knitr::opts_chunk$set(fig.width=5, fig.height=4)
suppressPackageStartupMessages(library("lessR"))
Plot ordered data values collected over time in one of two ways that correspond to how the values are labeled.
Meaningful for sequentially ordered numerical data values such as by time, plot a run chart of a single variable according to the parameter run
. Analogous to a time series visualization, the run chart plots the data values sequentially, but without dates or times. An analysis of the runs is also provided.
Illustrate with the lessR Employee data.
d <- Read("Employee")
The data values for the variable Salary are not actually collected over time, but for illustration, here create a run chart of Salary as if the data were collected over time. The indices, the sequence of integers from 1 to the last data value, are created by Plot()
. Only the data values are specified. Invoke the run
parameter to instruct Plot()
to plot the data in sequential order as a run chart.
Plot(Salary, run=TRUE)
The default run chart displays the plotted points in a small size with connecting line segments. Change the size of the points with the parameter size
, here set to zero to remove the points entirely. Fill the area under the line segments with the parameter area_fill
, here set to the default on
but can express any color. Remove the center line with the parameter center_line
set to off
.
Plot(Salary, run=TRUE, size=0, area_fill="on", center_line="off")
Plot()
can plot a time series from three different forms of the data:
Plot()
can also similarly plot a run chart in which it generates the index values sequentially ordered. A time series requires two variables, the time/date and each corresponding measured value to be plotted.
Plotting a variable of type Date as the x-variable in a scatterplot automatically creates a time series visualization. Plot() draws the connecting line segments, without the points at each time period (size=0). To add the area fill, for
lessR set the area parameter to TRUE
for the default color from the current color theme. Or, set to a specific color.
Read time series data of stock Price for three companies: Apple, IBM, and Intel. The data table is in long form, part of lessR.
d <- Read("StockPrice") d[1:5,]
Activate a time series plot by setting the $x$-variable to a variable of R type Date
, which is true of the variable date in this data set. Can also plot a time series by passing a time series object, created with the base R function ts()
as the variable to plot.
Here plot just for Apple, with the two variables date and Price, stock price. The parameter rows
specifies what rows of the input data frame to retain for the analysis.
Plot(date, Price, rows=(Company=="Apple"))
Here, add the default fill color by setting the area_fill
parameter to "on"
. Can also specify a custom color.
Plot(date, Price, rows=(Company=="Apple"), area_fill="on")
With the by
parameter, plot all three companies on the same panel.
Plot(date, Price, by=Company)
Stack the plots by setting the parameter stack
to TRUE
.
Plot(date, Price, by=Company, stack=TRUE)
With the by1
parameter, plot all three companies on the different panels, a Trellis plot.
Plot(date, Price, by1=Company)
Do the Trellis plot with some color. Learn more about customizing visualizations in the vignette utlities
.
style(sub_theme="black", window_fill="gray10") Plot(date, Price, by1=Company, n_col=1, fill="darkred", color="red", trans=.55)
Return to the default style, then turn off text output for subsequent analyses.
style() style(quiet=TRUE)
Set a baseline of 25 with the area_origin
parameter for a Trellis plot, with default fill color.
Plot(date, Price, by1=Company, xlab="", area_fill="on", area_origin=25)
Change the aspect ratio with the aspect
parameter defined as height divided by width.
Plot(date, Price, by1=Company, aspect=.5, area_fill="slategray3")
Stack the three time series, fill under each curve with a version of the lessR sequential range "emeralds"
.
Plot(date, Price, by=Company, trans=0.4, stack=TRUE, area_fill="emeralds")
Plot()
also reads wide-format data. We have no available wide form time data with lessR, so first convert the long form as read to the wide form. In the wide form, the three companies each have their own column of data, repeated for each date. Use the lessR function reshape_wide()
to do the conversion.
dw <- reshape_wide(d, group="Company", response="Price", ID="date") head(dw)
Now the analysis, which repeats a previous analysis, but with wide-form data. Because the data frame is not the default d, explicitly indicate with the data
parameter.
Plot(date, c(Intel, Apple, IBM), area_fill="blues", stack=TRUE, trans=.4, data=dw)
Can also plot directly from an R time series object, created with the base R ts()
function.
a1.ts <- ts(dw$Apple, frequency=12, start=c(1980, 12)) Plot(a1.ts)
With style()
many themes can be selected, such as "lightbronze"
, "dodgerblue"
, "darkred"
, and "gray"
for gray scale. When no theme
or any other parameter value is specified, return to the default theme, colors
.
style()
The annotations in the following visualization consist of the text field "iPhone" with an arrowhead that points to the time that the first iPhone became available. With lessR, list each component of the annotation as a vector for add. Any value listed that is not a keyword such as "rect" or "arrow" is interpreted as a text field. Then, in order of their occurrence in the vector for add, list the needed coordinates for the objects. To place the text field "iPhone" requires one coordinate, <x1,y1>
. To place an "arrow" requires two coordinates, <x1,y1>
and <x2,y2>
. For example, the second element of the y1
vector is the y1
value for the "arrow". The text field does not require a second coordinate, so specify x2
and y2
as single elements instead of vectors.
x <- as.Date("2007-06-01") Plot(date, Price, rows=(Company == "Apple"), fill="on", add=c("iPhone", "arrow"), x1=c(x,x), y1=c(100,90), x2=x, y2=30)
Use the base R help()
function to view the full manual for Plot()
. Simply enter a question mark followed by the name of the function.
?Plot
More on Scatterplots, Time Series plots, and other visualizations from lessR and other packages such as ggplot2 at:
Gerbing, D., R Visualizations: Derive Meaning from Data, CRC Press, May, 2020, ISBN 978-1138599635.
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