R/mxc_mulruns.R
In SindisoNyathi/MxC: Processes the entire set of Mexico City Results.
Defines functions
mxc_mulruns
Documented in
mxc_mulruns
#' @title MxC Mulruns
#' @author Sindiso Nyathi
#' @description This function will process all the runs in a single experiment given the folder name, and write results to .csv files.
#' @param home Parent directory path.
#' @param foldername String name of file with the runs eg. "Baseline"
#' @return Will write multiple csv files into the Folders with the experiments.
#' @details This function will aggregate each run into age and gender groupings, find the averages for the Time and
#' Events variables in each run and average these out across all the runs. Method is as follows:
#' Step 1: Create a dataframe to store all the group average values.
#' Step 2: Run a for loop from 1 to N (N is the number of runs in this experiment)
#' 2a: Call mxc_indrun, which will calculate individual run averages for all the variables in run N.
#' 2b: Store the resulting values in the data frame created.
#' Step 3: Calculate the overall means, SD and CI accross all the x runs.
#'
#'@seealso {\code{\link{mxc_master}}, \code{\link{mxc_indrun}}, \code{\link{mxc_dealsd}}}
#'@examples
#'mxc_mulruns(home, "Baseline")
#'mxc_mulruns(home, "Intervention 1")
#'@family
#'
#' @export
mxc_mulruns <- function(home, filename) {
#Set the working directory to input folder.
setwd(paste(home, filename, sep = ""))
#All of the runs should be in the current folder, and should be named mxc run_1.csv to mxc run_N.csv where N
#is the run number.
#Set runs based on the number of .csv files in the folder.
run = length(list.files(pattern = ".csv$"))
#Set the simulation length; 5 years. This value is important because it determines the length of time
#an adolescent of a given age was in the model.
simulation_length = 5
#check to make sure there are actually csv files in the folder.
if (length(run) == 0) {stop("Error: There are no csv files in this folder.")}
#Create the two dataframes that will store the results of these multiple runs.
#This first one stores the raw results for the N runs, so store all these variable for each run,
#each row is one run and has a column for each variable of interest.
mulruns_agg <- data.frame(RunNo. = numeric(run),
#Males
#6 - 9 yrs, 10 variables per age group.
"Male, 6 - 9, Ave, Events" = numeric(run),
"Male, 6 - 9, Events (0 - 3)" = numeric(run), "Male, 6 - 9, Events (3 - 5)" = numeric(run), "Male, 6 - 9, Events (5 or more)" = numeric(run),
"Male, 6 - 9, Ave, Time" = numeric(run), "Male, 6 - 9, PE Ave, Time" = numeric(run), "Male, 6 - 9, ASPA Ave, Time" = numeric(run),
"Male, 6 - 9, Time (0 - 3)" = numeric(run), "Male, 6 - 9, Time (3 - 7)" = numeric(run), "Male, 6 - 9, Time (7 or more)" = numeric(run),
#10 - 14 yrs
"Male, 10 - 14, Ave, Events" = numeric(run),
"Male, 10 - 14, Events (0 - 3)" = numeric(run), "Male, 10 - 14, Events (3 - 5)" = numeric(run), "Male, 10 - 14, Events (5 or more)" = numeric(run),
"Male, 10 - 14, Ave, Time" = numeric(run), "Male, 10 - 14, PE Ave, Time" = numeric(run), "Male, 10 - 14, ASPA Ave, Time" = numeric(run),
"Male, 10 - 14, Time (0 - 3)" = numeric(run), "Male, 10 - 14, Time (3 - 7)" = numeric(run), "Male, 10 - 14, Time (7 or more)" = numeric(run),
