Plotting multiple time series on the same plot using ggplot in R

Time series data is hierarchical data.  It is a series of data associated with a timestamp. An example of a time series is gold prices over a period or temperature range or precipitation during yearly storms. To visualize this data, R provides a handy library called ggplot. Using ggplot, we can see all sorts of plots.  Along with ggplot, R also provides libraries to clean up data and transform or manipulate it to fit our visualization requirements.

This article will look at one dataset from the R datasets and one dataset obtained from a CSV file.

Dataset 1: EU Covid deaths for March 2020

The dataset gives us the daily death counts from Covid-19 for all European Countries for March 2020. We will plot the number of deaths(y-axis) vs. day(x-axis) for every country.

Data in use can be downloaded from here.

Plot 1: Daily Death Count

The steps for plotting are as follows:

• Open R Studio and open an R notebook (has more options).
• Save this file as .rmd, preferably in the same folder as your data.
• Select the Working directory to where your data is
• Import all the R libraries
• Read the data from the CSV.
• The data above is spread across columns. To make plotting easier, we need to format the data in the required format.
• Plot data
• Display data

Example:

R

library(ggplot2)
library(reshape2)
library(dplyr)

covid1 =(read.csv(file="EUCOVIDdeaths.csv",header=TRUE)[,-c(2)])

head(covid1)


covid_deaths <- melt(covid1,id.vars=c("Country"),value.name="value",
                     variable.name="Day")

head(covid_deaths)

covid_plot <- ggplot(data=covid_deaths, aes(x=Day, y=value, group = Country,
                                            colour = Country))
+ geom_line() +labs(y= "Deaths", x = "Day")
covid_plot + ggtitle("Daily Deaths for European countries in March,2020")+geom_point()

covid_plot

Output:

Daily Deaths timeseries plot with points

Plot 2: Plotting covid deaths per capita.

We will be using the same data as the previous example. But here we will be dealing with per capita data.

R

library(ggplot2)
library(reshape2)
library(dplyr)

covid1 =(read.csv(file="EUCOVIDdeaths.csv",header=TRUE)[,-c(2)])

head(covid1)

covid_perCapita <- covid1[,c(2:17)] / covid$PopulationM

covid_perCapita$Country <- covid1$Country

head(covid_perCapita)

covid_perCapita_deaths <- melt(covid_perCapita,id.vars=c("Country"),
                               value.name="value", variable.name="Day")

covidPerCapitaPlot <- ggplot(data=covid_perCapita_deaths,
   aes(x=Day, y=value, group = Country, colour = Country)) + geom_line()
   +labs(y= "Deaths per Capita", x = "Day")  + theme_bw(base_size = 16)
+ theme(axis.text.x=element_text(angle=60,hjust=1))
+ ggtitle("Day-wise Covid-Deaths per Capita in Europe in 2020")

covid_perCapitaPlot

Output:

Capita Plot

Dataset 2: Rainfall for US counties during tropical storms.

First install the package: hurricaneexposuredata

Before installing the package, please check the R version. To check the R version in RStudio go to Tools -> Global Options. In the window that opens, in the Basic Tab, we see the R version.  

#If the R version is the greater than 4  

install.packages(“hurricaneexposuredata”)

#For R versions lower than 4.0, please install this way

install.packages(‘hurricaneexposuredata’, repos=’https://geanders.github.io/drat/’, type=’source’)

Example:

R

library(hurricaneexposuredata)
library(hurricaneexposure)

rain_data <- county_rain(counties = c("01001","36005", "36047",
                                      "36061","36085", "36081",
                                      "36119","22071", "51700"),
              start_year = 1995, end_year = 2005, rain_limit = 50,
                         dist_limit = 500, days_included = c(-1, 0, 1))

ggplot(data = rain_data, aes(x=fips, y=tot_precip, group=storm_id,
                             color=storm_id)) + geom_line()

Output: