For the exercises in this lab it is expected that you will work in groups as it will expose you to different views, ideas and opinions. However, please make an effort to complete your own report by typing your own code and (most importantly) expressing your own conclusions and interpretations.
The data set ToothGrowth.txt has measurements of tooth
growth (len) of guinea pigs for different dosages of Vitamin C (dose)
and two different delivery methods (supp). The response is the length of
odontoblasts (len) in each of 10 guinea pigs at each of three dose
levels of Vitamin C (0.5, 1, and 2 mg) with each of two delivery methods
(orange juice or ascorbic acid). (McNeil, et al, 1977) References:
McNeil, D. R. (1977) Interactive Data Analysis. New York:
Wiley. Perform a two-way analysis of variance of tooth growth modelled
by dosage and delivery method. The following questions help you with
this analysis.
library(ggplot2)
library(tidyr)
library(datasets)
# The ToothGrowth dataset is loaded in the 'datasets' package
tooth = ToothGrowth
ggplot(tooth, aes(y = len, x = supp, colour = factor(dose))) + geom_boxplot() + theme_classic() +
xlab("Supplement") + ylab("Length") + labs(colour = "Supplement")VCdata = tooth[tooth[, "supp"] == "VC", ]
dim(VCdata)
head(VCdata)
boxplot(len ~ dose, VCdata)
aov1 = aov(len ~ dose, VCdata)
summary(aov1)
aov2 = aov(len ~ factor(dose), VCdata)
summary(aov2)
ggplot(VCdata, aes(y = len, x = factor(dose))) + geom_boxplot() + theme_classic() +
xlab("Dose") + ylab("Length")
## Another way of fitting anova in R
fit = lm(len ~ factor(dose), VCdata)
anova(fit)interaction.plot as well as
in ggplot.# Change dose to factor
tooth$dose = as.factor(tooth$dose)
## Without ggplot
attach(tooth)
interaction.plot(dose, supp, len)
detach()
## With ggplot
ggplot(tooth, aes(x = factor(dose), y = len, colour = supp)) + geom_boxplot() + stat_summary(fun.y = mean,
geom = "line", aes(group = supp)) + theme_classic() + xlab("Dose") + ylab("Length") +
labs(colour = "Supplement")tooth.aov = aov(len ~ supp * dose, tooth)
summary(tooth.aov)
tooth.aov.additive = aov(len ~ supp + dose, tooth)
summary(tooth.aov.additive)
anova(tooth.aov, tooth.aov.additive)Only this section needs to be included in your report. This report will be due at the end of Week 6, Friday 11:59pm 29th March.
While I expect that you should explore the ANZdata, I am not opposed to you submiting a report on a dataset that you are incredibly engaged with.
Report purpose
The purpose of this report is to:
Provide you with an opportunity to apply the methods and skills you have been exposed to in class.
Create a document that can aid you when asking for feedback from tutors.
Practice creating reports for your mid-semester exam.
Because of this, do not feel restricted to using the dataset I have provided. Feel free to download any dataset that you find interesting and apply what you have learnt in class.
Report guidelines
There are no hard and fast guidelines to the final content of your submitted lab reports. For this lab you will be assessed on your ability to generate statistical questions, explore these with graphical summaries and interpret your findings. Your report will also need to be well-presented:
It is expected that your report will construct and communicate an interesting story in 4 - 6 paragraphs (ish). To do this, you should be a ‘bad’ scientist and explore the data until you find something that you think is interesting, or, can use to address the marking criteria. When preparing your report always think “is your report something that you would be proud to show your friends?”, “would your family be interested in the conclusions you made?” and “would they find it easy to read?”
Marking criteria
Lab instructions
ANZDATA is the Australia and New Zealand Dialysis and Transplant Registry. The Registry records the incidence, prevalence and outcome of dialysis and transplant treatment for patients with end stage renal failure. ANZDATA is located at The Royal Adelaide Hospital, South Australia.
Use ANOVA to perform some hypothesis tests.
As some example questions, consider comparing
Also consider looking for interaction effects
What are your conclusions? Make sure to check your assumptions.
Tip: If your data is right-skewed, try a square-root or a log transform. Does this alter your conclusion? Why?
Let’s start by reading in the data.
library(tidyverse)
# set your working directory to the same location as the data.
# setwd('/dataLocation/')
patientData <- read.csv("42264_AnzdataPatients.csv")
transplantData <- read.csv("42264_AnzdataTransplants.csv")
creatineData <- read.csv("42264_AnzdataTransplantSerumCreatinine.csv")
# For simplicity let's ignore patient's multiple transplants. What does the !
# do?
transplantData2 <- transplantData[!duplicated(transplantData$id), ]
# Join patientData and transplantData
Data <- full_join(patientData, transplantData2, by = "id")
# Extract some interesting variables
use <- c("gendercode", "latereferralcode", "creatinineatentry", "height", "weight",
"smokingcode", "cancerever", "chroniclungcode", "coronaryarterycode", "peripheralvascularcode",
"cerebrovasularcode", "diabetescode", "graftno", "transplantcentrestate", "recipientantibodycmvcode",
"recipientantibodyebvcode", "donorsourcecode", "donorage", "donorgendercode",
"ischaemia", "ageattransplant", "hlamismatchesa", "hlamismatchesb", "hlamismatchesdr",
"hlamismatchesdq", "maxcytotoxicantibodies", "currentcytotoxicantibodies", "timeondialysis",
"transplantstatus")
Data <- Data[, use]
# Recode some NA
Data[Data == ""] = NA
Data[Data == "-"] = NA