ezrsurvey: tidyverse helpers for everyday consumer-survey work, designed so common tasks require minimal R experience.
ezrsurvey packages the patterns a research manager
reaches for again and again: loading and stacking survey exports,
turning questions into percentages without the
count() %>% mutate() %>% pivot_wider() ritual,
recoding Likert and NPS scales, importance–performance modelling, survey
precision diagnostics, and a generalised plotting toolkit (clean themes,
semantic palettes, dynamic axes and automatic “BAD / OK / GOOD” decision
bands). It ships with two simulated survey datasets –
podracing_survey (a Star Wars pod-racing fan survey) and
shopping_survey (an Edwardian shopping survey) – so every
example runs out of the box.
# install.packages("pak")
pak::pak("mashud37/ezr-research/ezr-survey")The analysis core depends only on dplyr, ggplot2, tidyr, tibble,
readr, stringr and purrr, which it attaches for you. A few extras unlock
optional features: rwa (importance analysis),
treemapify (quote treemaps) and ggrepel
(non-overlapping IPM labels).
To draft the narrative that goes around these tables with a language
model, add the companion ezrintelligence
package; it takes any summary table ezrsurvey produces.
library(ezrsurvey) # attaches dplyr, ggplot2 and the rest with it
# 1. Percentages, no count/mutate/pivot_wider
calc_percentage(podracing_survey, demo_gender, sort = "desc")
#> # A tibble: 3 x 3
#> demo_gender n pct
#> <fct> <int> <dbl>
#> 1 Male 525 55
#> 2 Female 399 42
#> 3 Non-binary 27 3
# Drop catch-all answers; the kept ones re-base to ~100%
calc_percentage(podracing_survey, demo_job, drop = "Unemployed")
# Check-all-that-apply questions, by prefix
calc_percentage_multi(podracing_survey, "motivations_", id = respondent_id, sort = "desc")
# ...or packed into one cell per respondent, the way spreadsheets export them.
# The delimiter (";", "|" or ",") is found for you
packed <- data.frame(respondent = 1:3,
motivations = c("Speed; Drivers", "Speed", "Betting"))
calc_percentage_multi(packed, "motivations", id = respondent)
# split_multi() widens that column into one column per answer instead, so
# crosstabs, plots and everything else can use it too
split_multi(packed, motivations)
# Grouped + pivoted to a wide cross-tab
calc_percentage(podracing_survey, satis_return, by = region, wide = TRUE)
# A master banner table: questions down the side, groups across the top, an
# Overall column, and column percentages (numeric questions turn into a
# mean/median/sd/quartile block automatically)
crosstab_banner(podracing_survey, rows = c(satis_return, nps_value),
cols = c(demo_gender, region))
# Pass only the data frame to cross every variable against every variable
# (identifier and free-text columns are skipped automatically)
crosstab_banner(podracing_survey)
# 2. A labelled bar chart in one pipe -- orientation, wrapping and order
# are chosen automatically (here: many long driver names -> horizontal bars)
calc_percentage(podracing_survey, fav_driver) %>%
plot_bars()
# A whole block of rating questions as one stacked chart, features ordered
# by their average and coloured worst-to-best
plot_rating_grid(podracing_survey, "ratings_")
# 3. NPS and the gauge
nps <- calc_nps(podracing_survey, nps_value)$nps
plot_nps_gauge(nps)
# 4. Importance / performance modelling
ipm_model(podracing_survey, nps_value, "ratings_") %>%
plot_ipm()
# 5. Survey precision diagnostics (the appendix "data info" block, as one call)
diagnose(podracing_survey, demo_gender, dplyr::starts_with("ratings_"))
# 6. Quick saves, pipe-friendly, format inferred from the extension
calc_percentage(podracing_survey, demo_gender) %>% save_data("gender.xlsx")
