Winter 2025 Computing Workshop Survey Responses

Number of responses

Code 
library(tidyverse)
library(bslib)
library(shiny)
library(bsicons)
source("scripts/helper_functions.R")

# list of workshop IDs to filter results
workshops <- c("2025-01-23-ucsb-geospatial")

results <- read_csv("data-joined/all_workshops.csv") %>% 
  filter(workshop %in% workshops)
  
# Fix comma separator
results <- results %>% 
  mutate(findout_select.pre = str_replace_all(
  findout_select.pre, 
  "Twitter, Facebook, etc.", 
  "Twitter; Facebook; etc."))

pre_survey <- results %>%
  select(ends_with(".pre"))

post_survey <- results %>%
  select(ends_with(".post"))

shoreline <- read_csv("data/shoreline/2025-01-23-ucsb-geospatial-shoreline.csv") %>% 
  filter(`RSVP'ed`=='Yes')

n_pre <- sum(apply(post_survey, 1, function(row) all(is.na(row))))
n_post <- sum(apply(pre_survey, 1, function(row) all(is.na(row))))
n_total <- nrow(results)
n_both <- nrow(results) - n_pre - n_post

layout_columns(
  value_box(
    title = "RSVP'ed Yes - Shoreline", value = nrow(shoreline), ,
    theme = NULL, showcase = bs_icon("card-checklist"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Total survey responses", value = n_total, ,
    theme = NULL, showcase = bs_icon("people-fill"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Both pre- and post-", value = n_both, , theme = NULL,
    showcase = bs_icon("arrows-expand-vertical"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Only pre-workshop", value = n_pre, ,
    theme = NULL, showcase = bs_icon("arrow-left-short"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Only post-workshop", value = n_post, , theme = NULL,
    showcase = bs_icon("arrow-right-short"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  )
)

RSVP'ed Yes - Shoreline

56

Total survey responses

29

Both pre- and post-

7

Only pre-workshop

16

Only post-workshop

6

Departments (Shoreline info)

Code 
name_var <- shoreline %>% select(starts_with("Please")) %>% names()
depts_2 <- shoreline %>% rename(dept_select.pre = name_var) %>% 
  select(dept_select.pre) %>% 
  separate_rows(dept_select.pre, sep=",") %>%
  mutate(dept_select.pre = str_trim(dept_select.pre)) %>%
  count(dept_select.pre, name = "count") %>% 
  mutate(percent = (count / nrow(shoreline)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(depts_2, aes(y=reorder(dept_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) +  
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(depts_2$count)*1.1))

Current occupation / Career stage (Shoreline info)

Code 
name_var <- shoreline %>% select(starts_with("What is")) %>% names()
ocup_2 <- shoreline %>% rename(occupation.pre = name_var) %>% 
  select(occupation.pre) %>% 
  separate_rows(occupation.pre, sep=",") %>%
  mutate(occupation.pre = str_trim(occupation.pre)) %>%
  count(occupation.pre, name = "count") %>% 
  mutate(percent = (count / nrow(shoreline)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(ocup_2, aes(y=reorder(occupation.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(ocup_2$count)*1.2))

Motivation - Why are you participating in this workshop?

Code 
motiv <- results %>% select(motivation_select.pre) %>% 
  separate_rows(motivation_select.pre, sep=",")  %>% 
  mutate(motivation_select.pre = str_trim(motivation_select.pre)) %>%
  count(motivation_select.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(motiv, aes(y=reorder(motivation_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(motiv$count)*1.2))

How did you find out about this workshop?

Code 
findw <- results %>% select(findout_select.pre) %>% 
  separate_rows(findout_select.pre, sep=",")  %>% 
  mutate(findout_select.pre = str_trim(findout_select.pre)) %>%
  count(findout_select.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(findw, aes(y=reorder(findout_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(findw$count)*1.2))

What you most hope to learn?

Code 
results %>% group_by(workshop) %>% 
  select(workshop, hopes.pre) %>% 
  drop_na()
workshop hopes.pre
2025-01-23-ucsb-geospatial I’m interested in learning how to process, analyze, and visualize geospatial data from remote sensors. Ultimately, I’m trying to build skills toward effectively gathering and processing multiband sonar data of near-shore bathymetry for visualization, analysis, and modeling phenomenon.
2025-01-23-ucsb-geospatial Refresh skills in R and learn how to analyze spatial data
2025-01-23-ucsb-geospatial the R language and data management environments like SQL
2025-01-23-ucsb-geospatial Ability to handle high resolution spatial data within R
2025-01-23-ucsb-geospatial how to maneuver Geospatial data
2025-01-23-ucsb-geospatial I hope to become more confident in working with raster data. I am hoping that I can take what I learn during this workshop and make maps/figures using the information that I gain during the workshop.
2025-01-23-ucsb-geospatial more on handling raster spatial data
2025-01-23-ucsb-geospatial Confident in my skills :)
2025-01-23-ucsb-geospatial I hope to learn a project based system on how to do data analytics
2025-01-23-ucsb-geospatial To make maps in r without hating my data organization skills
2025-01-23-ucsb-geospatial I hope to learn at least some basic skills that can help with geospatial data
2025-01-23-ucsb-geospatial I hope to learn new skills and deepen my understanding of data structures and applications
2025-01-23-ucsb-geospatial I hope to better my understanding of R especially as it relates to geographical analysis
2025-01-23-ucsb-geospatial I hope to be able to use R when doing GIS

Learning environment in the workshop

Code 
orderedq <- c("Strongly Disagree", "Somewhat Disagree", "Neither Agree or Disagree","Somewhat Agree", "Strongly Agree")
addNA(orderedq)
Code 
agree_questions <- results %>% 
  select(join_key, agree_apply.post,    agree_comfortable.post, agree_clearanswers.post,
         agree_instr_enthusiasm.post, agree_instr_interaction.post, agree_instr_knowledge.post
) %>% 
  filter(!if_all(-join_key, is.na))

n_agree_questions <- nrow(agree_questions)
  
agree_questions <- agree_questions %>%
  pivot_longer(cols = -join_key, names_to = "Question", values_to = "Response") %>% 
  mutate(Response = factor(Response, levels = orderedq),
         Question = recode(Question,
                     "agree_apply.post" = "Can immediatly apply 
 what they learned",
                     "agree_comfortable.post" = "Comfortable learning in 
 the workshop environment",
                     "agree_clearanswers.post" = "Got clear answers 
 from instructors",
                     "agree_instr_enthusiasm.post" = "Instructors were enthusiastic",
                     "agree_instr_interaction.post" = "Comfortable interacting 
 with instructors",
                     "agree_instr_knowledge.post" = "Instructors were knowledgeable 
 about the material"
      ))

summary_data <- agree_questions %>%
  count(Question, Response, name = "count") %>% 
  mutate(percent = (count / n_agree_questions) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(summary_data, aes(x = Question, y = count, fill = Response)) +
  geom_col(position = "fill", color = "black", show.legend = TRUE) +
  scale_y_continuous(labels = scales::percent_format()) + 
  scale_fill_manual(values = c("Strongly Disagree" = "#d01c8b", 
                               "Somewhat Disagree" = "#f1b6da", 
                               "Neither Agree or Disagree" = "#f7f7f7", 
                               "Somewhat Agree" = "#b8e186", 
                               "Strongly Agree" = "#4dac26"), 
                    na.translate = TRUE, na.value = "#cccccc", 
                    breaks = orderedq, drop = FALSE) +
  geom_text(aes(label = text), size = 3,
             position = position_fill(vjust = 0.5)) +
  labs(y = "# respondents (Percentage)", x = element_blank(), fill = "Responses",
       subtitle = paste0("Number of responses: ", n_agree_questions)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.subtitle = element_text(hjust = 0.5, size = 12))

