library(geckor)
#> R client for the CoinGecko API
#> Developed by Next Game Solutions (http://nextgamesolutions.com)The CoinGecko API allows one to query market data on cryptocurrencies in real time. There are several functions in geckor that can be used to collect such data.
The current prices for a set of cryptocurrencies of interest can be queried with the current_price() function. The two important arguments expected by this function are coin_ids (a character vector with coin IDs; see ?supported_coins for details) and vs_currencies (a character vector with abbreviated names of reference currencies, which are used to express the coin price in; see ?supported_currencies for details). In the example below, we are requesting the current prices for Cardano, Tron, and Polkadot, expressed in USD, EUR, and GBP:
prices <- current_price(
coin_ids = c("cardano", "tron", "polkadot"),
vs_currencies = c("usd", "eur", "gbp")
)
prices
#> # A tibble: 9 x 7
#> coin_id price vs_currency market_cap vol_24h price_percent_change_24h
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 cardano 2 usd 63542311560. 2003905200. 2.31
#> 2 cardano 1.73 eur 54907102046. 1732065440. 2.18
#> 3 cardano 1.46 gbp 46524282593. 1466800493. 2.49
#> 4 polkadot 44.3 usd 45677466802. 1324266979. 5.92
#> 5 polkadot 38.3 eur 39485201015. 1144623542. 5.77
#> 6 polkadot 32.4 gbp 33436545184. 969325025. 6.10
#> 7 tron 0.102 usd 7235134893. 2252996359. 1.19
#> 8 tron 0.0881 eur 6251901766. 1947366138. 1.05
#> 9 tron 0.0746 gbp 5297406596. 1649127998. 1.36
#> # ... with 1 more variable: last_updated_at <dttm>See ?current_price for definitions of columns in the resultant tibble.
The exchange_rate() function can be used to obtain the current exchange rates for any supported reference currency, expressed in Bitcoin. The currency argument of this function specifies the list of currencies of interest. If currency = NULL, data for all supported currencies will be returned:
all_rates <- exchange_rate(currency = NULL)
head(all_rates, 10)
#> # A tibble: 10 x 5
#> timestamp currency name price_in_btc type
#> <dttm> <chr> <chr> <dbl> <chr>
#> 1 2021-11-01 09:49:51 aed United Arab Emirates Dirham 228339. fiat
#> 2 2021-11-01 09:49:51 ars Argentine Peso 6197280. fiat
#> 3 2021-11-01 09:49:51 aud Australian Dollar 82814. fiat
#> 4 2021-11-01 09:49:51 bch Bitcoin Cash 105. crypto
#> 5 2021-11-01 09:49:51 bdt Bangladeshi Taka 5368581. fiat
#> 6 2021-11-01 09:49:51 bhd Bahraini Dinar 23438. fiat
#> 7 2021-11-01 09:49:51 bits Bits 1000000 crypto
#> 8 2021-11-01 09:49:51 bmd Bermudian Dollar 62168. fiat
#> 9 2021-11-01 09:49:51 bnb Binance Coin 117. crypto
#> 10 2021-11-01 09:49:51 brl Brazil Real 350489. fiat
some_rates <- exchange_rate(currency = c("btc", "usd", "rub"))
some_rates
#> # A tibble: 3 x 5
#> timestamp currency name price_in_btc type
#> <dttm> <chr> <chr> <dbl> <chr>
#> 1 2021-11-01 09:49:51 btc Bitcoin 1 crypto
#> 2 2021-11-01 09:49:51 rub Russian Ruble 4398435. fiat
#> 3 2021-11-01 09:49:51 usd US Dollar 62168. fiatThe current_market() function retrieves a rich set of data points describing the current market status of the cryptocurrencies of interest. Let’s collect such data for Cardano, Tron, and Polkadot:
cm <- current_market(
coin_ids = c("cardano", "tron", "polkadot"),
vs_currency = "usd"
)
dplyr::glimpse(cm)
#> Rows: 3
#> Columns: 32
#> $ coin_id <chr> "cardano", "polkadot", "tron"
#> $ symbol <chr> "ada", "dot", "trx"
#> $ name <chr> "Cardano", "Polkadot", "TRON"
#> $ vs_currency <chr> "usd", "usd", "usd"
#> $ last_updated_at <dttm> 2021-11-01 09:49:28, 2021-11-~
#> $ current_price <dbl> 2.000000, 44.330000, 0.101936
#> $ market_cap <dbl> 63542311560, 45677466802, 723~
#> $ market_cap_rank <int> 5, 8, 30
#> $ fully_diluted_valuation <dbl> 89171371039, NA, NA
#> $ total_volume <dbl> 2003905200, 1324266979, 22529~
#> $ high_24h <dbl> 2.000000, 44.460000, 0.102011
#> $ low_24h <dbl> 1.92000, 41.33000, 0.09815
#> $ price_change_24h <dbl> 0.04525739, 2.48000000, 0.001~
#> $ price_change_percentage_24h <dbl> 2.31414, 5.91567, 1.18962
#> $ market_cap_change_24h <dbl> 628750748, 2106541495, -5246~
#> $ market_cap_change_percentage_24h <dbl> 0.99939, 4.83474, -0.07246
#> $ circulating_supply <dbl> 32066390668, 1046004149, 7166~
#> $ total_supply <dbl> 45000000000, 1126954665, 1008~
#> $ max_supply <dbl> 4.5e+10, NA, NA
#> $ ath <dbl> 3.090000, 49.350000, 0.231673
#> $ ath_change_percentage <dbl> -35.11797, -10.28816, -56.097~
#> $ ath_date <dttm> 2021-09-02 06:00:10, 2021-05-~
#> $ atl <dbl> 0.01925275, 2.70000000, 0.001~
#> $ atl_change_percentage <dbl> 10302.933, 1541.246, 5536.967
#> $ atl_date <dttm> 2020-03-13 02:22:55, 2020-08-~
#> $ price_change_percentage_1h_in_currency <dbl> 1.212416, 1.598759, 1.024652
#> $ price_change_percentage_24h_in_currency <dbl> 2.314139, 5.915670, 1.189617
#> $ price_change_percentage_7d_in_currency <dbl> -5.994873, 4.252299, 2.277023
#> $ price_change_percentage_14d_in_currency <dbl> -7.270068, 5.226417, 3.095765
#> $ price_change_percentage_30d_in_currency <dbl> -10.941119, 38.628612, 7.5294~
#> $ price_change_percentage_200d_in_currency <dbl> 37.417789, 4.315348, -27.8561~
#> $ price_change_percentage_1y_in_currency <dbl> 2054.5777, 961.0734, 294.2327See ?current_market for definitions of columns in the resultant tibble.
