Dallas’s miscellanious helper functions, namely for summarizing and reporting data.
You can install the development version of novahelpers from GitHub with:
# install.packages("devtools")
devtools::install_github("DallasNovakowski/novahelpers")library(tidyverse)
library(emmeans)
library(palmerpenguins) # dataset
library(novahelpers)Load data
df <- palmerpenguins::penguins
# drop rows with missing values
df <- df[complete.cases(df)==TRUE, ]Summarizes our relevant descriptive stats
flipper_summary <- run_summary(df, c("sex", "species"), "flipper_length_mm")
knitr::kable(flipper_summary)| sex | species | missing_count | n | mean | std_dev | se | loci | upci | min | max | y16 | y25 | y50 | y75 | y84 | coef_var | skewness | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| female | Adelie | 0 | 73 | 187.7945 | 5.595035 | 0.6548493 | 186.5110 | 189.0780 | 172 | 202 | 182.52 | 185.00 | 188.0 | 191.00 | 193.00 | 0.0297934 | -0.2983825 | 3.487783 |
| female | Chinstrap | 0 | 34 | 191.7353 | 5.754096 | 0.9868194 | 189.8011 | 193.6695 | 178 | 202 | 187.00 | 187.25 | 192.0 | 195.75 | 198.00 | 0.0300106 | -0.4085811 | 2.680323 |
| female | Gentoo | 0 | 58 | 212.7069 | 3.897857 | 0.5118136 | 211.7037 | 213.7101 | 203 | 222 | 209.00 | 210.00 | 212.0 | 215.00 | 217.00 | 0.0183250 | 0.2175358 | 2.620230 |
| male | Adelie | 0 | 73 | 192.4110 | 6.599317 | 0.7723917 | 190.8971 | 193.9248 | 178 | 210 | 185.00 | 189.00 | 193.0 | 197.00 | 198.00 | 0.0342980 | 0.0399511 | 3.012400 |
| male | Chinstrap | 0 | 34 | 199.9118 | 5.976559 | 1.0249713 | 197.9028 | 201.9207 | 187 | 212 | 194.28 | 196.00 | 200.5 | 203.00 | 205.72 | 0.0298960 | 0.1802548 | 2.499375 |
| male | Gentoo | 0 | 61 | 221.5410 | 5.673252 | 0.7263855 | 220.1173 | 222.9647 | 208 | 231 | 215.00 | 218.00 | 221.0 | 225.00 | 228.40 | 0.0256081 | -0.1079373 | 2.351827 |
Running ANOVA, nothing i take credit for.
# basic fit
# Setting contrasts
contrasts(df$species) <- contr.sum
contrasts(df$sex) <- contr.sum
# consider just using afex - https://www.rdocumentation.org/packages/afex/versions/1.3-0
#a <- aov_ez("id", "log_rt", fhch, between = "task", within = c("stimulus", "length"))
# Type 3 anova with orthogonal contrasts
flipper_fit <- lm(flipper_length_mm ~ species*sex, data = df)
flipper_anova <- car::Anova(flipper_fit, type = 3)
flipper_emmeans <- emmeans::emmeans(flipper_fit, specs = pairwise ~ species*sex)Combines our run_summary() output with emmeans object
flipper_summary <- merge_emmeans_summary(flipper_summary, flipper_emmeans)
knitr::kable(flipper_summary)| sex | species | missing_count | n | mean | std_dev | se | loci | upci | min | max | y16 | y25 | y50 | y75 | y84 | coef_var | skewness | kurtosis | emmean | emmean_se | emmean_loci | emmean_upci |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| female | Adelie | 0 | 73 | 187.7945 | 5.595035 | 0.6548493 | 186.5110 | 189.0780 | 172 | 202 | 182.52 | 185.00 | 188.0 | 191.00 | 193.00 | 0.0297934 | -0.2983825 | 3.487783 | 187.7945 | 0.6618982 | 186.4924 | 189.0966 |
