I wrote a python script that generates a tree and outputs some variable creation and function calls in Julia syntax to a text file (I am testing the correctness of some Julia tree algorithms in phylogenetics).
I was wondering if there is a way to "run" the text file in a Julia Jupyter notebook?
It gets tedious to manually copy the file and run it as I am generating many files.
You can run include("treealgos.jl") in a Jupyter cell, to run the entire file there. It's equivalent to copy-pasting the file contents to that cell, and all the variables and functions defined in the file become available in the notebook after that.
Note that this is very different from using or importing a module, which require a module name and come with extra features like namespacing and exports. An include in contrast is a more basic and simpler feature, similar to a #include in C language, just bringing the code that was included into wherever the include statement happens to be.
Related
What is the difference between using R console vs writing R code in a text file? I wrote this question on Kaggle but there were no previous questions on this matter.
When you supply code via text file (.R file) you "run the file with R" without visualizing it and it can stop somewhere due to error i.e. (which can be handled, etc.). Also running an .R file with R (for example via .bat file) generates a .Rout file, which is basically a print out of the console and some aditional info like runtime, etc.
If you feed the code in the console, each line is treated independently: even if there is an error you can process an aditional line (if it depends on the failed comand then it will fail also though) and you get to see each result as soon as the comand is run. In comparision to the .R file you will have no copy of the code other than that stored in the session - meaning you will end up needing to save to disk the code you have written if you want it to persist between session. Now you can choose to use whatever text format you like for this task from simple .txt to .docx BUT if you use .R format you can manipulate with notepad++ or the notepad editor and still run/complipe the file with R (via .bat file for example). In case of opting against .R file to store the written code, you will have to feed it to the console again to run.
In R Studio you can open .R files and manage (extend, correct) your code and feed it comand per comand or as a block to the console. So one could say you use .R files to manage you code, having the possiblity to compile/run these .R files directly with R to execute on a button click or repeatedly for example.
Not sure if that is what you are looking for?
I am trying to copy a file using Julia functions with the hope of manipulating the file and then use that copied version for various tasks in the Julia programming language. Can someone provide some example code of copying a file in Julia?
I guess I could do use read then write but it seems like I would be reinventing the wheel.
Is there a standard library function for this?
Inspired by this question.
Just use the built in function cp(src, dst).
Copy the file, link, or directory from src to dst. force=true will first remove an existing dst.
Afterwards you can open the file and manipulate it. Of course you could also open both source an destination files simultaneously and copy and manipulate it line by line.
I have a few R files that contain functions imported and used by several other R files. I import these functions with the source function. Naturally, the scope of a particular file might change over time, and recently I wanted to rename a file I had already sourced in many other places.
I'm using RStudio, and I have been unable to find a way to do this except for either manually updating each dependent file, or creating some external code to scan through the files.
Is there no way to do consistent renaming in RStudio? Alternatively, am I doing something wrong by using source to add functions?
You may or may not find this satisfactory. Create a parent script with the old name that sources the script with the new name.
Extending this, you could just create a general preamble script, called something like "preamble.R", that sources all general utility scripts you have. Such an approach is common (I believe) with TeX. Then you only have one place to update file names.
How do I run xquery using Saxon HE 9.5 on a directory of files using the build in command-line? I want to take one file as input and produce one file as output.
This sounds very obvious, but I can't figure it out without using saxon extensions that are only available in PE and EE.
I can read in the files in a directory using fn:collection() or using input parameters. But then I can only produce one output file.
To keep things simple, let's say I have a directory "input" with my files 01.xml, 02.xml, ... 99.xml. Then I have an "output" directory where I want to produce the files with the same names -- 01.xml, 02.xml, ... 99.xml.
Any ideas?
My real data set is large enough (tens of thousands of files) that I don't want to fire off the jvm, so writing a shell script to call the saxon command-line for each file is out of the question.
If there are no built-in command-line options, I may just write my own quick java class.
The capability to produce multiple output files from a single query is not present in the XQuery language (only in XSLT), and the capability to process a batch of input files is not present in Saxon's XQuery command line (only in the XSLT command line).
