How to check if a variable is available in the data else use a value provided by user in a R custom function?

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英文:

How to check if a variable is available in the data else use a value provided by user in a R custom function?

问题

  1. 我编写了一个自定义函数，其中的数据参数如下所示：
  2. ``` R
  3. Ploy <- function(data, Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  4.                  Petal.Width = "Petal.Width", Petal.Length = "Petal.Length"){
  6.   Sepal.Length = data[[Sepal.Length]]
  7.   Sepal.Width = data[[Sepal.Width]]
  8.   Petal.Width = data[[Petal.Width]]
  9.   Petal.Length = data[[Petal.Length]]
  11.   #计算一些派生参数
  12.   deltak <- (Sepal.Length - Sepal.Width)/390
  13.   ARk <- Petal.Width*2
  14.   
  15.   dat <- cbind.data.frame(deltak, ARk)
  16.   
  17.   #拟合二次模型
  18.   mod <- lm(deltak ~ poly(ARk, 2, raw = TRUE))
  19.   
  20.   deltaK0 <- abs(mod$coefficients[[1]])
  21.   
  22.   Ks <- Petal.Length - deltaK0
  23.   
  24.   return(list(`DeltaK0` = deltaK0, `Ks` = Ks))
  25. }

如果所有变量都在数据框中可用，它能够正常工作，如下所示：

  1. Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  2.      Petal.Width = "Petal.Width", Petal.Length = "Petal.Length", data = iris)

现在，如果我想要为 Petal.Length 使用一个数值，比如：

  1. Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  2.      Petal.Width = "Petal.Width", Petal.Length = 55, data = iris)

它会返回以下错误：
>Error in .subset2(x, i, exact = exact) : subscript out of bounds

实际上，我想要有一个选项，即如果数据中存在 Petal.Length，则应该使用先前的函数，否则将使用 Petal.Length 的值。我该如何做呢？

  2. <details>
  3. <summary>英文:</summary>
  5. I have written a custom function with data aurgument like

Ploy <- function(data, Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width",
Petal.Width = "Petal.Width", Petal.Length = "Petal.Length"){

Sepal.Length = data[[Sepal.Length]]
Sepal.Width = data[[Sepal.Width]]
Petal.Width = data[[Petal.Width]]
Petal.Length = data[[Petal.Length]]

#Calculate some derived parameters
deltak <- (Sepal.Length - Sepal.Width)/390
ARk <- Petal.Width*2

dat <- cbind.data.frame(deltak, ARk)

#Fitting quadratic model
mod <- lm(deltak ~ poly(ARk, 2, raw = TRUE))

deltaK0 <- abs(mod$coefficients[[1]])

Ks <- Petal.Length - deltaK0

return(list(DeltaK0 = deltaK0, Ks = Ks))
}

  1. It is working fine if all the variables are available in  the data frame like

Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width",
Petal.Width = "Petal.Width", Petal.Length = "Petal.Length", data = iris)

  1. Now, if I want to use one numeric value for `Petal.Length` like

Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width",
Petal.Width = "Petal.Width", Petal.Length = 55, data = iris)

  1. It returns the following error
  2. &gt;Error in .subset2(x, i, exact = exact) : subscript out of bounds
  4. Actually, I want to have an option like if the `Petal.Length` is available in the data, then the earlier function should work, else it will utilise the value of `Petal.Length`. How can I do that?
  6. </details>
  9. # 答案1
  10. **得分**: 3
  12. 如果我们要检查所有参数
  14. ```R
  15.  Ploy <- function(data, Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  16.                    Petal.Width = "Petal.Width", Petal.Length = "Petal.Length"){
  17.   
  18.    # 获取参数的名称并返回其值
  19.    tmpLst <- mget(names(formals()))
  20.    # 从列表中删除'data'组件
  21.    tmpLst <- tmpLst[setdiff(names(tmpLst), "data")]
  22.    # 通过unlisting创建一个向量
  23.    tmpVec <- unlist(tmpLst)
  24.    # 通过检查它们是否作为数据的列而创建一个逻辑索引
  25.    indx <- tmpVec %in% names(data)
  26.    if (any(!indx)) # 如果有任何不在的
  27.    {
  28.  
  29.     # 使用相应列表的值更新数据的列
  30.     data[names(tmpVec)[!indx]] <- tmpLst[!indx]
  31.    }
  32.   
  33.   # 从这里开始使用OP的函数...
  34.   Sepal.Length <- data[["Sepal.Length"]]
  35.   Sepal.Width <- data[["Sepal.Width"]]
  36.   Petal.Width <- data[["Petal.Width"]]
  37.   Petal.Length <- data[["Petal.Length"]]
  39.   # 计算一些派生参数
  40.   deltak <- (Sepal.Length - Sepal.Width)/390
  41.   ARk <- Petal.Width*2
  42.   
  43.   dat <- cbind.data.frame(deltak, ARk)
  44.   
  45.   # 拟合二次模型
  46.   mod <- lm(deltak ~ poly(ARk, 2, raw = TRUE))
  47.   
  48.   deltaK0 <- abs(mod$coefficients[[1]])
  49.   
  50.   Ks <- Petal.Length - deltaK0
  51.   
  52.   return(list(`DeltaK0` = deltaK0, `Ks` = Ks))
  53.   }
  • 测试
  1. > Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  2. +        Petal.Width = "Petal.Width", Petal.Length = 55, data = iris)
  3. $DeltaK0
  4. [1] 0.002795248
  6. $Ks
  7.  ...
  9. > Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  10. +         Petal.Width = 25, Petal.Length = "Petal.Length", data = iris)
  11. $DeltaK0
  12. [1] 0.00714359
  14. $Ks
  15.   [1] 1.3928564 1.3928564 1.2928564 1.4928564 1.3928564 1.6928564 ..
  17. > Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  18. +       Petal.Width = "Petal.Width", Petal.Length = "Petal.Length", data = iris)
  19. $DeltaK0
  20. [1] 0.002795248
  22. $Ks
  23.   [1] 1.3972048 1.3972048 1.2972048 1.4972048 1.3972048 1.6972048 ...

