问题
I'm trying to use the R code from this answer to convert a bunch of rdata files to CSV.
resave <- function(file){
e <- new.env(parent = emptyenv())
load(file, envir = e)
objs <- ls(envir = e, all.names = TRUE)
for(obj in objs) {
.x <- get(obj, envir =e)
message(sprintf('Saving %s as %s.csv', obj,obj) )
write.csv(.x, file = paste0(obj, '.csv'))
}
}
resave('yourData.RData')
However on one of the files I'm getting this error:
Error in data.frame(`2` = list(pos = c(6506L, 6601L, 21801L, 21811L, 21902L, :
arguments imply differing number of rows: 7670, 9729, 114, 2422
Calls: resave ... as.data.frame -> as.data.frame.list -> eval -> eval -> data.frame
I tried searching for the error message but I can't really make heads or tails of it.
Was that rdata file created improperly somehow?
Is there a better way I should convert arbitrary Rdata files to CSV? (I Don't know the names of the objects inside the files ahead of time.)
Update:
Here's what I'm seeing in that rdata file. If it's any help?? (Keep in mind I can't really edit the rdata files so I'm trying to figure out something that will convert them to CSV as is.)
> load("indiv8-hmmprob.RData")
> ls()
[1] "dataa"
> write.csv(dataa, file="greg.csv")
Error in data.frame(`2` = list(pos = c(6506L, 6601L, 21801L, 21811L, 21902L, :
arguments imply differing number of rows: 7670, 9729, 114, 2422
> names(dataa)
[1] "2" "3" "4" "X"
> str(dataa)
List of 4
$ 2:'data.frame': 7670 obs. of 23 variables:
..$ pos : int [1:7670] 6506 6601 21801 21811 21902 21931 22487 24071 26674 26713 ...
..$ ref : chr [1:7670] "C" "A" "G" "A" ...
..$ cons : chr [1:7670] "T" "T" "A" "G" ...
..$ reads : chr [1:7670] "ttt" "tttt" "AAAAA" "GGGGG" ...
..$ quals : chr [1:7670] "FBB" "IIIB" "IFIII" "FFIII" ...
..$ A : int [1:7670] 0 0 5 0 0 0 1 0 0 1 ...
..$ C : int [1:7670] 0 0 0 0 0 0 0 0 2 0 ...
..$ G : int [1:7670] 0 0 0 5 11 0 0 0 0 0 ...
..$ T : int [1:7670] 3 4 0 0 0 10 0 2 0 0 ...
..$ N : int [1:7670] 0 0 0 0 0 0 0 0 0 0 ...
..$ bad : chr [1:7670] NA NA NA NA ...
..$ par1ref : chr [1:7670] "C" "A" "G" "A" ...
..$ par2ref : chr [1:7670] "T" "T" "A" "G" ...
..$ read : Factor w/ 8397 levels "1","2","3","4",..: 2 2 3 3 3 3 4 7 9 9 ...
..$ count : int [1:7670] 3 4 5 5 11 10 1 2 2 1 ...
..$ read_allele : chr [1:7670] "T" "T" "A" "G" ...
..$ Pr(y| par1/par1 ): num [1:7670] 9.30e-04 5.69e-04 3.47e-04 1.42e-04 1.90e-08 ...
..$ Pr(y| par1/par2 ): num [1:7670] 4.58e-02 1.64e-02 2.41e-03 4.09e-03 8.89e-07 ...
..$ Pr(y| par2/par2 ): num [1:7670] 1.61e-01 8.40e-02 8.94e-03 2.09e-02 3.29e-06 ...
..$ est : int [1:7670] 3 3 3 3 3 3 3 3 3 3 ...
..$ Pr( par1/par1 |y): num [1:7670] 4.67e-25 2.25e-27 1.98e-31 2.93e-32 2.82e-34 ...
..$ Pr( par1/par2 |y): num [1:7670] 2.95e-11 2.86e-11 2.49e-14 1.98e-14 1.08e-14 ...
..$ Pr( par2/par2 |y): num [1:7670] 1 1 1 1 1 ...
..- attr(*, "badpos")= int [1:11386] 21900 21905 22840 24029 27149 27170 28024 42187 46927 46990 ...
$ 3:'data.frame': 9729 obs. of 23 variables:
..$ pos : int [1:9729] 6001 22537 25304 27228 28817 28842 30540 48903 48938 48943 ...
