# Examples

## Overview

Author: Yong-Han Hank Cheng

This package allows you to generate and compare power spectral density (PSD) plots given time series data. FFT is used to take a time series data, analyze the oscillations, and then output the frequencies of these oscillations in the time series in the form of a PSD plot.

## Installation

# Install the package from GitHub
# devtools::install_github("yhhc2/psdr")
# Load package
library("psdr")

## Examples

All functions with example code is run in this section. The functions are listed below in alphabetical order with example code to illustrate how each function should be used. The example code should be very similar to the example code in the function reference.

To see detailed descriptions for each function, please visit the package’s website.

### AutomatedCompositePlotting()

#I want to create a plot that shows two curves:
#1. Composite of time series signals 1, 2, and 3.
#2. Composite of time series signals 3 and 4.

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#First signal
#1. 1 Hz with amplitude of 2
S1 <- 2*sin(2*pi*1*t)
level1.vals <- rep("a", length(S1))
level2.vals <- rep("1", length(S1))
S1.data.frame <- as.data.frame(cbind(t, S1, level1.vals, level2.vals))
colnames(S1.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S1.data.frame[,"Signal"] <- as.numeric(S1.data.frame[,"Signal"])

#Second signal
#1. 1 Hz with amplitude of -4
#2. 2 Hz with amplitude of -2
S2 <- (-4)*sin(2*pi*1*t) - 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S2))
level2.vals <- rep("2", length(S2))
S2.data.frame <- as.data.frame(cbind(t, S2, level1.vals, level2.vals))
colnames(S2.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S2.data.frame[,"Signal"] <- as.numeric(S2.data.frame[,"Signal"])

#Third signal
#1. 1 Hz with amplitude of 2
#2. 2 Hz with amplitude of 2
S3 <- 2*sin(2*pi*1*t) + 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S3))
level2.vals <- rep("3", length(S3))
S3.data.frame <- as.data.frame(cbind(t, S3, level1.vals, level2.vals))
colnames(S3.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S3.data.frame[,"Signal"] <- as.numeric(S3.data.frame[,"Signal"])

#Fourth signal
#1. 1 Hz with amplitude of -2
S4 <- -2*sin(2*pi*1*t)
level1.vals <- rep("b", length(S4))
level2.vals <- rep("3", length(S4))
S4.data.frame <- as.data.frame(cbind(t, S4, level1.vals, level2.vals))
colnames(S4.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S4.data.frame[,"Signal"] <- as.numeric(S4.data.frame[,"Signal"])

windows <- list(S1.data.frame, S2.data.frame, S3.data.frame, S4.data.frame)

#Gets the composite of the first, second, and third signal. Should result in a flat signal.
FirstComboToUse <- list( c("a"), c(1, 2, 3) )

#Gets the composite of the third and fourth signal
SecondComboToUse <- list( c("a", "b"), c(3) )

#Timeseries-----------------------------------------------------------------

timeseries.results <- AutomatedCompositePlotting(list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
x_start = 0,
x_end = 999,
x_increment = 1,
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3", "Signal 3 + 4"),
plot.title = "Example",
plot.xlab = "Time",
plot.ylab = "Original units",
combination.index.for.envelope = NULL,
TimeSeries.PSD.LogPSD = "TimeSeries",
sampling_frequency = NULL)

ggplot.obj.timeseries <- timeseries.results[[2]]

#Plot. Will see the 1+2+3 curve as a flat line. The 3+4 curve will only have 2 Hz.
#dev.new()
ggplot.obj.timeseries

#PSD-------------------------------------------------------------------------

#Note that the PSDs are not generated directly from the "Signal 1 + 2 + 3" and
#the "Signal 3 + 4" time series. Instead, PSDs are generated individually
#for signals 1, 2, 3, and 4, and then then are summed together.

PSD.results <- AutomatedCompositePlotting(list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
x_start = 0,
x_end = 50,
x_increment = 0.01,
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3", "Signal 3 + 4"),
plot.title = "Example",
plot.xlab = "Hz",
plot.ylab = "(Original units)^2/Hz",
combination.index.for.envelope = 2,
TimeSeries.PSD.LogPSD = "PSD",
sampling_frequency = 100)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
ggplot.obj.PSD <- PSD.results[[2]]

#Plot. For both plots, two peaks will be present, 1 Hz and 2 Hz. 1 Hz should be
#stronger in both cases because more signals have this frequency (even if amp is negative).
#Error envelope is specified for the second (red) curve. Envelope should only
#be present for 2 Hz signal.
#dev.new()
ggplot.obj.PSD

