Plot: Scatterplots including Time Series and Violin/Box/Scatterplot

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Scatterplots including Time Series and Violin/Box/Scatterplot

Description

Abbreviation:
⁠ ⁠Violin Plot only: vp, ViolinPlot
⁠ ⁠Box Plot only: bx, BoxPlot
⁠ ⁠Scatter Plot only: sp, ScatterPlot

A scatterplot displays the values of a distribution, or the relationship between the two distributions, in terms of their joint values, as a set of points in an n-dimensional coordinate system, in which the coordinates of each point are the values of n variables for a single observation (row of data). From the identical syntax, from any combination of continuous or categorical variables variables x and y, Plot(x) or Plot(x,y), where x or y can be a vector, by default generates a family of related 1- or 2-dimensional scatterplots, possibly enhanced, plus related statistical analyses. Define a categorical variable as an R factor. If x is a Date variable, then a time series is plotted.

Plot() produces a wide variety of scatterplots as outlined in the following list.

Variable Type	Meaning
--------------------------	--------------------------------------
x, y, or z	single continuous variable
xDate	date variable, defined as a R Date type, which can be implicitly created from entered numeric dates
xCat, yCat, or zCat	categorical variable, typically defined as an R factor
xUnique or yUnique	categorical variable with all values unique
X or Y	vector of continuous variables
Xcat	vector of categorical variables
--------------------------	--------------------------------------

Two variables
Plot(x,y): traditional scatterplot of two continuous variables
Plot(xDate,y): a variable of type Date paired with a continuous variable yields a time-series plot
Plot(xCat,yCat): to solve the over-plot problem, plot a scatterplot of two categorical variables as a bubble scatterplot, the size of each bubble based on the corresponding joint frequency
Plot(xCat,y) or Plot(x,yCat): one variable categorical and the other variable continuous, yields a scatterplot with means at each level of the categorical variable
Plot(xCat,y, stat="mean") or Plot(x,yCat, stat="mean"): one variable categorical and the other variable continuous, yields a Cleveland dot plot with a specified statistic such as the "mean" of the continuous variable at each level of the categorical variable
Plot(xUnique,y) or Plot(x,yUnique): one categorical with unique (ID) values and the other variable continuous, yields a Cleveland dot (lollipop) plot, where the unique values can be variable row.names

One variable
Plot(x): one continuous variable generates either a violin/box/scatterplot (VBS plot), named here, or a run chart, generated from the x-variable named .Index. Or x can be an R time series object created with ts() for a time series visualization
Plot(xCat): one categorical variable yields a 1-dimensional bubble plot to solve the over-plot problem for a more compact replacement of the traditional bar chart

Three, four, or more variables
Plot(x,y, size=z): x and y continuous yields a bubble of two continuous variables with z setting the size of the corresponding plotted point, i.e., bubble
Plot(x,y, by=zCat): plots a different scatterplot of x and y for each level of zCat on the same panel
Plot(x,y, facet1=zCat): plots a different scatterplot of x and y for each level of zCat on separate panels, i.e., Trellis or facet plots
Plot(x,y, facet1=z1Cat, facet2=z2Cat): plots a different scatterplot of x and y for each combination of levels of zCat1 and zCat2 on separate panels, i.e., Trellis or facet plots
Plot(X,y) or Plot(x,Y): one vector variable of several continuous variables, paired with another single continuous variable, yields multiple scatterplots on the same graph
Plot(Y,xUnique): one categorical with unique (ID) values, such as row.names and the other variable a vector of continuous variables yields a Cleveland dot plot of all the continuous variables, usually two

One vector of variables
Plot(X): one vector of numerical variables, with no y-variable, results in a scatterplot matrix of the variables
Plot(Xcat): one vector of categorical x-variables coded as character or factor variables, with no y-variable, generalizes to a matrix of 1-dimensional bubble plots, here called the bubble plot frequency matrix, to replace a series of bar charts

Usage

Plot(

    # -------------------------------------------------------
    # Data from which to construct the plot for x- and y-axis
    x, y=NULL, data=d, filter=NULL,


    # -------------------------------
    # Enhancements and customizations
    # -------------------------------

    # --------------------------------------------------
    # Stratification: Same panel or Trellis (facet) plot [x, or x and y]
    by=NULL, facet1=NULL, facet2=NULL,
    n_row=NULL, n_col=NULL, aspect="fill",

    # --------------------------------------------------------------------
    # Analogy of physical Marks on paper that create the points and labels
    # See  ?style  for more options with the  style()  function
    theme=getOption("theme"),
    fill=NULL, color=NULL,
    transparency=getOption("trans_pt_fill"),

    enhance=FALSE, means=TRUE,
    size=NULL, size_cut=NULL, shape="circle", line_width=1.5,
    segments=FALSE, segments_y=FALSE, segments_x=FALSE,

    # ----------------------
    # Sort and jitter points
    sort_yx=c("0", "-", "+"),
    jitter_x=NULL, jitter_y=NULL,

    # ----------------
    # Outlier analysis
    ID="row.name", ID_size=0.60,
    MD_cut=0, out_cut=0, out_shape="circle", out_size=1,

    # -------------------------------------------------
    # Fit line, confidence interval, confidence ellipse
    fit=c("off", "loess", "lm", "ls", "null", "exp", "quad",
          "power", "log"),
    fit_power=1, fit_se=0.95,
    fit_color=getOption("fit_color"), fit_new=NULL, 
    plot_errors=FALSE, ellipse=0,


    # ----------------------------------------------------------
    # Types of plots
    # ----------------------------------------------------------

    # -----------------------------------------------------------------------
    # Time series and forecasting, plot x values sequentially [xDate, y or Y]
    ts_unit=NULL, ts_agg=c("sum", "mean"), ts_NA=NULL,
    ts_ahead=0, ts_method=c("es", "lm"), ts_format=NULL,
    ts_fitted=FALSE, ts_level=NULL, ts_trend=NULL, ts_seasons=NULL,
    ts_type=c("additive", "multiplicative"), ts_PI=0.95,
    stack=FALSE, area_fill="transparent", area_split=0, n_date_tics=NULL,
    # Run chart (indicate with .Index for the name of the x-variable)
    show_runs=FALSE, center_line=c("off", "mean", "median", "zero"),

    # -----------------------------------
    # Lollipop chart from aggregated data [xCategorical and y]
    stat=c("mean", "sum", "sd", "deviation", "min", "median", "max"),
    stat_x=c("count", "proportion", "%"),

    # ----------------------------------
    # Integrated violin/box/scatter plot  [x only]
    vbs_plot="vbs", vbs_size=0.9, bw=NULL, bw_iter=10,
    violin_fill=getOption("violin_fill"),
    box_fill=getOption("box_fill"),
    vbs_pt_fill="black",
    vbs_mean=FALSE, fences=FALSE,
    k=1.5, box_adj=FALSE, a=-4, b=3,

    # -----------
    # Bubble plot [xCategorical, or xCategorical and yCategorical]
    radius=NULL, power=0.55, low_fill=NULL, hi_fill=NULL,

    # --------------------------------------
    # Large data sets, smoothing and binning  [x and y]
    smooth=FALSE, smooth_points=100, smooth_size=1,
    smooth_exp=0.25, smooth_bins=128, n_bins=1,

    # ------------------------------------------------------
    # Bins for frequency polygon or text output of VBS plots
    bin=FALSE, bin_start=NULL, bin_width=NULL, bin_end=NULL,
    breaks="Sturges", cumulate=FALSE,


