Overview
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 value. 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,
- a provenance graph that documents how a gridded modelled data product was built,
- 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.
Installation
Install the official version from CRAN:
# install.packages("bitfield")Install the latest development version from github:
devtools::install_github("EhrmannS/bitfield")Examples
Let’s first load an example dataset
tbl_bityield$x # invalid (259) and improbable (0) coordinate value
#> [1] 25.3 27.9 27.8 27.0 259.0 27.3 26.1 26.5 0.0 25.7
tbl_bityield$y # Inf and NaN value
#> [1] 59.5 58.1 57.8 59.2 Inf 59.1 58.4 59.0 0.0 NaN
tbl_bityield$commodity # NA value or mislabelled term ("honey")
#> [1] "soybean" "maize" NA "maize" "honey" "maize" "soybean"
#> [8] "maize" "soybean" "maize"
tbl_bityield$yield # correct range?!
#> [1] 11.192915 11.986793 13.229386 4.431376 12.997422 8.548882 11.276921
#> [8] 10.640715 9.010452 13.169897
tbl_bityield$year # flags (*r)
#> [1] "2021" NA "2021r" "2021" "2021" "2021" "2021" "2021" "2021"
#> [10] "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
yieldReg <- bf_registry(name = "yield_QA",
description = "this bitfield documents quality assessment in a table of yield data.")Then, individual bit flags need to be grown by specifying the respective mapping function. These functions create flags for the most common applications, such as bf_na() (to test for missing values), bf_case() (to test what case/class the observations are part of),bf_length() (to count the number of digits of a variable), or bf_numeric() to encode a numeric (floating point) variable as bit sequence.
# tests for longitude availability
yieldReg <-
bf_na(x = tbl_bityield, # specify where to determine flags
test = "x", # ... and which variable to test
pos = 1, # specify at which position to store the flag
registry = yieldReg) # provide the registry to update
# test which case an observation is part of
yieldReg <-
bf_case(x = tbl_bityield, exclusive = FALSE,
yield >= 11, yield < 11 & yield > 9, yield < 9 & commodity == "maize",
registry = yieldReg)
# test the length (number of digits) of values
yieldReg <-
bf_length(x = tbl_bityield, test = "y",
registry = yieldReg)
# store a simplified (e.g. rounded) numeric value
yieldReg <-
bf_numeric(x = tbl_bityield, source = "yield", precision = "half",
registry = yieldReg)Various derived functions build on these and thus require bits according to the same rules. The resulting data structure is a record of all the things that are grown on the bitfield.
yieldReg
#> width 22
#> flags 4 -|--|---|----------------
#>
#> pos encoding type col
#> 1 0.0.1/0 na x
#> 2 0.0.2/0 cases {OBS}
#> 4 0.0.3/0 length y
#> 7 1.5.10/15 numeric yieldThis 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().
(bitYield <- bf_encode(registry = yieldReg))
#> # A tibble: 10 × 1
#> bf_int1
#> <int>
#> 1 280984
#> 2 281086
#> 3 281245
#> 4 1328238
#> 5 19071
#> 6 1329222
#> 7 280995
#> 8 674130
#> 9 608385
#> 10 19093The bitfield can be decoded based on the registry with the function bf_decode() at a later point in time, where the metadata contained in the bitfield can be studied or extended in a downstream application.
flags <- bf_decode(x = bitYield, registry = yieldReg, sep = "-")
#> # A tibble: 7 × 4
#> # Rowwise:
#> pos name flag desc
#> <chr> <chr> <chr> <chr>
#> 1 1 na_x 0 "{FALSE} the value in column 'x' is not …
#> 2 1 na_x 1 "{TRUE} the value in column 'x' is NA."
#> 3 2:3 cases 00 "the observation has case 1 [yield >= 11…
#> 4 2:3 cases 01 "the observation has case 2 [yield < 11 …
#> 5 2:3 cases 10 "the observation has case 3 [yield < 9 &…
#> 6 4:6 length_y xxx "the bits encode the value length in col…
#> 7 7:22 numeric_yield xxxxxxxxxxxxxxxx "the bits encode the numeric value in co…
# -> prints legend by default, which is also available in bf_env$legend
tbl_bityield |>
dplyr::bind_cols(flags) |>
kable()| x | y | commodity | yield | year | bf_bin |
|---|---|---|---|---|---|
| 25.3 | 59.5 | soybean | 11.192915 | 2021 | 0-00-100-0100100110011000 |
| 27.9 | 58.1 | maize | 11.986793 | NA | 0-00-100-0100100111111110 |
| 27.8 | 57.8 | NA | 13.229386 | 2021r | 0-00-100-0100101010011101 |
| 27.0 | 59.2 | maize | 4.431376 | 2021 | 0-10-100-0100010001101110 |
| 259.0 | Inf | honey | 12.997422 | 2021 | 0-00-000-0100101001111111 |
| 27.3 | 59.1 | maize | 8.548882 | 2021 | 0-10-100-0100100001000110 |
| 26.1 | 58.4 | soybean | 11.276921 | 2021 | 0-00-100-0100100110100011 |
| 26.5 | 59.0 | maize | 10.640715 | 2021 | 0-01-010-0100100101010010 |
| 0.0 | 0.0 | soybean | 9.010452 | 2021 | 0-01-001-0100100010000001 |
| 25.7 | NaN | maize | 13.169897 | 2021 | 0-00-000-0100101010010101 |
The column bf_binary, 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 10).
However, 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 bf_env.
bf_decode(x = bitYield, registry = yieldReg, verbose = FALSE)
#> # A tibble: 10 × 4
#> na_x cases length_y numeric_yield
#> <chr> <chr> <chr> <chr>
#> 1 0 00 100 0100100110011000
#> 2 0 00 100 0100100111111110
#> 3 0 00 100 0100101010011101
#> 4 0 10 100 0100010001101110
#> 5 0 00 000 0100101001111111
#> 6 0 10 100 0100100001000110
#> 7 0 00 100 0100100110100011
#> 8 0 01 010 0100100101010010
#> 9 0 01 001 0100100010000001
#> 10 0 00 000 0100101010010101
# access values manually
ls(bf_env)
#> [1] "cases" "legend" "length_y" "na_x"
#> [5] "numeric_yield"
bf_env[["length_y"]]
#> [1] 4 4 4 4 0 4 4 2 1 0Beware 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 when only rounded values are of interest). This can be adjusted by setting the respective parameters in any bitfield operator that encodes numeric values such as bf_numeric() (a vignette explaining this in detail will follow).
old <- options(pillar.sigfig = 7)
tibble::tibble(original = tbl_bityield$yield,
bitfield = bf_env$numeric_yield)
#> # A tibble: 10 × 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
#> 10 13.16990 13.16406
options(old)