| female | Chinstrap | 0 | 34 | 191.7353 | 5.754096 | 0.9868194 | 189.8011 | 193.6695 | 178 | 202 | 187.00 | 187.25 | 192.0 | 195.75 | 198.00 | 0.0300106 | -0.4085811 | 2.680323 | 191.7353 | 0.9698693 | 189.8273 | 193.6433 |
| female | Gentoo | 0 | 58 | 212.7069 | 3.897857 | 0.5118136 | 211.7037 | 213.7101 | 203 | 222 | 209.00 | 210.00 | 212.0 | 215.00 | 217.00 | 0.0183250 | 0.2175358 | 2.620230 | 212.7069 | 0.7425722 | 211.2461 | 214.1677 |
| male | Adelie | 0 | 73 | 192.4110 | 6.599317 | 0.7723917 | 190.8971 | 193.9248 | 178 | 210 | 185.00 | 189.00 | 193.0 | 197.00 | 198.00 | 0.0342980 | 0.0399511 | 3.012400 | 192.4110 | 0.6618982 | 191.1088 | 193.7131 |
| male | Chinstrap | 0 | 34 | 199.9118 | 5.976559 | 1.0249713 | 197.9028 | 201.9207 | 187 | 212 | 194.28 | 196.00 | 200.5 | 203.00 | 205.72 | 0.0298960 | 0.1802548 | 2.499375 | 199.9118 | 0.9698693 | 198.0038 | 201.8197 |
| male | Gentoo | 0 | 61 | 221.5410 | 5.673252 | 0.7263855 | 220.1173 | 222.9647 | 208 | 231 | 215.00 | 218.00 | 221.0 | 225.00 | 228.40 | 0.0256081 | -0.1079373 | 2.351827 | 221.5410 | 0.7240820 | 220.1165 | 222.9654 |
See https://aosmith.rbind.io/2019/04/15/custom-contrasts-emmeans/ and https://cran.r-project.org/web/packages/emmeans/vignettes/comparisons.html#linfcns for custom contrasts
flipper_just_emmeans <- emmeans::emmeans(flipper_fit, specs = ~ species*sex)
# correponds to row numbers in flipper_just_emmeans
chinstrap_female = c(0, 1, 0, 0, 0, 0)
adelie_female = c(1, 0, 0, 0, 0, 0)
contrast(flipper_just_emmeans, method = list("Adelie female - Chinstrap female" = adelie_female-chinstrap_female) )
#> contrast estimate SE df t.ratio p.value
#> Adelie female - Chinstrap female -3.94 1.17 327 -3.356 0.0009Merges raw comparisons and statistical tests with cohen’s d effect sizes. Currently only does all tukey-adjusted comparisons
flipper_contrasts <- calculate_and_merge_effect_sizes(flipper_emmeans, flipper_fit)
#> Since 'object' is a list, we are using the contrasts already present.
knitr::kable(flipper_contrasts)| contrast | estimate | SE | df_error | t.ratio | p | d | d_se | d_ci_low | d_ci_high |
|---|---|---|---|---|---|---|---|---|---|
| Adelie female - Chinstrap female | -3.9407736 | 1.1742043 | 327 | -3.3561226 | 0.0113141 | -0.6968332 | 0.2094108 | -1.1087955 | -0.2848709 |
| Adelie female - Gentoo female | -24.9123760 | 0.9947476 | 327 | -25.0439178 | 0.0000000 | -4.4051682 | 0.2461951 | -4.8894943 | -3.9208420 |
| Adelie female - Adelie male | -4.6164384 | 0.9360655 | 327 | -4.9317473 | 0.0000191 | -0.8163086 | 0.1685709 | -1.1479290 | -0.4846883 |
