Hello, World!

Week 1 Overview - Introductory Material

Overview

1. Syllabus, Objectives & Opener
2. Intro to/Foundations of Qualitative Research
3. Let’s do some data science!
4. Meet the Toolbox

Prepare

• Getting to know you survey

Materials

• 📂 Week 1 Google Drive folder
• 👀 Syllabus
• 📗 R for Non-Programmers (R4NP) Ch. 1.1, 1.3, 1.4, 2)

Assignments

• Complete 00-hello_world.qmd, render to PDF and submit to the Class activity submission folder in Google Drive.

Part 1: Syllabus, Objectives & Opener

Slides from class

Meet each other!

Syllabus & course objectives

By the end of this course, you’ll be able to:

• Contextualize project foundations, goals, and data through consultation with subject matter experts and researchers
• Identify and apply a variety of methodological tools to conduct qualitative research
• Conduct systematic qualitative data analysis — coding schemes, theme generation, patterns
• Implement reproducible qualitative workflows in R
• Evaluate and responsibly use AI/LLM tools in qualitative analysis
• Practice public scholarship — present analyses to campus/community decision-makers

Navigating this course in Canvas

• Each instructor has separate Canvas classrooms. Not our fault, but we’re stuck.
  • Students were semi-randomly split into the two separate sections.
  • The divide is artificial
  • We will strive to duplicate all items put into Canvas
• Where to find things –> Logistics page

Part 2: Intro to/Foundations of Qualitative Research

Why wicked problems demand cross-disciplinary methods.

Part 3: Let’s do some data science!

Data Collection

• Yesterday we collected some data from you!
• Today we’re going to explore that data together, following the data science cycle.
• Didn’t take it yet? Scan the QR code and do it now!

The analysis cycle

You took a survey, built in Google Forms:

We want to explore that data and get to know you!

Import

Load some packages

More on what packages are, in a nutshell “get your tools out of the toolbox”.

Click on the down arrow to expand the box and see the code!

library(tidyverse)      # for data wrangling and visualization
library(googlesheets4)  # to import data directly from Google
library(scales)         # for better axis labels
library(tidytext)       # for handling text data

Import the data

We could download the data and save it as a csv file on our computer, edit out the student names, name it survey-anonymized.csv and save it in a folder called data. Then we would import it into R using the read_csv() function:

survey <- read_csv("data/survey-anonymized.csv")

Alternatively, we can authorize R to directly read the Google Sheet that holds the responses from the Google Form you filled out.

survey <- read_sheet("https://docs.google.com/spreadsheets/d/1nUvOJN8z91c3dPN5UrV653qJxlbMCvvj846JDgOscdM")

Take a peek at the data

survey

# A tibble: 28 × 7
   location     programming_experience qual_research_experi…¹ qual_data_analysis
   <chr>        <chr>                  <chr>                  <chr>             
 1 Butte County A little — I’ve writt… A little - for exampl… None              
 2 Rest of Cal… None                   Some - I've worked on… A little - for ex…
 3 Outside of … None                   Some - I've worked on… Some - I've worke…
 4 Butte County Some — I’ve worked on… Some - I've worked on… A little - for ex…
 5 Broader Nor… A little — I’ve writt… A little - for exampl… A little - for ex…
 6 Butte County None                   Some - I've worked on… A little - for ex…
 7 Broader Nor… None                   None                   None              
 8 Rest of Cal… None                   A lot - I've worked o… A lot - I've work…
 9 Butte County Some — I’ve worked on… Some - I've worked on… Some - I've worke…
10 Broader Nor… A little — I’ve writt… A little - for exampl… A little - for ex…
# ℹ 18 more rows
# ℹ abbreviated name: ¹​qual_research_experience
# ℹ 3 more variables: data_interests <chr>, learn_best <chr>, hopes <chr>

Visualize

One way to make sense of data collected is to visualize it.

Multiple Choice Questions

We asked you a couple multiple-choice question where you could only pick one option:

Where are you from?