 #PSD Zoomed in---------------------------------------------------------------

PSD.results <- AutomatedCompositePlotting(list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
x_start = 0,
x_end = 5,
x_increment = 0.01,
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3", "Signal 3 + 4"),
plot.title = "Example",
plot.xlab = "Hz",
plot.ylab = "(Original units)^2/Hz",
combination.index.for.envelope = 2,
TimeSeries.PSD.LogPSD = "PSD",
sampling_frequency = 100)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
 ggplot.obj.PSD <- PSD.results[[2]]

#Plot. For both plots, two peaks will be present, 1 Hz and 2 Hz. 1 Hz should be
#stronger in both cases because more signals have this frequency (even if amp is negative).
#Error envelope is specified for the second (red) curve. Envelope should only
#be present for 2 Hz signal.
#dev.new()
ggplot.obj.PSD

#LogPSD-------------------------------------------------------------------------

LogPSD.results <- AutomatedCompositePlotting(list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
x_start = 0,
x_end = 50,
x_increment = 0.01,
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3", "Signal 3 + 4"),
plot.title = "Example",
plot.xlab = "Hz",
plot.ylab = "log((Original units)^2/Hz)",
combination.index.for.envelope = NULL,
TimeSeries.PSD.LogPSD = "LogPSD",
sampling_frequency = 100)

ggplot.obj.LogPSD <- LogPSD.results[[2]]

#Plot. For both plots, two peaks will be present, 1 Hz and 2 Hz. 1 Hz should
#be stronger in both cases because more signals have this frequency (even if amp is negative).
#Error envelope is specified for the second (red) curve. Envelope should only
#be present for 1 Hz signal.
#dev.new()
ggplot.obj.LogPSD

#Are dominant frequencies different---------------------------------------------
comparison_results <- PSD.results[[3]]

#Table used for statistical testing
comparison_results[[1]]
##   vals.to.compare.combined combo.labels.combined
## 1                        1           Signal1+2+3
## 2                        1           Signal1+2+3
## 3                        2           Signal1+2+3
## 4                        2             Signal3+4
## 5                        1             Signal3+4
#Kruskal Wallis results
comparison_results[[2]]
##
##  Kruskal-Wallis rank sum test
##
## data:  vals.to.compare.combined by combo.labels.combined
## Kruskal-Wallis chi-squared = 0.11111, df = 1, p-value = 0.7389

### CountWindows()

#Example using a dataframe with 5 homogeneous windows.

#Windows are homogeneous if looking at col.two and col.three values.
window.name.column <- c(10, 10, 10, 20, 20, 20, 30, 30, 30, 30, 40, 40, 50, 50, 50, 50)
col.two <- c("a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "b", "b", "a", "a", "a", "a")
col.three <- c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3)

multi.window.data <- data.frame(window.name.column, col.two, col.three)

list.of.homogeneous.windows <- GetHomogeneousWindows(multi.window.data,
"window.name.column", c("col.two", "col.three"))

matrix <- CountWindows(list.of.homogeneous.windows, "col.two", "col.three",
c("a", "b"), c("1", "2", "3"))

matrix
##   1 2 3
## a 2 1 1
## b 1 0 0

### FindHomogeneousWindows()

col.one <- c(1, 2, 3, 4, 5)
col.two <- c("a", "a", "a", "a", "a")
col.three <- c(1, 1, 1, 1, 1)

single.window.data <- data.frame(col.one, col.two, col.three)

#Example of inhomogeneous window if looking at col.one and col.two because
#col.one does not only have a single unique value.
result <- FindHomogeneousWindows(single.window.data , c("col.one", "col.two"))

result
## [1] FALSE
#Example of homogeneous window if looking at col.two and col.three because
#col.two and col.three both only have a single unique value.
result <- FindHomogeneousWindows(single.window.data , c("col.two", "col.three"))

result
## [1] TRUE

### GetHomogeneousWindows

#Example using a dataframe with 3 windows.

#Windows 20 and 30 are homogeneous if looking at col.two and col.three values.
window.name.column <- c(10, 10, 10, 20, 20, 20, 30, 30, 30, 30)
col.two <- c("a", "a", "a", "a", "a", "a", "a", "a", "a", "a")
col.three <- c(1, 1, 0, 1, 1, 1, 2, 2, 2, 2)

multi.window.data <- data.frame(window.name.column, col.two, col.three)

result <- GetHomogeneousWindows(multi.window.data, "window.name.column", c("col.two", "col.three"))

#As expected, it looks like two windows are homogeneous.
str(result)
## List of 2
##  $:'data.frame': 3 obs. of 3 variables: ## ..$ window.name.column: num [1:3] 20 20 20
##   ..$col.two : chr [1:3] "a" "a" "a" ## ..$ col.three         : num [1:3] 1 1 1
##  $:'data.frame': 4 obs. of 3 variables: ## ..$ window.name.column: num [1:4] 30 30 30 30
##   ..$col.two : chr [1:4] "a" "a" "a" "a" ## ..$ col.three         : num [1:4] 2 2 2 2
result[[1]]
##   window.name.column col.two col.three
## 4                 20       a         1
## 5                 20       a         1
## 6                 20       a         1
result[[2]]
##    window.name.column col.two col.three
## 7                  30       a         2
## 8                  30       a         2
## 9                  30       a         2
## 10                 30       a         2

### GetSubsetOfWindows()

#Example using a dataframe with 3 windows.

