This package is designed to build sequences of bits (i.e., bitfields) to capture the computational footprint of any (scientific) model workflow or output. The bit sequence is then encoded as an integer value that stores a range of information into a single column of a table or a raster layer. This 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.
Install the official version from CRAN:
# install.packages("bitfield")Install the latest development version from github:
devtools::install_github("EhrmannS/bitfield")library(bitfield)
library(dplyr, warn.conflicts = FALSE); library(CoordinateCleaner); library(stringr)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 = 3,
# 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 6
#> flags 3 -|--|---
#>
#> pos encoding type col
#> 1 0.0.1/0 na x
#> 2 0.0.2/0 cases
#> 4 0.0.3/0 length yThis 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().
(intBit <- bf_encode(registry = yieldReg))
#> # A tibble: 10 × 1
#> bf_int1
#> <int>
#> 1 4
#> 2 4
#> 3 4
#> 4 20
#> 5 0
#> 6 20
#> 7 4
#> 8 10
#> 9 9
#> 10 0The 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.
bitfield <- bf_decode(x = intBit, registry = yieldReg, sep = "-")
#> # A tibble: 6 × 4
#> # Rowwise:
#> bits name flag desc
#> <chr> <chr> <chr> <chr>
#> 1 1 na_x 0 "{FALSE} the value in column 'x' is not NA."
#> 2 1 na_x 1 "{TRUE} the value in column 'x' is NA."
#> 3 2:3 cases 00 "the observation has the case [yield >= 11]."
#> 4 2:3 cases 01 "the observation has the case [yield < 11 & yield > 9]."
#> 5 2:3 cases 10 "the observation has the case [yield < 9 & commodity == …
#> 6 4:6 length_y 000 "the bits encode the value length in column 'y'."
# -> prints legend by default, which is also available in bf_env$legend
tbl_bityield |>
bind_cols(bitfield) |>
kable()| x | y | commodity | yield | year | bf_bin |
|---|---|---|---|---|---|
| 25.3 | 59.5 | soybean | 11.192915 | 2021 | 0-00-100 |
| 27.9 | 58.1 | maize | 11.986793 | NA | 0-00-100 |
| 27.8 | 57.8 | NA | 13.229386 | 2021r | 0-00-100 |
| 27.0 | 59.2 | maize | 4.431376 | 2021 | 0-10-100 |
| 259.0 | Inf | honey | 12.997422 | 2021 | 0-00-000 |
| 27.3 | 59.1 | maize | 8.548882 | 2021 | 0-10-100 |
| 26.1 | 58.4 | soybean | 11.276921 | 2021 | 0-00-100 |
| 26.5 | 59.0 | maize | 10.640715 | 2021 | 0-01-010 |
| 0.0 | 0.0 | soybean | 9.010452 | 2021 | 0-01-001 |
| 25.7 | NaN | maize | 13.169897 | 2021 | 0-00-000 |
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”.
Not only tabular data are supported, but also gridded data such as rasters (wip).
library(terra, warn.conflicts = FALSE)
#> terra 1.7.71
rst_bityield <- rast(system.file("ex/rst_bityield.tif", package="bitfield"))
levels(rst_bityield$commodity) <- tibble(id = 1:3, commodity = c("soybean", "maize", "honey"))
plot(rst_bityield)
- write unit tests
- include MD5 sum for a bitfield and update it each time the bitfield is grown further
- document the provenance stuff in here