load: Loading data with dabestr

In dabestr: Data Analysis using Bootstrap-Coupled Estimation

Loading data with dabestr

Description

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 documentation built on Oct. 13, 2023, 5:10 p.m.