Fall 2023 Python 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("2023-11-14-ucsb-python")

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"))

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 = "Total 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
  )
)

Total responses

11

Both pre- and post-

2

Only pre-workshop

7

Only post-workshop

2

Departments

Code

depts <- results %>% 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 / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(depts, 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$count)*1.1))

“Other” Departments

Code

other_depts <- results %>% 
  count(dept_other.pre, name = "count") %>% 
  drop_na() %>% 
  mutate(percent = (count / (n_total - n_post)) * 100,
         text = sprintf("%.0f (%.0f%%)", count, percent))

ggplot(other_depts, aes(y=reorder(dept_other.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(other_depts$count)*1.1))

Warning in max(other_depts$count): no non-missing arguments to max; returning
-Inf

Current occupation / Career stage

Code

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

ggplot(ocup, 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$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
2023-11-14-ucsb-python	I want to be able to write simple code and understand what other people are doing with Python.
2023-11-14-ucsb-python	How to become more comfortable working with programming softwares such as Python and R
2023-11-14-ucsb-python	Work with data more efficiently
2023-11-14-ucsb-python	I want to learn basic Python programming skills
2023-11-14-ucsb-python	developing a clean python workflow and a good understanding of the syntax
2023-11-14-ucsb-python	i hope to learn more about data analysis in python, and aboit how i can apply this to my current and future work
2023-11-14-ucsb-python	To recall my python skills
2023-11-14-ucsb-python	get a start on python to do specialized ML applications

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
2023-11-14-ucsb-python	Seth and Greg answered my questions and it was helpful to see them do the coding in real time
2023-11-14-ucsb-python	They were always willing to help with specific technical issues and made sure we could catch up again
2023-11-14-ucsb-python	instructor and helpers were available to help when I had issues with the codes, access to the site or clarifications about the content

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
2023-11-14-ucsb-python	fairly basic but great for getting a foundational background and intro to Python; well paced; casual and helpful environment; having the Jupyter notebook environment set up for us was great
2023-11-14-ucsb-python	basic intro without necessary prior knowledge, good speed, in principle the technical working environment was set up in a robust way
2023-11-14-ucsb-python	small group to get enough support from instructor and helpers, content, suggested resources/links for future reference

Ways to improve the workshop

Code

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

workshop	workshop_improved.post
2023-11-14-ucsb-python	The server for of the jupyter notebook environment went down several times and avoiding that would be helpful
2023-11-14-ucsb-python	1)If JupyterLab allows, make all tabs the instructors use available, including the reviews that cover the questions from attendees. 2)Use more days or reduce the content; sometimes it felt that we were going fast and for those with no previous code experience, could be harder.

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
2023-11-14-ucsb-python	more Python, R, data visualization