请注意，我已经忽略了代码部分，只翻译了您提供的R函数和测试部分。

英文:

If we are checking for all arguments

  1.  Ploy &lt;- function(data, Sepal.Length = &quot;Sepal.Length&quot;, Sepal.Width = &quot;Sepal.Width&quot;, 
  2.                    Petal.Width = &quot;Petal.Width&quot;, Petal.Length = &quot;Petal.Length&quot;){
  3.   
  4.    # get the names of the arguments and return its value with mget
  5.    tmpLst &lt;- mget(names(formals()))
  6.    # subset the list without the &#39;data&#39; component
  7.    tmpLst &lt;- tmpLst[setdiff(names(tmpLst), &quot;data&quot;)]
  8.    # create a vector by unlisting
  9.    tmpVec &lt;- unlist(tmpLst)
  10.   # create a logical index by checking whether those are present as
  11.   # columns in the data
  12.   indx &lt;- tmpVec %in% names(data)
  13.     if(any(!indx)) # if there are any not present
  14.     {
  15.  
  16.     # update the columns of data with the value of the corresponding list
  17.     data[names(tmpVec)[!indx]] &lt;- tmpLst[!indx]
  18.    }
  19.   
  20.   # using OP&#39;s function from here on...
  21.   Sepal.Length &lt;- data[[&quot;Sepal.Length&quot;]]
  22.   Sepal.Width &lt;- data[[&quot;Sepal.Width&quot;]]
  23.   Petal.Width &lt;- data[[&quot;Petal.Width&quot;]]
  24.   Petal.Length &lt;- data[[&quot;Petal.Length&quot;]]
  26.   #Calculate some derived parameters
  27.   deltak &lt;- (Sepal.Length - Sepal.Width)/390
  28.   ARk &lt;- Petal.Width*2
  29.   
  30.   dat &lt;- cbind.data.frame(deltak, ARk)
  31.   
  32.   #Fitting quadratic model
  33.   mod &lt;- lm(deltak ~ poly(ARk, 2, raw = TRUE))
  34.   
  35.   deltaK0 &lt;- abs(mod$coefficients[[1]])
  36.   
  37.   Ks &lt;- Petal.Length - deltaK0
  38.   
  39.   return(list(`DeltaK0` = deltaK0, `Ks` = Ks))
  40.   
  41.   
  42.   
  43.    
  44.    
  45.   }

-testing

  1. &gt; Ploy(Sepal.Length = &quot;Sepal.Length&quot;, Sepal.Width = &quot;Sepal.Width&quot;, 
  2. +        Petal.Width = &quot;Petal.Width&quot;, Petal.Length = 55, data = iris)
  3. $DeltaK0
  4. [1] 0.002795248
  6. $Ks
  7.  ...
  9. &gt; Ploy(Sepal.Length = &quot;Sepal.Length&quot;, Sepal.Width = &quot;Sepal.Width&quot;, 
  10. +         Petal.Width = 25, Petal.Length = &quot;Petal.Length&quot;, data = iris)
  11. $DeltaK0
  12. [1] 0.00714359
  14. $Ks
  15.   [1] 1.3928564 1.3928564 1.2928564 1.4928564 1.3928564 1.6928564 ..
  16. ...
  18. &gt; &gt; Ploy(Sepal.Length = &quot;Sepal.Length&quot;, Sepal.Width = &quot;Sepal.Width&quot;, 
  19. +       Petal.Width = &quot;Petal.Width&quot;, Petal.Length = &quot;Petal.Length&quot;, data = iris)
  20. $DeltaK0
  21. [1] 0.002795248
  23. $Ks
  24.   [1] 1.3972048 1.3972048 1.2972048 1.4972048 1.3972048 1.6972048 1.3972048 1.4972048 1.3972048 1.4972048 1.4972048 1.5972048 1.3972048 1.0972048
  25.  [15] 1.1972048 1.4972048 1.2972048 1.3972048 1.6972048 1.4972048 1.6972048 1.4972048 0.9972048 1.6972048 1.8972048 1.5972048 1.5972048 1.4972048
  26.  [29] 1.3972048 1.5972048 1.5972048 1.4972048 1.4972048 1.3972048 1.497 ...

