Bernd Fischer 1 and Mike L. Smith 2. The package is an R interface for HDF5. On the one hand it implements R interfaces to many of the low level functions from the C interface. On the other hand it provides high level convenience functions on R level to make a usage of HDF5 files more easy.
After installing R you can run the following commands from the R command shell to install rhdf5.
Subscribe to RSS
The HDF5 file can contain a group hierarchy. We create a number of groups and list the file content afterwards. Objects can be written to the HDF5 file. Attributes attached to an object are written as well, if write. Note that not all R -attributes can be written as HDF5 attributes. If a dataset with the given name does not yet exist, a dataset is created in the HDF5 file and the object obj is written to the HDF5 file.
If a dataset with the given name already exists and the datatype and the dimensions are the same as for the object objthe data in the file is overwritten. If the dataset already exists and either the datatype or the dimensions are different, h5write fails. File, group and dataset handles are a simpler way to read and partially to write HDF5 files. A file is opened by H5Fopen. Both of the following code lines return the matrix C. Remind that this can have severe consequences for large datasets and datastructures.
One can as well return a dataset handle for a matrix and then read the matrix in chunks for out-of-memory computations. Remind again that in the following code the first version does not change the data on disk, but the second does.
The rhdf5 package provides two ways of subsetting. One can specify the submatrix with the R -style index lists or with the HDF5 style hyperslabs. Note, that the two next examples below show two alternative ways for reading and writing the exact same submatrices.
Before writing subsetting or hyperslabbing, the dataset with full dimensions has to be created in the HDF5 file. This can be achieved by writing once an array with full dimensions as in Section or by creating a dataset. Afterwards the dataset can be written sequentially. The chosen chunk size and compression level have a strong impact on the reading and writing time as well as on the resulting file size.NetCDF is a widely used format for exchanging or distributing climate data, and has also been adopted in other fields, particularly in bioinformatics, and in other disciplines where large multidimensional arrays of data are generated.
NetCDF files are also machine-independent because can be transferred among servers and computers that are running different operating systems, without having to convert the files in some way. Originally developed for storing and distributing climate data, such as those generated by climate simulation or reanalysis models, the format and protocols can be used for other gridded data sets. There are two versions of netCDF; netCDF3, which is widely used, but has some size and performance limitations, and netCDF4, which supports larger data sets and includes additional capabilities like file compression.
The ncdf4. The data are available on ClimateLab.
netCDF in R
Download the netCDF file to a convenient folder. The file is assumed to be a CF-compliant netCDF file, in which the three main spatiotemporal dimensions allear the the relative order of time T-coordinateheight or depth Z-coordinatelatitude or Y-coordinateand longitude or X-coordinate.
In this example, the file is a 3-D file with T, Y and X coordinates month of the year, latitude, and longitude. First, set the values for some temporary variables. Open the NetCDF data set, and print some basic information. The print function applied to the ncin object produces information similar to that produced by the command-line utility ncdump. Note that in an ncdump of the file, the coordinates of the variable tmp are listed in the reverse order as they are here e.
The number of longitude and latitude values can be verified using the dim function:. Print the time units string. Note the structure of the time units attribute. Get the global attributes. The attributes can be listed, by simply typing an attribute name at the command line.
NetCDF files or data sets are naturally 2-D raster slabs e. There is an exception to this expectation in some cases like principle components analysis PCA in which variables are locations and the observations are times. Here are some example conversions:. In a netCDF file, values of a variable that are either missing or simply not available i.
The total number of non-missing i. NetCDF variables are read and written as one-dimensional vectors e.NetCDF files are often used to distribute gridded, multidimensional spatial data such as sea surface temperature, chlorophyll-a levels and so on.
I think it's more likely that the netcdf library was not compiled with netcdf version 4 support enabled. I hope this is helpful and please let me know if you know of a better solution. Provides a high-level R interface to data files written using Unidata's netCDF library version 4 or earlierwhich are binary data files that are portable across platforms and include metadata information in addition to the data sets.
