Assignment submission

.Rhistory

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+setwd("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11")
+x = '/hi/cd'
+is.character(x)
+if(TRUE && TRUE){print('jho')}
+x = FALSE
+is.boolean(x)
+booltype(x)
+logical(x)
+is.logical(x)
+x = ','
+is.character(x)
+raw <- read.table(file = 'wages.csv',header = FALSE,sep = ',')
+View(raw)
+View(raw)
+raw <- read.table(file = 'wages.csv',header = TRUE,sep = ',')
+View(raw)
+View(raw)
+data <- raw[,3]
+data2 <- raw[,5]
+length(data)
+nrow(data)
+nrow(raw)
+ncol(raw)
+nav <- is.na(data)
+sum(checkNA)
+sum(nav)
+sum(!is.numeric(data))
+sum(is.numeric(data))
+is.numeric(data)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+data <- read.csv('wages.csv')
+data <- data[,3]
+sd(data)/mean(data)
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 10)
+source("~/My Files/Notes/NDU/Coursework/Biocomputing/Biocomp_tutorial11/function.R")
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+myfunction(dir = 'wages.csv',sep = ',', col = 3)
+myfunction(dir = 'wages.csv',sep = ',', col = 3,smallDataOverride = TRUE)

function.R

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+myfunction <- function(dir,sep=',',col=1,smallDataOverride=FALSE){
+  #This function takes in four arguments:
+  #   dir: directory to the file that is to be imported, in char
+  #   sep: separation character
+  #   col: specific column number to consider. Defaults to 1
+  #   smallDataOverride: lets the function continue even with less than 50 points, defaults to FALSE
+
+
+  #Perform input argument check
+  #============================
+  if(!is.character(dir) | !is.character(sep) | !is.logical(smallDataOverride) | !is.numeric(col)){
+    print('One or more input values are of wrong data type')
+    return()
+  }
+
+
+  #import data and take the relevant column. Check if the num of column is correct
+  #============================
+  raw <- read.table(file = dir, header = TRUE, sep = sep)
+  if (ncol(raw) < col | col <= 0){
+    print('ERROR: Invalid column number')
+    return()
+  }
+  data <- raw[,col]
+
+
+  #Check the data size, and override if given the option
+  #============================
+  if (length(data) <= 50 & !smallDataOverride){
+    print('ERROR: Data size under 50. Override this with smallDataOverride argument')
+    return()
+  }else if (length(data) <= 50 & smallDataOverride){
+    print('WARNING: The data set is small. Results might not be accurate')
+  }
+
+
+  #check if the data includes NA values or non numeric values
+  #============================
+  if(sum(is.na(data)) != 0){
+    print('ERROR: Data includes NA values')
+    return()
+  }
+  if(sum(!is.numeric(data)) != 0){
+    print('ERROR: Data includes non numeric values')
+    return()
+  }
+
+
+  #Calculate and return
+  #============================
+  return(sd(data) / mean(data))
+}