R 正确使用 na.locf() 的 across() 函数
R Proper use for across() function with na.locf()
我正在尝试复制 ，但通过使用更新的语法，该语法使用 across() 函数并摆脱了已弃用的 summarise_all() 和 funs()。
起始数据
我有一个数据库提取每个事件类型一行，如下所示：
library(tidyverse)
library(zoo)
df_start <- tibble(shipment = c(rep("A",4), rep("B",4)),
stop = rep(c(1,1,2,2), 2),
arrive_pickup = as.POSIXct(c("2021-01-01 07:00:00 UTC",NA, NA, NA,"2021-06-05 12:10:00 UTC", NA, NA, NA)),
depart_pickup = as.POSIXct(c(NA,"2021-01-01 08:40:00 UTC", NA, NA, NA, "2021-06-05 16:58:00 UTC", NA, NA)),
arrive_delivery = as.POSIXct(c(NA, NA, "2021-01-05 10:00:00 UTC",NA, NA, NA,"2021-06-08 10:58:00 UTC", NA)),
depart_delivery = as.POSIXct(c(NA, NA, NA, "2021-01-05 11:30:00 UTC",NA, NA, NA,"2021-06-08 13:50:00 UTC"))
)
> df_start
# A tibble: 8 x 6
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 1 2021-01-01 07:00:00 NA NA NA
2 A 1 NA 2021-01-01 08:40:00 NA NA
3 A 2 NA NA 2021-01-05 10:00:00 NA
4 A 2 NA NA NA 2021-01-05 11:30:00
5 B 1 2021-06-05 12:10:00 NA NA NA
6 B 1 NA 2021-06-05 16:58:00 NA NA
7 B 2 NA NA 2021-06-08 10:58:00 NA
8 B 2 NA NA NA 2021-06-08 13:50:00
期望的结果
... 我想通过按出货量和停靠点分组，甚至仅按出货量分组来折叠行数（我不确定在最终数据框中保留 NA 是否会影响答案，但我正在寻求能够以任何一种方式解决它）。
df_finish1 # 一个想要的结果
# A tibble: 4 x 6
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 1 2021-01-01 07:00:00 2021-01-01 08:40:00 NA NA
2 A 2 NA NA 2021-01-05 10:00:00 2021-01-05 11:30:00
3 B 1 2021-06-05 12:10:00 2021-06-05 16:58:00 NA NA
4 B 2 NA NA 2021-06-08 10:58:00 2021-06-08 13:50:00
df_finish2 # Second/alternative 期望的结果
# A tibble: 2 x 5
shipment arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dttm> <dttm> <dttm> <dttm>
1 A 2021-01-01 07:00:00 2021-01-01 08:40:00 2021-01-05 10:00:00 2021-01-05 11:30:00
2 B 2021-06-05 12:10:00 2021-06-05 16:58:00 2021-06-08 10:58:00 2021-06-08 13:50:00
我研究和尝试过的
基于 ，它确实有效：
df_1 <- df_start %>%
group_by(shipment, stop) %>% # Two groupings
summarise_all(funs(na.locf(., na.rm = FALSE, fromLast = FALSE))) %>%
filter(row_number()==n())
> df_1
# A tibble: 4 x 6
# Groups: shipment, stop [4]
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 1 2021-01-01 07:00:00 2021-01-01 08:40:00 NA NA
2 A 2 NA NA 2021-01-05 10:00:00 2021-01-05 11:30:00
3 B 1 2021-06-05 12:10:00 2021-06-05 16:58:00 NA NA
4 B 2 NA NA 2021-06-08 10:58:00 2021-06-08 13:50:00
df_2 <- df_start %>%
group_by(shipment) %>% # Single grouping
summarise_all(funs(na.locf(., na.rm = FALSE, fromLast = FALSE))) %>%
filter(row_number()==n())
> df_2
# A tibble: 2 x 6
# Groups: shipment [2]
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 2 2021-01-01 07:00:00 2021-01-01 08:40:00 2021-01-05 10:00:00 2021-01-05 11:30:00
2 B 2 2021-06-05 12:10:00 2021-06-05 16:58:00 2021-06-08 10:58:00 2021-06-08 13:50:00
但我看到的是 summarise_all() 函数和 funs() 函数已被弃用，以后不再使用，所以我试图了解如何使用 across() 正常运行，但没有成功：
df_3 <- df_start %>%
group_by(shipment) %>%
summarise(across(everything()), na.locf(., na.rm = FALSE, fromLast = FALSE))
> df_3 <- df_start %>%
+ group_by(shipment) %>%
+ summarise(across(everything()), na.locf(., na.rm = FALSE, fromLast = FALSE))
Error: Problem with `summarise()` input `..2`.
