Psyc240_HWanalysis. Compare means across TA's

Psyc240_HWanalysis

hwanalysis <- function(hwnumber = NULL, base_graphing=FALSE) { 

  #   A standalone R function, so to use it, use source("hwanalysis.R") from the command line and then call hwanalysis() with 
  #     1). the numeric number of the HW you want to analyze grades for and 
  #     2). the graphing style you want. The default base_graphing= FALSE uses ggplot (requires ggplot2 and dplyr be
  #         installed). base_graphing=TRUE gives you old school base R plots, thus no additional dependencies. (but we should all
  #         have ggplot2 and dplyr anyway right?)

  if (is.null(hwnumber)) {
    stop('hwnumber must be an integer which defines which hw you want to analyze grades for!')
  }

  hw <- read.csv(file.choose())
  hw <- hw[hw$Lab.instr!='NULL',]
  col <- paste('HW',as.character(hwnumber),sep='')
  aov.hw <- aov(hw[[col]] ~ hw$Lab.instr)
  

  if (base_graphing==TRUE) {  
    #base
    par(mfrow = c(2,2))
    hist(hw[hw$Lab.instr == "Andrea",col], breaks = 20, main = "Andrea", xlab = col)
    hist(hw[hw$Lab.instr == "Fiona",col], breaks = 20, main = "Fiona", xlab = col)
    hist(hw[hw$Lab.instr == "Wanette",col], breaks = 20, main = "Wanette", xlab = col)
    hist(hw[hw$Lab.instr == "Will",col], breaks = 20, main = "Will", xlab = col)
  } else if (base_graphing == FALSE) {
    #new school
    library(ggplot2)
    p <- ggplot(hw, aes(x=HW6,fill=Lab.instr))+
      geom_histogram(alpha=.6,position='identity',binwidth=2.5)
    # use this if you don't want to source by hand and include the print.eval=T option
    print(p) 
    library(dplyr)
    # means by TA
    call <- substitute(summarise(group_by(hw, Lab.instr),mean_score=round(mean(col, na.omit=T), 1)), 
                       list(col = as.name(col)))
    tmp<-eval(call) 
    print(tmp)
  }
  print(summary(aov.hw))
  
 }