I have multiple excel files in multiple directories that I am reading into R. However, I don't want to read in EVERY excel file; I only want to read in the most recent ones (for example, only the ones created in the last month). Is there a way to do this?
Currently I am using this to read in all of the excel files, which is working just fine:
filenames <- Sys.glob(file.path('(name of dir)', "19*", "Electrode*02.xlsx")) <br>
elecsheet <- do.call("cbind", lapply(filenames, read_excel))
Somewhere in this second line of code (I think), I need to tell R to look at the metadata and only read in the excel files that have been created since a certain date.
Thank you!
Related
I have a sample .nc file that contains a number of variables (5 to be precise) and is being read into a program. I want to create a new .nc file containing different data (and different dimensions) that will also be read into that program.
I have created a .nc file that looks the same as my sample file (I have included all of the necessary attributes for each of the variables that were included in the original file).
However, my file is still not being ingested.
My question is: is there a way to test for differences in the layout/structure of .nc files?
I have examined each of the variables/attributes within Rstudio and I have also opened them in panoply and they look the same. There are obviously differences (besides the actual data that they contain) since the file is not being read.
I see that there are options to compare the actual data within .nc files online (Comparison of two netCDF files), but that is not what I want. I want to compare the variable/attributes names/states/descriptions/dimensions to see where my file differs. Is that possible?
The ideal situation here would be to create a .nc template from the variables that exist within the original file and then fill in my data. I could do this by defining the dimensions (ncdim_def), creating the file(nc_create), getting my data (ncvar_get) and putting it in the file (ncvar_put), but that is what I have done so far, and it is too reliant on me not making an error (which I obviously have as they are not the same).
If you are on unix this is more easily achieved using CDO. See the Information section of the reference card: https://code.mpimet.mpg.de/projects/cdo/embedded/cdo_refcard.pdf.
For example, if you wanted to check that the descriptions are the same in files just do:
cdo griddes example1.nc
cdo griddes example2.nc
You can easily use system in R, to wrap around this.
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.
i want to read out several csv files from the web and save the data into a data frame.
If the files were on my computer this would be very easy as I have seen but I don't always want to download the files.
The example:
"https://www.football-data.co.uk/mmz4281/1819/F1.csv",
"https://www.football-data.co.uk/mmz4281/1718/F1.csv",
"https://www.football-data.co.uk/mmz4281/1617/F1.csv",
"https://www.football-data.co.uk/mmz4281/1516/F1.csv",
"https://www.football-data.co.uk/mmz4281/1415/F1.csv",
"https://www.football-data.co.uk/mmz4281/1314/F1.csv",
"https://www.football-data.co.uk/mmz4281/1213/F1.csv",
"https://www.football-data.co.uk/mmz4281/1112/F1.csv",
"https://www.football-data.co.uk/mmz4281/1011/F1.csv"
These are the CSV files. maybe its possible with a function or a loop but i dont know how.
Maybe you can help me.
Greetings
Reading files from the web is just as easy as reading them from your file system; you can just pass a URL instead of a file-path to readr::read_csv() (you tagged your question with readr so I assume you want to use that).
Assuming your files are in a vector:
files <- c("https://www.football-data.co.uk/mmz4281/1819/F1.csv",
"https://www.football-data.co.uk/mmz4281/1718/F1.csv",
"https://www.football-data.co.uk/mmz4281/1617/F1.csv",
"https://www.football-data.co.uk/mmz4281/1516/F1.csv",
"https://www.football-data.co.uk/mmz4281/1415/F1.csv",
"https://www.football-data.co.uk/mmz4281/1314/F1.csv",
"https://www.football-data.co.uk/mmz4281/1213/F1.csv",
"https://www.football-data.co.uk/mmz4281/1112/F1.csv",
"https://www.football-data.co.uk/mmz4281/1011/F1.csv")
You can use readr::read_csv to read a specific file, and combine them into one data-frame with purrr::map_dfr:
df <- purrr::map_dfr(files, readr::read_csv)
This iterates over the contents of files, applies readr::read_csv to each of those elements, and combines them into one data frame, rowwise (hence dfr).
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 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.