The rpredictit package provides an interface to the PredictIt public API (https://www.predictit.org/api/). In addition to providing a wrapper to retrieve market data, this package includes visualization and analysis functions.

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.

Installation

You may install the stable version from CRAN, or the development version using devtools:

# install from CRAN
install.packages('rpredictit')

# or the development version, via devtools
devtools::install_github('danielkovtun/rpredictit')

Usage

Demo Shiny Application

To start off, try running a demo Shiny application included with the package by running:

library(rpredictit)
runExample('demo')

All Markets

Try rpredictit::all_markets() to return a tibble containing bid and ask data for all PredictIt markets:

library(rpredictit)
all_markets()

# A tibble: 1,096 x 20
      id name  shortName image url   timeStamp           status contract_id dateEnd contract_image contract_name contract_shortN… contract_status lastTradePrice bestBuyYesCost
   <int> <chr> <chr>     <chr> <chr> <dttm>              <chr>        <int> <chr>   <chr>          <chr>         <chr>            <chr>                    <dbl>          <dbl>
 1  2721 Whic… Which pa… http… http… 2019-12-23 14:53:45 Open          4390 N/A     https://az620… Democratic    Democratic       Open                      0.52           0.53
 2  2721 Whic… Which pa… http… http… 2019-12-23 14:53:45 Open          4389 N/A     https://az620… Republican    Republican       Open                      0.49           0.49
 3  2721 Whic… Which pa… http… http… 2019-12-23 14:53:45 Open          4388 N/A     https://az620… Libertarian   Libertarian      Open                      0.02           0.03
 4  2721 Whic… Which pa… http… http… 2019-12-23 14:53:45 Open          4391 N/A     https://az620… Green         Green            Open                      0.02           0.03
 5  2747 Will… Will Cub… http… http… 2019-12-23 14:53:45 Open          4495 2020-1… https://az620… Will Mark Cu… Will Cuban run … Open                      0.05           0.07
 6  2875 Will… Will Cuo… http… http… 2019-12-23 14:53:45 Open          5121 2020-1… https://az620… Will Andrew … Will Cuomo run … Open                      0.06           0.09
 7  2901 Will… Woman pr… http… http… 2019-12-23 14:53:45 Open          5215 N/A     https://az620… Will a woman… Woman president… Open                      0.12           0.12
 8  2902 Will… Will the… http… http… 2019-12-23 14:53:45 Open          5216 N/A     https://az620… Will the 202… Will the 2020 D… Open                      0.22           0.23
 9  2903 Will… Will the… http… http… 2019-12-23 14:53:45 Open          5217 N/A     https://az620… Will the 202… Will the 2020 G… Open                      0.04           0.05
10  2992 Will… Will Zuc… http… http… 2019-12-23 14:53:45 Open          5534 2020-1… https://az620… Will Faceboo… Will Zuckerberg… Open                      0.03           0.04
# … with 1,086 more rows, and 5 more variables: bestBuyNoCost <dbl>, bestSellYesCost <dbl>, bestSellNoCost <dbl>, lastClosePrice <dbl>, displayOrder <int>

Interactive Table

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)

Interactive Plot

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_ohlcv(csv_path)
rpredictit::historical_plot(contract_data)

Twitter Markets

If you are only interested in “Tweet count” markets, use rpredictit::tweet_markets() to return all available “Tweet” markets:

data <- rpredictit::tweet_markets()
rpredictit::markets_table(data)

Individual Market

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)

# A tibble: 4 x 20
     id name  shortName image url   timeStamp           status contract_id dateEnd contract_image contract_name contract_shortN… contract_status lastTradePrice bestBuyYesCost
  <int> <chr> <chr>     <chr> <chr> <dttm>              <chr>        <int> <chr>   <chr>          <chr>         <chr>            <chr>                    <dbl>          <dbl>
1  2721 Whic… Which pa… http… http… 2019-12-23 14:51:03 Open          4390 N/A     https://az620… Democratic    Democratic       Open                      0.52           0.53
2  2721 Whic… Which pa… http… http… 2019-12-23 14:51:03 Open          4389 N/A     https://az620… Republican    Republican       Open                      0.49           0.49
3  2721 Whic… Which pa… http… http… 2019-12-23 14:51:03 Open          4388 N/A     https://az620… Libertarian   Libertarian      Open                      0.02           0.03
4  2721 Whic… Which pa… http… http… 2019-12-23 14:51:03 Open          4391 N/A     https://az620… Green         Green            Open                      0.02           0.03
# … with 5 more variables: bestBuyNoCost <dbl>, bestSellYesCost <dbl>, bestSellNoCost <dbl>, lastClosePrice <dbl>, displayOrder <int>

See the full documentation at https://danielkovtun.github.io/rpredictit.