Asymptotic Memory Usage Plot function

Function to plot timings vs data sizes from the data frame returned by asymptoticMemoryUsage()

plotMemoryUsage(
  data.df,
  titles = list("", ""),
  labels = list("Data sizes", "Memory usage (in bytes)"),
  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 'Memory Usage' and 'Data sizes', which can be obtained by asymptoticMemoryUsage()
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

# Memory profiling must be available in the running system:
if(capabilities("profmem")) {
# Quantifying the memory usage for the allocation of a square matrix (N*N dimensions)
# against a set of input data sizes:
input.sizes = 10^seq(1, 3, by = 0.1)
memory.usage.data <- asymptoticMemoryUsage(matrix(data = N:N, nrow = N, ncol = N), input.sizes)
# Plotting the trend between computed memory allocations and data sizes:
plotMemoryUsage(memory.usage.data)
}