Accessing Bitcoin Data with R

November 4, 2015

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

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 and, 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 here. This can be easily read with the fromJSON() function from the RJSONIO package and put into a data frame with the help of

url <- ""
bs_data <- fromJSON(url) # returns a list
bs_df <-,lapply(bs_data,data.frame,stringsAsFactors=FALSE))
     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.

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
url_m <- ""
mkt_data <- fromJSON(url_m)
mkt_data2 <- lapply(mkt_data,nullToNA)
mkt_df <-,lapply(mkt_data2,data.frame,stringsAsFactors=FALSE))
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 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
# Data Source
bitcoin_file <- "bitstampUSD.csv.gz"
URL <- ""
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)
        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"))
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.

data <- select(raw,-unixtime)
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")
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$m_value,$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 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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