More sample exams

  2020/08/18

  certify

  Greg Wilson

To help everyone who is preparing to certify as an RStudio instructor, here are examples of the kinds of questions we have been using recently. Candidates can use any reference material they want (books, online resources, old code, or YouTube videos), but may not ask another person for help, and must complete the exams in 90 minutes each. (Most people schedule them at least a couple of weeks apart rather than back to back.)

Tidyverse Exam

Brendan Cullen has posted a solution guide with source available on GitHub.

Basic Operations

  1. Read the file person.csv and store the result in a tibble called person.

  2. Create a tibble containing only family and personal names, in that order. You do not need to assign this tibble or any others to variables unless explicitly asked to do so. However, as noted in the introduction, you must use the pipe operator %>% and code that follows the tidyverse style guide.

  3. Create a new tibble containing only the rows in which family names come before the letter M. Your solution should work for tables with more rows than the example, i.e., you cannot rely on row numbers or select specific names.

  4. Display all the rows in person sorted by family name length with the longest name first.

Cleaning and Counting

  1. Read the file measurements.csv to create a tibble called measurements. (The strings "rad", "sal", and "temp" in the quantity column stand for “radiation”, “salinity”, and “temperature” respectively.)

  2. Create a tibble containing only rows where none of the values are NA and save in a tibble called cleaned.

  3. Count the number of measurements of each type of quantity in cleaned. Your result should have one row for each quantity "rad", "sal", and "temp".

  4. Display the minimum and maximum value of reading separately for each quantity in cleaned. Your result should have one row for each quantity "rad", "sal", and "temp".

  5. Create a tibble in which all salinity ("sal") readings greater than 1 are divided by 100. (This is needed because some people wrote percentages as numbers from 0.0 to 1.0, but others wrote them as 0.0 to 100.0.)

Combining Data

  1. Read visited.csv and drop rows containing any NAs, assigning the result to a new tibble called visited.

  2. Use an inner join to combine visited with cleaned using the visit_id column for matches.

  3. Find the highest radiation ("rad") reading at each site. (Sites are identified by values in the site_id column.)

  4. Find the date of the highest radiation reading at each site.

Plotting

  1. The code below is supposed to read the file home-range-database.csv to create a tibble called hra_raw, but contains a bug. Describe and fix the problem. (There are several ways to fix it: please use whichever you prefer.)

    hra_raw <- read_csv(here::here("data", "home-range-database.csv"))

  2. Convert the class column (which is text) to create a factor column class_fct and assign the result to a tibble hra. Use forcats to order the factor levels as:

    1. mammalia
    2. reptilia
    3. aves
    4. actinopterygii

  3. Create a scatterplot showing the relationship between log10.mass and log10.hra in hra.

  4. Colorize the points in the scatterplot by class_fct.

  5. Display a scatterplot showing only data for birds (class aves) and fit a linear regression to that data using the lm function.

Functional Programming

  1. Write a function called summarize_table that takes a title string and a tibble as input and returns a string that says something like, “title has # rows and # columns”. For example, summarize_table('our table', person) should return the string "our table has 5 rows and 3 columns".

  2. Write another function called show_columns that takes a string and a tibble as input and returns a string that says something like, “table has columns name, name, name". For example, show_columns('person', person) should return the string "person has columns person_id, personal_name, family_name".

  3. The function rows_from_file returns the first N rows from a table in a CSV file given the file’s name and the number of rows desired. Modify it so that if no value is specified for the number of rows, a default of 3 is used.

    rows_from_file <- function(filename, num_rows) {
  readr::read_csv(filename) %>% head(n = num_rows)
}

rows_from_file("measurements.csv") # should show 3 rows

  4. The function long_name checks whether a string is longer than 4 characters. Use this function and a function from purrr to create a logical vector that contains the value TRUE where family names in the tibble person are longer than 4 characters, and FALSE where they are 4 characters or less.

    long_name <- function(name) {
  stringr::str_length(name) > 4
}

Wrapping Up

  1. Modify the YAML header of this file so that a table of contents is automatically created each time this document is knit, and fix any errors that are preventing the document from knitting cleanly. 
    ---
title: "Tidyverse Exam Version 2.0"
output:
html_document:
    theme: flatly
---

Teaching Exam

Demonstration Lesson

Present the demonstration lesson you have developed for this examination.

Formative Assessment

You are teaching a one-day introductory workshop on the tidyverse to learners with little or no previous programming experience. You have shown them how to create a dplyr pipeline and how to use the basic verbs select, filter, and mutate, as well as how to create simple plots using ggplot. The next step in your lesson shows them group_by and summarize.

  1. Create a multiple choice question to test their understanding of these two functions. Include one right answer and at least two wrong answers, and explain clearly what misconceptions the wrong answers are intended to diagnose.

  2. Create a short fill-in-the-blanks coding exercise to test their ability to use these two functions in a dplyr pipeline. Provide the explanatory text you would give the learners and the template code they would fill in, and explain what answers you expect and what errors you expect learners to make.

Teaching Methods

To support a popular online class for people who are learning how to use regular expressions in R, your colleague has suggested having the learners grade each other’s exercises.

  • Each learner submits their code by pasting it into an online system.

  • They are then shown the submissions of 3 other learners, one at a time.

  • For each submission, they are able to put a + or - beside each line of code to indicate whether they like it or don’t like it. (They don’t have to mark or comment on lines, but are not able to submit their review unless they have marked at least 5 lines.)

  • They are also able to add an overall paragraph-length comment on each submission.

Please describe two strengths and two weaknesses of this tool from the point of view of the learner and from the point of view of the teacher. Please connect your points to specific aspects of educational theory where you can.

Mental Models

You are preparing a lesson on handling missing values in data science. Draw a concept map with 4-5 concepts and 6-8 links.

Being an Ally

For each of the scenarios below, describe three things you would do in escalation order (i.e., the first thing you would try, then what you would do if that didn’t work, and then your final option).

Scenario 1

You have been sent to a company to deliver a week-long introduction to R and the tidyverse to a team of 8 statisticians who have been using SAS for many years. Most of them seem interested, but one is obviously not: they are answering email during lessons, not bothering to do exercises, shrugging off questions, etc.

You spoke with them at the end of the first day to say that their behavior is demotivating other learners. After telling you that they don’t think the class is worthwhile—they can do everything they need to in SAS and they don’t see why the company is forcing them to switch—they promised to do better. However, it is now mid-morning on day 2 and if anything their behavior is worse. What are the next three steps you would take, assuming that they continue to promise to make changes but actually don’t?

Scenario 2

You are co-teaching a workshop at a conference with a senior (relatively famous) data scientist you have never met in person before. After an hour, you notice that they only ever ask male students to share their work with the class. You have pointed this out as gently as you can and they have become very defensive. What are the next steps you would take, assuming that each step makes them even more defensive or argumentative?

Feedback

Watch 4 minutes of the video embedded below and list feedback you would give the presenter about what they’re doing well and what they could improve.

https://youtu.be/whmLRd5nibc?t=1265

Content Presentation
Positive
Negative
Contents