---
title: "Controlling Plot Aesthetics"
output: rmarkdown::html_vignette
description: |
Loading in of sample datasets for dabestr.
vignette: >
%\VignetteIndexEntry{Controlling Plot Aesthetics}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
Controlling plot aesthetics is straightforward in `dabestr`. A key feature of `dabestr` is the ability for users to freely customize various components of a `dabestr` estimation plot, allowing for the creation of an optimal-looking plot.
## Getting started
At this point, it is assumed that you have already obtained the effectsize object named `dabest_effectsize_obj`.
To add and customize specific plot components, include them as arguments in the
`dabest_plot()` function.
```{r, warning = FALSE, eval = FALSE}
dabest_plot(
dabest_effectsize_obj,
float_contrast = TRUE,
plot_component = "adjustment_value"
)
```
## Adjusting Text
All text elements in the estimation plot can be modified. This includes the value,
the size and even the removal of the text elements completely.
### Size
The following parameters are responsible for adjusting the size of the text elements.
- `swarm_x_text`: Numeric value determining the font size of the x-axis of the swarm plot. The default value is 11.
- `swarm_y_text`: Numeric value determining the font size of the y-axis of the swarm plot. The default value is 15.
- `contrast_x_text`: Numeric value determining the font size of the x-axis of the contrast plot. The default value is 11.
- `contrast_y_text`:Numeric value determining the font size of the y-axis of the contrast plot. The default value is 15.
Check how the size has been changed in this example:
```{r, warning = FALSE, include = FALSE}
library(dabestr)
data(non_proportional_data)
data(proportional_data)
data(deltadelta_data)
dabest_twogroup_obj.mean_diff <- load(non_proportional_data, x = Group, y = Measurement, idx = c("Control 1", "Test 1")) %>%
mean_diff()
dabest_multigroup_obj.mean_diff <- load(non_proportional_data,
x = Group, y = Measurement,
idx = list(c("Control 1", "Test 1", "Test 2"), c("Control 2", "Test 3"))
) %>%
mean_diff()
dabest_unpaired_props.mean_diff <- load(proportional_data,
x = Group, y = Success,
idx = list(c("Control 1", "Test 1")),
proportional = TRUE
) %>%
mean_diff()
dabest_paired_props.mean_diff <- load(proportional_data,
x = Group, y = Success,
idx = list(c("Control 1", "Test 1", "Test 2", "Test 3"), c("Control 2", "Test 4")),
proportional = TRUE, paired = "sequential",
id_col = ID
) %>%
mean_diff()
```
```{r, warning = FALSE}
dabest_plot(
dabest_twogroup_obj.mean_diff,
float_contrast = TRUE,
swarm_x_text = 30,
swarm_y_text = 1,
contrast_x_text = 30,
contrast_y_text = 5
)
```
### Content
The following parameters are responsible for adjusting the content of the text elements.
- `swarm_label`: string value for the label of the y-axis in the swarm plot. This is the default "value" or "proportion of success" for proportion plots.
- `contrast_label`: string value for the label of the y-axis in the contrast plot. This is the default "effect size" based on the effect sizes as given in `effect_size()`.
- `delta2_label`: string value for the label of the y-axis in the swarm plot. The default value is NULL.
```{r, warning = FALSE}
dabest_plot(
dabest_twogroup_obj.mean_diff,
float_contrast = TRUE,
swarm_label = "I love estimation statistics.",
contrast_label = "I love it more than you do!"
)
```
## Adjusting Visual Elements
Visual elements in `dabestr` refer to the shapes, lines, symbols or other visual representations that convey data and relationship in a plot. Many of these elements can be adjusted in `dabestr`.
### Markers
The following parameters are responsible for adjusting the properties of various markers
in the plot.
- `raw_marker_size`: Numeric value determining the size of the points used in the swarm plot. The default value is 1.5.
- `raw_marker_alpha`: Numeric value determining the transparency of the points in the swarm plot. The default value is 1.
- `raw_bar_width`: Numeric value determining the width of the bar in the *Sankey* diagram. The default value is 0.3.
- `raw_marker_spread`: The distance between the points if it is a swarm plot. The default value is 2.
- `raw_marker_side_shift`: The horizontal distance that the swarm plot points are moved in the
direction of the `asymmetric_side`. The default value is 0.
- `tufte_size`: Numeric value determining the size of the tufte line in the swarm plot. The default value is 0.8.
- `es_marker_size`: Numeric value determining the size of the points used in the delta plot. The default value is 0.5.