#15 - 18 yrs
"Male, 15 - 18, Ave, Events" = numeric(run),
"Male, 15 - 18, Events (0 - 3)" = numeric(run), "Male, 15 - 18, Events (3 - 5)" = numeric(run), "Male, 15 - 18, Events (5 or more)" = numeric(run),
"Male, 15 - 18, Ave, Time" = numeric(run), "Male, 15 - 18, PE Ave, Time" = numeric(run), "Male, 15 - 18, ASPA Ave, Time" = numeric(run),
"Male, 15 - 18, Time (0 - 3)" = numeric(run), "Male, 15 - 18, Time (3 - 7)" = numeric(run), "Male, 15 - 18, Time (7 or more)" = numeric(run),
#Females
#6 - 9 yrs
"Female, 6 - 9, Ave, Events" = numeric(run),
"Female, 6 - 9, Events (0 - 3)" = numeric(run), "Female, 6 - 9, Events (3 - 5)" = numeric(run), "Female, 6 - 9, Events (5 or more)" = numeric(run),
"Female, 6 - 9, Ave, Time" = numeric(run), "Female, 6 - 9, PE Ave, Time" = numeric(run), "Female, 6 - 9, ASPA Ave, Time" = numeric(run),
"Female, 6 - 9, Time (0 - 3)" = numeric(run), "Female, 6 - 9, Time (3 - 7)" = numeric(run), "Female, 6 - 9, Time (7 or more)" = numeric(run),
#10 - 14 yrs
"Female, 10 - 14, Ave, Events" = numeric(run),
"Female, 10 - 14, Events (0 - 3)" = numeric(run), "Female, 10 - 14, Events (3 - 5)" = numeric(run), "Female, 10 - 14, Events (5 or more)" = numeric(run),
"Female, 10 - 14, Ave, Time" = numeric(run), "Female, 10 - 14, PE Ave, Time" = numeric(run), "Female, 10 - 14, ASPA Ave, Time" = numeric(run),
"Female, 10 - 14, Time (0 - 3)" = numeric(run), "Female, 10 - 14, Time (3 - 7)" = numeric(run), "Female, 10 - 14, Time (7 or more)" = numeric(run),
#15 - 18 yrs
"Female, 15 - 18, Ave, Events" = numeric(run),
"Female, 15 - 18, Events (0 - 3)" = numeric(run), "Female, 15 - 18, Events (3 - 5)" = numeric(run), "Female, 15 - 18, Events (5 or more)" = numeric(run),
"Female, 15 - 18, Ave, Time" = numeric(run), "Female, 15 - 18, PE Ave, Time" = numeric(run), "Female, 15 - 18, ASPA Ave, Time" = numeric(run),
"Female, 15 - 18, Time (0 - 3)" = numeric(run), "Female, 15 - 18, Time (3 - 7)" = numeric(run), "Female, 15 - 18, Time (7 or more)" = numeric(run),
#Overall
#Males
"Male, Ave, Events" = numeric(run),
"Male, Events (0 - 3)" = numeric(run), "Male, Events (3 - 5)" = numeric(run), "Male, Events (5 or more)" = numeric(run),
"Male, Ave, Time" = numeric(run), "Male, PE Ave, Time" = numeric(run), "Male, ASPA Ave, Time" = numeric(run),
"Male, Time (0 - 3)" = numeric(run), "Male, Time (3 - 7)" = numeric(run), "Male, Time (7 or more)" = numeric(run),
#Females
"Female, Ave, Events" = numeric(run),
"Female, Events (0 - 3)" = numeric(run), "Female, Events (3 - 5)" = numeric(run), "Female, Events (5 or more)" = numeric(run),
"Female, Ave, Time" = numeric(run), "Female, PE Ave, Time" = numeric(run), "Female, ASPA Ave, Time" = numeric(run),
"Female, Time (0 - 3)" = numeric(run), "Female, Time (3 - 7)" = numeric(run), "Female, Time (7 or more)" = numeric(run),
#All
"Ave, Events" = numeric(run),
"Events (0 - 3)" = numeric(run), "Events (3 - 5)" = numeric(run), "Events (5 or more)" = numeric(run),
"Ave, Time" = numeric(run), "PE Ave, Time" = numeric(run), "ASPA Ave, Time" = numeric(run),
"Time (0 - 3)" = numeric(run), "Time (3 - 7)" = numeric(run), "Time (7 or more)" = numeric(run)
)
#This data frame only inlcuded the results that would have confidence intervals within a run, i.e. for which a mean would be taken.
