# corrViz

# Introduction

`corrViz` is an R package designed to help users visualize correlations between variables in their datasets. With the ever-growing size and complexity of datasets, it is crucial to have a clear understanding of the relationships between variables. This package aims to provide an easy and effective way to explore and visualize these correlations, making it easier to interpret and communicate results.

This vignette will introduce you to the main features and functions of the `corrViz` package, guiding you through the process of creating visually appealing and informative correlation plots. By the end of this tutorial, you should be able to use `corrViz` to create various types of correlation visualizations, customize their appearance, and gain insights into the relationships within your dataset.

Download the development version from GitHub with:

    # install.packages("devtools")
    devtools::install_github("AlanInglis/corrViz")

You can then load the package with:

    library(corrViz)

# Methods

The `corrViz` package offers several visualization methods to help you explore and communicate correlations effectively, with an emphasis on interactivity. The only required inputs for all `corrViz` functions are either a dataset or a square matrix of correlations to be visualized. Each visualization has multiple customization options, which are outlined in this document. The `plotly` package1 is used to create interactive graphics.

To begin we will create a correlation matrix using the `mtcars` data set.

    cm <- cor(mtcars)

# Heat map

The following code produces an interactive heatmap displaying variable correlations, which can be seen in Figure 1. The additional arguments here are:

- `display`: Choose to display the upper, lower, or full matrix of values using `"upper"`, `"lower"`, or `"all"`, respectively.
- `reorder`: If TRUE (the default) then the heatmap is reordered to group correlations.
- `pal`: The colour palette to use for displaying values.

Hovering the mouse over a cell in the heatmap will display a box containing the variable pair names and their associated correlation value. Dragging the mouse will zoom in on the selected area and double-clicking the plot will reset the zoom.

    corrHeatmap(mat = cm,
                display = 'all',
                reorder = TRUE,
                pal = colorRampPalette(c("darkblue", 'white', 'darkred'))(100))

Figure 1: Interactive heatmap displaying all correlations.

In Figure 1 we set `display = "all"` which shows the full heatmap. To display only the upper or lower portion of the heatmap we use the appropriate argument in `display`, as shown in Figure 2 (which shows the lower section).

    corrHeatmap(mat = cm,
                display = 'lower',
                reorder = FALSE)

Figure 2: Interactive heatmap displaying correlations from the lower section of the heatmap.

# Network

For build our network plots we use the `visNetwork` package2. the additional arguments for producing a network plot are:

- `threshold`: Used to filter correlations with an absolute value lower than selected value.
- `layout`: Use an `igraph` package3 layout to display network.
- `width`: The width of the viewing window
- `height`: The height of the vewing window
- `physics`: If TRUE (the default) then physics is enabled on nodes. This will alter the layout to be roughly circular.

    corrNetwork(mat = cm,
                threshold = 0,
                layout = "layout_nicely",
                width = "100%",
                height = "400px",
                physics = FALSE)

Figure 3: Interactive network plot displaying correlations.

Figure 3 shows a network in which every node corresponds to a variable present in the data, and each connecting edge denotes the correlation between them. The edges are color-coded to signify the correlation, with red indicating a positive correlation and blue representing a negative one. The thickness of each edge corresponds to the magnitude of the correlation between two variables. Hovering the mouse over an edge will display a box containing the correlation value for that edge.

When clicking on a node, the selected node will change colour and only nodes that connected to the selected node are highlighted (a better example of this can be seen in Figure 4). Additionally, the top right of the plot has a drop-down box which can be used to select a particular node/variable. DOing so will have the same effect as clicking on the node. The nodes themselves can be re-positioned by dragging them (only works when `physics = FALSE`). The bottom left of the plot contains controls to zoom in or out, whereas the bottom right has controls to pan the image.