You could call a single-document query repeatedly from Ant, XProc, or xmlsh (or of course from Java), or you could write the code in XSLT instead.
my question(s) might be less general than the title suggests. I am running R on Mac OS X with a MySQL database to store the data. I have been working with the Komodo / Sciviews-R for some time. Recently I had the need for auto-generated reports and looked into Sweave. I guess StatET / Eclipse appears to be the "standard" solution for Sweavers.
1) Is it reasonable to switch from Komodo to StatET Eclipse? I tried StatET before but chose Komodo over StatET because I liked the calltip / autosuggest and the more convenient config from Komodo so much.
2) What´s a reasonable workflow to generate Sweave files? Usually I develop my R code first and then care about the report later. I just learned today that there is one file in Sweave that contains R code and Latex code at once and that from this file the .tex document is created. While the example files look handily and can't really imagine how to enter my 250 + lines of R code to a file and mixed it up with Latex.
Is it possible to just enter the qplot() and ggplot() statements to a such a document and source the functionality like database connection and intermediate results somehow?
Or is it just a matter of being used to the mix of Latex and R code?
Thx for any suggestions, hints, links and back-to-the-roots-shout-outs…
You've asked several questions, so here's several answers;
Is StatEt/Eclipse the right way to do Sweave ?
Not nessarily (note: I'm an avid StatEt/Eclipse user, and use it for both pure R and Sweave/R and love it, I haven't used Komodo / sciviews-R). You should be able to run the sweave command from any R command line which will generate a .tex file. You can then turn the .tex file into something readable (like pdf) from any tex environment.
What's a good Sweave workflow ?
When I have wanted to turn an r script into a sweave report I generaly start with an empty sweave template and copy/paste my entire R script into a sweave R block just after the title, i.e;
<<label=myEntireRScript, echo=false, include=false>>
#Insert code here
myTable<-dataframe(...)
myPlot<-qplot(....)
#
Then I go through and find the parts I want to report. For instance, if i want to put a table into the report, I'll cut the R block and put an xtable block in, and the same for variables and plots.
<<label=myEntireRScript, echo=false, include=false>>=
#Insert code here
#
Put any text I want before my table here, maybe with a \Sexpr{print(variable)} named variable
<<label=myTable, result=Tex>>=
myTable<-dataframe(...)
print(xtable(mytable,...),...)
#
Any text I want before my figure
<label=myplot, result=figure>>=
myPlot<-qplot(....)
print(qplot)
#
You may want to look at these related SO posts. The rest of my post relates to your question 2.
When creating reports with Sweave, I usually keep most of the R code and the report text separate. If the R code is fast to run, then I prefer I will include something like the following at the start of the .Rnw file:
<<>>
source('/path/to/script.r')
#
On the other hand, if the R code takes a long time, I will often include something like the following at the end of the R script:
Sweave('/path/to/report.Rnw'); system('pdflatex report.tex')
That way, I can re-generate the report quickly, without needing to run all the R code again. Then, the only work R has to do in the Sweave file is print tables, make graphs and maybe extract a few figures.
Like nullglob, I prefer to keep the R and Sweave files separate, but I prefer to save the workspace with save.image() rather than to source() the file. This avoids running the R calculations with each .Rnw file compiling (and I always end up tinkering with the typesetting more than I'd like).
My general work flow is to do each paper/project in it's own folder with it's own R file(s). When the calculation side is "done", I save.image() to store all the workspace variables as-is.
Then, in the .Rnw file in the same directory I set the working directory with setwd() and load all variables with load(".Rdata"). Of course, you can change the name you use for your workspace, but I do one workspace per folder and keep the default name. Oh, and if you tinker with the R file, be sure save the workspace image and watch out for variables that linger in the workspace and .Rnw file, but are no longer part of the R file... this is where the save.image() approach can cause some headaches.
I am on a Mac and I suggest TextMate if you're mildly geeky and emacs/ess if you're really geeky. I use vim and command line R, but emacs/ess works best for most. If you're in this for the long haul, I doubt you'll regret learning emacs/ess for R, Sweave, and LaTeX.