#So this dataframe will store the standard deviation for these values in order to later calculate the Confidence Interval
mulruns_aggU <- data.frame(RunNo. = numeric(run),
#Males
#6 - 9
"Male, 6 - 9, Ave, Events" = numeric(run),
"Male, 6 - 9, Ave, Time" = numeric(run), "Male, 6 - 9, PE Ave, Time" = numeric(run), "Male, 6 - 9, ASPA Ave, Time" = numeric(run),
#10 - 14
"Male, 10 - 14, Ave, Events" = numeric(run),
"Male, 10 - 14, Ave, Time" = numeric(run), "Male, 10 - 14, PE Ave, Time" = numeric(run), "Male, 10 - 14, ASPA Ave, Time" = numeric(run),
#15 - 18
"Male, 15 - 18, Ave, Events" = numeric(run),
"Male, 15 - 18, Ave, Time" = numeric(run), "Male, 15 - 18, PE Ave, Time" = numeric(run), "Male, 15 - 18, ASPA Ave, Time" = numeric(run),
#Females
#6 - 9
"Female, 6 - 9, Ave, Events" = numeric(run),
"Female, 6 - 9, Ave, Time" = numeric(run), "Female, 6 - 9, PE Ave, Time" = numeric(run), "Female, 6 - 9, ASPA Ave, Time" = numeric(run),
#10 - 14
"Female, 10 - 14, Ave, Events" = numeric(run),
"Female, 10 - 14, Ave, Time" = numeric(run), "Female, 10 - 14, PE Ave, Time" = numeric(run), "Female, 10 - 14, ASPA Ave, Time" = numeric(run),
#15 - 18
"Female, 15 - 18, Ave, Events" = numeric(run),
"Female, 15 - 18, Ave, Time" = numeric(run), "Female, 15 - 18, PE Ave, Time" = numeric(run), "Female, 15 - 18, ASPA Ave, Time" = numeric(run),
#Overall
#Males
"Male, Ave, Events" = numeric(run),
"Male, Ave, Time" = numeric(run), "Male, PE Ave, Time" = numeric(run), "Male, ASPA Ave, Time" = numeric(run),
#Females
"Female, Ave, Events" = numeric(run),
"Female, Ave, Time" = numeric(run), "Female, PE Ave, Time" = numeric(run), "Female, ASPA Ave, Time" = numeric(run),
#All
"Ave, Events" = numeric(run),
"Ave, Time" = numeric(run), "PE Ave, Time" = numeric(run), "ASPA Ave, Time" = numeric(run))
#This file stores the averages of all the runs and for each parameter and the CI and Std Deviation.
finalres <- data.frame(Group = numeric(90), MeanV = numeric(90), StdDev = numeric(90), CI_L = numeric(90), CI_U = numeric(90))
#The following for loop will iterate through all the runs (all the csv's) and calculate the means, and groupings
#that we need for each variable.
for (i in 1:run) {
#Read in the run file, name it "runN".
runN = read.csv(paste("mxc run_", i, ".csv", sep = ""))
#Call the "mxc_indrun()" function, which returns the formated results, a 7 by 9 table.
run_f = mxc_indrun(home, runN, i, filename, simulation_length)
#The mxc_indrunU will return the SD of the variables for use later.
run_fU = mxc_indrunSD(home, runN, i, filename, simulation_length)
#Write the values from the result into the mulruns agg file.
#The Run Number.
mulruns_agg$RunNo.[i] = i
mulruns_aggU$RunNo.[i] = i
#All the other columns for each of the variables.
mulruns_agg[i, c(2:91)] = c(run_f[1, 3:12], run_f[2, 3:12], run_f[3, 3:12],
run_f[4, 3:12], run_f[5, 3:12], run_f[6, 3:12],
run_f[7, 3:12], run_f[8, 3:12], run_f[9, 3:12])
#Write the standard deviations.
mulruns_aggU[i, c(2:37)] = c(run_fU[1, 3:6], run_fU[2, 3:6], run_fU[3, 3:6],
run_fU[4, 3:6], run_fU[5, 3:6], run_fU[6, 3:6],
run_fU[7, 3:6], run_fU[8, 3:6], run_fU[9, 3:6])
#That ends this.
}
#Now that the multiple aggregated runs, "mulruns_agg" is made, add the means and the StdDevs
#and the Confidence Intervals to finalres.
#Add the agegroup column
finalres$Group[1:90] = colnames(mulruns_agg[,2:91])
#Add means, StdDev and Confidence Intervals.
finalres$MeanV[1:90] = apply(mulruns_agg[ ,2:91], 2, mean)
finalres$StdDev[1:90] = apply(mulruns_agg[ ,2:91], 2, sd)
#Only find CI if run is more than 1.
if (run > 1) {
finalres$CI_U[1:90] = (apply(mulruns_agg[ ,2:91], 2, Rmisc::CI))[1,]
finalres$CI_L[1:90] = (apply(mulruns_agg[ ,2:91], 2, Rmisc::CI))[3,]
}
#This is where it gets even more complex. Leave most of the ones above the same, but only change the ones in the U and L files below.
