如何在R中循环使用此高阶函数？

Question

This question relates to the reply I received here with a nice little function from thelatemail . The dataframe I'm using is not optimal, but it's what I've got and I'm simply trying to loop this function across all rows.

This is my df

dput(SO_Example_v1)
structure(list(Type = structure(c(3L, 1L, 2L), .Label = c("Community", 
"Contaminant", "Healthcare"), class = "factor"), hosp1_WoundAssocType = c(464L, 
285L, 24L), hosp1_BloodAssocType = c(73L, 40L, 26L), hosp1_UrineAssocType = c(75L, 
37L, 18L), hosp1_RespAssocType = c(137L, 77L, 2L), hosp1_CathAssocType = c(80L, 
34L, 24L), hosp2_WoundAssocType = c(171L, 115L, 17L), hosp2_BloodAssocType = c(127L, 
62L, 12L), hosp2_UrineAssocType = c(50L, 29L, 6L), hosp2_RespAssocType = c(135L, 
142L, 6L), hosp2_CathAssocType = c(95L, 24L, 12L)), .Names = c("Type", 
"hosp1_WoundAssocType", "hosp1_BloodAssocType", "hosp1_UrineAssocType", 
"hosp1_RespAssocType", "hosp1_CathAssocType", "hosp2_WoundAssocType", 
"hosp2_BloodAssocType", "hosp2_UrineAssocType", "hosp2_RespAssocType", 
"hosp2_CathAssocType"), class = "data.frame", row.names = c(NA, 
-3L))
####################
#what it looks like#
####################
require(dplyr)
df <- tbl_df(SO_Example_v1)
head(df)
         Type hosp1_WoundAssocType hosp1_BloodAssocType hosp1_UrineAssocType
1  Healthcare                  464                   73                   75
2   Community                  285                   40                   37
3 Contaminant                   24                   26                   18
Variables not shown: hosp1_RespAssocType (int), hosp1_CathAssocType (int), hosp2_WoundAssocType
  (int), hosp2_BloodAssocType (int), hosp2_UrineAssocType (int), hosp2_RespAssocType (int),
  hosp2_CathAssocType (int)

The function I have is to perform a chisq.test across all categories in df$Type . Ideally the function should switch to a fisher.test() if the cell count is <5, but that's a separate issue (extra brownie points for the person who comes up with how to do that though).

This is the function I'm using to go row by row

func <- Map(
  function(x,y) {
    out <- cbind(x,y)
    final <- rbind(out[1,],colSums(out[2:3,]))
    chisq <- chisq.test(final,correct=FALSE)
    chisq$p.value
  },
  SO_Example_v1[grepl("^hosp1",names(SO_Example_v1))],
  SO_Example_v1[grepl("^hosp2",names(SO_Example_v1))] 
)
func

But ideally, i'd want it to be something like this

for(i in 1:nrow(df)){func}

But that doesn't work. A further hook is, that when for example, row two is taken, the final call looks like this

func <- Map(
  function(x,y) {
    out <- cbind(x,y)
    final <- rbind(out[2,],colSums(out[c(1,3),]))
    chisq <- chisq.test(final,correct=FALSE)
    chisq$p.value
  },
  SO_Example_v1[grepl("^hosp1",names(SO_Example_v1))],
  SO_Example_v1[grepl("^hosp2",names(SO_Example_v1))] 
)
func

so the function should understand that the cell count its taking for out[x,] has to be excluded from colSums() . This data.frame only has 3 rows, so it's easy, but I've tried applying this function to a separate data.frame I have that consists >200 rows, so it would be nice to be able to loop this somehow.

Any help appreciated.

Cheers

Answer 1

You were missing two things:

1. To select the line i and select all but this line you want to use u[i] and u[-i]
2. If an item is not the same length than the others given to Map, it is recycled, a very general property of the language. You then just have to add an argument to the function that corresponds to the line you want to oppose to the others, it will be recycled for all the items of the vectors passed.

The following does what you asked for

    # the function doing the stats
    FisherOrChisq <- function(x,y,lineComp) {
        out <- cbind(x,y)
        final <- rbind(out[lineComp,],colSums(out[-lineComp,]))
        test <- chisq.test(final,correct=FALSE)

        return(test$p.value)
    }

    # test of the stat function
    FisherOrChisq(SO_Example_v1[grep("^hosp1",names(SO_Example_v1))[1]],
    SO_Example_v1[grep("^hosp2",names(SO_Example_v1))[1]],2)

    # making the loop
    result <- c()
    for(type in SO_Example_v1$Type){
        line <- which(SO_Example_v1$Type==type)
        res <- Map(FisherOrChisq,
                    SO_Example_v1[grepl("^hosp1",names(SO_Example_v1))],
                    SO_Example_v1[grepl("^hosp2",names(SO_Example_v1))],
                    line
                )
        result <- rbind(result,res)
    }
    colnames(result) <- gsub("^hosp[0-9]+","",colnames(result))
    rownames(result) <- SO_Example_v1$Type

That said, what you are doing is very heavy multiple testing. I would be extremely cautious with the use of the corresponding p-values, you need at least to use a multiple testing correction such as what is suggested here .