Content of files and groups

With respect to groups and files, we also want to have a simple way to extract the contents. With the names function, we can get all names of objects in a group or in the root directory of a file

names(file.h5)

## [1] "flights" "mtcars"

names(flights.grp)

## [1] "flights"    "weather"    "wind_dir"   "wind_speed"

Another option that gives more information is ls, a method of the classes H5File and H5Group

flights.grp$ls()

##         name     link.type    obj_type num_attrs group.nlinks group.mounted
## 1    flights H5L_TYPE_HARD H5I_DATASET         0           NA            NA
## 2    weather H5L_TYPE_HARD H5I_DATASET         0           NA            NA
## 3   wind_dir H5L_TYPE_HARD H5I_DATASET         2           NA            NA
## 4 wind_speed H5L_TYPE_HARD H5I_DATASET         2           NA            NA
##   dataset.rank dataset.dims dataset.maxdims dataset.type_class
## 1            1       336776             Inf       H5T_COMPOUND
## 2            1        26115             Inf       H5T_COMPOUND
## 3            2     364 x 24       Inf x Inf        H5T_INTEGER
## 4            2     364 x 24       Inf x Inf        H5T_INTEGER
##   dataset.space_class committed_type
## 1          H5S_SIMPLE           <NA>
## 2          H5S_SIMPLE           <NA>
## 3          H5S_SIMPLE           <NA>
## 4          H5S_SIMPLE           <NA>

Information on attributes, datatypes and datasets

If you have an HDF5-File, it is of course important to look up various information not only about groups, but also about the information contained in it. First, we want to get more information about the dataset. ls on the group already gives a lot of information about the datatype, the size, the maximum size etc. However there are also other, more direct, ways to get the same information. In order to investigate the datatype we can

weather_ds <- flights.grp[["weather"]]
weather_ds_type <- weather_ds$get_type()
weather_ds_type$get_class()

## [1] H5T_COMPOUND
## 13 Levels: H5T_NO_CLASS H5T_INTEGER H5T_FLOAT H5T_TIME ... H5T_NCLASSES
## 13 Values: -1 0 1 2 ... 11

cat(weather_ds_type$to_text())

## H5T_COMPOUND {
##       H5T_STRING {
##          STRSIZE H5T_VARIABLE;
##          STRPAD H5T_STR_NULLTERM;
##          CSET H5T_CSET_ASCII;
##          CTYPE H5T_C_S1;
##       } "origin" : 0;
##       H5T_STD_I32LE "year" : 8;
##       H5T_STD_I32LE "month" : 12;
##       H5T_STD_I32LE "day" : 16;
##       H5T_STD_I32LE "hour" : 20;
##       H5T_IEEE_F64LE "temp" : 24;
##       H5T_IEEE_F64LE "dewp" : 32;
##       H5T_IEEE_F64LE "humid" : 40;
##       H5T_IEEE_F64LE "wind_dir" : 48;
##       H5T_IEEE_F64LE "wind_speed" : 56;
##       H5T_IEEE_F64LE "wind_gust" : 64;
##       H5T_IEEE_F64LE "precip" : 72;
##       H5T_IEEE_F64LE "pressure" : 80;
##       H5T_IEEE_F64LE "visib" : 88;
##       H5T_IEEE_F64LE "time_hour" : 96;
##    }

telling us that our dataset consists of a H5T_COMPOUND datatype and prints more detailed information on its content of every column. Regarding the size of the dataset and the size of the chunks (datasets are by default chunked; more about this below) we do:

weather_ds$dims
weather_ds$maxdims
weather_ds$chunk_dims

## [1] 26115
## [1] Inf
## [1] 78

In order to get information on attributes we also have various function available. Which attributes are attached to an object we can see with

h5attr_names(flights.grp[["wind_dir"]])

## [1] "colnames" "rownames"

and the content of one attribute can be extracted with h5attr, the content of all of them with a list as h5attributes.

h5attr(flights.grp[["wind_dir"]], "colnames")

##  [1] "0"  "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14"
## [16] "15" "16" "17" "18" "19" "20" "21" "22" "23"

Detailed information about various objects

In HDF5, there are also various ways of getting more detailed information about objects. The most detailed methods for this are

