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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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.