Tutorial: Basics

This vignette documents the basic functionalities of dabestr. It illustrates the order in which the functions are meant to be used procedurally.

The dataset is first processed into the dabestr format using the load() function. Next, the effect sizes are calculated using the effect_size() function. Finally, the estimation plots are generated using dabest_plot().

library(dabestr)

Create dataset for demo

Here, we create a dataset to illustrate how dabest functions. In this dataset, each column corresponds to a group of observations.

set.seed(12345) # Fix the seed so the results are replicable.
# pop_size = 10000 # Size of each population.
N <- 20

# Create samples
c1 <- rnorm(N, mean = 3, sd = 0.4)
c2 <- rnorm(N, mean = 3.5, sd = 0.75)
c3 <- rnorm(N, mean = 3.25, sd = 0.4)

t1 <- rnorm(N, mean = 3.5, sd = 0.5)
t2 <- rnorm(N, mean = 2.5, sd = 0.6)
t3 <- rnorm(N, mean = 3, sd = 0.75)
t4 <- rnorm(N, mean = 3.5, sd = 0.75)
t5 <- rnorm(N, mean = 3.25, sd = 0.4)
t6 <- rnorm(N, mean = 3.25, sd = 0.4)

# Add a `gender` column for coloring the data.
gender <- c(rep("Male", N / 2), rep("Female", N / 2))

# Add an `id` column for paired data plotting.
id <- 1:N

# Combine samples and gender into a DataFrame.
df <- tibble::tibble(
  `Control 1` = c1, `Control 2` = c2, `Control 3` = c3,
  `Test 1` = t1, `Test 2` = t2, `Test 3` = t3, `Test 4` = t4, `Test 5` = t5, `Test 6` = t6,
  Gender = gender, ID = id
)

df <- df %>%
  tidyr::gather(key = Group, value = Measurement, -ID, -Gender)

Note that we have 9 groups (3 Control samples and 6 Test samples). Our dataset also has a non-numerical column indicating gender, and another column indicating the identity of each observation.

This is known as a long dataset. See this writeup for more details.

knitr::kable(head(df))
Gender ID Group Measurement
Male 1 Control 1 3.234211
Male 2 Control 1 3.283786
Male 3 Control 1 2.956279
Male 4 Control 1 2.818601
Male 5 Control 1 3.242355
Male 6 Control 1 2.272818

Step 1: Loading Data

Before generating estimation plots and deriving confidence intervals for our effect sizes, we must first load the data and the corresponding groups.

To achieve this, we merely provide the DataFrame to the load() function, along with ‘x’ and ‘y’ representing the columns containing the treatment groups and measurement values, respectively. Additionally, we need to specify the two groups you wish to compare in the idx argument, either as a vector or a list.

two_groups_unpaired <- load(df,
  x = Group, y = Measurement,
  idx = c("Control 1", "Test 1")
)

Printing this dabestr object gives you a gentle greeting, as well as the comparisons that can be computed.

print(two_groups_unpaired)
#> DABESTR v2025.3.14
#> ==================
#> 
#> Good morning!
#> The current time is 09:55 AM on Wednesday February 26, 2025.
#> 
#> ffect size(s) with 95% confidence intervals will be computed for:
#> 1. Test 1 minus Control 1
#> 
#> 5000 resamples will be used to generate the effect size bootstraps.

Changing statistical parameters

You can change the width of the confidence interval that will be produced by manipulating the ci argument.

two_groups_unpaired_ci90 <- load(df,
  x = Group, y = Measurement,
  idx = c("Control 1", "Test 1"), ci = 90
)
print(two_groups_unpaired_ci90)
#> DABESTR v2025.3.14
#> ==================
#> 
#> Good morning!
#> The current time is 09:55 AM on Wednesday February 26, 2025.
#> 
#> ffect size(s) with 90% confidence intervals will be computed for:
#> 1. Test 1 minus Control 1
#> 
#> 5000 resamples will be used to generate the effect size bootstraps.

