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library(ezrsurvey)
#> Loading required package: dplyr
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#> Loading required package: ggplot2
#> Loading required package: tidyr
#> Loading required package: tibble
#> Loading required package: readr
#> Loading required package: stringr
#> Loading required package: purrrThe banner is the table a client actually works from: every question down the side, every way of splitting the sample across the top, and one column for the sample as a whole. It is the first deliverable of most projects and the one that takes longest to build by hand, because it is the same cross-tab written out dozens of times.
crosstab_banner() writes it in one call.
Name the questions as rows and the splits as
cols.
crosstab_banner(
podracing_survey,
rows = c(satis_return, demo_edu),
cols = c(demo_gender, region)
)
#> # A tibble: 12 × 11
#> variable item Overall Female Male `Non-binary` Asia Europe `Latin America`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 demo_edu Bach… 25 26 25 22 23 28 15
#> 2 demo_edu Doct… 4 3 4 4 4 3 5
#> 3 demo_edu Lowe… 11 12 11 15 12 10 10
#> 4 demo_edu Mast… 13 12 13 19 11 15 9
#> 5 demo_edu Prim… 4 3 5 7 7 6 4
#> 6 demo_edu Shor… 13 14 12 4 19 11 17
#> 7 demo_edu Uppe… 30 30 31 30 25 26 41
#> 8 satis_r… Like… 27 28 27 15 24 28 30
#> 9 satis_r… Not … 24 25 22 33 25 25 21
#> 10 satis_r… Unli… 16 14 17 26 12 16 11
#> 11 satis_r… Very… 26 24 26 26 34 25 28
#> 12 satis_r… Very… 8 9 7 0 5 6 10
#> # ℹ 2 more variables: `North America` <dbl>, Oceania <dbl>Three things are worth noticing in that output. Every question keeps
its own answers as rows, so questions with different scales stack in one
table. The Overall column is the whole sample, which is
what a reader compares each group against. And the percentages are
column percentages: they run down each group, so they answer “what does
this group look like”, not “where do this answer’s respondents come
from”.
tidyselect helpers work, which is how a block of rating
questions goes in:
crosstab_banner(
podracing_survey,
rows = dplyr::starts_with("ratings_"),
cols = demo_gender
)
#> # A tibble: 30 × 6
#> variable item Overall Female Male `Non-binary`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 ratings_atmosphere Bad 14 13 15 7
#> 2 ratings_atmosphere Good 30 29 32 15
#> 3 ratings_atmosphere Ok 25 28 21 48
#> 4 ratings_atmosphere Very bad 5 5 6 0
#> 5 ratings_atmosphere Very good 26 25 26 30
#> 6 ratings_commentary Bad 33 33 33 33
#> 7 ratings_commentary Good 15 15 16 11
#> 8 ratings_commentary Ok 32 33 32 37
#> 9 ratings_commentary Very bad 16 15 17 15
#> 10 ratings_commentary Very good 4 4 3 4
#> # ℹ 20 more rowsA numeric question has no answers to count, so counting it would produce a row per distinct value. Handed one, the banner switches to a statistics block instead, and says so by labelling the rows:
crosstab_banner(
podracing_survey,
rows = c(nps_value, demo_age),
cols = demo_gender
)
#> # A tibble: 10 × 6
#> variable item Overall Female Male `Non-binary`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 demo_age mean 32.6 32.9 32.3 31.2
#> 2 demo_age median 32 33 32 31
#> 3 demo_age sd 11.2 11.4 11.1 11.1
#> 4 demo_age p25 24 25 24 23.5
#> 5 demo_age p75 40 40 40 37
#> 6 nps_value mean 7.56 7.51 7.61 7.56
#> 7 nps_value median 8 8 8 8
#> 8 nps_value sd 1.76 1.81 1.72 1.69
#> 9 nps_value p25 6 6 7 7
#> 10 nps_value p75 9 9 9 8cell overrides the choice when you want something
specific: "pct", "count", "mean",
or "diff" for each group’s distance from
Overall, which is the version to read when you are hunting
for what makes a group different.
