Displaying 16 results from an estimated 16 matches for "adpli".
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adcli
2017 Oct 11
6
changing "," to "." in data.frame
Dear friends - I have a data.frame with "," instead of "." and found the
discussion
http://r.789695.n4.nabble.com/How-to-replace-all-commas-with-semicolon-in-a-string-tt4721187.html#a4721192
so copying the code of Ulrik(I hope:-)) I tried
(making some data)
AX <-
2010 Dec 01
1
Difference between loops and vectorization
Hello R-helpers,
A fundamental question ...I'm trying to understand the differences
between loop and vectorization ... I understand that it should be a
natural choice to use apply / adply when it is needed to perform the
same function across all rows of a data frame.
Any pointers on why this is so? Unable to find the right reading place
on the WWW which explains the concept.
Thanks for your
2017 Oct 11
0
changing "," to "." in data.frame
And as follow up,
fff<-function(x) gsub(",", ".", x)
BX <- apply(apply(AX, 2, fff), 2, as.numeric)
this seems to be easier.
Cheers
Petr
S pozdravem | Best Regards
RNDr. Petr PIKAL
Vedouc? V?zkumu a v?voje | Research Manager
PRECHEZA a.s.
n?b?. Dr. Edvarda Bene?e 1170/24 | 750 02 P?erov | Czech Republic
Tel: +420 581 252 256 | GSM: +420 724 008 364
petr.pikal at
2011 May 16
1
Extracting the dimnames of an array with variable dimensions
Hi list,
In a function I am writing, I need to extract the dimension names of
an array. I know this can be acheived easily using dimnames() but my
problem is that I want my function to be robust when the number of
dimensions varies. Consider the following case:
foo <- array(data = rnorm(32), dim = c(4,4,2),
dimnames=list(letters[1:4], LETTERS[1:4], letters[5:6]))
# What I want is to extract
2010 Nov 16
1
Batch Processing Files
Hi All!
I have some experience with R, but less experience writing scripts using
R and have run into a challenge that I hope someone can help me with.
I have multiple .csv files of data each with the same 3 columns of
data, but potentially of varying lengths (some data files are from short
measurements, others from longer ones). One file for example might look
like this...
Time, O2_conc,
2011 Aug 05
2
Which is more efficient?
Greetings all,
I am curious to know if either of these two sets of code is more efficient?
Example1:
## t-test ##
colA <- temp [ , j ]
colB <- temp [ , k ]
ttr <- t.test ( colA, colB, var.equal=TRUE)
tt_pvalue [ i ] <- ttr$p.value
or
Example2:
tt_pvalue [ i ] <- t.test ( temp[ , j ], temp[ , k ], var.equal=TRUE)
-------------
I have three loops, i, j, k.
One to test the all of
2013 Apr 10
6
means in tables
Hi.
I have 2 tables, with same dimensions (8000 x 5). Something like:
tab1:
V1 V2 V3 V4 V5
14.23 1.71 2.43 15.6 127
13.20 1.78 2.14 11.2 100
13.16 2.36 2.67 18.6 101
14.37 1.95 2.50 16.8 113
13.24 2.59 2.87 21.0 118
tab2:
V1 V2 V3 V4 V5
1.23 1.1 2.3 1.6 17
1.20 1.8 2.4 1.2 10
1.16 2.6 2.7 1.6 11
1.37 1.5 2.0 1.8 13
1.24 2.9 2.7 2.0 18
I need generate a table of averages, the
2008 Sep 30
0
New package: plyr
plyr is a set of tools that solves a common set of problems: you need
to break a big problem down into manageable pieces, operate on each
pieces and then put all the pieces back together. It's already
possible to do this with split and the apply functions, but plyr just
makes it all a bit easier with:
* consistent names, arguments and outputs
* input from and output to data.frames,
2008 Sep 30
0
New package: plyr
plyr is a set of tools that solves a common set of problems: you need
to break a big problem down into manageable pieces, operate on each
pieces and then put all the pieces back together. It's already
possible to do this with split and the apply functions, but plyr just
makes it all a bit easier with:
* consistent names, arguments and outputs
* input from and output to data.frames,
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 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
2012 Apr 19
3
How to "flatten" a multidimensional array into a dataframe?
Hi,
I have a three dimensional array, e.g.,
my.array = array(0, dim=c(2,3,4), dimnames=list( d1=c("A1","A2"),
d2=c("B1","B2","B3"), d3=c("C1","C2","C3","C4")) )
what I would like to get is then a dataframe:
d1 d2 d3 value
A1 B1 C1 0
A2 B1 C1 0
.
.
.
A2 B3 C4 0
I'm sure there is one function to do
2013 Apr 29
0
interesting behavior from aaply
Dear helpers,
I'm using plyr to process a large matrix for the first time. My code is set
up to work with matrixes, since I learned the hard way that dataframes
are considerably slower to process.
I started using aaply(), but the data was rearranged from a flat matrix to
a [, , 4] array for larger input matrixes. I'm sure something clever is
happening that I'm just not seeing -
2013 Apr 09
4
Converting matrix to data frame without losing an assigned dimname
Hello All,
Would like to be able to convert a matrix to a dataframe without losing an assigned dimname.
Here is an example that should illustrate what I'm talking about.
tableData <- state.x77[c(7, 38, 20, 46), c(7, 1, 8)]
names(dimnames(tableData)) <- c("State", "")
tableData
State Frost Population Area
Connecticut 139 3100 4862
2010 Nov 09
3
Row-wise recurive function call
Dear Group,
I have a following dataset:
> a
A B C D
1 22 3 31 40
2 26 31 36 32
3 3 7 49 16
4 24 40 27 26
5 20 45 47 0
6 34 43 11 18
7 48 48 24 2
8 3 16 39 48
9 20 49 7 21
10 17 36 47 10
> dput(a)
structure(list(A = c(22L, 26L, 3L, 24L, 20L, 34L, 48L, 3L, 20L,
17L), B = c(3L, 31L, 7L, 40L, 45L, 43L, 48L, 16L, 49L, 36L),
C = c(31L, 36L, 49L, 27L, 47L, 11L, 24L,