| Adelie female - Chinstrap male | -12.1172442 | 1.1742043 | 327 | -10.3195367 | 0.0000000 | -2.1426498 | 0.2238977 | -2.5831115 | -1.7021881 |
| Adelie female - Gentoo male | -33.7464631 | 0.9810219 | 327 | -34.3992957 | 0.0000000 | -5.9672688 | 0.2907559 | -6.5392570 | -5.3952806 |
| Chinstrap female - Gentoo female | -20.9716024 | 1.2214990 | 327 | -17.1687430 | 0.0000000 | -3.7083350 | 0.2601543 | -4.2201223 | -3.1965476 |
| Chinstrap female - Adelie male | -0.6756648 | 1.1742043 | 327 | -0.5754235 | 0.9925701 | -0.1194754 | 0.2076830 | -0.5280388 | 0.2890879 |
| Chinstrap female - Chinstrap male | -8.1764706 | 1.3716023 | 327 | -5.9612547 | 0.0000001 | -1.4458166 | 0.2490378 | -1.9357351 | -0.9558982 |
| Chinstrap female - Gentoo male | -29.8056895 | 1.2103475 | 327 | -24.6257285 | 0.0000000 | -5.2704356 | 0.2971169 | -5.8549373 | -4.6859339 |
| Gentoo female - Adelie male | 20.2959376 | 0.9947476 | 327 | 20.4031038 | 0.0000000 | 3.5888595 | 0.2250201 | 3.1461898 | 4.0315293 |
| Gentoo female - Chinstrap male | 12.7951318 | 1.2214990 | 327 | 10.4749425 | 0.0000000 | 2.2625183 | 0.2334102 | 1.8033432 | 2.7216935 |
| Gentoo female - Gentoo male | -8.8340871 | 1.0371635 | 327 | -8.5175452 | 0.0000000 | -1.5621007 | 0.1933027 | -1.9423745 | -1.1818268 |
| Adelie male - Chinstrap male | -7.5008058 | 1.1742043 | 327 | -6.3879905 | 0.0000000 | -1.3263412 | 0.2140100 | -1.7473513 | -0.9053311 |
| Adelie male - Gentoo male | -29.1300247 | 0.9810219 | 327 | -29.6935513 | 0.0000000 | -5.1509602 | 0.2658223 | -5.6738977 | -4.6280226 |
| Chinstrap male - Gentoo male | -21.6292189 | 1.2103475 | 327 | -17.8702550 | 0.0000000 | -3.8246190 | 0.2610972 | -4.3382611 | -3.3109768 |
# Extract effect size (partial eta squared) from anova
flipper_anova_pes <- effectsize::eta_squared(flipper_anova,
alternative="two.sided",
verbose = FALSE)
# Convert anova table into dataframe
flipper_anova <- data.frame(flipper_anova)
flipper_anova <- flipper_anova[!(rownames(flipper_anova) == "(Intercept)"), ]
# import effect size estimates and confidence intervals to anova dataframe
flipper_anova[1:3,"pes_ci95_lo"] <- flipper_anova_pes$CI_low
flipper_anova[1:3,"pes_ci95_hi"] <- flipper_anova_pes$CI_high
flipper_anova[1:3,"pes"] <- flipper_anova_pes$Eta2
# round all numeric columns to 2 decimal places
flipper_anova <- flipper_anova %>%
dplyr::mutate_if(is.numeric, function(x) round(x, 2))Used for extracting results from anova table into reportable text
report_tidy_anova_etaci(flipper_anova, "sex")
#> [1] "<U+03B7>^2^ = 0.27, 95% CI [0.2, 0.35], *F*(1, 327) = 122, *p* < .001"You can use this inline function inline as well, η2 = 0.27, 95% CI [0.2, 0.35], F(1, 327) = 122, p < .001
flipper_sex_ttest <- t.test(flipper_length_mm ~ sex, data=df) %>%
report::report() %>%
data.frame() %>%
janitor::clean_names()
#> Warning: Unable to retrieve data from htest object.
