Extracting data frames or expressions from {ggstatsplot} plots
Source: R/extract-stats.R
extract_stats.RdExtracting data frames or expressions from {ggstatsplot} plots
Value
A list of tibbles containing summaries of various statistical analyses. The exact details included will depend on the function.
Details
These are convenience functions to extract data frames or expressions with
statistical details that are used to create expressions displayed in
{ggstatsplot} plots as subtitle, caption, etc. Note that all of this
analysis is carried out by the {statsExpressions}
package. And so if you
are using these functions only to extract data frames, you are better off
using that package.
The only exception is the ggcorrmat() function. But, if a data frame is
what you want, you shouldn't be using ggcorrmat() anyway. You can use
correlation::correlation() function which provides tidy data frames by
default.
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)
#> 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)
#> 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
#>
extract_stats(p2[[1L]])
#> $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 Yes 316 74.4 74%
#> 2 Male Yes 338 20.3 20%
#> 3 Female No 109 25.6 26%
#> 4 Male No 1329 79.7 80%
#>
#> $one_sample_data
#> # A tibble: 2 × 10
#> Sex counts perc N statistic df p.value method .label .p.label
#> <fct> <int> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <glue> <glue>
#> 1 Male 1667 79.7 (n = 1,6… 589. 1 3.87e-130 Chi-s… list(… list(~i…
#> 2 Female 425 20.3 (n = 425) 101. 1 1.01e- 23 Chi-s… list(… list(~i…
#>
#> $tidy_data
#> NULL
#>
#> $glance_data
#> NULL
#>
extract_stats(p2[[2L]])
#> $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 0.131 0 0.328
#> 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 Yes 28 62.2 62%
#> 2 Male Yes 29 45.3 45%
#> 3 Female No 17 37.8 38%
#> 4 Male No 35 54.7 55%
#>
#> $one_sample_data
#> # A tibble: 2 × 10
#> Sex counts perc N statistic df p.value method .label .p.label
#> <fct> <int> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <glue> <glue>
#> 1 Male 64 58.7 (n = 64) 0.562 1 0.453 Chi-squa… list(… list(~i…
#> 2 Female 45 41.3 (n = 45) 2.69 1 0.101 Chi-squa… list(… list(~i…
#>
#> $tidy_data
#> NULL
#>
#> $glance_data
#> NULL
#>