plot_bars(calc_percentage(podracing_survey, demo_gender)) %>% save_plot("gender.svg")
# A tabbed Excel workbook: one sheet per question, each with its table and chart
export_summary_xlsx(podracing_survey, demo_gender, satis_return, nps_value,
path = "summary.xlsx")| Area | Functions |
|---|---|
| Import | read_folder(), select_prefix(),
select_suffix(), parse_filename() |
| Recode | na_blank(), drop_items(),
ensure_numeric(), bin_numeric(),
recode_age(), recode_generation(),
recode_likert(), nps_group(),
split_multi(), clean_label() |
| Country → region | add_region(), recode_region(),
recode_subregion(), country_region |
| Config / profile | ezrsurvey_options(),
reset_ezrsurvey_options(),
use_ezrsurvey_profile(),
load_ezrsurvey_profile() |
| Comments | sample_comments(),
sample_comments_diverse() |
| Summaries | calc_percentage(),
calc_percentage_multi(),
calc_percentage_batch(), calc_summary(),
crosstab(), crosstab_banner() |
| Modelling | calc_nps(), calc_importance(),
ipm_model() |
| Diagnostics | se_mean(), se_prop(), rse(),
margin_of_error(), diagnose() |
| Scales | nice_max(), scale_y_pct(),
label_pct() |
| Decisions | annotate_bands(), mark_value(),
bands_rating_3(), bands_rating_5(),
bands_nps(), bands_nps_score() |
| Themes & palettes | theme_ezrsurvey() (+
_x/_y/_xy,
transparent), pal_rating,
pal_nps, scale_fill_rating(),
scale_fill_nps() |
| Branding | use_brand(), brand_info(),
clear_brand(), pal_brand(),
scale_fill_brand()/scale_colour_brand() |
| Plots | plot_bars(), plot_stacked_rating(),
plot_rating_grid(), plot_nps(),
plot_nps_gauge(), plot_gauges(),
plot_ipm(), plot_quotes_tree() |
| Reporting | report_new(), report_layouts(),
report_slide()/report_section()/report_title_slide(),
report_add_slide()/_plot()/_table(),
report_deck(), scaffold_report() (Quarto
pptx/html/pdf/docx), example_report() |
| Quick save | save_plot() (png/svg/pdf), save_data()
(csv/tsv/xlsx), save_output() (auto-dispatch),
export_xlsx() (multi-tab),
export_summary_xlsx() (table + chart per question) |
| Data | podracing_survey (1,000 simulated pod-racing fans),
shopping_survey (800 Edwardian shoppers) |
use_dataset(podracing_survey) at the top of a script lets
every helper drop the data argument entirely –
calc_percentage(demo_gender),
calc_nps(nps_value),
diagnose(starts_with("ratings_")). An explicit data frame
or a pipe always wins over the default, and clear_dataset()
ends it.podracing_survey %>% calc_percentage(<Tab> offers
demo_gender and the rest; the un-piped
calc_percentage(df, <Tab> cannot. That is the
mechanism RStudio and Positron use, not a limitation of this package:
they only offer a data frame’s columns inside a pipe chain, for any
package including the tidyverse.Every save writes the file you name, where you name it:
save_plot(p, "nps.png") # nps.png in the working directory
save_plot(p, "charts/nps.png") # charts/nps.png, folder created
report_deck(items, path = "q2.pptx")
scaffold_report("html", path = "report.qmd")To collect bare names in one folder, set
ezrsurvey_options(output_dir = "outputs"), for a session or
in your profile.
The front page is the tour. The details live in their own articles:
| Article | What it covers |
|---|---|
| Getting started | A whole analysis end to end on the bundled data |
| Banner tables | The client’s working table: cell types, weighting, automatic variable choice, checkpointed runs |
| Countries, regions and currency | Free-text country columns, ISO 3166-1 in and out, converting money people answered in their own currency |
| PowerPoint decks | One-call decks, slide-at-a-time decks, your org template, Google Slides |
| Quarto reports | A report you edit and re-render each wave, in html, pdf, docx or pptx |
MIT © Andreas Schellewald