    # -------------
    # Miscellaneous
    # -------------

    # Labels for axes, values, legend if x and by variables, margins
    xlab=NULL, ylab=NULL, main=NULL, sub=NULL,
    label_adjust=c(0,0), margin_adjust=c(0,0,0,0),  # top, right, bottom, left

    rotate_x=getOption("rotate_x"), rotate_y=getOption("rotate_y"),
    offset=getOption("offset"),

    xy_ticks=TRUE, origin_x=NULL, origin_y=NULL,
    scale_x=NULL, scale_y=NULL,
    pad_x=c(0,0), pad_y=c(0,0),
    legend_title=NULL,

    # ----------------------------------------------------
    # Add one or more objects, text, or geometric figures
    add=NULL, x1=NULL, y1=NULL, x2=NULL, y2=NULL,

    # ---------------------------------------------
    # Output: turn off, to PDF file, decimal digits
    quiet=getOption("quiet"), do_plot=TRUE,
    pdf_file=NULL, width=6.5, height=6,
    digits_d=NULL,

    # -------------------------------------------------------------
    # Deprecated, to be removed in future versions
    n_cat=getOption("n_cat"), value_labels=NULL,  # use R factors instead
    rows=NULL, by1=NULL, by2=NULL,

    # -----
    # Other
    eval_df=NULL, fun_call=NULL, ...)


ScatterPlot(...)
sp(...)
BoxPlot(...)
bx(...)
ViolinPlot(...)
vp(...)

Arguments

x	By itself, or with y, by default, a primary variable, that is, plotted by its values mapped to coordinates. The data values can be continuous or categorical, cross-sectional or a time series. If x is sorted, with equal intervals separating the values, or is a time series, then by default plots the points sequentially, joined by line segments. If named .Index, then a run chart is generated from the corresponding y variable. Can specify multiple x-variables or multiple y-variables as vectors, but not both. Can be in a data frame or defined in the global environment.
y	An optional second primary variable. Variable with values to be mapped to coordinates of points in the plot on the vertical axis. Can be continuous or categorical. Can be in a data frame or defined in the global environment.
data	Optional data frame that contains one or both of x and y. Default data frame is d.
filter	A logical expression that specifies a subset of rows of the data frame to analyze.

by	A categorical variable to provide a scatterplot for each level of the numeric primary variables x and y on the same plot, a grouping variable. For two-variable plots, applies to the panels of a Trellis graphic if facet1 is specified.
facet1	A categorical variable, the conditioning variable, which activates Trellis (facet) graphics, provided by Deepayan Sarkar's (2007) lattice package, to provide a separate panel of numeric primary variables x and y for each level of the facet1 variable. Re-order the levels by first converting to a factor variable with factor or lessR factors.
facet2	A second conditioning variable to generate Trellis (facet) plots jointly conditioned on both the facet1 and facet2 variables, with facet2 as the row variable, which yields a scatterplot (panel) for each cross-classification of the levels of numeric x and y variables.
n_row	Optional specification for the number of rows in the layout of a multi-panel display with Trellis graphics. Specify n_col or n_row, but not both.
n_col	Optional specification for the number of columns in the layout of a multi-panel display with Trellis (facet) graphics. Specify n_col or n_row, but not both. The default sets to 1.
aspect	Lattice parameter for the aspect ratio of the Trellis panels or facets, defined as height divided by width. The default value is "fill" to have the panels expand to occupy as much space as possible. Set to 1 for square panels. Set to "xy" to specify a ratio calculated to display at 45 degrees, that is, with the line slope approximately 45 degrees.

theme	Color theme for this analysis. Make persistent across analyses with style.
fill	Either fill color of the points or the area under a line chart. Can also set with the lessR function getColors to select from a variety of color palettes. For points, default is pt_fill and for area under a line chart, violin_fill. For a line chart, set to "on" for default color.
color	Border color of the points or line_color for line plot. Can be a vector to customize the color for each point or a color range such as "blues" (see getColors. Default is pt_color from the lessR style function.
transparency	Transparency factor of the fill color of each point. Default is trans_pt_fill from the lessR style function.

enhance	For a two-variable scatterplot, if TRUE, automatically add the 0.95 data ellipse, labeling of outliers beyond a Mahalanobis distance of 6 from the ellipse center, the best-fitting least squares line of all the data, the best-fitting least squares line of the regular data without the outliers, and a horizontal and vertical line to represent the mean of each of the two variables.
means	If the one variable is categorical, expressed as a factor, and the other variable continuous, then if TRUE, by default, plot means with the scatterplot. Also applies to a 1-D scatterplot.
size	When set to a constant, the scaling factor for standard points (not bubbles) or a line, with default of 1.0 for points and 2.0 for a line. Set to 0 to not plot the points or lines. If area_fill for a line chart, then default is 0. When set to a variable, activates a bubble plot with the size of each bubble further determined by the value of radius. Applies to the standard two-variable scatterplot as well as to the scatterplot component of the integrated Violin-Box-Scatterplot (VBS) of a single continuous variable.
size_cut	If 1 (or TRUE), then for a bubble plot in which the bubble sizes are defined by a size variable, show the value of the sizing variable for selected bubbles in the center of the bubbles, unless the bubble is too small. If 0 (or FALSE), no value is displayed. If a number greater than 1, then display the value only for the indicated number of values, such as just the max and min for a setting of 2, the default value when bubbles represent a size variable. Color of the displayed text set by bubble_text from the style function.
shape	The plot character(s). The default value is "circle" with both a default exterior color and filled interior, explicitly specified with "color" and "fill". Other possible values, with fillable interiors, are "circle", "square", "diamond", "triup" (triangle up), and "tridown" (triangle down), all uppercase and lowercase letters, all digits, and most punctuation characters. The numbers 0 through 25 as defined by the R points function also apply. If plotting levels for different groups according to by, then list as a vector with with one shape for each level to be plotted or set to "vary" to have shapes selected by default across the by groups.
line_width	Width of the line segments that connect adjacent points, such as plotting time series data. Set to zero to remove the line segments.
segments	Designed for interaction plots of means, connects each pair of successive points with a line segment. Pass a data frame of the means, such as from pivot. To turn off connecting line segments for sorted, equal intervals data, set to FALSE. Currently, does not apply to Trellis plots.
segments_y	For one x-variable, draw a line segment from the y-axis to each plotted point, such as for the Cleveland dot plot. For two x-variables, the line segments connect the two points.
segments_x	Draw a line segment from the x-axis for each plotted point.

sort_yx	Sort the values of y by the values of x, such as for a Cleveland dot plot, that is, a numeric x-variable paired with a categorical y-variable with unique values. If a x is a vector of two variables, sort by their difference.
jitter_x	Randomly perturbs the plotted points of a scatterplot horizontally within the limits of the explicitly specified value, or set to NULL to rely upon the computed default value.
jitter_y	Defaults to 0. Same as jitter_x except vertical jitter.

