I use Visual Studio Code for everything aside from generating reports with R Markdown. For that, I still use RStudio. When I am trying out some new code and exploring data, I usually use an R Notebook file. The killer feature for me is being able to preview it: I can add some text and some code and see how it will look like in the HTML file without having to knit it all over. My understanding is that, as of 2021, that is main difference between R notebooks and "plain" R Markdown files.
I prefer VS Code over RStudio any day but I miss that preview feature. Every time I need to see how simple modifications I did on the .Rmd file look on the HTML file, I have to knit it. Currently, I am using Yuke Ueda's R extension for VS Code: https://marketplace.visualstudio.com/items?itemName=Ikuyadeu.r
My question for the community is: is there a way to enable the same preview capabilities of RStudio on VS Code? Essentially, I would like to see changes I did on the .Rmd file be reflected on the .nb.html file as soon as I save the .Rmd, as in RStudio. Thanks!
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Whenever I knit to a PDF in RStudio, the error "The system cannot find the file specified."
Here is the code that I'm using:
##importing data
library(readr)
Quiz1data_2 <- read_csv("C:/Users/erinp/Downloads/Quiz1data-2.csv")
I have restarted RStudio multiple times and I have copied and pasted the exact link that my file is saved to and it's still not working.
What am I not seeing or what am I not thinking?
Some suggestions/questions:
Without knitting, when you run the line reading in the csv, does it work?
Also, are you sure that the error is referring to the data csv? Could it be referring to the (I'm assuming) markdown file you are writing your code in? Have you moved that file since you started working in it?
Are you able to knit other documents to pdf? You need MiKTeX on a windows machine. Does knitting to html work?
I've found R to be a little tricky reading in files. I usually use the base function like this:go to the environment tab>import dataset>from text(base)> (select the file you want, hit open)>(select settings so that the dataframe preview looks right>import. Code that does this will run in the console, and I copy it into my markdown file so that every time it knits, it replicates that successful process.
I figured it out. It's because I forgot to assign a value to an object so it wouldn't knit into a PDF. I made a comment earlier, but it's small so I thought I would add this to the answer section.
I've been moving some of the code for a report I'm writing to child .rmd files. I want to run these chunks by clicking on the green arrow (top right):
But this doesn't work in RStudio, is this a feature or a bug?
This has not been implemented in RStudio yet, and probably won't be for some time.
However, you can write your R code in a separate file, reference it in R Markdown chunks, and execute those chunks interactively in RStudio. The way to do this is with knitr's code externalization feature. You can read about how to use it in R Markdown notebooks here:
https://rmarkdown.rstudio.com/r_notebooks.html#executing_code (scroll down a bit to Executing External Chunks)
More on code externalization with knitr here:
https://yihui.name/knitr/demo/externalization/
I'm trying to build a webpage displaying a few graphs. I want to use R to build the graphs, and I want these graphs to automatically update themselves periodically. For example, a webpage showing a graph of the stock price of a particular company over time, which refreshes every day with new data.
what is the best way to approach this? Using Rook to run the R-scripts? can I use it along Markdown, for example, to make the html webpage? Or do you suggest something else?
You can make your plots in a R file and write your webpage in markdown in which you call your R objects and plots. Alternatively you can also run the R code directly in the markdown file. With the knit2html function of the knitr package you can create the html page with the desired output. You can find basic examples on the knitr webpage.
You can schedule these file(s) on your own computer or on a server to update the data of the html output every day. If you have a machine that runs on Windows you can use the Windows Task Manager to run the batch file.
EDIT:
Here you can find a worked out example.
I'm trying to write a Bash script in Ubuntu 10.04 that opens a Python file which exports a CSV, and then runs the following Rscript with the goal of exporting a HTML with plots from Dashboard.Rmd:
require(knitr)
setwd('/home/sensors/Desktop/')
knit2html('Dashboard.Rmd')
browseURL('Dashboard.html')
Dashboard.Rmd is an R markdown that calls read.csv on the csv from the first step, makes a data frame and creates plots, but that part's working fine. According to this, I figure that Rscript should replicate the action of pressing "Knit HTML" in R Studio. However, the html it creates is identical to the last time Knit HTML was pressed; i.e. even if the CSV is different, the html doesn't reflect the change.
I also tried using a separate line for knit and markdownToHTML with the same effect. It seems like it doesn't source the code from the Rmd when performing knit. It does update the html properly when I enter the commands from that Rscript into the console of R Studio with Dashboard.Rmd open. However I'm not sure how to translate that into a Bash script. I also tried knit2html with envir=new.env(), envir=R_GlobalEnv, and envir=parent.frame() with no luck. Any help would be appreciated!
So it turns out that this was an artifact of cache=TRUE -- the HTML file was not changed because everything was cached.
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.