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bigml (version 0.1.2)

quickSource: Quickly Creating BigML Sources

Description

Quickly Creating BigML Sources

Usage

quickSource(data, name = deparse(substitute(data)), header = !is.null(names(data)), locale = "en-US", missing_tokens = c("NA"), quote = "\"", trim = TRUE, flatten = TRUE, ...)

Arguments

data
A matrix or data frame containing data to upload to bigml.
name
A string giving the name of the source.
header
A logical value indicating whether to use the first row of data as a header row.
locale
A string indicating the desired locale.
missing_tokens
A vector listing strings that should be treated as missing.
quote
A string giving the quote character to use.
trim
A logical value indicating whether to trim white space.
flatten
A logical value indicating whether to flatten the response into a data frame.
...
Arbitrary named arguments that are passed on to formEncodeURL in order to create form-encoded URL options.

Value

category
numeric
code
numeric
content_type
character
created
character
credits
numeric
description
character
fields
data.frame (or list if flatten=FALSE)
file_name
character
md5
character
name
character
number_of_datasets
numeric
number_of_models
numeric
number_of_predictions
numeric
private
logical
resource
character
size
numeric
source_parser
list
status
list
tags
AsIs
type
numeric
updated
character

Details

quickSource will take its "data" dataframe argument and attempt to create an equivalent BigML source. It does this by converting the dataframe to a csv file, compressing it, and uploading it directly to BigML. Generally, it's better to use quickDataset, since this method attempts to preserve any type information in the data frame.

References

https://bigml.com/developers/sources

See Also

Other quick methods: quickDataset; quickModel; quickPrediction

Other source methods: createSource; getSource; listSources