• get_obj_info: Various information on the number of attributes, the type of the object, the reference count, access times (if set to be recorded) and other more technical information
• get_link_info: For links, mainly yields information on the link type, i.e. hard link or soft link. The difference between them and how to create them will be discussed further below.
• get_group_info: Information about the storage type of the group, if a file is mounted to the group and the number of items in the group. For the casual user, the most interesting information is the number of items in the group, which can also be retrieved using the names function. For very large groups, this way is however more efficient.
• get_file_name: For an H5File or H5Group, H5D or H5T (where D is for dataset and T stands for a committed type) object, returns the name of the file it is in.
• get_obj_name: Similar as get_file_name, applies to the same object, but returns the path inside the file to the object
• file_info: It extracts relatively technical information about a file. It can only be applied to an object of class H5File. This function is usually not of interest to the casual user

Most of these are somewhat advanced. They key information can usually also be extracted with one of the “higher-level” methods shown above, but sometimes the info methods are more efficient.

Integer, Float

For integer-datatypes we have already seen that we have control over essentially everything, i.e. signed/unsigned as well as precision down to the exact number of bits. For floats we have similar control, being able to customize the size of the mantissa as well as the exponent (although in practice this is likely less relevant than being able to customize integer types). To learn more about this functionality for floats, we recommend to read the relevant section of the manual.

Strings

HDF5 itself provides access to both C-type strings and FORTRAN type strings. As R internally uses C-strings, only C-type strings are supported (i.e. strings that are NULL delimited). In terms of the size of the strings, there are fixed and variable length strings available.

str_fixed_len <- H5T_STRING$new(size = 20)
str_var_length <- H5T_STRING$new(size = Inf)

These two types of strings have implications for efficiency and usability. For obvious reasons, variable length strings are more convenient as they are never too small hold a piece of information. However, internally in HDF5, these aren’t stored in the dataset itself - only a pointer to the HDF5-internal heap is stored. This has 2 implications:

• Retrieving the string is somewhat slower
• As the heap is not compressed, compression of datasets does not yield much space saving for variable length data

From this perspective, fixed length strings are considerably better as they are both faster (if not too long) and compressible. However, the user has to be careful that their strings aren’t getting too long, or they will be truncated.

Enum

The equivalent to factors in R are ENUM datatypes. These are stored internally as integers, but each integer has a string label attached to it. In contrast to R-factor variables, the integer values do not have to start at 1 and do not have to to consecutive either. In order to support this more flexible datatype also optimally on the R side, hdf5r comes with the factor_extended class. In the HDF5 API - each enum level is inserted one at a time. As this is rather inconvenient for a vector-oriented language like R, this functionality has not been exposed. We instead provide an R6-class constructor that lets us set all labels and values in one go.

enum_example <- H5T_ENUM$new(c("Label 1", "Label 2", "Label 3"), values = c(-3, 5,
    10))

For efficiency reasons, an integer datatype is automatically generated that provides exactly the needed precision in order to store the values of the enum. Given an enum, variable, we can also find out what labels and values it has

enum_example$get_labels()
enum_example$get_values()

## [1] "Label 1" "Label 2" "Label 3"
## [1] -3  5 10

In addition, we can also get the datatype back that the enum is based on

enum_example$get_super()

## Class: H5T_INTEGER
## Datatype: undefined integer

logical_example <- H5T_LOGICAL$new(include_NA = TRUE)
## we could also use h5types$H5T_LOGICAL or h5types$H5T_LOGICAL_NA
logical_example$get_labels()
logical_example$get_values()

## [1] "FALSE" "TRUE"  "NA"   
## [1] 0 1 2

Compounds (Tables)

Tables are represented as COMPOUND HDF5 objects, which are the equivalent of C-struct. As R does not know this datatype natively, it has to be converted from structs to the list-based construct of R data-frames. Similar as with ENUMs, we don’t expose the underlying C-API that builds the compound on element at a time but instead provide constructors that create it in one go.

cpd_example <- H5T_COMPOUND$new(c("Double_col", "Int_col", "Logical_col"), dtypes = list(h5types$H5T_NATIVE_DOUBLE,
    h5types$H5T_NATIVE_INT, logical_example))

and similar to enums, we can also get back the column names, the classes of the datatypes as well as identifiers for the datatypes itself.

cpd_example$get_cpd_labels()