ID	Name of variable to provide the labels for the selected plotted points for outlier identification, row names of data frame by default. To label all the points use the add parameter described later.
ID_size	Size of the plotted labels. Modify text color of the labels with the style function parameter ID_color.
MD_cut	Mahalanobis distance cutoff to define an outlier in a 2-variable scatterplot.
out_cut	Count or proportion of plotted points to label, in order of their distance from the scatterplot center (means), counting down from the more extreme point. For two-variable plots, assess distance from the center with Mahalanobis distance. For VBS plots of a single continuous variable, refers to outliers on each side of the plot.
out_shape	Shape of outlier points in a 2-variable scatterplot or a VBS plot. Modify fill color from the current theme with the style function parameters out_fill and out2_fill.
out_size	Size of outlier points in a 2-variable scatterplot or VBS plot.

fit	The best fit line. Default value is "off", with options "loess" for non-linear fit, "lm" for linear model least squares, "null" for the null model, "exp" for exponential growth or decay, "power" for the general power model in conjunction with fit_power, and "quad" for an increasing or decreasing function for the specific power value of 2. If potential outliers are identified according to out_cut, a second (dashed) fit line is displayed calculated without the outliers.
fit_power	Power that describes response Y as a power function of the predictor variable X, required for the value of fit of "power". Optionally, and experimentally, applies to fit values "exp".
fit_se	Confidence level for the error band displayed around the line of best fit. On by default at 0.95 if a fit line is specified, but turned off if plot_errors=TRUE. Can be a vector to display multiple ranges. Set to 0 to turn off.
fit_color	Color of the fit line.
fit_new	When parameter fit is set to a fit curve such as "lm" or "quad", then predicted values from the model are predicted for these specified values.
plot_errors	Plot the line segment that joins each point to the regression line, "loess" or "lm", illustrating the size of the residuals.
ellipse	Confidence level of a data ellipse for a scatterplot of only a single x-variable and a single y-variable according to the contours of the corresponding bivariate normal density function. Can specify the confidence level(s) for a single or vector of numeric values from 0 to 1, to plot one or more specified ellipses. For Trellis graphics, only the maximum level applies with only one ellipse per panel. Modify fill and border colors with the style function parameters ellipse_fill and ellipse_color.

ts_unit	Specify the time unit from which to plot time series data, plotted when the x-variable is of type Date. Default value is the time unit that describes the time intervals as they occur in the data. Aggregation according to the time unit will occur as specified, such as a daily time series aggregated to "months". Dates are currently stored as variable type Date() which stores information as calendar dates without times of the day. Valid values include: "days", "weeks", "months", "quarters", and "years", as well as "days7" to provide seasonality for daily data on a weekly instead of annual basis. Otherwise, for forecasting, the time unit for detecting seasonality will usually be '"months"' or '"quarters"'.
ts_agg	Function by which to aggregate over time according to ts_unit. Default is "sum" with an option for "means".
ts_NA	By default, y missing values, those with value NA, do not plot, leaving a blank space. Or, specify a value, usually 0, to replace the NA to plot a y-value such as 0 for the corresponding date on the x-axis. However, forecasting with missing data does not work.
ts_ahead	Forecast this specified number of ts_units ahead of the last time period in the time series data.
ts_method	Default is "es" for exponential smoothing forecasting. Or, choose "lm" for at least squares linear regression model.
ts_format	A specified format for R function as.Date() that describes the values of the date variable on the x-axis, needed if the function cannot identify the date format to properly decode the given date values. For example, describe a character string date such as "09/01/2024" by the format "%m/%d/%Y". See details for more information.
ts_fitted	If TRUE, for each data value display the fitted value, ts_level, trend, and seasonal component.
ts_level	Holt-Winters exponential smoothing level parameter, alpha. By default, the algorithm chooses an optimal numerical value from 0 to 1, or user specify.
ts_trend	Trend parameter. If FALSE, then no trend in the model estimation. For Holt-Winters exponential smoothing, the trend parameter, beta. By default, the algorithm chooses an optimal numerical value from 0 to 1, or user specify.
ts_seasons	The seasonality parameter, which applies to both exponential smoothing, gamma, and de-seasonalized regression forecasting. For exponential smoothing, by default, the algorithm chooses an optimal numerical value from 0 to 1, or user specify. Or set to FALSE to remove the effect. Defaults to FALSE for annual data, which cannot exhibit intra-annual seasonality. For linear regression time series forecasting, set to FALSE to not de-seasonalize.
ts_type	Type of seasonal model for exponential smoothing forecasting. Default is "additive" with a "multiplicative" option.
ts_PI	Level of the prediction interval about the forecasted Holt-Winters values with a default of 0.95.
stack	If TRUE, multiple time plots are stacked on each other, with area set to TRUE by default.
area_fill	Specifies the area under the line segments, if present. If stack is TRUE, then default is gradation from default color range, e.g., "blues". If not specified, and fill is specified with no plotted points and area_fill is not specified,, then fill generally specifies the area under the line segments.
area_split	[Applies only to a Trellis plot activated with parameter facet1.] Value of y that defines a reference line that splits the filled area under the time series line. Values of y less than this value are below the corresponding reference line, values larger are above the line.
n_date_tics	Suggested number of ticks for the dates on the x-axis to override the default of approximately 7.
show_runs	If TRUE, display the individual runs in the run analysis. Also, sets run to TRUE. Customize the color of the line segments with segments_color with function style.
center_line	Plots a dashed line through the middle of a run chart. Provides a center line for the "median" by default, when the values randomly vary about the mean. "mean" and "zero" specify that the center line goes through the mean or zero, respectively. Currently does not apply to Trellis plots.

stat	Apply specified aggregation such as "mean" for the numerical y variable to each of the levels of categorical variable x. The resulting dot plot, or Cleveland plot, is analogous to a bar chart.
stat_x	If no y variable is specified, for constructing a frequency polygon, with access to the bin_width parameter. Either do the default count for each bin or the proportion, also indicated by %.

vbs_plot	A character string that specifies the components of the integrated Violin-Box-Scatterplot (VBS) of a continuous variable. A "v" in the string indicates a violin plot, a "b" indicates a box plot with flagged outliers, and a "s" indicates a 1-variable scatterplot. Default value is "vbs". The characters can be in any order and upper- or lower-case. Generalize to Trellis plots with the facet1 and facet2 parameters, but currently only applies to horizontal displays. Modify fill and border colors from the current theme with the style function parameters violin_fill, violin_color, box_fill and box_color.
vbs_size	Width of the violin plot to the plot area. Make the violin (and also the accompanying box plot) larger or smaller by making the plot area and/or this value larger or smaller.
bw	Bandwidth for the smoothness of the violin plot. Higher values for smoother plots. Default is to calculate a bandwidth that provides a relative smooth density plot.
bw_iter	Number of iterations used to modify default R bandwidth to further smooth the obtained density estimate. When set, also displays the iterations and corresponding results.
violin_fill	Fill color for a violin plot.
box_fill	Fill color for a box plot.
vbs_pt_fill	Points in a VBS scatterplot are black by default because the background is the violin, which is based on the current theme color. To use the values for pt_fill and pt_color specified by the style function, set to "default". Or set to any desired color.
vbs_mean	Show the mean on the box plot with a strip the color of out_fill, which can be changed with the style function.
fences	If TRUE, draw the inner upper and lower fences as dotted line segments.
k	IQR multiplier for the basis of calculating the distance of the whiskers of the box plot from the box. Default is Tukey's setting of 1.5.
box_adj	Adjust the box and whiskers, and thus outlier detection, for skewness using the medcouple statistic as the robust measure of skewness according to Hubert and Vandervieren (2008).
a, b	Scaling factors for the adjusted box plot to set the length of the whiskers. If explicitly set, activates box_adj.

