra 2017 30797.
## 8 Bali Nusra 2018 33866.
## 9 Bali Nusra 2019 38116.
## 10 Bali Nusra 2020 29400.
## # ... with 34 more rows
Memilih variable Daerah dan Kasus
library(dplyr)
BaliNusraUp <- select(dataLength, Keterangan, Kasus)
BaliNusraUp
## # A tibble: 44 x 2
## Keterangan Kasus
## <chr> <dbl>
## 1 Bali Nusra 10322.
## 2 Bali Nusra 14613.
## 3 Bali Nusra 17512.
## 4 Bali Nusra 20807.
## 5 Bali Nusra 23008.
## 6 Bali Nusra 30965.
## 7 Bali Nusra 30797.
## 8 Bali Nusra 33866.
## 9 Bali Nusra 38116.
## 10 Bali Nusra 29400.
## # ... with 34 more rows
Menyeleksi baris atau observasi berdasarkan nilai.
library(dplyr)
BaliNusraUp1 <- dataLength %>%
filter(Keterangan > 'NTB') %>%
select('Keterangan', 'Tahun', 'Kasus')
BaliNusraUp1
## # A tibble: 22 x 3
## Keterangan Tahun Kasus
## <chr> <chr> <dbl>
## 1 Nusa Tenggara Barat 2011 1803.
## 2 Nusa Tenggara Barat 2012 3676.
## 3 Nusa Tenggara Barat 2013 7024.
## 4 Nusa Tenggara Barat 2014 5704.
## 5 Nusa Tenggara Barat 2015 6285.
## 6 Nusa Tenggara Barat 2016 8842.
## 7 Nusa Tenggara Barat 2017 8383.
## 8 Nusa Tenggara Barat 2018 9140.
## 9 Nusa Tenggara Barat 2019 9614.
## 10 Nusa Tenggara Barat 2020 8007.
## # ... with 12 more rows
BaliNusraUp2 <- dataLength %>%
filter(Keterangan <= 'Bali Nusra', Tahun <= '2019') %>%
select('Keterangan', 'Tahun', 'Kasus')
BaliNusraUp2
## # A tibble: 18 x 3
## Keterangan Tahun Kasus
## <chr> <chr> <dbl>
## 1 Bali Nusra 2011 10322.
## 2 Bali Nusra 2012 14613.
## 3 Bali Nusra 2013 17512.
## 4 Bali Nusra 2014 20807.
## 5 Bali Nusra 2015 23008.
## 6 Bali Nusra 2016 30965.
## 7 Bali Nusra 2017 30797.
## 8 Bali Nusra 2018 33866.
## 9 Bali Nusra 2019 38116.
## 10 Bali 2011 6394.
## 11 Bali 2012 8202.
## 12 Bali 2013 5066.
## 13 Bali 2014 11590.
## 14 Bali 2015 13072.
## 15 Bali 2016 17914.
## 16 Bali 2017 16962.
## 17 Bali 2018 18610.
## 18 Bali 2019 21422.
pivot_wider() “widens” data, increasing the number of columns and decreasing the number of rows. The inverse transformation is pivot_longer().
BaliNusraID <- dataLength %>%
pivot_wider(names_from = "Tahun",
values_from = "Kasus")
BaliNusraID
## # A tibble: 4 x 12
## Keterangan `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Bali Nusra 10322. 14613. 17512. 20807. 23008. 30965. 30797. 33866. 38116.
## 2 Bali 6394. 8202. 5066. 11590. 13072. 17914. 16962. 18610. 21422.
## 3 Nusa Tenggara ~ 1803. 3676. 7024. 5704. 6285. 8842. 8383. 9140. 9614.
## 4 Nusa Tenggara ~ 2125. 2735. 5422. 3512. 3651. 4210. 5452. 6116. 7080.
## # ... with 2 more variables: `2020` <dbl>, `2021` <dbl>
ggplot(data = dataLength,
mapping = aes(x = Keterangan, y = Kasus, color = Keterangan)) +
geom_jitter(alpha = 0.9)
ggplot(data = dataLength,
mapping = aes(x = Keterangan, y = Kasus, color = Keterangan)) +
geom_jitter(alpha = 0.9)
ggplot(data = dataLength, mapping = aes(x = Tahun, y = Kasus)) +
geom_point(color = "pink")
ggplot(data = dataLength,
mapping = aes(x = Keterangan, y = Kasus)) +
geom_point(color = "dark green")
ggplot(data = dataLength, mapping = aes(x = Tahun, y = Kasus)) +
geom_point(color = "dark red") +
facet_wrap( ~ Keterangan) +
theme(axis.text.x = element_text(angle = 90))
ggplot(data = dataLength, mapping = aes(x = Keterangan, y = Kasus)) +
geom_point(color = "dark blue") +
facet_wrap( ~ Tahun) +
theme(axis.text.x = element_text(angle = 90))
ggplot(data = dataLength,
mapping = aes(x = Keterangan, y = Kasus)) +
geom_boxplot(alpha = 0) + # Do not show outliers
geom_jitter(alpha = 0.7, color = "dark orange") +
theme_bw()
ggplot(data = dataLength,
mapping = aes(x = Keterangan, y = Kasus)) +
geom_boxplot(alpha = 0) + # Do not show outliers
geom_jitter(alpha = 0.7, color = "yellow") +
theme_bw()