## [1] "Double_col"  "Int_col"     "Logical_col"

cpd_example$get_cpd_classes()

## [1] H5T_FLOAT   H5T_INTEGER H5T_ENUM   
## 13 Levels: H5T_NO_CLASS H5T_INTEGER H5T_FLOAT H5T_TIME ... H5T_NCLASSES
## 13 Values: -1 0 1 2 ... 11

cpd_example$get_cpd_types()

## [[1]]
## Class: H5T_FLOAT
## Datatype: H5T_IEEE_F64LE
## 
## [[2]]
## Class: H5T_INTEGER
## Datatype: H5T_STD_I32LE
## 
## [[3]]
## Class: H5T_LOGICAL
## Datatype: H5T_ENUM {
##       undefined integer;
##       "FALSE"            0;
##       "TRUE"             1;
##       "NA"               2;
##    }

A textual description is also available

cat(cpd_example$to_text())

## H5T_COMPOUND {
##       H5T_IEEE_F64LE "Double_col" : 0;
##       H5T_STD_I32LE "Int_col" : 8;
##       H5T_ENUM {
##          undefined integer;
##          "FALSE"            0;
##          "TRUE"             1;
##          "NA"               2;
##       } "Logical_col" : 12;
##    }

cplx_example <- H5T_COMPLEX$new()
cplx_example$get_cpd_labels()
cplx_example$get_cpd_classes()

## [1] "Real"      "Imaginary"
## [1] H5T_FLOAT H5T_FLOAT
## 13 Levels: H5T_NO_CLASS H5T_INTEGER H5T_FLOAT H5T_TIME ... H5T_NCLASSES
## 13 Values: -1 0 1 2 ... 11

Arrays

A special datatype is the H5T_ARRAY. As datasets are itself arrays, they are not needed to represent arrays itself. Rather, the are useful in cases where one datatype is wrapped inside another, so mainly if a column of a compound object is supposed to be an array. So lets create an array and put it into a compound object together with some other columns

array_example <- H5T_ARRAY$new(dims = c(3, 4), dtype_base = h5types$H5T_NATIVE_INT)
cpd_several <- H5T_COMPOUND$new(c("STRING_fixed", "Double", "Complex", "Array"),
    dtypes = list(str_fixed_len, h5types$H5T_NATIVE_DOUBLE, cplx_example, array_example))
cat(cpd_several$to_text())

## H5T_COMPOUND {
##       H5T_STRING {
##          STRSIZE 20;
##          STRPAD H5T_STR_NULLTERM;
##          CSET H5T_CSET_ASCII;
##          CTYPE H5T_C_S1;
##       } "STRING_fixed" : 0;
##       H5T_IEEE_F64LE "Double" : 20;
##       H5T_COMPOUND {
##             H5T_IEEE_F64LE "Real" : 0;
##             H5T_IEEE_F64LE "Imaginary" : 8;
##          } "Complex" : 28;
##       H5T_ARRAY {
##          [4][3] H5T_STD_I32LE
##       } "Array" : 44;
##    }

And to see what this would look like as an R object

obj_empty <- create_empty(1, cpd_several)
obj_empty

## Warning in format.data.frame(if (omit) x[seq_len(n0), , drop = FALSE] else x, :
## corrupt data frame: columns will be truncated or padded with NAs

obj_empty$Array

##   STRING_fixed Double Complex Array
## 1                   0    0+0i     0
##  [1] 0 0 0 0 0 0 0 0 0 0 0 0

Variable length data types

And last, there are also variable length datatypes - corresponding to a list in R where each item of the list has the same datatype (general R list, where each item can have a different type cannot be represented in HDF5).

vlen_example <- H5T_VLEN$new(dtype_base = cpd_several)

This would represent a list where each item is a table with an arbitrary number of rows.

Opening and closing of files

In this context, let us quickly also discuss the special way HDF5 handles files. In HDF5, in principle a file can always only be opened once. This can lead to problems as users in R are used to being able to open files as often as they like. Furthermore, it is possible in HDF5 to close the ID of a file without closing all objects in the file. Then, however, the file actually stays open until the last ID pointing into the file is closed and it cannot be opened again without it.

Therefore, as already explained above (and as recommended by the HDF5 manual), do not discard or close files that still have open objects in them. It is preferable to keep the HDF5-file-id pointer around and close it when it is no longer needed (and all objects inside the file) using the close_all method.

