Displaying 20 results from an estimated 800 matches similar to: "Calculating frequencies of multiple values in 200 colomns"
2017 Nov 10
0
Calculating frequencies of multiple values in 200 colomns
Thank you for your effort Bert..,
I knew what is the problem now, the values (1,2,3) were only an example. The values I have are 0 , 1, 2 . Tabulate () function seem to ignore calculating the frequency of 0 values and this is my exact problem as the frequency of 0 values should also be calculated for the maf to be calculated correctly.
________________________________
From: Bert Gunter
2017 Nov 09
2
Calculating frequencies of multiple values in 200 colomns
Always reply to the list. I am not a free, private consultant!
"For example, if I have the values : 1 , 2 , 3 in each column, applying
Tabulate () would calculate the frequency of 1 and 2 without 3"
Huh??
> x <- sample(1:3,10,TRUE)
> x
[1] 1 3 1 1 1 3 2 3 2 1
> tabulate(x)
[1] 5 2 3
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people
2017 Nov 10
2
Calculating frequencies of multiple values in 200 colomns
|> x <- sample(0:2, 10, replace = TRUE)
|> x
[1] 1 0 2 1 0 2 2 0 2 1
|> tabulate(x)
[1] 3 4
|> table(x)
x
0 1 2
3 3 4
B.
> On Nov 10, 2017, at 4:32 AM, Allaisone 1 <allaisone1 at hotmail.com> wrote:
>
>
>
> Thank you for your effort Bert..,
>
>
> I knew what is the problem now, the values (1,2,3) were only an example. The values I have are
2017 Nov 10
0
Calculating frequencies of multiple values in 200 colomns
Hi,
To clarify the default behavior that Boris is referencing below, note the definition of the 'bin' argument to the tabulate() function:
bin: a numeric vector ***(of positive integers)***, or a factor. Long vectors are supported.
I added the asterisks for emphasis.
This is also noted in the examples used for the function in ?tabulate at the bottom of the help page.
The second
2007 Oct 19
0
calculating power of log rank test
hie
Im trying to calculate the power of the logrank test for different values of rho .I was just wandering whether the following programme would do it. any suggestions are welcome
s=50
number=1
count1=0;count2=0;count3=0;count4=0;count5=0;count6=0;count7=0;count7=0;
count8=0;count9=0
while(s!=0){
n=20
n1=n/2
2008 Feb 14
0
help in simplyfiying programme
my program given below can some one make it presentable. I trying to simulate survival data and calculate the power. I think i could have done better.
s=10
number=0
count1=0;count2=0;count3=0;count4=0;count5=0;count6=0;count7=0;count8=0;
count9=0;
count11=0;count22=0;count33=0;count44=0;count55=0;count66=0;count77=0;
count88=0;count99=0;
while(s!=0){
n=100
n1=n/2
n2=n/4
2012 Feb 10
3
problem subsetting data frame with variable instead of constant
Hello,
I've encountered a very weird issue with the method subset(), or maybe this
is something I don't know about said method that when you're subsetting
based on the columns of a data frame you can only use constants (0.1, 2.3,
2.2) instead of variables?
Here's a look at my data frame called 'ea.cad.pwr':
*>ea.ca.pwr[1:5,]
MAF OR POWER
1 0.02 0.01 0.9999
2 0.02
2010 Nov 29
2
FW: how to use by() ?
Thank you for the suggestion, Bill. The result is not quite what I would like. Here's sample code for you or anyone else who may be interested:
Al1 = c('A','C','C','C')
Al2 = c('G','G','G','T')
Freq1 = c(0.0078,0.0567,0.9434,0.9908)
MAF = c(0.0078,0.0567,0.0566,0.0092)
m1 = data.frame(Al1=Al1,
2017 Nov 18
0
Complicated analysis for huge databases
Something like the following?
