README.md

In ChandlerLutz/ggts: Easy time series visualization

The ggts package allows for easy time series visualization of dataframes, ts, zoo, and xts objects via ggplot2. The main functions, ggts, ggts_facet, and geom_cycle allow for easy variable selection, different linetypes and colors, standardizing or indexing, and shading of cycles such as rececessions or bear markets.

See below and ?ggts ?ggts_facet, and ?geom_cycle for more examples.

Main Functions

• ggts to plot one or more time series on a single graph

• ggts_facet to plot multiple time series using faceting

• geom_cycle to add customized (e.g. NBER recession) shading to a ggplot2 plot

Installation and Usage:

     #install.packages("devtools")
     library(devtools)
     install_github("ChandlerLutz/ggts")
     library(ggts)

     data(AirPassengers) ##A ts object
     ggts(djia.data,bear=TRUE)
     ggts(AirPassengers)

     library(AER)
     data(USMacroSW)  ##a mts object
     ggts(USMacroSW, ffrate, tbill, tbond)
     ##Index values for CPI and Japanese GDP from SW datasets
     ggts(USMacroSW, cpi, gdpjp, index = TRUE)
     ##Standardized unemployment rate and Pound/Dollar exchange rate
     ggts(USMacroSW, cpi, gdpjp, standardize = TRUE)

     ##Can also easily plot a faceted graph
     ##This is handy for plotting several time series
     ggts_facet(USMacroSW)

     library(xts)
     data(AAPL.data) ##xts object
     ##Plot everything but volume using dplyr matches helper function.
     ##Note that all dplyr select helper functions can be used, but
     ##either dplyr must be attached or the package explicitly
     referenced
     ##(below example)
     ggts(AAPL.data, -dplyr::matches("Volume"))

     ##If we have a randomly placed time column in a dataframe
     ##ggts will find it
     USMacroSW2 <- as_ts_df(USMacroSW)[, c("ffrate",
                                            "tbill",
                                            "time",
                                            "tbond")]
     names(USMacroSW2)[3] <- "date"
     ggts(USMacroSW2)

     ##geom_cycle() will automatically plot NBER recessions
     ##This is the default cycle
     ggts_facet(USMacroSW) + geom_cycle()
     ##Can also plot bear market dates
     data(bear_dates)
     ggts_facet(USMacroSW) + geom_cycle(dates = bear_dates)
     ##Or both recessions and bear markets
     ggts_facet(USMacroSW) + geom_cycle() +
         geom_cycle(dates=bear_dates, fill="grey50")


     #Using the quantmod package
     library(quantmod)
     data(bear_dates)
     getSymbols("^VIX")
     class(VIX)  # "xts" "zoo"
     ggts(VIX, VIX.Close) + geom_cycle(dates = bear_dates)

A comparison to Hadley Whickam's ggplot2 book (first ed, p. 164-166):

     data(economics)
     ## From Hadley's book ##
     require(ggplot2)
     require(plyr)
     require(reshape)
     emp <- melt(economics, id = "date",measure = c("unemploy", "uempmed"))

     range01 <- function(x) {
             rng <- range(x, na.rm = TRUE)
         (x - rng[1]) / diff(rng)
     }
     emp2 <- ddply(emp, .(variable), transform, value = range01(value))
     #Regular plot
     qplot(date, value, data = emp2, geom = "line",colour =
             variable,
             linetype = variable)
     #faceted plot
     qplot(date, value, data = emp, geom = "line") +
        facet_grid(variable ~ ., scales = "free_y")

     ## Using the ggts package ##
     require(ggts)
     #Regular Plot
     ggts(economics, uempmed, unemploy)
     #Faceted plot
     ggts_facet(economics)
     #Add nber recessions cycles
     ggts_facet(economics) + geom_cycle()

Chandler Lutz http://chandlerlutz.com/

ChandlerLutz/ggts documentation built on May 6, 2019, 9:56 a.m.