#> Returning an approximate effect size using t_to_d().
knitr::kable(flipper_sex_ttest)| parameter | group | mean_group1 | mean_group2 | difference | ci | ci_low | ci_high | t | df_error | p | method | alternative | d | d_ci_low | d_ci_high |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| flipper_length_mm | sex | 197.3636 | 204.506 | -7.142316 | 0.95 | -10.06481 | -4.219821 | -4.807866 | 325.2784 | 2.3e-06 | Welch Two Sample t-test | two.sided | -0.5331566 | -0.7539304 | -0.3115901 |
report_tidy_t(flipper_sex_ttest, teststat = T)
#> [1] "*d* = -0.53, 95% CI [-0.75, -0.31], *t* (325.28) = -4.81, *p* < .001"Works on t-tests, d = -0.53, 95% CI [-0.75, -0.31], t (325.28) = -4.81, p < .001
… and it works on emmmean contrasts from ANOVA, d = -0.82, 95% CI [-1.15, -0.48], p < .001
report_tidy_t(
flipper_contrasts[flipper_contrasts$contrast =="Adelie female - Adelie male",],
italicize = FALSE,
ci = FALSE)
#> [1] "d = -0.82, p < .001"#Just for this plotting workflow
library(ggpp) # for position_dodge2nudge
library(cowplot)
library(ggdist)
library(ggpubr) # significance brackets
library(ggtext)
# define-colors
nova_palette <- c("#4ED0CD","#FFD966") ## my custom palette
# very important to get the right amount of fading for your palette choice
dark_aqua <- colorspace::darken("#4ED0CD", amount = 0.5, space = "HLS") ## darken colors
dark_yellow <- colorspace::darken("#FFD966", amount = 0.35, space = "HLS") ## darken colors
contrast_nova_palette <- c(dark_aqua, dark_yellow)
# Define color palette
nova_palette <- c("#78AAA9", "#FFDB6E")
ggplot(data = df,
aes(y = flipper_length_mm, # our dependent/response/outcome variable
x = species, # our grouping/independent/predictor variable
fill = sex)) + # our third grouping/independent/interaction variable
ggdist::stat_slab( alpha = .4,
adjust = 2,
side = "left",
scale = 0.4,
show.legend = F,
position = position_dodge(width = .6),
.width = c(.50, 1),
aes(fill_ramp = after_stat(level))) +
## Add stacked dots
ggdist::stat_dots(alpha = .35,
side = "left",
scale = 0.4,
aes(color = sex),
position = position_dodge(width = .6)) +
geom_text(data = flipper_summary,
aes(x = species,
y = mean,
label = round(mean,1)),
color="black",
size = 2,
position = position_dodge2nudge(x = .08, width = .6)) +
geom_pointrange(data = flipper_summary, # our externally-defined summary dataframe
aes(x = species, # our independent variable
y = mean, # our outcome/dependent variable
ymin = loci, # lower-bound confidence interval
ymax = upci # upper-bound confidence interval
),
show.legend = F,
position = position_dodge2nudge(x= -.03, width = .6)) +
scale_colour_manual(values = contrast_nova_palette,
aesthetics = c("fill", "color")) +
## define amount of fading
ggdist::scale_fill_ramp_discrete(range = c(0.2, 1),
aesthetics = c("fill_ramp")) +
labs(subtitle = paste0("**species**: ", report_tidy_anova_etaci(flipper_anova,"species"), "<br>",
"**sex**: ", report_tidy_anova_etaci(flipper_anova,"sex"), "<br>",
"**species*sex**: ",report_tidy_anova_etaci(flipper_anova,"species:sex"))
) + # add a density slab
ggpubr::geom_bracket(inherit.aes = FALSE, # necessary for factorial design
tip.length = 0.02,
vjust = -0.6,
xmin = 1.12, # You need to play with these by hand