The coin_tickers() function allows one to query the current data on all trading pairs of a cryptocurrency from a given exchange. In the example below, we collect such data for Cardano traded at Binance:
cardano_tickers <- coin_tickers(
coin_id = "cardano",
exchange_id = "binance"
)
dplyr::glimpse(cardano_tickers)
#> Rows: 14
#> Columns: 23
#> $ exchange_id <chr> "binance", "binance", "binance", "binan~
#> $ exchange_name <chr> "Binance", "Binance", "Binance", "Binan~
#> $ coin_id <chr> "cardano", "cardano", "cardano", "carda~
#> $ name <chr> "Cardano", "Cardano", "Cardano", "Carda~
#> $ base <chr> "ADA", "ADA", "ADA", "ADA", "ADA", "ADA~
#> $ target <chr> "USDT", "BTC", "USDC", "EUR", "ETH", "T~
#> $ trust_score <chr> "green", "green", "green", "green", "gr~
#> $ last_price <dbl> 2.0010e+00, 3.2210e-05, 1.9990e+00, 1.7~
#> $ last_fetch_at <dttm> 2021-11-01 09:35:33, 2021-11-01 09:36:~
#> $ last_traded_at <dttm> 2021-11-01 09:35:33, 2021-11-01 09:36:~
#> $ bid_ask_spread_percentage <dbl> 0.050659, 0.031172, 0.100756, 0.058275,~
#> $ trading_volume_24h <dbl> 145934666.5, 35074897.2, 2012438.9, 513~
#> $ last_price_btc <dbl> 3.224e-05, 3.221e-05, 3.220e-05, 3.225e~
#> $ last_price_eth <dbl> 0.00046217, 0.00046170, 0.00046152, 0.0~
#> $ last_price_usd <dbl> 2.00, 2.00, 2.00, 2.00, 2.00, 1.99, 2.0~
#> $ trading_volume_24h_btc <dbl> 4705.000000, 1130.000000, 64.807000, 16~
#> $ trading_volume_24h_eth <dbl> 67447.00000, 16194.00000, 928.78300, 23~
#> $ trading_volume_24h_usd <int> 292236992, 70188027, 4026249, 10296358,~
#> $ cost_to_move_up_2percent_usd <dbl> 5192544.77, 835390.95, 600475.96, 24128~
#> $ cost_to_move_down_2percent_usd <dbl> 2125641.25, 809426.57, 198268.53, 29561~
#> $ is_anomaly <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALS~
#> $ is_stale <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALS~
#> $ trade_url <chr> "https://www.binance.com/en/trade/ADA_U~See ?coin_tickers for definitions of columns in the resultant tibble.
Finally, one can use the trending_coins() function to obtain a list of top-7 trending coins in terms of their search popularity on CoinGecko:
trending_coins()
#> # A tibble: 7 x 7
#> timestamp popularity_rank_~ coin_id name symbol market_cap_rank
#> <dttm> <int> <chr> <chr> <chr> <int>
#> 1 2021-11-01 09:49:54 1 ariva Ariva ARV 674
#> 2 2021-11-01 09:49:54 2 taboo-to~ Taboo ~ TABOO 304
#> 3 2021-11-01 09:49:54 3 richquack Rich Q~ QUACK 687
#> 4 2021-11-01 09:49:54 4 wagyuswap WagyuS~ WAG 711
#> 5 2021-11-01 09:49:54 5 shiba-inu Shiba ~ SHIB 9
#> 6 2021-11-01 09:49:54 6 unilayer UniLay~ LAYER 899
#> 7 2021-11-01 09:49:54 7 decentra~ Decent~ MANA 55
#> # ... with 1 more variable: price_btc <dbl>