AllMAFs <- list()
for (i in length(SeparatedGroupsofmealsCombs) {
AllMAFs[[i]] <- apply(SeparatedGroupsofmealsCombs[[i]], 2, function(x)maf(tabulate(x+1)))
}
(untested, of course)
Also the solution is a bit generic since I don't know what the output of maf() looks like in your case, and I don't understand why you use tabulate because I would have
2017 Nov 18
0
Complicated analysis for huge databases
On 18/11/2017 4:40 PM, Allaisone 1 wrote:
>
> The loop :
>
>
> AllMAFs <- list()
>
> for (i in length(SeparatedGroupsofmealsCombs) {
> AllMAFs[[i]] <- apply( SeparatedGroupsofmealsCombs[[i]], 2, function(x)maf( tabulate( x+1) ))
> }
>
>
> gives these errors (I tried this many times and I'm sure I copied it entirely) :-
>
> Error in
2017 Nov 18
2
Complicated analysis for huge databases
Although the loop seems to be formulated correctly I wonder why
it gives me these errors :
-object 'i' not found
- unexpected '}' in "}"
the desired output is expected to be very large as for each dataframe in the list of dataframes I expect to see maf value for each of the 600 columns! and this is only for
for one dataframe in the list .. I have around 150-200
2017 Nov 18
0
Complicated analysis for huge databases
> On Nov 18, 2017, at 1:52 AM, Allaisone 1 <allaisone1 at hotmail.com> wrote:
>
> Although the loop seems to be formulated correctly I wonder why
> it gives me these errors :
>
> -object 'i' not found
> - unexpected '}' in "}"
You probably did not copy the entire code offered. But we cannot know since you did not "show your code",
2017 Nov 19
1
Complicated analysis for huge databases
Thanks but a new error appeared with the loop :
Error in x + 1 : non-numeric argument to binary operator
I think this can be solved by converting columns (I,II,II,..600) into "numeric" instead of
the current "int" type as shown below in the structure of "33_55" dataframe .
$ 33_55:'data.frame': 256 obs. of 600 variables:
..$ MealsCombinations
2017 Nov 18
3
Complicated analysis for huge databases
The loop :
AllMAFs <- list()
for (i in length(SeparatedGroupsofmealsCombs) {
AllMAFs[[i]] <- apply( SeparatedGroupsofmealsCombs[[i]], 2, function(x)maf( tabulate( x+1) ))
}
gives these errors (I tried this many times and I'm sure I copied it entirely) :-
Error in apply(SeparatedGroupsofmealsCombs[[i]], 2, function(x) maf(tabulate(x + :
object 'i' not found
> }
2017 Nov 18
2
Complicated analysis for huge databases
Thanks Boris , this was very helpful but I'm struggling with the last part.
1) I combined the first 2 columns :-
library(tidyr)
SingleMealsCode <-unite(MyData, MealsCombinations, c(MealA, MealB), remove=FALSE)
SingleMealsCode <- SingleMealsCode[,-2]
2) I separated this dataframe into different dataframes based on "MealsCombination"
column so R will recognize each meal
2005 Jul 15
1
2D contour predictions
Hi All
I have been fitting regression models and would now like to produce some
contour & image plots from the predictors.
Is there an easy way to do this? My current (newbie) experience with R
would suggest there is but that it's not always easy to find it!
f3 <- lm( fc ~ poly( speed, 2 ) + poly( torque, 2 ) + poly( sonl, 2 ) +
poly( p_rail, 2 ) + poly( pil_sep, 2 ) + poly( maf, 2
2017 Nov 17
3
Complicated analysis for huge databases
Hi all ..,
I have a large dataset of around 600,000 rows and 600 columns. The first col is codes for Meal A, the second columns is codes for Meal B. The third column is customers IDs where each customer had a combination of meals. Each column of the rest columns contains values 0,1,or 2. The dataset is organised in a way so that the first group of customers had similar meals combinations, this
2017 Nov 17
0
Complicated analysis for huge databases
Combine columns 1 and 2 into a column with a single ID like "33.55", "44.66" and use split() on these IDs to break up your dataset. Iterate over the list of data frames split() returns.
B.
> On Nov 17, 2017, at 12:59 PM, Allaisone 1 <allaisone1 at hotmail.com> wrote:
>
>
> Hi all ..,
>
>
> I have a large dataset of around 600,000 rows and 600
2003 Sep 17
2
CART analysis
Greetings,
Does anyone know of an R code for classification and regression tree
analysis (CART)?
Thank you
Ron
Ron Thornton BVSc, PhD, MACVSc (pathology, epidemiology)
Programme Co-ordinator, Active Surveillance
Animal Biosecurity
MAF Biosecurity Authority
P O Box 2526
Wellington, New Zealand
phone: 64-4-4744156
027 223 7582
fax: 64-4-474-4133
e-mail: ron.thornton at maf.govt.nz
2007 Jan 21
2
efficient code. how to reduce running time?
Hi,
I am new to R.
and even though I've made my code to run and do what it needs to .
It is taking forever and I can't use it like this.
I was wondering if you could help me find ways to fix the code to run
faster.
Here are my codes..
the data set is a bunch of 0s and 1s in a data.frame.
What I am doing is this.
I pick a column and make up a new column Y with values associated with that