---
title: "Plot Objects"
output:
rmarkdown::html_vignette:
self_contained: FALSE
vignette: >
%\VignetteIndexEntry{Plot Objects}
%\VignetteEngine{knitr::knitr}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## Overview
The lionfish package offers multiple different types of display elements.
These have to be provided to the interactive_tour function that launches
the GUI. Each plot object is a list containing a type and an object. The
type specifies, which kind of display should be generated. The object
provides additional information required to construct the display. The
plot objects then have to be stored in a list that can be given to the
interactive_tour function. The currently supported displays are:
- 1d tour
- 2d tour
- scatterplot
- histogram
- mosaic plot
- heatmap
- categorical cluster interface
Examples for all displays can be found below.
## Setup
```{r load packages}
# Load required packages
library(tourr)
library(lionfish)
if (requireNamespace("flexclust")) {library(flexclust)}
```
```{r initialize python backend, eval = FALSE}
# Initialize python backend
if (check_venv()){
init_env(env_name = "r-lionfish", virtual_env = "virtual_env")
} else if (check_conda_env()){
init_env(env_name = "r-lionfish", virtual_env = "anaconda")
}
```
### Dataset 1 - Flea data
```{r Load and prepare flea data}
# Load dataset
data("flea")
# Prepare objects for later us
clusters_flea <- as.numeric(flea$species)
flea_subspecies <- unique(flea$species)
# Standardize data and calculate half_range
flea <- apply(flea[,1:6], 2, function(x) (x-mean(x))/sd(x))
feature_names_flea <- colnames(flea)
half_range_flea <- max(sqrt(rowSums(flea^2)))
```
### Dataset 2 - Winter activities data
```{r Load and prepare flea data winter activities data}
if (requireNamespace("flexclust")) {
# Load dataset and set seed
data("winterActiv")
set.seed(42)
# Perform kmeans clustering
clusters_winter <- stepcclust(winterActiv, k=6, nrep=20)
clusters_winter <- clusters_winter@cluster
# Get the names of our features
feature_names_winter <- colnames(winterActiv)
}
```
## Currently supported display types
### One dimensional tour
To display one dimensional tours, they first have to be generated and
saved using the
[save_history](https://ggobi.github.io/tourr/reference/save_history.html)
function of the tourr package. For more information on the available
types of tours please visit [tour path
construction](https://ggobi.github.io/tourr/reference/index.html#tour-path-construction).
#### Example plot object
```{r 1d tour}
guided_tour_flea_1d <- save_history(flea,
tour_path=guided_tour(holes(),1))
obj_flea_1d_tour <- list(type="1d_tour", # type of display
obj=guided_tour_flea_1d) # 1d tour history
```
#### Interactivity
- Changing frames of tour: The frames of the current tour can be changed by
pressing the left arrow-key for the last frame and the right arrow-key for the
next frame. The current frame is displayed in the frame on the left.
- Projection manipulation: the projection can be manipulated clicking on one of
the arrowheads with the right mouse-button and dragging it.
- Subselection: The datapoins within each subset can be changed by clicking
on one of the bars of the histogram. The datapoints within the bar are then
moved to the subset currently selected on the left.
{width=100%}
### Two dimensional tour
To display two dimensional tours, they first have to be generated and
saved using the
[save_history](https://ggobi.github.io/tourr/reference/save_history.html)
function of the tourr package. For more information on the available
types of tours please visit [tour path
construction](https://ggobi.github.io/tourr/reference/index.html#tour-path-construction).
#### Example plot object
```{r 2d tour}
grand_tour_flea_2d <- save_history(flea,
tour_path = grand_tour(d=2))
obj_flea_2d_tour <- list(type="2d_tour", # type of display
obj=grand_tour_flea_2d) # 2d tour history
```
#### Interactivity
- Changing frames of tour: The frames of the current tour can be changed by
pressing the left arrow-key for the last frame and the right arrow-key for the
next frame. The current frame is displayed in the frame on the left.
- Projection manipulation: the projection can be manipulated clicking on one of
the arrowheads with the right mouse-button and dragging it.
- Subselection: The datapoins within each subset can be changed by pressing
the left mouse-button and drawing a lasso around the datapoints to be selected.
The datapoints within the lasso are then moved to the subset currently selected
on the left.
{width=100%}
### Scatterplot
To display a scatter plot, the features to be displayed on the x and y
axis have to be provided in form of a two dimensional vector.
