Numerical Answers with R

This page is specific to the R questions (without coding). The objectives are:

  • Use the necessary R function in the server.py to generate the solutions, and grade the questions
  • Specify the randomized variables in the server.py
  • Specify the specific files (e.g., figure) in the server.py

Overview

The easiest way to create a R question (without coding) is by copying an existing R question, and change certain files. Then you don’t need to create the UUID by yourself.

Note: Each UUID will be assigned to a question only.

Step 1: Copy a R question

  • Follow the Step 1 to Step 4 in the Routine work. Then click PrarieLearn logo (next to Admin) in the upper left.
  • Click a course such as CEE 202: Engineering Risk & Uncertainty in the Courses (not Course instances) list.
  • Click the Questions (next to Issues) on the top line.
  • Find a question you want to copy (for example: AS4_Prob5_2020_AngTang).
  • Click Settings between Preview and Statistics.
  • Click Make a copy of this question
  • Click Change QID

Step 2: Modify the questions

Before you modifying the question, I strongly suggest creating a spreadsheet to keep track of the questions (including title, topic, tags) and corresponding UUID.

Note: Each question folder contain the following files

Folder/File Name Usage
info.json The information of the question such as title, topic, tags, and uuid.
question.html The main body of the question
server.py The solution to the question, but it also species
clientFilesQuestion Save the figures for the question.
clientFilesQuestion
/dist.png		 The figure needes to be added to the question

info.json

  • Click Edit under Settings
  • Define the title, topic, tags, and type

server.py

  • Click Files (under PrairieLearn in the upper left) → Edit the server.py, then you need to finish the following tasks:
import rpy2.robjects as robjects
import prairielearn as pl

def generate(data):
    # here is the start the R function
    values = robjects.r("""
    # prob 1
    #a_r = 4.0
    a_r = sample(seq(3.8,4.3,0.1),1)
    ans_a_r = 1 + a_r
    
    # Export
    list(  
         ans = list(a=a_r,
                    answer_a=ans_a_r)
       )
    """)
    # here is the end of the R function
    ans = values[0]
    # Convert from R lists to python dictionaries
    ans = { key : ans.rx2(key)[0] for key in ans.names }
    # Setup output 
    data['correct_answers'] = ans
    # Setup randomized variables
    data["params"] = ans
    # define the figure name
    image_name = "dist.png"
    data["params"]["image"] = image_name
  • Change the randomized variable using a_r=sample(seq(start,end,interval),1)
  • Change the answers (ans_a_r, ans_b_r, …), and export (list(...))

Note: a corresponds to ${{params.a}}$, answer_a corresponds to answers-name="answer_a" in the question.html

  • Change the image_name (if you have figures(s))

question.html

  • Click Files (under PrairieLearn in the upper left) → Edit the question.html, then you need to finish the following tasks:
<pl-question-panel>
	<p> 
	This is the problem statement.
	</p>
	<pl-figure file-name={{params.image}} directory="clientFilesQuestion"></pl-figure>
</pl-question-panel>

<pl-question-panel><hr></pl-question-panel>
<pl-question-panel>
	<p>
	    (a) Determine the probability that the settlement will exceed ${{params.a}}$ cm.
	</p>
</pl-question-panel>

<div class="card my-2">
  <div class="card-body">
    <pl-question-panel>
      <p>The answer is: (0.XX)</p>
    </pl-question-panel>
    <pl-number-input answers-name="answer_a" weight = "3" comparison="relabs" rtol="0.01" atol="0.01"></pl-number-input>
  </div>
</div>
  • Replace “This is the problem statement.” with your problem statement
  • Replace ${{params.a}}$ with your randomized variable from server.py
  • Replace "answer_a" with your answer from server.py
  • Define the tolerance. Sotiria suggests that:
    • for the answer (0.XX), comparison="relabs" rtol="0.01" atol="0.01"
    • for the answer (0.XXX), comparison="relabs" rtol="0.001" atol="0.001"

Alternatives: Integer

Reference: (link)

If the answer is an integer, you need to replace

<pl-number-input answers-name="answer_a" weight = "3" comparison="relabs" rtol="0.01" atol="0.01"></pl-number-input>

with

<pl-integer-input answers-name="answer_a" weight = 3></pl-integer-input>

Where the answer “answer_a” has to be an integer.

Step 3: Test your questions

  • Click Preview to test
  • Click New variant to have another test

Step 4: Commit and push the changes

  • Using Git to commit and push the changes

Note: You may do this after you finish all the questions

Step 5: Sync and test

  • Log in the website https://prairielearn.engr.illinois.edu/pl/, and select your course
  • Click Sync, then Pull from remote git repository
  • Find your questions by clicking Questions and test them again

Appendix: Answers from R function output

The following server.py shows the workflow of doing a simple linear regression

import rpy2.robjects as robjects
import prairielearn as pl

def generate(data):
    # here is the start the R function 
    values = robjects.r("""

    # Read in the data
    my_data = read.csv(paste0('./clientFilesQuestion/mydata.csv'))

    # Form a model
    lm_model = lm(y ~ x, data = my_data)
    beta_hats = coef(lm_model)
    
    # View all the attributes, e.g., adj.r.squared
    #attributes(summary(lm_model))
    
    # New predictions
    #new_data <- data.frame(x = c(20))
    #predicted = predict(lm_model,newdata=new_data,interval="confidence", level=0.95)
  
    # Export
    list(  
         ans = list(beta1 = beta_hats[2],
                    beta0 = beta_hats[1],
                    pvalue = summary(lm_model)$coefficients[2,4],
                    slope_se = summary(lm_model)$coefficients[2,2],
                    r_mult=summary(lm_model)$r.squared,
                    lo=confint(lm_model)[2,1],
                    hi=confint(lm_model)[2,2],
                    pred_low = predicted[2],
                    pred_high = predicted[3],
                    corr = cor(x,y))
        )
    """)
    # Extract parameter and answer lists
    ans = values[0]
    # Convert from R lists to python dictionaries
    ans = { key : ans.rx2(key)[0] for key in ans.names }
    # Setup output dictionaries
    data['correct_answers'] = ans