Prepare data for Cadenza

Usage

as_cadenza_enrichment_calculation(
  data,
  parameters = empty_named_list,
  container_type = "text/csv",
  ...
)

Arguments

data: Either a dataframe or a list of either with data to send back to Cadenza.
parameters: Parameters to return to Cadenza. Defaults to an empty named list.
container_type: String: The HTML content type for serializing data. Defaults to "text/csv". If data is a list, supply a vector with the content types for each element.
...: For future expansion: Further arguments passed on to generating the metadata for the Cadenza response-
role: A list of the same length as data with vectors of length ncol(data[[el]]). Values "dimension" or "measure", indicating the role for Cadenza. Default values are "measure" for each column except IDs, Geometries or strings.

Value

List of class cadenza_response with elements metadata

and other elements passed in as dataargument.

Examples


data <- data.frame(a = rnorm(10L), b = runif(10L), c = letters[1L:10L])

# specify the container content type; this part will be serialized
# as JSON in the response.
as_cadenza_enrichment_calculation(data, container_type = "application/json")
#> $metadata
#> $metadata$parameters
#> named list()
#> 
#> $metadata$dataContainers
#> $metadata$dataContainers[[1]]
#> $metadata$dataContainers[[1]]$type
#> [x] "application/json"
#> 
#> $metadata$dataContainers[[1]]$name
#> [x] "data"
#> 
#> $metadata$dataContainers[[1]]$columns
#>   name printName attributeGroupName      role dataType
#> 1    a         a         1DIdTQhwBD   measure  float64
#> 2    b         b         1DIdTQhwBD   measure  float64
#> 3    c         c         1DIdTQhwBD dimension   string
#> 
#> 
#> 
#> 
#> $data
#>               a          b c
#> 1  -1.400043517 0.28989230 a
#> 2   0.255317055 0.67838043 b
#> 3  -2.437263611 0.73531960 c
#> 4  -0.005571287 0.19595673 d
#> 5   0.621552721 0.98053967 e
#> 6   1.148411606 0.74152153 f
#> 7  -1.821817661 0.05144628 g
#> 8  -0.247325302 0.53021246 h
#> 9  -0.244199607 0.69582388 i
#> 10 -0.282705449 0.68855600 j
#> 
#> attr(,"class")
#> [1] "cadenza_response" "list"            

# List of data elements to return to Cadenza
data_list <- list(data, list(param = "a", vec = 1:13))
as_cadenza_enrichment_calculation(data_list,
  container_type = c("text/csv", "application/json"))
#> $metadata
#> $metadata$parameters
#> named list()
#> 
#> $metadata$dataContainers
#> $metadata$dataContainers[[1]]
#> $metadata$dataContainers[[1]]$type
#> [x] "text/csv"
#> 
#> $metadata$dataContainers[[1]]$name
#> [x] "data_1"
#> 
#> $metadata$dataContainers[[1]]$columns
#>   name printName attributeGroupName      role dataType
#> 1    a         a         gUde5llzyO   measure  float64
#> 2    b         b         gUde5llzyO   measure  float64
#> 3    c         c         gUde5llzyO dimension   string
#> 
#> 
#> $metadata$dataContainers[[2]]
#> $metadata$dataContainers[[2]]$type
#> [x] "application/json"
#> 
#> $metadata$dataContainers[[2]]$name
#> [x] "data_2"
#> 
#> $metadata$dataContainers[[2]]$columns
#>    name printName attributeGroupName      role dataType
#> 1 param     param         SJAWUmCi4L dimension   string
#> 2   vec       vec         SJAWUmCi4L   measure    int64
#> 
#> 
#> 
#> 
#> $data_1
#>               a          b c
#> 1  -1.400043517 0.28989230 a
#> 2   0.255317055 0.67838043 b
#> 3  -2.437263611 0.73531960 c
#> 4  -0.005571287 0.19595673 d
#> 5   0.621552721 0.98053967 e
#> 6   1.148411606 0.74152153 f
#> 7  -1.821817661 0.05144628 g
#> 8  -0.247325302 0.53021246 h
#> 9  -0.244199607 0.69582388 i
#> 10 -0.282705449 0.68855600 j
#> 
#> $data_2
#> $data_2$param
#> [1] "a"
#> 
#> $data_2$vec
#>  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13
#> 
#> 
#> attr(,"class")
#> [1] "cadenza_response" "list"