radius	Scaling factor of the bubbles in a bubble plot, which sets the radius of the largest displayed bubble in inches. To activate, either set the value of size to a third variable where the default is 0.10, or for categorical variables, a factor, the size of the bubbles represents frequency, with a default of 0.22.
power	Relative size of the scaling of the bubbles to each other. Value of 0.5 scales the bubbles so that the area of each bubble is the value of the corresponding sizing variable. Default value is 0.55, a slight emphasis toward emphasizing differences in bubble area beyond area. Value of 1 scales so the radius of the bubble is the value of the sizing variable, increasing the discrepancy of size between the variables.
low_fill	For a categorical variable and the resulting bubble plot, or a matrix of these plots, sets a color gradient of the fill color beginning with this color.
hi_fill	For a categorical variable and the resulting bubble plot, or a matrix of these plots, sets a color gradient of the fill color ending with this color.

smooth	Smoothed density plot for two numerical variables.
smooth_points	Number of points superimposed on the density plot in the areas of the lowest density to help identify outliers, which controls how dark are the smoothed points.
smooth_size	Size of points superimposed on the density plot.
smooth_exp	Exponent of the function that maps the density scale to the color scale. Smaller than default of 0.25 yields darker plots.
smooth_bins	Number of bins in both directions for the density estimation.
n_bins	Specify the number of bins to bin a single numeric x-variable from which to compute the mean or median for a numeric y-variable for each bin of x. Points are plotted with the size dependent on the sample size for the bin, unless size is specified at a constant value. Default value is 1 for no binning. Available parameters include fill, color, transparency, segments, scale_x, and scale_y.

bin	If TRUE, display the default frequency distribution for the text output of the Violin-Box-Scatter (VBS) Plot, or, if values is set to "count", a frequency polygon.
bin_start	Optional specified starting value of the bins for a frequency polygon or for the text output of a Violin-Box-Scatter (VBS) Plot. Also, sets bin to TRUE.
bin_width	Optional specified bin width value. Also, sets bin to TRUE.
bin_end	Optional specified value that is within the last bin, so the actual endpoint of the last bin may be larger than the specified value.
breaks	The method for calculating the bins, or an explicit specification of the bins, such as with the standard R seq function or other options provided by the hist function. Also, sets bin to TRUE.
cumulate	Specify a cumulative frequency polygon.

xlab, ylab	Axis label for x-axis or y-axis. If not specified, then the label becomes the name of the corresponding variable label if it exists, or, if not, the variable name. If xy_ticks is FALSE, no ylab is displayed. Customize these and related parameters with parameters such as lab_color from the style function.
main	Label for the title of the graph. If the corresponding variable labels exist, then the title is set by default from the corresponding variable labels.
sub	Sub-title of graph, below xlab. Not yet implemented.
label_adjust	Two-element vector – x-axis label, y-axis label – adjusts the position of the axis labels in approximate inches. + values move the labels away from plot edge. Not applicable to Trellis graphics.
margin_adjust	Four-element vector – top, right, bottom and left – adjusts the margins of the plotted figure in approximate inches. + values move the corresponding margin away from plot edge. Can use in conjunction with offset that can move axis values into a larger margin space. Not applicable to Trellis graphics.

rotate_x	Rotation in degrees of the value labels on the x-axis, usually to accommodate longer values, typically used in conjunction with offset. When equal 90 the value labels are perpendicular to the x-axis and a different algorithm places the labels so that offset is not needed.
rotate_y	Degrees that the axis values for the value labels on the y-axis are rotated, usually to accommodate longer values, typically used in conjunction with offset.
offset	The amount of spacing between the axis values and the axis. Default is 0.5. Larger values such as 1.0 create additional space for the label when longer axis value names are rotated. Can use in conjunction with margin_adjust to create space in the margin to accommodate the axis values.

xy_ticks	Flag that indicates if tick marks and associated value labels on the axes are to be displayed. To rotate the axis values, use rotate_x, rotate_y, and offset from the style function.
origin_x	Origin of x-axis. Starting value of x, by default the minimum value of x, except for time series plots and when stat is set to "count" or related where the origin is zero by default.
origin_y	Origin of y-axis. Starting value of y, by default the minimum value of x, except for time series plots and when stat is set to "count" or related where the origin is zero by default.
scale_x	If specified, a vector of three values that define the x-axis with numerical values: starting value, ending value, and number of intervals.
scale_y	If specified, a vector of three values that define the y-axis with numerical values: starting value, ending value, and number of intervals.
pad_x	Proportion of padding added to left and right sides of the x-axis, respectively. Value from 0 to 1 for each of the two elements. If only one element specified, value is applied to both sides.
pad_y	Proportion of padding added to bottom and top sides of the y-axis, respectively. Value from 0 to 1 for each of the two elements. If only one element specified, value is applied to both sides.
legend_title	Title of the legend for a multiple-variable x or y plot.

add	Overlay one or more objects, text or a geometric figures, on the plot. Possible values are any text to be written, the first argument, which is "text", or, "labels" to label each point with the row name, or, "rect" (rectangle), "line", "arrow", "v_line" (vertical line), and "h_line" (horizontal line). The value "means" is short-hand for vertical and horizontal lines at the respective means. Does not apply to Trellis graphics. Customize with parameters such as add_fill and add_color from the style function.
x1	First x-coordinate to be considered for each object, can be "mean_x". Not used for "h_line".
y1	First y-coordinate to be considered for each object, can be "mean_y". Not used for"v_line".
x2	Second x-coordinate to be considered for each object, can be "mean_x". Only used for "rect", "line" and arrow.
y2	Second y-coordinate to be considered for each object, can be "mean_y". Only used for "rect", "line" and arrow.

quiet	If set to TRUE, no text output. Can change system default with style function.
do_plot	If TRUE, the default, then generate the plot.
pdf_file	Indicate to direct pdf graphics to the specified name of the pdf file.
width	Width of the plot window in inches, defaults to 5 except in RStudio to maintain an approximate square plotting area.
height	Height of the plot window in inches, defaults to 4.5 except for 1-D scatterplots and when in RStudio.
digits_d	Number of significant digits for each of the displayed summary statistics.

n_cat	Number of categories, specifies the largest number of unique, equally spaced integer values of a variable for which the variable will be analyzed as categorical instead of continuous. Default is 0. Use to specify that such variables are to be analyzed as categorical, a kind of informal R factor. [deprecated]: Best to convert a categorical integer variable to a factor.
value_labels	For factors, default is the factor labels, and for character variables, default is the character values. Or, provide labels for the x-axis on the graph to override these values. If the variable is a factor and value_labels is not specified (is NULL), then the value_labels are set to the factor levels with each space replaced by a new line character. If x and y-axes have the same scale, they also apply to the y-axis. Control the plotted size with axis_cex and axis_x_cex from the lessR style function. [deprecated]: Better to convert a categorical integer variable to a factor.
rows	Deprecated old parameter name that is now called filter.
by1	Deprecated old parameter name, replaced with the more descriptive facet1.
by2	Deprecated old parameter name, replaced with the more descriptive facet2.

eval_df	Determines if to check for existing data frame and specified variables. By default is TRUE unless the shiny package is loaded then set to FALSE so that Shiny will run. Needs to be set to FALSE if using the pipe %\>% notation.
fun_call	Function call. Used with knitr to pass the function call when obtained from the abbreviated function call sp.

