你能在 R 中使用 AssertR 提取有缺陷的行吗？
Can you extract defective rows using AssertR in R?
下面的示例是一个简单的示例，它试图断言列 y 始终为正值 (y>0)。如何提取错误数据（负值的第 3 行，可能放入数据框或任何方便的对象中），同时允许工作流继续处理“已清理”的数据？
library(tidyverse)
library(assertr)
#> Warning: package 'assertr' was built under R version 4.0.5
test = tribble(
~x,~y,
1,4,
3,8,
1,-2,
3,1
)
test %>%
assert(within_bounds(0,100),y) %>%
group_by(x) %>%
summarise(avg = mean(y))
#> Column 'y' violates assertion 'within_bounds(0, 100)' 1 time
#> verb redux_fn predicate column index value
#> 1 assert NA within_bounds(0, 100) y 3 -2
#> Error: assertr stopped execution
由 reprex package (v0.3.0)
于 2021-04-12 创建
更新
尝试@Sirius 解决方案后，我第一次尝试它时它对我有用，但随后的试验都失败了，并显示以下 reprex 中显示的消息：
library(tidyverse)
library(assertr)
#> Warning: package 'assertr' was built under R version 4.0.5
test = tribble(
~x,~y,
1,4,
3,8,
1,-2,
3,1,
5,81
)
my_error_fun = function( ... ) {
args = list(...)
do.call( just_warn, args )
bad.data = args[[1]][[1]]$error_df
these.failed.rows = args$data %>%
slice( bad.data$index )
if(!exists("my.failed.rows", inherits=TRUE)) {
my.failed.rows = NULL
}
my.failed.rows = rbind( my.failed.rows, these.failed.rows )
assign( "my.failed.rows", my.failed.rows, envir=parent.frame(n=3) )
good.rows = args$data %>% slice(-bad.data$index)
return(good.rows)
}
my.result <- test %>%
assert(within_bounds(0,100),y, error_fun = my_error_fun) %>%
group_by(x) %>%
summarise(avg = mean(y))
#> Column 'y' violates assertion 'within_bounds(0, 100)' 1 time
#> verb redux_fn predicate column index value
#> 1 assert NA within_bounds(0, 100) y 3 -2
#> Warning: assertr encountered errors
#> `summarise()` ungrouping output (override with `.groups` argument)
print(my.result)
#> # A tibble: 3 x 2
#> x avg
#> <dbl> <dbl>
#> 1 1 4
#> 2 3 4.5
#> 3 5 81
print(my.failed.rows)
#> Error in print(my.failed.rows): object 'my.failed.rows' not found
由 reprex package (v0.3.0)
于 2021-05-02 创建
devtools::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 4.0.2 (2020-06-22)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz Africa/Nairobi
#> date 2021-05-02
#>
#> - Packages -------------------------------------------------------------------
#> package * version date lib source
#> assertr * 2.8 2021-01-25 [1] CRAN (R 4.0.5)
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
#> backports 1.1.9 2020-08-24 [1] CRAN (R 4.0.2)
#> blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.2)
#> broom 0.7.0 2020-07-09 [1] CRAN (R 4.0.2)
#> callr 3.4.4 2020-09-07 [1] CRAN (R 4.0.2)
#> cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.2)
#> cli 2.0.2 2020-02-28 [1] CRAN (R 4.0.2)
#> colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.2)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.2)
#> DBI 1.1.0 2019-12-15 [1] CRAN (R 4.0.2)
#> dbplyr 1.4.4 2020-05-27 [1] CRAN (R 4.0.2)
#> desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.2)
#> devtools 2.3.2 2020-09-18 [1] CRAN (R 4.0.3)
#> digest 0.6.25 2020-02-23 [1] CRAN (R 4.0.2)
#> dplyr * 1.0.2 2020-08-18 [1] CRAN (R 4.0.2)
#> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.2)
#> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.2)
#> forcats * 0.5.0 2020-03-01 [1] CRAN (R 4.0.2)
#> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
#> generics 0.0.2 2018-11-29 [1] CRAN (R 4.0.2)
#> ggplot2 * 3.3.2 2020-06-19 [1] CRAN (R 4.0.2)
#> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
#> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.2)
#> haven 2.3.1 2020-06-01 [1] CRAN (R 4.0.3)
#> highr 0.8 2019-03-20 [1] CRAN (R 4.0.2)
#> hms 0.5.3 2020-01-08 [1] CRAN (R 4.0.2)
#> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2)
#> httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
#> jsonlite 1.7.1 2020-09-07 [1] CRAN (R 4.0.2)
#> knitr 1.29 2020-06-23 [1] CRAN (R 4.0.2)
#> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.2)
#> lubridate 1.7.9 2020-06-08 [1] CRAN (R 4.0.2)
#> magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.2)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.3)
#> modelr 0.1.8 2020-05-19 [1] CRAN (R 4.0.2)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
#> pillar 1.4.6 2020-07-10 [1] CRAN (R 4.0.2)
#> pkgbuild 1.1.0 2020-07-13 [1] CRAN (R 4.0.2)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.2)
#> pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.2)
#> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.2)
#> processx 3.4.4 2020-09-03 [1] CRAN (R 4.0.2)
#> ps 1.3.4 2020-08-11 [1] CRAN (R 4.0.2)
#> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.0.2)
#> R6 2.4.1 2019-11-12 [1] CRAN (R 4.0.2)
#> Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2)
#> readr * 1.3.1 2018-12-21 [1] CRAN (R 4.0.2)
#> readxl 1.3.1 2019-03-13 [1] CRAN (R 4.0.2)
#> remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.3)
#> reprex 0.3.0 2019-05-16 [1] CRAN (R 4.0.2)
#> rlang 0.4.7 2020-07-09 [1] CRAN (R 4.0.2)
#> rmarkdown 2.7 2021-02-19 [1] CRAN (R 4.0.2)
#> rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.4)
#> rvest 0.3.6 2020-07-25 [1] CRAN (R 4.0.2)
#> scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.2)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.3)
#> stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2)
#> stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
#> testthat 2.3.2 2020-03-02 [1] CRAN (R 4.0.2)
#> tibble * 3.0.3 2020-07-10 [1] CRAN (R 4.0.2)
#> tidyr * 1.1.2 2020-08-27 [1] CRAN (R 4.0.2)
#> tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.2)
#> tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 4.0.2)
#> usethis 1.6.3 2020-09-17 [1] CRAN (R 4.0.3)
#> utf8 1.1.4 2018-05-24 [1] CRAN (R 4.0.2)
#> vctrs 0.3.4 2020-08-29 [1] CRAN (R 4.0.2)
#> withr 2.2.0 2020-04-20 [1] CRAN (R 4.0.2)
#> xfun 0.16 2020-07-24 [1] CRAN (R 4.0.2)
#> xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.2)
#> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
#>
#> [1] C:/Users/tnkil/OneDrive/Documents/R/win-library/4.0
#> [2] C:/Program Files/R/R-4.0.2/library
这很棘手，下面的答案并不能 100% 解决这个问题。现在有许多不同的方式 assertr 让你处理 errors/stops，看看 ?error_stop（这是默认的）。
您不仅需要过滤掉失败的行，还需要收集（全部）它们以供以后检查。
下面我写了我自己的错误处理程序。它获取那些失败的行，将它们过滤掉，并将它们存储在 全局环境 变量 my.failed.rows.
下
library(tidyverse)
library(assertr)
#> Warning: package 'assertr' was built under R version 4.0.5
test = tribble(
~x,~y,
1,4,
3,8,
1,-2,
3,1
)
my_error_fun <- function( ... ) {
args <- list(...)
