Hi all,
I have the following sql query that I am executing on a machine with single
core. I want to know how can I execute the same sqery on a maching that is
running with 4 cores. Please provide me the code.
NEW_TABLE - rhive.query(SELECT A, B, COUNT(C) FROM TABLE_A WHERE
A='01-01-2012')
Hi all,
I have the following sql query that I am executing on a machine with single
core. I want to know how can I execute the same sqery on a maching that is
running with 4 cores. Please provide me the code.
NEW_TABLE - rhive.query(SELECT A, B, COUNT(C) FROM TABLE_A WHERE
A='01-01-2012')
Also
Hi All,
I am trying to install RODBC Package in Linux version of R. PFB the error
message. Request all, if you have any solution how to overcome this error.
configure: error: ODBC headers sql.h and sqlext.h not found
Regards,
Madana
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Hi All,
I want to join a table (Dataset) that is created in R with a table that is
in oracle database. Can some one help me in accomplishing this task in R?
Example Code:
library(RODBC)
DB_CONNECT - odbcConnect(DSN_NAME)
TABLE_JOIN - sqlQuery(DB_CONNECT, SELECT * FROM DB_TABLE WHERE COL_1 NOT
data frame
Hi Madana,
No example data, so untested, but I think this will do what you want:
do.call(rbind, DF)
Cheers,
Josh
On Sun, Jul 24, 2011 at 5:38 PM, Madana_Babu [hidden
email]/user/SendEmail.jtp?type=nodenode=3691342i=0 wrote:
Hi all,
I have multiple data frames created with equal
This is working for me... however when i use multicore there are more than
60K DF's getting created and when i use do.call() function it is taking huge
time. Is there any function which can perform this operation at faster rate?
Regards,
Madana
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check the components of DF by DF[[1]], DF[[2]] ..., which is a bit
different from rbind
#feel free to add more arguments to function read.data.example and add those
to mclapply accordingly
Hope this helps.
Regards,
Lei
On Fri, Jul 22, 2011 at 11:35 AM, Madana_Babu [hidden
email]/user
Hi all,
I have multiple data frames created with equal number of columns in each
data frame by using mclapply() on multicore processor. The data frames are
like DF[[1]], DF[[2]], ... DF[[150]]. Now i want to bind (Similar like
rbind()) all these data frames and create one single data frame called
Hi,
Can you please explain me that how can i perform this on a multicore
processor? since i have a machine with 16-cores. I can do this much faster
if i use all cores.
Thanks in advance...
Regards,
Madana
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Hi all,
Currently i am trying to this on R which is running on multicore processor.
I am not sure how to use mclapply() function on this task. Can anyone help
me.
# Setting up directory
setwd(/XXX////2011/07/20)
library(sqldf)
# Data is available in the form of multiple
Hi all,
Currently i am trying this on R which is running on multicore processor. I
am not sure how to use mclapply() function on this task. Can anyone help me.
# Setting up directory
setwd(/XXX////2011/07/20)
library(sqldf)
# Data is available in the form of multiple
Hi all,
I have R installed on a box, which is running on a machine with 16 core and
Redhat - Linux. I am handling huge (size of dataset will be 5 GB) dataset.
Lets assume that my data is in the form of structured (multiple) logs. I
access the data by using all.files(). Since by default basic
Hi David,
PFB the details of my query. Request your help in getting this resolved.
# TESTING is my dataset with almost 40K rows. I am importing this dataset
from my local desktop
TESTING - read.table(/Users/madana/Desktop/testing.txt, header=FALSE,
sep=\t, na.strings=, dec=.,
Hi,
This is working with when i have few lines and when i give those input lines
in R window. But i want to apply this function on a variable which is a part
of dataset and the data set is very large in size. Any help in this aspect
will really help me a lot.
Regards,
Madana
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Hi i have the data in the following format:
rent,100,1,common,674
pipe,200,0,usual,864
car,300,1,uncommon,392:jump,700,0,common,664
car,200,1,uncommon,864:snap,900,1,usual,746
stint,600,1,uncommon,257
pull,800,0,usual,594
where as i want the above 6 lines data into 8 lines as below (Spliting row
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