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+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 56 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 132 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 135 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 145 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 150 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 155 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 160 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 162 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+# model for simulating muscle oxygen content depending on activity level (exercise) and heart rate
+# model parameters; https://data-flair.training/blogs/r-vector/
+# 1 ml O2 is 1.43 mg O2 at Standard Temperature and Pressure
+Oblood = 0.2*1.43 # mg O2 (ml blood)-1
+f = 0.7  # maximum fraction of oxygen transfered from blood to tissue
+b = 0.0357 # ml blood /(beat 100 ml muscle)-1
+Odemand = 0.11*1.43 # mg O2 (100 ml muscle minute)-1
+Omax = 10 # max oxygen content in mg O2/100ml
+timesteps = 120 # run each simulation for 120 timesteps (minutes)
+O = numeric(timesteps) # vector for the results of the simulations of muscle oxygen content
+O[1] = 5 # initial oxygen content for the models is 5 mg O2/100ml
+# multiple model simulations follow to calculate muscle oxygen content
+# with different input heart rates and activity levels for 120 minutes each; https://campus.datacamp.com/courses/intermediate-r-for-finance/loops-3?ex=9
+# and an initial muscle oxygen content of 5 mg O2
+# muscle oxygen content is updated each minute based on previous muscle oxygen content
+# first simulation model inputs
+Hrate = 68 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 1 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O[i-1]/Omax)
+Demand = Odemand*Alevel
+O[i] = O[i-1]+Supply-Demand
+}
+# second simulation model inputs
+O2 = O #results of the second simulation of muscle oxygen content
+Hrate = 164 # 50-200; heart rate beats per minute  UPDATE YOUR HEARTRATE HERE
+Alevel = 2.5 # 1 for rest and >2 for exercise; activity level of skeletal muscle
+for(i in 2:length(O)){
+Supply = Oblood*Hrate*f*b*(1-O2[i-1]/Omax)
+Demand = Odemand*Alevel
+O2[i] = O2[i-1]+Supply-Demand
+}
+# plot the results of each simulation to compare the change in muscle oxygen content over time
+# depending on the input heart rate and activity level
+# https://www.datacamp.com/community/tutorials/15-questions-about-r-plots
+plot(O, type='l', xlab="Timestep (minute)", ylab="Oxygen Content (mg O2/100ml)", ylim=c(0, 10)) # first simulation results, line colored black
+lines(O2, col='red') # second simulation results, line colored red
+abline(h=1.2, lty=3) # draw a horizontal line at 1.2 = 1/(273*8.3e3)*1000*100*32; this assumes 100 ml skeletal muscle
+legend("top", bty="n", inset=0, legend=c("Resting", "Active"), col=c("black", "red"), lty=1:1, cex=0.8, horiz = TRUE)
+install.packages(c("swirl","swirlify"))
+install.packages("swirl")
+library("siwrl")
+library("swirl")
+swirl()
+swirl()
+swirl()
+library(swirl)
+swirl()
+library()
+swirl()
+library("swirl")
+swirl()
+swirl()
+5+7
+x <- 5 + 7
+x
+y <- x - 3
+y
+c(1.1, 9, 3.14)
+z <- (1.1, 9, 3.14)
+z <- c(1.1, 9, 3.14)
+?c
+z
+c(z, 555, z)
+z * 2 + 100
+my_sqrt <- sqrt(z-1)
+swirl()
+my_sqrt
+my_div <- z/my_sqrt
+my_div
+c(1, 2, 3, 4) + c(0, 10)
+c( 1, 2, 3, 4) + c(0, 10, 100)
+c( 1, 2, 3, 4) + c(0, 10, 1000)
+c( 1, 2, 3, 4) + c(0, 10, 1000)
+info()
+c( 1, 2, 3, 4) + c(0, 10, 1000)
+c( 1, 2, 3, 4) + c(0, 10, 1000)
+c( 1, 2, 3, 4) + c(0, 10, 1000)
+c( 1, 2, 3, 4) + c(0, 10, 1000)
+info()
+nxt()
+z * 2 + 1000
+my_div
+getwd()
+ls()
+x <- 9
+ls()
+list.files()
+?list.files
+args(list.files())
+args(list.files)
+old.dir <- wkdir
+old.dir <- list.files
+old.dir <- getwd()
+dir.create(testdir)
+dir.create("testdir")
+setwd("testdir")
+file.create("mytest.R")
