I want to create a covariance matrix from a data frame which is not yet suitable for creating one.
After using RPostgreSQL to query the database I have a data frame of the following type:
pg_id item_id value date
1 67808755896 23.5 2016-11-12
2 223337345 0 2016-11-12
3 254337000000 1 2016-11-12
4 34604777037 0 2016-11-12
5 142223438000 14.3 2016-11-12
6 170555690000 22 2016-11-12
The entire data frame is of ~500 000 rows with roughly 16 000 item_id's. The item_id's are repeated (looking back a couple of months here).
What I want to do eventually is to create a covariance matrix for the values of the item_id's.
In order to to so, as a first step I want to re-arrange the data frame in a way that I end up with a data frame that would look like the following:
item_id
date 67808755896 223337345 254337000000 ...
2016-11-12 value value value
2016-11-12 value value value
2016-11-12 value value value
2016-11-12 value value value
2016-11-12 value value value
2016-11-12 value value value
My problem is, that I don't know of a way to reorder the data frame the way I need to.
If there is a SQL query that would give me the option at the time of retrieval to get the desired structure, I guess that would be best.
Within RI tried a couple of things from using melt as well as spread but the computation seemed to be to heavy for my local mac which the last time I tried it just shut down at some point.
Thanks in advance for any help!
In R, this should run pretty fast:
library(data.table)
set.seed(1)
n_items <- 15996L; n_days <- floor(500000/n_items)
df <- data.frame(
item_id = 1:n_items,
date = rep(seq(Sys.Date(), Sys.Date()+n_days, by=1), each=n_items)
)
df$value <- runif(nrow(df))
dim(df)
# [1] 511872 3
uniqueN(df$item_id)
# [1] 15996
setDT(df)
system.time(wide <- dcast(df, date~item_id, value.var = "value", fun.aggregate = mean))
# User System verstrichen
# 0.19 0.00 0.20
wide[1:5, 1:5]
# date 1 2 3 4
# 1: 2017-01-05 0.26550866 0.3721239 0.57285336 0.9082078
# 2: 2017-01-06 0.09235838 0.3801334 0.03702181 0.5900971
# 3: 2017-01-07 0.24687042 0.9922133 0.53181526 0.5044988
# 4: 2017-01-08 0.29523145 0.2263145 0.33291640 0.1165338
# 5: 2017-01-09 0.83870267 0.3274892 0.95595348 0.3889042
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