• Butte County
• Broader North State (Sac & up)
• Rest of California
• United States - Outside California
• Outside of the United States

survey |>
  count(location) |>
  mutate(prop = n / sum(n)) |>
  ggplot(aes(y = location, x = prop))+
  geom_col(show.legend = FALSE) +
  scale_y_discrete(labels = label_wrap(20)) +
  scale_x_continuous(labels = percent_format(accuracy = 1), breaks = c(0, 0.25, 0.5)) +
  labs(
    title = "Where are you from?",
    y = NULL,
    x = "Count"
  ) 

“How much experience do you have with qualitative research, broadly (e.g. project conceptualization, identifying a sampling strategy, interview instrument design, survey design, data management, etc.)?”

• None
• A little - for example, I have taken a research methods class where we were taught qualitative methods
• Some - I’ve worked on at least one project where I have had to practice some elements of qualitative research
• A lot - I’ve worked on multiple projects where I have gained experience with a variety of qualitative research methods

survey |>
  count(programming_experience) |>
  mutate(prop = n / sum(n)) |>
  ggplot(aes(y = fct_reorder(programming_experience, prop), x = prop, fill = prop))+
  geom_col(show.legend = FALSE) +
  scale_y_discrete(labels = label_wrap(25)) +
  scale_x_continuous(labels = percent_format(accuracy = 1), breaks = c(0, 0.1, 0.2, 0.3, 0.4)) +
  scale_fill_viridis_c(option = "E") +
  labs(
    title = "Prior qualitative research experience",
    y = NULL,
    x = "Count"
  ) +
  theme_minimal(base_size = 16)

Boo, there are still special characters in the bar labels

Multiple Answer

We also asked you the following question where you could as many options as you liked:

What types of data interest you?

• Crime
• Economics
• Education
• Entertainment (e.g., books, movies, music)
• Environment/Climate
• Health (e.g., social determinants of health, medical)
• Politics
• Sports
• Other
• No preference

Peek at the data

survey |>
  select(data_interests)

# A tibble: 28 × 1
   data_interests                                                               
   <chr>                                                                        
 1 - Education                                                                  
 2 - Education, - Entertainment (e.g., books, movies, music), - Health (e.g., s…
 3 - Environment/Climate, - Health (e.g., social determinants of health, medica…
 4 - Education, - Entertainment (e.g., books, movies, music), - Environment/Cli…
 5 - Education, - Environment/Climate, - Other                                  
 6 - Crime, - Education, - Sports                                               
 7 - Economics, - Education, - Environment/Climate, - Health (e.g., social dete…
 8 - Other                                                                      
 9 - Health (e.g., social determinants of health, medical), - Politics, - Other 
10 - Crime, - Economics, - Education, - Environment/Climate, - Health (e.g., so…
# ℹ 18 more rows

Before we can visualize this variable, we need to tidy and transform it.

Tidy + Transform

survey |>
  # remove text in parentheses
  mutate(data_interests = str_remove_all(data_interests, "\\s*\\(.*?\\)")) |>
  # separate into multiple rows for each interest, using a semi-colon as delimiter
  separate_longer_delim(data_interests, delim = ",") |>
  mutate(data_interests = trimws(data_interests)) |> # remove leading and trailing spaces
  count(data_interests, sort = TRUE) |>
  mutate(prop = n / sum(n))

# A tibble: 9 × 3
  data_interests            n   prop
  <chr>                 <int>  <dbl>
1 - Education              21 0.191 
2 - Health                 18 0.164 
3 - Environment/Climate    16 0.145 
4 - Politics               16 0.145 
5 - Crime                  11 0.1   
6 - Entertainment           9 0.0818
7 - Economics               8 0.0727
8 - Sports                  7 0.0636
9 - Other                   4 0.0364

survey |>
  mutate(data_interests = str_remove_all(data_interests, "\\s*\\(.*?\\)")) |>
  separate_longer_delim(data_interests, delim = ",") |>
  mutate(data_interests = trimws(data_interests)) |>
  count(data_interests, sort = TRUE) |>
  mutate(prop = n / sum(n)) |>
  filter(!is.na(data_interests)) |>
  ggplot(aes(y = fct_reorder(data_interests, prop), x = prop, fill = prop)) +
  geom_col(show.legend = FALSE) +
  scale_y_discrete(labels = label_wrap(20)) +
  scale_x_continuous(labels = percent_format(accuracy = 1)) +
  scale_fill_distiller(palette = "RdPu") +
  labs(title = "Data interests", y = NULL, x = "Count") 

Open Ended

We also asked you a few open-ended questions.