xmax = 1.82,
y.position = 210 ,
label.size = 2.1,
label = paste0(
report_tidy_t(
flipper_contrasts[flipper_contrasts$contrast =="Chinstrap female - Adelie male",],
italicize = FALSE,
ci = FALSE)) # content of your bracket text
) +
ggpubr::geom_bracket(inherit.aes = FALSE,
tip.length = -0.02,
vjust = 0.3,
xmin = 2.12,
xmax = 3.12,
y.position = 192 ,
label.size = 2.1,
label = paste0(
report_tidy_t(
flipper_contrasts[flipper_contrasts$contrast =="Chinstrap male - Gentoo male",],
italicize = FALSE,
ci = FALSE)) # content of your bracket text
) +
theme_basic() +
guides(fill_ramp = "none")
For posterity, here is a plot with just faded density slabs
fadeslab <- ggplot(data = df,
aes(y = flipper_length_mm, # our dependent/response/outcome variable
x = species, # our grouping/independent/predictor variable
fill = sex)) + # our third grouping/independent/interaction variable
ggdist::stat_slab(
side = "left",
scale = 0.5,
position = position_dodge(width = .6),
.width = c(.50, 1),
aes(fill_ramp = after_stat(level))) +
ggdist::scale_fill_ramp_discrete(range = c(0.2, 1),
aesthetics = c("fill_ramp")) +
scale_colour_manual(values = nova_palette,
aesthetics = c("fill")) +
guides(fill_ramp = "none") +
geom_text(data = flipper_summary,
aes(x = species,
y = y25 - .1*sd(df$flipper_length_mm),
label = round(mean,1)),
color="black",
size = 2.5,
position = position_dodge2nudge(x= -.07, width = .6)) +
geom_pointrange(data = flipper_summary, # our externally-defined summary dataframe
aes(x = species, # our independent variable
y = mean, # our outcome/dependent variable
ymin = loci, # lower-bound confidence interval
ymax = upci # upper-bound confidence interval
),
show.legend = F,
position = position_dodge2nudge(x= -.05, width = .6)) +
theme_basic()
fadeslab
Further posterity, fading based on standard deviation
ggplot(data = df,
aes(y = flipper_length_mm, # our dependent/response/outcome variable
x = species, # our grouping/independent/predictor variable
fill = sex)) + # our third grouping/independent/interaction variable
ggdist::stat_slab(
side = "left",
scale = 0.5,
position = position_dodge(width = .6),
.width = c(.68, 1),
aes(fill_ramp = after_stat(level))) +
ggdist::scale_fill_ramp_discrete(range = c(0.2, 1),
aesthetics = c("fill_ramp")) +
scale_colour_manual(values = nova_palette,
aesthetics = c("fill")) +
guides(fill_ramp = "none") +
geom_text(data = flipper_summary,
aes(x = species,
y = y16 - .07*sd(df$flipper_length_mm),
label = round(mean,1)),
color="black",
size = 2.5,
position = position_dodge2nudge(x= -.07, width = .6)) +
geom_pointrange(data = flipper_summary, # our externally-defined summary dataframe
aes(x = species, # our independent variable
y = mean, # our outcome/dependent variable
ymin = loci, # lower-bound confidence interval
ymax = upci # upper-bound confidence interval
),
show.legend = F,
position = position_dodge2nudge(x= -.05, width = .6)) +
theme_basic()
Often, you may need to run your analysis on multiple variables (e.g.,
mediators). The many family of functions lets you designate multiple
dependent variables to run summaries, lm, emmeans, anovas, and merging
emmeans/summaries. These functions each return a list corresponding to
your dependent variable name.
