The rpredictit package provides an interface to the
PredictIt public API.
In addition to providing a wrapper to retrieve market data, this package includes visualization functions for plotting historical price data and exploring available markets. The package also comes with a demo shiny application for illustrating example use cases.
rpredictit is not affiliated with any predictive markets
and is presented for informational purposes only. Always confirm with
your own research before making an investment. License to use
data made available via the API is for non-commercial use and PredictIt
is the sole source of such data.
Once released, you may install the stable version from CRAN, or the development version using devtools:
# development version, via devtools
devtools::install_github('danielkovtun/rpredictit')To start off, try running a demo Shiny application included with the package by running:
library(rpredictit)
rpredictit::runExample('demo') Try rpredictit::all_markets() to return a tibble
containing bid and ask data for all PredictIt markets:
rpredictit::all_markets()
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
#> Warning: `as.tbl()` was deprecated in dplyr 1.0.0.
#> Please use `tibble::as_tibble()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
#> # A tibble: 664 × 20
#> id name shortName image url timeStamp status contract_id
#> <int> <chr> <chr> <chr> <chr> <dttm> <chr> <int>
#> 1 6867 Which par… Which pa… http… http… 2022-07-18 07:54:51 Open 23546
#> 2 6867 Which par… Which pa… http… http… 2022-07-18 07:54:51 Open 23547
#> 3 6867 Which par… Which pa… http… http… 2022-07-18 07:54:51 Open 23545
#> 4 6867 Which par… Which pa… http… http… 2022-07-18 07:54:51 Open 23548
#> 5 6874 Which par… Who will… http… http… 2022-07-18 07:54:51 Open 23587
#> 6 6874 Which par… Who will… http… http… 2022-07-18 07:54:51 Open 23586
#> 7 6892 Which par… Which pa… http… http… 2022-07-18 07:54:51 Open 23690
#> 8 6892 Which par… Which pa… http… http… 2022-07-18 07:54:51 Open 23689
#> 9 6953 Will Kama… Harris f… http… http… 2022-07-18 07:54:51 Open 24205
#> 10 6975 Will Mike… Pence fi… http… http… 2022-07-18 07:54:51 Open 24361
#> # … with 654 more rows, and 12 more variables: dateEnd <chr>,
#> # contract_image <chr>, contract_name <chr>, contract_shortName <chr>,
#> # contract_status <chr>, lastTradePrice <dbl>, bestBuyYesCost <dbl>,
#> # bestBuyNoCost <dbl>, bestSellYesCost <dbl>, bestSellNoCost <dbl>,
#> # lastClosePrice <dbl>, displayOrder <int>Alternatively, to return an interactive htmlwidget (DT::datatable)
table containing HTML formatted market data, pass the returned bid/ask
data to rpredictit::markets_table():
data <- rpredictit::all_markets()
rpredictit::markets_table(data)To plot historical prices, download a ‘csv’ file for a specific
contract from PredictIt’s website and pass the file path to
rpredictit::parse_historical_ohlcv(). Then, pass in the
returned contract data object to
rpredictit::historical_plot():
filename <- "What_will_be_the_balance_of_power_in_Congress_after_the_2020_election.csv"
csv_path <- system.file("extdata", filename, package = "rpredictit")
contract_data <- rpredictit::parse_historical_csv(csv_path)
rpredictit::historical_plot(contract_data)To return data for a specific market, use
rpredictit::single_market(id), where id refers
to the numerical code pertaining to the market of interest. You can find
a market’s numerical code by consulting its URL or by first calling the
all markets API (all_markets())
markets <- rpredictit::all_markets()
id <- markets$id[1]
rpredictit::single_market(id)See the full documentation at https://danielkovtun.github.io/rpredictit/.