..$ ref : chr [1:9729] "A" "A" "A" "C" ...
..$ cons : chr [1:9729] "A" "G" "T" "C" ...
..$ reads : chr [1:9729] "," "GGG" "TTTTT" "," ...
..$ quals : chr [1:9729] "F" "BBB" "BFFFF" "B" ...
..$ A : int [1:9729] 1 0 0 0 0 0 0 0 0 0 ...
..$ C : int [1:9729] 0 0 0 1 1 0 0 0 0 1 ...
..$ G : int [1:9729] 0 3 0 0 0 0 0 0 0 0 ...
..$ T : int [1:9729] 0 0 5 0 0 1 1 1 1 0 ...
..$ N : int [1:9729] 0 0 0 0 0 0 0 0 0 0 ...
..$ bad : chr [1:9729] NA NA NA NA ...
..$ par1ref : chr [1:9729] "A" "A" "A" "C" ...
..$ par2ref : chr [1:9729] "G" "G" "T" "T" ...
..$ read : Factor w/ 10640 levels "1","2","3","4",..: 1 3 4 5 7 7 8 10 10 10 ...
..$ count : int [1:9729] 1 3 5 1 1 1 1 1 1 1 ...
..$ read_allele : chr [1:9729] "A" "G" "T" "C" ...
..$ Pr(y| par1/par1 ): num [1:9729] 0.969856 0.002707 0.000372 0.969639 0.969856 ...
..$ Pr(y| par1/par2 ): num [1:9729] 0.48995 0.0567 0.00228 0.48988 0.48995 ...
..$ Pr(y| par2/par2 ): num [1:9729] 0.01005 0.26071 0.00798 0.01012 0.01005 ...
..$ est : int [1:9729] 1 3 3 1 1 1 1 3 1 3 ...
..$ Pr( par1/par1 |y): num [1:9729] 2.18e-10 2.82e-11 2.67e-11 2.65e-11 2.63e-11 ...
..$ Pr( par1/par2 |y): num [1:9729] 0.688 0.688 0.688 0.688 0.688 ...
..$ Pr( par2/par2 |y): num [1:9729] 0.312 0.312 0.312 0.312 0.312 ...
..- attr(*, "badpos")= int [1:13707] 25259 27250 27810 27880 27888 28836 30507 48975 55998 58734 ...
$ 4:'data.frame': 114 obs. of 23 variables:
..$ pos : int [1:114] 21119 21194 42177 64136 64146 74463 74465 74521 79860 79884 ...
..$ ref : chr [1:114] "T" "T" "C" "C" ...
..$ cons : chr [1:114] "C" "A" "Y" "Y" ...
..$ reads : chr [1:114] "cCCCCCCCCCCCCCcc" "aa" "T" "T" ...
..$ quals : chr [1:114] "IBFFBFBFFFFFFBBF" "FF" "F" "I" ...
..$ A : int [1:114] 0 2 0 0 0 0 0 0 2 0 ...
..$ C : int [1:114] 16 0 0 0 1 0 1 1 0 0 ...
..$ G : int [1:114] 0 0 0 0 0 0 0 0 0 2 ...
..$ T : int [1:114] 0 0 1 1 0 1 0 0 0 0 ...
..$ N : int [1:114] 0 0 0 0 0 0 0 0 0 0 ...
..$ bad : chr [1:114] NA NA NA NA ...
..$ par1ref : chr [1:114] "T" "T" "C" "C" ...
..$ par2ref : chr [1:114] "C" "A" "T" "T" ...
..$ read : Factor w/ 130 levels "1","2","3","4",..: 3 3 6 8 8 10 10 10 14 14 ...
..$ count : int [1:114] 16 2 1 1 1 1 1 1 2 2 ...
..$ read_allele : chr [1:114] "C" "A" "T" "T" ...
..$ Pr(y| par1/par1 ): num [1:114] 9.34e-12 4.99e-03 1.00e-02 1.00e-02 1.00e-02 ...
..$ Pr(y| par1/par2 ): num [1:114] 4.56e-10 2.33e-01 4.90e-01 4.90e-01 4.90e-01 ...
..$ Pr(y| par2/par2 ): num [1:114] 9.04e-10 8.61e-01 9.70e-01 9.70e-01 9.70e-01 ...
