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The goal of cereal is to provide methods toserialize objectsfromvctrs toJSON, as well as back fromJSON to vctrs objects.

Installation

install.packages("cereal")

# install.packages("pak")pak::pak("r-lib/cereal")

Example

A data frame is a rectangular collection of variables (in thecolumns) and observations (in the rows). Each variable is a vector ofone data type, 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 2023-06-08 2019-01-01 00:01:40 z     blue  small#> 2   2.3     3 2023-06-09 2019-01-01 00:01:41 c     blue  large#> 3   3.4     4 2023-06-10 2019-01-01 00:01:42 t     green medium

The vctrs package has aconcept of avector prototype which captures the metadataassociated with a vector without keeping any of the 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, forexample, the levels of a factor and the timezone for a datetime. Thiscan be useful or important information when deploying code or models,such as when usingvetiver.We could store this vector prototype as an R binary object saved as an.rds file, but with cereal, you can store this vectorprototype 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": "2023-06-08",#>     "details": []#>   },#>   "d": {#>     "type": "POSIXct",#>     "example": "2019-01-01 00:01:40",#>     "details": {#>       "tzone": "America/New_York"#>     }#>   },#>   "e": {#>     "type": "character",#>     "example": "z",#>     "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)means it can be used as plain-text metadata for a model.

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>

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cereal 🥣

Installation

Example

Contributing