I have 2 excel files which have macros in it. The file extension ends with .xlsb and .xlsm. I want to read these files into R and do exactly what excel is doing with these files in terms of data inputs in R. What is the way to go about it?
For example: if the excel file calculates house prices in sheet 2 based on data input in sheet 1, how can the same results for house price calculation be obtained in R?
You might take a look at the R package RDCOMClient:
https://github.com/omegahat/RDCOMClient
Here is a nice example shown:
https://www.r-bloggers.com/2021/07/rdcomclient-read-and-write-excel-and-call-vba-macro-in-r/
Related
I have some excel file with simple formulas like =SUM(A1:A3).
I need to import the file into R, but before that I need to refresh the formulas. Is there a way to refresh the file from within R? There are good packages for importing the data in a R dataframe (eg. the R xslx package) but I need to refresh my formulas first.
Any suggestions?
Thanks!
You should be able to do this with RDCOMClient:
library(RDCOMClient)
ex = COMCreate("Excel.Application")
book = ex$Workbooks()$Open("my_file.xlsx")
book$Worksheets("Sheet1")$Calculate() # if you have many sheets you could loop through them or use apply functions based on their actual names
book$Save()
book$Close()
Here's another thread on the underlying VBA
I am wondering is it possible to read an excel file that is currently open, and capture things you manually test into R?
I have an excel file opened (in Windows). In my excel, I have connected to a SSAS cube. And I do some manipulations using PivotTable Fields (like changing columns, rows, and filters) to understand the data. I would like to import some of the results I see in excel into R to create a report. (I mean without manually copy/paste the results into R or saving excel sheets to read them later). Is this a possible thing to do in R?
UPDATE
I was able to find an answer. Thanks to awesome package created by Andri Signorell.
library(DescTools)
fxls<-GetCurrXL()
tttt<-XLGetRange(header=TRUE)
I was able to find an answer. Thanks to awesome package created by Andri Signorell.
library(DescTools)
fxls<-GetCurrXL()
tttt<-XLGetRange(header=TRUE)
Copy the values you are interested in (in a single spread sheet at a time) to clipboard.
Then
dat = read.table('clipboard', header = TRUE, sep = "\t")
You can save the final excel spreadsheet as a csv file (comma separated).
Then use read.csv("filename") in R and go from there. Alternatively, you can use read.table("filename",sep=",") which is the more general version of read.csv(). For tab separated files, use sep="\t" and so forth.
I will assume this blog post will be useful: http://www.r-bloggers.com/a-million-ways-to-connect-r-and-excel/
In the R console, you can type
?read.table
for more information on the arguments and uses of this function. You can just repeat the same call in R after Excel sheet changes have been saved.
I want to export a dataset in the MASS package to SPSS for further investigation. I'm looking for the EuStockMarkets data set in the package.
As described in http://www.statmethods.net/input/exportingdata.html, I did:
library(foreign)
write.foreign(EuStockMarkets, "c:/mydata.txt", "c:/mydata.sps", package="SPSS")
I got a text file but the sps file is not a valid SPSS file. I'm really looking for a way to export the dataset to something that a SPSS can open.
As Thomas has mentioned in the comments, write.foreign doesn't generate native SPSS datafiles (.sav). What it does generate is the data in a comma delimited format (the .txt file) and a basic syntax file for reading that data into SPSS (the .sps file). The EuStockMarkets data object class is multivariate time series (mts) so when it's exported the metadata is lost and the resulting .sps file, lacking variable names, throws an error when you try to run it in SPSS. To get around this you can export it as a data frame instead:
write.foreign(as.data.frame(EuStockMarkets), "c:/mydata.txt", "c:/mydata.sps", package="SPSS")
Now you just need to open mydata.sps as a syntax file (NOT as a datafile) in SPSS and run it to read in the datafile.
Rather than exporting it, use the STATS GET R extension command. It will take a specified data frame from an R workspace/dataset and convert it into a Statistics dataset. You need the R Essentials for Statistics and the extension command, which are available via the SPSS Community site (www.ibm.com/developerworks/spssdevcentral)
I'm not trying to answer a question that has been answered. I just think there is something else to complement for other users looking for this.
On your SPSS window, you just need to find the first line of code and edit it. It should be something like this:
"file-name.txt"
You need to find the folder path where you're keeping your file:
"C:\Users\DELL\Google Drive\Folder-With-Your-File"
Then you just need to add this path to your file's name:
"C:\Users\DELL\Google Drive\Folder-With-Your-File\file-name.txt"
Otherwise SPSS will not recognize the .txt file.
Sorry if I'm repeating some information here, I just wanted to make it easier to understand.
I suppose that EuStockMarkets is a (labelled) data frame.
This should work and even keep the variable and value labels:
require(sjlabelled)
write_spss(EuStockMarkets, "mydata.sav")
Or you try rio:
rio::export(EuStockMarkets, "mydata.sav")
I have a RMA normalized data ( from the CEL files ) and would like to write it into a file that I could open in excel but have some problems.
library(affy)
cel <- ReadAffy()
pre<-rma(cel)
write.table(pre, file="norm.txt", sep="\t")
write.table(pre, file="norma.txt")
The outut is arranged row-wise in the text file that is written using the above command and hence when exported to excel it is in a wrong form and many of the information is cut off as the maximum rows are used up .The output looks the following way :
GSM 133971.CEL 5.85302 3.54678 6.57648 9.45634
GSM 133972.CEL 4.65784 3.64578 3.54213 7.89566
GSM 133973.CEL 6.78543 3.54623 2.54345 7.89767
How to write it in a proper format from CEL files in R to a notepad or excel ?