How do you learn best?

Peek at the data

survey |>
  select(learn_best)

# A tibble: 28 × 1
   learn_best                                                                   
   <chr>                                                                        
 1 I'm not sure. I like to read about things I'll need to know, but I also try …
 2 Solo projects & putting stuff into practice                                  
 3 With more smaller assignments rather than fewer larger assignments           
 4 Hands on activities and learning by observation                              
 5 I learn best from detailed instructions , hands on  experience and being abl…
 6 Visual learner, with detailed explanations spoken slowly                     
 7 Reading, discussion                                                          
 8 I like deadlines so I can hold myself accounatble. With coding I might need …
 9 I learn best with a mixture of in class discussion, lectures, and readings.  
10 I learn best by first listening, then seeing, and finally doing it myself. I…
# ℹ 18 more rows

Tidy + Transform + Summarize

We can use text mining techniques, like tokenizing to words to explore this open-ended question:

survey |>
  select(learn_best) |>
  unnest_tokens(word, learn_best) |>
  anti_join(stop_words, by = "word") |>
  count(word, sort = TRUE) |>
  filter(n > 1) |>
  print(n = Inf)

# A tibble: 22 × 2
   word             n
   <chr>        <int>
 1 hands            9
 2 learn            7
 3 practice         5
 4 assignments      4
 5 learning         4
 6 visual           4
 7 class            3
 8 learner          3
 9 reading          3
10 activities       2
11 applying         2
12 coding           2
13 detailed         2
14 discussion       2
15 experience       2
16 explanations     2
17 im               2
18 instructions     2
19 lectures         2
20 listening        2
21 notes            2
22 understand       2

We can also tokenize to bigrams (pairs of words):

survey |>
  select(learn_best) |>
  tidytext::unnest_tokens(bigrams, learn_best, token = "ngrams", n = 2) |>
  count(bigrams, sort = TRUE) |>
  filter(n > 2) |>
  print(n = Inf)

# A tibble: 8 × 2
  bigrams            n
  <chr>          <int>
1 hands on           9
2 i learn            6
3 learn best         5
4 i am               4
5 best by            3
6 i also             3
7 i can              3
8 visual learner     3

What do you hope to get out of this course?

Not so easy to tidy

And the answers are non-trivial to tidy up, e.g.,

survey$hopes[1:3]

[1] "I hope to learn more about how to organize and report qualitative data and, if it is appropriate, how to quantify qualitative responses. I'm also interested in learning more about properly gathering and dealing with quantitative data."
[2] "Get more comfortable with statistics and qualitative research."                                                                                                                                                                            
[3] "Some basic R knowledge and overall improvement in my understanding and confidence with qualitative research methods"                                                                                                                       

Can AI help?

Prompt:

Summarize the following responses to the question “What do you hope to get out of this course?”. Write your response in a short paragraph.

Response:

Most students hope to develop practical skills for analyzing qualitative data in a more systematic and rigorous way, particularly through coding interview transcripts, case notes, and other text-based data. Many want to strengthen their research toolkit by combining qualitative and quantitative approaches, applying data analysis to real-world problems in fields such as social work, sociology, criminal justice, political science, public policy, and environmental research. Several students also expressed a desire to become more confident with coding, data analysis, and mixed-methods research, while a few are looking to broaden their methodological training or explore research approaches outside their current area of expertise.

Can it be trusted 100%?

