stackoom
  简体   繁体   中英

How to select columns with names as dates using dplyr R

 原文  2021-07-20 09:00:40  4   3    r/ dplyr/ data-analysis

Question

I have dataframe where some headers have names a character and dates. I want to select all the columns which dont have dates as header and all the columns which have the dates less than the current date or system date(sys.date()). How can I do thins using select statement in dplyr.

Below is the dataframe

> dput(job_times[1:5,])
structure(list(Skill = c("KAC", "KAC", "KAC", "KAC", "KAC"), 
    Patch = c("A1", "A2", "A3", "A4", "A5"), `Work Code` = c("W01", 
    "W01", "W01", "W01", "W01"), Product = c("KAC Repair", "KAC Repair", 
    "KAC Repair", "KAC Repair", "KAC Repair"), `Visit Time` = c(45.68, 
    42.55, 46.45, 51.86, 43.49), Travel = c(32.5, 21.66, 26.33, 
    28.63, 27.03), Success = c(0.69, 0.66, 0.67, 0.65, 0.67), 
    `Completion Time` = c(1.9, 1.61, 1.8, 2.05, 1.74), `28-12-2020` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `04-01-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `11-01-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `18-01-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `25-01-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `01-02-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `08-02-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `15-02-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `22-02-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `01-03-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `08-03-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `15-03-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `22-03-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `29-03-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `05-04-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `12-04-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `19-04-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `26-04-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `03-05-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `10-05-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `17-05-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `24-05-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `31-05-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `07-06-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `14-06-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `21-06-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `28-06-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `05-07-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `12-07-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `19-07-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `26-07-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `02-08-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `09-08-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `16-08-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `23-08-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `30-08-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `06-09-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `13-09-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `20-09-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `27-09-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `04-10-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `11-10-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `18-10-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `25-10-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `01-11-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `08-11-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `15-11-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `22-11-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `29-11-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `06-12-2021` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `13-12-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `20-12-2021` = c(1.9, 1.61, 1.8, 2.05, 1.74), `27-12-2021` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `03-01-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `10-01-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `17-01-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `24-01-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `31-01-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `07-02-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `14-02-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `21-02-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `28-02-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `07-03-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `14-03-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `21-03-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `28-03-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `04-04-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `11-04-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `18-04-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `25-04-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `02-05-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `09-05-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `16-05-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `23-05-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `30-05-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `06-06-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `13-06-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `20-06-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `27-06-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `04-07-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `11-07-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `18-07-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `25-07-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `01-08-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `08-08-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `15-08-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `22-08-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `29-08-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `05-09-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `12-09-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `19-09-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `26-09-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `03-10-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `10-10-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `17-10-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `24-10-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `31-10-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `07-11-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `14-11-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `21-11-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `28-11-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74), `05-12-2022` = c(1.9, 1.61, 1.8, 
    2.05, 1.74), `12-12-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), 
    `19-12-2022` = c(1.9, 1.61, 1.8, 2.05, 1.74), `26-12-2022` = c(1.9, 
    1.61, 1.8, 2.05, 1.74)), row.names = c(NA, -5L), class = c("tbl_df", 
"tbl", "data.frame"))

I want the Skill, Patch, Work Code, Product, Visit Time, Travel, Success, Completion Time columns along with all the columns which have their dates less than or equal to sys.Date(). Using dplyr and select statements.