..$ est : int [1:114] 3 3 3 3 3 3 3 3 3 3 ...
..$ Pr( par1/par1 |y): num [1:114] 6.50e-24 4.49e-24 1.10e-26 2.53e-31 1.51e-31 ...
..$ Pr( par1/par2 |y): num [1:114] 1.56e-10 1.54e-10 5.77e-11 6.60e-12 6.59e-12 ...
..$ Pr( par2/par2 |y): num [1:114] 1 1 1 1 1 ...
..- attr(*, "badpos")= int [1:73] 16621 16638 34177 34180 74448 74464 78954 79664 80045 94170 ...
$ X:'data.frame': 2422 obs. of 23 variables:
..$ pos : int [1:2422] 34630 45427 70728 70744 166279 189892 207276 207424 213012 232229 ...
..$ ref : chr [1:2422] "T" "G" "G" "C" ...
..$ cons : chr [1:2422] "T" "G" "G" "C" ...
..$ reads : chr [1:2422] "a" "..." "^F." "." ...
..$ quals : chr [1:2422] "<" "IIF" "F" "B" ...
..$ A : int [1:2422] 1 0 0 0 0 0 0 4 0 1 ...
..$ C : int [1:2422] 0 0 0 1 1 0 2 0 0 0 ...
..$ G : int [1:2422] 0 3 1 0 0 1 0 1 1 0 ...
..$ T : int [1:2422] 0 0 0 0 0 0 0 0 0 0 ...
..$ N : int [1:2422] 0 0 0 0 0 0 0 0 0 0 ...
..$ bad : chr [1:2422] NA NA NA NA ...
..$ par1ref : chr [1:2422] "T" "G" "G" "C" ...
..$ par2ref : chr [1:2422] "A" "A" "A" "T" ...
..$ read : Factor w/ 2433 levels "1","2","3","4",..: 1 6 8 8 13 16 18 18 19 20 ...
..$ count : int [1:2422] 1 3 1 1 1 1 2 5 1 1 ...
..$ read_allele : chr [1:2422] "A" "G" "G" "C" ...
..$ Pr(y| par1/par1 ): num [1:2422] 0.0105 0.2732 0.9699 0.9696 0.9699 ...
..$ Pr(y| par1/par2 ): num [1:2422] 0.4895 0.0642 0.49 0.4899 0.49 ...
..$ Pr(y| par2/par2 ): num [1:2422] 0.96856 0.00134 0.01005 0.01012 0.01005 ...
..$ est : int [1:2422] 3 1 1 1 1 1 1 1 1 1 ...
..$ Pr( par1/par1 |y): num [1:2422] 1 1 1 1 1 ...
..$ Pr( par1/par2 |y): num [1:2422] 3.70e-08 2.00e-08 1.06e-08 1.06e-08 1.59e-09 ...
..$ Pr( par2/par2 |y): num [1:2422] 3.70e-18 9.35e-20 2.36e-23 2.23e-23 3.26e-26 ...
..- attr(*, "badpos")= int [1:2327] 34776 45619 86591 86607 166220 193151 193159 212997 232221 233552 ...
回答1:
That answer was designed to handle object of class-'data.frame'. You only have an object of class-'list' which happens to have items that are dataframes. So there isn't an object with the name "2" in you workspace but there is an element in the 'dataa'-list that is named "2" and all of the other elements appear to also be dataframes, so why not use:
lapply( names(dataa), function(nam) write.csv( data[[nam]], file=paste0(nam, ".Rdata") ) )
回答2:
I'll vote for the other answer, but here's some almost working code:
resave <- function(file){
e <- new.env(parent = emptyenv())
load(file, envir = e)
obj <- get('dataa', envir =e)
lapply( names(obj), function(nam) {
write.csv( obj[[nam]], file=paste(nam, ".csv", sep="") )
cat(sprintf('%s.csv
', nam) )
}
)
}
resave("indiv8-hmmprob.RData")
Here's the output. which works but it's throwing in some wierd printed stuff at the end, the [[1]] NULL, etc.
2.csv
3.csv
4.csv
X.csv
[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL
来源：https://stackoverflow.com/questions/23413728/converting-rdata-files-to-csv-error-in-data-frame-arguments-imply-differing-nu