About

This page has code, images, and data visualizations that support the material presented in the workshop.

To reveal or hide the code, click on the button at the upper right.

Tip

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Setup

Code 
library(ggplot2)

Registrant data

We try to practice what we preach, so we include below the code we use to download the registration data and visualize it. Click on the code arrow to show or hide the code. The data file itself remains private. We have added csv/ to the .gitignore file so that the data remains available only to users who have it locally on their computers.

Note

Rick Gilmore has stored his Gmail account information in an environment variable called GMAIL_SURVEY.

To do something similar, use the usethis package to open your .Renviron variable.

install.packages("usethis") # if not installed already
usethis::edit_r_environ()

Add an entry like GMAIL_SURVEY="your.google.acct@gmail.com" to the file. Save it, and restart R.

Then, run the following line in your console:

install.packages("googledrive") # if not installed already
googledrive::drive_auth()

You will see something like this:

Select ‘1’ to initiate a Google authentication screen in your default browser. The browser should launch with a window asking your permission to authorize access to your Google account. Grant access to Tidyverse API Packages. Remember, you continue to control what code from the Tidyverse accesses specific Google sheets.

Once these steps are complete, the automated updating code below should work.

We download the data as a CSV (if params$update_data == TRUE).

Code 
update_figs = TRUE
update_date_time = Sys.Date()

# Create include/csv if it doesn't exist
csv_path <- file.path(here::here(), "src", "include", "csv")
if (!dir.exists(csv_path)) {
  message("Creating missing '", csv_path, "'.")
  dir.create(csv_path)
}

if (params$update_data) {
  options(gargle_oauth_email = Sys.getenv("GMAIL_SURVEY"))
  googledrive::drive_auth()
  
  csv_full_fn <- file.path(csv_path, params$csv_fn)
  
  # If no file (1st run of script), create
  if (!file.exists(csv_full_fn)) {
    x <- data.frame(x="")
    readr::write_csv(x, csv_full_fn)
  }
  
  # new_fn <- csv_full_fn
  # file.rename(new_fn, stringr::str_replace(new_fn, "data-", "_data-"))
              
  googledrive::drive_download(
    params$google_fn,
    path = csv_full_fn,
    type = "csv",
    overwrite = TRUE
  )
  message("Data updated.")
} else {
  if (!file.exists(new_fn)) {
    warning("File not found: '", new_fn, "'.")
    update_figs = FALSE
  } else {
    message("Using previously stored data.")
  }
}

We load the saved CSV.

Code 
if (update_figs) {
  registrations <-
    readr::read_csv(csv_full_fn, show_col_types = FALSE)
}

Then we clean the data by renaming the variables, dropping the “no’s”, wrangling the unit names, and adding a college variable.

Note

TO-DO: Remove duplicate entries. Drop timestamp beforehand since it will be unique even among duplicates in other fields.

