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Dr. Chester Ismay

Educator, Data Scientist, and R/Python Consultant

world_bank_data 的列名

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"

用 select() 调整顺序

reordered_wb <- world_bank_data %>% 
    select(iso:year,

    matches("^perc"),

    everything())

names(reordered_wb)

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

改用 last_col()

world_bank_data %>% 
    select(iso:year, 
    matches("^perc"),

    infant_mortality_rate:last_col()) %>%

    names()

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

更简单：relocate() 与 .after

world_bank_data %>% 

    relocate(matches("^perc"), .after = year) %>%

    names()

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

使用 .before 的 relocate()

world_bank_data %>%
  relocate(matches("^perc"), 
           .before = infant_mortality_rate) %>%

  names()

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

Passons à la pratique !

使用 dplyr 编程