To access HDF, you can use 3 different R packages. Barring bugs, it should not be possible to install ncdf4 correctly without it knowing where the libraries are. For data in netCDF4, an R-package, ncdf4, is available. One of the main advantages of NetCDF4 include its support for larger files, unlimited dimensions e.
The R package ncdf4 can read either format. Version 4 of the netcdf library stores data in HDF5 format files; earlier versions stored data in a custom format. Unfortunately, clicking the install button in RStudio and typing 'ncdf' will only work at the user level. There are several 'models' of HDF4 which can be a bit confusing. The netCDF data file format from Unidata is a platform-independent, binary file that also contains metadata describing the contents and format of the data in the file.
I originally used the hdf5 package. The package will not be installed for all users or even show up in all of your RStudio projects. Are there any other ways to convert? Many thanks. That script uses the function wasp in the R package seewave to estimate sound speeds for each gridded value of latitude, longitude and depth using the Mackenzie model.
R: Functions for handling time series and meteorological data, e. In this example, I am working with dead fuel moisture data available from Dr. If you are using the R package, they have specific instructions on their web site for dealing with NetCDF4 files: ncdf4. R packages for those fields usually include functions to read directly from those file formats. There is a common "design pattern" in analyzing data stored as netCDF, HDF or in the native format of the raster package, that includes.
The R code for performing the analysis is shown below. R is a free software environment for statistical computing and graphics. The solution is to import the netCDF file into R as an array and then reorganize the array into the proper dimensions.
HDF5 in R. Unfortunately, neither array nor matrix are the fundamental data storage in R. Newer package "ncdf4" is designed to work with the netcdf library version 4, and supports features such as compression and chunking. The files are HDF5, but may be able to be read using the netcdf4 reader for R You probably want to read the Mapped data and not the Binned data, as the bin files are not for the faint of heart - they are NOT raster files and require a bit of work to properly use.Provides a high-level R interface to data files written using Unidata's netCDF library version 4 or earlierwhich are binary data files that are portable across platforms and include metadata information in addition to the data sets.
Using this package, netCDF files either version 4 or "classic" version 3 can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files.
This package replaces the former ncdf package, which only worked with netcdf version 3 files. For various reasons the names of the functions have had to be changed from the names in the ncdf package. The old ncdf package is still available at the URL given below, if you need to have backward compatibility.
It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem. However, the ncdf package does not provide an interface for netcdf version 4 files.
SpatialSpatioTemporal.For example, you can slice into multi-terabyte datasets stored on disk, as if they were real NumPy arrays. The netCDF data file format from Unidata is a platform-independent, binary file that also contains metadata describing the contents and format of the data in the file. For example, you might have a variable named "Temperature" that is a function of lon-gitude, latitude, and height.
On the one hand it implements R interfaces to many of the low level functions from the C interface. I've installed R I was able to open the file with the ncdf4 package since ncdf does not seem to be backwards compatible with version 4 files: "However, the ncdf package does not provide an interface for netcdf version 4 files.
These are sadly not the hdf5 files that I know are readable in R. Version 4 of the netcdf library stores data in HDF5 format files; earlier versions stored data in a custom format. It is One of my favorite formats I love MatLab is hdf.
For data in netCDF4, an R-package, ncdf4, is available. The getValues function in raster reshapes a raster object; if the argument of the function is a raster layer, the function returns a vector, while if the argument is a raster stack or raster brick e.
Installation of the HDF5 package To install the rhdf5 package, you need a current version 1. In this case, it is essentially the same as the ncdf4 package. R packages for those fields usually include functions to read directly from those file formats. The R package ncdf4 can read either format.
The main functions are nc open which opens the NetCDF Many fields of science have field-specific data formats. The file. The files are HDF5, but may be able to be read using the netcdf4 reader for R You probably want to read the Mapped data and not the Binned data, as the bin files are not for the faint of heart - they are NOT raster files and require a bit of work to properly use. NET applications. Read a kallisto object from an HDF5 file. They can also be downloaded separately.
