I have several netCDF files with one variable that I need to merge into one netCDF file and open it in Arc Map.The following are the examples of the file name that I use.
2005.nc
2006.nc
2007.nc
2008.nc
2009.nc
2010.nc
2011.nc
.......
I would be grateful if anyone could help me with this issue.
If you are on Linux or macOS it is better to do this using CDO as follows:
cdo mergetime 20*.nc outfile
which will merge the files.
As a side point, please show evidence of your attempts (as SO asks) before asking questions.
I have just downloaded some climate data in grib format. I want to use "R" to convert it to NetCDF format.
Furthermore, as the file consists of different variables, I would like to extract one variable at a time into individual files.
It's hard to answer this without your specific file. You should look into producing reproducible examples, especially if you're posting to the R board.
For R, check out library(raster) and library(ncdf4). I just grabbed the first grib1 file I saw, and put together a quick example.
library(raster)
library(ncdf4)
download.file(url = 'ftp://ftp.hpc.ncep.noaa.gov/grib/20130815/p06m_2013081500f030.grb', destfile = 'test.grb')
(r <- raster('test.grb'))
n <- writeRaster(r, filename = 'netcdf_in_youR_comp.nc', overwrite = TRUE)
1. RNOMADS
The package Rnomads has a function readgrib providing wrappers to external libraries allowing one to read grib files
2. converting to netcdf
If the GRIB data is on a regular lat-lon grid, then probably an easier way is to convert to netcdf as the support for reading that is more developed (and you are probably already used to using it)
You can convert grib in several ways, two of the easiest are
CDO:
cdo -f nc copy test.grb test.nc
Use "-f nc4" if you want netcdf4 conventions.
ECCODES (on a mac install with brew install eccodes)
grib_to_netcdf -o test.nc test.grb
you can use ncl installed on you computer
library(ncdf)
system(ncl_convert2nc xxxx.grb, internal = TRUE)
my.nc <- open.ncdf("result.nc")
print(my.nc)
How can I read and write the following file using R ?
https://www.dropbox.com/s/vlnrlxjs7f977zz/3B42_daily.2012.11.23.7.nc
In other words, I would like to read the "3B42_daily.2012.11.23.7.nc" file and write with the same structure that it is written.
Best regards
Package ncdf have functions to do this. You should also read other Q&A on this site tagged with netcdf and r.
Basically to read a netcdf file:
library(ncdf)
a <- open.ncdf('your/path/to/your/file.nc') #that opens a connection to the file
Then function get.var.ncdf helps you extract the data, variable by variable.
The process to write one is described in this Q&A.
The idea is to create dimensions first using dim.def.ncdf then the variables with var.def.ncdf and finally the file itself using create.ncdf.
I have just downloaded some climate data in grib format. I want to use "R" to convert it to NetCDF format.
Furthermore, as the file consists of different variables, I would like to extract one variable at a time into individual files.
It's hard to answer this without your specific file. You should look into producing reproducible examples, especially if you're posting to the R board.
For R, check out library(raster) and library(ncdf4). I just grabbed the first grib1 file I saw, and put together a quick example.
library(raster)
library(ncdf4)
download.file(url = 'ftp://ftp.hpc.ncep.noaa.gov/grib/20130815/p06m_2013081500f030.grb', destfile = 'test.grb')
(r <- raster('test.grb'))
n <- writeRaster(r, filename = 'netcdf_in_youR_comp.nc', overwrite = TRUE)
1. RNOMADS
The package Rnomads has a function readgrib providing wrappers to external libraries allowing one to read grib files
2. converting to netcdf
If the GRIB data is on a regular lat-lon grid, then probably an easier way is to convert to netcdf as the support for reading that is more developed (and you are probably already used to using it)
You can convert grib in several ways, two of the easiest are
CDO:
cdo -f nc copy test.grb test.nc
Use "-f nc4" if you want netcdf4 conventions.