ch <- c()
for (i in 1:9) {
ch2 <- c((((i-1)*10)+1), (((i-1)*10)+5), (((i-1)*10)+6), (((i-1)*10)+7))
ch <- c(ch, ch2)
}
#Use deal SD to get the actual SD from the mulruns_aggU file, which contains the Standard Deviations.
plus_minus <- mxc_dealsd(mulruns_aggU)
#Add the SD, and the CI.
finalres$StdDev[ch] <- apply(mulruns_aggU[, c(2:37)], 2, mean) #This is likely wrong. A standard deviation is not the mean of Standard deviations.
finalres$CI_U[ch] <- finalres$MeanV[ch] + plus_minus #The CIs are correct and in order to get correct SDs work backwards from CIs.
finalres$CI_L[ch] <- finalres$MeanV[ch] - plus_minus
#Make a Third file in a better format, final_format.
groups = 90
final_form <- data.frame(Gender = numeric(groups), Age = numeric(groups),
Variable = numeric(groups), Level = numeric(groups), Values = numeric(groups))
#The following couple of lines are not the best code, but they name the columns and rows.
final_form$Gender[c(1:30, 61:70)] = "Male"
final_form$Gender[c(31:60, 71:80)] = "Female"
final_form$Gender[c(81:90)] = "All"
final_form$Age[c(1:10, 31:40)] = "6 - 9.99 yrs"
final_form$Age[c(11:20, 41:50)] = "10 - 14.99 yrs"
final_form$Age[c(21:30, 51:60)] = "15 - 18.99 yrs"
final_form$Age[c(61:90)] = "All"
final_form$Level[c(1, 11, 21, 31, 41, 51, 61, 71, 81)] = "Average Events"
final_form$Level[c(2, 12, 22, 32, 42, 52, 62, 72, 82)] = "Low (0 - 3/week)"
final_form$Level[c(3, 13, 23, 33, 43, 53, 63, 73, 83)] = "Intermediate (3 - 5/week)"
final_form$Level[c(4, 14, 24, 34, 44, 54, 64, 74, 84)] = "High (or more/week)"
final_form$Level[c(5, 15, 25, 35, 45, 55, 65, 75, 85)] = "Average Time"
final_form$Level[c(6, 16, 26, 36, 46, 56, 66, 76, 86)] = "Average PE Time"
final_form$Level[c(7, 17, 27, 37, 47, 57, 67, 77, 87)] = "Average ASPA Time"
final_form$Level[c(8, 18, 28, 38, 48, 58, 68, 78, 88)] = "Low (0 - 3hrs/week)"
final_form$Level[c(9, 19, 29, 39, 49, 59, 69, 79, 89)] = "Intermediate (3 - 7hrs/week)"
final_form$Level[c(10, 20, 30, 40, 50, 60, 70, 80, 90)] = "High (7 or more hrs/week)"
final_form$Variable[c(1:4, 11:14, 21:24, 31:34, 41:44, 51:54, 61:64, 71:74, 81:84)] = "Events"
final_form$Variable[c(5:10, 15:20, 25:30, 35:40, 45:50, 55:60, 65:70, 75:80, 85:90)] = "Time"
#Copy the relevant columns from finalres to final_form
final_form$Values = finalres$MeanV
#Do some more formatting.
#Here we want to seperate the Events and Time variables.
final_form_e = dplyr::filter(final_form, Variable == "Events")
final_form_e = subset(final_form_e, select = -Variable)
final_form_t = dplyr::filter(final_form, Variable == "Time")
final_form_t = subset(final_form_t, select = -Variable)
#Reshape the file for clarity.
final_form_events = stats::reshape(final_form_e, idvar = c("Gender", "Age"), v.names = "Values", timevar = c("Level"), direction = "wide")
final_form_time = stats::reshape(final_form_t, idvar = c("Gender", "Age"), v.names = "Values", timevar = c("Level"), direction = "wide")
#Save all three files.
write.csv(mulruns_agg, paste(home, filename, "/Raw Results ", run, " Runs.csv", sep = ""))
write.csv(finalres, paste(home, filename, "/Summary ", run, " Runs.csv", sep = ""))
write.csv(final_form_events, paste(home, filename, "/Final ", run, " Format (Events).csv", sep = ""))
write.csv(final_form_time, paste(home, filename, "/Final ", run, " Format (Time).csv", sep = ""))
#On exit setwd to home.
on.exit(setwd(home))
#Print the progress check step.
print("Multiple Runs complete for Physical Activity Variables. Moving on the the next step.")
