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Plot Summary Scores as Scatter Plots

Source: R/8-plot-scores.R
scatterplotSigScores.Rd

This function generates a ggplot object.

scatterplotSigScores uses the internal function ggPlot which contains different function calls to ggplot2 functions in order to create a pre-defined plot.

Usage

scatterplotSigScores(
  data,
  scores = NULL,
  runs = NULL,
  add.points = T,
  point.shape = 16,
  point.jitter.w = 0.2,
  point.jitter.h = NULL,
  labs.title = "Summary Scores",
  labs.x = "Sample IDs",
  labs.y = "Scores",
  labs.col = "Summary\nScores",
  axis.text.x.angle = 90,
  axis.text.x.size = 9,
  axis.text.y.angle = 0,
  axis.text.y.size = 9,
  ...
)

Arguments

data

data frame, output of computeSigScores

scores

(optional) character vector, indicating the summary score(s) to plot from data

runs

(optional) numeric vector, indicating the repeats to plot from data

add.points

logical, whether to add the computed scores of the individual summary measures as points in the plot

point.shape

the shape to use to plot the scores. It can take five types of values:

  • An integer in `[0, 25]`

  • The name of the shape

  • A single character, used as a plotting symbol

  • A . to draw the smallest rectangle that is visible, usually 1 pixel

  • An NA, to draw nothing

See vignette("ggplot2-specs") for further details

point.jitter.w, point.jitter.h

Amount of vertical and horizontal jitter. The jitter is added in both positive and negative directions, so the total spread is twice the value specified here. See position_jitter for further details

labs.title

The text for the title

labs.x

The title of the x axis

labs.y

The title of the y axis

labs.col

The title of the legend

axis.text.x.angle, axis.text.y.angle

Specify the x and y axis tick labels angles (in [0, 360]) See element_text for further details

axis.text.x.size, axis.text.y.size

Specify the x and y axis tick labels size in pts. See element_text for further details

...

further arguments to ggPlot

Value

A ggplot object.

See also

ggPlot

Author

Alessandro Barberis

Examples

#Set seed for reproducibility
set.seed(seed = 5381L)

#Define row/col size
n = 10

#Create input matrix
x = matrix(
 data = stats::runif(n = n*n, min = 0, max = 100),
 nrow = n,
 ncol = n,
 dimnames = list(
    paste0("g",seq(n)),
    paste0("S",seq(n))
 )
)

#Compute Summary Scores
x = computeSigScores(
 x = x,
 i = rownames(x)
)
#> [1] "Calculating ranks..."
#> [1] "Calculating absolute values from ranks..."
#> [1] "Normalizing..."

#Plot scores
scatterplotSigScores(data = x)
#> Warning: Use of `data[[x]]` is discouraged.
#> ℹ Use `.data[[x]]` instead.
#> Warning: Use of `data[[y]]` is discouraged.
#> ℹ Use `.data[[y]]` instead.
#> Warning: Use of `data[[color]]` is discouraged.
#> ℹ Use `.data[[color]]` instead.
#> Warning: Removed 10 rows containing missing values (`geom_point()`).


#Plot scatter plots per summary score
scatterplotSigScores(
 data       = x,
 scores     = c("mean", "median"),
 facet.rows = "summaryScore"
)
#> Warning: Use of `data[[x]]` is discouraged.
#> ℹ Use `.data[[x]]` instead.
#> Warning: Use of `data[[y]]` is discouraged.
#> ℹ Use `.data[[y]]` instead.
#> Warning: Use of `data[[color]]` is discouraged.
#> ℹ Use `.data[[color]]` instead.