crosstab_banner(
podracing_survey,
rows = satis_return,
cols = demo_gender,
cell = "diff"
)
#> # A tibble: 5 × 6
#> variable item Overall Female Male `Non-binary`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 satis_return Likely 27 1 0 -12
#> 2 satis_return Not sure 24 1 -2 9
#> 3 satis_return Unlikely 16 -2 1 10
#> 4 satis_return Very likely 26 -2 0 0
#> 5 satis_return Very unlikely 8 1 -1 -8A banner built on a weighted sample has to be weighted in every cell, not corrected afterwards. Set the scheme once and it applies throughout:
set_weights(c(variable = "demo_gender",
"Male" = 0.49, "Female" = 0.50, "Non-binary" = 0.01))
#> Weighting scheme stored for: demo_gender. It applies once a dataset is in play.
crosstab_banner(
podracing_survey,
rows = satis_return,
cols = region
)
#> # A tibble: 5 × 8
#> variable item Overall Asia Europe `Latin America` `North America` Oceania
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 satis_retu… Like… 27 23 28 29 27 31
#> 2 satis_retu… Not … 24 26 25 23 24 13
#> 3 satis_retu… Unli… 16 12 15 11 17 25
#> 4 satis_retu… Very… 25 33 25 28 24 21
#> 5 satis_retu… Very… 8 6 7 9 8 10
clear_weights()Weights are computed once per dataset and scheme and then reused,
rather than re-running the raking loop for every cell.
clear_weights_cache() empties that store if you need to
force a recalculation.
Called with no rows or cols, the banner
picks them itself: every question that looks like a question, crossed by
every variable that looks like a split. That choice decides the entire
table, so in an interactive session it shows the choice and waits:
Banner variables chosen automatically: 34 question(s) across 12 grouping variable(s).
Questions (34): satis_return, demo_gender, ...
Grouping variables (12): demo_gender, region, ...
Skipped (11): respondent_id, start_date, nps_com, ...
Name `rows` / `cols` yourself, or raise `max_levels` (now 20), to change this.
Continue? [Y/n]Identifier columns, free text and anything with more distinct answers
than max_levels are left out. Naming the variables yourself
is your own decision and is never questioned. The prompt only appears
when someone is there to answer it, so an unattended script cannot stall
on it; ezrsurvey_options(confirm = FALSE) removes it and
TRUE forces it.
A full every-variable banner on a wide survey is minutes of work. While it runs it says where it is, with an estimate built from the throughput measured so far rather than a guess:
Banner table: 34 question(s) across 12 grouping variable(s)
[7/34] satis_return (about 2m left)Progress reporting is on in an interactive session and silent in
scripts, vignettes and R CMD check.
ezrsurvey_options(progress = FALSE) turns it off
everywhere.
For a table long enough that losing it would hurt, ask for a checkpoint:
crosstab_banner(survey, checkpoint = TRUE) # managed for you
crosstab_banner(survey, checkpoint = "banner.rds") # or choose the locationEach question is saved as it finishes, so re-running the identical
call after a crash carries on from where it stopped. The managed file is
named after the run’s own fingerprint, so a call finds its own
interrupted run and never sees one belonging to different data or
different arguments: a stale file cannot quietly corrupt a table. A
finished run deletes the file checkpoint = TRUE made for
it, and clear_checkpoints() clears anything left behind by
a run that was never repeated. A path you chose yourself is your file:
the package leaves it in place when the run finishes, so delete it when
you no longer need it.
A banner is usually delivered as a spreadsheet, and
long = TRUE gives the tidy form to pivot or join instead of
the printed one.
banner <- crosstab_banner(survey, rows = ..., cols = ...)
export_xlsx("Banner" = banner, path = "banner.xlsx")
save_data(banner, "banner.csv")flextable = TRUE returns a formatted table ready to drop
on a slide or into a Word document through report_add_table().
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