Extracting data frames or expressions from {ggstatsplot} plots
Source: R/extract-stats.R
extract_stats.RdExtracting data frames or expressions from {ggstatsplot} plots
Value
For extract_stats(), a list with the following elements, each either a
data frame or NULL if not relevant for the given plot:
subtitle_data: results of the test displayed in the subtitlecaption_data: results of the test displayed in the caption (typically, the Bayesian test)pairwise_comparisons_data: results of pairwise comparisonsdescriptive_data: counts and percentages (ggpiestats()andggbarstats())one_sample_data: proportion tests for each level ofy(ggpiestats()andggbarstats())tidy_data: tidy model parameters (ggcoefstats())glance_data: model performance indices (ggcoefstats())
For extract_subtitle() and extract_caption(), the statistical expression
used in the subtitle or caption, respectively (or NULL if unavailable).
User-supplied subtitle and caption text is not returned.
For plots from grouped_* functions, a list with one such element per plot.
Details
These helpers retrieve the statistical data frames and expressions retained
by {ggstatsplot} plots. If you only need tabular results, call the
underlying analysis functions directly: {statsExpressions} for statistical
tests, parameters::model_parameters() for model coefficients, and
performance::model_performance() for model fit indices.
ggcorrmat() does not retain statistical data frames, so all elements
returned by extract_stats() are NULL. Use
correlation::correlation() to obtain its statistical results as data.
Examples
set.seed(123)
# non-grouped plot
p1 <- ggbetweenstats(mtcars, cyl, mpg)
# grouped plot
p2 <- grouped_ggbarstats(Titanic_full, Survived, Sex, grouping.var = Age)
# extracting expressions -----------------------------
extract_subtitle(p1)
#> list(italic("F")["Welch"](2, 18.03) == "31.62", italic(p) ==
#> "1.27e-06", widehat(omega["p"]^2) == "0.74", CI["95%"] ~
#> "[" * "0.53", "1.00" * "]", italic("n")["obs"] == "32")
extract_caption(p1)
#> list(log[e] * (BF["01"]) == "-14.92", widehat(italic(R^"2"))["Bayesian"]^"posterior" ==
#> "0.71", CI["95%"]^HDI ~ "[" * "0.57", "0.79" * "]", italic("r")["Cauchy"]^"JZS" ==
#> "0.71")
extract_subtitle(p2)
#> [[1]]
#> list(chi["Pearson"]^2 * "(" * 1 * ")" == "460.87", italic(p) ==
#> "3.11e-102", widehat(italic("V"))["Cramer"] == "0.47", CI["95%"] ~
#> "[" * "0.43", "0.51" * "]", italic("n")["obs"] == "2,092")
#>
#> [[2]]
#> list(chi["Pearson"]^2 * "(" * 1 * ")" == "3.03", italic(p) ==
#> "0.08", widehat(italic("V"))["Cramer"] == "0.14", CI["95%"] ~
#> "[" * "0.00", "0.34" * "]", italic("n")["obs"] == "109")
#>
extract_caption(p2)
#> [[1]]
#> list(log[e] * (BF["01"]) == "-213.79", widehat(italic("V"))["Cramer"]^"posterior" ==
#> "0.47", CI["95%"]^ETI ~ "[" * "0.43", "0.51" * "]", italic("a")["Gunel-Dickey"] ==
#> "1.00")
#>
#> [[2]]
#> list(log[e] * (BF["01"]) == "-0.03", widehat(italic("V"))["Cramer"]^"posterior" ==
#> "0.13", CI["95%"]^ETI ~ "[" * "0.00", "0.33" * "]", italic("a")["Gunel-Dickey"] ==
#> "1.00")
#>
# extracting data frames -----------------------------
extract_stats(p1)
#> $subtitle_data
#> # A tibble: 1 × 14
#> statistic df df.error p.value
#> <dbl> <dbl> <dbl> <dbl>
#> 1 31.6 2 18.0 0.00000127
#> method effectsize estimate
#> <chr> <chr> <dbl>
#> 1 One-way analysis of means (not assuming equal variances) Omega2 0.744
#> conf.level conf.low conf.high conf.method conf.distribution n.obs expression
#> <dbl> <dbl> <dbl> <chr> <chr> <int> <list>
#> 1 0.95 0.531 1 ncp F 32 <language>
#>
#> $caption_data
#> # A tibble: 6 × 17
#> term pd prior.distribution prior.location prior.scale bf10
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 mu 1 cauchy 0 0.707 3008850.
#> 2 cyl-4 1 cauchy 0 0.707 3008850.
#> 3 cyl-6 0.780 cauchy 0 0.707 3008850.
#> 4 cyl-8 1 cauchy 0 0.707 3008850.
#> 5 sig2 1 cauchy 0 0.707 3008850.
#> 6 g_cyl 1 cauchy 0 0.707 3008850.
