R mutate() 具有不同的多个条件，由 case_when() 给出

如何解决R mutate() 具有不同的多个条件，由 case_when() 给出

我正在尝试使用 mutate() 和 case_when() 创建一个包含 R 中值的表，因为 mutate() 的计算可能因 case_when() 的多个条件而异。但是 case_when() 的多个条件的数量也可能因我使用的模型而异。 下面是一个可重现的示例（来源：https://doseresponse.github.io/medrc/articles/medrc.html）：

library(medrc)
library(dplyr)
library(tidyr)

data(spinach)
spinach$CURVE <- as.factor(spinach$CURVE)

#I can have a model using LL.4() or LL.3() function (as in 'fct' bellow)
sm1 <- Metadrm(SLOPE ~ DOSE,data=spinach,fct=LL.3(),ind=CURVE,cid2=HERBICIDE,struct="UN")

#or in other situations
sm1 <- Metadrm(SLOPE ~ DOSE,fct=LL.4(),struct="UN")

#Extracting the coefficients from the model
minor_coef_table <- as.data.frame(sm1$estimates$ind)
minor_coef_table <- cbind(minor_coef_table,sm1$estimates$coefficient)
minor_coef_table <- cbind(minor_coef_table,sm1$estimates$estimate)
colnames(minor_coef_table) <- c("curves","minor_coef","minor_estimates")

#I need to create a "table" ('pdata') with a calculated column "SLOPE_per_CURVE" in a way that it is independent of the number of levels of CURVE in the data (spinach,in the example).
pdata <- spinach %>%
  group_by(CURVE,HERBICIDE) %>%
  expand(DOSE=exp(seq(-5,5,length=50)))

#One of the conditions is the fct used in the model [LL.4() or LL.3()]
#Other conditions are the curve IDs
ncurves <- length(levels(spinach$CURVE))

#This is an IDEA of what I need,but it is not working as the column "SLOPE_per_CURVE is not created
pdata <- pdata %>% mutate(
  SLOPE_per_CURVE =
    for(i in 1:ncurves){
    case_when(
      sm1$fct$name == "LL.4" & CURVE == levels(pdata$CURVE)[i] ~ minor_coef_table[(ncurves+i),3]+((minor_coef_table[(2*ncurves+i),3]-minor_coef_table[(ncurves+i),3])/((1+ exp(minor_coef_table[i,3] * (log(DOSE) - log(minor_coef_table[(3*ncurves+i),3])))))),sm1$fct$name == "LL.3" & CURVE == levels(pdata$CURVE)[i] ~ ((minor_coef_table[(ncurves+i),3] * (log(DOSE) - log(minor_coef_table[(2*ncurves+i),3]))))))
    )
  }
)

#The bellow code gives what I need and is an example of the final desired result,but it is not independent of the number of levels in CURVE as I have to write every condition. 
pdata <- pdata %>% mutate(
  SLOPE_per_CURVE =
    case_when(
      sm1$fct$name == "LL.4" & CURVE == levels(pdata$CURVE)[1] ~ minor_coef_table[6,3]+((minor_coef_table[11,3]-minor_coef_table[6,3])/((1+ exp(minor_coef_table[1,3] * (log(DOSE) - log(minor_coef_table[16,sm1$fct$name == "LL.4" & CURVE == levels(pdata$CURVE)[2] ~ minor_coef_table[7,3]+((minor_coef_table[12,3]-minor_coef_table[7,3])/((1+ exp(minor_coef_table[2,3] * (log(DOSE) - log(minor_coef_table[17,sm1$fct$name == "LL.4" & CURVE == levels(pdata$CURVE)[3] ~ minor_coef_table[8,3]+((minor_coef_table[13,3]-minor_coef_table[8,3])/((1+ exp(minor_coef_table[3,3] * (log(DOSE) - log(minor_coef_table[18,sm1$fct$name == "LL.4" & CURVE == levels(pdata$CURVE)[4] ~ minor_coef_table[9,3]+((minor_coef_table[14,3]-minor_coef_table[9,3])/((1+ exp(minor_coef_table[4,3] * (log(DOSE) - log(minor_coef_table[19,sm1$fct$name == "LL.4" & CURVE == levels(pdata$CURVE)[5] ~ minor_coef_table[10,3]+((minor_coef_table[15,3]-minor_coef_table[10,3])/((1+ exp(minor_coef_table[5,3] * (log(DOSE) - log(minor_coef_table[20,sm1$fct$name == "LL.3" & CURVE == levels(pdata$CURVE)[1] ~ ((minor_coef_table[6,3] * (log(DOSE) - log(minor_coef_table[11,sm1$fct$name == "LL.3" & CURVE == levels(pdata$CURVE)[2] ~ ((minor_coef_table[7,3] * (log(DOSE) - log(minor_coef_table[12,sm1$fct$name == "LL.3" & CURVE == levels(pdata$CURVE)[3] ~ ((minor_coef_table[8,3] * (log(DOSE) - log(minor_coef_table[13,sm1$fct$name == "LL.3" & CURVE == levels(pdata$CURVE)[4] ~ ((minor_coef_table[9,3] * (log(DOSE) - log(minor_coef_table[14,sm1$fct$name == "LL.3" & CURVE == levels(pdata$CURVE)[5] ~ ((minor_coef_table[10,3] * (log(DOSE) - log(minor_coef_table[15,3]))))))
    )
)

编辑：

经过 M.Viking 的回答，这是解决方案：

for(i in 1:ncurves){
pdata <- coalesce(pdata %>% mutate(
  SLOPE_per_CURVE =
      case_when(
        sm1$fct$name == "LL.4" & CURVE == levels(pdata$CURVE)[i] ~ minor_coef_table[(ncurves+i),3]))))))
      )
    ),pdata)
  }

解决方法

我使用 iris 数据集制作了一个 for 循环 mutate case_when 的玩具示例

第一个复杂点是在一个简单的 for (j in 1:10){out<-j} 循环中，输出数据被 j 的每次后续迭代覆盖，最后，只有 10th 运行的结果是保留。

然后我了解了coalesce()函数；它组合（合并？联合？连接？）等长的稀疏数据（类似于 Microsoft Excel 的粘贴特殊“跳过空白”转换）。

在下面的示例代码中，我们取 iris 数据集，从 1 到 10 循环，如果 iris$Sepal.Length（又名 iris[,1]）等于循环迭代 j，我们将结果变量（斜率）更改为 100+j。

iris4 <- tibble(iris)

for (j in 1:10) {
  iris4 <- coalesce(iris4 %>% mutate(Slope = case_when(Sepal.Length == j ~ 100+j)),iris4)}

table(iris4$Slope)