...	Other parameter values for non-Trellis graphics as defined by and processed by standard R functions plot and par, including cex.main for the size of the title col.main for the color of the title sub and col.sub for a subtitle and its color

Details

VARIABLES and TRELLIS PLOTS
There is at least one primary variable, x, which defines the coordinate system for plotting in terms of the x-axis, the horizontal axis. Plots may also specify a second primary variable, y, which defines the y-axis of the coordinate system. One of these primary variables may be a vector. The simplest plot is from the specification of only one or two primary variables, each as a single variable, which generates a single scatterplot of either one or two variables, necessarily on a single plot, called a panel, defined by a single x-axis and usually a single y-axis_

For numeric primary variables, a single panel may also contain multiple plots of two types. Form the first type from subsets of observations (rows of data) based on values of a categorical variable. Specify this plot with the by parameter, which identifies the grouping variable to generate a scatterplot of the primary variables for each of its levels. The points for each group are plotted with a different shape and/or color. By default, the colors vary, though to maintain the color scheme, if there are only two levels of the grouping variable, the points for one level are filled with the current theme color and the points for the second level are plotted with transparent interiors.

Or, obtain multiple scatterplots on the same panel with multiple numeric x-variables, or multiple y-variables. To obtain this graph, specify one of the primary variables as a vector of multiple variables.

Trellis graphics (facets), from Deepayan Sarkar's (2009) lattice package, may be implemented in which multiple panels for one numeric x-variable and one numeric y-variable are displayed according to the levels of one or two categorical variables, called conditioning variables. A variable specified with by is a conditioning variable that results in a Trellis plot, the scatterplot of x and y produced at each level of the facet1 variable. The inclusion of a second conditioning variable, facet2, results in a separate scatterplot panel for each combination of cross-classified values of both facet1 and facet2. A grouping variable according to by may also be specified, which is then applied to each panel. If there are 1000 or less unique values of x, an analysis of the maximum number of repetitions for each value of facet1 is provided.

Control the panel dimensions and the overall size of the Trellis plot with the following parameters: width and height for the physical dimensions of the plot window, n_row and n_col for the number of rows and columns of panels, and aspect for the ratio of the height to the width of each panel. The plot window is the standard graphics window that displays on the screen, or it can be specified as a pdf file with the pdf_file parameter.

CATEGORICAL VARIABLES
Conceptually, there are continuous variables and categorical variables. Categorical variables have relatively few unique data values. However, categorical variables can be defined with non-numeric values, but also with numeric values, such as responses to a five-point Likert scale from Strongly Disagree to Strongly Agree, with responses coded 1 to 5. The three by –variables – facet1, facet2 and by – only apply to graphs created with numeric x and/or y variables, continuous or categorical.

A scatterplot of Likert type data is problematic because there are so few possibilities for points in the scatterplot. For example, for a scatterplot of two five-point Likert response data, there are only 26 possible paired values to plot, so most of the plotted points overlap with others. In this situation, that is, when a single variable or two variables with Likert response scales are specified, a bubble plot is automatically provided, with the size of each point relative to the joint frequency of the paired data values. To request a sunflower plot in lieu of the bubble plot, set the shape to "sunflower".

DATA
The default input data frame is d. Specify another name with the data option. Regardless of its name, the data frame need not be attached to reference the variables directly by its name, that is, no need to invoke the d$name notation. The referenced variables can be in the data frame and/or the user's workspace, the global environment.

The data values themselves can be plotted, or for a single variable, counts or proportions can be plotted on the y-axis. For a categorical x-variable paired with a continuous variable, means and other statistics can be plotted at each level of the x-variable. If x is continuous, it is binned first, with the standard Histogram binning parameters available, such as bin_width, to override default values. The stat parameter sets the values to plot, with data the default. By default, the connecting line segments are provided, so a frequency polygon results. Turn off the line segments by setting line_width=0.

The rows parameter subsets rows (cases) of the input data frame according to a logical expression. Use the standard R operators for logical statements as described in Logic such as & for and, | for or and ! for not, and use the standard R relational operators as described in Comparison such as == for logical equality != for not equals, and > for greater than. See the Examples.

VALUE LABELS
[DEPRECATED. Use factor() instead.] The value labels for each axis can be over-ridden from their values in the data to user supplied values with the value_labels option. This option is particularly useful for Likert-style data coded as integers. Then, for example, a 0 in the data can be mapped into a "Strongly Disagree" on the plot. These value labels apply to integer categorical variables, and also to factor variables. To enhance the readability of the labels on the graph, any blanks in a value label translate into a new line in the resulting plot. Blanks are also transformed as such for the labels of factor variables.

However, the lessR function factors allows for the easy creation of factors, one variable or a vector of variables, in a single statement, and is generally recommended as the method for providing value labels for the variables.

VARIABLE LABELS
Although standard R does not provide for variable labels, lessR can store the labels in the data frame with the data, obtained from the Read function or VariableLabels. If variable labels exist, then the corresponding variable label is by default listed as the label for the corresponding axis and on the text output.

ONE VARIABLE PLOT
The one variable plot of one continuous variable generates either a violin/box/scatterplot (VBS plot), or a run chart with if .Index appears as the name of the first variable listed, or x can be an R time series variable for a time series chart. For the box plot, for gray scale output potential outliers are plotted with squares and outliers are plotted with diamonds, otherwise shades of red are used to highlight outliers. The default definition of outliers is based on the standard boxplot rule of values more than 1.5 IQR's from the box. The definition of outliers may be adjusted (Hubert and Vandervieren, 2008), such that the whiskers are computed from the medcouple index of skewness (Brys, Hubert, & Struyf, 2004).

The plot can also be obtained as a bubble plot of frequencies for a categorical variable.

TWO VARIABLE PLOT
When two variables are specified to plot, by default if the values of the first variable, x, are unsorted, or if there are unequal intervals between adjacent values, or if there is missing data for either variable, a scatterplot is produced from a call to the Base R plot function. By default, sorted values with equal intervals between adjacent values of the first of the two specified variables yields a function plot if there is no missing data for either variable, that is, a call to the standard R plot function with segments=TRUE, which connects each adjacent pair of points with a line segment.

Specifying multiple, continuous x-variables against a single y variable, or vice versa, results in multiple plots on the same graph. The color of the points of the second variable is the same as that of the first variable, but with a transparent fill. For more than two x-variables, multiple colors are displayed, one for each x-variable.

BUBBLE PLOT FREQUENCY MATRIX (BPFM)
Multiple categorical variables for x may be specified in the absence of a y variable. A bubble plot results that illustrates the frequency of each response for each of the variables in a common figure in which the x-axis contains all of the unique labels for all of the variables plotted. Each line of information, the bubbles and counts for a single variable, replaces the standard bar chart in a more compact display. Usually the most meaningful when each variable in the matrix has the same response categories, that is, levels, such as for a set of shared Likert scales. The BPFM is considerably condensed presentation of frequencies for a set of variables than are the corresponding bar charts.

SCATTERPLOT MATRIX
A single vector of continuous variables specified as x, with no y-variable, generates a scatterplot matrix of the specified variable.

The scatterplot matrix is displayed according to the current color theme. Specific colors such as fill, color, etc. can also be provided. The upper triangle shows the correlation coefficient, and the lower triangle each corresponding scatterplot, with, by default, the non-linear loess best fit line. The code fit option can be used to provide the linear least squares line instead, along with the corresponding fit_color for the color of the fit line.

