[R] How do we do correlation for big matrices?

Peter Langfelder
Sat, 26 Dec 2015 23:44:46 -0000

My guess is that a mapply would take forever to run. I would split it
up into smaller blocks - not too large so the calculation can fit into
the RAM, and not too small to make the calculation tun too long. Say
500 columns per block, that way each correlation matrix takes up
500*500*8 bytes = 1.9 MB, so a even the full 1000 blocks would fit
into a reasonably sized RAM (hopefully R will do a garbage collection
from time to time anyway). At the risk of tooting my own horn,

library(WGCNA) ## For allocateJobs
n = ncol(df1)
blocks = allocateJobs(n, 1000) # With 1000 blocks, roughly 500 columns
per block...
results.lst = lapply(blocks, function(index) diag(cor(df1[, index],
df2[, index])));
result = unlist(results.lst)

I haven't tested this code, but it shouldn't be too far from correct.

On Sat, Dec 26, 2015 at 11:14 AM, William Dunlap via R-help
<r-help at r-project.org> wrote:
> Since you only want the diagonal of the correlation matrix, the following
> will probably
> do the job using less memory.  The mapply versions works on the data.frames
> you supplied, but will not work on matrices - be careful not to conflate
> the two classes of data objects.
>
>   > vapply(colnames(df1), function(i)cor(df1[,i],df2[,i]), 0)
>          site1        site2        site3        site4        site5
>  site6        site7
>   -0.540644946  0.006898188 -0.035279748 -0.261648270  0.274059055
> -0.076396648   -0.147696334
>          site8        site9       site10
>   -0.138916728  0.330632540  0.366095090
>   > mapply(FUN=cor, df1, df2)
>          site1        site2        site3        site4        site5
>  site6        site7
>   -0.540644946  0.006898188 -0.035279748 -0.261648270  0.274059055
> -0.076396648   -0.147696334
>          site8        site9       site10
>   -0.138916728  0.330632540  0.366095090
> Compare to your:
>   > diag(cor(df1,df2))
>          site1        site2        site3        site4        site5
>  site6        site7
>   -0.540644946  0.006898188 -0.035279748 -0.261648270  0.274059055
> -0.076396648   -0.147696334
>          site8        site9       site10
>   -0.138916728  0.330632540  0.366095090
>
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com
>
> On Sat, Dec 26, 2015 at 10:55 AM, Marna Wagley <marna.wagley at
gmail.com>
> wrote:
>
>> Hi R users,
>> I have a very big two matrices of 12 columns and over 0.5 million
columns
>> (50,4710) and trying to get correlation value between two tables but I
>> could not compute it because of big files.
>> Would you give me any suggestion on how I can do the correlations for
the
>> big files?
>>
>> I used the following codes and the example data.
>>
>> df1<-structure(list(X = structure(c(1L, 5L, 6L, 7L, 8L, 9L, 10L,
11L,
>> 12L, 2L, 3L, 4L), .Label = c("env1", "env10",
