ggplot2.zoo

Convenience Functions for Plotting zoo Objects with ggplot2

Description

Convenience interface for visualizing zoo objects with ggplot2. autoplot.zoo uses fortify.zoo (with melt = TRUE) to convert the zoo object into a data frame and then uses a suitable aes() mapping to visiualize the series.

Usage

  ## S3 method for class 'zoo'
autoplot(object, geom = "line", facets, ...)
  ## S3 method for class 'zoo'
fortify(model, data, 
    names = c("Index", "Series", "Value"),
    melt = FALSE, sep = NULL, ...)
  facet_free(facets = Series ~ ., margins = FALSE, scales = "free_y", ...)

  yearmon_trans(format = "%b %Y", n = 5)
  scale_x_yearmon(..., format = "%b %Y", n = 5)
  scale_y_yearmon(..., format = "%b %Y", n = 5)

  yearqtr_trans(format = "%Y-%q", n = 5)
  scale_x_yearqtr(..., format = "%Y-%q", n = 5)
  scale_y_yearqtr(..., format = "%Y-%q", n = 5)

  scale_type.yearmon(x)
  scale_type.yearqtr(x)

Arguments

object an object of class “zoo”.

geom character (e.g., “line”) or function (e.g., geom_line) specifying which geom to use.

facets specification of facets for facet_grid. The default in the autoplot method is to use facets = NULL for univariate series and facets = Series ~ . for multivariate series.

… further arguments passed to aes for autoplot (e.g., linetype = Series and/or shape = Series). For fortify the arguments are not used. For the scale_*_ functions the arguments are passed on to scale__continuous.

model an object of class “zoo” to be converted to a “data.frame”.

data not used (required by generic fortify method).

names (list of) character vector(s). New names given to index/time column, series indicator (if melted), and value column (if melted). If only a subset of characters should be changed, either NAs can be used or a named vector.

sep If specified then the Series column is split into multiple columns using sep as the split character.

melt Should the resulting data frame be in long format (melt = TRUE) or wide format (melt = FALSE).

margins As in facet_grid.

scales As in facet_grid except it defaults to “free_y”.

format A format acceptable to format.yearmon or format.yearqtr.

n Approximate number of axis ticks.

x an object of class “yearmon” or “yearqtr”.

Details

fortify.zoo takes a zoo object and converts it into a data frame (intended for ggplot2). autoplot.zoo takes a zoo object and returns a ggplot2 object. It essentially uses the mapping aes(x = Time, y = Value, group = Series) and adds colour = Series in the case of a multivariate series with facets = NULL.

Value

fortify.zoo returns a data.frame either in long format (melt = TRUE) or in wide format (melt = FALSE). The long format has three columns: the time Index, a factor indicating the Series, and the corresponding Value. The wide format simply has the time Index plus all columns of coredata(model).

autoplot.zoo returns a ggplot object.

Author(s)

Trevor L. Davis trevor.l.davis@gmail.com, Achim Zeileis

Examples

library("zoo")


library("ggplot2")
library("scales")
suppressWarnings(RNGversion("3.5.0"))
set.seed(1)

## example data
x.Date <- as.Date(paste(2003, 02, c(1, 3, 7, 9, 14), sep = "-"))
x <- zoo(rnorm(5), x.Date)
xlow <- x - runif(5)
xhigh <- x + runif(5)
z <- cbind(x, xlow, xhigh)

## univariate plotting
autoplot(x)

## by hand
ggplot(aes(x = Index, y = Value), data = fortify(x, melt = TRUE)) +
  geom_line() + xlab("Index") + ylab("x")

## adding series one at a time
last_plot() + geom_line(aes(x = Index, y = xlow), colour = "red", data = fortify(xlow))

## add ribbon for high/low band
ggplot(aes(x = Index, y = x, ymin = xlow, ymax = xhigh), data = fortify(x)) +
  geom_ribbon(fill = "darkgray") + geom_line()