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have a file in hdf5 format. I know that it is supposed to be a matrix, but I want to read that matrix in R so that I can study it. Is such a tutorial available online. Specifically, How do you read a hdf5 object with this package, and how to actually extract the matrix? The interface is relatively easier to understand the the documentation and example code is quite clear.
I could use it without problems. My problem it seems was the input file. The matrix that I wanted to read was actually stored in the hdf5 file as a python pickle. So every time I tried to open it and access it through R i got a segmentation fault. I did figure out how to save the matrix from within python as a tsv file and now that problem is solved.
The rhdf5 package works really well, although it is not in CRAN. Install it from Bioconductor. And inspect the structure :. Note that multidimensional arrays may appear transposed. Also you can read groups, which will be named lists in R. Compared to rhdf5 it has the following features:.
I used the rgdal package to read HDF5 files. You do need to take care that probably the binary version of rgdal does not support hdf5. In that case, you need to build gdal from source with HDF5 support before building rgdal from source. Alternatively, try and convert the files from hdf5 to netcdf. Once they are in netcdf, you can use the excellent ncdf package to access the data. The conversion I think could be done with the cdo tool.
In practice, ncdf4 provides a simple interface, and migrating code from using older hdf5 and ncdf packages to a single ncdf4 package has made our code less buggy and easier to write some of my trials and workarounds are documented in my previous answer.
Learn more. How to deal with hdf5 files in R?
rhdf5 - HDF5 interface for R
Ask Question. Asked 7 years, 6 months ago. Active 1 month ago. Viewed 46k times. Sam Sam 6, 14 14 gold badges 40 40 silver badges 58 58 bronze badges.
Active Oldest Votes. Rich Pauloo 4, 2 2 gold badges 18 18 silver badges 45 45 bronze badges. Mike T Mike T 32k 15 15 gold badges silver badges bronze badges. Very good package indeed. I was thinking about using the h5r package from CRAN first but it seems underdocumented.You can report issue about the content on this page here Want to share your content on R-bloggers?
NetCDF files are often used to distribute gridded, multidimensional spatial data such as sea surface temperature, chlorophyll-a levels and so on.
NetCDF is more than just a file format, and so googling it can be a little intimidating. I hope this helps make these files a little easier to use in R. A full specification for NetCDF can be found here. Additionally if you have a very large NetCDF data file, you can only pull out the subset of data you are interested in instead of opening the whole thing.
These are gridded longitude by latitude values. Bear in mind that your NetCDF files may contain higher dimensions e. The output can be quite long so I prefer to save it to a text file. That way I can keep it open and avoid continuous scrolling in my console window. The term attributes might get a little confusing now. Calling the R attributes of the NetCDF file connection provides access to some information about the file, e. These names in turn give us access to the data and associated NetCDF attributes units etc.
The flags data is associated with cloud cover and the error describes error associated with each data point. These are fully described in the Product User Guide.
Now we will retrieve the latitude and longitude data stored as NetCDF dimensions "dim". We can compare the extents of these dimensions with those of our data matrix to confirm that they match. We will then use them to assign meaningful row and column names to our data.
This also matches what we saw in the text file. We can make the data a little easier to think about with a bit of labelling and by transposing the data matrix so that the latitudes are in the rows and longitudes are in the columns. We saw above and in the text file that there were 52 global attributes in this file and they contain all kinds of useful info.
Which attributes you will need is dependent on your analysis. The names function will give us a list of attribute names. These names will give us access to the relevant values. If you need other data structures it should be easy to adjust the function as required. To leave a comment for the author, please follow the link and comment on their blog: R — Phil's blog.
Want to share your content on R-bloggers? Never miss an update! Subscribe to R-bloggers to receive e-mails with the latest R posts. You will not see this message again.