search for: l_pli

Dear All, to produce output of several columns of a data frame, I tried to use lapply and also l_ply. In both cases, I would like to print a header line containing also the name of the respective column in the data frame. For example, I would like the following lapply(data.frame(a=1:3, b=2:4), function(x) print(deparse(substitute(x)))) to produce: [1] "a" [1] "b" and

Hi! I want to write portions of my data (3573 columns at a time) to twenty folders I have available titled "A_1" to "A_20" such that the first 3573 columns will go to folder A_1, next 3573 to folder A_2 and so on. This code below ensures that the data is written into all 20 folders, but only the last iteration of the loop (last 3573 columns) is being written into ALL of the

I am estimating a large model by groups. How do you save the results and?returns the associated quantiles? For this example I need a data frame n?? ?xi??????? mu????????beta 1?? 0.1033614? 2.5389580 0.9092611 2? ?0.3401922? 0.5192882 1.5290615 3?? 0.5130798? 0.5668308 1.2105666 I also want to apply gevrlevelPlot() for each "n" or group. ? #Example n <- c(1, 1, 1, 1, 1, 1, 2, 2, 2,

Dear List, I have a wrapper function that draws a graph that I'd like to use in a vector-like manner. The for-loop version I currently use is below. library(ggplot2) data(economics) h <- 600 w <- 800 #---------------------------------------------------------- draw_metric_by_date <- function( df, i, smooth=FALSE, BASEPATH ) { mlabel <- names(df)[i] qmetric

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,

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,

There are a few version of apply() (e.g., lapply(), sapply()). I'm wondering if there is one that does not return anything but just silently apply a function to the list argument. For example, the plot function is applied to each element in 'alist'. It is redundant to return anything from apply. apply(alist,function(x){ plot each element of alist})

Hi, Working on a report that is going to have a large number of graphs and summaries. We have 80 "groups" with 20 variables each. Ideally, I'd like to produce ONE page for each group. It would have two columns of 10 graphs and then the 5 number summary of the variables at the bottom. So, perhaps the top 2/3 of the page has the graphs and the bottom third has 20 rows of data

I have a simple 1 way anova coded like summary(ANOVA1way <- aov(Value ~ WellID, data = welldata)) How can I use the BY function to do this ANOVA for each group using another variable in the dataset?? I tried coding it like this, but it doesn't seem to work. summary(ANOVA1way <- by(welldata, Analyte, function(x) aov(Value ~ WellID, data = welldata))) In SAS I would code it like this:

I'm running a long MCMC chain that is generating samples for 22 variables. I have each run of the chain as a row in a matrix. So: Chain[,1] is the column with all the samples for variable one. Chain[,2] is the column with all the samples for variable 2, etc. I'd like to fit all 22 on a single page to print a nice summary. It is OK if the graphs are small, I just need to show the

Hi, I have been examining large data and need to do simple linear regression with the data which is grouped based on the values of a particular attribute. For instance, consider three columns : ID, x, y, and I need to regress x on y for each distinct value of ID. Specifically, for the set of data corresponding to each of the 4 values of ID (76,111,121,168) in the below data, I should invoke