ECCODES (on a mac install with brew install eccodes)
grib_to_netcdf -o test.nc test.grb
you can use ncl installed on you computer
library(ncdf)
system(ncl_convert2nc xxxx.grb, internal = TRUE)
my.nc <- open.ncdf("result.nc")
print(my.nc)
Please can someone help me on the best way to import an excel 2007 (.xlsx) file into R. I have tried several methods and none seems to work. I have upgraded to 2.13.1, windows XP, xlsx 0.3.0, I don't know why the error keeps coming up. I tried:
AB<-read.xlsx("C:/AB_DNA_Tag_Numbers.xlsx","DNA_Tag_Numbers")
OR
AB<-read.xlsx("C:/AB_DNA_Tag_Numbers.xlsx",1)
but I get the error:
Error in .jnew("java/io/FileInputStream", file) :
java.io.FileNotFoundException: C:\AB_DNA_Tag_Numbers.xlsx (The system cannot find the file specified)
Thank you.
For a solution that is free of fiddly external dependencies*, there is now readxl:
The readxl package makes it easy to get data out of Excel and into R.
Compared to many of the existing packages (e.g. gdata, xlsx,
xlsReadWrite) readxl has no external dependencies so it's easy to
install and use on all operating systems. It is designed to work with
tabular data stored in a single sheet.
Readxl supports both the legacy .xls format and the modern xml-based
.xlsx format. .xls support is made possible the with libxls C library,
which abstracts away many of the complexities of the underlying binary
format. To parse .xlsx, we use the RapidXML C++ library.
It can be installed like so:
install.packages("readxl") # CRAN version
or
devtools::install_github("hadley/readxl") # development version
Usage
library(readxl)
# read_excel reads both xls and xlsx files
read_excel("my-old-spreadsheet.xls")
read_excel("my-new-spreadsheet.xlsx")
# Specify sheet with a number or name
read_excel("my-spreadsheet.xls", sheet = "data")
read_excel("my-spreadsheet.xls", sheet = 2)
# If NAs are represented by something other than blank cells,
# set the na argument
read_excel("my-spreadsheet.xls", na = "NA")
* not strictly true, it requires the Rcpp package, which in turn requires Rtools (for Windows) or Xcode (for OSX), which are dependencies external to R. But they don't require any fiddling with paths, etc., so that's an advantage over Java and Perl dependencies.
Update There is now the rexcel package. This promises to get Excel formatting, functions and many other kinds of information from the Excel file and into R.
You may also want to try the XLConnect package. I've had better luck with it than xlsx (plus it can read .xls files too).
library(XLConnect)
theData <- readWorksheet(loadWorkbook("C:/AB_DNA_Tag_Numbers.xlsx"),sheet=1)
also, if you are having trouble with your file not being found, try selecting it with file.choose().
I would definitely try the read.xls function in the gdata package, which is considerably more mature than the xlsx package. It may require Perl ...
Update
As the Answer below is now somewhat outdated, I'd just draw attention to the readxl package. If the Excel sheet is well formatted/lain out then I would now use readxl to read from the workbook. If sheets are poorly formatted/lain out then I would still export to CSV and then handle the problems in R either via read.csv() or plain old readLines().
Original
My preferred way is to save individual Excel sheets in comma separated value (CSV) files. On Windows, these files are associated with Excel so you don't loose the double-click-open-in-Excel "feature".
CSV files can be read into R using read.csv(), or, if you are in a location or using a computer set up with some European settings (where , is used as the decimal place), using read.csv2().
These functions have sensible defaults that makes reading appropriately formatted files simple. Just keep any labels for samples or variables in the first row or column.
Added benefits of storing files in CSV are that as the files are plain text they can be passed around very easily and you can be confident they will open anywhere; one doesn't need Excel to look at or edit the data.
