有没有办法在一定数量的块之后停止readr::read_tsv_chunked()?
我试图在一个大的.tsv文件上使用有没有办法在一定数量的块之后停止readr::read_tsv_chunked()?,r,readr,R,Readr,我试图在一个大的.tsv文件上使用read_tsv_chunked(),并希望在一定数量的块之后停止 @jimhester提出了一种有用的方法,可以使用browse()::以交互方式查看给定的块,但我想编写一个函数,1)只返回感兴趣的块;2)返回块后停止读取文件 我修改了Jim的响应以返回数据块,这样我就可以将它与DataFrameCallback一起使用,但是我不知道如何在read\u tsv\u chunked()中停止读取 我目前的做法是: get_problem_chunk <-
read_tsv_chunked()
,并希望在一定数量的块之后停止
@jimhester提出了一种有用的方法,可以使用browse()
::以交互方式查看给定的块,但我想编写一个函数,1)只返回感兴趣的块;2)返回块后停止读取文件
我修改了Jim的响应以返回数据块,这样我就可以将它与DataFrameCallback一起使用,但是我不知道如何在read\u tsv\u chunked()中停止读取
我目前的做法是:
get_problem_chunk <- function(num) {
i <- 1
function(x, pos) {
if (i == num) {
i <<- i + 1
return(x)
}
i <<- i + 1
message(pos) # to see that it's scanning the whole file
return(NULL) # break() or error() cause errors
}
}
write_tsv(mtcars, "mtcars.tsv")
read_tsv_chunked("mtcars.tsv", DataFrameCallback$new(get_problem_chunk(3)), chunk_size = 3)
get\u problem\u chunk由于readr
包中的read\u tsv\u chunked()
函数没有提供停止读取的函数,我想,也许可以使用更基本的read\u tsv()
函数,该函数在读取n行后提供跳过和停止的可能性:
require(readr)
write.table(mtcars, "mtcars.tsv", sep = "\t", quote = FALSE)
read_tsv_chunk <- function(fpath, start.row, end.row, ...) {
# Read read_tsv() but only from row n to m
# For the column names, read one line:
df.1 <- suppressWarnings(read_tsv(fpath, skip = 0, n_max = 1))
# Then read again, from start.row to end.row, both included
skip.row = start.row - 1
df <- suppressWarnings((read_tsv(fpath, skip = skip.row, n_max = end.row - skip.row , ...))
colnames(df) <- colnames(df.1)
df
}
给出:
## Parsed with column specification:
## cols(
## mpg = col_character(),
## cyl = col_integer(),
## disp = col_integer(),("mtcars.tsv", chunk_size=3, col_names = TRUE, skip = 6, g
## hp = col_integer(),
## drat = col_integer(),d("mtcars.tsv", chunk_size = 3, skip = 6, col_names = TRUE
## wt = col_double(),
## qsec = col_double(),
## vs = col_double(),
## am = col_integer(),
## gear = col_integer(),
## carb = col_integer()
## )
## Parsed with column specification:
## cols(
## Valiant = col_character(),
## `18.1` = col_double(),
## `6` = col_integer(),
## `225` = col_double(),
## `105` = col_integer(),
## `2.76` = col_double(),
## `3.46` = col_double(),
## `20.22` = col_double(),
## `1` = col_integer(),
## `0` = col_integer(),
## `3` = col_integer(),
## `1_1` = col_integer()
## )
## # A tibble: 3 x 12
## mpg cyl disp hp drat wt qsec vs am gear carb `NA`
## <chr> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
## 1 Duster 360 14.3 8 360. 245 3.21 3.57 15.8 0 0 3 4
## 2 Merc 240D 24.4 4 147. 62 3.69 3.19 20.0 1 0 4 2
## 3 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
返回所需内容:
X18.1 X6 X225 X105 X2.76 X3.46 X20.22 X1 X0 X3 X1.1
1 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4
2 24.4 4 146.7 62 3.69 3.19 20.00 1 0 4 2
3 22.8 4 140.8 95 3.92 3.15 22.90 1 0 4 2
@jimhester又来营救了-
您可以通过使用SideEffectCallback(这是默认值)来实现这一点
当传递一个正常函数)并使用返回结果时,使用readr
包的原因是什么?这对你有什么好处?为什么你的coldisp
中有NA?我在发布我的问题时,通过使用readr
标记表示我正在使用readr
。我现在也对我的问题进行了编辑,使之更加明确。NAs源于readr分配列类型的方式—在本例中,该列的类型为int
,值为双精度—很容易在函数调用中修复。我之所以使用readr
,是因为我发现使用tidyverse函数而不是非tidyverse替代方法对于包或函数内的一致性非常有用。如果你愿意,你可以在这里了解更多关于readr的信息:好的,明白了。。。但是看文档,我看不到限制的方法。因此,我在上面编写了一个新函数。
## Parsed with column specification:
## cols(
## mpg = col_character(),
## cyl = col_integer(),
## disp = col_integer(),("mtcars.tsv", chunk_size=3, col_names = TRUE, skip = 6, g
## hp = col_integer(),
## drat = col_integer(),d("mtcars.tsv", chunk_size = 3, skip = 6, col_names = TRUE
## wt = col_double(),
## qsec = col_double(),
## vs = col_double(),
## am = col_integer(),
## gear = col_integer(),
## carb = col_integer()
## )
## Parsed with column specification:
## cols(
## Valiant = col_character(),
## `18.1` = col_double(),
## `6` = col_integer(),
## `225` = col_double(),
## `105` = col_integer(),
## `2.76` = col_double(),
## `3.46` = col_double(),
## `20.22` = col_double(),
## `1` = col_integer(),
## `0` = col_integer(),
## `3` = col_integer(),
## `1_1` = col_integer()
## )
## # A tibble: 3 x 12
## mpg cyl disp hp drat wt qsec vs am gear carb `NA`
## <chr> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
## 1 Duster 360 14.3 8 360. 245 3.21 3.57 15.8 0 0 3 4
## 2 Merc 240D 24.4 4 147. 62 3.69 3.19 20.0 1 0 4 2
## 3 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
df <- read_tsv_chunked("mtcars.tsv", chunk_size = 3, skip = 6, col_names = TRUE, guess_max = 3)
df
## # A tibble: 3 x 12
## mpg cyl disp hp drat wt qsec vs am gear carb `NA`
## <chr> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
## 1 Duster 360 14.3 8 360. 245 3.21 3.57 15.8 0 0 3 4
## 2 Merc 240D 24.4 4 147. 62 3.69 3.19 20.0 1 0 4 2
## 3 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
read.table(file, header = TRUE, sep = "\t", quote = "\"",
dec = ".", fill = TRUE, comment.char = "#", nrow = 3, skip = 2 * 3)
X18.1 X6 X225 X105 X2.76 X3.46 X20.22 X1 X0 X3 X1.1
1 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4
2 24.4 4 146.7 62 3.69 3.19 20.00 1 0 4 2
3 22.8 4 140.8 95 3.92 3.15 22.90 1 0 4 2
library(readr)
get_problem_chunk <- function(num) {
i <- 1
function(x, pos) {
if (i == num) {
res <<- x
return(FALSE)
}
i <<- i + 1
}
}
write_tsv(mtcars, "mtcars.tsv")
read_tsv_chunked("mtcars.tsv", get_problem_chunk(3), chunk_size = 2, col_types = cols())
#> NULL
res
#> # A tibble: 2 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 2 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1