#Windows 20 and 30 are homogeneous if looking at col.two and col.three values.
window.name.column <- c(10, 10, 10, 20, 20, 20, 30, 30, 30, 30)
col.two <- c("a", "a", "a", "a", "a", "a", "a", "a", "a", "a")
col.three <- c(1, 1, 0, 1, 1, 1, 2, 2, 2, 2)

multi.window.data <- data.frame(window.name.column, col.two, col.three)

list.of.homogeneous.windows <- GetHomogeneousWindows(multi.window.data,
"window.name.column", c("col.two", "col.three"))

#Get a subset of windows where col.three has a value of 2
subset.list.of.homogeneous.windows <- GetSubsetOfWindows(list.of.homogeneous.windows,
"col.three", "2")

str(subset.list.of.homogeneous.windows)
## List of 1
##  $:'data.frame': 4 obs. of 3 variables: ## ..$ window.name.column: num [1:4] 30 30 30 30
##   ..$col.two : chr [1:4] "a" "a" "a" "a" ## ..$ col.three         : num [1:4] 2 2 2 2
subset.list.of.homogeneous.windows[[1]]
##    window.name.column col.two col.three
## 7                  30       a         2
## 8                  30       a         2
## 9                  30       a         2
## 10                 30       a         2

### GetSubsetOfWindowsTwoLevels()

#Example using a dataframe with 5 homogeneous windows.

#Windows are homogeneous if looking at col.two and col.three values.
window.name.column <- c(10, 10, 10, 20, 20, 20, 30, 30, 30, 30, 40, 40, 50, 50, 50, 50)
col.two <- c("a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "b", "b", "a", "a", "a", "a")
col.three <- c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3)

multi.window.data <- data.frame(window.name.column, col.two, col.three)

list.of.homogeneous.windows <- GetHomogeneousWindows(multi.window.data,
"window.name.column", c("col.two", "col.three"))

result <- GetSubsetOfWindowsTwoLevels(list.of.homogeneous.windows, "col.two", "col.three",
c("a"), c("1", "2"))

#Should contain windows 10, 20, 30 because col.two is "a" and col.three can be "1" or "2"
result
## [[1]]
##   window.name.column col.two col.three
## 1                 10       a         1
## 2                 10       a         1
## 3                 10       a         1
##
## [[2]]
##   window.name.column col.two col.three
## 4                 20       a         1
## 5                 20       a         1
## 6                 20       a         1
##
## [[3]]
##    window.name.column col.two col.three
## 7                  30       a         2
## 8                  30       a         2
## 9                  30       a         2
## 10                 30       a         2

### IdentifyMaxOnXY()

#I want to create a plot that shows two curves:
#1. Composite of time series signals 1, 2, and 3.
#2. Composite of time series signals 3 and 4.

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#First signal
#1. 1 Hz with amplitude of 2
S1 <- 2*sin(2*pi*1*t)
level1.vals <- rep("a", length(S1))
level2.vals <- rep("1", length(S1))
S1.data.frame <- as.data.frame(cbind(t, S1, level1.vals, level2.vals))
colnames(S1.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S1.data.frame[,"Signal"] <- as.numeric(S1.data.frame[,"Signal"])

#Second signal
#1. 1 Hz with amplitude of -4
#2. 2 Hz with amplitude of -2
S2 <- (-4)*sin(2*pi*1*t) - 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S2))
level2.vals <- rep("2", length(S2))
S2.data.frame <- as.data.frame(cbind(t, S2, level1.vals, level2.vals))
colnames(S2.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S2.data.frame[,"Signal"] <- as.numeric(S2.data.frame[,"Signal"])

#Third signal
#1. 1 Hz with amplitude of 2
#2. 2 Hz with amplitude of 2
S3 <- 2*sin(2*pi*1*t) + 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S3))
level2.vals <- rep("3", length(S3))
S3.data.frame <- as.data.frame(cbind(t, S3, level1.vals, level2.vals))
colnames(S3.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S3.data.frame[,"Signal"] <- as.numeric(S3.data.frame[,"Signal"])

#Fourth signal
#1. 1 Hz with amplitude of -2
S4 <- -2*sin(2*pi*1*t)
level1.vals <- rep("b", length(S4))
level2.vals <- rep("3", length(S4))
S4.data.frame <- as.data.frame(cbind(t, S4, level1.vals, level2.vals))
colnames(S4.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S4.data.frame[,"Signal"] <- as.numeric(S4.data.frame[,"Signal"])