}
SindisoNyathi/MxC documentation built on May 20, 2019, 9:13 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions mxc_mulruns
Documented in mxc_mulruns
#' @title MxC Mulruns
#' @author Sindiso Nyathi
#' @description This function will process all the runs in a single experiment given the folder name, and write results to .csv files.
#' @param home Parent directory path.
#' @param foldername String name of file with the runs eg. "Baseline"
#' @return Will write multiple csv files into the Folders with the experiments.
#' @details This function will aggregate each run into age and gender groupings, find the averages for the Time and
#' Events variables in each run and average these out across all the runs. Method is as follows:
#' Step 1: Create a dataframe to store all the group average values.
#' Step 2: Run a for loop from 1 to N (N is the number of runs in this experiment)
#' 2a: Call mxc_indrun, which will calculate individual run averages for all the variables in run N.
#' 2b: Store the resulting values in the data frame created.
#' Step 3: Calculate the overall means, SD and CI accross all the x runs.
#'
#'@seealso {\code{\link{mxc_master}}, \code{\link{mxc_indrun}}, \code{\link{mxc_dealsd}}}
#'@examples
#'mxc_mulruns(home, "Baseline")
#'mxc_mulruns(home, "Intervention 1")
#'@family
#'
#' @export
mxc_mulruns <- function(home, filename) {
#Set the working directory to input folder.
setwd(paste(home, filename, sep = ""))
#All of the runs should be in the current folder, and should be named mxc run_1.csv to mxc run_N.csv where N
#is the run number.
#Set runs based on the number of .csv files in the folder.
run = length(list.files(pattern = ".csv$"))
#Set the simulation length; 5 years. This value is important because it determines the length of time
#an adolescent of a given age was in the model.
simulation_length = 5
#check to make sure there are actually csv files in the folder.
if (length(run) == 0) {stop("Error: There are no csv files in this folder.")}
#Create the two dataframes that will store the results of these multiple runs.
#This first one stores the raw results for the N runs, so store all these variable for each run,
#each row is one run and has a column for each variable of interest.
mulruns_agg <- data.frame(RunNo. = numeric(run),
#Males
#6 - 9 yrs, 10 variables per age group.
"Male, 6 - 9, Ave, Events" = numeric(run),
"Male, 6 - 9, Events (0 - 3)" = numeric(run), "Male, 6 - 9, Events (3 - 5)" = numeric(run), "Male, 6 - 9, Events (5 or more)" = numeric(run),
"Male, 6 - 9, Ave, Time" = numeric(run), "Male, 6 - 9, PE Ave, Time" = numeric(run), "Male, 6 - 9, ASPA Ave, Time" = numeric(run),
"Male, 6 - 9, Time (0 - 3)" = numeric(run), "Male, 6 - 9, Time (3 - 7)" = numeric(run), "Male, 6 - 9, Time (7 or more)" = numeric(run),
#10 - 14 yrs
"Male, 10 - 14, Ave, Events" = numeric(run),
"Male, 10 - 14, Events (0 - 3)" = numeric(run), "Male, 10 - 14, Events (3 - 5)" = numeric(run), "Male, 10 - 14, Events (5 or more)" = numeric(run),
"Male, 10 - 14, Ave, Time" = numeric(run), "Male, 10 - 14, PE Ave, Time" = numeric(run), "Male, 10 - 14, ASPA Ave, Time" = numeric(run),
"Male, 10 - 14, Time (0 - 3)" = numeric(run), "Male, 10 - 14, Time (3 - 7)" = numeric(run), "Male, 10 - 14, Time (7 or more)" = numeric(run),