SIZE VARIABLE
A variable specified with size= is a numerical variable that activates a bubble plot in which the size of each bubble is determined by the value of the corresponding value of size, which can be a variable or a constant.

To explicitly vary the shapes, use shape and a list of shape values in the standard R form with the c function to combine a list of values, one specified shape for each group, as shown in the examples. To explicitly vary the colors, use fill, such as with R standard color names. If fill is specified without shape, then colors are varied, but not shapes. To vary both shapes and colors, specify values for both options, always with one shape or color specified for each level of the by variable.

Shapes beyond the standard list of named shapes, such as "circle", are also available as single characters. Any single letter, uppercase or lowercase, any single digit, and the characters "+", "*" and "#" are available, as illustrated in the examples. In the use of shape, either use standard named shapes, or individual characters, but not both in a single specification.

SCATTERPLOT ELLIPSE
For a scatterplot of two numeric variables, the ellipse=TRUE option draws the .95 data ellipse as computed by the ellipse function, written by Duncan Murdoch and E. D. Chow, from the ellipse package. The axes are automatically lengthened to provide space for the entire ellipse that extends beyond the maximum and minimum data values. The specific level of the ellipse can be specified with a numerical value in the form of a proportion. Multiple numerical values of ellipse may also be specified to obtain multiple ellipses.

BOXPLOTS
For a single variable the preferred plot is the integrated violin/box/scatter plot or VBS plot. Only the violin or box plot can be obtained with the corresponding aliases ViolinPlot and BoxPlot, or by setting vbs_plot to "v" or "b". To view a box plot of a continuous variable (Y) across the levels of a categorical variable (X), either as part of the full VBS plot, or by itself, there are two possibilities:
1. Plot(Y,X) or BoxPlot(Y, X)
2. Plot(Y, facet1=X) or BoxPlot(Y, facet1=X)
Both styles produce the same information. What differs is the color scheme.

The first possibility places the multiple box plots on a single pane and also, for the default color scheme "colors", displays the sequence of box plots with the default qualitative color palette from the lessR function getColors. All colors are displayed at the same level of gray-scale saturation and brightness to avoid perceptual bias. BarChart and PieChart use the same default colors as well.

The second possibility with facet1 produces the different box plots on a separate panel, that is, a Trellis chart. These box plots are displayed with a single hue, the first color, blue, in the default qualitative sequence.

TIME CHARTS
See https://web.pdx.edu/~gerbing/lessR/examples/Time.html for more explanation and examples.

Specify .Index as the name of the x-variable. The y variable is then plotted as a run chart. The values of the specified x-variable are plotted on the y-axis, with Index on the x-axis. Index is the ordinal position of each data value, from 1 to the number of values.

If the specified x-variable is of type Date, or is an R time series, a time series plot is generated for each specified variable. If data are represented as a formal R time-series, univariate or multivariate, specify as the x-variable. One possibility is to specify the x-variable of type Date, or, have Plot do the as.Date() conversion implicitly from entered character-string numerical dates such as "08/18/1952". Then specify the y-variable as one or more time series to plot. The y-variable can be formatted as long-form data with all the values in a single column, or as wide-formatted data with the time-series variables in separate columns.

Plot() makes a reasonable attempt to decode a character string decimal date value as the x-axis variable as read from a text data file such as a csv file. However, some date formats are not available for conversion by default, such as date values that include the name of the month instead of its number. In general, there can be no guarantee that a date format is not miss-inferred as they can be inherently ambiguous.

If the default date conversion is not working or is not available, then manually supply the date format following one of the format examples in the following table according to the parameter ts_format.

Example Date	Format
---------------------------	-----------------
"2022-09-01"	"%Y-%m-%d"
"2022/9/1"	"%Y/%m/%d"
"2022.09.01"	"%Y.%m.%d"
"09/01/2022"	"%m/%d/%Y"
"9/1/22"	"%m/%d/%y"
"September 1, 2022"	"%B %d, %Y"
"Sep 1, 2022"	"%b %d, %Y"
"20220901"	"%Y%m%d"
---------------------------	-----------------

Also, Plot() will convert character string dates such as 2024 Aug and 2024 Q1. Use three-letter abbreviations for the months or use Q1, Q2, Q3, or Q4 for the quarter.

The parameter ts_unit aggregates the date variable according to its specified value. The aggregation is based on two functions from the xts package, endpoints() and period.apply(). For example, a data variable has daily values but is plotted with aggregated quarterly values.

Specify the function by which to aggregate with the parameter ts_agg. The default is "sum".

In terms of missing data, if the date value exists and the corresponding y-value is missing, with value NA, then the visualization leaves a corresponding y-value blank. If the date value is also missing, then the nearest adjacent points are connected by line segment which runs over the missing data value. For example, consider a daily time series such that "2021-01-07" and "2021-01-09" are both present with their corresponding y values, but there is no date value or y value for January 8, that is, "2021-01-08". The entire row of data is missing. The resulting visualization plot the y-value for January 7 and also for January 9, with a line segment connecting those two points. There is no corresponding label on the x-axis for the missing data value but the January 9 value is appropriately placed two days after the January 7 value on the visualization.

2-D KERNEL DENSITY
With smooth=TRUE, the R function smoothScatter is invoked according to the current color theme. Useful for very large data sets. The smooth_points parameter plots points from the regions of the lowest density. The smooth_bins parameter specifies the number of bins in both directions for the density estimation. The smooth_exp parameter specifies the exponent in the function that maps the density scale to the color scale to allow customization of the intensity of the plotted gradient colors. Higher values result in less color saturation, de-emphasizing points from regions of lessor density. These parameters are respectively passed directly to the smoothScatter nrpoints, nbin and transformation parameters. Grid lines are turned off, by default, but can be displayed by setting the grid_color parameter.

COLORS
A color theme for all the colors can be chosen for a specific plot with the colors option with the lessR function style. The default color theme is "lightbronze". A gray scale is available with "gray", and other themes are available as explained in style, such as "sienna" and "darkred". Use the option style(sub_theme="black") for a black background and partial transparency of plotted colors.

Colors can also be changed for individual aspects of a scatterplot as well with the style function. To provide a warmer tone by slightly enhancing red, try a background color such as panel_fill="snow". Obtain a very light gray with panel_fill="gray99". To darken the background gray, try panel_fill="gray97" or lower numbers. See the lessR function showColors, which provides an example of all available named R colors with their RGB valu

For the color options, such as violin_color, the value of "off" is the same as "transparent".

ANNOTATIONS
Use the add and related parameters to annotate the plot with text and/or geometric figures. Each object is placed according from one to four corresponding coordinates, the required coordinates to plot that object, as shown in the following table. x-coordinates may have the value of "mean_x" and y-coordinates may have the value of "mean_y".

Value	Object	Required Coordinates
-----------	-------------------	-----------------------
"text"	text	x1, y1
"point"	text	x1, y1
"rect"	rectangle	x1, y1, x2, y2
"line"	line segment	x1, y1, x2, y2
"arrow"	arrow	x1, y1, x2, y2
"v_line"	vertical line	x1
"h_line"	horizontal line	y1
"means"	horiz, vert lines
-----------	-------------------	-----------------------

The value of add specifies the object. For a single object, enter a single value. Then specify the value of the needed corresponding coordinates, as specified in the above table. For multiple placements of that object, specify vectors of corresponding coordinates. To annotate multiple objects, specify multiple values for add as a vector. Then list the corresponding coordinates, for up to each of four coordinates, in the order of the objects listed in add.

