I'm looking for a better way to view output of the data frame in the console.
The computer that I'm using has high security restrictions, so installing many of the more popular packages such as tidyr and tibble is not possible.
What I want is for the ouput to be more compact and not wrap in the console.
Is there a way to use base R to improve the console output for data frames?
You could edit your data.frame without changing it. It will open a new window for you to see. There is an editor parameter which allows to choose an editor of your choise.
Or you could page through the data:
page(mtcars, method = "print")
Related
When opening file 'TestFile.RData' in BlueSky Statistics it is opened with this name PLUS Dataset3 attached. Looks like this in tab TestFile.RData(Dataset3)
I would like to use my original name when using r code in the r command editor but from what I see BlueSky wants me to use the Dataset3 name.
Please clarify this file name issue for me.
If my original name is changed I see issues with reproducing things - as the given name of Dataset3 is not controllable.
Regards
Your observation is correct. When ever a file is opened in BlueSky Statistics (that is not an R datafile) we create a dataframe object in R. We name these objects sequentially namely Dataset1, Dataset2,Dataset3, etc. We could always use the name of the original file, however we went with Dataset1,Dataset2,Dataset3 for compatibility with SPSS. Many of our users come from SPSS and that is exactly what SPSS does. There is a simple work around, see below.
To work around this you need to change the default code we use to open the dataset. To see the code in the output window, Go to the top level menu Tools , Tools->Configuration settings->Select the Output tab and select the checkbox near the text "Show syntax in output window"
The code you will see when you open a dataset in the output Window is
BSkyloadDataset(fullpathfilename='C:/Users/Aaron_2/Documents/BlueSky Statistics/Sample Datasets/IRT/engagement.csv', filetype='CSV', worksheetName='',load.missing=FALSE, character.to.factor=FALSE, csvHeader=TRUE, isBasketData=FALSE, trimSPSStrailing=FALSE, sepChar=',', deciChar='.', datasetName='Dataset2')
All you need to do is change the datasetName parameter to the name you want to use
I will also add an enhancement to make the default behavior of naming the dataset when opening files to be the name of the file. This is easy to do.
With R datasets this is not a problem because we load all dataframe objects into the grid. The name of the dataset in the grid, continues to be the dataset object
BlueSky is one of the few packages that use R and allow you to open and work on multiple data files at once. This naming approach is its way of allowing that while using files that have not yet been stored as R data files (.RData). After importing data from a non-R file, simply use "File> Save as" and save it as an R Object (.RData). The next time you open that file, it will maintain the name you've given it.
Power BI has a feature that lets you create visuals from R scripts. When you add data (columns) to the Values field, it automatically creates a data frame from those columns, which is calls "dataset"
It even shows the code it runs:
dataset <- data.frame(Col1, Col2, Col3, etc.)
My question is, how could I go about viewing the data in this dataframe?
I've tried running code like:
g <- xtabs(dataset)
g
print(g)
but it just returns the error: "No image was created. The code didn't result in creation of any visuals. Make sure your R script results in a plot to the R default device."
On the PowerBI website it says: 'Only plots that are plotted to the R default display device are displayed correctly on the canvas'. In simpler terms it means that if an object is printed to the console, it will not be displayed in PowerBI.
The tableHTML package let's you create HTML tables that will be displayed in the R default display.
library (tableHTML)
g <- tableHTML(dataset, rownames = FALSE)
print(g)
Note: you need to make sure tableHTML is installed in the library of R that is used by PowerBI. You can see the path for R used by PowerBI in the Global.options under 'R scripting'. Use the path that is displayed there in the code snipped below (this needs to be run from R/RStudio rather that PowerBI):
install.packages('tableHTML','/path/to/R/R-x.x.x/library)
You need to use a function that turns the table into a visual. If you install the gridExtra package in R, you should be able to do this in PowerBI:
g <- xtabs(dataset)
gridExtra::grid.table(dataset)
Bear in mind, the grid.table() requires a lot of detailed programming to control the image size, margins, font size, etc.
If you're just doing something simple like a crosstab, that's something you should be able to calculate as a Measure in PowerBI, and then use the built in table or matrix visuals.
My question:
Can I change the parameters in R to use the source editor to also view >5MB data sets in R?
If not, what is your advice?
Background:
I recently stopped looking at data in Excel and switched to R entirely. As I did in Excel and still prefer to do in R, I like to look at the entire frame and then decide on filters.
Problem: Working with the World Development Indicators (WDI) data set which is over 100MB, opening it in the source editor does not work. View(df) opens an empty tab in RStudio as also shown below:
R threw another error when I selected the data set from the Files Tab in column on the right of RStudio which read:
The selected file 'wdi.csv' is too large to open in the source editor (the file is 104.5 MB and the maximum file size is 5MB).