In Figure 4, below, we set the `threshold` argument to equal 0.8. This will filter any correlations from our visualization with an absolute value lower than 0.8. Additionally, in Figure 4, `physics = TRUE`. By enabling physics, each node is repelled from each other and the edges act like springs. When `physics = TRUE`, the nodes can still be dragged, however as each node repels each other with equal force, the display will try to push each node away from each other.

    corrNetwork(mat = cm,
                threshold = 0.8,
                physics = TRUE)

Figure 4: Interactive network plot displaying variables with an absolute correlation value above 0.8.

# Solar plot

In a solar system correlation plot, the dependent (sun) variable of interest is positioned at the center, represented as the sun. The explanatory (planet) variables are depicted as planets orbiting around the sun, with their distance from the sun corresponding to the absolute value of their correlation with the dependent variable. Therefore, the greater the distance of a planet from the sun, the weaker the correlation between the explanatory variable and the dependent variable. # Solar plot animation

In Figure 5 we show an animated version which displays the planet variables orbiting the sun. The additional arguments are:

- `export`: If TRUE, then the animation is exported to the user’s machine as a gif. If FALSE (default), then the animation is played in the viewer window.
- `num_frames`: The number of frames to render.
- `path`: Path of the directory to save plot to.
- `gif_name`: File name to create on disk. Must be in the format “myFile.gif”.
- `fps`: The frame rate of the exported animation in frames/sec. Default is 60 fps and is only used when exporting a gif via `export = TRUE`.

    animSolar(mat = cm,
              sun = 'mpg',
              export = FALSE,
              num_frames = 100,
              path = NULL,
              gif_name = "solar_system.gif",
              fps = 60)

Figure 5: Animated solar system correlation plot with the variable `mpg` as the sun.

The `num_frames` argument is used to select the number of frames. Setting this to a low value will produce the plot quicker, however having a low number of frames will result in the “planets” jumping as the frames transition. Additionally, a low values of `num_frames` will affect the orbit of the animation when setting `export = FALSE`. This differs from the `fps` argument which sets the number of frames to play per second for use when exporting a gif. When `export = FALSE` (as is the case in figure 6), a ‘play’ button is provided to start the animation. Hovering over a planet will display the correlation value.

# Bar plot

Figure 6 shows an interactive barplot displaying correlation values. The red bars extending right represent the positive correlations whereas the blue bars extending left represent the negative correlations. The additional arguements here are: * `interactive`: If TRUE then an interactive version of the barplot is displayed. * `pal`: The colour palette to use for displaying values.

    corrBarplot(mat = cm,
                interactive = TRUE,
                pal = colorRampPalette(c("cornflowerblue", 'white', 'tomato'))(100))

Figure 6: Bar plot displaying correlation values.

Hovering the mouse over a bar will display the name of the variable pair and their associated correlation.

# Bubble

Figure 7 is similar to the heatmap shown in Figures 1 and 2, except this time each variable pair is represented as a circle, with the size corresponding the the magnitude of the correlation. Hovering over a bubble will display the variable pair names and their associated correlation. The additional arguments for this function are:

- `display`: Choose to display the upper, lower, or full matrix of values using “upper”, “lower”, or “all”, respectively.
- `pal`: The colour palette to use for displaying values.

    corrBubble(mat = cm,
               display = 'all',
               pal = colorRampPalette(c("cornflowerblue", "white", "tomato"))(100))

Figure 7: Bubble plot displaying correlation values.

As with the heatmap, we can also display a portion of the plot using the `display` argument, as shown in Figure 8.

    corrBubble(mat = cm,
               display = 'upper')

Figure 8: Bubble plot displaying correlation values from the upper portion of the correlation matrix.

# Scatterplot matrix pairs plot

Our next plot is a scatterplot matrix (or pairs plot) displaying correlation values in the upper triangle of the plot, with the stars representing the correlation significance. The diagonal contains density plots of the data. The lower triangle displays a scatterplot of the data values with a smooth-line (which is coloured according the the correlation value for the variable pair). The additional arguments are:

- `interactive`: A logical value indicating whether the output plot should be interactive (TRUE) or static (FALSE). Default is TRUE.
- `col_by`: An optional character string specifying the name of the column in the data frame to be used for coloring points. Default is NULL.