x Input `..2` must be size 4 or 1, not 8.
i An earlier column had size 4.
i Input `..2` is `na.locf(., na.rm = FALSE, fromLast = FALSE)`.
i The error occurred in group 1: shipment = "A".
我已经通读了 vignette("colwise")，其中描述了不同之处，并建议我只替换上面所示的语法，但显然我没有做对。帮助？
这是一个选项，在按 'shipment'、'stop' 分组后，根据 NA 值对列进行排序，然后 filter 离开所有 NA 的行
library(dplyr)
df_start %>%
group_by(shipment, stop) %>%
mutate(across(everything(), ~ .[order(is.na(.))])) %>%
filter(!if_all(everything(), is.na)) %>%
ungroup
# A tibble: 4 x 6
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 1 2021-01-01 07:00:00 2021-01-01 08:40:00 NA NA
2 A 2 NA NA 2021-01-05 10:00:00 2021-01-05 11:30:00
3 B 1 2021-06-05 12:10:00 2021-06-05 16:58:00 NA NA
4 B 2 NA NA 2021-06-08 10:58:00 2021-06-08 13:50:00
对于第二种情况，使用across
df_start %>%
group_by(shipment) %>%
dplyr::summarise(across(contains("_"), ~ na.omit(.)))
# A tibble: 2 x 5
shipment arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dttm> <dttm> <dttm> <dttm>
1 A 2021-01-01 07:00:00 2021-01-01 08:40:00 2021-01-05 10:00:00 2021-01-05 11:30:00
2 B 2021-06-05 12:10:00 2021-06-05 16:58:00 2021-06-08 10:58:00 2021-06-08 13:50:00
在 OP 中，它使用 na.locf 而不是 na.omit 并且还有一个拼写错误，即 across 是在没有任何参数的情况下关闭的，即如果我们检查此 post，使用的语法是
...across(everything(), ~ .. # correct
...across(everything()) ... # incorrect
因此，我们只需要将 ) 和指定 lambda 函数的 ~ 一起更改到末尾（否则 function(.) .
df_start %>%
group_by(shipment) %>%
summarise(across(everything(), ~ na.locf(., na.rm = FALSE, fromLast = FALSE)))
代码中有几个语法问题。
1 - 参数 .cols 和 .fns 在 across 内，在您的代码中 across 函数在 everything() 之后关闭（across(everything())).