#15 - 18 yrs
"Male, 15 - 18, Ave, Events" = numeric(run),
"Male, 15 - 18, Events (0 - 3)" = numeric(run), "Male, 15 - 18, Events (3 - 5)" = numeric(run), "Male, 15 - 18, Events (5 or more)" = numeric(run),
"Male, 15 - 18, Ave, Time" = numeric(run), "Male, 15 - 18, PE Ave, Time" = numeric(run), "Male, 15 - 18, ASPA Ave, Time" = numeric(run),
"Male, 15 - 18, Time (0 - 3)" = numeric(run), "Male, 15 - 18, Time (3 - 7)" = numeric(run), "Male, 15 - 18, Time (7 or more)" = numeric(run),
#Females
#6 - 9 yrs
"Female, 6 - 9, Ave, Events" = numeric(run),
"Female, 6 - 9, Events (0 - 3)" = numeric(run), "Female, 6 - 9, Events (3 - 5)" = numeric(run), "Female, 6 - 9, Events (5 or more)" = numeric(run),
"Female, 6 - 9, Ave, Time" = numeric(run), "Female, 6 - 9, PE Ave, Time" = numeric(run), "Female, 6 - 9, ASPA Ave, Time" = numeric(run),
"Female, 6 - 9, Time (0 - 3)" = numeric(run), "Female, 6 - 9, Time (3 - 7)" = numeric(run), "Female, 6 - 9, Time (7 or more)" = numeric(run),
#10 - 14 yrs
"Female, 10 - 14, Ave, Events" = numeric(run),
"Female, 10 - 14, Events (0 - 3)" = numeric(run), "Female, 10 - 14, Events (3 - 5)" = numeric(run), "Female, 10 - 14, Events (5 or more)" = numeric(run),
"Female, 10 - 14, Ave, Time" = numeric(run), "Female, 10 - 14, PE Ave, Time" = numeric(run), "Female, 10 - 14, ASPA Ave, Time" = numeric(run),
"Female, 10 - 14, Time (0 - 3)" = numeric(run), "Female, 10 - 14, Time (3 - 7)" = numeric(run), "Female, 10 - 14, Time (7 or more)" = numeric(run),
#15 - 18 yrs
"Female, 15 - 18, Ave, Events" = numeric(run),
"Female, 15 - 18, Events (0 - 3)" = numeric(run), "Female, 15 - 18, Events (3 - 5)" = numeric(run), "Female, 15 - 18, Events (5 or more)" = numeric(run),
"Female, 15 - 18, Ave, Time" = numeric(run), "Female, 15 - 18, PE Ave, Time" = numeric(run), "Female, 15 - 18, ASPA Ave, Time" = numeric(run),
"Female, 15 - 18, Time (0 - 3)" = numeric(run), "Female, 15 - 18, Time (3 - 7)" = numeric(run), "Female, 15 - 18, Time (7 or more)" = numeric(run),
#Overall
#Males
"Male, Ave, Events" = numeric(run),
"Male, Events (0 - 3)" = numeric(run), "Male, Events (3 - 5)" = numeric(run), "Male, Events (5 or more)" = numeric(run),
"Male, Ave, Time" = numeric(run), "Male, PE Ave, Time" = numeric(run), "Male, ASPA Ave, Time" = numeric(run),
"Male, Time (0 - 3)" = numeric(run), "Male, Time (3 - 7)" = numeric(run), "Male, Time (7 or more)" = numeric(run),
#Females
"Female, Ave, Events" = numeric(run),
"Female, Events (0 - 3)" = numeric(run), "Female, Events (3 - 5)" = numeric(run), "Female, Events (5 or more)" = numeric(run),
"Female, Ave, Time" = numeric(run), "Female, PE Ave, Time" = numeric(run), "Female, ASPA Ave, Time" = numeric(run),
"Female, Time (0 - 3)" = numeric(run), "Female, Time (3 - 7)" = numeric(run), "Female, Time (7 or more)" = numeric(run),
#All
"Ave, Events" = numeric(run),
"Events (0 - 3)" = numeric(run), "Events (3 - 5)" = numeric(run), "Events (5 or more)" = numeric(run),
"Ave, Time" = numeric(run), "PE Ave, Time" = numeric(run), "ASPA Ave, Time" = numeric(run),
"Time (0 - 3)" = numeric(run), "Time (3 - 7)" = numeric(run), "Time (7 or more)" = numeric(run)
)
#This data frame only inlcuded the results that would have confidence intervals within a run, i.e. for which a mean would be taken.