do.call( just_warn, args )
bad.data <- args[[1]][[1]]$error_df
these.failed.rows <- args$data %>% slice( bad.data$index )
if(!exists("my.failed.rows", inherits=TRUE)) {
my.failed.rows <- NULL
}
my.failed.rows <- rbind( my.failed.rows, these.failed.rows )
assign( "my.failed.rows", my.failed.rows, envir=parent.frame(n=3) )
good.rows <- args$data %>% slice( -bad.data$index )
return( good.rows )
}
my.result <- test %>%
assert(within_bounds(0,100),y, error_fun = my_error_fun ) %>%
group_by(x) %>%
summarise(avg = mean(y))
print(my.result)
print(my.failed.rows)
输出：
> print(my.result)
# A tibble: 2 x 2
x avg
<dbl> <dbl>
1 1 4
2 3 4.5
> print(my.failed.rows)
# A tibble: 1 x 2
x y
<dbl> <dbl>
1 1 -2
下次，它将继续附加到 my.failed.rows，因此您可以在完成检查后截断或删除它。我还没有想出一种方法来自动执行此操作。本质上是一种检测是否正在进行新的链式 dplyr 操作的方法。
下面的示例是一个简单的示例，它试图断言列 y 始终为正值 (y>0)。如何提取错误数据（负值的第 3 行，可能放入数据框或任何方便的对象中），同时允许工作流继续处理“已清理”的数据？
library(tidyverse)
library(assertr)
#> Warning: package 'assertr' was built under R version 4.0.5
test = tribble(
~x,~y,
1,4,
3,8,
1,-2,
3,1
)
test %>%
assert(within_bounds(0,100),y) %>%
group_by(x) %>%
summarise(avg = mean(y))
#> Column 'y' violates assertion 'within_bounds(0, 100)' 1 time
#> verb redux_fn predicate column index value
#> 1 assert NA within_bounds(0, 100) y 3 -2
#> Error: assertr stopped execution
由 reprex package (v0.3.0)
于 2021-04-12 创建更新
尝试@Sirius 解决方案后，我第一次尝试它时它对我有用，但随后的试验都失败了，并显示以下 reprex 中显示的消息：
library(tidyverse)
library(assertr)
#> Warning: package 'assertr' was built under R version 4.0.5
test = tribble(
~x,~y,
1,4,
3,8,
1,-2,
3,1,
5,81
)
my_error_fun = function( ... ) {
args = list(...)
do.call( just_warn, args )
bad.data = args[[1]][[1]]$error_df
these.failed.rows = args$data %>%
slice( bad.data$index )
if(!exists("my.failed.rows", inherits=TRUE)) {
my.failed.rows = NULL
}
my.failed.rows = rbind( my.failed.rows, these.failed.rows )
assign( "my.failed.rows", my.failed.rows, envir=parent.frame(n=3) )
good.rows = args$data %>% slice(-bad.data$index)
return(good.rows)
}
my.result <- test %>%
assert(within_bounds(0,100),y, error_fun = my_error_fun) %>%
group_by(x) %>%
summarise(avg = mean(y))
#> Column 'y' violates assertion 'within_bounds(0, 100)' 1 time
#> verb redux_fn predicate column index value
#> 1 assert NA within_bounds(0, 100) y 3 -2
#> Warning: assertr encountered errors
#> `summarise()` ungrouping output (override with `.groups` argument)
print(my.result)
#> # A tibble: 3 x 2
#> x avg
#> <dbl> <dbl>
#> 1 1 4
#> 2 3 4.5
#> 3 5 81
print(my.failed.rows)
#> Error in print(my.failed.rows): object 'my.failed.rows' not found
由 reprex package (v0.3.0)
于 2021-05-02 创建devtools::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 4.0.2 (2020-06-22)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz Africa/Nairobi
#> date 2021-05-02
#>
#> - Packages -------------------------------------------------------------------
#> package * version date lib source
#> assertr * 2.8 2021-01-25 [1] CRAN (R 4.0.5)
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
#> backports 1.1.9 2020-08-24 [1] CRAN (R 4.0.2)
#> blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.2)
#> broom 0.7.0 2020-07-09 [1] CRAN (R 4.0.2)
#> callr 3.4.4 2020-09-07 [1] CRAN (R 4.0.2)
#> cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.2)
#> cli 2.0.2 2020-02-28 [1] CRAN (R 4.0.2)
#> colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.2)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.2)
#> DBI 1.1.0 2019-12-15 [1] CRAN (R 4.0.2)
#> dbplyr 1.4.4 2020-05-27 [1] CRAN (R 4.0.2)
#> desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.2)
#> devtools 2.3.2 2020-09-18 [1] CRAN (R 4.0.3)
#> digest 0.6.25 2020-02-23 [1] CRAN (R 4.0.2)
#> dplyr * 1.0.2 2020-08-18 [1] CRAN (R 4.0.2)
#> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.2)
#> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.2)
#> forcats * 0.5.0 2020-03-01 [1] CRAN (R 4.0.2)
#> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