- `es_line_size`: Numeric value determining the size of the ci line in the delta plot. The default value is 0.8.
```{r, warning = FALSE}
A <- dabest_plot(dabest_twogroup_obj.mean_diff,
float_contrast = TRUE,
swarm_label = "", contrast_label = "",
raw_marker_size = 1, raw_marker_alpha = 1
)
B <- dabest_plot(dabest_twogroup_obj.mean_diff,
float_contrast = TRUE,
swarm_label = "", contrast_label = "",
raw_marker_size = 2, raw_marker_alpha = 0.5
)
cowplot::plot_grid(
plotlist = list(A, B),
nrow = 1,
ncol = 2,
labels = "AUTO"
)
```
### Axes
The following parameters are responsible for adjusting the y-axis limits for both the raw data and contrast axes of the plot. By modifying the range, it creates the effect of zooming in or out of the plot.
- `swarm_ylim`: Vector containing the y-limits for the swarm plot. The default value is NULL.
- `contrast_ylim`: Vector containing the y-limits for the delta plot. The default value is NULL.
- `delta2_ylim`: Vector containing the y-limits for the delta-delta plot. The default value is NULL.
If your effect size is qualitatively inverted (i.e., a smaller value indicates a better outcome), you can invert the vector passed to `contrast_ylim.`
```{r, warning = FALSE}
dabest_plot(dabest_multigroup_obj.mean_diff,
float_contrast = FALSE,
contrast_label = "More negative is better!",
swarm_ylim = c(1, 5), contrast_ylim = c(0.7, -1.2)
)
```
### Palettes
The following parameters are responsible for adjusting the plot palettes of the plot.
- `custom_palette`: string to set the colour palette. The default value is "d3". The following palettes are also available:
npg, aaas, nejm, lancet, jama, jco, ucscgb, d3, locuszoom, igv, cosmic, uchicago, brewer,
ordinal and viridis_d.
```{r, warning = FALSE}
npg <- dabest_plot(dabest_unpaired_props.mean_diff,
swarm_label = "", contrast_label = "",
custom_palette = "npg"
)
nejm <- dabest_plot(dabest_unpaired_props.mean_diff,
swarm_label = "", contrast_label = "",
custom_palette = "nejm"
)
jama <- dabest_plot(dabest_unpaired_props.mean_diff,
swarm_label = "", contrast_label = "",
custom_palette = "jama"
)
locuszoom <- dabest_plot(dabest_unpaired_props.mean_diff,
swarm_label = "", contrast_label = "",
custom_palette = "locuszoom"
)
cowplot::plot_grid(
plotlist = list(npg, nejm, jama, locuszoom),
nrow = 2,
ncol = 2
)
```
### Misc
- `sankey`: this is a boolean value determining if the flows between the bar charts will be plotted. The default value is TRUE.
```{r, warning = FALSE}
dabest_plot(dabest_paired_props.mean_diff, sankey = FALSE, raw_bar_width = 0.15)
```
- `flow`: this is a boolean value determining whether the bars will be plotted in pairs. The default value is TRUE.
```{r, warning = FALSE}
dabest_plot(dabest_paired_props.mean_diff, flow = FALSE, raw_bar_width = 0.15)
```
- `asymmetric_side`: this is a string value that controls at which side the swarm points are shown. Accepted values are: "right" or "left". The default one is "right".
```{r, warning = FALSE}
right <- dabest_plot(dabest_twogroup_obj.mean_diff,
float_contrast = FALSE,
swarm_label = "", contrast_label = "",
asymmetric_side = "right"
)
left <- dabest_plot(dabest_twogroup_obj.mean_diff,
float_contrast = FALSE,
swarm_label = "", contrast_label = "",
asymmetric_side = "left"
)
cowplot::plot_grid(
plotlist = list(right, left),
nrow = 1,
ncol = 2
)
```
- `show_delta2`: this is a boolean value determining if the delta-delta plot is shown. The default value is FALSE.
- `show_mini_meta`: this is a boolean value determining if the weighted average plot is shown. When `FALSE`, the resulting graph would be identical to a multiple two-groups plot. The default value is FALSE.
- `show_zero_dot`: this is a boolean value determining if there is a dot on the zero line of the effect size for the control-control group. The default value is TRUE.
- `show_baseline_ec`: this is a boolean value determining whether the baseline curve is shown. The default value is FALSE.
```{r, warning = FALSE}
dabest_plot(dabest_multigroup_obj.mean_diff,
float_contrast = FALSE,
show_baseline_ec = TRUE
)
```