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by Joseph Rickert
I am not yet a Bitcoin advocate. Nevertheless, I am impressed with the amount of Bitcoin activity and the progress that advocates are making towards having Bitcoin recognized as a legitimate currency. Right now, I am mostly interested in the technology behind bitcoin and the possibility of working with some interesting data sets. A good bit of historical data is located on sites like bitstamp.net and bitcoincharts.com, and most of it is easily accessible from R with just a little data munging. In this post, I present some code that may be helpful to someone who wants to get started working with Bitcoin data in R.
Transaction data is available in a JSON file from bitstamp.net here. This can be easily read with the fromJSON() function from the RJSONIO package and put into a data frame with the help of do.call().
library(RJSONIO)
url <- "https://www.bitstamp.net/api/transactions/"
bs_data <- fromJSON(url) # returns a list
bs_df <- do.call(rbind,lapply(bs_data,data.frame,stringsAsFactors=FALSE))
head(bs_df)
date tid price type amount
1 1446329342 9614372 310.78 0 0.15350400
2 1446329337 9614371 310.10 1 3.00000000
3 1446329278 9614370 310.10 1 5.40000000
4 1446329273 9614369 310.79 1 0.15682615
5 1446329273 9614368 310.70 0 0.02679437
6 1446329273 9614367 310.60 0 0.02680299
If you are a newcomer you might find the one-liner using the plyr library more intuitive; but do have a look at the efficiency discussion on stackoverflow.
library(plyr) bs_df2 <- ldply(bs_data,data.frame)
In the data frame returned above "tid" is the transaction id. "date" is the time stamp of the transaction in unix time. (We will see how to work with that below.) "price" is the bitcoin price. "amount" the amount of bitcoin, and "type" = 0 for a buy and 1 for a sell.
Bitcoincharts has some market data in JSON format here. However, applying exactly the same code will most likely result in a very unhelpful error message something like this:
Error in data.frame(volume = 0, latest_trade = 1415135720, bid = 342.87015289, :
arguments imply differing number of rows: 1, 0
What's going on here is that Nulls in the data will keep R from building a data frame because some rows will have more columns than others. On way around it is to use a function like nullToNA()to turn Nulls into NAs on the list returned by fromJSON().
nullToNA <- function(x) {
x[sapply(x, is.null)] <- NA
return(x)
}
url_m <- "http://api.bitcoincharts.com/v1/markets.json"
mkt_data <- fromJSON(url_m)
str(mkt_data)
mkt_data2 <- lapply(mkt_data,nullToNA)
mkt_df <- do.call(rbind,lapply(mkt_data2,data.frame,stringsAsFactors=FALSE))
head(mkt_df)
volume latest_trade bid high currency currency_volume ask close avg
1 1580.379 1446339230 4534200.0000 4800000.00 IDR 7313517821.8 4534300.0000 4534300.0000 4627699.9493
2 1656.868 1446338556 158168.0300 963.39 USD 637542.2 158.4900 597.8300 384.7876
3 0.000 1415135720 342.8702 NA USD 0.0 347.8674 327.4206 NA
4 0.000 1444487795 383.6141 NA SGD 0.0 409.2476 341.7299 NA
5 0.000 1446224056 291.2600 NA EUR 0.0 291.3500 291.3400 NA
6 0.000 1414593608 25000.0000 NA XRP 0.0 74998.0000 64001.0000 NA
symbol low
1 btcoidIDR 4508600.0
2 localbtcUSD 248.9
3 rippleUSD NA
4 anxhkSGD NA
5 zyadoEUR NA
6 justXRP NA
The easiest way to get Bitcoin transaction data is from the cache of zipped .csv files at http://api.bitcoincharts.com/v1/csv/. The following bit of code downloads the bitstamp transaction data in US dollars, un-zips it and reads all 8 million rows or so into a data frame. Then the unix timestamp is converted into a date.
# Code to read compressed .gz files
# http://api.bitcoincharts.com/v1/csv/
# Data Source
bitcoin_file <- "bitstampUSD.csv.gz"
URL <- "http://api.bitcoincharts.com/v1/csv"
source_file <- file.path(URL,bitcoin_file)
# Data destination on local disk
dataDir <-"C:/DATA/Bitcoin"
dest_file <- file.path(dataDir,bitcoin_file)
# Download to disk
download.file(source_file,destfile = dest_file)
# Uncompress .gz file and read into a data frame
raw <- read.csv(gzfile(dest_file),header=FALSE)
head(raw,2)
V1 V2 V3
1 1315922016 5.80 1.0000
2 1315922024 5.83 3.0000
names(raw) <- c("unixtime","price","amount")
raw$date <- as.Date(as.POSIXct(raw$unixtime, origin="1970-01-01"))
head(raw,2)
unixtime price amount date
1 1315922016 5.80 1 2011-09-13
2 1315922024 5.83 3 2011-09-13
Now for the payoff: we use dplyr functions and xts() to aggregate the transactions into a time series and digraph() visualize the results.
library(dplyr)
library(xts)
library(dygraphs)
data <- select(raw,-unixtime)
rm(raw)
data <- mutate(data,value = price * amount)
by_date <- group_by(data,date)
daily <- summarise(by_date,count = n(),
m_price <- mean(price, na.rm = TRUE),
m_amount <- mean(amount, na.rm = TRUE),
m_value <- mean(value, na.rm = TRUE))
names(daily) <- c("date","count","m_value","m_price","m_amount")
head(daily,2)
Source: local data frame [6 x 5]
date count m_value m_price m_amount
(date) (int) (dbl) (dbl) (dbl)
1 2011-09-13 12 5.874167 4.864282 28.84145
2 2011-09-14 14 5.582143 4.367570 24.41820
# Make the m_value variable into a time series object
daily_ts <- xts(daily$m_value,order.by=daily$date)
# Plot with htmlwidget dygraph
dygraph(daily_ts,ylab="US Dollars",
main="Average Value of bitstampUSD Buys") %>%
dySeries("V1",label="Avg_Buy") %>%
dyRangeSelector(dateWindow = c("2011-09-13","2015-11-02"))
This series tells the story of bitcoin so far: a long slow start then a rocket ride to a high followed by a roller coaster ride down to a what looks like it might be a plateau of respectability stability.
Finally note that there R some packages to help explore Bitcoin. Rbitcoin provides a unified API interface to the bitstamp, kraken, btce and bitmarket sites while rbitcoinchartsapi provides an interface to the BitCoinCharts.com API. For a nice example of what the Rbitcoin package can do have a look at Benedikt Koehler's post from earlier this year.
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