#> generics 0.0.2 2018-11-29 [1] CRAN (R 4.0.2)
#> ggplot2 * 3.3.2 2020-06-19 [1] CRAN (R 4.0.2)
#> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
#> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.2)
#> haven 2.3.1 2020-06-01 [1] CRAN (R 4.0.3)
#> highr 0.8 2019-03-20 [1] CRAN (R 4.0.2)
#> hms 0.5.3 2020-01-08 [1] CRAN (R 4.0.2)
#> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2)
#> httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
#> jsonlite 1.7.1 2020-09-07 [1] CRAN (R 4.0.2)
#> knitr 1.29 2020-06-23 [1] CRAN (R 4.0.2)
#> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.2)
#> lubridate 1.7.9 2020-06-08 [1] CRAN (R 4.0.2)
#> magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.2)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.3)
#> modelr 0.1.8 2020-05-19 [1] CRAN (R 4.0.2)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
#> pillar 1.4.6 2020-07-10 [1] CRAN (R 4.0.2)
#> pkgbuild 1.1.0 2020-07-13 [1] CRAN (R 4.0.2)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.2)
#> pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.2)
#> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.2)
#> processx 3.4.4 2020-09-03 [1] CRAN (R 4.0.2)
#> ps 1.3.4 2020-08-11 [1] CRAN (R 4.0.2)
#> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.0.2)
#> R6 2.4.1 2019-11-12 [1] CRAN (R 4.0.2)
#> Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2)
#> readr * 1.3.1 2018-12-21 [1] CRAN (R 4.0.2)
#> readxl 1.3.1 2019-03-13 [1] CRAN (R 4.0.2)
#> remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.3)
#> reprex 0.3.0 2019-05-16 [1] CRAN (R 4.0.2)
#> rlang 0.4.7 2020-07-09 [1] CRAN (R 4.0.2)
#> rmarkdown 2.7 2021-02-19 [1] CRAN (R 4.0.2)
#> rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.4)
#> rvest 0.3.6 2020-07-25 [1] CRAN (R 4.0.2)
#> scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.2)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.3)
#> stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2)
#> stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
#> testthat 2.3.2 2020-03-02 [1] CRAN (R 4.0.2)
#> tibble * 3.0.3 2020-07-10 [1] CRAN (R 4.0.2)
#> tidyr * 1.1.2 2020-08-27 [1] CRAN (R 4.0.2)
#> tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.2)
#> tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 4.0.2)
#> usethis 1.6.3 2020-09-17 [1] CRAN (R 4.0.3)
#> utf8 1.1.4 2018-05-24 [1] CRAN (R 4.0.2)
#> vctrs 0.3.4 2020-08-29 [1] CRAN (R 4.0.2)
#> withr 2.2.0 2020-04-20 [1] CRAN (R 4.0.2)
#> xfun 0.16 2020-07-24 [1] CRAN (R 4.0.2)
#> xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.2)
#> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
#>
#> [1] C:/Users/tnkil/OneDrive/Documents/R/win-library/4.0
#> [2] C:/Program Files/R/R-4.0.2/library
这很棘手，下面的答案并不能 100% 解决这个问题。现在有许多不同的方式 assertr 让你处理 errors/stops，看看 ?error_stop（这是默认的）。
您不仅需要过滤掉失败的行，还需要收集（全部）它们以供以后检查。
下面我写了我自己的错误处理程序。它获取那些失败的行，将它们过滤掉，并将它们存储在 全局环境 变量 my.failed.rows.
library(tidyverse)
library(assertr)
#> Warning: package 'assertr' was built under R version 4.0.5
test = tribble(
~x,~y,
1,4,
3,8,
1,-2,
3,1
)
my_error_fun <- function( ... ) {
args <- list(...)
do.call( just_warn, args )
bad.data <- args[[1]][[1]]$error_df
these.failed.rows <- args$data %>% slice( bad.data$index )
if(!exists("my.failed.rows", inherits=TRUE)) {
my.failed.rows <- NULL
}
my.failed.rows <- rbind( my.failed.rows, these.failed.rows )
assign( "my.failed.rows", my.failed.rows, envir=parent.frame(n=3) )
good.rows <- args$data %>% slice( -bad.data$index )
return( good.rows )
}
my.result <- test %>%
assert(within_bounds(0,100),y, error_fun = my_error_fun ) %>%
group_by(x) %>%
summarise(avg = mean(y))
print(my.result)
print(my.failed.rows)
输出：
> print(my.result)
# A tibble: 2 x 2
x avg
<dbl> <dbl>
1 1 4
2 3 4.5
> print(my.failed.rows)
# A tibble: 1 x 2
x y
<dbl> <dbl>
1 1 -2
下次，它将继续附加到 my.failed.rows，因此您可以在完成检查后截断或删除它。我还没有想出一种方法来自动执行此操作。本质上是一种检测是否正在进行新的链式 dplyr 操作的方法。