1 Goal

The goal of this tutorial is to learn how to draw manually additional lines in ggplot.

2 Preparing the data

We will use the Bitcoin Price Prediction dataset which can be downloaded through this link.

library(ggplot2)
library(lubridate)
library(dplyr)

bit_data <- read.csv("bitcoin_price_Training - Training.csv", header = T, sep = ",")
str(bit_data)
## 'data.frame':    1556 obs. of  7 variables:
##  $ Date      : Factor w/ 1556 levels "Apr 01, 2014",..: 763 758 753 748 743 738 733 728 723 718 ...
##  $ Open      : num  2763 2724 2807 2680 2539 ...
##  $ High      : num  2890 2759 2809 2897 2693 ...
##  $ Low       : num  2721 2645 2693 2680 2529 ...
##  $ Close     : num  2875 2757 2726 2809 2672 ...
##  $ Volume    : Factor w/ 1314 levels "-","1,064,730,000",..: 1245 1129 1205 28 1187 1286 31 1246 3 13 ...
##  $ Market.Cap: Factor w/ 1552 levels "1,000,070,000",..: 946 942 949 937 920 925 945 944 948 933 ...
# Setting Date as Date variable. If your local time is set in different type than C you will get NA, in order to fix that we must set the local type into C.
Sys.setlocale("LC_TIME", "C")
## [1] "C"
bit_data$Date <- as.Date(bit_data$Date, format = "%B %d, %Y")

For this tutorial we will only use the data from 2017. As we will use only the variable “Close” we will not change “Volume”nor “Market.Cap” variables into numeric.

bit_2017 <- filter(bit_data, year(Date) == 2017)

3 Visualization

Now we are going to plot the closing price of the bitcoin from January to July of 2017.

ggplot(bit_2017, aes(x = Date, y = Close)) + geom_line(color = "blue")

If we want to add additional lines showing, for example, the mean of the close price we can do it adding a new geom_line:

ggplot(bit_2017, aes(x = Date, y = Close)) + geom_line(color = "blue") +
 geom_line(aes(y = mean(Close)), color = "red", linetype = "dotted")

And not only mean can be plotted. Also quantile borders can be plotted in order to identify better the distribution of the close price.

ggplot(bit_2017, aes(x = Date, y = Close)) + geom_line(color = "blue") +
  geom_line(aes(y = mean(Close)), color = "red", linetype = "dotted") +
  geom_line(aes(y = quantile(Open, 0.75)), color = "black", linetype = "dashed") +
  geom_line(aes(y = quantile(Open, 0.25)), color = "black", linetype = "dashed")

Finally, we are going to put labels to the lines in order to know what their meaning are.

ggplot(bit_2017, aes(x = Date, y = Close)) + geom_line(color = "blue") +
  geom_line(aes(y = mean(Close)), color = "red", linetype = "dotted") +
  geom_line(aes(y = quantile(Close, 0.75)), color = "black", linetype = "dashed") +
  geom_line(aes(y = quantile(Close, 0.25)), color = "black", linetype = "dashed") +
  geom_text(aes(Date[210], mean(Close) , label = "Mean"), vjust= -0.3) +
  geom_text(aes(Date[210], quantile(Close, 0.75) , label = "75%"), vjust= -0.3) +
  geom_text(aes(Date[210], quantile(Close, 0.25) , label = "25%"), vjust= -3)

4 Conclusion

In this tutorial we have learnt how to put additional information in a visualization. This is very useful when presenting information both technical nor not technical audience.