3 answers

solution1
 1 ACCPTED  2021-07-20 09:16:25

This is how I would solve it -

cols <- grep('\\d{2}-\\d{2}-\\d{2}', names(job_times), value = TRUE)
result <- job_times[, c(setdiff(names(job_times), cols), 
              cols[Sys.Date() > as.Date(cols, '%d-%m-%Y')])]

You can integrate this in dplyr pipe as -

library(dplyr)

job_times %>%
  select({
    cols <- grep('\\d{2}-\\d{2}-\\d{2}', names(.), value = TRUE)
    c(setdiff(names(.), cols),
      cols[Sys.Date() > as.Date(cols, '%d-%m-%Y')])
  })

solution2
 0  2021-07-20 09:42:17

I would suggest creating a helper function and then you can use select like this:

library(tidyverse)
library(lubridate)

is_before_today <- function(x) {
  (dmy(x, quiet = TRUE) < Sys.Date()) %>% coalesce(FALSE)
}

df %>% 
  select(
    matches("^\\D"), all_of(colnames(.) %>% keep(is_before_today))
  )
#> # A tibble: 5 x 38
#>   Skill Patch `Work Code` Product   `Visit Time` Travel Success `Completion Tim~
#>   <chr> <chr> <chr>       <chr>            <dbl>  <dbl>   <dbl>            <dbl>
#> 1 KAC   A1    W01         KAC Repa~         45.7   32.5    0.69             1.9 
#> 2 KAC   A2    W01         KAC Repa~         42.6   21.7    0.66             1.61
#> 3 KAC   A3    W01         KAC Repa~         46.4   26.3    0.67             1.8 
#> 4 KAC   A4    W01         KAC Repa~         51.9   28.6    0.65             2.05
#> 5 KAC   A5    W01         KAC Repa~         43.5   27.0    0.67             1.74
#> # ... with 30 more variables: 28-12-2020 <dbl>, 04-01-2021 <dbl>,
#> #   11-01-2021 <dbl>, 18-01-2021 <dbl>, 25-01-2021 <dbl>, 01-02-2021 <dbl>,
#> #   08-02-2021 <dbl>, 15-02-2021 <dbl>, 22-02-2021 <dbl>, 01-03-2021 <dbl>,
#> #   08-03-2021 <dbl>, 15-03-2021 <dbl>, 22-03-2021 <dbl>, 29-03-2021 <dbl>,
#> #   05-04-2021 <dbl>, 12-04-2021 <dbl>, 19-04-2021 <dbl>, 26-04-2021 <dbl>,
#> #   03-05-2021 <dbl>, 10-05-2021 <dbl>, 17-05-2021 <dbl>, 24-05-2021 <dbl>,
#> #   31-05-2021 <dbl>, 07-06-2021 <dbl>, 14-06-2021 <dbl>, 21-06-2021 <dbl>,
#> #   28-06-2021 <dbl>, 05-07-2021 <dbl>, 12-07-2021 <dbl>, 19-07-2021 <dbl>

Created on 2021-07-20 by the reprex package (v1.0.0)

solution3
 -1  2021-07-20 09:41:20

Ronak Shah's answer is extremely great. Here is how I'll do.

    ## Get the List of All Column Names
    ColumnNames <- names(TestDF)

    ## Retain Only those don't have Dates
    CharacterColumnNames <- ColumnNames[grepl( "[[:alpha:]]" , names( TestDF ) )] 

    ## Get the List of all Date Column Names
    DateColumns <- setdiff(names(TestDF),CharacterColumnNames)

    ## Filter Required Date Column Names
    RequiredDateColumns <- DateColumns[ Sys.Date() > as.Date(DateColumns, '%d-%m-%Y')]

    ## Get the Modified DF
    ModifiedDF <- TestDF[, c(CharacterColumnNames, RequiredDateColumns)]

暂无
暂无

The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.

Related Question How do filter dates in r using dplyr? Select multiple columns with dplyr::select() with numbers as names How to select columns based on string using dplyr R dplyr select not removing columns R dplyr using all columns again after select select columns that do NOT start with a string using dplyr in R How to mutate a new column with row means for select columns in grouped_tbl using dplyr r? R dplyr - Rearrange columns by pattern of names How to select columns with dplyr/tidyvese depending on minimal value in column in R How to rename multiple columns that have numeric column names using dplyr?
Related Tags
 
粤ICP备18138465号  © 2020-2024 STACKOOM.COM