This is a useful function for running run_summary() over many variables, and storing them all in one place
dv_vars <- c("bill_length_mm", "flipper_length_mm", "body_mass_g")
grouping_vars <- c("species", "sex")
summary_list <- run_many_summaries(data = df,
summarization_vars = dv_vars,
group_vars = grouping_vars)
summary_list
#> $bill_length_mm
#> # A tibble: 6 x 19
#> # Groups: species [3]
#> species sex missing_count n mean std_dev se loci upci min max
#> <fct> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Adelie fema~ 0 73 37.3 2.03 0.237 36.8 37.7 32.1 42.2
#> 2 Adelie male 0 73 40.4 2.28 0.267 39.9 40.9 34.6 46
#> 3 Chinstr~ fema~ 0 34 46.6 3.11 0.533 45.5 47.6 40.9 58
#> 4 Chinstr~ male 0 34 51.1 1.56 0.268 50.6 51.6 48.5 55.8
#> 5 Gentoo fema~ 0 58 45.6 2.05 0.269 45.0 46.1 40.9 50.5
#> 6 Gentoo male 0 61 49.5 2.72 0.348 48.8 50.2 44.4 59.6
#> # i 8 more variables: y16 <dbl>, y25 <dbl>, y50 <dbl>, y75 <dbl>, y84 <dbl>,
#> # coef_var <dbl>, skewness <dbl>, kurtosis <dbl>
#>
#> $flipper_length_mm
#> # A tibble: 6 x 19
#> # Groups: species [3]
#> species sex missing_count n mean std_dev se loci upci min max
#> <fct> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 Adelie fema~ 0 73 188. 5.60 0.655 187. 189. 172 202
#> 2 Adelie male 0 73 192. 6.60 0.772 191. 194. 178 210
#> 3 Chinstr~ fema~ 0 34 192. 5.75 0.987 190. 194. 178 202
#> 4 Chinstr~ male 0 34 200. 5.98 1.02 198. 202. 187 212
#> 5 Gentoo fema~ 0 58 213. 3.90 0.512 212. 214. 203 222
#> 6 Gentoo male 0 61 222. 5.67 0.726 220. 223. 208 231
#> # i 8 more variables: y16 <dbl>, y25 <dbl>, y50 <dbl>, y75 <dbl>, y84 <dbl>,
#> # coef_var <dbl>, skewness <dbl>, kurtosis <dbl>
#>
#> $body_mass_g
#> # A tibble: 6 x 19
#> # Groups: species [3]
#> species sex missing_count n mean std_dev se loci upci min max
#> <fct> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 Adelie fema~ 0 73 3369. 269. 31.5 3307. 3431. 2850 3900
#> 2 Adelie male 0 73 4043. 347. 40.6 3964. 4123. 3325 4775
#> 3 Chinstr~ fema~ 0 34 3527. 285. 48.9 3431. 3623. 2700 4150
#> 4 Chinstr~ male 0 34 3939. 362. 62.1 3817. 4061. 3250 4800
#> 5 Gentoo fema~ 0 58 4680. 282. 37.0 4607. 4752. 3950 5200
#> 6 Gentoo male 0 61 5485. 313. 40.1 5406. 5563. 4750 6300
#> # i 8 more variables: y16 <dbl>, y25 <dbl>, y50 <dbl>, y75 <dbl>, y84 <dbl>,
#> # coef_var <dbl>, skewness <dbl>, kurtosis <dbl>lm_results_list <- run_many_lm(data = df,
independent_variable = grouping_vars,
dependent_variables = dv_vars)
# or just run
# results <- list()
# for (var in c("bill_length_mm", "flipper_length_mm", "body_mass_g")) {
# formula <- as.formula(paste(var, "~", paste(c("species", "sex"),
# collapse = "*")))
# model <- lm(formula, data = df)
# results[[var]] <- model
# }emmeans_specs <- ~ species * sex
emmeans_results_list <- run_many_emmeans(lm_results_list, emmeans_specs)
emmeans_results_list
#> $bill_length_mm
#> species sex emmean SE df lower.CL upper.CL
#> Adelie female 37.3 0.271 327 36.7 37.8
#> Chinstrap female 46.6 0.397 327 45.8 47.4
#> Gentoo female 45.6 0.304 327 45.0 46.2
#> Adelie male 40.4 0.271 327 39.9 40.9
