1 Goal

The goal of this tutorial is to plot time series and forecast objects as ggplot plots that can be later customized using the ggplot grammar.

2 Data import

#First we load the libraries
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date
library(ggplot2)
library(forecast)

# In this tutorial we will use the dataset of minimum temperature in melbourne
# https://datamarket.com/data/set/2324/daily-minimum-temperatures-in-melbourne-australia-1981-1990
Temperatures <- read.csv("daily-minimum-temperatures-in-me.csv", stringsAsFactors = FALSE)
head(Temperatures)
##         Date Daily.minimum.temperatures.in.Melbourne..Australia..1981.1990
## 1 1981-01-01                                                          20.7
## 2 1981-01-02                                                          17.9
## 3 1981-01-03                                                          18.8
## 4 1981-01-04                                                          14.6
## 5 1981-01-05                                                          15.8
## 6 1981-01-06                                                          15.8
colnames(Temperatures) <- c("Date", "Temperature")

# First we have to change the date to POSIXct
Temperatures$Date <- strptime(Temperatures$Date, "%Y-%m-%d" )
Temperatures$Date <- as.POSIXct(Temperatures$Date)
Temperatures$Temperature <- as.numeric(Temperatures$Temperature)
## Warning: NAs introduced by coercion
# Let's check the structure of the table
str(Temperatures)
## 'data.frame':    3652 obs. of  2 variables:
##  $ Date       : POSIXct, format: "1981-01-01" "1981-01-02" ...
##  $ Temperature: num  20.7 17.9 18.8 14.6 15.8 15.8 15.8 17.4 21.8 20 ...
# Now we create different columns for different time configurations
# Month
Temperatures <- mutate(Temperatures, MonthYear = paste(year(Date),formatC(month(Date), width = 2, flag = "0")))

# Day of the week
Temperatures <- mutate(Temperatures, Yearday = paste(year(Date), formatC(month(Date), width = 2, flag = "0"),
                                                     formatC(day(Date), width = 2, flag = "0")))

# Week of the year
Temperatures <- mutate(Temperatures, Week = week(Date))

# Year
Temperatures <- mutate(Temperatures, Year = year(Date))
Temperatures$Year <- as.factor(Temperatures$Year)
# Let's check the structure of the table
str(Temperatures)
## 'data.frame':    3652 obs. of  6 variables:
##  $ Date       : POSIXct, format: "1981-01-01" "1981-01-02" ...
##  $ Temperature: num  20.7 17.9 18.8 14.6 15.8 15.8 15.8 17.4 21.8 20 ...
##  $ MonthYear  : chr  "1981 01" "1981 01" "1981 01" "1981 01" ...
##  $ Yearday    : chr  "1981 01 01" "1981 01 02" "1981 01 03" "1981 01 04" ...
##  $ Week       : num  1 1 1 1 1 1 1 2 2 2 ...
##  $ Year       : Factor w/ 10 levels "1981","1982",..: 1 1 1 1 1 1 1 1 1 1 ...

3 Time Series and forecast

# First we need to use the year column to aggregate
Temps_month <- aggregate(Temperatures$Temperature, by = list(Temperatures$MonthYear), FUN = function(x) mean(x, na.rm=T))

# Now we create the time series adding the right period
# It's better to define the end of the time series if it's not at the end of one year
myts <- ts(Temps_month$x, frequency=12, start = c(1981, 01), end = c(1990, 12))
plot(myts)

# Now that we have several periods we can decompose 
myds_month <- decompose(myts)
plot(myds_month)

# We can forecast the next period using a Holt Winters model
myholtts <-HoltWinters(myts)

myhw <- forecast(myholtts, h = 24, findfrequency = TRUE)
plot(myhw)

4 Plotting time series and forecasts with ggplot: autoplot

4.1 Plotting a time series

# We can use autoplot to plot a time series in ggplot
autoplot(myts)

# We can now use everything we know about ggplot to make the plot nicer
autoplot(myts) + ggtitle("Melbourne temperatures time series") + 
  xlab("Time (year)") + ylab("Temperatures (C)") +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5))

5 Plotting time series and forecasts with ggplot: autoplot

5.1 Plotting a forecast object

# We can also plot a forecast using autoplot
autoplot(myhw)

# We can now use everything we know about ggplot to make the plot nicer
autoplot(myhw) + ggtitle("Melbourne temperatures forecast using Holt Winters") + 
  xlab("Time (year)") + ylab("Temperatures (C)") +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5)) +
  scale_x_continuous(breaks = seq(from = 1982, to =  1992, by = 2))
## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.

6 Conclusion

In this tutorial we have learnt how to plot time series and forecast objects using autoplot. This could be useful if we want to generate ggplot objects to be manipulated afterwards.