How an instructor or helper affected your learning experience

Code 
results %>% 
  group_by(workshop) %>% 
  select(workshop, instructor_example.post) %>%
  drop_na()
workshop instructor_example.post
2025-01-23-ucsb-geospatial For somebody coming outside of the field of geospatial science, the instructors were helpful in communicating and relating geographic metadata and other concepts.
2025-01-23-ucsb-geospatial Sigrid was great at addressing the comments we made on the purple and yellow stickies!
2025-01-23-ucsb-geospatial Helped me whenever I put up the colored sticky note
2025-01-23-ucsb-geospatial I ran into small issues with my code as a result of typing errors. Instructors were very helpful at identifying these errors and providing helpful tips along the way to avoid errors in the future
2025-01-23-ucsb-geospatial Explaining commands
2025-01-23-ucsb-geospatial They were extremely helpful and created a lighthearted learning environment. It was clear that they respect each other as well as the students. They did a fantastic job.
2025-01-23-ucsb-geospatial hands on help(esp when things went wrong->stopped me from panicing and shutting down in frustration)
2025-01-23-ucsb-geospatial They were all super attentitve and fast in providing help
2025-01-23-ucsb-geospatial Great! Helping me determine what coding issues I was encountering rapidly.
2025-01-23-ucsb-geospatial Plenty of people to offer one on one help so it was a very supportive and comfortable working environment that moved at a good slow pace for everyone too follow along. If I ever had trouble, I could put up a yellow sticky note and someone would come work with me to figure out my problem or catch up if I got behind.
2025-01-23-ucsb-geospatial I didn’t show up the second to last day but when I came in the final day, one of the instructors got me all caught up on the code I needed which was super helpful

Skills and perception comparison

Code 
# Calculate mean scores and make graph for all respondents (only_matched=FALSE)
tryCatch(
  {
mean_nresp <- get_mean_scores_nresp(results, only_matched=FALSE)
graph_pre_post(mean_nresp$mean_scores, mean_nresp$n_resp_pre, mean_nresp$n_resp_post, mean_nresp$n_resp_pre_post, only_matched=FALSE)
},
error = function(cond) {
message("Could not do the plots as there are no pre or post results to show")
}
)

Code 
# Calculate mean scores and make graph for only matched respondents in pre and post (only_matched=TRUE)
tryCatch(
  {
mean_nresp <- get_mean_scores_nresp(results, only_matched=TRUE)
graph_pre_post(mean_nresp$mean_scores, mean_nresp$n_resp_pre, mean_nresp$n_resp_post, mean_nresp$n_resp_pre_post, only_matched=TRUE)
},
error = function(cond) {
message("Could not do the plots as there are no pre or post results to show")
}
)

Workshop Strengths

Code 
results %>% 
  group_by(workshop) %>% 
  select(workshop, workshop_strengths.post) %>% 
  drop_na()
workshop workshop_strengths.post
2025-01-23-ucsb-geospatial Organization, pacing.
2025-01-23-ucsb-geospatial A lot of support throughout the workshop with the lecture and helpers
2025-01-23-ucsb-geospatial Real-time communication with sticky note colors
2025-01-23-ucsb-geospatial applicability
2025-01-23-ucsb-geospatial Loved the sticky note system, it is much easier to stick a yellow note on your laptop than to interrupt and raise a hand. Loved the visual elements and direct applications during the workshop.
2025-01-23-ucsb-geospatial i loved it all tbh
2025-01-23-ucsb-geospatial helping us navigate step-by-step with the functions and building up from every episode. the way it was organized throughout 4 days really helped. Perhaps it could be spread out into more days even.
2025-01-23-ucsb-geospatial Instructors & Pacing
2025-01-23-ucsb-geospatial Lots of staff to help troubleshoot and interesting data. Also explored a variety of data types
2025-01-23-ucsb-geospatial This workshop really helped me with ggplot. I know so much now