Example 2012:
library("xlsx")
FirstTable <- read.xlsx("MyExcelFile.xlsx", 1 , stringsAsFactors=F)
SecondTable <- read.xlsx("MyExcelFile.xlsx", 2 , stringsAsFactors=F)
I would try 'xlsx' package for it is easy to handle and seems mature enough
worked fine for me and did not need any additionals like Perl or whatever
Example 2015:
library("readxl")
FirstTable <- read_excel("MyExcelFile.xlsx", 1)
SecondTable <- read_excel("MyExcelFile.xlsx", 2)
nowadays I use readxl and have made good experience with it.
no extra stuff needed
good performance
This new package looks nice http://cran.r-project.org/web/packages/openxlsx/openxlsx.pdf
It doesn't require rJava and is using 'Rcpp' for speed.
If you are running into the same problem and R is giving you an error -- could not find function ".jnew" -- Just install the library rJava. Or if you have it already just run the line library(rJava). That should be the problem.
Also, it should be clear to everybody that csv and txt files are easier to work with, but life is not easy and sometimes you just have to open an xlsx.
For me the openxlx package worked in the easiest way.
install.packages("openxlsx")
library(openxlsx)
rawData<-read.xlsx("your.xlsx");
I recently discovered Schaun Wheeler's function for importing excel files into R after realising that the xlxs package hadn't been updated for R 3.1.0.
https://gist.github.com/schaunwheeler/5825002
The file name needs to have the ".xlsx" extension and the file can't be open when you run the function.
This function is really useful for accessing other peoples work. The main advantages over using the read.csv function are when
Importing multiple excel files
Importing large files
Files that are updated regularly
Using the read.csv function requires manual opening and saving of each Excel document which is time consuming and very boring. Using Schaun's function to automate the workflow is therefore a massive help.
Big props to Schaun for this solution.
What's your operating system? What version of R are you running: 32-bit or 64-bit? What version of Java do you have installed?
I had a similar error when I first started using the read.xlsx() function and discovered that my issue (which may or may not be related to yours; at a minimum, this response should be viewed as "try this, too") was related to the incompatability of .xlsx pacakge with 64-bit Java. I'm fairly certain that the .xlsx package requires 32-bit Java.
Use 32-bit R and make sure that 32-bit Java is installed. This may address your issue.
You have checked that R is actually able to find the file, e.g. file.exists("C:/AB_DNA_Tag_Numbers.xlsx") ? – Ben Bolker Aug 14 '11 at 23:05
Above comment should've solved your problem:
require("xlsx")
read.xlsx("filepath/filename.xlsx",1)
should work fine after that.
I have tried very hard on all the answers above. However, they did not actually help because I used a mac. The rio library has this import function which can basically import any type of data file into Rstudio, even those file using languages other than English!
Try codes below:
library(rio)
AB <- import("C:/AB_DNA_Tag_Numbers.xlsx")
AB <- AB[,1]
Hope this help.
For more detailed reference: https://cran.r-project.org/web/packages/rio/vignettes/rio.html
You may be able to keep multiple tabs and more formatting information if you export to an OpenDocument Spreadsheet file (ods) or an older Excel format and import it with the ODS reader or the Excel reader you mentioned above.
As stated by many here, I am writing the same thing but with an additional point!
At first we need to make sure that our R Studio has these two packages installed:
"readxl"
"XLConnect"
In order to load a package in R you can use the below function:
install.packages("readxl/XLConnect")
library(XLConnect)
search()
search will display the list of current packages being available in your R Studio.
Now another catch, even though you might have these two packages but still you may encounter problem while reading "xlsx" file and the error could be like "error: more columns than column name"
To solve this issue you can simply resave your excel sheet "xlsx" in to
"CSV (Comma delimited)"
and your life will be super easy....
Have fun!!
The installation of xlsx package require rJava and xlsxjars. Indirectly they require the specific (32 or 64 bit) java runtime environment on the system.
Pro of read.xlsx: In the same package there are read.xlsx and write.xlsx
Con: Very low speed
As suggested, the easy way is to save in .csv format from excel.
Simple benchmark on a 5800x15 dataset (median)
read.xlsx: >10000ms
read_xlsx: 70ms
read.csv: 15ms