Step 2: Effect sizes

dabestr features a range of effect sizes:

The output of the load() function, a dabest object, is then passed into these effect_size() functions as a parameter.

two_groups_unpaired.mean_diff <- mean_diff(two_groups_unpaired)

print(two_groups_unpaired.mean_diff)
#> DABESTR v2025.3.14
#> ==================
#> 
#> Good morning!
#> The current time is 09:55 AM on Wednesday February 26, 2025.
#> 
#> The character(0) mean difference between Test 1 and Control 1 is 0.585 [95%CI 0.307, 0.869].
#> The p-value of the two-sided permutation t-test is 0.0004, calculated for legacy purposes only.
#> 
#> 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
#> Any p-value reported is the probability of observing the effect size (or greater),
#> assuming the null hypothesis of zero difference is true.
#> For each p-value, 5000 reshuffles of the control and test labels were performed.

For each comparison, the type of effect size is reported (here, it’s the “unpaired mean difference”). The confidence interval is reported as:

[confidenceIntervalWidth LowerBound, UpperBound]

This confidence interval is generated through bootstrap resampling. See Bootstrap Confidence Intervals for more details.

P-values and statistical tests

Permutation P values are only provided to allow analysts to satisfy a customary requirement of scientific journals. DABEST’s provision of P values does not constitute an endorsement of P values or null-hypothesis significance testing (NHST). If users need to include these in a study, we recommend that they:

  1. avoid performing NHST, i.e. do not compare P to an alpha.
  2. never refer to the P values in the ‘Results’ text.
  3. state in their Methods section that “No null-hypothesis significance testing was performed; P values are provided for legacy purposes only.”

Step 3: Producing estimation plots

To produce a Gardner-Altman estimation plot, simply use the dabest_plot() function. You can read more about its genesis and design inspiration at Robust and Beautiful Statistical Visualization.

dabest_plot() requires only one compulsory parameter to run: the dabest_effectsize_obj obtained from the effect_size() function. This means that you can quickly create plots for various effect sizes with ease.

dabest_plot(two_groups_unpaired.mean_diff)

Instead of a Gardner-Altman plot, you can produce a Cumming estimation plot by setting float_contrast = FALSE in the dabest_plot() function. This will plot the bootstrap effect sizes below the raw data, and will also display the mean (gap) and ± standard deviation of each group (vertical ends) as gapped lines. This design was inspired by Edward Tufte’s dictum to maximise the data-ink ratio.

dabest_plot(two_groups_unpaired.mean_diff,
  float_contrast = FALSE,
  contrast_ylim = c(-0.3, 1.3)
)
#> Warning in get_plot_component(plot, "guide-box"): Multiple components found;
#> returning the first one. To return all, use `return_all = TRUE`.

The dabestr package also implements a variety of estimation plot designs aimed at depicting common experimental designs.

The multi-two-group estimation plot tiles two or more Cumming plots horizontally. To create this plot, you can pass a nested list to the idx parameter when invoking the load() function for the first time.

As a result, the lower axes in the Cumming plot is effectively a forest plot, commonly used in meta-analyses to aggregate and compare data from different experiments.

multi_2group <- load(df,
  x = Group, y = Measurement,
  idx = list(
    c("Control 1", "Test 1"),
    c("Control 2", "Test 2")
  )
)
multi_2group %>%
  mean_diff() %>%
  dabest_plot()

The shared control plot displays another common experimental paradigm, where several test samples are compared against a common reference sample.

This type of Cumming plot is automatically generated if the vector passed to the parameter idx has more than two data columns.

shared_control <- load(df,
  x = Group, y = Measurement,
  idx = c(
    "Control 1", "Test 1", "Test 2", "Test 3",
    "Test 4", "Test 5", "Test 6"
  )
)

print(shared_control)
#> DABESTR v2025.3.14
#> ==================
#> 
#> Good morning!
#> The current time is 09:55 AM on Wednesday February 26, 2025.
#> 
#> ffect size(s) with 95% confidence intervals will be computed for:
#> 1. Test 1 minus Control 1
#> 2. Test 2 minus Control 1
#> 3. Test 3 minus Control 1
#> 4. Test 4 minus Control 1
#> 5. Test 5 minus Control 1
#> 6. Test 6 minus Control 1
#> 
#> 5000 resamples will be used to generate the effect size bootstraps.
shared_control.mean_diff <- mean_diff(shared_control)

