Produktif dengan dplyr

Pemrograman dengan dplyr

Dr. Chester Ismay

Educator, Data Scientist, and R/Python Consultant

Prasyarat kursus

Menggabungkan data dengan dplyr
Pengantar menulis fungsi di R

Garis besar kursus

Bab 1

Menyegarkan pipeline dplyr
Memilih kolom berdasarkan pola

Bab 2

Memindahkan kolom dalam data
Transformasi pada banyak kolom data

Bab 3

Memperkuat pengetahuan join dplyr
Gunakan klausa teori himpunan untuk meningkatkan pemrograman dengan banyak sumber data

Bab 4

Membuat fungsi untuk membungkus kode dplyr dan ggplot2
Gunakan paket rlang untuk memahami tidy evaluation

Tibble world_bank_data

country	region	year	infant_mortality_rate	fertility_rate	perc_rural_pop
Saudi Arabia	Western Asia	2013	13.3	2.64	17.260
Greece	Southern Europe	2014	3.7	1.54	22.298
Latvia	Northern Europe	2014	7.2	1.62	32.048
Romania	Eastern Europe	2014	10.1	1.43	46.100
Netherlands	Western Europe	2015	3.2	1.78	9.827

Kolom world_bank_data

names(world_bank_data)

 [1] "iso"                   "country"               "continent"            
 [4] "region"                "year"                  "infant_mortality_rate"
 [7] "fertility_rate"        "perc_electric_access"  "perc_college_complete"
[10] "perc_cvd_crd_70"       "unemployment_rate"     "perc_rural_pop"

Pilih beberapa kolom dari world_bank_data

world_bank_data %>%
    select(country, continent, region, year, perc_rural_pop, perc_college_complete)

# A tibble: 300 x 6
   country      continent region           year perc_rural_pop perc_college_complete
   <chr>        <fct>     <fct>           <dbl>          <dbl>                 <dbl>
 1 Portugal     Europe    Southern Europe  2000          45.6                   7.26
 2 Armenia      Asia      Western Asia     2001          35.6                  20.4 
 3 Bulgaria     Europe    Eastern Europe   2001          30.8                  18.0 
 4 Portugal     Europe    Southern Europe  2001          45.0                   7.57
 5 Qatar        Asia      Western Asia     2004           2.91                 20.9 
 6 Saudi Arabia Asia      Western Asia     2004          19.2                  14.9 
 7 Pakistan     Asia      Southern Asia    2005          66.0                   3.92
# ... with 293 more rows

Saring baris sesuai nilai benua

continents_vector <- c("Africa", "Asia")

asia_africa_results <- world_bank_data %>%
    select(country, continent, region, year, perc_rural_pop, perc_college_complete) %>%
    filter(continent %in% continents_vector)

Hasil penyaringan baris

asia_africa_results

# A tibble: 111 x 6
   country      continent region              year perc_rural_pop perc_college_complete
   <chr>        <fct>     <fct>              <dbl>          <dbl>                 <dbl>
 1 Armenia      Asia      Western Asia        2001          35.6                  20.4 
 2 Qatar        Asia      Western Asia        2004           2.91                 20.9 
 3 Saudi Arabia Asia      Western Asia        2004          19.2                  14.9 
 4 Pakistan     Asia      Southern Asia       2005          66.0                   3.92
 5 Nigeria      Africa    Western Africa      2006          60.1                   9.04
 6 Pakistan     Asia      Southern Asia       2006          65.8                   6.30
 7 Singapore    Asia      South-Eastern Asia  2006           0                    19.6 
 8 Azerbaijan   Asia      Western Asia        2007          47.2                  14.9 
 9 Qatar        Asia      Western Asia        2007           2.08                 25.1 
10 Singapore    Asia      South-Eastern Asia  2007           0                    20.1 
# ... with 101 more rows

Mutasi kolom baru

asia_africa_results <- asia_africa_results %>%
    mutate(perc_urban_pop = 100 - perc_rural_pop)

Hasil mutasi

# A tibble: 111 x 7
   country      continent region              year perc_rural_pop perc_college_complete perc_urban_pop
   <chr>        <fct>     <fct>              <dbl>          <dbl>                 <dbl>          <dbl>
 1 Armenia      Asia      Western Asia        2001          35.6                  20.4            64.4
 2 Qatar        Asia      Western Asia        2004           2.91                 20.9            97.1
 3 Saudi Arabia Asia      Western Asia        2004          19.2                  14.9            80.8
 4 Pakistan     Asia      Southern Asia       2005          66.0                   3.92           34.0
 5 Nigeria      Africa    Western Africa      2006          60.1                   9.04           39.9
 6 Pakistan     Asia      Southern Asia       2006          65.8                   6.30           34.2
 7 Singapore    Asia      South-Eastern Asia  2006           0                    19.6           100  
 8 Azerbaijan   Asia      Western Asia        2007          47.2                  14.9            52.8
 9 Qatar        Asia      Western Asia        2007           2.08                 25.1            97.9
10 Singapore    Asia      South-Eastern Asia  2007           0                    20.1           100  
# ... with 101 more rows

Analisis persentase urban antar wilayah

asia_africa_results %>%

    group_by(region) %>%

    summarize(
      mean_urban = mean(perc_urban_pop)
    )

# A tibble: 9 x 2
  region             mean_urban
  <fct>                   <dbl>
1 Central Asia             49.2
2 Eastern Africa           19.5
3 Eastern Asia             74.2
4 Middle Africa            42.4
5 South-Eastern Asia       79.8
6 Southern Africa          64.8
7 Southern Asia            40.0
8 Western Africa           39.6
9 Western Asia             78.9

Ayo berlatih!

Pemrograman dengan dplyr