#> Chinstrap male 51.1 0.397 327 50.3 51.9
#> Gentoo male 49.5 0.296 327 48.9 50.1
#>
#> Confidence level used: 0.95
#>
#> $flipper_length_mm
#> species sex emmean SE df lower.CL upper.CL
#> Adelie female 188 0.662 327 186 189
#> Chinstrap female 192 0.970 327 190 194
#> Gentoo female 213 0.743 327 211 214
#> Adelie male 192 0.662 327 191 194
#> Chinstrap male 200 0.970 327 198 202
#> Gentoo male 222 0.724 327 220 223
#>
#> Confidence level used: 0.95
#>
#> $body_mass_g
#> species sex emmean SE df lower.CL upper.CL
#> Adelie female 3369 36.2 327 3298 3440
#> Chinstrap female 3527 53.1 327 3423 3632
#> Gentoo female 4680 40.6 327 4600 4760
#> Adelie male 4043 36.2 327 3972 4115
#> Chinstrap male 3939 53.1 327 3835 4043
#> Gentoo male 5485 39.6 327 5407 5563
#>
#> Confidence level used: 0.95anova_results_list <- run_many_anovas(lm_results_list, type = "III")
anova_results_list
#> $bill_length_mm
#> Anova Table (Type III tests)
#>
#> Response: bill_length_mm
#> Sum Sq Df F value Pr(>F)
#> (Intercept) 609824 1 1.1373e+05 <2e-16 ***
#> species 6973 2 6.5021e+02 <2e-16 ***
#> sex 1116 1 2.0806e+02 <2e-16 ***
#> species:sex 24 2 2.2841e+00 0.1035
#> Residuals 1753 327
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> $flipper_length_mm
#> Anova Table (Type III tests)
#>
#> Response: flipper_length_mm
#> Sum Sq Df F value Pr(>F)
#> (Intercept) 12136922 1 3.7949e+05 < 2.2e-16 ***
#> species 50076 2 7.8288e+02 < 2.2e-16 ***
#> sex 3902 1 1.2202e+02 < 2.2e-16 ***
#> species:sex 329 2 5.1442e+00 0.006314 **
#> Residuals 10458 327
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> $body_mass_g
#> Anova Table (Type III tests)
#>
#> Response: body_mass_g
#> Sum Sq Df F value Pr(>F)
#> (Intercept) 5232595969 1 54661.828 < 2.2e-16 ***
#> species 143001222 2 746.924 < 2.2e-16 ***
#> sex 29851220 1 311.838 < 2.2e-16 ***
#> species:sex 1676557 2 8.757 0.0001973 ***
#> Residuals 31302628 327
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary_list <- merge_many_emmeans_summary(summary_list,
emmeans_results_list,
grouping_vars = c("species", "sex"))
summary_list
#> $bill_length_mm
#> species sex missing_count n mean std_dev se loci
#> 1 Adelie female 0 73 37.25753 2.028883 0.2374628 36.79211
#> 2 Adelie male 0 73 40.39041 2.277131 0.2665180 39.86804
#> 3 Chinstrap female 0 34 46.57353 3.108669 0.5331324 45.52859
#> 4 Chinstrap male 0 34 51.09412 1.564558 0.2683196 50.56821
#> 5 Gentoo female 0 58 45.56379 2.051247 0.2693419 45.03588
#> 6 Gentoo male 0 61 49.47377 2.720594 0.3483364 48.79103
#> upci min max y16 y25 y50 y75 y84 coef_var skewness
#> 1 37.72296 32.1 42.2 35.500 35.900 37.00 38.800 39.500 0.05445564 0.02785804
#> 2 40.91279 34.6 46.0 38.008 39.000 40.60 41.500 42.396 0.05637800 0.05642379
#> 3 47.61847 40.9 58.0 43.976 45.425 46.30 47.375 49.324 0.06674755 1.32612795
#> 4 51.62002 48.5 55.8 49.528 50.050 50.95 51.975 52.144 0.03062111 0.80466563
#> 5 46.09170 40.9 50.5 43.312 43.850 45.50 46.875 47.500 0.04501923 -0.03016883
#> 6 50.15651 44.4 59.6 46.760 48.100 49.50 50.500 51.180 0.05499064 0.91023881