You need to extract the values from the normalised probes using the exprs function. Something like:
write.csv(exprs(pre), file="output.csv", row.names=FALSE)
should do the trick.
I'm not totally clear about what the problem is, what do you mean by "proper format"? Excel will struggle with huge tables and doing your analysis in R with Bioconductor is likely a better way to go, you could then export a smaller results or summary table to excel.
Nevertheless, if you want the file written columnwise, you could try:
write.csv(t(pre),file="norm.txt")
But excel (at least used to) allow many more rows than columns.
I downloaded the X12 seasonal adjustment program located here: http://www.census.gov/srd/www/x12a/x12downv03_pc.html
I followed the setup and got the setting correct. When I go to select a file to input I have four options for file extensions to import which are ".spc" ".mta" ".dta" and "."
The problem is that I have data in excel and I have searched extensively through search engines and I do cannot figure out a way to get data from excel into one of these formats so I can do a seasonal adjustment on my data. Thanks
ADDED: After converting to a dta file (using R thanks to the comments left below) it looks like the program makes you convert it also to a .spc file as well. Anyone have a lead on how to do this? thanks
My first reaction is to:
(1) export the data from excel in something simple like csv.
(2) import that data into R
(3) use the R library "foreign" to export the data in .dta format.
So with the file "test.csv" containing:
V1,V2
1,2
3,4
5,6
you could do the following to produce "test.dta":
library(foreign)
testdata <- read.csv("test.csv")
write.dta(testdata,"test.dta")
Voila, data in .dta format. Would this work for what you have?
I've only ever used the command-line version of X12, but it sounds like you may be using the windows interface instead? If so the following might not be entirely accurate, but it should be close enough (I hope!).
The .dta and .mta files you refer to are just metafiles containing text lists of either spec files or data files to be processed; in particular the .dta files X12 uses are NOT Stata data format files like those produced by Nathan's R-based answer. It's probably best to ignore using metafiles until you are comfortable enough using the software to adjust a single time series.
You can export your data in tab separated variable format (year month/quarter value) without headings and use that as your data file. You can also use a simple list of data values separated by spaces, tabs, or newlines and then tell X12ARIMA what the start and end dates of the series are in the .spc file.
The .spc file doesn't contain the input data, it's a specification file telling X12 where to find the data file and how you want those data to be processed -- you'll have to write them yourself or create them in Win X-12.
Ideally you should write a separate .spc file for each time series to be adjusted; while you can write a .spc file which invokes many of X12's autoselection and identification procedures, it's usually not a good idea to treat the process as a black box, and a bit of manual intervention in the .spc is often necessary to get a good quality adjustment (and essential if there's a seasonal break involved). I find it helpful to start with a fairly generic skeleton .spc file suitable for your computing environment to begin with and then tweak it from there as appropriate for each series.
If you really want to use a single .spc file to adjust multiple series, then you can provide a list of data files in a .dta file and a single .spc file instructing X12ARIMA how to adjust them, but take care to ensure this is appropriate for your data!
The "Getting started with X-12-ARIMA input files on your PC" document on that site is probably a good place to start reading, but you'll probably end up having to consult the complete reference documentation (in particular Chapters 3 and 7) as well.
Edit postscript:
The UK Office for National Statistics have a draft of their guide to seasonal adjustment with X12ARIMA available online here here (archive.org), and is worth a look. It's a good bit easier to work through than the Census Bureau documentation.
Ryan,
This is not elegant, but it might work for you. In this example I'm trying to replicate the spec file from the Example 3.2 in the Census documentation.
Concatentate the data into one text string, then save this single text string using the MS-DOS (TXT) format under the SAVE AS command. To make the text string, first insert two cells above your column header and in the second one type the following text into it.
series{title=
Next, insert double quotation marks before and after the text in your column header, like this:
"Monthly Retail Sales of Household Appliance Stores"
Directly below the last data row, insert rows of texts that list the model specifications, like the following:
)
start= 1972.jul}
transform{function = log}
regression{variables=td}
indentify[diff=(0,1) sdiff=(0,1)}
So you should have something like the following:
<blank row>
series{title=
"Monthly Retail Sales of Household Appliance Stores"
530
529
...
592
590
start= 1972.jul}
transform{function = log}
regression{variables=td}
indentify{diff=(0,1) sdiff=(0,1)}
For the next instructions I am assuming that the text *series{title=
* appears in cell A2 and that cell B1 is empty. In cell B2, insert the following:
=CONCATENATE(B1,A2," ")
Then copy this formula into every cell down the column to concatentate all of the text in column A into a single cell at the end of column B. Finally, copy the final cell to a new spreadsheet's cell A1 using PASTE SPECIAL/VALUE, and save this spreadsheet using SAVE AS: *TXT(MS-DOS), but change the extension to ".spc".
Good luck (and from the little I read of the Census documentation - you'll need it).