The tidymodels team fielded a short survey to gather community feedback on development priorities and possible next steps in 2024. This report summarizes the survey results.

tl;dr

  • Almost 340 people responded to the survey (a significant decrease from last year).
  • About half of respondents say they have used tidymodels packages many times.
  • About 60% of respondents say they work in industry.
  • The priority given the most weight by our respondents (across most groups) is by far causal inference.
  • Priorities involving the chattr package, cost-sensitive learning, and sparse tibbles were among the most likely to be given zero weight.

Exploring the data

Let’s start by exploring the characteristics of the survey respondents.

library(tidyverse)
library(qualtRics)
library(glue)

survey_id <- "SV_aWw8ocGN5aPgeZE"

survey_raw <- fetch_survey(survey_id, verbose = FALSE, force_request = TRUE) %>%
  filter(Status != "Survey Preview", Finished)

survey_select <- survey_raw %>%
  select(Q5_1:Q5_12, Q1002, Q12)

metadata_raw <- metadata(survey_id)

choice_text <- metadata_raw$questions$QID2001$choices %>% 
  map_chr("choiceText")

question_text <- survey_questions(survey_id) %>%
  filter(qname %in% c("Q1002", "Q12"))

labels_df <-
  enframe(choice_text) %>% 
  transmute(qname = glue("Q5_{name}"), 
            question = map(value, xml2::read_html)) %>% 
  mutate(question = map(question, xml2::as_list), 
         question = map_chr(question, ~.$html$body$strong[[1]])) %>%
  bind_rows(question_text)

tidy_survey <- survey_select %>% 
  pivot_longer(Q5_1:Q5_12, names_to = "qname", values_to = "dollars") %>% 
  inner_join(labels_df) %>%
  filter(question != "Other")

survey_raw %>%
  count(StartDate = as.Date(StartDate)) %>%
  ggplot(aes(StartDate, n)) +
  geom_col(alpha = 0.8) +
  labs(x = NULL, 
       y = "Number of survey responses",
       title = "Survey responses over time",
       subtitle = glue("There are ", {nrow(survey_raw)}, " total responses"))

survey_raw %>%
  mutate(Q1002 = fct_relabel(Q1002, str_wrap, width = 20)) %>%
  count(Q1002) %>%
  ggplot(aes(x = n, y = Q1002)) +
  geom_col(alpha = 0.8) +
  scale_x_continuous(expand = c(0,0)) +
  labs(x = "Number of survey responses", 
       y = NULL,
       title = "Familiarity with tidymodels",
       subtitle = glue("Of the respondents, ", 
                       {percent(mean(str_detect(survey_raw$Q1002, "many times")))}, 
                       " say they have used tidymodels many times"))

survey_raw %>%
  filter(`Duration (in seconds)` < 5e4) %>%
  mutate(Q1002 = fct_relabel(Q1002, str_wrap, width = 20)) %>%
  ggplot(aes(Q1002, `Duration (in seconds)`, fill = Q1002)) +
  geom_boxplot(show.legend = FALSE, alpha = 0.7) +
  scale_y_log10() +
  labs(x = NULL,
       y = "Time to take the survey (seconds)",
       title = "Survey length in seconds",
       subtitle = glue(
         "The median time to take the survey was ",
         {round(median(survey_raw$`Duration (in seconds)`) / 60, 2)},
         " minutes")
  )

survey_raw %>%
  mutate(Q12 = fct_relabel(Q12, str_wrap, width = 20)) %>%
  count(Q12) %>%
  ggplot(aes(x = n, y = Q12)) +
  geom_col(alpha = 0.8) +
  scale_x_continuous(expand = c(0,0)) +
  labs(x = "Number of survey responses", 
       y = NULL,
       title = "Current role",
       subtitle = glue("Of the respondents, ", 
                       {percent(mean(str_detect(survey_raw$Q12, "in industry")))}, 
                       " say they work in industry"))

Perspectives on priorities

The main question on the survey asked:

If you had a hypothetical $100 to spend on tidymodels development, how would you allocate those resources right now?

The possible priorities were presented in a randomized order to respondents, except for the “Other” option at the bottom.

Mean dollars allocated

Overall

tidy_survey %>%
  mutate(question = str_wrap(question, width = 25)) %>%
  group_by(question) %>%
  summarise(dollars_mean = mean(dollars)) %>%
  mutate(question = fct_reorder(question, dollars_mean)) %>%
  ggplot(aes(dollars_mean, question)) +
  geom_col(alpha = 0.8) +
  scale_x_continuous(labels = dollar_format(),
                     expand = c(0,0)) +
  labs(x = "Mean hypothetical dollars allocated",
       y = NULL,
       title = "What are the average dollars allocated to each priority?",
       subtitle = "Causal inference had by far the highest mean scores")

By experience

library(tidytext)

tidy_survey %>%
  mutate(question = str_wrap(question, width = 25),
         Q1002 = fct_relabel(Q1002, str_wrap, width = 50)) %>%
  group_by(Q1002, question) %>%
  summarise(dollars_mean = mean(dollars)) %>%
  ungroup %>%
  mutate(question = reorder_within(question, dollars_mean, as.character(Q1002))) %>%
  ggplot(aes(dollars_mean, question, fill = Q1002)) +
  geom_col(alpha = 0.8, show.legend = FALSE) +
  facet_wrap(~Q1002, scales = "free_y") +
  scale_x_continuous(labels = dollar_format(),
                     expand = c(0,0)) +
  scale_y_reordered() +
  labs(x = "Mean hypothetical dollars allocated",
       y = NULL,
       title = "What are the average dollars allocated to each priority?",
       subtitle = "Folks who have contributed to or taught tidymodels prefer causal inference less")