Processes and converts a tidy dataset into the dabestr format. The output of this function is then used as an input for various procedural functions within dabestr to create estimation plots.

## Usage

```
load(
data,
x,
y,
idx = NULL,
paired = NULL,
id_col = NULL,
ci = 95,
resamples = 5000,
colour = NULL,
proportional = FALSE,
minimeta = FALSE,
delta2 = FALSE,
experiment = NULL,
experiment_label = NULL,
x1_level = NULL
)
```

## Arguments

- data
A tidy dataframe.

- x
Column in

`data`

that contains the treatment groups.- y
Column in

`data`

that contains the measurement values.- idx
List of control-test groupings for which the effect size will be computed for.

- paired
Paired ("sequential" or "baseline"). Used for plots for experiments with repeated-measures designs.

If "sequential", comparison happens between each measurement to the one directly preceding it. (control vs group i)

If "baseline", comparison happens between each group to a shared control. (group i vs group i+1)

- id_col
Column in

`data`

indicating the identity of the datapoint if the data is tagged. Compulsory parameter if paired is TRUE.- ci
Default 95. Determines the range of the confidence interval for effect size and bootstrap calculations. Only accepts values between 0 to 100 (inclusive).

- resamples
The number of resamples to be used to generate the effect size bootstraps.

- colour
Column in

`data`

that determines the groupings for colour of the swarmplot as opposed to`x`

.- proportional
Boolean value determining if proportion plots are being produced.

- minimeta
Boolean value determining if mini-meta analysis is conducted.

- delta2
Boolean value determining if delta-delta analysis for 2 by 2 experimental designs is conducted.

- experiment
Experiment column name for delta-delta analysis.

- experiment_label
String specifying the experiment label that is used to distinguish the experiment and the factors (being used in the plotting labels).

- x1_level
String setting the first factor level in a 2 by 2 experimental design.

## Value

Returns a `dabest_obj`

list with 18 elements. The following are the elements contained within:

`raw_data`

The tidy dataset passed to`load()`

that was cleaned and altered for plotting.`proportional_data`

List of calculations related to the plotting of proportion plots.`enquo_x`

Quosure of x as initially passed to`load()`

.`enquo_y`

Quosure of y as initially passed to`load()`

.`enquo_id_col`

Quosure of id_col as initially passed to`load()`

.`enquo_colour`

Quosure of colour as initially passed to`load()`

.`proportional`

Boolean value determining if proportion plots are being produced.`minimeta`

Boolean value determining if mini-meta analysis is conducted.`delta2`

Boolean value determining if delta-delta analysis for 2 by 2 experimental designs is conducted.`idx`

List of control-test groupings for which the effect size will be computed for.`resamples`

The number of resamples to be used to generate the effect size bootstraps.`is_paired`

Boolean value determining if it is a paired plot.`is_colour`

Boolean value determining if there is a specified colour column for the plot.`paired`

Paired ("sequential" or "baseline") as initially passed to`load()`

.`ci`

Numeric value which determines the range of the confidence interval for effect size and bootstrap calculations. Only accepts values between 0 to 100 (inclusive).`Ns`

List of labels for x-axis of the rawdata swarm plot.`control_summary`

Numeric value for plotting of control summary lines for float_contrast= TRUE.`test_summary`

Numeric value for plotting of test summary lines for float_contrast = TRUE.`ylim`

Vector containing the y limits for the rawdata swarm plot.

## Examples

```
# Loading in of the dataset
data(non_proportional_data)
# Creating a dabest object
dabest_obj <- load(
data = non_proportional_data, x = Group, y = Measurement,
idx = c("Control 1", "Test 1")
)
# Printing dabest object
print(dabest_obj)
#> DABESTR v2023.9.12
#> ==================
#>
#> Good morning!
#> The current time is 06:03 AM on Tuesday December 12, 2023.
#>
#> Effect 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.
#>
```