Importing Data and Data Visualization

bitcoin <- readr::read_csv("C:\\Users\\CARRIE.VENNEFRON\\Desktop\\Bitcoin Historical Data - Investing.csv")

bitcoin$Date <- anydate(bitcoin$Date)
bitcoin$Date <- as.Date(bitcoin$Date)

ggplot(data = bitcoin, aes(x = as.Date(Date), y = Price)) + 
        geom_point(aes(color = `Moon Phase`, shape = `Moon Phase`)) + 
        geom_smooth() +
        scale_x_date(NULL,
        breaks = scales::breaks_width("6 months"), 
        labels = scales::label_date_short()) + 
        scale_y_continuous(name = "Price of Bitcoin on said day") +
        ggtitle("The Effect of Lunar Cycles on Bitcoin Value",
                subtitle = "Data from https://www.investing.com/crypto/bitcoin/historical-data")

Statement

The data used is from investing.com which captures the price of bitcoin daily, along with the high for the day, the low for the day, the price for market open, the volume of trades for that day, as well as the percent change in price from the previous day. I have manually added the data for “Moon Phase” to indicate on which days had either a Full Moon or New Moon based on the historical data found here. I added this data because some astrologers and cryptocurrency holders have claimed that bitcoin market highs seem to occur during New Moons, whereas bitcoin market lows seem to occur during Full Moons.

However, based on the above data visualization, this appears to not actually be the case. The blue smooth line indicates the line of best fit for all data points (whether collected on a Full Moon, New Moon, or neither). Although the terms “market high” and “market low” are subjective and relative, it appears that there is no correlation between the lunar phase being above or below said best-fit line. If there were indeed a correlation between lunar phase and the bitcoin market price, we would expect to see one moon phase consistently being above the line and the other moon phase being consistently below the line. However, this is not the case – it appears that there are an equal number of occurrences of both Full Moons and New Moons both below and above the trendline.