Code 
registrations_clean <- registrations |>
  dplyr::rename(
    timestamp = "Timestamp",
    email = 'Email Address',
    attend = "Will you attend the workshop on Thursday, March 27, 2025?",
    name = "What is your name?",
    unit = "What is your department or unit?",
    position = "What is your current position?",
    comments = "Any comments?"
  ) |>
  # Remove duplicate rows
  dplyr::distinct() |>
  # Convert timestamp into proper dates
  dplyr::mutate(timestamp = lubridate::mdy_hms(timestamp,
                                               tz = "America/New_York")) |>
  # Normalize unit names
  dplyr::mutate(
    unit = dplyr::recode(
      unit,
      `ESM-Ecosystem Science and Mgmt` = "Ecosystem Science & Mgmt",
      `Earth and Environmental Systems Institute` = "Earth & Environmental Systems Institute",
      `Earth and Environmental Systems Institute` = "Earth & Environmental Systems Institute",
      `Civil and Environmental Engineering` = "Civil & Environmental Engineering",
      `Civil and Environmental Eng` = "Civil & Environmental Engineering",
      `chemical engineering` = "Chemical Engineering",
      `FOOD SCIENCE` = "Food Science",
      `Earth and Mineral Sciences, Energy Institute` = "Energy Institute",
      `Center for Healthy Aging / HHD` = "Center for Healthy Aging",
      `BMB` = "Biochemistry, Microbiology, & Molecular Biology",
      `Biochemistry, Microbiology and Molecular Biology` = "Biochemistry, Microbiology, & Molecular Biology",
      `BMMB` = "Biochemistry, Microbiology, & Molecular Biology",
      `Spanish, Italian and Portuguese` = "Spanish, Italian, & Portuguese",
      `Spanish, Italian, and Portuguese` = "Spanish, Italian, & Portuguese",
      `SIP` = "Spanish, Italian, & Portuguese",
      `Department of Nutritional Sciences` = "Nutritional Sciences",
      `Department of Medicine, Division of Endocrinology` = "Medicine",
      `Nutrition` = "Nutritional Sciences",
      `Department of Statistics` = "Statistics",
      `Dept of Statistics` = "Statistics",
      `RPTM` = "Recreation, Park, & Tourism Management",
      `Kinesiology & Developmental Psychology` = "Kinesiology",
      `Educational psychology` = "Educational Psychology",
      `Clinical Psychology` = "Psychology",
      `Developmental Psychology` = "Psychology",
      `Communication Sciences and Disorders` = "Communication Sciences & Disorders",
      `Center for Healthy Aging; Department of Psychology` = "Center for Healthy Aging",
      `MCIBS` = "Molecular, Cellular, & Integrative Biosciences",
      `Department of Chemical Engineering` = "Chemical Engineering",
      `Biobehavioral health` = "Biobehavioral Health",
      `EE` = "Electrical Engineering",
      `Agricultural and biological engineering` = "Agricultural & Biological Engineering",
      `Nutrition: Kathleen Keller` = "Nutritional Sciences"
      )
  ) |>
  # Drop no attends
  dplyr::filter(attend == "Yes") |>
  # Add college
  dplyr::mutate(
    college = dplyr::case_match(
      unit,
      "Statistics" ~ "ECoS",
      "Biology" ~ "ECoS",
      "Biochemistry, Microbiology, & Molecular Biology" ~ "ECoS",
      "Biomedical engineering" ~ "Eng",
      "Physics" ~ "ECoS",
      "Chemistry" ~ "ECoS",
      "Astronomy & Astrophysics" ~ "ECoS",
      "Eberly" ~ "ECos",
      "Eberly" ~ "ECos",
      "Eberly College of Science" ~ "ECoS",
      "ECos" ~ "ECoS",
      "Poli Sci" ~ "CLA",
      "Prevention research center" ~ "HHD",
      "Center for Healthy Aging (HHD)" ~ "HHD",
      "Sociology and Criminology" ~ "CLA",
      "Psychology" ~ "CLA",
      "Spanish, Italian, & Portuguese" ~ "CLA",
      "Research Informatics and Publishing" ~ "Libraries",
      "Political Science" ~ "CLA",
      "Applied Linguistics" ~ "CLA",
      "Global Languages & Literatures" ~ "CLA",
      "Sociology" ~ "CLA",
      "English" ~ "CLA",
      "C-SoDA" ~ "CLA",
      "Office of Digital Pedagogies and Initiatives" ~ "CLA",
      "Asian Studies" ~ "CLA",
      "Anthropology" ~ "CLA",
      "Linguistics" ~ "CLA",
      "Center for Language Science" ~ "CLA",
      "Foreign Languages" ~ "CLA",
      "Languages and Literature" ~ "CLA",
      "Developmental Psychology" ~ "CLA",
      "IST" ~ "IST",
      "Chemical Engineering" ~ "Eng",
      "Civil & Environmental Engineering" ~ "Eng",
      "Material Science and Engineering" ~ "Eng",
      "Engineering Science & Mechanics" ~ "Eng",
      "Biomedical Engineering" ~ "Eng",
      "Mechanical Engineering" ~ "Eng",
      "Electrical Engineering" ~ "Eng",
      "Chemical Engineering" ~ "Eng",
      "Nutritional Sciences" ~ "HHD",
      "HDFS" ~ "HHD",
      "Communication Sciences & Disorders" ~ "HHD",
      "Kinesiology" ~ "HHD",
      "Recreation, Park, & Tourism Management" ~ "HHD",
      "Prevention Center" ~ "HHD",
      "Bellisario College of Communication" ~ "Comm",
      "Marketing" ~ "Smeal",
      "Food Science" ~ "Ag",
      "Ecosystem Science & Mgmt" ~ "Ag",
      "Entomology" ~ "Ag",
      "Plant Pathology & Environmental Microbiology" ~ "Ag",
      "Plant Science" ~ "Ag",
      "Agricultural & Biological Engineering" ~ "Ag",
      "Landscape Architecture" ~ "Arts & Arch",
      "Neuroscience" ~ "Med",
      "Medicine" ~ "Med",
      "College of Human and Health Development" ~ "HHD",
      "Center for Healthy Aging" ~ "HHD",
      "Center for Childhood Obesity Research" ~ "HHD",
      "Biobehavioral Health" ~ "HHD",
      "Earth & Environmental Systems Institute" ~ "EMS",
      "Geography" ~ "EMS",
      "Energy Institute" ~ "EMS",
      "Geosciences" ~ "EMS",
      "Nursing" ~ "Nursing",
      "Educational Psychology" ~ "Ed",
      "Molecular, Cellular, & Integrative Biosciences" ~ "Huck",
      "University Libraries" ~ "Libraries"
    ),
    .default = "Unknown",
    .missing = "Unknown"
  )

# registrations_clean <- registrations_clean |>
#   dplyr::mutate(
#   confirmed = dplyr::case_match(
#       confirmation_response,
#       "Sorry, can't make it" ~ "Decline",
#       "No response" ~ "No response",
#       "Yes,  I'll be there" ~ "Confirm",
#       NA ~ "No response",
#       "presenter" ~ "Confirm"),
#       .default = "Unknown",
#     .missing = "Unknown"
#   )

The following is used to modify the above code to capture new unit names, clean/normalize them, and assign them to colleges.

Code 
college_na <- is.na(registrations_clean$college)
registrations_clean$unit[college_na]
[1] NA

Time series

Code 
registrations_clean |>
  dplyr::arrange(timestamp) |>
  dplyr::mutate(resp_index = seq_along(timestamp)) |>
  ggplot() +
  aes(x = timestamp, y = resp_index, color = position) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(x = NULL, y = 'n') +
  theme(legend.position = "bottom", legend.title = element_blank())
Figure 1: Time series of registrations.

By college and position

Code 
registrations_clean |>
  dplyr::filter(!is.na(college)) |>
  dplyr::count(position, college, sort = TRUE) |>
  ggplot() +
  aes(college, n, fill = position) +
  geom_col() +
  theme(legend.position = "bottom", legend.title = element_blank()) +
  scale_y_continuous(breaks = c(5,10,15))
Figure 2: Workshop registrations by college and position.