Displaying 20 results from an estimated 1000 matches similar to: "plyr version 1.6"
2011 Apr 11
0
plyr: version 1.5
# plyr
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise
2011 Apr 11
0
plyr: version 1.5
# plyr
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise
2011 Dec 30
0
Plyr 1.7
# plyr
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise
2011 Dec 30
0
Plyr 1.7
# plyr
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise
2010 Sep 10
0
plyr: version 1.2
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations
2010 Sep 10
0
plyr: version 1.2
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations
2011 Jan 04
0
plyr 1.4
# plyr
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise
2011 Jan 04
0
plyr 1.4
# plyr
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise
2009 Apr 15
0
plyr version 0.1.7
plyr is a set of tools for a common set of problems: you need to break
down a big data structure into manageable pieces, operate on each
piece and then put all the pieces back together. For example, you
might want to:
* fit the same model to subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations like scaling or standardising
*
2009 Apr 15
0
plyr version 0.1.7
plyr is a set of tools for a common set of problems: you need to break
down a big data structure into manageable pieces, operate on each
piece and then put all the pieces back together. For example, you
might want to:
* fit the same model to subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations like scaling or standardising
*
2010 Jul 27
0
plyr version 1.1
plyr is a set of tools for a common set of problems: you need to break
down a big data structure into manageable pieces, operate on each
piece and then put all the pieces back together. For example, you
might want to:
* fit the same model to subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations like scaling or standardising
2010 Jul 27
0
plyr version 1.1
plyr is a set of tools for a common set of problems: you need to break
down a big data structure into manageable pieces, operate on each
piece and then put all the pieces back together. For example, you
might want to:
* fit the same model to subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations like scaling or standardising
2009 Jun 23
0
plyr 0.1.9
plyr is a set of tools for a common set of problems: you need to break
down a big data structure into manageable pieces, operate on each
piece and then put all the pieces back together. For example, you
might want to:
* fit the same model to subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations like scaling or standardising
*
2009 Jun 23
0
plyr 0.1.9
plyr is a set of tools for a common set of problems: you need to break
down a big data structure into manageable pieces, operate on each
piece and then put all the pieces back together. For example, you
might want to:
* fit the same model to subsets of a data frame
* quickly calculate summary statistics for each group
* perform group-wise transformations like scaling or standardising
*
2012 Jan 12
1
parallel computation in plyr 1.7
Dear all,
I have a question regarding the possibility of parallel computation in plyr
version 1.7.
The help files of the following functions mention the argument '.parallel':
ddply, aaply, llply, daply, adply, dlply, alply, ldply, laply
However, the help files of the following functions do not mention this
argument: ?d_ply, ?aply, ?lply
Is it because parallel computation is not
2011 Oct 12
3
Applying function to only numeric variable (plyr package?)
My data frame consists of character variables, factors, and proportions,
something like
c1 <- c("A", "B", "C", "C")
c2 <- factor(c(1, 1, 2, 2), labels = c("Y","N"))
x <- c(0.5234, 0.6919, 0.2307, 0.1160)
y <- c(0.9251, 0.7616, 0.3624, 0.4462)
df <- data.frame(c1, c2, x, y)
pct <- function(x) round(100*x, 1)
I want to
2012 Jan 31
0
Error in linearHypothesis.mlm: The error SSP matrix is apparently of deficient rank
Hi,
I have encountered this error when attempting a One-way Repeated-measure ANOVA
with my data.
I have read the "Anova in car: SSPE apparently deficient rank" thread
by I'm not sure the within-subject interaction has more degrees of freedom
than subjects in my case.
I have prepared the following testing script:
rm(list = ls())
2013 Aug 30
0
ddply for comparing simulation results
This might do it:
> lhs=c('a','a','a','b')
> rhs=c('a','b','b','b')
>
>
> # function to determine differences
> f_diff <- function(l, r){
+ t_l <- table(l)
+ t_r <- table(r)
+ # compare 'l' to 'r'
+ sapply(names(t_l), function(x){
+ if (is.na(t_r[x])) return(t_l[x])
2010 Apr 07
1
unexpected behaviour with ddply and colwise
Hi,
I am confused by results from:
> ddply(aa, names(aa), colwise(sum))
I thought ddply was just calling colwise(sum)() with each column.
However ddply() returns a 13 x 5 result !!
The general result I expected is similar to that of apply() , or
using colwise(sum)() alone. Shouldn't ddply() produce the same ?
Thanks in advance for your help,
- Stuart Andrews
>
2010 Jun 09
2
Read in dataset without saving it
A simple question - I have a small dataset to read in and want to copy and
paste part from Excel and paste it into an R script file without creating
more files saving it as a .txt/.csv and then reading that in. I want to
read in 3 columns e.g.
1 2.5 3.4
1 2.3 3.1
1 2.6 3.9
2 2.9 2.8
2 2.6 2.9
2 2.7 2.9
3 2.3 3.3
3 2.4 3.0
3 2.7 3.2
I thought I could use scan() but don't know how to extend it