我喜欢this RStudio blog post中描述的有关列规范的工作流程。基本上,可以在导入read_csv后获取列规范,然后将其保存下来以备后用。例如,从该帖子中:

mtcars2 <- read_csv(readr_example("mtcars.csv"))
#> Parsed with column specification:
#> cols(
#>   mpg = col_double(),
#>   cyl = col_integer(),
#>   disp = col_double(),
#>   hp = col_integer(),
#>   drat = col_double(),
#>   wt = col_double(),
#>   qsec = col_double(),
#>   vs = col_integer(),
#>   am = col_integer(),
#>   gear = col_integer(),
#>   carb = col_integer()
#> )
# Once you've figured out the correct types
mtcars_spec <- write_rds(spec(mtcars2), "mtcars2-spec.rds")

# Every subsequent load
mtcars2 <- read_csv(
  readr_example("mtcars.csv"),
  col_types = read_rds("mtcars2-spec.rds")
)

不幸的是,spec对象本身是具有属性的列表,但是与通过read_csv参数提供给col_types函数的不同列规范不匹配
> mtcars_spec$cols$cyl
<collector_integer>
> str(mtcars_spec$cols$cyl)
 list()
 - attr(*, "class")= chr [1:2] "collector_integer" "collector"
> class(mtcars_spec)
[1] "col_spec"

此外,.rds文件在Windows中进行编辑很丑陋(至少对我而言)。

我希望能够编辑一个较大的col_spec对象(例如,跳过某些列,或者以其他方式编辑该类)。我可以不断猜测编辑列表所需的字符串,如下所示:
attr(mtcars_spec$cols$cyl,"class")[1] = "collector_skip"` # this worked!
> mtcars_spec
cols(
  mpg = col_double(),
  cyl = col_skip(),
  disp = col_double(),
  hp = col_integer(),
  drat = col_double(),
  wt = col_double(),
  qsec = col_double(),
  vs = col_integer(),
  am = col_integer(),
  gear = col_integer(),
  carb = col_integer()
)

但这似乎很尴尬。有没有更优雅的方法来更新列分类,例如在我的示例中,尝试跳过mtcars$cyl列?或者,如果不是一种优雅的方式,那么一种涵盖所有可能类型的方式?我不想对如何使用各种日期格式实现<collector_date>进行大量猜测。

最佳答案

这是Jim Hester's Github post的最低版本

library(readr)
test_spec <- spec_csv('x,y,theDate,skipCol
  1,a,"21/01/2018", "skip1
  2,z,"31/01/2018", "skip2')

test_spec
#> cols(
#>   x = col_integer(),
#>   y = col_character(),
#>   theDate = col_character(),
#>   skipCol = col_character()
#> )

test_spec$cols[["theDate"]] <- col_date("%d/%m/%Y")
test_spec$cols[["skipCol"]] <- col_skip()

test_spec
#> cols(
#>   x = col_integer(),
#>   y = col_character(),
#>   theDate = col_date(format = "%d/%m/%Y"),
#>   skipCol = col_skip()
#> )

笔记
  • 您需要知道数据的日期格式。
  • 您可以在文件
  • 上使用readr::spec_csv()

    关于阅读器-如何从spec()更新col_spec对象,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/39135129/

    10-11 16:06