I have a list with the following example structure:
> dput(test)
structure(list(id = 1, var1 = 2, var3 = 4, section1 = structure(list(
var1 = 1, var2 = 2, var3 = 3), .Names = c("var1", "var2",
"var3")), section2 = structure(list(row = structure(list(var1 = 1,
var2 = 2, var3 = 3), .Names = c("var1", "var2", "var3")),
row = structure(list(var1 = 4, var2 = 5, var3 = 6), .Names = c("var1",
"var2", "var3")), row = structure(list(var1 = 7, var2 = 8,
var3 = 9), .Names = c("var1", "var2", "var3"))), .Names = c("row",
"row", "row"))), .Names = c("id", "var1", "var3", "section1",
"section2"))
> str(test)
List of 5
$ id : num 1
$ var1 : num 2
$ var3 : num 4
$ section1:List of 3
..$ var1: num 1
..$ var2: num 2
..$ var3: num 3
$ section2:List of 3
..$ row:List of 3
.. ..$ var1: num 1
.. ..$ var2: num 2
.. ..$ var3: num 3
..$ row:List of 3
.. ..$ var1: num 4
.. ..$ var2: num 5
.. ..$ var3: num 6
..$ row:List of 3
.. ..$ var1: num 7
.. ..$ var2: num 8
.. ..$ var3: num 9
Notice that the section2 list contains elements named rows. These represent multiple records. What I have is a nested list where some elements are at the root level and others are multiple nested records for the same observation. I would like the following output in a data.frame format:
> desired
id var1 var3 section1.var1 section1.var2 section1.var3 section2.var1 section2.var2 section2.var3
1 1 2 4 1 2 3 1 4 7
2 NA NA NA NA NA NA 2 5 8
3 NA NA NA NA NA NA 3 6 9
Root-level elements should populate the first row, while row elements should have their own rows. As an added complication, the number of variables in the row entries can vary.
Here's a general approach. It doesn't assume that you'll have only three row; it will work with however many rows you have. And if a value is missing in the nested structure (e.g. var1 doesn't exist for some sub-lists in section2), the code correctly returns an NA for that cell.
E.g. if we use the following data:
test <- structure(list(id = 1, var1 = 2, var3 = 4, section1 = structure(list(var1 = 1, var2 = 2, var3 = 3), .Names = c("var1", "var2", "var3")), section2 = structure(list(row = structure(list(var1 = 1, var2 = 2), .Names = c("var1", "var2")), row = structure(list(var1 = 4, var2 = 5), .Names = c("var1", "var2")), row = structure(list( var2 = 8, var3 = 9), .Names = c("var2", "var3"))), .Names = c("row", "row", "row"))), .Names = c("id", "var1", "var3", "section1", "section2"))
The general approach is to use melt to create a dataframe that includes information about the nested structure, and then dcast to mold it into the format you desire.
library("reshape2")
flat <- unlist(test, recursive=FALSE)
names(flat)[grep("row", names(flat))] <- gsub("row", "var", paste0(names(flat)[grep("row", names(flat))], seq_len(length(names(flat)[grep("row", names(flat))])))) ## keeps track of rows by adding an ID
ul <- melt(unlist(flat))
split <- strsplit(rownames(ul), split=".", fixed=TRUE) ## splits the names into component parts
max <- max(unlist(lapply(split, FUN=length)))
pad <- function(a) {
c(a, rep(NA, max-length(a)))
}
levels <- matrix(unlist(lapply(split, FUN=pad)), ncol=max, byrow=TRUE)
## Get the nesting structure
nested <- data.frame(levels, ul)
nested$X3[is.na(nested$X3)] <- levels(as.factor(nested$X3))[[1]]
desired <- dcast(nested, X3~X1 + X2)
names(desired) <- gsub("_", "\\.", gsub("_NA", "", names(desired)))
desired <- desired[,names(flat)]
> desired
## id var1 var3 section1.var1 section1.var2 section1.var3 section2.var1 section2.var2 section2.var3
## 1 1 2 4 1 2 3 1 4 7
## 2 NA NA NA NA NA NA 2 5 8
## 3 NA NA NA NA NA NA 3 6 9
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