Objectives
The Lab 2 Assignment provides an opportunity to experience aspects of the Data stage of the PPDAC scientific inquiry framework. Specifically, this assignment focuses on aspects of
i. collecting data according to a previously described Plan,
ii. exploring and cleaning data in advance of further analysis,
iii. applying your understanding of vocabulary and concepts associated with summarizing and exploring data, and,
iv. summarizing data in appropriate scientific formats.
To achieve these objectives, students will need to draw on content from virtual lab meeting, and from course material covered in the first three lab topics (Intro to Lab, Lab 1 and Lab 2) and lecture topics up to and including topic 5: Summarizing and Exploring Data.
Background for Assignment
In the first Lab Assignment, you were introduced to a little background information that led/explained the logic behind the following Research Objective for 2244 labs:
In Lab Assignment 1, you were tasked with characterizing the Problem of interest and creating a Plan for a sampling and study design to collect data to address a specific research question related to this Objective. Rather than have each of you implement your described Plan, we will use published data from a research study with a similar objective:
Jeter PE, Haaz Moonaz S, Bittner AK, Dagnelie G (2015) Ashtanga-Based Yoga Therapy Increases the Sensory Contribution to Postural Stability in Visually-Impaired Persons at Risk for Falls as Measured by the Wii Balance Board: A Pilot Randomized Controlled Trial. PLoS ONE 10(6):e0129646. doi:10.1371/journal.pone.0129646.
An annotated pdf of the paper is provided with the lab material for this assignment. You should read up to page 10 (the Abstract, Introduction and Methods (except sections on Secondary Outcomes, Instrumentation and Statistical Analysis). It is not necessary for you to understand every technical detail of the paper, but you should understand
• the sampling design,
• study design,
• methods of randomization, replication, and control
• how the explanatory and response variables were measured (it is okay to somewhat confused about the calculations of the Stability Indices, but you should understand what they are comparing.)
Data for this lab are presented in two files, balance_data.csv contains the raw primary outcomes data and demographic.csv contains some demographic data about the participants. The data description file includes details about symbols and units used in the two data files. You should familiarize yourself with the information in that file and/or keep it handy as you work with the data. It will likely be useful as you answer the questions on this Assignment, as well as some questions on future Assignments in the course.
Instructions for Assignment
To help you approach this Assignment in a logical/organized fashion, you are encouraged to follow these steps (in the order presented). Ask questions about any step if you don’t understand the information!
1. Read the Abstract, Introduction, and the sections of the Methods so you understand the sampling and study design, and the dataset with which we are working.
2. Read through the remainder of this Assignment file, including the “Reminders/Tips for Success”, the Assignment Questions, and the comments related to Marking Rubrics so that you know what you are being asked to do. Be sure to ask questions (e.g. during your section’s Zoom meeting, by OWL message to your lab TA, or on the Forum) if you need clarification!
3. Open the “Answer template” file. Use this file to type/enter your answers to the Assignment Questions; it is set up with the proper headings for this Assignment; you just need to input your answers (use whatever space you need to do so). Refer back to any feedback you received from the “Practice Lab Assignment” on the formatting of your assignments.
4. Import the data file provided, “balance_data.csv” and “demographic_data.csv” into R or R Studio as a dataframes called balance_data and demographic_data, respectively. If you refer to these names in your R code at any point in your assignment, we will assume that they are the files that you were provided. This way, if you choose to save the data file under a different name that we don’t recognize (i.e. other than balance_data or demographic_data), you won’t be penalized if you always refer to it in code as balance_data or demographic_data. It might be useful to check the structure of the data using a function like str() to ensure it imported properly and/or is consistent with the quality check information provided in the Data Description file.
5. Explore the data a little bit to get familiar with it. To do this, consider the data types of the variables, and use appropriate numerical and/or graphical summaries to explore the distribution of individual variables, and/or the relationship between variables. You may also find it helpful to simply View() the datafile (e.g. scroll through the rows to get a visual sense of the data).
6. Answer the Assignment Questions (below).
Reminders/Tips for Success
1. Make interpreting your R code easy and ensure that it is functional. The best approach is to generate an R script file for each numbered Question that requires use of R in any way. Put everything in that R script file that takes you from attaching the dataset to completing the question. Annotate your more complicated lines of code with #comments. Then, simply copy/paste the contents of that script file into your answer (and include the Output if applicable) when asked for R code. We should then be able to copy/paste what you’ve included, and the code should run without problems!
2. We only know names/variables that are part of the original data files. So, when we are looking through/grading your R code, if we see a name for a variable or datafile that is NOT part of the original data files (e.g. you’ve renamed a variable or created a new one), you must provide information on what that R object represents. As discussed during the second in-lab session, this is best achieved by simply providing the line(s) of R code (plus #comments!) used to create that new object. Failing to provide such new R object definitions is one way to not be awarded full marks for your R code.
