Welcome Aboard
August 25, 2026
tidy way – more on that later!
You took a survey, built in Google Forms:
We want to explore that data and get to know you!
One way to make sense of data collected via a question like this is to visualize it.
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 programming experience among DATA 385 students",
y = NULL,
x = "Proportion of responses"
) +
labs(
caption = "Data are self-reported, collected from DATA 385 students on August 24, 2026."
) +
theme_minimal(base_size = 16)Before we can visualize this variable, we need to tidy and transform it.
survey |>
mutate(data_interests = str_remove_all(data_interests, "\\s*\\(.*?\\)|\\s*-\\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 = "YlOrBr") +
labs(
title = "Data interests among DATA 385 students",
y = NULL,
x = "Proportion of Responses",
caption = "Data are self-reported, collected from DATA 385 students on August 24, 2026."
) +
theme_minimal(base_size = 16)Tip
How long does it take you to commute from home to this classroom in minutes?
survey |>
ggdensity(
x = "commute_minutes",
fill = "lightblue",
color = "darkblue",
add_density = TRUE,
add = "mean",
rug = TRUE,
title = "Commute time to class among DATA 385 students",
xlab = "Commute time (minutes)",
ylab = "Density"
) +
labs(caption = "Data are self-reported, collected from DATA 385 students on August 24, 2026.")We can see the most frequently used words using a word cloud. Further tidying could be to remove words like “learn” and “lots” to remove “noise”
survey |>
select(learn_best) |>
unnest_tokens(word, learn_best) |>
anti_join(stop_words, by = "word") |>
count(word, sort = TRUE) |>
ggplot(aes(label = word, size = n, color = n)) +
ggwordcloud::geom_text_wordcloud_area(shape = "square",
rm_outside=TRUE) +
scale_color_gradient(low = "darkblue", high = "blue") +
scale_size_area(max_size = 24) +
theme_minimal(base_size = 16) 
survey_locations <- survey |>
mutate(zipcode = str_pad(zipcode, width = 5, side = "left", pad = "0")) |>
left_join(zip_code_db, by = "zipcode")
survey_locations |>
filter(!is.na(lat), zipcode != "00000") |>
ggplot(aes(x = lng, y = lat)) +
borders("state", colour = "gray85", fill = "gray95") +
geom_point(color = "#2C4A5E", size = 3, alpha = 0.7) +
coord_quickmap() +
theme_void() +
labs(
title = "Where DATA 385 students call home",
caption = "Data are self-reported, collected from DATA 385 students on August 24, 2026."
)Note: rows with zipcode 00000 (international students) are excluded from the map — worth calling out as a group separately rather than silently dropping them.