- 当您在
across 中使用 . 时，您需要在其前面加上 ~ 以指定您正在为传递的函数使用 lambda 表达式。 （参见 ?across 中的 .fns 论点）。
合并此更改，您可以使用 -
library(dplyr)
library(zoo)
df_start %>%
group_by(shipment) %>%
summarise(across(everything(), ~na.locf(., na.rm = FALSE, fromLast = FALSE)))
然而，across 有 everything() 作为默认的 .cols 参数，你也可以在不需要 ~ 的情况下应用该函数，所以另一种写法是是-
df_start %>%
group_by(shipment) %>%
summarise(across(.fns = na.locf, na.rm = FALSE, fromLast = FALSE))
我正在尝试复制 across() 函数并摆脱了已弃用的 summarise_all() 和 funs()。
起始数据
我有一个数据库提取每个事件类型一行，如下所示：
library(tidyverse)
library(zoo)
df_start <- tibble(shipment = c(rep("A",4), rep("B",4)),
stop = rep(c(1,1,2,2), 2),
arrive_pickup = as.POSIXct(c("2021-01-01 07:00:00 UTC",NA, NA, NA,"2021-06-05 12:10:00 UTC", NA, NA, NA)),
depart_pickup = as.POSIXct(c(NA,"2021-01-01 08:40:00 UTC", NA, NA, NA, "2021-06-05 16:58:00 UTC", NA, NA)),
arrive_delivery = as.POSIXct(c(NA, NA, "2021-01-05 10:00:00 UTC",NA, NA, NA,"2021-06-08 10:58:00 UTC", NA)),
depart_delivery = as.POSIXct(c(NA, NA, NA, "2021-01-05 11:30:00 UTC",NA, NA, NA,"2021-06-08 13:50:00 UTC"))
)
> df_start
# A tibble: 8 x 6
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 1 2021-01-01 07:00:00 NA NA NA
2 A 1 NA 2021-01-01 08:40:00 NA NA
3 A 2 NA NA 2021-01-05 10:00:00 NA
4 A 2 NA NA NA 2021-01-05 11:30:00
5 B 1 2021-06-05 12:10:00 NA NA NA
6 B 1 NA 2021-06-05 16:58:00 NA NA
7 B 2 NA NA 2021-06-08 10:58:00 NA
8 B 2 NA NA NA 2021-06-08 13:50:00
期望的结果
... 我想通过按出货量和停靠点分组，甚至仅按出货量分组来折叠行数（我不确定在最终数据框中保留 NA 是否会影响答案，但我正在寻求能够以任何一种方式解决它）。
df_finish1 # 一个想要的结果
# A tibble: 4 x 6
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 1 2021-01-01 07:00:00 2021-01-01 08:40:00 NA NA
2 A 2 NA NA 2021-01-05 10:00:00 2021-01-05 11:30:00
3 B 1 2021-06-05 12:10:00 2021-06-05 16:58:00 NA NA
4 B 2 NA NA 2021-06-08 10:58:00 2021-06-08 13:50:00
df_finish2 # Second/alternative 期望的结果
# A tibble: 2 x 5
shipment arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dttm> <dttm> <dttm> <dttm>
1 A 2021-01-01 07:00:00 2021-01-01 08:40:00 2021-01-05 10:00:00 2021-01-05 11:30:00
2 B 2021-06-05 12:10:00 2021-06-05 16:58:00 2021-06-08 10:58:00 2021-06-08 13:50:00
我研究和尝试过的
基于
df_1 <- df_start %>%
group_by(shipment, stop) %>% # Two groupings
summarise_all(funs(na.locf(., na.rm = FALSE, fromLast = FALSE))) %>%
filter(row_number()==n())
> df_1
# A tibble: 4 x 6
# Groups: shipment, stop [4]
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 1 2021-01-01 07:00:00 2021-01-01 08:40:00 NA NA
2 A 2 NA NA 2021-01-05 10:00:00 2021-01-05 11:30:00
3 B 1 2021-06-05 12:10:00 2021-06-05 16:58:00 NA NA
4 B 2 NA NA 2021-06-08 10:58:00 2021-06-08 13:50:00
df_2 <- df_start %>%
group_by(shipment) %>% # Single grouping
summarise_all(funs(na.locf(., na.rm = FALSE, fromLast = FALSE))) %>%
filter(row_number()==n())
> df_2
# A tibble: 2 x 6
# Groups: shipment [2]
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 2 2021-01-01 07:00:00 2021-01-01 08:40:00 2021-01-05 10:00:00 2021-01-05 11:30:00
2 B 2 2021-06-05 12:10:00 2021-06-05 16:58:00 2021-06-08 10:58:00 2021-06-08 13:50:00
但我看到的是 summarise_all() 函数和 funs() 函数已被弃用，以后不再使用，所以我试图了解如何使用 across() 正常运行，但没有成功：
df_3 <- df_start %>%
group_by(shipment) %>%
summarise(across(everything()), na.locf(., na.rm = FALSE, fromLast = FALSE))
> df_3 <- df_start %>%
+ group_by(shipment) %>%
+ summarise(across(everything()), na.locf(., na.rm = FALSE, fromLast = FALSE))
Error: Problem with `summarise()` input `..2`.