#So this dataframe will store the standard deviation for these values in order to later calculate the Confidence Interval
mulruns_aggU <- data.frame(RunNo. = numeric(run),
#Males
#6 - 9
"Male, 6 - 9, Ave, Events" = numeric(run),
"Male, 6 - 9, Ave, Time" = numeric(run), "Male, 6 - 9, PE Ave, Time" = numeric(run), "Male, 6 - 9, ASPA Ave, Time" = numeric(run),
#10 - 14
"Male, 10 - 14, Ave, Events" = numeric(run),
"Male, 10 - 14, Ave, Time" = numeric(run), "Male, 10 - 14, PE Ave, Time" = numeric(run), "Male, 10 - 14, ASPA Ave, Time" = numeric(run),
#15 - 18
"Male, 15 - 18, Ave, Events" = numeric(run),
"Male, 15 - 18, Ave, Time" = numeric(run), "Male, 15 - 18, PE Ave, Time" = numeric(run), "Male, 15 - 18, ASPA Ave, Time" = numeric(run),
#Females
#6 - 9
"Female, 6 - 9, Ave, Events" = numeric(run),
"Female, 6 - 9, Ave, Time" = numeric(run), "Female, 6 - 9, PE Ave, Time" = numeric(run), "Female, 6 - 9, ASPA Ave, Time" = numeric(run),
#10 - 14
"Female, 10 - 14, Ave, Events" = numeric(run),
"Female, 10 - 14, Ave, Time" = numeric(run), "Female, 10 - 14, PE Ave, Time" = numeric(run), "Female, 10 - 14, ASPA Ave, Time" = numeric(run),
#15 - 18
"Female, 15 - 18, Ave, Events" = numeric(run),
"Female, 15 - 18, Ave, Time" = numeric(run), "Female, 15 - 18, PE Ave, Time" = numeric(run), "Female, 15 - 18, ASPA Ave, Time" = numeric(run),
#Overall
#Males
"Male, Ave, Events" = numeric(run),
"Male, Ave, Time" = numeric(run), "Male, PE Ave, Time" = numeric(run), "Male, ASPA Ave, Time" = numeric(run),
#Females
"Female, Ave, Events" = numeric(run),
"Female, Ave, Time" = numeric(run), "Female, PE Ave, Time" = numeric(run), "Female, ASPA Ave, Time" = numeric(run),
#All
"Ave, Events" = numeric(run),
"Ave, Time" = numeric(run), "PE Ave, Time" = numeric(run), "ASPA Ave, Time" = numeric(run))
#This file stores the averages of all the runs and for each parameter and the CI and Std Deviation.
finalres <- data.frame(Group = numeric(90), MeanV = numeric(90), StdDev = numeric(90), CI_L = numeric(90), CI_U = numeric(90))
#The following for loop will iterate through all the runs (all the csv's) and calculate the means, and groupings
#that we need for each variable.
for (i in 1:run) {
#Read in the run file, name it "runN".
runN = read.csv(paste("mxc run_", i, ".csv", sep = ""))
#Call the "mxc_indrun()" function, which returns the formated results, a 7 by 9 table.
run_f = mxc_indrun(home, runN, i, filename, simulation_length)
#The mxc_indrunU will return the SD of the variables for use later.
run_fU = mxc_indrunSD(home, runN, i, filename, simulation_length)
#Write the values from the result into the mulruns agg file.
#The Run Number.
mulruns_agg$RunNo.[i] = i
mulruns_aggU$RunNo.[i] = i
#All the other columns for each of the variables.
mulruns_agg[i, c(2:91)] = c(run_f[1, 3:12], run_f[2, 3:12], run_f[3, 3:12],
run_f[4, 3:12], run_f[5, 3:12], run_f[6, 3:12],
run_f[7, 3:12], run_f[8, 3:12], run_f[9, 3:12])
#Write the standard deviations.
mulruns_aggU[i, c(2:37)] = c(run_fU[1, 3:6], run_fU[2, 3:6], run_fU[3, 3:6],
run_fU[4, 3:6], run_fU[5, 3:6], run_fU[6, 3:6],
run_fU[7, 3:6], run_fU[8, 3:6], run_fU[9, 3:6])
#That ends this.
}
#Now that the multiple aggregated runs, "mulruns_agg" is made, add the means and the StdDevs
#and the Confidence Intervals to finalres.
#Add the agegroup column
finalres$Group[1:90] = colnames(mulruns_agg[,2:91])
#Add means, StdDev and Confidence Intervals.
finalres$MeanV[1:90] = apply(mulruns_agg[ ,2:91], 2, mean)
finalres$StdDev[1:90] = apply(mulruns_agg[ ,2:91], 2, sd)
#Only find CI if run is more than 1.
if (run > 1) {
finalres$CI_U[1:90] = (apply(mulruns_agg[ ,2:91], 2, Rmisc::CI))[1,]
finalres$CI_L[1:90] = (apply(mulruns_agg[ ,2:91], 2, Rmisc::CI))[3,]
}
#This is where it gets even more complex. Leave most of the ones above the same, but only change the ones in the U and L files below.
