search for: numcolwise

I'm trying to use ddply to compute summary statistics for many variables splitting on the variable site. however, it seems to work fine for mean() but if i use max() or min() things fall apart. whats going on? test.set<-data.frame(site=1:10,x=.Random.seed[1:100],y=rnorm(100)) means<-ddply(test.set,.(site),mean) means site x y 1 1 -97459496 -0.14826303 2

I've got a problem with the sparseby command (reshape library), and I have reached the peak of my R knowledge (it isn't really that high). I have a small data frame of 23 rows and 15 columns, here is a subset, the first four columns are factors and the rest are numeric (only one, line54 is provided). bearID YEAR Season SEX line54 5 1900 8 3 0 16.3923519 11 2270

I want to use aggregate with the mean function on specific columns gender <- factor(c("m", "m", "f", "f", "m")) student <- c(0001, 0002, 0003, 0003, 0001) score <- c(50, 60, 70, 65, 60) basicSub <- data.frame(student, gender, score) basicSubMean <- aggregate(basicSub, by=list(basicSub$student), FUN=mean, na.rm=TRUE) This

...oid using images to show dataset. Also, please read the posting guide esp. regarding home work, assignments etc.) res <- sapply(Gene[,-1],function(x) tapply(x,list(Gene$Genotype),mean)) #or res2 <-? aggregate(.~Genotype, data=Gene,mean) #or library(plyr) ?res3 <- ddply(Gene,.(Genotype),numcolwise(mean)) identical(res2,res3) #[1] TRUE resNew <- data.frame(Genotype=rownames(res),res,stringsAsFactors=FALSE) ?attr(resNew,"row.names") <- attr(res2,"row.names") ?identical(resNew,res2) #[1] TRUE A.K. So I'm having a problem with the part b of the assignment....

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

Hi R-users, I want to apply a function to each column of a data frame that is numeric. Thus I tried to check it for each column first: > apply(df, 2, function(x) is.numeric(x)) A60 A64 A66a A67 A71 A75a A80 A85 A91 A95 A96 A97 A98 A99 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

Does anyone know if there is an R function that will take a matrix like this jim 1 0 0 0 0 0 jim 0 1 0 0 0 0 jim 0 0 1 0 0 0 bob 1 0 0 0 0 0 bob 0 0 1 0 0 0 harry 0 0 1 0 0 0 harry 0 0 0 1 0 0 harry 0 0 0 0 1 0 harry 0 0 0 0 0 1 and make it like this? (that is, add together rows that have the same name?) jim 1 1 1 0 0 0 bob

...:2]) ?identical(res1,res2) #[1] TRUE # Also if you need to find means for each group ("Ungrazed vs. "Grazed") by(mydata[,-3],mydata[,3],colMeans) #or if column names are "V1", "V2", "V3" aggregate(.~V3,mydata,mean) #or library(plyr) ?ddply(mydata,.(V3),numcolwise(mean)) A.K. I have a data set with two columns of data that I want to find the mean of. ? 1 ? 6.225 ?59.77 Ungrazed 2 ? 6.487 ?60.98 Ungrazed 3 ? 4.919 ?14.73 Ungrazed 4 ? 5.130 ?19.28 Ungrazed 5 ? 5.417 ?34.25 Ungrazed 6 ? 5.359 ?35.53 Ungrazed 7 ? 7.614 ?87.73 Ungrazed 8 ? 6.352 ?63.2...

...values.? You didn't provide information on how to select those unique genes.? Here, the first row of every duplicated gene will be selected and others are removed. > >But suppose, you want to get the mean values of those rows. >library(plyr) >?res<-ddply(dat1[,c(1,6:32)],.(gene), numcolwise(mean,na.rm=TRUE)) >dim(res) >#[1] 639? 28 > >A.K. > > > > > > > >________________________________ >From: Vivek Das <vd4mmind at gmail.com> >To: arun <smartpink111 at yahoo.com> >Sent: Monday, September 9, 2013 1:35 PM >Subject: Urgent help...