#### Example plot object
```{r scatterplot}
obj_flea_scatter <- list(type="scatter", # type of display
obj=c("tars1", "tars2")) # x and y axis of plot
```
#### Interactivity
- Subselection: The datapoins within each subset can be changed by pressing
the left mouse-button and drawing a lasso around the datapoints to be selected.
The datapoints within the lasso are then moved to the subset currently selected
on the left.
{width=100%}
### Histogram
To display a histogram, the feature to be displayed on the x axis has to
be provided in form of a two dimensional vector.
#### Example plot object
```{r histogram}
obj_flea_histogram <- list(type="hist", # type of display
obj="head") # x axis of histogram
```
#### Interactivity
- Subselection: The datapoins within each subset can be changed by clicking
on one of the bars of the histogram. The datapoints within the bar are then
moved to the subset currently selected on the left.
{width=100%}
### Mosaic
To display a mosaic plot, one has to provide whether the
subgroups/clusters should be on the x or y axis, either with
"subgroups_on_y" or "subgroups_on_x".
#### Example plot object
```{r mosaic}
obj_winter_mosaic <- list(type="mosaic", # type of display
obj=c("subgroups_on_y")) # whether subgroups x or y axis
```
#### Interactivity
Currently there is no way of directly interacting with the mosaic plot.
{width=100%}
### Heatmap
To display a heatmap, the metric to be calculated and plotted has to be
selected. One can choose between "Total fraction", "Intra cluster
fraction" and "Intra feature fraction".
Consider the matrix
\[
C = \left[ \begin{array}{cccc} c_{11} & c_{12} & \dots & c_{1p} \\
c_{21} & c_{22} & \dots & c_{2p} \\
\vdots & \vdots & \ddots & \vdots \\
c_{k1} & c_{k2} & \dots & c_{kp}
\end{array} \right]
\]
where $c_{ij}, i=1, ..., k$ (number of clusters); $j=1, ..., p$ (number of
features) are a summary of each feature in each cluster. In case of binary
data $c_{ij}$ are the positive counts of the cluster/feature combination.
Then the total fraction is calculated by $f_{ij}^{o} = \frac{c_{ij}}{n}$
the intra cluster fraction by $f_{ij}^{c} = \frac{c_{ij}}{n_{i}}$ and
the intra feature fraction by $f_{ij}^{f} = \frac{c_{ij}}{n_{j}}$,
where $n_i, n_j$ are the row and column totals.
#### Example plot object
```{r heatmap}
obj_winter_heatmap <- list(type="heatmap", # type of display
obj=c("Total fraction")) # initial metric
```
#### Interactivity
- Metric selection: The currently displayed metric can be changed by using the
dropdown menu within the frame on the left. The details on the metrics can be
found above.
{width=100%}
### Categorical cluster interface
To display a categorical cluster interface, the metric to be calculated
and plotted has to be selected. One can choose between "Total fraction",
"Intra cluster fraction" and "Intra feature fraction". For details see "Heatmap"
#### Example plot object
```{r categorical cluster interface}
obj_winter_cat_clust <- list(type="cat_clust_interface", # type of display
obj=c("Total fraction")) # initial metric
```
#### Interactivity
- Metric selection: The currently displayed metric can be changed by using the
dropdown menu within the frame on the left. The details on the metrics can be
found above.
{width=100%}
## Generating the displays
The various plot objects can the be displayed with the
[interactive_tour](https://mmedl94.github.io/lionfish/reference/interactive_tour.html)
function.
```{r interactive_tour flea}
# interactive_tour call of flea dataset. Insert plot objects of your liking.
if (interactive()){
interactive_tour(data=flea,
feature_names=feature_names_flea,
plot_objects=list(obj_flea_2d_tour),
half_range=half_range_flea,
n_plot_cols=2,
preselection=clusters_flea,
n_subsets=3,
preselection_names=flea_subspecies,
display_size=5)
}
```
```{r interactive_tour winterActiv}
# interactive_tour call of winterActiv dataset. Insert plot objects of your liking.
if (interactive()){
interactive_tour(data=winterActiv,
feature_names=feature_names_winter,
plot_objects=list(obj_winter_cat_clust),
half_range=3,
n_plot_cols=2,
preselection=clusters_winter,
n_subsets=10,
display_size=5)
}
```