Ways to improve the workshop

Code 
results %>% 
  group_by(workshop) %>% 
  select(workshop, workshop_improved.post) %>% 
  drop_na()
workshop workshop_improved.post
2025-01-23-ucsb-geospatial More information about data sets and information being visualized, and information about data collection techniques.
2025-01-23-ucsb-geospatial Talk more about the data we are working with, what all of the standards mean, what plot are we actually making?
2025-01-23-ucsb-geospatial I think more could be done to contextualize the data and explain the options of all of the function options. For example, in some ggplots, we added titles and subtitles, but I would appreciate a deeper explanation of all of the options, perhaps by making one very fancy graph
2025-01-23-ucsb-geospatial More in-depth explanations of what is going on behind the scenes or detailed descriptions of rasters etc.
2025-01-23-ucsb-geospatial more days:)
2025-01-23-ucsb-geospatial I would consider adding one more day, the last day felt a bit rushed in the last part. Explain the functions in more details, for instance, why we are using a function in a certain line – sometimes this was said, but not often.
2025-01-23-ucsb-geospatial More checkpoints through out the workshop
2025-01-23-ucsb-geospatial Explain certain acronyms or functions a little better when we use them even if they were explained once a few days ago
2025-01-23-ucsb-geospatial I think we should copy and paste code more often because it was a bit tedious especially with all the ggplot setup

How likely are you to recommend this workshop? Scale 0 - 10

Code 
orderedq <- c("Detractor", "Passive", "Promoter")

nps <- results %>% 
  count(recommend_group.post, recommende_score.post, name = "count") %>% 
  drop_na() %>% 
  mutate(recommend_group.post = factor(recommend_group.post, levels = orderedq),
         percent = (count/sum(count)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

nps %>% 
ggplot(aes(x=recommende_score.post, y=count, fill=recommend_group.post)) +
  geom_col(color="black", show.legend = TRUE) +
  scale_fill_manual(values = c("Detractor" = "#af8dc3", "Passive" = "#f7f7f7", "Promoter" = "#7fbf7b"), breaks = c("Detractor", "Passive", "Promoter"), drop = FALSE) +
  geom_label(aes(label = text, vjust = -0.5), fill = "white", size= 3) +
  scale_x_continuous(breaks = 1:10) +
  labs(x = "NPS Score", y = "# respondents", subtitle = paste0("Number of responses: ", sum(nps$count), "
 Mean score: ", format(weighted.mean(nps$recommende_score.post, nps$count), digits = 3))) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    plot.subtitle = element_text(hjust = 0.5, size = 12)
  ) +
  expand_limits(x = c(1,10),
                y = c(0, max(nps$count)*1.1))

Topic Suggestions

Code 
results %>% 
  group_by(workshop) %>% 
  select(workshop, suggest_topics.post) %>% 
  drop_na()
workshop suggest_topics.post
2025-01-23-ucsb-geospatial No, thank you!
2025-01-23-ucsb-geospatial Git and GitHub version control as well as working in iOS, windows, and Linus environments
2025-01-23-ucsb-geospatial Would love a more career focused workshop that goes over skills that are used in tech/business industry
2025-01-23-ucsb-geospatial keep the env data vibes
2025-01-23-ucsb-geospatial GIS/ Remote Sensing
2025-01-23-ucsb-geospatial Utilizing Kaggle Data
2025-01-23-ucsb-geospatial Intermediate Python Data Analysis
2025-01-23-ucsb-geospatial None right now

Number of responses

#| message: false
#| warning: false

library(tidyverse)
library(bslib)
library(shiny)
library(bsicons)
source("scripts/helper_functions.R")

# list of workshop IDs to filter results
workshops <- c("2025-02-03-ucsb-computing")

results <- read_csv("data-joined/all_workshops.csv") %>% 
  filter(workshop %in% workshops)
  
# Fix comma separator
results <- results %>% 
  mutate(findout_select.pre = str_replace_all(
  findout_select.pre, 
  "Twitter, Facebook, etc.", 
  "Twitter; Facebook; etc."))

pre_survey <- results %>%
  select(ends_with(".pre"))

post_survey <- results %>%
  select(ends_with(".post"))

shoreline <- read_csv("data/shoreline/2025-02-03-ucsb-computing-shoreline.csv") %>% 
  filter(`RSVP'ed`=='Yes')

n_pre <- sum(apply(post_survey, 1, function(row) all(is.na(row))))
n_post <- sum(apply(pre_survey, 1, function(row) all(is.na(row))))
n_total <- nrow(results)
n_both <- nrow(results) - n_pre - n_post

layout_columns(
  value_box(
    title = "RSVP'ed Yes - Shoreline", value = nrow(shoreline), ,
    theme = NULL, showcase = bs_icon("card-checklist"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Total survey responses", value = n_total, ,
    theme = NULL, showcase = bs_icon("people-fill"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Both pre- and post-", value = n_both, , theme = NULL,
    showcase = bs_icon("arrows-expand-vertical"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Only pre-workshop", value = n_pre, ,
    theme = NULL, showcase = bs_icon("arrow-left-short"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  ),
  value_box(
    title = "Only post-workshop", value = n_post, , theme = NULL,
    showcase = bs_icon("arrow-right-short"), showcase_layout = "left center",
    full_screen = FALSE, fill = TRUE, height = NULL
  )
)

