I got the data dictionary from data provider which contains hundreds vars in different word files and looks like this:
In order to add this dictionary to my current dataset, I need to convert it to certain format in Excel. For example,for first var:"intarm_actual", i would like to create columns in a spreadsheet: col of "variable" puts the left top words, col of "label" store content of "label" (for this var, it is NA, but for second var, it should be "tpe_lab"), col of "type" stors the words of " string(str2), col of "value" stores "4", col of "missing" stores "46/102", col of "tabulation" stores "46 "", 14 "RO",14 "RV",14 "TO",14 "TV"". Ideally, it should look like this:
Could anyone who happens have done this before help to provide some suggestions for this? (I appreciate for any suggestion like what package I should refer and use, any related posts article I should read, similar type of code i can learn...)Can R package "labelled" handle this type of task? Thanks a lot~~!!
update:_________________________________________________
I use package qdapTool to imported one of the docx files, it looks like this:
How can I retrieve the demanded words and assign them to right place in my spreadsheet? Thanks~~!
Update 2:--------------------------------------------
Issue has been solved in another way.
In case someone will encounter the similar situation, 1) This type of codebook file is generated by STATA; 2) Instead of reading this complex text file, the alternative solution is using package of "codebook" in R to generate the new .csv codebook which contains both these information and even more.
assuming that indeed, you have zero clue, I would recommend you to get started with regular expressions in R. I often use the R package stringr to work with regular expressions, and you find the respective cheat sheet here. They will allow you to, e.g., select the word following a ":".
I have never worked with Word Documents in R, but I guess that there are packages out there that allow you to read Word documents into R. Just Google them. :) I am sure they also have good instructions on how to use them.
Another issue you might encounter is encoding. If you have issues with reading the text into read in the correct way, e.g. reading in strange character combinations, that is most likely the source of the problem.
Once you have looked at these things and started working on your own code, you will be able to ask more precise questions.
Related
I have a relatively simple issue when writing out in R with fwrite from the data.table package I am getting a character vector interpreted as scientific notation by Excel. You can run the following code to create the data issue:
#create example
samp = data.table(id = c("7E39", "7G32","5D99999"))
fwrite(samp,"test.csv",row.names = F)
When you read this back into R you get values back no problem if you have scinote disable. My less code capable colleagues work with the csv directly in excel and they see this:
They can attempt to change the variable to text but excel then interprets all the zeros. I want them to see the original "7E39" from the data table created. Any ideas how to avoid this issue?
PS: I'm working with millions of rows so write.csv is not really an option
EDIT:
One workaround I've found is to just create a mock variable with quotes:
samp = data.table(id = c("7E39", "7G32","5D99999"))[,id2:=shQuote(id)]
I prefer a tidyr solution (pun intended), as I hate unnecessary columns
EDIT2:
Following R2Evan's solution I adapted it to data table with the following (factoring another numerical column, to see if any changes occured):
#create example
samp = data.table(id = c("7E39", "7G32","5D99999"))[,second_var:=c(1,2,3)]
fwrite(samp[,id:=sprintf("=%s", shQuote(id))],
"foo.csv", row.names=FALSE)
It's a kludge, and dang-it for Excel to force this (I've dealt with it before).
write.csv(data.frame(id=sprintf("=%s", shQuote(c("7E39", "7G32","5D99999")))),
"foo.csv", row.names=FALSE)
This is forcing Excel to consider that column a formula, and interpret it as such. You'll see that in Excel, it is a literal formula that assigns a static string.
This is obviously not portable and prone to all sorts of problems, but that is Excel's way in this regard.
(BTW: I used write.csv here, but frankly it doesn't matter which function you use, as long as it passes the string through.)
Another option, but one that your consumers will need to do, not you.
If you export the file "as is", meaning the cell content is just "7E39", then an auto-import within Excel will always try to be smart about that cell's content. However, you can manually import the data.
Using Excel 2016 (32bit, on win10_64bit, if it matters):
Open Excel (first), have an (optionally empty) worksheet already open
On the ribbon: Data > Get External Data > From Text
Navigate to the appropriate file (CSV)
Select "Delimited" (file type), click Next, select "Comma" (and optionally deselect any others that may default to selected), Next
Click on the specific column(s) and set the "Default data format" to "Text" (this will need to be done for any/all columns where this is a problem). Multiple columns can be Shift-selected (for a range of columns), but not Ctrl-selected. Finish.
Choose the top-left cell to import/paste the data (or a new worksheet)
Select Properties..., and deselect "Save query definition". Without this step, the data is considered a query into an external data source, which may not be a problem but makes some things a little annoying. (For example, try to highlight all data and delete it ... Excel really wants to make sure you know what you're doing there.)
This method provides a portable solution. It "punishes" the Excel users, but anybody/anything else will still be able to consume the files directly without change. The biggest disadvantage with this method is that you won't know if somebody loads it incorrectly unless/until they get odd results when the try to use the data and some fields are silently converted.
I am using a shiney interface under R to read in a CSV file and load it into one sheet of an excel xlsm file. The file then allows user input and preforms calculations based on VBA macros.
The R xlsx package is working well for preserving the VBA and formatting in the original excel sheet. However some of the data is being converted to a different data type than intended. For example a cell containing the string "F" is causing the column containing it to be converted to type boolean, or a miss-entered number in one cell is causing the entire column to be converted to string.
