Last updated 2 days ago
library("redivis") # Specify the table's container, either a dataset or workflow dataset <- redivis$organization("Demo")$dataset("iris_species") table <- dataset$table("Iris") table$to_tibble() # Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species # 0 33 5.2 4.1 1.5 0.1 Iris-setosa # ... # Other methods to read data: # table$to_arrow_batch_reader() # table$to_arrow_dataset() # table$to_arrow_dataset() # table$to_data_frame() # table$to_data_table() # table$to_sf_tibble()
library("redivis") table <- redivis$organization("Demo")$dataset("iris_species")$table("iris") variable <- table$variable("sepalLengthCm") variable$get(wait_for_statistics=TRUE) print(variable$properties) { "kind": "variable", ... (see variable resource definition) "statistics": { "status": "completed", "count": 150, "numDistinct": 35, "min": 4.3, "max": 7.9, "mean": 5.8433333333333355, "approxMedian": 0.8280661279778625 } }
# In a notebook, all tables are scoped to the current workflow. # Additionally, the notebook's source table can simply be referenced as _source_ table = redivis$table("_source_") table$to_tibble()