#Extra representation of S2 dataframe to show an example that has enough samples
#to have significance for Kruskal-Wallis test
windows <- list(S1.data.frame, S2.data.frame, S2.data.frame, S2.data.frame, S2.data.frame,
S2.data.frame, S2.data.frame, S2.data.frame, S2.data.frame, S2.data.frame, S3.data.frame,
S4.data.frame)

#Gets the composite of the first, second, and third signal. Should result in a flat signal.
FirstComboToUse <- list( c("a"), c(1, 2, 3) )

#Gets the composite of the third and fourth signal
SecondComboToUse <- list( c("a", "b"), c(3) )

#PSD-------------------------------------------------------------------------

PSD.results <- AutomatedCompositePlotting(list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
x_start = 0,
x_end = 10,
x_increment = 0.01,
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3", "Signal 3 + 4"),
plot.title = "Example",
plot.xlab = "Hz",
plot.ylab = "(Original units)^2/Hz",
combination.index.for.envelope = 2,
TimeSeries.PSD.LogPSD = "PSD",
sampling_frequency = 100)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
ggplot.obj.PSD <- PSD.results[[2]]

#Plot
ggplot.obj.PSD

dataframes.plotted <- PSD.results[[1]]
first.curve <- dataframes.plotted[[1]]
second.curve <- dataframes.plotted[[2]]

#Identify maximum
first.curve.max <- IdentifyMaxOnXY(first.curve$xvals, first.curve$yvals, 0, 10, 0.01)
second.curve.max <- IdentifyMaxOnXY(second.curve$xvals, second.curve$yvals, 0, 10, 0.01)

first.curve.max
##     xval       yval
## 101    1 0.06909091
second.curve.max
##     xval yval
## 101    1 0.02

### MakeCompositePSDForAllWindows()

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#First signal
#1. 10 Hz with amplitude of 4
#2. 25 Hz with amplitude of 4
S1 <- 1*sin(2*pi*10*t) + 2*sin(2*pi*25*t);
S1 <- S1 + rnorm(length(t)) #Add some noise
S1.data.frame <- as.data.frame(cbind(t, S1))
colnames(S1.data.frame) <- c("Time", "Signal")

#Second signal
#1. 5 Hz with amplitude of 2
#2. 8 Hz with amplitude of 2
S2 <- 2*sin(2*pi*5*t) + 2*sin(2*pi*8*t);
S2 <- S2 + rnorm(length(t)) #Add some noise
S2.data.frame <- as.data.frame(cbind(t, S2))
colnames(S2.data.frame) <- c("Time", "Signal")

#Third signal
#1. 5 Hz with amplitude of 2
#2. 8 Hz with amplitude of 2
S3 <- 2*sin(2*pi*5*t) + 2*sin(2*pi*8*t);
S3 <- S3 + rnorm(length(t)) #Add some noise
S3.data.frame <- as.data.frame(cbind(t, S3))
colnames(S3.data.frame) <- c("Time", "Signal")

#Add all signals to a List
list.of.windows <- list(S1.data.frame, S2.data.frame, S3.data.frame)

results <- MakeCompositePSDForAllWindows(list.of.windows, "Signal", Fs, 0, 30, 0.1)

frequencies <- results[[1]]

averaged.PSD <- results[[2]]

stddev.PSD <- results[[3]]

plot(frequencies, averaged.PSD, type = "l")

### MakeCompositeXYPlotForAllWindows

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#First signal
#1. 1 Hz with amplitude of 4
S1 <- 4*sin(2*pi*1*t)
S1.data.frame <- as.data.frame(cbind(t, S1))
colnames(S1.data.frame) <- c("Time", "Signal")

#Second signal
#1. 1 Hz with amplitude of -2
#2. 2 Hz with amplitude of -2
S2 <- (-2)*sin(2*pi*1*t) - 2*sin(2*pi*2*t);
S2.data.frame <- as.data.frame(cbind(t, S2))
colnames(S2.data.frame) <- c("Time", "Signal")

#Third signal
#1. 1 Hz with amplitude of 2
#2. 2 Hz with amplitude of 2
S3 <- 2*sin(2*pi*1*t) + 2*sin(2*pi*2*t);
S3.data.frame <- as.data.frame(cbind(t, S3))
colnames(S3.data.frame) <- c("Time", "Signal")

#Add all signals to a List
list.of.windows <- list(S1.data.frame, S2.data.frame, S3.data.frame)

results <- MakeCompositeXYPlotForAllWindows(list.of.windows, "Signal", 0, 999, 1)

x.values <- results[[1]]

y.values <- results[[2]]

stddev.y.values <- results[[3]]