3. Your answers should be written specifically for the research study/context (in terms of variables, sample, units, measurements, etc.) with which we are dealing. For example, it’s insufficient to talk about the “response variable”; we should be talking about the actual name of that variable. Being specific means using the context of the research; a sentence like, “the distribution of student heights in my sample of 2244 students needs to be symmetric” is explicit about what needs to be symmetric AND uses the vocabulary of the study.
4. Demonstrate your understanding of course content through application, not definition. A question like Question 3b, which asks you to compare and discuss something with reference to particular course concepts (e.g. sampling variability) requires an application of those course concepts to the current scenario/situation. That means that simply providing the definition of those concepts is not going to result in points awarded for the Question. Your answers should demonstrate you understand that concept and why it applies (or doesn’t!) or is useful in the particular context.
Assignment Questions
Question 1.
The first step in working with a dataset is “cleaning” the data. This involves inspecting the dataset for potential errors and ensuring that our software has imported the data completely and in a manner consistent with the nature of the data.
a) Use the str() function in R (as introduced in Online R Module 1) to quickly inspect the dataset. Using your knowledge of types of variables (e.g. quantitative, ordinal, ratio, etc.) and the labelling of data types in R (e.g. character, factor, numeric [num], integer [int]) to check that all variables have been imported and identified correctly by R (hint: go review the variable descriptions from the Data Description file so you know what kind of variable each should be, then compare this to what R has identified). Write R code that will re-class any variables that are mis-classed by R.
b) Use R to create graphs to help you inspect the dataset for outliers and missing values. You will discover that at least one variable has an outlier (or more than one). Identify ONE (1) variable in which there is an outlier in the dataset.
c) Briefly identify the most plausible reason for why the outlier exists in the variable you identified, and explain why you suspect that is the reason.
d) Briefly describe the most appropriate method of dealing with the outlier you identified, and justify your decision.
e) Report the R code(s) plus output that you used that explicitly helped you find/identify the outlier for the variable that you have discussed. If you graphed more than one variable separately, you only need to include the code that produced the graph which showed the outlier(s) you identified in 1a.
question 2.
a) In Online Lab Module on data manipulation, you were introduced to the distinction between datafiles organized in the ‘wide’ vs. ‘long’ formats. In what format is our balance_data.csv file? Briefly describe/explain the specific elements of the datafile’s structure that led you to your answer.
b) Covert the data to the other format. Include the R code you used to convert and show the str() of the resulting data frame.
Question 3.
In most research involving humans, researchers describe the ‘demographics’ of their sample (i.e. age, gender, and other characteristics, etc.). This description often occurs because the researchers want to highlight the degree to which their sample represents (or possibly deviates from) the population of interest, or, to identify the similarity/differences between their sample and samples used in related research. According to the US Census Bureau, the population of the United States is 50.7% female and has a median age of 38.2 years. The National Research Council (US) Working Group on Mobility Aids for the Visually Impaired and Blind reports that the median age of legally blind Americans is greater than 64 years. A systematic review study by Abou_Gareeb and colleagues (Abou-Gareeb I, Lewallen S, Bassett K, Courtright P. Gender and blindness: A meta-analysis of population-based prevalence surveys. Ophthalmic Epidemiol. 2001;8:39–56.) found that after controlling for age, women are approximately 40% more likely to be legally blind than men.
a) Use R to calculate the median age and proportion female of all study participants. Report and
describe the values you calculated, and the R code(s) you used to compute the summaries.
b) Briefly compare and discuss whether you think our sample has characteristics consistent with the population of interest based on the information as described above. Be sure to apply your understanding of sampling strategies, sampling variability, and sampling error in your discussion.
Question 4.
When conducting studies that involve comparing two or more groups, researchers commonly compare the groups at a ‘baseline’ stage of the study. In this question, you will undertake this type of comparison visually with a graph.
a. Create a SINGLE (i.e. one pane) graph in R, that can be used to compare groups in our data to address the following question: Directly before beginning the Yoga treatment, do the individuals in the randomly created experimental groups differ in with respect to their MTV of COP in the Firm_EO condition? Make sure that all axes on your graph are correctly labelled including units.
b. Provide all the R code you used to generate your graph (with any # comments necessary to help us interpret the code; be sure to include any preliminary data manipulation, variable creation, etc. that you did!).
c. Graphs that appear in scientific papers are accompanied by legends to fully describe all variables, axes and symbols included in the graph. For example:
CS 340 Milestone One Guidelines and Rubric Overview: For this assignment, you will implement the fundamental operations of create, read, update,
Retail Transaction Programming Project Project Requirements: Develop a program to emulate a purchase transaction at a retail store. This
7COM1028 Secure Systems Programming Referral Coursework: Secure
Create a GUI program that:Accepts the following from a user:Item NameItem QuantityItem PriceAllows the user to create a file to store the sales receip
CS 340 Final Project Guidelines and Rubric Overview The final project will encompass developing a web service using a software stack and impleme
