撰寫R函式，僅在宣告時進行子集

我正在嘗試撰寫一個函式，該函式從特定列（深度）的資料框中提取平均值、最小值和最大值，并且它可以按兩個分類變數進行分類，因此在函式中，一個按型別變數分組。另一個分類變數是在 2020 年或 2021 年收集的資料。我希望默認函式提取所有年份的資料，除非在引數中說明，然后按年份對資料進行子集化。如果我可以更改變數（例如長度而不是深度）也很好。這是我的代碼

analysis <- function(data=measurements, yearX=2020){
  data %>%
    subset(year == yearX) %>%  ## Subsets the dataset by specific year
    group_by(type) %>%  ## groups the data by type 
    summarise(mBD=mean(depth), sdBD=sd(depth), minBD=min(depth),
              maxBD=max(depth), median=median(depth), 
              range=(max(depth) - min(depth)))
}

uj5u.com熱心網友回復：

實作所需結果的一種選擇可能如下所示：

set.seed(123)

measurements <- data.frame(
  year = rep(2020:2021, each = 10),
  type = rep(c("A", "B")),
  length = runif(20),
  depth = runif(20)
)

library(dplyr)

analysis <- function(data = measurements, x, yearX = NULL) {
  # Subset by year if given
  if (!is.null(yearX)) data <- filter(data, year %in% yearX) 
  data %>%
    group_by(type) %>%
    summarise(across({{x}}, .fns = list(
      mBD = mean, 
      sdBD = sd, 
      minBD = min, 
      maxBD = max, 
      median = median, 
      range = ~ diff(range(.x))), .names = "{.fn}"
      ))
}

analysis(x = depth)
#> # A tibble: 2 × 7
#>   type    mBD  sdBD  minBD maxBD median range
#>   <chr> <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl>
#> 1 A     0.577 0.290 0.0246 0.963  0.648 0.938
#> 2 B     0.576 0.299 0.147  0.994  0.643 0.847

analysis(measurements, depth, 2020)
#> # A tibble: 2 × 7
#>   type    mBD  sdBD minBD maxBD median range
#>   <chr> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>
#> 1 A     0.604 0.217 0.289 0.890  0.641 0.600
#> 2 B     0.627 0.307 0.147 0.994  0.693 0.847

analysis(measurements, length, 2021)
#> # A tibble: 2 × 7
#>   type    mBD  sdBD  minBD maxBD median range
#>   <chr> <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl>
#> 1 A     0.462 0.348 0.103  0.957  0.328 0.854
#> 2 B     0.584 0.370 0.0421 0.955  0.573 0.912

uj5u.com熱心網友回復：

為了從subset()函式中的方便中受益，我們可以使用match.call()和match它與formalArgsofsubset.default來創建一個我們可以使用的subset呼叫eval。如果未指定子集，則行將被省略。

對于其余部分，我們定義了一個匯總函式，我們應該在其中定義當存在 時會發生什么NA's，并將其aggregate()與易于創建的公式一起使用reformulate()。

通過案例處理，我們也可以省略分組。

FUN <- function(..., col, group=NA, na.rm=FALSE) {
  cll <- match.call()
  m <- match(formalArgs(subset.default), names(cll), 0L)
  m <- cll[c(1L, m)]
  m[[1L]] <- quote(subset)
  dat <- eval(m)
  mysum <- function(x) c(mBD=mean(x, na.rm=na.rm), sdBD=sd(x, na.rm=na.rm), 
                         minBD=min(x, na.rm=na.rm), maxBD=max(x, na.rm=na.rm), 
                         median=median(x, na.rm=na.rm), 
                         range=max(x, na.rm=na.rm) - min(x, na.rm=na.rm))
  if (!is.na(group)) {
    res <- aggregate(reformulate(group, col), dat, mysum)
  } else {
    res <- mysum(dat[, col])
  }
  return(res)
}

用法

FUN(x=measurements, col='depth', group='type')
#   type  depth.mBD depth.sdBD depth.minBD depth.maxBD depth.median depth.range
# 1    A 0.57739614 0.29037002  0.02461368  0.96302423   0.64810631  0.93841055
# 2    B 0.57604555 0.29862847  0.14711365  0.99426978   0.64347271  0.84715613

FUN(x=measurements, col='depth', group='type', subset=year == 2020)
#   type depth.mBD depth.sdBD depth.minBD depth.maxBD depth.median depth.range
# 1    A 0.6037955  0.2169419   0.2891597   0.8895393    0.6405068   0.6003796
# 2    B 0.6273719  0.3070970   0.1471136   0.9942698    0.6928034   0.8471561

FUN(x=measurements, col='length', group='type', subset=year == 2020)
#   type length.mBD length.sdBD length.minBD length.maxBD length.median length.range
# 1    A  0.5433124   0.2457008    0.2875775    0.9404673     0.5281055    0.6528898
# 2    B  0.6131826   0.3633747    0.0455565    0.8924190     0.7883051    0.8468625

FUN(x=measurements, col='depth', group=NA)
#        mBD       sdBD      minBD      maxBD     median      range 
# 0.57672085 0.28667353 0.02461368 0.99426978 0.64810631 0.96965609  

資料（借用stefan）：

measurements <- structure(list(year = c(2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 
2020L, 2020L, 2020L, 2020L, 2021L, 2021L, 2021L, 2021L, 2021L, 
2021L, 2021L, 2021L, 2021L, 2021L), type = c("A", "B", "A", "B", 
"A", "B", "A", "B", "A", "B", "A", "B", "A", "B", "A", "B", "A", 
"B", "A", "B"), length = c(0.287577520124614, 0.788305135443807, 
0.4089769218117, 0.883017404004931, 0.940467284293845, 0.0455564993899316, 
0.528105488047004, 0.892419044394046, 0.551435014465824, 0.456614735303447, 
0.956833345349878, 0.453334156190977, 0.677570635452867, 0.572633401956409, 
0.102924682665616, 0.899824970401824, 0.24608773435466, 0.0420595335308462, 
0.327920719282702, 0.954503649147227), depth = c(0.889539316063747, 
0.6928034061566, 0.640506813768297, 0.994269776623696, 0.655705799115822, 
0.708530468167737, 0.544066024711356, 0.59414202044718, 0.28915973729454, 
0.147113647311926, 0.963024232536554, 0.902299045119435, 0.690705278422683, 
0.795467417687178, 0.0246136845089495, 0.477795971091837, 0.758459537522867, 
0.216407935833558, 0.318181007634848, 0.231625785352662)), class = "data.frame", row.names = c(NA, 
-20L))

標籤：r 功能 争论 子集 选修的

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