#> kurtosis emmean emmean_se df emmean_loci emmean_upci
#> 1 2.690518 37.25753 0.2710177 327 36.72438 37.79069
#> 2 3.263138 40.39041 0.2710177 327 39.85725 40.92357
#> 3 6.753174 46.57353 0.3971180 327 45.79230 47.35476
#> 4 3.965757 51.09412 0.3971180 327 50.31289 51.87535
#> 5 2.596070 45.56379 0.3040500 327 44.96565 46.16193
#> 6 5.219030 49.47377 0.2964791 327 48.89052 50.05702
#>
#> $flipper_length_mm
#> species sex missing_count n mean std_dev se loci
#> 1 Adelie female 0 73 187.7945 5.595035 0.6548493 186.5110
#> 2 Adelie male 0 73 192.4110 6.599317 0.7723917 190.8971
#> 3 Chinstrap female 0 34 191.7353 5.754096 0.9868194 189.8011
#> 4 Chinstrap male 0 34 199.9118 5.976558 1.0249713 197.9028
#> 5 Gentoo female 0 58 212.7069 3.897856 0.5118136 211.7037
#> 6 Gentoo male 0 61 221.5410 5.673252 0.7263855 220.1173
#> upci min max y16 y25 y50 y75 y84 coef_var skewness
#> 1 189.0780 172 202 182.52 185.00 188.0 191.00 193.00 0.02979339 -0.29838252
#> 2 193.9248 178 210 185.00 189.00 193.0 197.00 198.00 0.03429803 0.03995108
#> 3 193.6695 178 202 187.00 187.25 192.0 195.75 198.00 0.03001063 -0.40858105
#> 4 201.9207 187 212 194.28 196.00 200.5 203.00 205.72 0.02989598 0.18025476
#> 5 213.7101 203 222 209.00 210.00 212.0 215.00 217.00 0.01832501 0.21753580
#> 6 222.9647 208 231 215.00 218.00 221.0 225.00 228.40 0.02560814 -0.10793726
#> kurtosis emmean emmean_se df emmean_loci emmean_upci
#> 1 3.487783 187.7945 0.6618982 327 186.4924 189.0966
#> 2 3.012400 192.4110 0.6618982 327 191.1088 193.7131
#> 3 2.680323 191.7353 0.9698693 327 189.8273 193.6433
#> 4 2.499375 199.9118 0.9698693 327 198.0038 201.8197
#> 5 2.620230 212.7069 0.7425722 327 211.2461 214.1677
#> 6 2.351827 221.5410 0.7240820 327 220.1165 222.9654
#>
#> $body_mass_g
#> species sex missing_count n mean std_dev se loci
#> 1 Adelie female 0 73 3368.836 269.3801 31.52856 3307.040
#> 2 Adelie male 0 73 4043.493 346.8116 40.59122 3963.934
#> 3 Chinstrap female 0 34 3527.206 285.3339 48.93436 3431.295
#> 4 Chinstrap male 0 34 3938.971 362.1376 62.10608 3817.243
#> 5 Gentoo female 0 58 4679.741 281.5783 36.97304 4607.274
#> 6 Gentoo male 0 61 5484.836 313.1586 40.09585 5406.248
#> upci min max y16 y25 y50 y75 y84 coef_var skewness
#> 1 3430.632 2850 3900 3063 3175.00 3400 3550.00 3700 0.07996238 -0.06386450
#> 2 4123.052 3325 4775 3713 3800.00 4000 4300.00 4400 0.08577028 0.15892067
#> 3 3623.117 2700 4150 3307 3362.50 3550 3693.75 3754 0.08089517 -0.61136344
#> 4 4060.699 3250 4800 3614 3731.25 3950 4100.00 4300 0.09193710 0.23421597
#> 5 4752.209 3950 5200 4400 4462.50 4700 4875.00 4950 0.06016963 -0.32891168
#> 6 5563.424 4750 6300 5180 5300.00 5500 5700.00 5800 0.05709534 0.07737749
#> kurtosis emmean emmean_se df emmean_loci emmean_upci
#> 1 2.175554 3368.836 36.21222 327 3297.597 3440.074
#> 2 2.354934 4043.493 36.21222 327 3972.255 4114.731
#> 3 4.143221 3527.206 53.06120 327 3422.821 3631.590
#> 4 2.768810 3938.971 53.06120 327 3834.586 4043.355
#> 5 2.627605 4679.741 40.62586 327 4599.820 4759.662
#> 6 2.785910 5484.836 39.61427 327 5406.905 5562.767