#plot each xy plot individually
plot(t, S1, ylim = c(-5, 5), type = "l")
lines(t, S2, col="blue")
lines(t, S3, col="green")

#plot the averaged plot
#The only curve remaining should be the 1Hz with amplitude of 4/3.
plot(x.values, y.values, type = "l")

### MakeOneSidedAmplitudeSpectrum()

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#Form a signal (time series) that contains two frequencies:
#1. 10 Hz with amplitude of 1
#2. 25 Hz with amplitude of 2
S <- 1*sin(2*pi*10*t) + 2*sin(2*pi*25*t);

results <- MakeOneSidedAmplitudeSpectrum(Fs, S)

frequencies <- results[[1]]

amplitudes <- results[[2]]

#dev.new()
plot(frequencies, amplitudes, type = "l")

### MakePowerSpectralDensity()

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz (sampling/second)
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector in seconds

#Form a signal (time series) that contains two frequencies:
#1. 10 Hz with amplitude of 1
#2. 25 Hz with amplitude of 2
S <- 1*sin(2*pi*10*t) + 2*sin(2*pi*25*t);

#Plot the signal
plot(t[1:100], S[1:100], type = "l")

#Make a PSD to see the frequencies in the signal
results <- MakePowerSpectralDensity(Fs, S)

frequencies <- results[[1]]

PSD <- results[[2]]

#dev.new()
plot(frequencies, PSD, type = "l")

### PSDDominantFrequencyForMultipleWindows()

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#First signal
#1. 1 Hz with amplitude of 2
S1 <- 2*sin(2*pi*1*t)
level1.vals <- rep("a", length(S1))
level2.vals <- rep("1", length(S1))
S1.data.frame <- as.data.frame(cbind(t, S1, level1.vals, level2.vals))
colnames(S1.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S1.data.frame[,"Signal"] <- as.numeric(S1.data.frame[,"Signal"])

#Second signal
#1. 1 Hz with amplitude of -4
#2. 2 Hz with amplitude of -2
S2 <- (-4)*sin(2*pi*1*t) - 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S2))
level2.vals <- rep("2", length(S2))
S2.data.frame <- as.data.frame(cbind(t, S2, level1.vals, level2.vals))
colnames(S2.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S2.data.frame[,"Signal"] <- as.numeric(S2.data.frame[,"Signal"])

#Third signal
#1. 1 Hz with amplitude of 2
#2. 2 Hz with amplitude of 2
S3 <- 2*sin(2*pi*1*t) + 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S3))
level2.vals <- rep("3", length(S3))
S3.data.frame <- as.data.frame(cbind(t, S3, level1.vals, level2.vals))
colnames(S3.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S3.data.frame[,"Signal"] <- as.numeric(S3.data.frame[,"Signal"])

#Fourth signal
#1. 1 Hz with amplitude of -2
S4 <- -2*sin(2*pi*1*t)
level1.vals <- rep("b", length(S4))
level2.vals <- rep("3", length(S4))
S4.data.frame <- as.data.frame(cbind(t, S4, level1.vals, level2.vals))
colnames(S4.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S4.data.frame[,"Signal"] <- as.numeric(S4.data.frame[,"Signal"])

windows <- list(S1.data.frame, S2.data.frame, S3.data.frame, S4.data.frame)

#Plot the PSD for each window
FirstComboToUse <- list( c("a"), c(1) )
SecondComboToUse <- list( c("a"), c(2) )
ThirdComboToUse <- list( c("a"), c(3) )
FourthComboToUse <- list( c("b"), c(3) )
PSD.results <- AutomatedCompositePlotting(list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
x_start = 0,
x_end = 5,
x_increment = 0.01,
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse,
ThirdComboToUse, FourthComboToUse),
level.combinations.labels = c("Signal 1", "Signal 2",
"Signal 3", "Signal 4"),
plot.title = "Example",
plot.xlab = "Hz",
plot.ylab = "(Original units)^2/Hz",
combination.index.for.envelope = 2,
TimeSeries.PSD.LogPSD = "PSD",
sampling_frequency = 100)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
ggplot.obj.PSD <- PSD.results[[2]]

#Plot
ggplot.obj.PSD

#Calculate the dominant frequency for each window
results <- PSDDominantFrequencyForMultipleWindows(windows, "Signal", Fs, 0, 5, 0.01)

results
## [1] 1 1 2 1

### PSDIdentifyDominantFrequency()

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#First signal
#1. 1 Hz with amplitude of 2
S1 <- 2*sin(2*pi*1*t)
level1.vals <- rep("a", length(S1))
level2.vals <- rep("1", length(S1))
S1.data.frame <- as.data.frame(cbind(t, S1, level1.vals, level2.vals))
colnames(S1.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S1.data.frame[,"Signal"] <- as.numeric(S1.data.frame[,"Signal"])

results <- PSDIdentifyDominantFrequency(Fs, S1.data.frame[,"Signal"], 0, 10, 0.01)

results
##     xval yval
## 101    1 0.02

### PSDIntegrationPerFreqBin()

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#Form a signal (time series) that contains two frequencies:
#1. 10 Hz with amplitude of 1
#2. 25 Hz with amplitude of 2
S <- 1*sin(2*pi*10*t) + 2*sin(2*pi*25*t);

results <- MakePowerSpectralDensity(Fs, S)

frequencies <- results[[1]]

