For this vignette, we will create and use a synthetic dataset.

```
library(dplyr)
set.seed(54321)
N = 40
c1 <- rnorm(N, mean = 100, sd = 25)
c2 <- rnorm(N, mean = 100, sd = 50)
g1 <- rnorm(N, mean = 120, sd = 25)
g2 <- rnorm(N, mean = 80, sd = 50)
g3 <- rnorm(N, mean = 100, sd = 12)
g4 <- rnorm(N, mean = 100, sd = 50)
gender <- c(rep('Male', N/2), rep('Female', N/2))
id <- 1: N
wide.data <-
tibble::tibble(
Control1 = c1, Control2 = c2,
Group1 = g1, Group2 = g2, Group3 = g3, Group4 = g4,
Gender = gender, ID = id)
my.data <-
wide.data %>%
tidyr::gather(key = Group, value = Measurement, -ID, -Gender)
head(my.data)
```

```
## # A tibble: 6 x 4
## Gender ID Group Measurement
## <chr> <int> <chr> <dbl>
## 1 Male 1 Control1 95.5
## 2 Male 2 Control1 76.8
## 3 Male 3 Control1 80.4
## 4 Male 4 Control1 58.7
## 5 Male 5 Control1 89.8
## 6 Male 6 Control1 72.6
```

This dataset is a tidy dataset, where each observation (datapoint) is a row, and each variable (or associated metadata) is a column. `dabestr`

requires that data be in this form, as do other popular R packages for data visualization and analysis.

The `dabest`

function is the main workhorse of the `dabestr`

package. To create a two-group estimation plot (*aka* a Gardner-Altman plot), specify:

- the
`x`

and`y`

columns, - whether the comparison is
`paired = TRUE`

or`paired = FALSE`

, - and the groups to be compared via
`idx`

.

```
library(dabestr)
two.group.unpaired <-
my.data %>%
dabest(Group, Measurement,
# The idx below passes "Control" as the control group,
# and "Group1" as the test group. The mean difference
# will be computed as mean(Group1) - mean(Control1).
idx = c("Control1", "Group1"),
paired = FALSE)
# Calling the object automatically prints out a summary.
two.group.unpaired
```

```
## DABEST (Data Analysis with Bootstrap Estimation) v0.2.2
## =======================================================
##
## Variable: Measurement
##
## Unpaired mean difference of Group1 (n=40) minus Control1 (n=40)
## 19.2 [95CI 7.16; 30.4]
##
##
## 5000 bootstrap resamples.
## All confidence intervals are bias-corrected and accelerated.
```

To create a two-group estimation plot (*aka* a Gardner-Altman plot), simply use `plot(dabest.object)`

.

*Advanced R users would be interested to learn that dabest produces an object of class dabest. There is a generic S3 plot method for dabest objects that produces the estimation plot.*

`plot(two.group.unpaired, color.column = Gender)`

This is known as a Gardner-Altman estimation plot, after Martin J. Gardner and Douglas Altman who were the first to publish it in 1986.

The key features of the Gardner-Altman estimation plot are:

- All data points are plotted.
- The mean difference (the effect size) and its 95% confidence interval (95% CI) is displayed as a point estimate and vertical bar respectively, on a separate but aligned axes.

The estimation plot produced by `dabest`

differs from the one first introduced by Gardner and Altman in one important aspect. `dabest`

derives the 95% CI through nonparametric bootstrap resampling. This enables visualization of the confidence interval as a graded sampling distribution.

The 95% CI presented is bias-corrected and accelerated (ie. a BCa bootstrap). You can read more about bootstrap resampling and BCa correction in this vignette.

If you have paired or repeated observations, you must specify the `id.col`

, a column in the data that indicates the identity of each paired observation. This will produce a Tufte slopegraph instead of a swarmplot.

```
two.group.paired <-
my.data %>%
dabest(Group, Measurement,
idx = c("Control1", "Group1"),
paired = TRUE, id.col = ID)
# The summary indicates this is a paired comparison.
two.group.paired
```

```
## DABEST (Data Analysis with Bootstrap Estimation) v0.2.2
## =======================================================
##
## Variable: Measurement
##
## Paired mean difference of Group1 (n=40) minus Control1 (n=40)
## 19.2 [95CI 7.45; 31]
##
##
## 5000 bootstrap resamples.
## All confidence intervals are bias-corrected and accelerated.
```

`plot(two.group.paired, color.column = Gender)`

To create a multi-two group plot, one will need to specify a list, with each element of the list corresponding to the each two-group comparison.

```
multi.two.group.unpaired <-
my.data %>%
dabest(Group, Measurement,
idx = list(c("Control1", "Group1"),
c("Control2", "Group2")),
paired = FALSE
)
multi.two.group.unpaired
```

```
## DABEST (Data Analysis with Bootstrap Estimation) v0.2.2
## =======================================================
##
## Variable: Measurement
##
## Unpaired mean difference of Group1 (n=40) minus Control1 (n=40)
## 19.2 [95CI 7.16; 30.4]
##
## Unpaired mean difference of Group2 (n=40) minus Control2 (n=40)
## -23.9 [95CI -44.8; -3.1]
##
##
## 5000 bootstrap resamples.
## All confidence intervals are bias-corrected and accelerated.
```

`plot(multi.two.group.unpaired, color.column = Gender)`