Departments (Shoreline info)

#| message: false
#| warning: false
name_var <- shoreline %>% select(starts_with("Please")) %>% names()
depts_2 <- shoreline %>% rename(dept_select.pre = name_var) %>% 
  select(dept_select.pre) %>% 
  separate_rows(dept_select.pre, sep=",") %>%
  mutate(dept_select.pre = str_trim(dept_select.pre)) %>%
  count(dept_select.pre, name = "count") %>% 
  mutate(percent = (count / nrow(shoreline)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(depts_2, aes(y=reorder(dept_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) +  
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(depts_2$count)*1.1))

Current occupation / Career stage (Shoreline info)

#| message: false
name_var <- shoreline %>% select(starts_with("What is")) %>% names()
ocup_2 <- shoreline %>% rename(occupation.pre = name_var) %>% 
  select(occupation.pre) %>% 
  separate_rows(occupation.pre, sep=",") %>%
  mutate(occupation.pre = str_trim(occupation.pre)) %>%
  count(occupation.pre, name = "count") %>% 
  mutate(percent = (count / nrow(shoreline)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(ocup_2, aes(y=reorder(occupation.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(ocup_2$count)*1.2))

Motivation - Why are you participating in this workshop?

motiv <- results %>% select(motivation_select.pre) %>% 
  separate_rows(motivation_select.pre, sep=",")  %>% 
  mutate(motivation_select.pre = str_trim(motivation_select.pre)) %>%
  count(motivation_select.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(motiv, aes(y=reorder(motivation_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(motiv$count)*1.2))

How did you find out about this workshop?

findw <- results %>% select(findout_select.pre) %>% 
  separate_rows(findout_select.pre, sep=",")  %>% 
  mutate(findout_select.pre = str_trim(findout_select.pre)) %>%
  count(findout_select.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(findw, aes(y=reorder(findout_select.pre, count), x=count)) +
    geom_col() +
    geom_label(aes(label = text, hjust = -0.1),
               size = 3) +
    labs(x = "# respondents", y = element_blank()) + 
    theme_minimal() +
    theme(
      panel.grid.minor = element_blank(),
      panel.grid.major.y = element_blank()
      ) +
    expand_limits(x = c(0,max(findw$count)*1.2))

What you most hope to learn?

results %>% group_by(workshop) %>% 
  select(workshop, hopes.pre) %>% 
  drop_na()

Learning environment in the workshop

#| message: false
#| output: false
orderedq <- c("Strongly Disagree", "Somewhat Disagree", "Neither Agree or Disagree","Somewhat Agree", "Strongly Agree")
addNA(orderedq)
agree_questions <- results %>% 
  select(join_key, agree_apply.post,    agree_comfortable.post, agree_clearanswers.post,
         agree_instr_enthusiasm.post, agree_instr_interaction.post, agree_instr_knowledge.post
) %>% 
  filter(!if_all(-join_key, is.na))

n_agree_questions <- nrow(agree_questions)
  
agree_questions <- agree_questions %>%
  pivot_longer(cols = -join_key, names_to = "Question", values_to = "Response") %>% 
  mutate(Response = factor(Response, levels = orderedq),
         Question = recode(Question,
                     "agree_apply.post" = "Can immediatly apply 
 what they learned",
                     "agree_comfortable.post" = "Comfortable learning in 
 the workshop environment",
                     "agree_clearanswers.post" = "Got clear answers 
 from instructors",
                     "agree_instr_enthusiasm.post" = "Instructors were enthusiastic",
                     "agree_instr_interaction.post" = "Comfortable interacting 
 with instructors",
                     "agree_instr_knowledge.post" = "Instructors were knowledgeable 
 about the material"
      ))

