testComplexity (version 0.1.1)

plotTimings: Asymptotic Timings Plot function

Description

Function to plot timings vs data sizes from the data frame returned by asymptoticTimings

Usage

plotTimings(
  data.df,
  titles = list("", ""),
  labels = list("Data size", "Runtime (in nanoseconds)"),
  point.alpha = 1,
  line.alpha = 1,
  point.color = "black",
  line.color = "black",
  point.size = 1.3,
  line.size = 0.7
)

Arguments

data.df

A data frame composed of columns 'Timings' and 'Data sizes', which can be obtained by asymptoticTimings()

titles

A list of two elements consisting of strings for the plot title and subtitle. Optional, with default values set to empty strings. (no titles/subtitles)

labels

A list of two elements containing strings for x and y labels respectively. Optional, with default values set to appropriate labels.

point.alpha

A numeric value denoting transparency level (in the range 0 to 1) for point geometry. Optional, with the default value set to 1. (no transparentness)

line.alpha

A numeric value denoting transparency level (in the range 0 to 1) for line geometry. Optional, with the default value set to 1. (no transparentness)

point.color

A string specifying a known color or a representation in hexcode for point geometry. Optional, with the default color set as black. (Hex equivalent: #000000)

line.color

A string specifying a known color or a representation in hexcode for line geometry. Optional, with the default color set as black. (Hex equivalent: #000000)

point.size

A numeric value denoting the size of point geometry. Optional, with the default value set to (1.3).

line.size

A numeric value denoting the size of line geometry. Optional, with the default value set to (0.7).

Value

A ggplot object.

Details

For more information regarding its implementation or functionality/usage, please check https://anirban166.github.io//Plotters/

Examples

# NOT RUN {
# Quantifying the runtimes for the substring function against a set of input data sizes:
input.sizes = 10^seq(1, 4, by = 0.5)
timings.df <- asymptoticTimings(substring(paste(rep("A", N), collapse = ""), 1:N, 1:N), input.sizes)
# Plotting the trend between benchmarked timings and data sizes:
plotTimings(timings.df)
# }