This package is designed to capture the computational footprint of any model workflow or output. It achieves this by encoding computational decisions into sequences of bits (i.e., bitfields) that are transformed to integer values. This allows storing a range of information into a single column of a table or a raster layer, which can be useful when documenting
- the metadata of any dataset by collecting information throughout the dataset creation process,
- intermediate data that accrue along a workflow, or
- a set of output metrics or parameters.
- …
Think of a bit as a switch representing off and on states. A combination of a pair of bits can store four states, and n bits can accommodate 2^n states. These states could be the outcomes of (simple) tests that document binary responses, cases or numeric values. The data produced in that way could be described as meta-analytic or meta-algorithmic data, because they can be re-used to extend an analysis pipeline or algorithm by downstream applications.
Install the official version from CRAN:
install.packages("bitfield")Install the latest development version from github:
devtools::install_github("bitfloat/bitfield")library(bitfield)
library(dplyr, warn.conflicts = FALSE)
library(terra, warn.conflicts = FALSE)
#> terra 1.8.54Let’s first load an example dataset
bf_tbl$x # invalid (259) and improbable (0) coordinate value
#> [1] 25.3 27.9 27.8 27.0 259.0 27.3 26.1 26.5 0.0
bf_tbl$y # Inf and NaN value
#> [1] 59.5 58.1 57.8 59.2 Inf 59.1 58.4 NaN 0.0
bf_tbl$commodity # NA value or mislabelled term ("honey")
#> [1] soybean maize soybean <NA> honey maize soybean maize soybean
#> Levels: honey maize soybean
bf_tbl$yield # correct range?!
#> [1] 11.192915 11.986793 13.229386 4.431376 12.997422 8.548882 11.276921
#> [8] 10.640715 9.010452
bf_tbl$year # flags (*r)
#> [1] "2021" NA "2021r" "2021" "2021" "2021" "2021" "2021" "2021"
# and there is a set of valid commodity terms
validComm <- c("soybean", "maize")The first step is in creating what is called registry in bitfield.
This registry captures all the information required to build the
bitfield (note, this is merely a minimal tech demo, for additional
information, check the respective function documentations).
reg <- bf_registry(name = "yield_QA",
description = "this example bitfield documents quality assessment of yield data.")Then, individual bit flags need to be grown by specifying the respective
protocols. These protocols create flags for the most common
applications, such as na (to test for missing values), case (to test
what case/class the observations are part of),nChar (to count the
number of characters of a variable), or numeric to encode a numeric
(floating point) variable as bit sequence.
# tests for longitude availability
reg <- bf_map(protocol = "na", # the protocol with which to build the bit flag
data = bf_tbl, # specify where to determine flags
x = x, # ... and which variable to test
pos = 1, # specify at which position to store the flag
registry = reg) # provide the registry to update
# test which case an observation is part of
reg <- bf_map(protocol = "case", data = bf_tbl, registry = reg, na.val = 0,
yield >= 11, yield < 11 & yield > 9, yield < 9 & commodity == "maize")
#> Lade nötiges Paket: purrr
# test the length (number of characters) of values
reg <- bf_map(protocol = "nChar", data = bf_tbl, registry = reg,
x = y)
# store a simplified (e.g. rounded) numeric value
reg <- bf_map(protocol = "numeric", data = bf_tbl, registry = reg,
x = yield, format = "half")These are functions that represent the possible encoding types boolean
(bool), enumerated cases (enum), (signed) integers (int), and
numeric floating-point (num). The encoding type determines various
storage parameters of the resulting flags. This is, however, not yet the
bitfield. The registry is merely the instruction manual, so to speak, to
create the bitfield and encode it as integer, with the function
bf_encode().
reg
#> width 23
#> flags 4 -|--|----|----------------
#>
#> pos encoding type col
#> 1 0.0.1/0 na x
#> 2 0.0.2/0 case yield-commodity
#> 4 0.0.4/0 nChar y
#> 8 1.5.10/15 numeric yield
(field <- bf_encode(registry = reg))
#> # A tibble: 9 × 1
#> bf_int1
#> <int>
#> 1 1591704
#> 2 1591806
#> 3 1591965
#> 4 541806
#> 5 1460863
#> 6 3688518
#> 7 1591715
#> 8 2509138
#> 9 2246785The bitfield can be decoded based on the registry with the function
bf_decode() at a later point in time or another workflow, where the
metadata contained in the bitfield can be used or extended in a
downstream application.
flags <- bf_decode(x = field, registry = reg, sep = "-")
#> # A tibble: 4 × 3
#> pos name desc
#> <chr> <chr> <chr>
#> 1 1:1 na_x 'x' contains NA-values.
#> 2 2:3 case_yield-commodity The observations are distinguished into cases.
#> 3 4:7 nChar_y 'y' is that many characters long.
#> 4 8:23 numeric_yield 'yield' is encoded as floating-point bit-sequence.
