Yet another solution. This time using the LaF package:
library(LaF)
d<-c(1,4,7,8)
P1 <- laf_open_csv("M1.csv", column_types=rep("double", 10), skip=1)
P2 <- laf_open_csv("M2.csv", column_types=rep("double", 10), skip=1)
for (i in d) {
M<-data.frame(P1[, i],P2[, i])
}
(The skip=1 is needed as l
On Tue, Nov 15, 2011 at 9:44 AM, Juliet Hannah wrote:
> In the solution below, what is the advantage of using "0L".
>
> M0 <- read.csv("M1.csv", nrows = 1)[0L, ]
>
As mentioned, you will find quite a bit of additional info on the
sqldf home page but to address the specific question regarding the
In the solution below, what is the advantage of using "0L".
M0 <- read.csv("M1.csv", nrows = 1)[0L, ]
Thanks!
2011/11/8 Gabor Grothendieck :
> 2011/11/8 Sergio René Araujo Enciso :
>> Dear all:
>>
>> I have two larges files with 2000 columns. For each file I am
>> performing a loop to extract t
have you considered reading in the data and then creating objects for each
column and then saving (save) each to disk. That way you incur the expense of
the read once and now have quick access (?load) to the column as you need them.
You could also use a database for this.
On Nov 8, 2011, at 5:
2011/11/8 Sergio René Araujo Enciso :
> Dear all:
>
> I have two larges files with 2000 columns. For each file I am
> performing a loop to extract the "i"th element of each file and create
> a data frame with both "i"th elements in order to perform further
> analysis. I am not extracting all the "i
Dear all:
I have two larges files with 2000 columns. For each file I am
performing a loop to extract the "i"th element of each file and create
a data frame with both "i"th elements in order to perform further
analysis. I am not extracting all the "i"th elements but only certain
which I am indicati
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