similar to: ggplot inside function doesn't plot

I don't really understand. I mean, I understand the solution is print(ggplot(...)). But why is that required in a function and not at the console? Shouldn't I be able to rely on what I do at the console working in a script? Is this inconsistent behavior by design? On Thu, Nov 2, 2017 at 11:54 AM, David Winsemius <dwinsemius at comcast.net> wrote: > >> On Nov 2, 2017,

> On Nov 2, 2017, at 9:27 AM, Ed Siefker <ebs15242 at gmail.com> wrote: > > I have a function: > > myplot <- function (X) { > d <- plotCounts(dds2, gene=X, intgroup="condition", returnData=TRUE) > png(paste("img/", X, ".png", sep="")) > ggplot(d, aes(x=condition, y=count, color=condition)) + >

Wait. Now I'm really confused. > > head(samples) quant_samples age sapply(yo, toupper) E11.5 F20het BA40 E11.5 F20het BA40 E11.5 F20HET E11.5 F20het BA45 E11.5 F20het BA45 E11.5 F20HET E11.5 F20het BB84 E11.5 F20het BB84 E11.5 F20HET E11.5 F9.20DKO KTr3 E11.5 F9.20DKO KTr3 E11.5 F9.20DKO E11.5

Why doesn't this work? > samples$geno <- as.data.frame(sapply(yo, toupper), col.names="geno") > samples quant_samples age sapply(yo, toupper) E11.5 F20het BA40 E11.5 F20het BA40 E11.5 F20HET E11.5 F20het BA45 E11.5 F20het BA45 E11.5 F20HET

You left out all the most important bits of information. What is yo? Are you trying to assign a data frame to a single column in another data frame? Printing head(samples) tells us nothing about what data types you have, especially if the things that look like text are really factors that were created when you used one of the read.*() functions. Use str(samples) to see what you are dealing with.

I am experimenting with a scsi2 HP SureStore DAT24 attached as an external device to an Intel P4 system. I am attempting to create a raw disk image of a dds2 tape made on a non linux system. My immediate problem is that I am trying to terminate a dd process and having no luck. dd was invoked thus: dd if=/dev/nst1 of=/tmp/disk.dds2.img bs=512 & pid=$! a ps reveals this: ps PID TTY

Dear R experts I am looking for a package which gives me latin hyper cube samples from the grid of values produced from the command "expand.grid". Any pointers to this issue might be very useful. Basically, I am doing the following: > a<-(1:10) > b<-(20:30) > dataGrid<-expand.grid(a,b) Now, is there a way to use this "dataGrid" in the package

I'm looking for a well-supported ExpressCard or PCMCIA/CardBus SCSI card that can do at least FastSCSI (Narrow, Wide not necessary) that is supported by CentOS 7. I have an Adaptec SlimSCSI 1460D, but it doesn't seem to be supported (it's previously supported by the aha152x driver). Any ideas are welcomed, other than 'just use a desktop.' I am not sure a USB or Firewire

Hi, I have the following matrix, which is printed %e format (in C's way). I am wondering how make it be printed in %f format (in C's way)? Regards, Peng > significant_analysis_results[,7:8] pval(ki-wt) pval(ko-wt) Nab2 1.913348979e-06 2.731944670e-09 Rasal1 2.482254110e-05 1.054711084e-05 Ccndbp1 6.307674516e-08 2.268947934e-04 Svep1 0.000000000e+00

Hi list, I´m working on a predictive modeling task using the caret package. I found the best model parameters using the train() and trainControl() command. Now I want to evaluate my model and make predictions on a test dataset. I tried to follow the instructions in the manual and the vignettes but unfortunately I´m getting an error message I can`t figure out. Here is my code: rfControl <-

I list, I'm new with backup ops and I'm searching a good system to accomplish this work. I know that on centos there are bacula and amanda but they are too tape oriented. Another is that they are very powerfull but more complex. I need a solution for small office for disk storage and I found Backup PC. Many people say that it is great for small stuff and for great number of data. What do

Dear R-users, I have a complex line by xy-values (ordered by z). And I would like to get interpolated y-values on the positions of x = 0:600. How do I get the correct points? x=c(790,790,790,790,790,786,783,778,778,766,763,761,761,761,715,628,521,350,160,134,134,129,108,101,93,111,161,249,288,243,139,45,7)