Am I right in assuming that this would be impossible with a compressed
file, simply because gzip requires to uncompress everything from the
beginning?
Indeed, for a short explanation let's take some dummy method as starting point:
AAAAVVBABBBC gzip would do something like: 4A2VBA3BC
Obviously you can't extract all A from the file without reading it all as you can't guess if there's an A at end or not.
For the other question "Loading part of a saved file" I can't see a solution on top of my head. You probably can with write.csv and read.csv (or fwrite and fread from the data.table package) with skipand nrows parameters could be an alternative.
By all means, using any function on a file already read would mean loading the whole file in memory before filtering, which is no more time than reading the file and then subsetting from memory.
You may craft something in Rcpp, taking advantage of streams for reading data without loading them in memory, but reading and parsing each entry before deciding if it should be kept or not won't give you a real better throughput.
saveDRS will save a serialized version of the datas, example:
> myvector <- c("1","2","3").
> serialize(myvector,NULL)
[1] 58 0a 00 00 00 02 00 03 02 03 00 02 03 00 00 00 00 10 00 00 00 03 00 04 00 09 00 00 00 01 31 00 04 00 09 00 00 00 01 32 00 04 00 09 00 00
[47] 00 01 33
It is of course parsable, but means reading byte per byte according to the format.
On the other hand, you could write as csv (or write.table for more complex data) and use an external tool before reading, something along the line:
z <- tempfile()
write.table(df, z, row.names = FALSE)
shortdf <- read.table(text= system( command = paste0( "awk 'NR > 5 && NR < 10 { print }'" ,z) ) )
You'll need a linux system with awk wich is able to parse millions of lines in a few milliseconds, or to use a windows compiled version of awk obviously.
Main advantage is that awk is able to filter on a regex or some other conditions each line of data.
Complement for case of data.frame, a data.frame is more or less a list of vectors (simple case), this list will be saved sequentially so if we have a dataframe like:
> str(ex)
'data.frame': 3 obs. of 2 variables:
$ a: chr "one" "five" "Whatever"
$ b: num 1 2 3
It's serialization is:
> serialize(ex,NULL)
[1] 58 0a 00 00 00 02 00 03 02 03 00 02 03 00 00 00 03 13 00 00 00 02 00 00 00 10 00 00 00 03 00 04 00 09 00 00 00 03 6f 6e 65 00 04 00 09 00
[47] 00 00 04 66 69 76 65 00 04 00 09 00 00 00 08 57 68 61 74 65 76 65 72 00 00 00 0e 00 00 00 03 3f f0 00 00 00 00 00 00 40 00 00 00 00 00 00
[93] 00 40 08 00 00 00 00 00 00 00 00 04 02 00 00 00 01 00 04 00 09 00 00 00 05 6e 61 6d 65 73 00 00 00 10 00 00 00 02 00 04 00 09 00 00 00 01
[139] 61 00 04 00 09 00 00 00 01 62 00 00 04 02 00 00 00 01 00 04 00 09 00 00 00 09 72 6f 77 2e 6e 61 6d 65 73 00 00 00 0d 00 00 00 02 80 00 00
[185] 00 ff ff ff fd 00 00 04 02 00 00 00 01 00 04 00 09 00 00 00 05 63 6c 61 73 73 00 00 00 10 00 00 00 01 00 04 00 09 00 00 00 0a 64 61 74 61
[231] 2e 66 72 61 6d 65 00 00 00 fe
Translated to ascii for an idea:
X
one five Whatever?ð@@ names a b row.names
ÿÿÿý class
data.frameþ
We have the header of the file, the the header of the list, then each vector composing the list, as we have no clue on how much size the character vector will take we can't skip to arbitrary datas, we have to parse each header (the bytes just before the text data give it's length). Even worse now to get the corresponding integers, we have to go to the integer vector header, which can't be determined without parsing each character header and summing them.
So in my opinion, crafting something is possible but will probably not be really much quicker than reading all the object and will be brittle to the save format (as R has already 3 formats to save objects).
Some reference here
Same view as the serialize output in ascii format (more readable to get how it is organized):
> write(rawToChar(serialize(ex,NULL,ascii=TRUE)),"")
A
2
197123
131840
787
2
16
3
262153
3
one
262153
4
five
262153
8
Whatever
14
3
1
2
3
1026
1
262153
5
names
16
2
262153
1
a
262153
1
b
1026
1
262153
9
row.names
13
2
NA
-3
1026
1
262153
5
class
16
1
262153
10
data.frame
254