Can also specify vectors of different properties, such as add_color. That is, different objects can be different colors, different transparency levels, etc.

PDF OUTPUT
To obtain pdf output, use the pdf_file option, perhaps with the optional width and height options. These files are written to the default working directory, which can be explicitly specified with the R setwd function.

ADDITIONAL OPTIONS
Commonly used graphical parameters that are available to the standard R function plot are also generally available to Plot, such as:

cex.main, col.lab, font.sub, etc.

Settings for main- and sub-title and axis annotation, see title and par.

main

Title of the graph, see title.

xlim

The limits of the plot on the x-axis, expressed as c(x1,x2), where x1 and x2 are the limits. Note that x1 > x2 is allowed and leads to a reversed axis.

ylim

The limits of the plot on the y-axis.

ONLY VARIABLES ARE REFERENCED
A referenced variable in a lessR function can only be a variable name. This referenced variable must exist in either the referenced data frame, such as the default d, or in the user's workspace, more formally called the global environment. That is, expressions cannot be directly evaluated. For example:

> Plot(rnorm(50), rnorm(50)) # does NOT work

Instead, do the following:

    > X <- rnorm(50)   # create vector X in user workspace
    > Y <- rnorm(50)   # create vector Y in user workspace
    > Plot(X,Y)     # directly reference X and Y

Value

The output can optionally be saved into an R object, otherwise it simply appears in the console. Each value in the output will only appear if activated in the analysis. For example, the outlier identification must be activated for the analysis, such as from parameter MD_cut, for out_outliers to appear in the output.

Here is an example of saving the output to an R object with any valid R name, such as p: p <- Plot(Years, Salary). To see the names of the output objects for that specific analysis, enter names(p). To display any of the objects, precede the name with p$, such as p$out_stats. View the output at the R console or within a markdown document that displays your results.

READABLE OUTPUT
out_stats: Correlational analysis.
out_outliers: Mahalanobis Distance of each outlier.
out_frcst: Forecasted values.
out_fitted: Fitted values to data.
out_coefs: Linear and seasonal coefficients from forecasting.
out_smooth: Smoothing parameters from exponential smoothing forecasting.
. out_bubble: Bubble plot parameters, radius and power.
. out_reg: Regression statistics from setting the fit parameter.
.

STATISTICS
outliers: Row numbers that contain the outliers.

Author(s)

David W. Gerbing (Portland State University; [e-mail])

References

Brys, G., Hubert, M., & Struyf, A. (2004). A robust measure of skewness. Journal of Computational and Graphical Statistics, 13(4), 996-1017.

Murdoch, D, and Chow, E. D. (2013). ellipse function from the ellipse package package.

Gerbing, D. W. (2023). R Data Analysis without Programming, 2nd edition, Chapter 10, NY: Routledge.

Gerbing, D. W. (2020). R Visualizations: Derive Meaning from Data, Chapter 5, NY: CRC Press.

Gerbing, D. W. (2021). Enhancement of the Command-Line Environment for use in the Introductory Statistics Course and Beyond, Journal of Statistics and Data Science Education, 29(3), 251-266, https://www.tandfonline.com/doi/abs/10.1080/26939169.2021.1999871.

Hubert, M. and Vandervieren, E. (2008). An adjusted boxplot for skewed distributions, Computational Statistics and Data Analysis 52, 51865201.

Sarkar, Deepayan (2008) Lattice: Multivariate Data Visualization with R, Springer. http://lmdvr.r-forge.r-project.org/

See Also

plot, stripchart, title, par, loess, Correlation, style.

Examples

# read the data
d <- rd("Employee", quiet=TRUE)
d <- d[.(random(0.6)),]  # less computationally intensive
dd=d

#---------------------------------------------------
# traditional scatterplot with two numeric variables
#---------------------------------------------------

Plot(Years, Salary, by=Gender, size=2, fit="lm",
     fill=c("olivedrab3", "gold1"),
     color=c("darkgreen", "gold4"))

# scatterplot with all defaults
Plot(Years, Salary)
# or use abbreviation sp in place of Plot
# or use full expression ScatterPlot in place of Plot

# maximum information, minimum input: scatterplot +
#  means, outliers, ellipse, least-squares lines with and w/o outliers
Plot(Years, Salary, enhance=TRUE)

# extend x and y axes
Plot(Years, Salary, scale_x=c(-10, 35, 10), scale_y=c(0,200000,10))

Plot(Years, Salary, add="Hi", x1=c(12, 16, 18), y1=c(80000, 100000, 60000))

Plot(Salary, row_names)

d <- factors(Gender, levels=c("M", "F"))
Plot(Years, Salary, facet1=Gender)
d <- dd



# just males employed more than 5 years
Plot(Years, Salary, filter=(Gender=="M" & Years > 5))

# plot 0.95 data ellipse with the points identified that represent
#   outliers defined by a Mahalanobis Distance larger than 6
# save outliers into R object out
d[1, "Salary"] <- 200000
out <- Plot(Years, Salary, ellipse=0.95, MD_cut=6)

# new shape and point size, no grid or background color
# then put style back to default
style(panel_fill="powderblue", grid_color="off")
Plot(Years, Salary, size=2, shape="diamond")
style()

# translucent data ellipses without points or edges
#  show the idealized joint distribution for bivariate normality
style(ellipse_color="off")
Plot(Years, Salary, size=0, ellipse=seq(.1,.9,.10))
style()

# bubble plot with size determined by the value of Pre
# display the value for the bubbles with values of  min, median and max
Plot(Years, Salary, size=Pre, size_cut=3)

# variables in a data frame not the default d
# plot 0.6 and 0.9 data ellipses with partially transparent points
# change color theme to gold with black background
style("gold", sub_theme="black")
Plot(eruptions, waiting, transparency=.5, ellipse=seq(.6,.9), data=faithful)

# scatterplot with two x-variables, plotted against Salary
# define a new style, then back to default
style(window_fill=rgb(247,242,230, maxColorValue=255),
  panel_fill="off", panel_color="off", pt_fill="black", transparency=0,
  lab_color="black", axis_text_color="black",
  axis_y_color="off", grid_x_color="off", grid_y_color="black",
  grid_lty="dotted", grid_lwd=1)
Plot(c(Pre, Post), Salary)
style()

# increase span (smoothing) from default of .7 to 1.25
# span is a loess parameter, which generates a caution that can be
#   ignored that it is not a graphical parameter -- we know that
# display confidence intervals about best-fit line at
#   0.95 confidence level
Plot(Years, Salary, fit="loess", span=1.25)

# 2-D kernel density (more useful for larger sample sizes)
Plot(Years, Salary, smooth=TRUE)


#------------------------------------------------------
# scatterplot matrix from a vector of numeric variables
#------------------------------------------------------

# with least squares fit line
Plot(c(Salary, Years, Pre), fit="lm")


#--------------------------------------------------------------
# Trellis graphics and by for groups with two numeric variables
#--------------------------------------------------------------

# Trellis plot with condition on 1-variable
# optionally re-order default alphabetical R ordering by converting
#   to a factor with lessR factors (which also does multiple variables)
# always save to the full data frame with factors
d <- factors(Gender, levels=c("M", "W"))
Plot(Years, Salary, facet1=Gender)
d <- Read("Employee", quiet=TRUE)