"env11", "env12",
>> "env2", "env3", "env4", "env5",
"env6", "env7", "env8", "env9"
>> ), class = "factor"), site1 = c(0.38, 0.83, 0.53, 0.48, 0.66,
>> 0.09, 0.21, 0.02, 0.76, 0.62, 0.2, 0.47), site2 = c(0.19, 0.14,
>> 0.66, 0.35, 0.18, 0.24, 0.18, 0.2, 0.86, 0.06, 0.51, 0.29), site3 =
c(0.95,
>> 0.51, 0.91, 0.48, 0.74, 0.67, 0.34, 0.72, 0.43, 0.49, 0.1, 0.48
>> ), site4 = c(0.89, 0.54, 0.93, 0.18, 0.99, 0.21, 0.69, 0.29,
>> 0.89, 0.84, 0.45, 0.2), site5 = c(0.38, 0.37, 0.01, 0.26, 0.97,
>> 0.49, 0.39, 0.31, 0.14, 0.83, 0.99, 0.2), site6 = c(0.68, 0.67,
>> 0.6, 0.92, 0.01, 0.04, 0.49, 0.38, 0.5, 0.37, 0.51, 0.17), site7 =
c(0.08,
>> 0.54, 0.31, 0.3, 0.77, 0.39, 0.03, 0.51, 0.28, 0.32, 0.86, 0.95
>> ), site8 = c(0.54, 0.26, 0.87, 0.91, 0.12, 0.51, 0.31, 0.67,
>> 0.69, 0.79, 0.76, 0.08), site9 = c(0.1, 0.68, 0.17, 0.44, 0.78,
>> 0.9, 0.16, 0.31, 0.13, 0.34, 0.9, 0.16), site10 = c(0.53, 0.31,
>> 0.88, 0.61, 0.92, 0.44, 0.92, 0.94, 0.55, 0.8, 0.27, 0.07)), .Names
>> c("X",
>> "site1", "site2", "site3",
"site4", "site5", "site6", "site7",
>> "site8", "site9", "site10"), class =
"data.frame", row.names = c(NA,
>> -12L))
>> df1<-df1[-1]
>>
>> df2<-structure(list(X = structure(c(1L, 5L, 6L, 7L, 8L, 9L, 10L,
11L,
>> 12L, 2L, 3L, 4L), .Label = c("env1", "env10",
"env11", "env12",
>> "env2", "env3", "env4", "env5",
"env6", "env7", "env8", "env9"
>> ), class = "factor"), site1 = c(0.36, 0.29, 0.09, 0.07, 0.82,
>> 0.88, 0.59, 0.57, 0.2, 0.29, 0.76, 0.2), site2 = c(0.91, 0.87,
>> 0.91, 0.54, 0.53, 0.2, 0.23, 0.16, 0.42, 0.44, 0.01, 0.29), site3 =
c(0.96,
>> 0.56, 0.34, 0.34, 0.6, 0.63, 0.28, 0.25, 0.73, 0.45, 0.88, 0.39
>> ), site4 = c(0.73, 0.79, 0.39, 0.59, 0.63, 0.24, 0.69, 0.94,
>> 0.07, 0.23, 0.01, 0.99), site5 = c(0.88, 0.18, 0.37, 0.24, 0.61,
>> 0.61, 0.54, 0.71, 0.12, 0.82, 0.26, 0.5), site6 = c(0.43, 0.52,
>> 0.01, 0.76, 0.41, 0.57, 0.08, 0.75, 0.82, 0.98, 0.61, 0.74),
>>     site7 = c(0.84, 0.14, 0.96, 0.04, 0.41, 0.84, 0.26, 0.59,
>>     0.29, 0.3, 0.76, 0.05), site8 = c(0.12, 0.18, 0.75, 0.23,
>>     0.96, 0.64, 0.33, 0.61, 0.25, 0.13, 0.99, 0.6), site9 = c(0.26,
>>     0.58, 0.32, 0.67, 0.11, 0.8, 0.87, 0.05, 0.03, 0.47, 0.95,
>>     0.81), site10 = c(0.94, 0.63, 0.64, 0.5, 0.94, 0.75, 0.44,
>>     0.57, 0.19, 0.23, 0.08, 0.18)), .Names = c("X",
"site1",
>> "site2", "site3", "site4",
"site5", "site6", "site7", "site8",
>> "site9", "site10"), class = "data.frame",
row.names = c(NA, -12L
>> ))
>> df2<-df2[-1]
>> df2
>> # here I put only 12 columns, but as I mentioned above I have more than
1/2
>> million columns
>> cor_site<-data.matrix(diag(cor(df1,df2)))
>> It works fine for a small data but this big files did not work.
>>
>> Thanks for your suggestions.
>> MW
>>
>>         [[alternative HTML version deleted]]
>>
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>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.