Hello List, i have been agonizing over this for days, any reply would be
greatly appreciated!
Situation:___________________________________
My original dataset is a .csv dataset (w/ 2M records) with 4 variables:
job_id (Primary key, won't be used for analysis, just used for join tables),
sector_id (categorical variable, for 19 industry sectors),
sqft (con't variable for square footage),
building_type (categorical, for 2 building types)
some values of sqft were inputed wrong, so i'd like to set sqft<1 to "NA" and
then use aregImpute() to impute those NAs.
Problem: the origianl dataset(.csv format) is too large. though i could read
that dataset into R, i could not get aregImpute() run even i set the memory
limit to 3G ! (yes, i did the switch in windows to reach 3G rather than 2G)
Goal: try to find a way to slim down my dataset so as to get aregImpute()
running.
What i did:________________________________
i searched in the archive, and found someone said, as R tends to inflate
memory, it is a good idea to first read the original dataset into R--> then
save it as a more compact binary file using save() --> and then reload the
compact binary file back into R using load(). this way would reduce the memory
allocation.
HOWEVER, after i saved my original dataset into a compact binary file using
save(), and used "load("filename.Rdata") to reload the new compact data format
into R, I could not figure out how to retrive all my variables!!! R shows the
new dataset is not a list, nor a matrix, or a dataframe, but just a character
with length 1 !!! and there is no way i could do attach().
i generated a 1K-row subset out of my original dataset to illustrate my
problem (does anyone know how to get my four variables back from this "compact
binary" new dataset? what did i do wrong?):
> data <- read.table (file.choose(),header=T,sep=",")
> summary(data)
job_id sector_id sqft building_type
Min. : 1.0 Min. : 6.000 Min. : 0.00 Min. :1.000
1st Qu.: 250.8 1st Qu.: 6.000 1st Qu.: 3.00 1st Qu.:2.000
Median : 500.5 Median :11.000 Median : 4.00 Median :2.000
Mean : 500.5 Mean : 9.455 Mean : 12.49 Mean :1.996
3rd Qu.: 750.3 3rd Qu.:11.000 3rd Qu.: 4.00 3rd Qu.:2.000
Max. :1000.0 Max. :12.000 Max. :192.00 Max. :2.000
>
> attach(data)
> sqft[sqft<1] <- NA
> sector.f <- as.factor(sector_id)
> building_type.f <- as.factor (building_type)
> d <- data.frame(job_id,sector.f,sqft, building_type.f)
> summary (d)
job_id sector.f sqft building_type.f
Min. : 1.0 6 :340 Min. : 3.00 1: 4
1st Qu.: 250.8 11:505 1st Qu.: 4.00 2:996
Median : 500.5 12:155 Median : 4.00
Mean : 500.5 Mean : 14.16
3rd Qu.: 750.3 3rd Qu.: 17.00
Max. :1000.0 Max. :192.00
NA's :118.00
> save (d, file="compact_d.Rdata", ascii=FALSE)
>
> newdata <- load ("compact_d.Rdata")
>
> summary(newdata)
Length Class Mode
1 character character
> attach(newdata)
Error in attach(newdata) : file 'd' not found
> is.data.frame (newdata)
[1] FALSE
> is.list (newdata)
[1] FALSE
> is.matrix (newdata)
[1] FALSE
>
_________________________________
btw, i also tried to just save (into compact binary) and reload (the new
compact binary data format) (as i could do the "NA" stuff in sql anyhow).
however, i still got stucked at the same spot:
> data <- read.table (file.choose(),header=T,sep=",")
> summary(data)
job_id sector_id sqft building_type
Min. : 1.0 Min. : 6.000 Min. : 0.00 Min. :1.000
1st Qu.: 250.8 1st Qu.: 6.000 1st Qu.: 3.00 1st Qu.:2.000
Median : 500.5 Median :11.000 Median : 4.00 Median :2.000
Mean : 500.5 Mean : 9.455 Mean : 12.49 Mean :1.996
3rd Qu.: 750.3 3rd Qu.:11.000 3rd Qu.: 4.00 3rd Qu.:2.000
Max. :1000.0 Max. :12.000 Max. :192.00 Max. :2.000
> save (data, file="compact_data.Rdata", ascii=FALSE)
> newdata <- load ("compact_data.Rdata")
> summary(newdata)
Length Class Mode
1 character character
> attach(newdata)
Error: restore file may be empty -- no data loaded
In addition: Warning message:
file 'data' has magic number ''
Use of save versions prior to 2 is deprecated
> is.data.frame (newdata)
[1] FALSE
> is.list (newdata)
[1] FALSE
> is.matrix (newdata)
[1] FALSE
>
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