PSD <- results[[2]]

plot(frequencies, PSD, type = "l")

bins <- list(
c(9, 11),
c(24,26),
c(9,26),
c(30,40)
)

integration.results <- PSDIntegrationPerFreqBin(Fs, S, bins)

for(i in 1:length(integration.results)){

message <- paste("Area in bin ", integration.results[[i]][[1]], " is ",
integration.results[[i]][[2]])

print(message)

}
## [1] "Area in bin  9 - 11  is  0.00050011493570859"
## [1] "Area in bin  24 - 26  is  0.00200024756472286"
## [1] "Area in bin  9 - 26  is  0.00249982073718224"
## [1] "Area in bin  30 - 40  is  1.40556982936425e-30"

### SingleBinPSDIntegrationForMultipleWindows()

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#First signal
#1. 1 Hz with amplitude of 2
S1 <- 2*sin(2*pi*1*t)
level1.vals <- rep("a", length(S1))
level2.vals <- rep("1", length(S1))
S1.data.frame <- as.data.frame(cbind(t, S1, level1.vals, level2.vals))
colnames(S1.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S1.data.frame[,"Signal"] <- as.numeric(S1.data.frame[,"Signal"])

#Second signal
#1. 1 Hz with amplitude of -4
#2. 2 Hz with amplitude of -2
S2 <- (-4)*sin(2*pi*1*t) - 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S2))
level2.vals <- rep("2", length(S2))
S2.data.frame <- as.data.frame(cbind(t, S2, level1.vals, level2.vals))
colnames(S2.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S2.data.frame[,"Signal"] <- as.numeric(S2.data.frame[,"Signal"])

#Third signal
#1. 1 Hz with amplitude of 2
#2. 2 Hz with amplitude of 2
S3 <- 2*sin(2*pi*1*t) + 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S3))
level2.vals <- rep("3", length(S3))
S3.data.frame <- as.data.frame(cbind(t, S3, level1.vals, level2.vals))
colnames(S3.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S3.data.frame[,"Signal"] <- as.numeric(S3.data.frame[,"Signal"])

#Fourth signal
#1. 1 Hz with amplitude of -2
S4 <- -2*sin(2*pi*1*t)
level1.vals <- rep("b", length(S4))
level2.vals <- rep("3", length(S4))
S4.data.frame <- as.data.frame(cbind(t, S4, level1.vals, level2.vals))
colnames(S4.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S4.data.frame[,"Signal"] <- as.numeric(S4.data.frame[,"Signal"])

windows <- list(S1.data.frame, S2.data.frame, S3.data.frame, S4.data.frame)

#Plot the PSD for each window
FirstComboToUse <- list( c("a"), c(1) )
SecondComboToUse <- list( c("a"), c(2) )
ThirdComboToUse <- list( c("a"), c(3) )
FourthComboToUse <- list( c("b"), c(3) )
PSD.results <- AutomatedCompositePlotting(list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
x_start = 0,
x_end = 5,
x_increment = 0.01,
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse,
ThirdComboToUse, FourthComboToUse),
level.combinations.labels = c("Signal 1", "Signal 2",
"Signal 3", "Signal 4"),
plot.title = "Example",
plot.xlab = "Hz",
plot.ylab = "(Original units)^2/Hz",
combination.index.for.envelope = 2,
TimeSeries.PSD.LogPSD = "PSD",
sampling_frequency = 100)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
ggplot.obj.PSD <- PSD.results[[2]]

#Plot
ggplot.obj.PSD

#For each of the 4 windows, calculate the area under the PSD from 0-2 Hz
results <- SingleBinPSDIntegrationForMultipleWindows(windows, "Signal", Fs, c(0,2))

results
## [1] 0.002000248 0.009000239 0.003000262 0.002000248

### SingleBinPSDIntegrationOrDominantFreqComparison()

#Create a vector of time that represent times where data are sampled.
Fs = 100; #sampling frequency in Hz
T = 1/Fs; #sampling period
L = 1000; #length of time vector
t = (0:(L-1))*T; #time vector

