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Serialize vctrs objects to JSON 🥣
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r-lib/cereal
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The goal of cereal is to provide methods toserialize objects fromvctrs toJSON, as well as back from JSON tovctrs objects.
You can install the released version of vetiver fromCRAN with:
install.packages("cereal")You can install the development version of cereal fromGitHub with:
# install.packages("pak")pak::pak("r-lib/cereal")
A data frame is a rectangular collection of variables (in the columns)and observations (in the rows). Each variable is a vector of one datatype, like factor or datetime:
df<-tibble::tibble(a= c(1.2,2.3,3.4),b=2L:4L,c= Sys.Date()+0:2,d= as.POSIXct("2019-01-01",tz="America/New_York")+100:102,e= sample(letters,3),f=factor(c("blue","blue","green"),levels= c("blue","green","red")),g= ordered(c("small","large","medium"),levels= c("small","medium","large")))df#> # A tibble: 3 × 7#> a b c d e f g#> <dbl> <int> <date> <dttm> <chr> <fct> <ord>#> 1 1.2 2 2023-06-09 2019-01-01 00:01:40 b blue small#> 2 2.3 3 2023-06-10 2019-01-01 00:01:41 p blue large#> 3 3.4 4 2023-06-11 2019-01-01 00:01:42 k green medium
The vctrs package has aconcept of avectorprototype whichcaptures the metadata associated with a vector without keeping any ofthe data itself.
vctrs::vec_ptype(df)#> # A tibble: 0 × 7#> # ℹ 7 variables: a <dbl>, b <int>, c <date>, d <dttm>, e <chr>, f <fct>,#> # g <ord>
The information stored in such a vector prototype includes, for example,the levels of a factor and the timezone for a datetime. This can beuseful or important information when deploying code or models, such aswhen usingvetiver. We could store thisvector prototype as an R binary object saved as an.rds file, but withcereal, you can store this vector prototype in plain text as JSON:
library(cereal)json<- cereal_to_json(df)json#> {#> "a": {#> "type": "numeric",#> "example": "1.2",#> "details": []#> },#> "b": {#> "type": "integer",#> "example": "2",#> "details": []#> },#> "c": {#> "type": "Date",#> "example": "2023-06-09",#> "details": []#> },#> "d": {#> "type": "POSIXct",#> "example": "2019-01-01 00:01:40",#> "details": {#> "tzone": "America/New_York"#> }#> },#> "e": {#> "type": "character",#> "example": "b",#> "details": []#> },#> "f": {#> "type": "factor",#> "example": "blue",#> "details": {#> "levels": ["blue", "green", "red"]#> }#> },#> "g": {#> "type": "ordered",#> "example": "small",#> "details": {#> "levels": ["small", "medium", "large"]#> }#> }#> }
Storing prototype information as JSON (rather than a binary file) meansit can be used as plain-text metadata for a model.
You can also convert from JSON back to the original prototype:
cereal_from_json(json)#> # A tibble: 0 × 7#> # ℹ 7 variables: a <dbl>, b <int>, c <date>, d <dttm>, e <chr>, f <fct>,#> # g <ord>
For an approach to this same task using Python,see Pydantic’smodel.json().
This project is released with aContributor Code ofConduct.By contributing to this project, you agree to abide by its terms.
If you think you have encountered a bug, pleasesubmit anissue.
Either way, learn how to create and share areprex(a minimal, reproducible example), to clearly communicate about yourcode.
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