Frutifique com dplyr

Programando com dplyr

Dr. Chester Ismay

Educator, Data Scientist, and R/Python Consultant

Pré-requisitos do curso

Unindo dados com dplyr
Introdução a funções em R

Descrição do curso

Capítulo 1

Rever pipelines do dplyr
Escolher colunas por padrões

Capítulo 2

Reorganizar colunas nos dados
Transformar várias colunas de uma vez

Capítulo 3

Aprofundar joins do dplyr
Usar cláusulas de teoria dos conjuntos para programar com múltiplas fontes

Capítulo 4

Criar funções que envolvam código dplyr e ggplot2
Usar o pacote rlang para entender tidy evaluation

O 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

Colunas de 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"

Selecionar algumas colunas de 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

Filtrar linhas por valores de continente

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)

Resultados do filtro de linhas

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

Criar uma nova coluna com mutate

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

Resultados do mutate

# 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

Analisar percentual urbano por região

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

Vamos praticar!

Programando com dplyr