## multivariate plotting in multiple or single panels
autoplot(z)                      ## multiple without color/linetype

autoplot(z, facets = Series ~ .) ## multiple with series-dependent color/linetype

autoplot(z, facets = NULL)       ## single with series-dependent color/linetype

## by hand with color/linetype and with/without facets
ggz <- ggplot(aes(x = Index, y = Value, group = Series, colour = Series, linetype = Series),
  data = fortify(z, melt = TRUE)) + geom_line() + xlab("Index") + ylab("")
ggz

ggz + facet_grid(Series ~ .)

## variations
autoplot(z, geom = "point")

autoplot(z, facets = NULL) + geom_point()

autoplot(z, facets = NULL) + scale_colour_grey() + theme_bw()

## for "ts" series via coercion
autoplot(as.zoo(EuStockMarkets))

autoplot(as.zoo(EuStockMarkets), facets = NULL)

autoplot(z) + 
    aes(colour = NULL, linetype = NULL) +
    facet_grid(Series ~ ., scales = "free_y")

autoplot(z) + aes(colour = NULL, linetype = NULL) + facet_free() # same

z.yq <- zooreg(rnorm(50), as.yearqtr("2000-1"), freq = 4)
autoplot(z.yq)

## mimic matplot
data <- cbind(A = c(6, 1, NA, NA), B = c(16, 4, 1, NA), C = c(25, 7, 2, 1))
autoplot(zoo(data), facet = NULL) + geom_point()

## with different line types
autoplot(zoo(data), facet = NULL) + geom_point() + aes(linetype = Series)

## illustrate just fortify() method
z <- zoo(data)
fortify(z)

  Index  A  B  C
1     1  6 16 25
2     2  1  4  7
3     3 NA  1  2
4     4 NA NA  1

fortify(z, melt = TRUE)

   Index Series Value
1      1      A     6
2      2      A     1
3      3      A    NA
4      4      A    NA
5      1      B    16
6      2      B     4
7      3      B     1
8      4      B    NA
9      1      C    25
10     2      C     7
11     3      C     2
12     4      C     1

fortify(z, melt = TRUE, names = c("Time", NA, "Data"))

   Time Series Data
1     1      A    6
2     2      A    1
3     3      A   NA
4     4      A   NA
5     1      B   16
6     2      B    4
7     3      B    1
8     4      B   NA
9     1      C   25
10    2      C    7
11    3      C    2
12    4      C    1

fortify(z, melt = TRUE, names = c(Index = "Time"))

   Time Series Value
1     1      A     6
2     2      A     1
3     3      A    NA
4     4      A    NA
5     1      B    16
6     2      B     4
7     3      B     1
8     4      B    NA
9     1      C    25
10    2      C     7
11    3      C     2
12    4      C     1

## with/without splitting
z <- zoo(cbind(a.A = 1:2, a.B = 2:3, b.A = 3:4, c.B = 4:5))
fortify(z)

  Index a.A a.B b.A c.B
1     1   1   2   3   4
2     2   2   3   4   5

fortify(z, melt = TRUE, sep = ".", names = list(Series = c("Lower", "Upper")))

  Index Lower Upper Value
1     1     a     A     1
2     2     a     A     2
3     1     a     B     2
4     2     a     B     3
5     1     b     A     3
6     2     b     A     4
7     1     c     B     4
8     2     c     B     5

## scale_x_yearmon with custom discrete breaks
df <-  data.frame(dates = as.yearmon("2018-08") + 0:6/12, values = c(2:6, 0, 1))
ggdf <- ggplot(df, aes(x = dates, y = values)) +
  geom_bar(position = "dodge", stat = "identity") + theme_light() +
  xlab("Month") + ylab("Values")
ggdf ## with default scale_x_yearmon

ggdf + scale_x_yearmon(breaks = df$dates) ## with custom discrete breaks