hi literally day one new coder
On the excel sheet, my data looks organized, but when I upload my file to R, it's not able to read the excel properly and the column headers are in the rows and the data seems randomized.
So far I have tried:
library(readxl)
dataset <-read_excel("pathname")
View(dataset)
Also tried:
dataset <-read_excel("pathname", sheet=1, colNames=TRUE)
Also tried to use the package openxlsx
but nothing is giving me the correct, organized data set.
I tried formatting my Excel to a CSV file, and the CSV file looks exactly like the data that shows up on R (both are messed up).
How should I approach this problem?
I deal with importing .xlsx into R frequently. It can be challenging due to the flexibility of the excel platform. I generally use readxl::read_xlsx() to fetch data from .xlsx files. My suggestions:
First, specify exactly the data you want to import with the range argument.
A cell range to read from, as described in cell-specification. Includes typical Excel
ranges like "B3:D87", possibly including the sheet name like "Budget!B2:G14"
Second, if there are there merged cells or other formatting challenges in column headers, I resort to setting col_names = FALSE. And supplying clean names after import with names(df) <- c("first_col", "second_col")
Third, if there are merged cells elsewhere in the spreadsheet I generally I resort to "fixing" them in excel (not ideal but easier for my use case), however, others may have suggestions on a programmatic fix.
It may be helpful to provide a screenshot of your spreadsheet.
Related
I am using R to analyze some text data. After doing some aggregation, I had a new dataframe I wanted to write to a csv file, so I can use it in other analyses. The dataframe looks correct in r- it only has 2 columns with text data- but once I write the csv and open it, the text is scattered across different columns. Here is the code I was using:
write.csv(new_df, "4.19 Group 1_agg by user try 2.csv")
I tried adding in an extra bit of code to specify that it should be using UTF-8, since I've heard this could be an encoding error, so the code then looked like this:
write.csv(new_df, "4.19 Group 1_agg by user try 2.csv", fileEncoding = "UTF-8")
I also tried reading in the file differently (using fread instead of read.csv)
Still, the csv file looks wrong/messy in many places. Here is what it should look like:
This is what it looks like currently:
Again, I think the error must be in writing the csv file, because everything looks good in R when I check it using names and head. Any help is appreciated, thank you!
This is more of a curiosity.
Sometimes I modify csv files from Excel rather than R (suppose I manage to find a missing piece of info and I type it in the csv file), of course maintaining commas and quotes as they were.
Every time I do this, R becomes unable to read the csv file, i.e. it imports a single column as it appears on Excel, rather than separating the values (no options like sep= or quote= change this).
Does anyone know why this happens?
Thanks a lot
An example
This was readable:
state,"city","county"
AK,"Anchorage",""
AK,"Haines",""
AK,"Juneau","Juneau"
After adding the missing info under "county", R fails to import it as a data frame, reading it instead as a single vector.
state,"city","county"
AK,"Anchorage","Anchorage"
AK,"Haines","Haines"
AK,"Juneau","Juneau"
Edit:
I'm just running the basic read.csv
df <- read.csv("C:/directory/df.csv")
I am using openxlsx in order to write the outputs of my data.
I have used the following code to read my data using readxl.
df1=read_excel("C:/my_data.xlsx",skip=2);
Now I want to write the output and keep the original Excel file using any possible package. I have used the following codes, but it does not keep the original Excel file. Can we do it it in R packages?
write.xlsx(df1, 'C:/mydata.xlsx',skip=2)
Given your code, you should nhave two different data files in your working directory:
"my_data.xlsx" (the one that you loaded), and "mydata.xlsx" (the one that you created through R). R shouldn't overwrite your files if you give them different names.
If there's only one file, are you sure you didn't use the same name for both files? If so, then everything should work fine if you give the files different names (e.g. "my_file1.xlsx" and "my_file2.xlsx")!
Also, in general, it's a good idea to give data files an informative name so that you don't accidentally delete/overwrite files that you need. For example, if the original excel data is you raw data, consider naming it "data_raw.xlsx", and make sure that you only read it, and whenever you make some changes to it, save it under a different name (e.g. "data_processed1.xlsx").
You can also save data files in the native R format .rds using the save_rds() function, this is especially helpful if you want to keep special attributes of variables such as factors, etc...
Hope this helps!
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.
Since I'm not able to find/decipher solutions to my problem I'll try asking.
Up until now I've only worked with other people's data (.csv files) in RStudio but this time around it's my own data, which I entered into Excel. All entries are tab-delimited, and I wouldn't enter data into Excel any other way. After some googling it seems like R and .xlsx files aren't best friends, so I saved my file in various other formats, .csv one of them. Thus I have tab-delimited .csv file.
The problem with loading tab-delimited .csv files also features here, but my problem is not with "reading some of the numeric variables in as factors" (whatever that means), but that data is loaded as semi-colon separated in R:
data <- read.table(file.choose(), sep="\t", header=T)
View(data)
Date.Miles.Time
2015-08-10;5;45
Apart from improper formatting there are 29 observations rather than 24; the last five entries are just ;;. Again, my problem is not the same as in the link but I figure there's no harm in trying Justin's suggestion in the answer, i.e. options(stringsAsFactors=FALSE) and then running the above again, but it achieves nothing.
read.csv() and read.delim() yield the same result. Any suggestions?