No. They can be misleading, inaccurate, hallucinate or in general sound more confident than they are. Humans should always stay “in the loop”. Hence – the interdisciplinary approach!

Do you have concerns that I gave an AI agent your responses?

Part 4: Meet the Toolbox

Buckle up! You’re going to learn the programming language R, using a program called RStudio, which is installed and fully setup for us inside JupyterHub, which you can access with a web browser.

This is where we will create Quarto files that interweave code, output, and narrative text to create a practical reproducible research pipeline that empowers you to be the boss of your own data.

Artwork by Allison Horst

Quarto is the next generation Markdown, but I like this image better

Why this toolkit?

Why R?

• Open source, cross-platform, and free
• Great for reproducibility
• Tons of learning resources
• Works on data of all shapes and sizes
• Produces high-quality graphics
• Large and welcoming community
• Flexible and extensible — doesn’t do something you want? Write a custom function
• Used by professionals across public health, economics and finance, political science and policy research, and social science research — not just academia

Why RStudio?

• Customizable workspace that docks all your windows together
• Notebook formats for easy sharing of code and output
• Syntax highlighting and helpful error warnings
• Cross-platform — works on Windows, macOS, and Linux
• Tab completion for functions — forget the syntax? Popup helpers are there
• One-button publishing of reproducible documents (reports, dashboards, presentations, websites — like this one)

Why JupyterHub?

• No install required — access from any computer with a browser
• Same environment every session — no “it works on my machine” problems
• Log in with your Chico credentials — no extra accounts to manage
• Everyone in class has the same package versions, so troubleshooting is shared, not solo
• Removes setup friction — you’re writing code in minutes, not after an afternoon of installation

Why Quarto?

• One document = narrative + code + output, always in sync — no copy-pasting results into a report
• Fully reproducible — anyone can re-run it and get the same result
• One source file renders to multiple formats (HTML, PDF, Word, slides)
• This course’s entire website is built with it — you’re learning the same tool powering these notes
• The direct successor to R Markdown, with broader language support

Follow along

1. Log into JupyterHub (will go through SSO)
2. Launch Server
3. Open RStudio
4. In the “Files” pane (lower right corner) Navigate into the “shared/Donatello Wicked” folder
5. Click the box next to 00-hello_world.qmd, then “More” and “Copy To”
   • Click the word “Home” to go back up to your home (root) folder
   • Replace the word world in the file name with your username (first part of your email).
   • e.g. 00-hello_rdonatello.qmd
6. Back in the “Files” pane, click “Home” to get back to your home directory
7. Click on 00-hello_rdonatello.qmd to open this file
8. Click the “Render” button on the top.
9. Make the right side window tall and compare what you see left to right.

Coding is a hands-on activity. You must do your own typing, do your own practice for your brain to connect what you are writing to what it is doing.

Only watching someone code and then trying to do something yourself would be like listening to someone read a book to you and then trying to go write one yourself without being taught how to write.

You try it

1. Change the author name in the YAML header to your name
2. Try to match the graph colors to the penguin colors
3. Answer the remaining questions in the quarto document itself.

Render to PDF and make sure it looks good.

If you don’t finish in class, the rest is homework.

Exporting your file for submission

1. In the files tab, click the box next to the PDF for the file you want to export.
2. Click “More” –> “Export”

This file will download to your computer. Since it already has your name on it, you can upload it to the Class activity submission folder in Google Drive.

Normalize and accept the struggle

• Hard things make you stronger.
• There is a “roller coaster of emotions” that comes with learning to code is temporary — and normal.
• Learning a programming language is like learning a foreign language: vocabulary, grammar, syntax, and yes — a steep learning curve that takes real commitment. If R feels less comfortable right now than point-and-click tools you’ve used before, that’s expected, not a sign you’re doing something wrong. (R4NP, Ch. 2)
• It’s worth it: R opens up analytical methods that point-and-click software doesn’t have, and the struggle itself builds abstract, conceptual thinking that makes you a stronger researcher — in this class and beyond it.
• Don’t go solo — lean on the learning community.