# -> prints legend by default, which is also available in bf_legend
bf_tbl |>
bind_cols(flags) |>
kable()| x | y | commodity | yield | year | bf_bin |
|---|---|---|---|---|---|
| 25.3 | 59.5 | soybean | 11.192915 | 2021 | 0-01-1000-0100100110011000 |
| 27.9 | 58.1 | maize | 11.986793 | NA | 0-01-1000-0100100111111110 |
| 27.8 | 57.8 | soybean | 13.229386 | 2021r | 0-01-1000-0100101010011101 |
| 27.0 | 59.2 | NA | 4.431376 | 2021 | 0-00-1000-0100010001101110 |
| 259.0 | Inf | honey | 12.997422 | 2021 | 0-01-0110-0100101001111111 |
| 27.3 | 59.1 | maize | 8.548882 | 2021 | 0-11-1000-0100100001000110 |
| 26.1 | 58.4 | soybean | 11.276921 | 2021 | 0-01-1000-0100100110100011 |
| 26.5 | NaN | maize | 10.640715 | 2021 | 0-10-0110-0100100101010010 |
| 0.0 | 0.0 | soybean | 9.010452 | 2021 | 0-10-0010-0100100010000001 |
The column bf_bin, in combination with the legend, can be read one
step at a time. For example, considering the first bit, we see that no
observation has an NA value and considering the second bit, we see
that observations 4 and 6 have a yield smaller than 9 and a
commodity value “maize” (case 3 with binary value 11).
Moreover, more computation friendly, we can also separate the bitfield
into distinct columns per flag and we can load the decoded values from
the package environment .GlobalEnv.
bf_decode(x = field, registry = reg, verbose = FALSE)
#> # A tibble: 9 × 4
#> na_x `case_yield-commodity` nChar_y numeric_yield
#> <chr> <chr> <chr> <chr>
#> 1 0 01 1000 0100100110011000
#> 2 0 01 1000 0100100111111110
#> 3 0 01 1000 0100101010011101
#> 4 0 00 1000 0100010001101110
#> 5 0 01 0110 0100101001111111
#> 6 0 11 1000 0100100001000110
#> 7 0 01 1000 0100100110100011
#> 8 0 10 0110 0100100101010010
#> 9 0 10 0010 0100100010000001
# access values manually
ls(.GlobalEnv)
#> [1] "bf_legend" "case_yield-commodity" "field"
#> [4] "flags" "na_x" "nChar_y"
#> [7] "numeric_yield" "reg" "validComm"
.GlobalEnv[["nChar_y"]]
#> [1] 8 8 8 8 6 8 8 6 2Beware that numeric values that have been encoded in this way, likely have a lower precision than the input values (which may not be a problem in the frequent case where only rounded values are of interest). This can be adjusted by setting the respective parameters in the protocol that encodes numeric values (a vignette explaining this in detail will follow).
old <- options(pillar.sigfig = 7)
tibble::tibble(original = bf_tbl$yield,
bitfield = .GlobalEnv$numeric_yield)
#> # A tibble: 9 × 2
#> original bitfield
#> <dbl> <dbl>
#> 1 11.19292 11.1875
#> 2 11.98679 11.98438
#> 3 13.22939 13.22656
#> 4 4.431376 4.429688
#> 5 12.99742 12.99219
#> 6 8.548882 8.546875
#> 7 11.27692 11.27344
#> 8 10.64072 10.64062
#> 9 9.010452 9.007812
options(old)An interesting use case is in encoding metadata for modelled gridded data. In the newest version of this package, this is possible simply by providing a raster instead of a table to the functions.
library(terra)
# define an example dataset
bf_rst <- rast(nrows = 3, ncols = 3, vals = bf_tbl$commodity,
names = "commodity") # with levels
bf_rst$yield <- rast(nrows = 3, ncols = 3, vals = bf_tbl$yield) # numeric
# build the registry
reg <- bf_registry(name = "reg_raster",
description = "bitfield for rasters.")
reg <- bf_map(protocol = "na", data = bf_rst, registry = reg,
x = commodity)
reg <- bf_map(protocol = "range", data = bf_rst, registry = reg,
x = yield, min = 5, max = 11)
reg <- bf_map(protocol = "category", data = bf_rst, registry = reg,
x = commodity, na.val = 0)
# encode as bitfield (and make raster out of it)
field <- bf_encode(registry = reg)
rst_field <- rast(bf_rst$yield, vals = field, names = names(field))
# decode (gridded) bitfield somewhere downstream
flags <- bf_decode(x = values(rst_field, dataframe = TRUE), registry = reg)
#> # A tibble: 3 × 3
#> pos name desc
#> <chr> <chr> <chr>
#> 1 1:1 na_commodity 'commodity' contains NA-values.
#> 2 2:2 range_yield The 'yield' values are between '5' and '11'.
#> 3 3:4 category_commodity 'commodity' is encoded as categorical enumeration.
bind_cols(flags, field)
#> # A tibble: 9 × 4
#> na_commodity range_yield category_commodity bf_int1
#> <chr> <chr> <chr> <int>
#> 1 0 0 10 2
#> 2 0 0 01 1
#> 3 0 0 10 2
#> 4 1 0 00 8
#> 5 0 0 00 0
#> 6 0 1 01 5
#> 7 0 0 10 2
#> 8 0 1 01 5
#> 9 0 1 10 6
plot(c(bf_rst, rst_field))