datana (version 1.0.9)

xyboxplot: Function for building a scatterplot with superposing boxplots

Description

The function creates a scatterplot with superposing boxplots for the Y-axis variable. To a scatterplot between a response variable 'y' and a predictor variable 'x', this function superposes boxplots of the response by groups of the predictor variable. The main aim of the above described graph is to get a sense of the distribution of the response variable depending upon the predictor variable.

Usage

xyboxplot(
  x = x,
  y = y,
  col.dots = "blue",
  xlab = NULL,
  ylab = NULL,
  try.x.ascategory = FALSE,
  transp.dots = 0.2,
  transp.boxp = 0.1
)

Value

The function returns the above described graph.

Arguments

x

A numeric vector representing the X-axis variable.

y

A numeric vector representing the Y-axis variable (response).

col.dots

A string specifying the dot colors. The default is set to "blue".

xlab

(optional) A string specifying X-axis label.

ylab

(optional) A string specifying Y-axis label.

try.x.ascategory

A logical statement, if set to TRUE, the X-axis variable will be treated as categorical for the drawing od the boxplots. The default is set to FALSE.

transp.dots

A numeric value to be used as transparency for the dots of the figure to be produced. The defauls is set to 0.2

transp.boxp

A numeric value to be used as transparency for the boxpot of the figure to be produced. The defauls is set to 0.1

Author

Christian Salas-Eljatib

Details

Notice that the superposing boxplots for the Y-axis variable are computed by grouping the X-axis variable in 10 classes. Those classes are set by computing the ten percentiles of the X-axis variable, therefore each group has the same number of observations.

References

• Salas-Eljatib C. 2021. Análisis de datos con el programa estadístico R: una introducción aplicada. Ediciones Universidad Mayor. Santiago, Chile. 170 p. https://eljatib.com

• Salas C, Stage AR, and Robinson AP. 2008. Modeling effects of overstory density and competing vegetation on tree height growth. Forest Science 54(1):107-122. tools:::Rd_expr_doi("10.1093/forestscience/54.1.107")

Examples

df <- datana::fishgrowth
xyboxplot(x=df$length,y=df$scale)
xyboxplot(x=df$length,y=df$scale,col.dots = "red",
xlab="Variable X")
xyboxplot(x=df$length,y=df$scale,xlab="Variable X")
xyboxplot(x=df$length,y=df$scale,xlab="Variable X",
transp.dots = 0.4)
xyboxplot(x=df$age,y=df$length,try.x.ascategory = TRUE)