ch <- c()
for (i in 1:9) {
ch2 <- c((((i-1)*10)+1), (((i-1)*10)+5), (((i-1)*10)+6), (((i-1)*10)+7))
ch <- c(ch, ch2)
}
#Use deal SD to get the actual SD from the mulruns_aggU file, which contains the Standard Deviations.
plus_minus <- mxc_dealsd(mulruns_aggU)
#Add the SD, and the CI.
finalres$StdDev[ch] <- apply(mulruns_aggU[, c(2:37)], 2, mean) #This is likely wrong. A standard deviation is not the mean of Standard deviations.
finalres$CI_U[ch] <- finalres$MeanV[ch] + plus_minus #The CIs are correct and in order to get correct SDs work backwards from CIs.
finalres$CI_L[ch] <- finalres$MeanV[ch] - plus_minus
#Make a Third file in a better format, final_format.
groups = 90
final_form <- data.frame(Gender = numeric(groups), Age = numeric(groups),
Variable = numeric(groups), Level = numeric(groups), Values = numeric(groups))
#The following couple of lines are not the best code, but they name the columns and rows.
final_form$Gender[c(1:30, 61:70)] = "Male"
final_form$Gender[c(31:60, 71:80)] = "Female"
final_form$Gender[c(81:90)] = "All"
final_form$Age[c(1:10, 31:40)] = "6 - 9.99 yrs"
final_form$Age[c(11:20, 41:50)] = "10 - 14.99 yrs"
final_form$Age[c(21:30, 51:60)] = "15 - 18.99 yrs"
final_form$Age[c(61:90)] = "All"
final_form$Level[c(1, 11, 21, 31, 41, 51, 61, 71, 81)] = "Average Events"
final_form$Level[c(2, 12, 22, 32, 42, 52, 62, 72, 82)] = "Low (0 - 3/week)"
final_form$Level[c(3, 13, 23, 33, 43, 53, 63, 73, 83)] = "Intermediate (3 - 5/week)"
final_form$Level[c(4, 14, 24, 34, 44, 54, 64, 74, 84)] = "High (or more/week)"
final_form$Level[c(5, 15, 25, 35, 45, 55, 65, 75, 85)] = "Average Time"
final_form$Level[c(6, 16, 26, 36, 46, 56, 66, 76, 86)] = "Average PE Time"
final_form$Level[c(7, 17, 27, 37, 47, 57, 67, 77, 87)] = "Average ASPA Time"
final_form$Level[c(8, 18, 28, 38, 48, 58, 68, 78, 88)] = "Low (0 - 3hrs/week)"
final_form$Level[c(9, 19, 29, 39, 49, 59, 69, 79, 89)] = "Intermediate (3 - 7hrs/week)"
final_form$Level[c(10, 20, 30, 40, 50, 60, 70, 80, 90)] = "High (7 or more hrs/week)"
final_form$Variable[c(1:4, 11:14, 21:24, 31:34, 41:44, 51:54, 61:64, 71:74, 81:84)] = "Events"
final_form$Variable[c(5:10, 15:20, 25:30, 35:40, 45:50, 55:60, 65:70, 75:80, 85:90)] = "Time"
#Copy the relevant columns from finalres to final_form
final_form$Values = finalres$MeanV
#Do some more formatting.
#Here we want to seperate the Events and Time variables.
final_form_e = dplyr::filter(final_form, Variable == "Events")
final_form_e = subset(final_form_e, select = -Variable)
final_form_t = dplyr::filter(final_form, Variable == "Time")
final_form_t = subset(final_form_t, select = -Variable)
#Reshape the file for clarity.
final_form_events = stats::reshape(final_form_e, idvar = c("Gender", "Age"), v.names = "Values", timevar = c("Level"), direction = "wide")
final_form_time = stats::reshape(final_form_t, idvar = c("Gender", "Age"), v.names = "Values", timevar = c("Level"), direction = "wide")
#Save all three files.
write.csv(mulruns_agg, paste(home, filename, "/Raw Results ", run, " Runs.csv", sep = ""))
write.csv(finalres, paste(home, filename, "/Summary ", run, " Runs.csv", sep = ""))
write.csv(final_form_events, paste(home, filename, "/Final ", run, " Format (Events).csv", sep = ""))
write.csv(final_form_time, paste(home, filename, "/Final ", run, " Format (Time).csv", sep = ""))
#On exit setwd to home.
on.exit(setwd(home))
#Print the progress check step.
print("Multiple Runs complete for Physical Activity Variables. Moving on the the next step.")
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.