Hola ¿Alguno tiene un código para sumarizar medidas? my.df <- data.frame( "ID" = c( rep("A", 3 ), rep("B",2),"C", rep("D",3) ), "Obs.1" = rnorm( 9,0,1 ), "Obs.2" = rnorm( 9,0,3 ) ) Algo que quede mas bonito que esto: by( my.df, my.df$ID, mean ) Gracias un saludo! -- Patricia García González [[alternative HTML

I want to apply a more complicated function than what I use in my example, but the idea is the same: Suppose you have a data frame named x and you want to a function applied to each variable, we'll just use the quantile function for this example. I'm trying all sorts of apply functions, but not having luck. My best guess would be: sapply(x, FUN=quantile) -- View this message in

Hello, I've got a data frame with a mix of numeric, integer and factor columns. I'd like to pull out (or just operate only on) the numeric/integer columns. Every thing I've found in searches is about how to subset by rows, or how to operate assuming you have the column names.  I'd like to pull by type. Thanks! Barry [[alternative HTML version deleted]]

Hi R-helpers, I have a dataframe with 60columns and I would like to convert several columns to factor, others to numeric, and yet others to dates. Rather than having 60 lines like this: data$Var1<-as.factor(data$Var1) I wonder if it's possible to write one line of code (per data type, e.g. factor) that would apply a function (e.g., as.factor) to several (non-contiguous) columns. So, I

Hi, i have that table Thesis Day A B C 1 0 83.43 90.15 22.97 1 0 85.50 94.97 16.62 1 0 83.36 95.38 20.70 1 0 84.47 92.16 23.58 1 0 83.98 95.33 19.39 1 0 82.86 93.78 24.55 1 0 83.39 92.67 19.56 1 0 85.17 95.24 17.95 1 0 81.62 93.32 28.49 1 0 82.99 92.85 19.73 1 0 81.11 95.67 27.20 1 0 83.39 94.69 16.51 1 0 79.56 89.87 30.39 1 0 80.54 93.32 21.76 1 0 82.11 92.58 22.17 1 14 85.65 94.00 19.19 1 14

Dear R-users, I have a dataset as like below, and I want to subtotal the values of rice,wheat and maize by year for each fid. fid      year     rice     wheat      maize ------------------------------------------------ 1        1995      5        NA           NA 1        1995      NA        3           NA 1        1995      NA       NA           2 1        1996      4        NA           NA 1  

I have a two part question one about statistical theory and the other about implementations in R. Thank you for all help in advance. (1) Am I correct in understanding that Heteroscedasticity is a problem for Generalized Additive Models as it is for standard linear models? I am asking particularly about the GAMs as implemented in the mgcv package. Based upon my online search it seems that some

...gt; > str(m) > 'data.frame':???9 obs. of? 7 > variables: > $ V1: Factor w/ 3 levels "bob","harry",..: 3 3 3 1 1 2 2 2 > 2 > $ V2: int? 1 0 0 1 0 0 0 0 0 > <V3 through V7 are also numeric> > > library(plyr) > ddply(m, "V1", numcolwise(sum)) > ? ???V1 V2 V3 V4 V5 V6 V7 > 1???bob? 1? 0? 1? > 0? 0? 0 > 2 harry? 0? 0? 1? 1? 1? 1 > 3???jim? 1? 1? 1? > 0? 0? 0 > > ddply() takes a data frame as input and outputs a data > frame. "V1" specifies > the (categorical) variable over which reductions ar...

...> ddply( x, .(GENEID), function(df) > {with(as.integer(c(col1=any(col1), col2=any(col2), col3=any(col3), col4=any(col4))))} > ) You can do this a little more succinctly with colwise: any_1 <- function(x) as.integer(any(x)) ddply(x, "GENEID", numcolwise(any_1)) Hadley -- Assistant Professor / Dobelman Family Junior Chair Department of Statistics / Rice University [80]http://had.co.nz/ ------------------------------ Message: 10 Date: Sat, 24 Jul 2010 08:55:01 -0500 From: Frank E Harrell Jr <[81]f.harr...