#> method log_e_bf10 effectsize estimate std.dev
#> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 Bayes factors for linear models 14.9 Bayesian R-squared 0.714 0.0503
#> 2 Bayes factors for linear models 14.9 Bayesian R-squared 0.714 0.0503
#> 3 Bayes factors for linear models 14.9 Bayesian R-squared 0.714 0.0503
#> 4 Bayes factors for linear models 14.9 Bayesian R-squared 0.714 0.0503
#> 5 Bayes factors for linear models 14.9 Bayesian R-squared 0.714 0.0503
#> 6 Bayes factors for linear models 14.9 Bayesian R-squared 0.714 0.0503
#> conf.level conf.low conf.high conf.method n.obs expression
#> <dbl> <dbl> <dbl> <chr> <int> <list>
#> 1 0.95 0.574 0.788 HDI 32 <language>
#> 2 0.95 0.574 0.788 HDI 32 <language>
#> 3 0.95 0.574 0.788 HDI 32 <language>
#> 4 0.95 0.574 0.788 HDI 32 <language>
#> 5 0.95 0.574 0.788 HDI 32 <language>
#> 6 0.95 0.574 0.788 HDI 32 <language>
#>
#> $pairwise_comparisons_data
#> # A tibble: 3 × 9
#> group1 group2 statistic p.value alternative distribution p.adjust.method
#> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 4 6 -6.67 0.00110 two.sided q Holm
#> 2 4 8 -10.7 0.0000140 two.sided q Holm
#> 3 6 8 -7.48 0.000257 two.sided q Holm
#> test expression
#> <chr> <list>
#> 1 Games-Howell <language>
#> 2 Games-Howell <language>
#> 3 Games-Howell <language>
#>
#> $descriptive_data
#> NULL
#>
#> $one_sample_data
#> NULL
#>
#> $tidy_data
#> NULL
#>
#> $glance_data
#> NULL
#>
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"
extract_stats(p2)
#> [[1]]
#> $subtitle_data
#> # A tibble: 1 × 13
#> statistic df p.value method effectsize
#> <dbl> <int> <dbl> <chr> <chr>
#> 1 461. 1 3.11e-102 Pearson's Chi-squared test Cramer's V (adj.)
#> estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <int>
#> 1 0.469 0.95 0.426 0.512 ncp chisq 2092
#> expression
#> <list>
#> 1 <language>
#>
#> $caption_data
#> # A tibble: 1 × 15
#> term conf.level effectsize estimate conf.low conf.high
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 Ratio 0.95 Cramers_v 0.468 0.426 0.509
#> prior.distribution prior.location prior.scale bf10
#> <chr> <dbl> <dbl> <dbl>
#> 1 independent multinomial 0 1 7.02e92
#> method conf.method log_e_bf10 n.obs expression
#> <chr> <chr> <dbl> <int> <list>
#> 1 Bayesian contingency table analysis ETI 214. 2092 <language>
#>
#> $pairwise_comparisons_data
#> NULL
#>
#> $descriptive_data
#> # A tibble: 4 × 5
#> Sex Survived counts perc .label
#> <fct> <fct> <int> <dbl> <chr>
#> 1 Female No 109 25.6 26%
#> 2 Female Yes 316 74.4 74%
#> 3 Male No 1329 79.7 80%
#> 4 Male Yes 338 20.3 20%
#>
#> $one_sample_data
#> # A tibble: 2 × 19
#> Sex counts perc N statistic df p.value method effectsize estimate
#> <fct> <int> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 Male 1667 79.7 (n =… 589. 1 3.87e-130 Chi-s… Pearson's… 0.511
#> 2 Female 425 20.3 (n =… 101. 1 1.01e- 23 Chi-s… Pearson's… 0.438
#> # ℹ 9 more variables: conf.level <dbl>, conf.low <dbl>, conf.high <dbl>,
#> # conf.method <chr>, conf.distribution <chr>, n.obs <int>, expression <list>,
#> # .label <glue>, .p.label <glue>
#>
#> $tidy_data
#> NULL
#>
#> $glance_data
#> NULL
#>
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"
#>
#> [[2]]
#> $subtitle_data
#> # A tibble: 1 × 13
#> statistic df p.value method effectsize estimate
#> <dbl> <int> <dbl> <chr> <chr> <dbl>
#> 1 3.03 1 0.0818 Pearson's Chi-squared test Cramer's V (adj.) 0.137
#> conf.level conf.low conf.high conf.method conf.distribution n.obs expression
#> <dbl> <dbl> <dbl> <chr> <chr> <int> <list>
#> 1 0.95 0 0.343 ncp chisq 109 <language>
#>
#> $caption_data
#> # A tibble: 1 × 15
#> term conf.level effectsize estimate conf.low conf.high
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 Ratio 0.95 Cramers_v 0.130 0 0.329
#> prior.distribution prior.location prior.scale bf10
#> <chr> <dbl> <dbl> <dbl>
#> 1 independent multinomial 0 1 1.03
#> method conf.method log_e_bf10 n.obs expression
#> <chr> <chr> <dbl> <int> <list>
#> 1 Bayesian contingency table analysis ETI 0.0313 109 <language>
#>
#> $pairwise_comparisons_data
#> NULL
#>
#> $descriptive_data
#> # A tibble: 4 × 5
#> Sex Survived counts perc .label
#> <fct> <fct> <int> <dbl> <chr>
#> 1 Female No 17 37.8 38%
#> 2 Female Yes 28 62.2 62%
#> 3 Male No 35 54.7 55%
#> 4 Male Yes 29 45.3 45%
#>
#> $one_sample_data
#> # A tibble: 2 × 19
#> Sex counts perc N statistic df p.value method effectsize estimate
#> <fct> <int> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 Male 64 58.7 (n = 6… 0.562 1 0.453 Chi-s… Pearson's… 0.0933
#> 2 Female 45 41.3 (n = 4… 2.69 1 0.101 Chi-s… Pearson's… 0.237
#> # ℹ 9 more variables: conf.level <dbl>, conf.low <dbl>, conf.high <dbl>,
#> # conf.method <chr>, conf.distribution <chr>, n.obs <int>, expression <list>,
#> # .label <glue>, .p.label <glue>
#>
#> $tidy_data
#> NULL
#>
#> $glance_data
#> NULL
#>
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"
#>