Lay your dreams, little darling, in a flower bed; let that sunshine in your hair (Where the skies are blue, The Lumineers)
I discovered this nice visualization some days ago. The author is also the creator of Highcharter, an incredible R wrapper for Highcharts javascript libray and its modules. I am a big fan of him.
Inspired by his radial plot, I did a visualization of the daily evolution of Daily Bitcoin exchange rate (BTC vs. EUR) on Localbtc. Data is sourced from here and I used Quandl to obtain the data frame. Quandl is a marketplace for financial and economic data delivered in modern formats for today’s analysts. There is a package called Quandl
to interact directly with the Quandl API to download data in a number of formats usable in R. You only need to locate the data you want in the Quandl site. In my case data are here.
After loading data, I do the folowing steps:
- Filtering data to obtain last 12 complete months
- Create a new variable with the difference between closing and opening price of Bitcoin (in Euros)
- Create a color variable to distinguish between positive and negative differences
- Create the graph using Fivethirtyeight theme for highcharts
This is the result:
Apart of its appealing, I think is a good way to to have a quick overview of the evolution of a stock price. This is the code to do the experiment:
library(Quandl) library(dplyr) library(highcharter) library(lubridate) bitcoin=Quandl("BCHARTS/LOCALBTCEUR") bitcoin %>% arrange(Date) %>% mutate(tmstmp = datetime_to_timestamp(Date)) -> bitcoin last_date=max(bitcoin$Date) if (day(last_date+1)==1) date_to=last_date else date_to=ymd(paste(year(last_date), month(last_date),1, sep="-"))-1 date_from=ymd(paste(year(date_to)-1, month(date_to)+1,1, sep="-")) bitcoin %>% filter(Date>=date_from, Date<=date_to) -> bitcoin var_bitcoin <- bitcoin %>% mutate(Variation = Close - Open, color = ifelse(Variation>=0, "green", "red"), y = Variation) %>% select(x = tmstmp, y, variation = Variation, name = Date, color, open = Open, close = Close) %>% list.parse3() x <- c("Open", "Close", "Variation") y <- sprintf("{point.%s}", tolower(x)) tltip <- tooltip_table(x, y) hc <- highchart() %>% hc_title(text = "Bitcoin Exchange Rate (BTC vs. EUR)") %>% hc_subtitle(text = "Daily Variation on Localbtc. Last 12 months")%>% hc_chart( type = "column", polar = TRUE) %>% hc_plotOptions( series = list( stacking = "normal", showInLegend = FALSE)) %>% hc_xAxis( gridLineWidth = 0.5, type = "datetime", tickInterval = 30 * 24 * 3600 * 1000, labels = list(format = "{value: %b}")) %>% hc_yAxis(showFirstLabel = FALSE) %>% hc_add_series(data = var_bitcoin) %>% hc_add_theme(hc_theme_538()) %>% hc_tooltip(useHTML = TRUE, headerFormat = as.character(tags$small("{point.x:%d %B, %Y}")), pointFormat = tltip) hc
Hi , your lab is not only really interesting, it’s funny and amazing! Congratulations!
I am a beginner in web scrapping with R (text mining); your powerful graphs are giving me a more visual approach to show educational data mining algorithms that I am studying. Thanks!
Thanks!