x Input `..2` must be size 4 or 1, not 8.
i An earlier column had size 4.
i Input `..2` is `na.locf(., na.rm = FALSE, fromLast = FALSE)`.
i The error occurred in group 1: shipment = "A".
我已经通读了 vignette("colwise")，其中描述了不同之处，并建议我只替换上面所示的语法，但显然我没有做对。帮助？
这是一个选项，在按 'shipment'、'stop' 分组后，根据 NA 值对列进行排序，然后 filter 离开所有 NA 的行
library(dplyr)
df_start %>%
group_by(shipment, stop) %>%
mutate(across(everything(), ~ .[order(is.na(.))])) %>%
filter(!if_all(everything(), is.na)) %>%
ungroup
# A tibble: 4 x 6
shipment stop arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dbl> <dttm> <dttm> <dttm> <dttm>
1 A 1 2021-01-01 07:00:00 2021-01-01 08:40:00 NA NA
2 A 2 NA NA 2021-01-05 10:00:00 2021-01-05 11:30:00
3 B 1 2021-06-05 12:10:00 2021-06-05 16:58:00 NA NA
4 B 2 NA NA 2021-06-08 10:58:00 2021-06-08 13:50:00
对于第二种情况，使用across
df_start %>%
group_by(shipment) %>%
dplyr::summarise(across(contains("_"), ~ na.omit(.)))
# A tibble: 2 x 5
shipment arrive_pickup depart_pickup arrive_delivery depart_delivery
<chr> <dttm> <dttm> <dttm> <dttm>
1 A 2021-01-01 07:00:00 2021-01-01 08:40:00 2021-01-05 10:00:00 2021-01-05 11:30:00
2 B 2021-06-05 12:10:00 2021-06-05 16:58:00 2021-06-08 10:58:00 2021-06-08 13:50:00
在 OP 中，它使用 na.locf 而不是 na.omit 并且还有一个拼写错误，即 across 是在没有任何参数的情况下关闭的，即如果我们检查此 post，使用的语法是
...across(everything(), ~ .. # correct
...across(everything()) ... # incorrect
因此，我们只需要将 ) 和指定 lambda 函数的 ~ 一起更改到末尾（否则 function(.) .
df_start %>%
group_by(shipment) %>%
summarise(across(everything(), ~ na.locf(., na.rm = FALSE, fromLast = FALSE)))
代码中有几个语法问题。
1 - 参数 .cols 和 .fns 在 across 内，在您的代码中 across 函数在 everything() 之后关闭（across(everything())).
- 当您在
across中使用.时，您需要在其前面加上~以指定您正在为传递的函数使用 lambda 表达式。 （参见?across中的.fns论点）。
合并此更改，您可以使用 -
library(dplyr)
library(zoo)
df_start %>%
group_by(shipment) %>%
summarise(across(everything(), ~na.locf(., na.rm = FALSE, fromLast = FALSE)))
然而，across 有 everything() 作为默认的 .cols 参数，你也可以在不需要 ~ 的情况下应用该函数，所以另一种写法是是-
df_start %>%
group_by(shipment) %>%
summarise(across(.fns = na.locf, na.rm = FALSE, fromLast = FALSE))