+ls("wkdir")
+ls()
+list.files()
+ls
+file.exists("mytest.R")
+file.info("mytest.R")
+file.rename("mytest.R")
+file.rename("mytest.R") to "mytest2.R"
+file.rename("mytest.R" to "mytest2.R")
+file.rename("mytest.R" "to" "mytest2.R")
+file.rename(mytest.R)
+?file.rename
+file.rename("mytest.R", to "mytest2.R")
+file.rename("mytest.R", "mytest2.R")
+file.copy(mytest2.R, mytest3.R)
+file.copy("mytest2.R", "mytest4.R")
+file.copy("mytest2.R", "mytest3.R")
+file.path("mytest3.R")
+file.path("folder1")
+file.path("folder1", "folder2")
+?dir.create
+dir.create("testdir2") file.path("testdir3")
+?file.path()
+dir.create("testdir2" file.path("testdir3"))
+dir.create(file.path("testdir2", "testdir3")
+dir.create(file.path("testdir2", "testdir3"))
+dir.create(file.path("testdir2", "testdir3"))
+dir.create(file.path("testdir2", "testdir3"), recursive = TRUE)
+setwd()
+setwd(old.dir)
+1:20
+pi:10
+15:1
+':'
+?':'
+seq(1,20)
+seq(0, 10, by=0.5)
+seq(5, 10, length=30)
+my_seq <- seq(5, 10, length=30)
+length("my_seq")
+length(my_seq)
+1:length(my_seq)
+seq(along.with = my_seq)
+seq_along(my_seq)
+rep(0, times = 40)
+rep(c(0, 1, 2), times =10)
+rep(c(0 , 1, 2), each = 10)
+num_vect(0.5, 55, -10, 6)
+num_vect("0.5, 55, -10, 6")
+c(0.5, 55, -10, 6)
+?c
+c(0.5, 55, -10, 6, recursive = FALSE)
+<- num_vect c(0.5, 55, -10, 6)
+num_vect <- c(0.5, 55, -10, 6)
+tf <- num_vect < 1
+swirl()
+swirl()
+swirl()
+swirl()
+tf
+num_vect >=6
+swirl()
+my_char <- c("My", "name", "is")
+my_char
+paste(my_char, collapse = " ")
+my_name <- c(my_char, "Emily")
+my_name
+paste(my_name, collapse = " ")
+paste("Hello", "world!", sep = " ")
+paste("1:3", c("X", "Y", "Z"), sep = "")
+paste(1:3, c("X", "Y", "Z"), sep = "")
+paste(LETTERS, 1:4, sep = "-")
+dt=data.frame(a=c)1:4),
+b=c(5:8),
+c=c(9:12))
+dt=data.frame(a=  c)1:4),
+b = c(5:8),
+c = c(9:12))
+dt=data.frame(a =  c)1:4),
+b = c(5:8),
+c = c(9:12))
+dt = data.frame(a =  c)1:4),
+b = c(5:8),
+c = c(9:12))
+dt=data.frame(a=c(1:4),)
+dt=data.frame(a=c)1:4),
+b=c(5:8),
+c=c(9:12))
+dt=data.frame(a=c(1:4),
+b=c(5:8),
+c=c(9:12))
+sumMale
+for(wages )
+source("~/.active-rstudio-document", echo=TRUE)
+library(ggplot2)
+library("ggplot2")
+source("~/.active-rstudio-document", echo=TRUE)
+install.packages("ggplot2")
+install.packages(‘lifecycle’)
+install.packages("lifecycle")
+setwd("~/Downloads/Notre Dame/Biocomputing/Biocomp_tutorial11")
+?function()
diff --git a/dirfunction.R b/dirfunction.R
new file mode 100644
index 0000000..ebd1c84
--- /dev/null
+++ b/dirfunction.R
@@ -0,0 +1,40 @@
+
+# define the function 
+coefficient_of_var<-function(dir,column_num,nrow=50){
+  
+# set working directory and create CoV vector
+  setwd(dir)
+  dataset <- list.files(dir)
+  CoV_vector <- vector()
+
+# calculate reliable coefficient of variation 
+  for(i in dataset){
+    i <- read.csv(dataset[i])
+    if (column_num > ncol(i)){
+      print("not valid -- choose lower column_num")
+      }
+    else {
+      # Report error for file with less than 50 observations
+      if(nrow(i) < 50){
+        print("error")
+        # Give option to override and continue with calculation 
+        x <- readline("Would you like to override? Please enter 'yes' or 'no': ")
+        if (x == "yes"){
+          print("Warning! Your file has less than 50 observations.")
+          # Calculate the coefficient of variation with an override
+          standard_dev<-sd(i[,column_num])
+          average<-mean(i[,col])
+          coeff_variation<-standard_dev/average
+        } else {
+          break
+        }
+  } else {
+      # Calculate the coefficient of variation for file with more than 50 obs
+      standard_dev<-sd(i[,column_num])
+      average<-mean(i[,column_num])
+      coeff_variation<-standard_dev/average
+      Cov_vector[i]<-coeff_variation
+      }
+    }
+  } return(CoV_vector)
+  }
\ No newline at end of file