[英]Passing a column name into R dplyr group_by and summarise function
我正在嘗試編寫一個函數,該函數采用數據框和變量名稱(或變量名稱列表)並使用 group_by 和匯總函數輸出摘要信息。 但是,我不斷收到以下錯誤:
Error: Problem with `mutate()` input `..1`.
x Input `..1` must be a vector, not a function.
i Input `..1` is `<fn>`.
或者這個錯誤:
Error in (function (x) : object 'ym' not found
最后一個錯誤說它找不到名為“value”的列,該列包含數據框的值(在熔化后)。
這是我的代碼:
tested <- melt(test_data, measure.vars = c('TA','PP','US','UD','UE','UG','UH','XR','RW','PA','TB4',
'TV2','TV4','TV8','TV20','TV40','MV2','MV4','MV8','MV20','MV40','VB'), id.vars = c('TmStamp','year','month','ym','day','hour'))
test_function <- function(data,col){
stats <- data %>% group_by(!!col,variable) %>%
summarize(N = length(value[!is.na(value)]),
Missing = length(value[is.na(value)]),
Per.Avail = (length(value[!is.na(value)])/(length(value[!is.na(value)]) + length(value[is.na(value)]))) * 100,
Mean = mean(value, na.rm=TRUE),
Median = median(value, na.rm=TRUE),
Min = min(value, na.rm=TRUE),
Max = max(value, na.rm=TRUE),
Range = max(value, na.rm=TRUE) - min(value, na.rm=TRUE),
Variance = var(value, na.rm=TRUE),
Std.Dev = sd(value, na.rm=TRUE),
Coef.Var = sd(value, na.rm=TRUE)/mean(value, na.rm=TRUE),
SE = sd(value, na.rm=TRUE)/sqrt(length(value[!is.na(value)])),
Skewness = e1071::skewness(value, na.rm=TRUE),
Kurtosis = e1071::kurtosis(value, na.rm=TRUE),
IQR = IQR(value, na.rm=TRUE),
MAD = mad(value, na.rm=TRUE)
)
return(stats)
}
test_function(tested, ym)
這是一個小數據樣本。 請注意,“變量”是一個始終傳遞到 group_by 函數的列,因此我決定對其進行硬編碼。
structure(list(TmStamp = c("2019-10-01 12:00:00 AM", "2019-10-01 12:05:00 AM",
"2019-10-01 12:10:00 AM", "2019-10-01 12:15:00 AM", "2019-10-01 12:20:00 AM",
"2019-10-01 12:25:00 AM", "2019-10-01 12:30:00 AM", "2019-10-01 12:35:00 AM",
"2019-10-01 12:40:00 AM", "2019-10-01 12:45:00 AM", "2019-10-01 12:50:00 AM",
"2019-10-01 12:55:00 AM", "2019-10-01 01:00:00 AM", "2019-10-01 01:05:00 AM",
"2019-10-01 01:10:00 AM", "2019-10-01 01:15:00 AM", "2019-10-01 01:20:00 AM",
"2019-10-01 01:25:00 AM", "2019-10-01 01:30:00 AM", "2019-10-01 01:35:00 AM"
), year = c(2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019
), month = c(10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10), ym = c("10-2019", "10-2019",
"10-2019", "10-2019", "10-2019", "10-2019", "10-2019", "10-2019",
"10-2019", "10-2019", "10-2019", "10-2019", "10-2019", "10-2019",
"10-2019", "10-2019", "10-2019", "10-2019", "10-2019", "10-2019"
), day = structure(c(18170, 18170, 18170, 18170, 18170, 18170,
18170, 18170, 18170, 18170, 18170, 18170, 18170, 18170, 18170,
18170, 18170, 18170, 18170, 18170), class = "Date"), hour = c(23L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("TA",
"PP", "US", "UD", "UE", "UG", "UH", "XR", "RW", "PA", "TB4",
"TV2", "TV4", "TV8", "TV20", "TV40", "MV2", "MV4", "MV8", "MV20",
"MV40", "VB"), class = "factor"), value = c(6.008, 6.013, 5.915,
5.777, 5.727, 5.679, 5.653, 5.591, 5.479, 5.353, 5.299, 5.249,
5.256, 5.171, 5.01, 4.901, 4.716, 4.487, 4.397, 4.25)), row.names = c(NA,
20L), class = "data.frame")
我將如何編寫此函數以使其接受 group_by 函數中的一個或多個列名?
要使您的功能正常工作,請使用例如{{col}}
而不是!!col
。 為了使您的函數適用於多個變量,您可以使用...
表示法,您也可以使用它來將變量傳遞給 group_by:
library(dplyr)
test_function <- function(data, ...){
stats <- data %>%
group_by(variable, ...) %>%
summarize(N = length(value[!is.na(value)]),
Missing = length(value[is.na(value)]),
Per.Avail = (length(value[!is.na(value)])/(length(value[!is.na(value)]) + length(value[is.na(value)]))) * 100,
Mean = mean(value, na.rm=TRUE),
Median = median(value, na.rm=TRUE),
Min = min(value, na.rm=TRUE),
Max = max(value, na.rm=TRUE),
Range = max(value, na.rm=TRUE) - min(value, na.rm=TRUE),
Variance = var(value, na.rm=TRUE),
Std.Dev = sd(value, na.rm=TRUE),
Coef.Var = sd(value, na.rm=TRUE)/mean(value, na.rm=TRUE),
SE = sd(value, na.rm=TRUE)/sqrt(length(value[!is.na(value)])),
Skewness = e1071::skewness(value, na.rm=TRUE),
Kurtosis = e1071::kurtosis(value, na.rm=TRUE),
IQR = IQR(value, na.rm=TRUE),
MAD = mad(value, na.rm=TRUE)
)
return(stats)
}
test_function(tested, ym)
#> `summarise()` regrouping output by 'variable' (override with `.groups` argument)
#> # A tibble: 1 x 18
#> # Groups: variable [1]
#> variable ym N Missing Per.Avail Mean Median Min Max Range Variance
#> <fct> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 TA 10-2~ 20 0 100 5.30 5.33 4.25 6.01 1.76 0.283
#> # ... with 7 more variables: Std.Dev <dbl>, Coef.Var <dbl>, SE <dbl>,
#> # Skewness <dbl>, Kurtosis <dbl>, IQR <dbl>, MAD <dbl>
test_function(tested, ym, year, month)
#> `summarise()` regrouping output by 'variable', 'ym', 'year' (override with `.groups` argument)
#> # A tibble: 1 x 20
#> # Groups: variable, ym, year [1]
#> variable ym year month N Missing Per.Avail Mean Median Min Max
#> <fct> <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 TA 10-2~ 2019 10 20 0 100 5.30 5.33 4.25 6.01
#> # ... with 9 more variables: Range <dbl>, Variance <dbl>, Std.Dev <dbl>,
#> # Coef.Var <dbl>, SE <dbl>, Skewness <dbl>, Kurtosis <dbl>, IQR <dbl>,
#> # MAD <dbl>
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