Demo Shiny Application

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

library(rpredictit)
rpredictit::runExample('demo')  

All Markets

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
#> # A tibble: 1,028 x 20
#>       id name  shortName image url   timeStamp           status contract_id
#>    <int> <chr> <chr>     <chr> <chr> <dttm>              <chr>        <int>
#>  1  2721 Whic… Which pa… http… http… 2020-03-17 21:42:45 Open          4390
#>  2  2721 Whic… Which pa… http… http… 2020-03-17 21:42:45 Open          4389
#>  3  2721 Whic… Which pa… http… http… 2020-03-17 21:42:45 Open          4388
#>  4  2721 Whic… Which pa… http… http… 2020-03-17 21:42:45 Open          4391
#>  5  2747 Will… Will Cub… http… http… 2020-03-17 21:42:45 Open          4495
#>  6  2875 Will… Will Cuo… http… http… 2020-03-17 21:42:45 Open          5121
#>  7  2901 Will… Woman pr… http… http… 2020-03-17 21:42:45 Open          5215
#>  8  2902 Will… Will the… http… http… 2020-03-17 21:42:45 Open          5216
#>  9  2903 Will… Will the… http… http… 2020-03-17 21:42:45 Open          5217
#> 10  2992 Will… Will Zuc… http… http… 2020-03-17 21:42:45 Open          5534
#> # … with 1,018 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>

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()
#> Warning in rpredictit::all_markets(): Error fetching PredictIt market data 
#> Check your internet connection and try again 
#> 
#> Note: do not abuse the API by requesting data more than once every 60 seconds
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_csv(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)

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