Solutions?
My alter ego would tell me to increase the threshold of datasets' file size for the source editor, so I could investigate it there. In brief: change 5 to 200 MB. My alter ego would also tell me that I would probably encounter performance issues (since I am using a MacAir).
How I resolved the issue:
I used head() and dplyr's glimpse() to get a better idea, but ended up looking at the wdi matrix in excel and then filtered it out in R. Newly created dataframes could be opened in the source editor without any problems.
Thanks in advance!
I have a dataframe loaded successfully in R.
I would like to give the data of df to someone else to use them with quick and easy way without need to load again the file into a df.
Which is the command to give the whole data of df (not the str())
You can save the file into a .RData using save or save.image, depending on your needs. First one will save specific objects while the latter will dump the whole workspace to a file. This method has the advantage of working on probably any R object.
Another option is as #user1945827 mentioned, using dput which will produce a string that is parseable into another R session. This will not work for complex (like S4) objects.
I am looking for an easy way to get objects into MS Excel.
(I am using the preinstalled "Puromycin"-dataset for the examples)
I would like to place the contents of these objects to a single excel file:
Puromycin
summary(Puromycin$rate)
summary(Purymycin$conc)
table(Puromycin$state)
lm( conc ~ rate , data=Puromycin)
By "contents" i mean what is shown in the console when i press enter. I dont know what to call it.
I tried to do this:
sink("datafilewhichexcelhopefullyunderstands.csv")
Puromycin
summary(Puromycin$rate)
summary(Purymycin$conc)
table(Puromycin$state)
lm( conc ~ rate , data=Puromycin)
sink()
This gives med a file with the CSV-extension, however when i open the file in notepad,
there is comma-separation. That means that i cant get Excel to open it properly. By properly
i mean that each number is in its own cell.
Others have suggested this for a similar problem
https://stackoverflow.com/a/13007555/1831980
But as a novice i feel that the solution is too complex, and I am hoping for a simpler method.
What I am doing now is this:
write.table(Puromycin, file="clipboard" , sep=";" , row.names=FALSE )
write.table(summary(Purymycin$conc), file="clipboard" , sep=";" , row.names=FALSE )
... etc...
But this requires i lot of copy-ing and pasting, which I hope to eliminate.
Any help would appreciated.
write.table and its friends are intended to write out columns of data separated by whatever separator is specified. Your clipboard contains several data types because you are using summary which always gives a unique output.
For writing the data values out, you can use write.csv on a data frame and then open with Excel. For example, Puromycin is already a data frame (which you can see with str(Puromycin)) so you can just write it out directly:
write.csv(file = "some file.csv", x = Puromycin)
Which will go into the current working directory (which can be determined with getwd()).
To write out/save the results of the regression model is a bit more of a challenge. You could definitely use sink as you did, but specify an extension of .txt on your file so a text editor can open it. There are fancier methods (sweave, knitr) which you might want to look into in the long run, as they can write really nice reports automatically.
In the meantime, get to know str(any R object) as it will be your friend. You can see all the objects in your workspace with ls().
This will only be helpful if you are prepared to use Excel's Data/Text to Columns functions:
capture.output( sapply( c(Puromycin,
summary(Puromycin$rate),
summary(Puromycin$conc),
table(Puromycin$state),
lm( conc ~ rate , data=Puromycin) ), FUN=print), file="datafilewhichexcelhopefullyunderstands.csv", append=TRUE)
The problem being that Excel will not read the whitespace as a cell separator unless you specifically tell it to. You can (and I have often done so) use the fixed filed input features offered by the Text-to-Columns dialog interface.
Your simplest option may be to use the RExcel tool, it transfers information between R and Excel. However it is not free software.
The XLConnect package is another option, it can be used to write information directly to an Excel file.
The tricky part is the lm call. lm does not return a simple vector, matrix, or data frame (all of which are easy to convert to csv or send directly) and there is not a clear way to convert the various parts of a list to cells in a spreadsheet. What would be better is to use extractor functions to pull the important parts from the return of lm or the summary of the lm object and send those to Excel using the other tools.
If you can tell us more about why you want the numbers in Excel and what you plan to do with them after, then we may be able to offer better help (you may be able to completely skip excel).
If the main goal is to share output with others then you should really look at the knitr package (or other related packages). This will not create Excel files, but can be used (along with the pandoc program and possibly other tools) to create a report file in a format easy to share with others not familiar with R. You could put everything into a .pdf file or a .docx file (the latter read by MS Word and would have tables wich can be edited using Word). There is not a simple way to get edits back into R, but with the track changes you can easily see what changes have been made and hand edit your R script/template accordingly.