Numeric datatypes

For numeric datatypes, the situation is in certain circumstances a bit tricky. In general, R numerical objects are either represented as 64-bit floating point values (doubles) or 32-but integers. R switches relatively transparently between these types as needed (for computations, integers are converted to doubles and conversely, array positions can be addressed by doubles). The main issue when working with HDF5 occurs as R doesn’t have either a 64bit signed or unsigned integer datatype (and also not a long double). In order to work around this issue, the following conventions are being used

• The package uses the bit64 package to provide support for 64-bit integers. These are used extensively (e.g. for ids) and also for numeric integer data types.
• 32 bit and 64 bit floats from HDF5 are always returned as 64 bit floats in R. Writing 32 bit floats from R, may always incur loss of precision of the underlying 64 bit double that is used to represent it in R.
• For integer data types, any HDF5 integer type that can accurately be represented as a 32-bit signed integer will be returned to R as a regular integer (can be changed using flags). Any HDF5 64-bit integer can be returned as a signed 64-bit integer - with the option of returning it as a 32 bit integer or double if it can be done without loss of precision. For unsigned 64-bit integers, they will be returned as floats, incurring loss of precision but avoiding truncation.

An overview of how the data conversion is being done can be seen here:

Schematic of dataype conversion

The underlying principle is that any internal conversion between R types is done by R (with the resulting handling of NA’s and overflows), whereas any conversion between R-types and Non-R-types is done by the HDF5 library (usually meaning that on overflow, truncation occurs).

Strings

In HDF5, strings can either be variable length or fixed length strings. In R, they are always variable length. Therefore, strings from R to HDF5 that are written into fixed-length fields will be truncated. Conversely, strings from HDF5 that are fixed length to R will only be returned up the the NULL character that ends strings in C.

Data-frames/Compounds

The situation is a bit more tricky for table-like objects. In R, these are data-frames, which internally are a list of vectors. In HDF5, a table is a Compound object, that is equivalent to C-struct - i.e. every row is represented together whereas in R every column is represented together. Each of these approaches has certain advantages, but the challenge here is to translate between them.

This is done in the straightforward manner. When converting from R to HDF5, the columns of the tables are copied into the struct whereas in the reverse direction, every struct is decomposed into the corresponding columns.

The Data-frame <-> Compound conversion is also extensively used for HDF5-API functions that return structs as result (and therefore return data-frames).

Arrays data-types

In HDF5, datasets itself can have arbitrary dimensions. In addition to that, there are also array-datatypes that allow for the inclusion for arrays e.g. inside a compound object. Translation to and from arrays is relatively straightforward and only involves setting the correct dim attribute in R.

In addition to that, however, there is small complication. In R, the first dimension is the fastest changing dimension. In HDF5 (same as in C), the last dimension is however the fastest changing one. For datasets, we work around this problem by always reversing the dimensions that are passed between R and HDF5 and therefore making the distinction transparent. For arrays, this is however a bit trickier. For example let us assume that we have a dataset that is a one-dimensional vector of length 10, each element of which is an array-datatype of length 4, resulting in a 10 x 4 dataset. However, it is now not quite clear how this should be represented in R. If we follow the notion, that the fastest changing dimension in R is the first one, the result would be a dataset with 4 rows and 10 columns, i.e. 10 x 4.

This does feel rather unintuitive, forcing a user to specify the second dimension to get all items of the array. Therefore, we have implemented it so that a 10 x 4 dataset is returned, with each row corresponding to the array-datatype. In order to achieve this we have to deviate from the ordering principle in HDF5. Where in HDF5, the elements of the first internal array are in position 1, 2, 3 and 4 (or 0 to 3 when you start counting at 0), in R they are now in position 1, 11, 21, and 31. In order to do this, we first internally read the HDF5 array into an R-array of shape 4 x 10 and then transpose the result.

Variable-length data types

In HDF5, there are also variable-length data types. Essentially, this corresponds to an R list-like object, with the additional restriction that every item of the list has to be of the same datatype. This is also how it is implemented. R list where all items are vectors (of arbitrary length) of the same type can be converted to HDF5-VLEN objects and vice versa.

Reference objects

As of the writing of this vignette, these have not yet been implemented.