#First signal
#1. 1 Hz with amplitude of 2
S1 <- 2*sin(2*pi*1*t)
level1.vals <- rep("a", length(S1))
level2.vals <- rep("1", length(S1))
S1.data.frame <- as.data.frame(cbind(t, S1, level1.vals, level2.vals))
colnames(S1.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S1.data.frame[,"Signal"] <- as.numeric(S1.data.frame[,"Signal"])

#Second signal
#1. 1 Hz with amplitude of -4
#2. 2 Hz with amplitude of -2
S2 <- (-4)*sin(2*pi*1*t) - 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S2))
level2.vals <- rep("2", length(S2))
S2.data.frame <- as.data.frame(cbind(t, S2, level1.vals, level2.vals))
colnames(S2.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S2.data.frame[,"Signal"] <- as.numeric(S2.data.frame[,"Signal"])

#Third signal
#1. 1 Hz with amplitude of 2
#2. 2 Hz with amplitude of 2
S3 <- 2*sin(2*pi*1*t) + 2*sin(2*pi*2*t);
level1.vals <- rep("a", length(S3))
level2.vals <- rep("3", length(S3))
S3.data.frame <- as.data.frame(cbind(t, S3, level1.vals, level2.vals))
colnames(S3.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S3.data.frame[,"Signal"] <- as.numeric(S3.data.frame[,"Signal"])

#Fourth signal
#1. 1 Hz with amplitude of -2
S4 <- -2*sin(2*pi*1*t)
level1.vals <- rep("b", length(S4))
level2.vals <- rep("3", length(S4))
S4.data.frame <- as.data.frame(cbind(t, S4, level1.vals, level2.vals))
colnames(S4.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S4.data.frame[,"Signal"] <- as.numeric(S4.data.frame[,"Signal"])

#Fifth signal
#1. 5 Hz with amplitude of -2
S5 <- -2*sin(2*pi*5*t)
level1.vals <- rep("c", length(S5))
level2.vals <- rep("1", length(S5))
S5.data.frame <- as.data.frame(cbind(t, S5, level1.vals, level2.vals))
colnames(S5.data.frame) <- c("Time", "Signal", "level1.ID", "level2.ID")
S5.data.frame[,"Signal"] <- as.numeric(S5.data.frame[,"Signal"])

#Extra representation of S2 dataframe to show an example that has enough samples
#to have significance for Kruskal-Wallis test
windows <- list(S1.data.frame, S2.data.frame, S2.data.frame, S2.data.frame, S2.data.frame,
S2.data.frame, S2.data.frame, S2.data.frame, S2.data.frame, S2.data.frame, S3.data.frame,
S4.data.frame,
S5.data.frame, S5.data.frame, S5.data.frame, S5.data.frame, S5.data.frame)

#Gets the composite of the first, second, and third signal. Should result in a flat signal.
FirstComboToUse <- list( c("a"), c(1, 2, 3) )

#Gets the composite of the third and fourth signal
SecondComboToUse <- list( c("a", "b"), c(3) )

#Gets the composite of fifth signal
ThirdComboToUse <- list( c("c"), c(1) )

#PSD-------------------------------------------------------------------------

PSD.results <- AutomatedCompositePlotting(list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
x_start = 0,
x_end = 10,
x_increment = 0.01,
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse,
SecondComboToUse,
ThirdComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3",
"Signal 3 + 4",
"Signal 5"),
plot.title = "Example",
plot.xlab = "Hz",
plot.ylab = "(Original units)^2/Hz",
combination.index.for.envelope = 2,
TimeSeries.PSD.LogPSD = "PSD",
sampling_frequency = 100)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
ggplot.obj.PSD <- PSD.results[[2]]

ggplot.obj.PSD

#Integration-------------------------------------------------------------------------