# two Trellis classification variables with a single continuous
Plot(Salary, facet1=Dept, facet2=Gender)

# all three by (categorical) variables
Plot(Years, Salary, facet1=Dept, facet2=Gender, by=Plan)

# vary both shape and color with a least-squares fit line for each group
style(color=c("darkgreen", "brown"))
Plot(Years, Salary, facet1=Gender, fit="lm", shape=c("F","M"), size=.8)
style("gray")

# compare the men and women Salary according to Years worked
#   with an ellipse for each group
Plot(Years, Salary, by=Gender, ellipse=.50)


#--------------------------------------------------
# analysis of a single numeric variable (or vector)
#--------------------------------------------------

# One continuous variable
# -----------------------
# integrated Violin/Box/Scatterplot, a VBS plot
Plot(Salary)

Plot(Years, Salary, by=Gender, size=2, fit="lm",
     fill=c("olivedrab3", "gold1"),
     color=c("darkgreen", "gold4"))



# by variable, different colors for different values of the variable
# two panels
Plot(Salary, facet1=Dept)

# large sample size
x <- rnorm(10000)
Plot(x)

# custom colors for outliers, which might not appear in this subset data
style(out_fill="hotpink", out2_fill="purple")
Plot(Salary)
style()

# no violin plot or scatterplot, just a boxplot
Plot(Salary, vbs_plot="b")
# or, the same with the mnemonic
BoxPlot(Salary)

# two related displays of box plots for different levels of a
#   categorical variable
BoxPlot(Salary, facet1=Dept)


# binned values to plot counts
# ----------------------------
# bin the values of Salary to plot counts as a frequency polygon
# the counts are plotted as points instead of the data
Plot(Salary, stat_x="count")  # bin the values

# time charts
#------------
# run chart, with default fill area
Plot(.Index, Salary, area_fill="on")

# two run charts in same panel
# or could do a multivariate time series
Plot(.Index, c(Pre, Post))

# Trellis graphics run chart with custom line width, no points
Plot(.Index, Salary, facet1=Gender, line_width=3, size=0)

# daily time series plot
# create the daily time series from R built-in data set airquality
oz.ts <- ts(airquality$Ozone, start=c(1973, 121), frequency=365)
Plot(oz.ts)

# multiple time series plotted from dates and stacked
# black background with translucent areas, then reset theme to default
style(sub_theme="black", color="steelblue2", transparency=.55,
  window_fill="gray10", grid_color="gray25")
date <- seq(as.Date("2013/1/1"), as.Date("2016/1/1"), by="quarter")
x1 <- rnorm(13, 100, 15)
x2 <- rnorm(13, 100, 15)
x3 <- rnorm(13, 100, 15)
df <- data.frame(date, x1, x2, x3)
rm(date); rm(x1); rm(x2); rm(x3)
Plot(date, x1:x3, data=df)
style()

# aggregate monthly data to plot by quarter
n.q <- 42
month <- seq(as.Date("2013/1/1"), length=n.q, by="months")
x <- rnorm(n.q, 100, 15)
Plot(month, x, ts_unit="quarters")


# trigger a time series with a Date variable specified first
# stock prices for three companies by month:  Apple, IBM, Intel
d <- rd("StockPrice")
# only plot Apple
Plot(Month, Price, filter=(Company=="Apple"))
# Trellis plots, one for each company
Plot(Month, Price, facet1=Company, n_col=1)
# all three plots on the same panel, three shades of blue
Plot(Month, Price, by=Company, color="blues")
# exponential smoothing forecast for next 12 months, 
#   aggregate monthly data by mean over quarters
Plot(Month, Price, ts_ahead=12, ts_unit="quarters")


#------------------------------------------
# analysis of a single categorical variable
#------------------------------------------
d <- rd("Employee")

# default 1-D bubble plot
# frequency plot, replaces bar chart
Plot(Dept)



# plot of frequencies for each category (level), replaces bar chart
Plot(Dept, stat_x="count")


#----------------------------------------------------
# scatterplot of numeric against categorical variable
#----------------------------------------------------

# generate a chart with the plotted mean of each level
# rotate x-axis labels and then offset from the axis
style(rotate_x=45, offset=1)
Plot(Dept, Salary)
style()


#-------------------
# Cleveland dot plot
#-------------------

# row.names on the y-axis
Plot(Salary, row_names)

# standard scatterplot
Plot(Salary, row_names, sort_yx="0", segments_y=FALSE)

# Cleveland dot plot with two x-variables
Plot(c(Pre, Post), row_names)


#------------
# annotations
#------------

# add text at the one location specified by x1 and x2
Plot(Years, Salary, add="Hi There", x1=12, y1=80000)
# add text at three different specified locations
Plot(Years, Salary, add="Hi", x1=c(12, 16, 18), y1=c(80000, 100000, 60000))

# add three different text blocks at three different specified locations
Plot(Years, Salary, add=c("Hi", "Bye", "Wow"), x1=c(12, 16, 18),
  y1=c(80000, 100000, 60000))

# add an 0.95 data ellipse and horizontal and vertical lines through the
#  respective means
Plot(Years, Salary, ellipse=0.95, add=c("v_line", "h_line"),
  x1="mean_x", y1="mean_y")
# can be done also with the following short-hand
Plot(Years, Salary, ellipse=0.95, add="means")

# a rectangle requires two points, four coordinates, <x1,y1> and <x2,y2>
style(add_trans=.8, add_fill="gold", add_color="gold4", add_lwd=0.5)
Plot(Years, Salary, add="rect", x1=12, y1=80000, x2=16, y2=115000)

# the first object, a rectangle, requires all four coordinates
# the vertical line at x=2 requires only an x1 coordinate, listed 2nd
Plot(Years, Salary, add=c("rect", "v_line"), x1=c(10, 2),
  y1=80000, x2=12, y2=115000)

# two different rectangles with different locations, fill colors and translucence
style(add_fill=c("gold3", "green"), add_trans=c(.8,.4))
Plot(Years, Salary, add=c("rect", "rect"),
  x1=c(10, 2), y1=c(60000, 45000), x2=c(12, 75000), y2=c(80000, 55000))


#----------------------------------------------------
# analysis of two categorical variables (Likert data)
#----------------------------------------------------

d <- rd("Mach4", quiet=TRUE)  # Likert data, 0 to 5

# use value labels for the integer values, modify color options
LikertCats <- c("Strongly Disagree", "Disagree", "Slightly Disagree",
   "Slightly Agree", "Agree", "Strongly Agree")
style(fill="powderblue", color="blue", bubble_text="darkred")
d <- factors(m01:m20, 0:5, labels=LikertCats)
Plot(m01:m10)
style()  # reset theme



Plot(m06, m07)

#-----------------------------
# Bubble Plot Frequency Matrix
#-----------------------------

#---------------
# function curve
#---------------

x <- seq(10,50,by=2)
y1 <- sqrt(x)
y2 <- x**.33
# x is sorted with equal intervals so run chart by default
Plot(x, y1)

# multiple plots from variable vectors need to have the variables
#  in a data frame
d <- data.frame(x, y1, y2)
# if variables are in the user workspace and in a data frame
#   with the same names, the user workspace versions are used,
#   which do not work with vectors of variables, so remove
rm(x); rm(y1); rm(y2)
Plot(x, c(y1, y2))

lessR documentation built on April 4, 2025, 12:31 a.m.