print(shared_control.mean_diff)
#> DABESTR v2025.3.14
#> ==================
#> 
#> Good morning!
#> The current time is 09:55 AM on Wednesday February 26, 2025.
#> 
#> The character(0) mean difference between Test 1 and Control 1 is 0.585 [95%CI 0.307, 0.869].
#> The p-value of the two-sided permutation t-test is 0.0004, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 2 and Control 1 is -0.286 [95%CI -0.585, 0.046].
#> The p-value of the two-sided permutation t-test is 0.0946, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 3 and Control 1 is 0.007 [95%CI -0.323, 0.383].
#> The p-value of the two-sided permutation t-test is 0.9673, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 4 and Control 1 is 0.543 [95%CI 0.073, 0.997].
#> The p-value of the two-sided permutation t-test is 0.0300, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 5 and Control 1 is 0.121 [95%CI -0.082, 0.335].
#> The p-value of the two-sided permutation t-test is 0.2789, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 6 and Control 1 is 0.248 [95%CI 0.024, 0.493].
#> The p-value of the two-sided permutation t-test is 0.0492, calculated for legacy purposes only.
#> 
#> 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
#> Any p-value reported is the probability of observing the effect size (or greater),
#> assuming the null hypothesis of zero difference is true.
#> For each p-value, 5000 reshuffles of the control and test labels were performed.
dabest_plot(shared_control.mean_diff)
#> Warning in get_plot_component(plot, "guide-box"): Multiple components found;
#> returning the first one. To return all, use `return_all = TRUE`.

The dabestr package empowers you to robustly perform statistical analyses and elegantly present complex visualizations.

multi_groups <- load(df,
  x = Group, y = Measurement,
  idx = list(
    c("Control 1", "Test 1"),
    c("Control 2", "Test 2", "Test 3"),
    c("Control 3", "Test 4", "Test 5", "Test 6")
  )
)

print(multi_groups)
#> DABESTR v2025.3.14
#> ==================
#> 
#> Good morning!
#> The current time is 09:55 AM on Wednesday February 26, 2025.
#> 
#> ffect size(s) with 95% confidence intervals will be computed for:
#> 1. Test 1 minus Control 1
#> 2. Test 2 minus Control 2
#> 3. Test 3 minus Control 2
#> 4. Test 4 minus Control 3
#> 5. Test 5 minus Control 3
#> 6. Test 6 minus Control 3
#> 
#> 5000 resamples will be used to generate the effect size bootstraps.
multi_groups.mean_diff <- mean_diff(multi_groups)

print(multi_groups.mean_diff)
#> DABESTR v2025.3.14
#> ==================
#> 
#> Good morning!
#> The current time is 09:55 AM on Wednesday February 26, 2025.
#> 
#> The character(0) mean difference between Test 1 and Control 1 is 0.585 [95%CI 0.307, 0.869].
#> The p-value of the two-sided permutation t-test is 0.0004, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 2 and Control 2 is -1.058 [95%CI -1.52, -0.577].
#> The p-value of the two-sided permutation t-test is 0.0002, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 3 and Control 2 is -0.765 [95%CI -1.236, -0.252].
#> The p-value of the two-sided permutation t-test is 0.0059, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 4 and Control 3 is 0.282 [95%CI -0.188, 0.771].
#> The p-value of the two-sided permutation t-test is 0.2714, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 5 and Control 3 is -0.14 [95%CI -0.402, 0.156].
#> The p-value of the two-sided permutation t-test is 0.3295, calculated for legacy purposes only.
#> 
#> The character(0) mean difference between Test 6 and Control 3 is -0.014 [95%CI -0.284, 0.294].
#> The p-value of the two-sided permutation t-test is 0.9286, calculated for legacy purposes only.
#> 
#> 5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
#> Any p-value reported is the probability of observing the effect size (or greater),
#> assuming the null hypothesis of zero difference is true.
#> For each p-value, 5000 reshuffles of the control and test labels were performed.
dabest_plot(multi_groups.mean_diff)
#> Warning in get_plot_component(plot, "guide-box"): Multiple components found;
#> returning the first one. To return all, use `return_all = TRUE`.

NOTE: Using wide datasets

Currently dabestr does not support the use of ‘wide’ data. To convert datasets from ‘wide’ to ‘long’/‘tidy’, consider taking a look at gather() as part of the tidyr package.