summary_data <- agree_questions %>%
  count(Question, Response, name = "count") %>% 
  mutate(percent = (count / n_agree_questions) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(summary_data, aes(x = Question, y = count, fill = Response)) +
  geom_col(position = "fill", color = "black", show.legend = TRUE) +
  scale_y_continuous(labels = scales::percent_format()) + 
  scale_fill_manual(values = c("Strongly Disagree" = "#d01c8b", 
                               "Somewhat Disagree" = "#f1b6da", 
                               "Neither Agree or Disagree" = "#f7f7f7", 
                               "Somewhat Agree" = "#b8e186", 
                               "Strongly Agree" = "#4dac26"), 
                    na.translate = TRUE, na.value = "#cccccc", 
                    breaks = orderedq, drop = FALSE) +
  geom_text(aes(label = text), size = 3,
             position = position_fill(vjust = 0.5)) +
  labs(y = "# respondents (Percentage)", x = element_blank(), fill = "Responses",
       subtitle = paste0("Number of responses: ", n_agree_questions)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.subtitle = element_text(hjust = 0.5, size = 12))

How an instructor or helper affected your learning experience

#| message: false
results %>% 
  group_by(workshop) %>% 
  select(workshop, instructor_example.post) %>%
  drop_na()

Skills and perception comparison

# Calculate mean scores and make graph for all respondents (only_matched=FALSE)
tryCatch(
  {
mean_nresp <- get_mean_scores_nresp(results, only_matched=FALSE)
graph_pre_post(mean_nresp$mean_scores, mean_nresp$n_resp_pre, mean_nresp$n_resp_post, mean_nresp$n_resp_pre_post, only_matched=FALSE)
},
error = function(cond) {
message("Could not do the plots as there are no pre or post results to show")
}
)


# Calculate mean scores and make graph for only matched respondents in pre and post (only_matched=TRUE)
tryCatch(
  {
mean_nresp <- get_mean_scores_nresp(results, only_matched=TRUE)
graph_pre_post(mean_nresp$mean_scores, mean_nresp$n_resp_pre, mean_nresp$n_resp_post, mean_nresp$n_resp_pre_post, only_matched=TRUE)
},
error = function(cond) {
message("Could not do the plots as there are no pre or post results to show")
}
)

Workshop Strengths

results %>% 
  group_by(workshop) %>% 
  select(workshop, workshop_strengths.post) %>% 
  drop_na()

Ways to improve the workshop

results %>% 
  group_by(workshop) %>% 
  select(workshop, workshop_improved.post) %>% 
  drop_na()

How likely are you to recommend this workshop? Scale 0 - 10

orderedq <- c("Detractor", "Passive", "Promoter")

nps <- results %>% 
  count(recommend_group.post, recommende_score.post, name = "count") %>% 
  drop_na() %>% 
  mutate(recommend_group.post = factor(recommend_group.post, levels = orderedq),
         percent = (count/sum(count)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

nps %>% 
ggplot(aes(x=recommende_score.post, y=count, fill=recommend_group.post)) +
  geom_col(color="black", show.legend = TRUE) +
  scale_fill_manual(values = c("Detractor" = "#af8dc3", "Passive" = "#f7f7f7", "Promoter" = "#7fbf7b"), breaks = c("Detractor", "Passive", "Promoter"), drop = FALSE) +
  geom_label(aes(label = text, vjust = -0.5), fill = "white", size= 3) +
  scale_x_continuous(breaks = 1:10) +
  labs(x = "NPS Score", y = "# respondents", subtitle = paste0("Number of responses: ", sum(nps$count), "
 Mean score: ", format(weighted.mean(nps$recommende_score.post, nps$count), digits = 3))) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    plot.subtitle = element_text(hjust = 0.5, size = 12)
  ) +
  expand_limits(x = c(1,10),
                y = c(0, max(nps$count)*1.1))

Topic Suggestions

results %>% 
  group_by(workshop) %>% 
  select(workshop, suggest_topics.post) %>% 
  drop_na()