将函数的相关输出放入数据框中

Question

随着时间的推移，我正在执行大量人口的相关性。我已将它们相应地分开，并通过 lapply 的函数将它们放在了一起。我想将每个相关性的输出放入一个数据框中（即：每一行将是一个相关性的信息，列：correlation's name, p-value 、t 统计量、df、CIs和corcoeff）。

我有两个问题：

1. 我不知道如何提取拆分中所做的关联名称
2. 我可以让我的函数在拆分上运行相关性（600+ 相关性），但我不能让它打印到数据框中。澄清一下：当我在没有循环的情况下运行该函数时，它会为每个组执行所有 600 个相关性。但是，当我添加循环时，它会为拆分中的所有组生成 NULL。

这是我目前所拥有的：

> head(Birds) #Shortened for this Post
Location      Species   Year Longitude Latitude Section Total Percent  Family
1 Chiswell A  Kittiwake 1976 -149.5847 59.59559 Central   310 16.78397 Gull

BigSplit<-split(Birds,list(Birds$Family, Birds$Location, 
Birds$Section,Birds$Species), drop=T) #A list of Dataframes

#Make empty data frame
resultcor <- data.frame(Name = character(),
                        tvalue = character(),
                        degreeF = character(),
                        pvalue = character(),
                        CIs = character(),
                        corcoeff = character(),stringsAsFactors = F)

WorkFunc <- function(dataset) {
     data.name = substitute(dataset) #Use "dataset" as substitute for actual dataset name

     #Correlation between Year and population Percent
     try({
          correlation <- cor.test(dataset$Year, dataset$Percent, method = "pearson")    
     }, silent = TRUE)

     for (i in 1:nrow(resultcor)) {
          resultcor$Name[i] <- ??? #These ??? are not in the code, just highlighting Issue 1
          resultcor$tvalue[i] <- correlation$dataset$statistic
          resultcor$degreeF[i] <- correlation$dataset$parameter
          resultcor$pvalue[i] <- correlation$dataset$p.value
          resultcor$CIs[i] <- correlation$dataset$conf.int
          resultcor$corcoeff[i] <- correlation$dataset$estimate
     }
}

lapply(BigSplit, WorkFunc)

任何帮助将不胜感激，谢谢！

Answer 1

考虑使用Map（mapply 的变体）来传递BigSplit 的元素和名称。使用Map 将输出一个数据框列表，然后您可以在末尾使用do.call() 进行行绑定。下面假设 BigSplit 是一个命名列表。

WorkFunc <- function(dataset, dataname) {
    # Correlation between Year and population Percent
    tryCatch({ 
        correlation <- cor.test(dataset$Year, dataset$Percent, method = "pearson")
        CIs <- correlation$conf.int

        return(data.frame(
                  Name = dataname,
                  tvalue = correlation$statistic,
                  degreeF = correlation$parameter,
                  pvalue = correlation$p.value,
                  CI_lower = ifelse(is.null(CIs), NA, CIs[[1]]),
                  CI_higher = ifelse(is.null(CIs), NA, CIs[[2]]),
                  corcoeff = correlation$estimate
             )
         ) 
     }, error = function(e) 
             return(data.frame(
                        Name = character(0),
                        tvalue = numeric(0),
                        degreeF = numeric(0),
                        pvalue = numeric(0),
                        CI_lower = numeric(0),
                        CI_higher = numeric(0),
                        corcoeff = numeric(0)
                    )
              )
      )
}    

# BUILD CORRELATION DATAFRAMES INTO LIST
cor_df_list <- Map(WorkFunc, BigSplit, names(BigSplit))
cor_df_list <- mapply(WorkFunc, BigSplit, names(BigSplit), SIMPLIFY=FALSE)   # EQUIVALENT

# ROW BIND ALL DATAFRAMES TO FINAL LARGE DATAFRAME
finaldf <- do.call(rbind, cor_df_list)