Visualizing Time-Series Data with Line Plots

The manual approach

To create the same plot with ggplot, we need to construct a data frame first. In this example, we want to consider the dates at which the measurements were taken when scaling the x-axis.

The problem here is that the mts object doesn’t store the years as dates but as floating point numbers. For example, a value of 1998.0 indicates a day in the beginning of 1998, while 1998.9 indicates a value at the end if 1998. Since I could not find a function that transforms such representations, we will create a function that transforms this numeric representation to dates.

scale.value.range <- function(x, old, new) {
   # scale value from interval (min/max) 'old' to 'new'
   scale <- (x - old[1]) / (old[2] - old[1])
   newscale <- new[2] - new[1]
   res <- scale * newscale + new[1]
   return(res)
}
float.to.date <- function(x) {
    # convert a float 'x' (e.g. 1998.1) to its Date representation
    year <- as.integer(x)
    # obtaining the month: consider decimals
    float.val <- x - year
    # months: transform from [0,1) value range to [1,12] value range
    mon.float <- scale.value.range(float.val, c(0,1), c(1,12))
    mon <- as.integer(mon.float)
    date <- get.date(year, mon.float, mon)
    return(date)
}
days.in.month <- function(year, mon) {
    # day: transform based on specific month and year (leap years!)
    date1 <- as.Date(paste(year, mon, 1, sep = "-"))
    date2 <- as.Date(paste(year, mon+1, 1, sep = "-"))
    days <- difftime(date2, date1)
    return(as.numeric(days))
}
get.date <- function(year, mon.float, mon) {
    max.nbr.days <- days.in.month(year, mon)
    day.float <- sapply(seq_along(year), function(x) 
        scale.value.range(mon.float[x] - mon[x], c(0,1), c(1,max.nbr.days[x])))
    day <- as.integer(day.float)
    date.rep <- paste(as.character(year), as.character(mon), 
                as.character(day), sep = "-")
    date <- as.Date(date.rep, format = "%Y-%m-%d")
    return(date)
}

mts.to.df <- function(obj) {
    date <- float.to.date(as.numeric(time(obj)))
    df <- cbind("Date" = date, as.data.frame(obj))
    return(df)
}
library(ggplot2)
df <- mts.to.df(EuStockMarkets)
# go from wide to long format
library(reshape2)
dff <- melt(df, "Date", variable.name = "Exchange", value.name = "Points")
# load scales to format dates on x-axis
library(scales)
ggplot(dff, aes(x = Date, y = Points)) + 
  geom_line(aes(color = Exchange), size = 1) + 
  # use date_breaks to have more frequent labels
  scale_x_date(labels = date_format("%m-%Y"), date_breaks = "4 months") +
  # rotate x-axis labels
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

Creating the ggplot visualization for this example involved more work because I wanted to have an improved representation of the dates as for the other two approaches for creating the plot. For a faster, yet less accurate representation, the plot could have also been created by ignoring the months and just using the years, as in the first example.

Creating the plot with the zoo package

To create a ggplot version of the plot, we can use the autoplot function from ggplot2, ater transforming the mts object to a zoo object via as.zoo:

library(zoo)
zooMarkets <- as.zoo(EuStockMarkets)
#autoplot(zooMarkets) # plot with facets
autoplot(zooMarkets, facet = NULL) # plot without facets

Rather than using the custom mts.to.df function, we could have also used the ggplot2’s fortify function on the zoo object in order to convert it to a data frame:

market.df <- fortify(zooMarkets)
print(head(market.df))

##      Index     DAX    SMI    CAC   FTSE
## 1 1991.496 1628.75 1678.1 1772.8 2443.6
## 2 1991.500 1613.63 1688.5 1750.5 2460.2
## 3 1991.504 1606.51 1678.6 1718.0 2448.2
## 4 1991.508 1621.04 1684.1 1708.1 2470.4
## 5 1991.512 1618.16 1686.6 1723.1 2484.7
## 6 1991.515 1610.61 1671.6 1714.3 2466.8

Note, however, that the Index column provides the date as a floating point number rather than as a Date as in the mts.to.df function.