答案2

得分: 2

一种方法是检查 Petal.Length 参数是否是数值或列名。

如果是数值，则直接赋给 Petal.Length。

如果是列名，则使用提供的值。

  1. Ploy <- function(data, Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  2.                  Petal.Width = "Petal.Width", Petal.Length = "Petal.Length") {
  3.   
  4.   Sepal.Length <- data[[Sepal.Length]]
  5.   Sepal.Width <- data[[Sepal.Width]]
  6.   Petal.Width <- data[[Petal.Width]]
  7.   
  8.   if (is.numeric(Petal.Length)) {
  9.     # 直接使用提供的数值
  10.     Petal.Length <- rep(Petal.Length, nrow(data))
  11.   } else if (is.character(Petal.Length)) {
  12.     # 根据列名提取列的值
  13.     Petal.Length <- data[[Petal.Length]]
  14.   } else {
  15.     stop("Petal.Length 参数无效。")
  16.   }
  17.   
  18.   # 计算一些派生参数
  19.   deltak <- (Sepal.Length - Sepal.Width) / 390
  20.   ARk <- Petal.Width * 2
  21.   
  22.   dat <- cbind.data.frame(deltak, ARk)
  23.   
  24.   # 拟合二次模型
  25.   mod <- lm(deltak ~ poly(ARk, 2, raw = TRUE))
  26.   
  27.   deltaK0 <- abs(mod$coefficients[[1]])
  28.   
  29.   Ks <- Petal.Length - deltaK0
  30.   
  31.   return(list(`DeltaK0` = deltaK0, `Ks` = Ks))
  32. }
  34. # 使用列名
  35. Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  36.                 Petal.Width = "Petal.Width", Petal.Length = "Petal.Length", 
  37.                 data = iris)
  39. # 使用数值
  40. Ploy(Sepal.Length = "Sepal.Length", Sepal.Width = "Sepal.Width", 
  41.                 Petal.Width = "Petal.Width", Petal.Length = 55, 
  42.                 data = iris)

希望这有帮助。

英文:

One way is to check if the Petal.Length argument is a numeric value or a column name.

If numeric value then assign directly to Petal.Length.

If a column name then use the provided value.

  1. Ploy &lt;- function(data, Sepal.Length = &quot;Sepal.Length&quot;, Sepal.Width = &quot;Sepal.Width&quot;, 
  2.                  Petal.Width = &quot;Petal.Width&quot;, Petal.Length = &quot;Petal.Length&quot;) {
  3.   
  4.   Sepal.Length &lt;- data[[Sepal.Length]]
  5.   Sepal.Width &lt;- data[[Sepal.Width]]
  6.   Petal.Width &lt;- data[[Petal.Width]]
  7.   
  8.   if (is.numeric(Petal.Length)) {
  9.     # Use the provided numeric value directly
  10.     Petal.Length &lt;- rep(Petal.Length, nrow(data))
  11.   } else if (is.character(Petal.Length)) {
  12.     # Extract the column values based on the column name
  13.     Petal.Length &lt;- data[[Petal.Length]]
  14.   } else {
  15.     stop(&quot;Invalid argument for Petal.Length.&quot;)
  16.   }
  17.   
  18.   # Calculate some derived parameters
  19.   deltak &lt;- (Sepal.Length - Sepal.Width) / 390
  20.   ARk &lt;- Petal.Width * 2
  21.   
  22.   dat &lt;- cbind.data.frame(deltak, ARk)
  23.   
  24.   # Fitting quadratic model
  25.   mod &lt;- lm(deltak ~ poly(ARk, 2, raw = TRUE))
  26.   
  27.   deltaK0 &lt;- abs(mod$coefficients[[1]])
  28.   
  29.   Ks &lt;- Petal.Length - deltaK0
  30.   
  31.   return(list(`DeltaK0` = deltaK0, `Ks` = Ks))
  32. }
  34. # Using a column name
  35. Ploy(Sepal.Length = &quot;Sepal.Length&quot;, Sepal.Width = &quot;Sepal.Width&quot;, 
  36.                 Petal.Width = &quot;Petal.Width&quot;, Petal.Length = &quot;Petal.Length&quot;, 
  37.                 data = iris)
  39. # Using a numeric value
  40. Ploy(Sepal.Length = &quot;Sepal.Length&quot;, Sepal.Width = &quot;Sepal.Width&quot;, 
  41.                 Petal.Width = &quot;Petal.Width&quot;, Petal.Length = 55, 
  42.                 data = iris)

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  • 本文由 发表于 2023年5月28日 13:28:59
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