Can this behavior be controlled so that, for example, cells with valid numbers are not converted to string type? Is there a work-around? Or can someone just help me to understand what is happening in the guts of the package to cause this effect so I can try to find a way around it?
Here are the calls in question:
#excelType() points to an excel xlsm template
data = read.csv("results.csv")
excelForm = loadWorkbook(excelType())
sheets = getSheets(excelForm)
addDataFrame(data, sheets[[1]], col.names = FALSE, row.names = FALSE, startRow=2, colStyle = NULL)
saveWorkbook(excelForm, "results.xlsm")
Thanks!
I hope this is the correct protocol for explaining the outcome which worked for me. I hope it will be of help to others if they end up doing something similar, though the solution is not very elegant!
I tried r2evans's suggestion of forcing column types I could not get that to work in this case. Using readxls gave the same problem, and also broke my VBA. Given lebelionz's comment suggesting that this is an R thing and not a package thing I followed his advice to deal with it after the fact. (I do not see how to credit a comment rather than an answer, but for the record this was very helpful, as were the others).
I therefore altered the program producing the CSV that was being loaded through R. I appended "::" to each cell produced, so that R saw all cells as strings, regardless of the original content. Thus "F" was stored as "::F", and therefore was not altered by R.
I added an autorun macro to the excel sheet thus created, so that when opened it automatically performed a global search and replace to remove the prefix "::" from the whole of the data. This forces Excel to choose a data type for each cell after it was restored, resulting in the types being detected cell by cell and in the correct format for my purposes.
It feels kludgy, but it works and is relatively transparent to the user. One hazard is that if the user data intentionally contained the string "::" it would be lost (I am confident this cannot arise in my particular application, but if someone would like to suggest a better prefix I would be interested). I still hope for an eventual solution rather than a work-around.
And here I thought it was only the movie industry that had to "fix it in post"!
my data frame has a column A with strings in character form
> df$A
[1] "2-60", "2-61", "2-62", "2-63" etc
I saved the table using write.csv, but when I open it with Excel column A appears formatted as date:
Feb-60
Feb-61
Feb-62
Feb-63
etc
Anyone knows what can I do to avoid this?
I tweaked the arguments of write.csv but nothing worked, and I can't seem to find an example in Stack Overflow that helps solve this problem.
As said in the comments, this is an excel behaviour, not R's. And that can't be deactivated:
Microsoft Excel is preprogrammed to make it easier to enter dates. For
example, 12/2 changes to 2-Dec. This is very frustrating when you
enter something that you don't want changed to a date. Unfortunately
there is no way to turn this off. But there are ways to get around it.
Microsoft Office Article
The first suggested way around it according to the article is not helpful, because it relies on changing the cell formatting, but that's too late when you open the .csv file in excel (it's already converted to an integer representing the date).
There is, however, a useful tip:
If you only have a few numbers to enter, you can stop Excel from
changing them into dates by entering:
An apostrophe (‘) before you enter a number, such as ’11-53 or ‘1/47. The apostrophe isn’t displayed in the cell after you press
Enter.
So you can make the data display as original by using
vec <- c("2-60", "2-61", "2-62", "2-63")
vec <- paste0("'", vec)
Just remember the values will still have the apostrophe if you read them again in R, so you might have to use
vec <- sub("'", "", vec)
This might not be ideal but at least it works.
One alternative is enclosing the text in =" ", as an excel formula, but that has the same end result and uses more characters.
Another solution - a bit tedious, Use Import Text File in Excel, click thru the dialog boxes and in Step 3 of 3 of the Text Import Wizard, you will have an option of setting the column data format, use "Text" for the column that has "2-60", "2-61", "2-62", "2-63". If you use General (the default), Excel tries to be smart and converts the answer for you.
I solved the problem by saving the file using the .xlsx format by using the function
write.xlsx()
from the package xlsx (https://www.rdocumentation.org/packages/xlsx/versions/0.6.5)
I have just started using R, so this may be a very dumb question. I am trying to import the data using:
emdata=read.csv(file="http://lottery.merseyworld.com/cgi-bin/lottery?days=19&Machine=Z&Ballset=0&order=1&show=1&year=0&display=CSV",header=TRUE)
My problem is that it reads the csv file into a single column ( by the way, the lottery data is simply because it is publicly available to download - using as an exercise to understand what I can and can't do in R), instead of formatting it into however many columns of data there are. Would someone mind helping out, please, even though this is trivial
Hm, that's kind of obnoxious for a page purporting to be in csv format. You can skip the first 5 lines, which will cause R to read (most of) the rest of the file correctly.
emdata=read.csv(file=...., header=TRUE, skip=5)
I got the number of lines to skip by looking at the source. You'll still have to remove the cruft in the middle and end, and then clean up the columns (they'll all be factors because of the embedded text).
It would be much easier to save the page to your hard disk, edit it to remove all the useless bits, then import it.
... to answer your REAL question, yes, you can import data directly from the web. In general, wherever you would read a file, you can substitute a fully qualified URL -- R is smart enough to do the Right Thing[tm]. This specific URL just happens to be particularly messy.
You could read text from the given url, filter out the obnoxious lines and then read the result as CSV like so:
lines <- readLines(url("http://lottery.merseyworld.com/cgi-bin/lottery?days=19&Machine=Z&Ballset=0&order=1&show=1&year=0&display=CSV"))
read.csv(text=lines[grep("([^,]*,){5,}", lines)])
The above regular expression matches any lines containing at least five commas.
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.