#Compare integration for the 1.5-2.5 Hz bin. P-value should not indicate
#significant difference
integration.compare.res <- SingleBinPSDIntegrationOrDominantFreqComparison(
list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3", "Signal 3 + 4"),
sampling_frequency = 100,
single.bin.boundary = c(1.5, 2.5),
integration.or.dominant.freq = "integration")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
#Kruskal-Wallis test results
integration.compare.res[[2]]
##
##  Kruskal-Wallis rank sum test
##
## data:  vals.to.compare.combined by combo.labels.combined
## Kruskal-Wallis chi-squared = 0, df = 1, p-value = 1
#Values used in Kruskal-Wallis test
integration.compare.res[[1]]
##    vals.to.compare.combined combo.labels.combined
## 1              4.733168e-35      Signal 1 + 2 + 3
## 2              2.000248e-03      Signal 1 + 2 + 3
## 3              2.000248e-03      Signal 1 + 2 + 3
## 4              2.000248e-03      Signal 1 + 2 + 3
## 5              2.000248e-03      Signal 1 + 2 + 3
## 6              2.000248e-03      Signal 1 + 2 + 3
## 7              2.000248e-03      Signal 1 + 2 + 3
## 8              2.000248e-03      Signal 1 + 2 + 3
## 9              2.000248e-03      Signal 1 + 2 + 3
## 10             2.000248e-03      Signal 1 + 2 + 3
## 11             2.000248e-03      Signal 1 + 2 + 3
## 12             2.000248e-03          Signal 3 + 4
## 13             4.733168e-35          Signal 3 + 4
#Compare integration for the 0.5-1.5 Hz bin. P-value should indicate
#significant difference
integration.compare.res2 <- SingleBinPSDIntegrationOrDominantFreqComparison(
list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3", "Signal 3 + 4"),
sampling_frequency = 100,
single.bin.boundary = c(0.5,1.5),
integration.or.dominant.freq = "integration")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
#Kruskal-Wallis test results
integration.compare.res2[[2]]
##
##  Kruskal-Wallis rank sum test
##
## data:  vals.to.compare.combined by combo.labels.combined
## Kruskal-Wallis chi-squared = 4.9091, df = 1, p-value = 0.02672
#Values used in Kruskal-Wallis test
integration.compare.res2[[1]]
##    vals.to.compare.combined combo.labels.combined
## 1               0.002000248      Signal 1 + 2 + 3
## 2               0.008000115      Signal 1 + 2 + 3
## 3               0.008000115      Signal 1 + 2 + 3
## 4               0.008000115      Signal 1 + 2 + 3
## 5               0.008000115      Signal 1 + 2 + 3
## 6               0.008000115      Signal 1 + 2 + 3
## 7               0.008000115      Signal 1 + 2 + 3
## 8               0.008000115      Signal 1 + 2 + 3
## 9               0.008000115      Signal 1 + 2 + 3
## 10              0.008000115      Signal 1 + 2 + 3
## 11              0.002000248      Signal 1 + 2 + 3
## 12              0.002000248          Signal 3 + 4
## 13              0.002000248          Signal 3 + 4
#Dominant Frequency---------------------------------------------------------------------

#Compare dominant frequency P-value should not indicate
#significant difference
integration.compare.res3 <- SingleBinPSDIntegrationOrDominantFreqComparison(
list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(FirstComboToUse, SecondComboToUse),
level.combinations.labels = c("Signal 1 + 2 + 3", "Signal 3 + 4"),
sampling_frequency = 100,
x_start = 0,
x_end = 10,
x_increment = 0.01,
integration.or.dominant.freq = "dominant_freq")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
#Kruskal-Wallis test results
integration.compare.res3[[2]]
##
##  Kruskal-Wallis rank sum test
##
## data:  vals.to.compare.combined by combo.labels.combined
## Kruskal-Wallis chi-squared = 2.0083, df = 1, p-value = 0.1564
#Values used in Kruskal-Wallis test
integration.compare.res3[[1]]
##    vals.to.compare.combined combo.labels.combined
## 1                         1      Signal 1 + 2 + 3
## 2                         1      Signal 1 + 2 + 3
## 3                         1      Signal 1 + 2 + 3
## 4                         1      Signal 1 + 2 + 3
## 5                         1      Signal 1 + 2 + 3
## 6                         1      Signal 1 + 2 + 3
## 7                         1      Signal 1 + 2 + 3
## 8                         1      Signal 1 + 2 + 3
## 9                         1      Signal 1 + 2 + 3
## 10                        1      Signal 1 + 2 + 3
## 11                        2      Signal 1 + 2 + 3
## 12                        2          Signal 3 + 4
## 13                        1          Signal 3 + 4
#Compare dominant frequency P-value should indicate
#significant difference
integration.compare.res4 <- SingleBinPSDIntegrationOrDominantFreqComparison(
list.of.windows = windows,
name.of.col.containing.time.series = "Signal",
level1.column.name = "level1.ID",
level2.column.name = "level2.ID",
level.combinations = list(SecondComboToUse, ThirdComboToUse),
level.combinations.labels = c("Signal 3 + 4", "Signal 5"),
sampling_frequency = 100,
x_start = 0,
x_end = 10,
x_increment = 0.01,
integration.or.dominant.freq = "dominant_freq")
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
#Kruskal-Wallis test results
integration.compare.res4[[2]]
##
##  Kruskal-Wallis rank sum test
##
## data:  vals.to.compare.combined by combo.labels.combined
## Kruskal-Wallis chi-squared = 5.8333, df = 1, p-value = 0.01573
#Values used in Kruskal-Wallis test
integration.compare.res4[[1]]
##   vals.to.compare.combined combo.labels.combined
## 1                        2          Signal 3 + 4
## 2                        1          Signal 3 + 4
## 3                        5              Signal 5
## 4                        5              Signal 5
## 5                        5              Signal 5
## 6                        5              Signal 5
## 7                        5              Signal 5