load {dabestr}R Documentation

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:

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)


[Package dabestr version 2023.9.12 Index]