Import Libraries

library(coinmarketcapr)

Plot top 5 cryptocurrencies by current price.

plot_top_5_currencies()

Examine how the market share is split among the top cryptocurrencies.

market_today <- get_marketcap_ticker_all()
head(market_today[,1:8])

##             id         name symbol rank     price_usd  price_btc
## 1      bitcoin      Bitcoin    BTC    1 4132.83948356        1.0
## 2       ripple          XRP    XRP    2  0.3672807888 0.00008889
## 3     ethereum     Ethereum    ETH    3  116.22829719  0.0281299
## 4      stellar      Stellar    XLM    4  0.1604106953 0.00003882
## 5 bitcoin-cash Bitcoin Cash    BCH    5 171.275465081 0.04145258
## 6          eos          EOS    EOS    6  2.8456477787 0.00068871
##   X24h_volume_usd market_cap_usd
## 1   5293482428.63  71928194608.0
## 2   337778730.768  14811457870.0
## 3    1860911271.0  12037225691.0
## 4   75895674.3008   3072586627.0
## 5   71862409.1687   2995554361.0
## 6    739888959.24   2578854406.0

Having extracted the complete data of various cryptocurrencies, we’ll visualize the marketshare split with a treemap. For plotting, let us extract only the two columns ID and market_cap_usd and convert the market_cap_usd into numeric and format numbers of treemap labels.

library(treemap)
df1 <- na.omit(market_today[,c('id','market_cap_usd')])
df1$market_cap_usd <- as.numeric(df1$market_cap_usd)
df1$formatted_market_cap <-  paste0(df1$id,'\n','$',format(df1$market_cap_usd,big.mark = ',',scientific = F, trim = T))
treemap(df1, index = 'formatted_market_cap', vSize = 'market_cap_usd', title = 'Cryptocurrency Market Cap', fontsize.labels=c(12, 8), palette='RdYlGn')