I am in the process of automating a number of graphs that are produced where I work through R that are currently in Excel.
Note that for now, I am not able to convince that doing the graphs directly in R is the best solution, so the solution cannot be "use ggplot2", although I will push for it.
So in the meantime, my path is to download, update and tidy data in R, then export it to an existing Excel file where the graph is already constructed.
The way I have been trying to do that is through openxlsx, which seems to be the most frequent recommendation (for instance here).
However, I am encountering an issue taht I cannot solve with this way (I asked a question there that did not inspire a lot of answers !).
Therefore, I am going to try other ways, but I seem to mainly be directed to the aforementioned solution. What are the existing alternatives ?
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So I'm working with a network dataset from Stanford's SNAP Datasets and "SNAP" has wrappers for Python and C++ but not R - however, the data is still usable since I believe it's a mix of CSV files.
I can actually read in the .edges file and form an igraph object but want to read in the other files, get the attributes & add those attributes to the igraph object for analysis. I'm just confused on how to work with the .circles, .egofeat, .feat, and .featnames files since the documentation on the dataset is very scarce. Hoping someone has worked with the dataset in R or even another language and has any pointers to get started.
Thank you!
I'm wondering if there are any packages for R which help to visualize workflows/code in a way Alteryx does. I find the visualization of the workflows within Alteryx quite helpful, but manually dragging an dropping the tools onto the canvas and set the parameters just takes so much longer than just writing the code in R. Also some functionally within Alteryx is not yet sufficient and has to be implemented via the R/Python-Tool anyway.
During my search I found this post which goes into the same direction, but the suggested packages don't really match what I am looking for.
Best regards
I have a large population survey dataset for a project and the first step is to make exclusions and have a final dataset for analyses. To organize my work, I must continue my work in a new file where I derive survey variables correctly. Is there a command used to continue work by saving all the previous data and code to the new file?
I don´t think I understand the problem you have. You can always create multiple .R files and split the code among them as you wish, and you can also arrange those files as you see fit in the file system (group them in the same folder with informative names and comments, etc...).
As for the data side of the problem, you can load your data into R, make any changes / filters needed, and then save it to another file with one of the billions of functions to write stuff to the disk: write.table() from base, fwrite() from data.table (which can be MUCH faster), etc...
I feel that my answer is way too obvious. When you say "project" you mean "something I have to get done" or the actual projects that you can create in rstudio. If it´s the first, then I think I have covered it. If it´s the second, I never got to use that feature so I am not going to be able to help :(
Maybe you can elaborate a bit more.
This question already has an answer here:
Automatic documentation of datasets
(1 answer)
Closed 8 years ago.
Is there any code self-documenting system for R?
I think writing documentation is a very important part of any statistical analysis. There are always important details in your code or the steps of data cleaning that are not reflected in the final analysis report. I wonder whether there is any efficient self-documenting system (or approach) in R that can help me documenting my codes including my comments o my codes with structure files explaining the structure of the datasets (or tables) used in my code?
Beyond using Sweave or knitr in R, is there any other way of doing that?
I'd suggest bundling up your code and data sets in an R package. There's a steep learning curve the first time you do it, but if you're at the point where you're asking, "how can I better manage this code documentation thing", you're likely ready to take the plunge.
Plus, doesn't the idea of typing ?myOwnFunction or ?myOwnDataset, and having the appropriate help file pop up (just as it does when you do ?mean or ?iris) sound appealing?
You might try writing your code as a Sweave or kntr file, which contains LaTeX text along with R code. This process produces a pdf of your text, including your code, and executes your code.
If you choose to organise your analysis as package, you can use roxygen2 for documentation of your code and data.
I frequently find myself doing some analysis in R and then wanting to make a quick map. The standard plot() function does a reasonable job of quick, but I quickly find that I need to go to ggplot2 when I want to make something that looks nice or has more complex symbology requirements. Ggplot2 is great, but is sometimes cumbersome to convert a SpatialPolygonsDataFrame into the format required by Ggplot2. Ggplot2 can also be a tad slow when dealing with large maps that require specific projections.
It seems like I should be able to use Mapnik to plot spatial objects directly from R, but after exhausting my Google-fu, I cannot find any evidence of bindings. Rather than assume that such a thing doesn't exist, I thought I'd check here to see if anyone knows of an R - Mapnik binding.
The Mapnik FAQ explicitly mentions Python bindings -- as does the wiki -- with no mention of R, so I think you are correct that no (Mapnik-sponsored, at least) R bindings currently exist for Mapnik.
You might get a more satisfying (or at least more detailed) answer by asking on the Mapnik users list. They will know for certain if any projects exist to make R bindings for Mapnik, and if not, your interest may incite someone to investigate the possibility of generating bindings for R.
I would write the SpatialWotsitDataFrames to Shapefiles and then launch a Python Mapnik script. You could even use R to generate the Python script (package 'brew' is handy for making files from templates and inserting values form R).