将多个不均匀的嵌套列表转换成R中的一个DataFrame

Question

I am trying to get to grips with R and as an experiment I thought that I would try to play around with some cricket data. In its rawest format it is a yaml file, which I used the yaml R package to turn into an R object.

However, I now have a number of nested lists of uneven length that I want to try and turn into a data frame in R. I have tried a few methods such as writing some loops to parse the data and some of the functions in the tidyr package. However, I can't seem to get it to work nicely.

I wondered if people knew of the best way to tackle this? Replicating the data structure would be difficult here, because the complexity comes in the multiple nested lists and the unevenness of their length (which would make for a very long code block. However, you can find the raw yaml data here: http://cricsheet.org/downloads/ (I was using the ODI internationals).

Thanks in advance!

Update I have tried this: 1)Using tidyr - seperate

d <- unnest(balls)
Name <- c("Batsman","Bowler","NonStriker","RunsBatsman","RunsExtras","RunsTotal","WicketFielder","WicketKind","PlayerOut")
a <- separate(d, x, Name, sep = ",",extra = "drop")

Which basically uses the tidyr package returns a single column dataframe that I then try to separate. However, the problem here is that in the middle there is sometimes extras variables that appear in some rows and not others, thereby throwing off the separation.

2) Creating vectors

ballsVector <- unlist(balls[[2]],use.names = FALSE)
names_vector <- c("Batsman","Bowler","NonStriker","RunsBatsman","RunsExtras","RunsTotal")
names(ballsVector) <- c(names_vector)
ballsMatrix <- matrix(ballsVector, nrow = 1, byrow = TRUE)
colnames(ballsMatrix) <- names_vector

The problem here is that the resulting vectors are uneven in length and therefore cant be combined into a data frame. It will also suffer from the issue that there are sporadic variables in the middle of the dataset (as above).

Answer 1

Caveat: not complete answer; attempt to arrange the innings data

plyr::rbind.fill may offer a solution to binding rows with a different number of columns.

I dont use tidyr but below is some rough code to get the innings data into a data.frame. You could then loop this through all the yaml files in the directory.

# Download and unzip data
download.file("http://cricsheet.org/downloads/odis.zip", temp<- tempfile())
tmp <- unzip(temp)

# Create lists - use first game
library(yaml)
raw_dat <- yaml.load_file(tmp[[2]])
#names(raw_dat)

# Function to process list into dataframe
p_fun <- function(X) {
          team = X[[1]][["team"]]

          # function to process each list subelement that represents each throw
          fn <- function(...) {
                    tmp = unlist(...)
                    tmp = data.frame(ball=gsub("[^0-9]", "", names(tmp))[1], t(tmp))
                    colnames(tmp) = gsub("[0-9]", "", colnames(tmp))
                    tmp
                    }
           # loop over all throws
           lst = lapply(X[[1]][["deliveries"]], fn )

           cbind(team, plyr:::rbind.fill(lst))
          }

# Loop over each innings
dat <- plyr::rbind.fill(lapply(raw_dat$innings, p_fun))

---

Some explanation

The list structure and subsetting it. To get an idea of the structure of the list use

str(raw_dat) # but this gives a really long list of data

You can truncate this, to make it a bit more useful

str(raw_dat, 3)
length(raw_dat)

So there are three main list elements - meta , info , and innings . You can also see this with

names(raw_dat)

To access the meta data, you can use

raw_dat$meta
#or using `[[1]]` to access the first element of the list (see ?'[[')
raw_dat[[1]]
#and get sub-elements by either
raw_dat$meta$data_version
raw_dat[[1]][[1]] # you can also use the names of the list elements eg [[`data_version`]]

The main data is in the innings element.

str(raw_dat$innings, 3)

Look at the names in the list element

lapply(raw_dat$innings, names)
lapply(raw_dat$innings[[1]], names)

There are two list elements, each with sub-elements. You can access these as

raw_dat$innings[[1]][[1]][["team"]] # raw_dat$innings[[1]][["1st innings"]][["team"]]
raw_dat$innings[[2]][[1]][["team"]] # raw_dat$innings[[2]][["2nd innings"]][["team"]]

---

The above function parsed the deliveries data in raw_dat$innings . To see what it does, work through it from the inside.

Use one record to see how it works (note the lapply , with p_fun , looped over raw_dat$innings[[1]] and raw_dat$innings[[2]] ; so this is the outer loop, and the lapply , with fn , loops through the deliveries, within an innings ; the inner loop)

X <- raw_dat$innings[[1]] 
tmp <- X[[1]][["deliveries"]][[1]]
tmp

#create a named vector
tmp <- unlist(tmp)
tmp
#      0.1.batsman       0.1.bowler  0.1.non_striker 0.1.runs.batsman  0.1.runs.extras   0.1.runs.total 
#        "IR Bell"       "DW Steyn"       "MJ Prior"              "0"              "0"              "0" 

To use rbind.fill , the elements to bind together need to be data.frames . We also want to remove the leading numbers / deliveries from the names, as otherwise we will have lots of uniquely names columns

# this regex removes all non-numeric characters from the string
# you could then split this number into over and delivery
gsub("[^0-9]", "", names(tmp)) 

# this regex removes all numeric characters from the string -
# allowing consistent names across all the balls / deliveries
# (if i was better at regex I would have also removed the leading dots)
gsub("[0-9]", "", names(tmp))

So for the first delivery in the first innings we have

tmp = data.frame(ball=gsub("[^0-9]", "", names(tmp))[1], t(tmp))
colnames(tmp) = gsub("[0-9]", "", colnames(tmp))
tmp
#   ball X..batsman X..bowler X..non_striker X..runs.batsman X..runs.extras X..runs.total
# 1   01    IR Bell  DW Steyn       MJ Prior               0              0             0

To see how the lapply works, use the first three deliveries (you will need to run the function fn in your workspace)

lst = lapply(X[[1]][["deliveries"]][1:3], fn )
lst
# [[1]]
#   ball X..batsman X..bowler X..non_striker X..runs.batsman X..runs.extras X..runs.total
# 1   01    IR Bell  DW Steyn       MJ Prior               0              0             0
# 
# [[2]]
#   ball X..batsman X..bowler X..non_striker X..runs.batsman X..runs.extras X..runs.total
# 1   02    IR Bell  DW Steyn       MJ Prior               0              0             0
# 
# [[3]]
#   ball X..batsman X..bowler X..non_striker X..runs.batsman X..runs.extras X..runs.total
# 1   03    IR Bell  DW Steyn       MJ Prior               3              0             3

So we end up with a list element for every delivery within an innings. We then use rbind.fill to create one data.frame.

---

If I was going to try and parse every yaml file I would use a loop.

Use the first three records as an example, and also add the match date.

tmp <- unzip(temp)[2:4]

all_raw_dat <- vector("list", length=length(tmp))

for(i in seq_along(tmp)) {
      d = yaml.load_file(tmp[i])
      all_raw_dat[[i]] <- cbind(date=d$info$date, plyr::rbind.fill(lapply(d$innings, p_fun)))
}

Then use rbind.fill .

---

Q1. from comments

A small example with rbind.fill

a <- data.frame(x=1, y=2)
b <- data.frame(x=2, z=1)

rbind(a,b) # error as names dont match
plyr::rbind.fill(a, b)

rbind.fill doesnt go back and add/update rows with the extra columns, where needed ( a still doesnt have column z ), Think of it as creating an empty dataframe with the number of columns equal to the number of unique columns found in the list of dataframes - unique(c(names(a), names(b))) . The values are then filled in each row where possible, and left missing (NA) otherwise..