Figure 9 shows an interactive scatterplot for the first 5 variables found in the `mtcars` dataset. In this case, the scatterplot points are coloured by the variable `cyl`.

    corrPairs(data = mtcars[1:5],
             interactive = TRUE,
             col_by = "cyl")

Figure 9: Correlation pairs plot for the first five variables from the `mtcars` dataset.

# Sankey plot

An alternative method for viewing correlation is by using a Sankey plot. Sankey plots are primarily used to represent the flow of information between nodes in a network. These diagrams consist of interconnected pathways, with their width proportionally representing the quantity being transferred (in this case, the absolute value of the correlation). Figure 10 provides an example. In this case the correlations are filtered to show correlations with an absolute value above 0.6. The additional arguments are:

- `threshold`: Filter correlations with an absolute value lower than selected value.
- `colour`: A logical value indicating whether to color the links based on positive or negative correlation. Default is FALSE (links are grey).

    corrSankey(mat = cm,
               threshold = 0.6,
               colour = FALSE)

Figure 10: Sankey plot displaying correlation values with a magnitude above 0.6.

Setting `colour = TRUE` colours the links based on their correlation. In figure 11, the `threshold` argument is set to equal 0.8.

    corrSankey(mat = cm,
               threshold = 0.8,
               colour = TRUE)

Figure 11: Coloured Sankey plot displaying correlation values with a magnitude above 0.8.

# Shiny Plot

We also provide an option to create an interactive Shiny plot. This plot is a useful tool for quickly checking the correlation between two variables. In Figure 12 below we show a screenshot of the Shiny app in use. The additional arguments are:

- `data`: A data frame with the variables to be analyzed.
- `x_var`: The name of the variable to be plotted on the X-axis.
- `y_var`: The name of the variable to be plotted on the Y-axis.
- `color_var`: The name of the variable to be used for coloring the points on the scatter plot.
- `size_var`: The name of the variable to be used for sizing the points on the scatter plot.
- `correlation_method`: The method to be used for computing the correlation coefficient, must be one of “pearson”, “spearman” or “kendall”, the default is “pearson”.

    corrShiny(data = mtcars,
              x_var = "wt",
              y_var = "mpg",
              color_var = "cyl",
              size_var = "hp")

Figure 12: Shiny correlation explorer.

In the code above, although we chose with variables to display, colour and size by, all of these options options can be changrd within the Shiny app. As can be seen in Figure 12, we provide the options to choose both the X and Y variable to display in the plotting window. The correlation value is displayed in the Figure caption or can be displayed in the bottom right of the plot by checking the show correlation coefficient on plot box. The correlation method can be changed by clicking on the appropriate drop-down menu and we provide three correlation method options. They are; Pearson, Spearman, or Kendall.

A line of best fit (with or without confidence intervals) can be overlayed on top off the data and provide two options to fit the line, i.e., \(lm\) of \(loess\). To aid in interpretability, we also provide options to colour the points in the plot by a selected variable, to size the points by a selected variable, or to show observation labels on the plot.

# Static Plots

In addition to interactive plots, `corrViz` also provides numerous static plots. In Figures 13 to 16 we show a selection of options to display correlations on a grid. The plot types can be any of square, circle, text, or pie, which can be set via the `type` argument. If the `type` argument is set to either `square` or `circle`, then the shapes are sized, with the size representing the absolute value of the correlation. The additional arguments are:

- `showDiag`: A logical value, if TRUE (default), the diagonal of the correlation matrix is shown.
- `pal`: A color palette function, used for the correlation coefficient colors.

In Figure 13, we set `type = 'square'`

    corrGrid(mat = cm[1:7,1:7],
             type = 'square')

Figure 13: Static heatmap correlation plot.

In Figure 14, we set `type = 'circle'`.

    corrGrid(mat = cm[1:7,1:7],
             type = 'circle')

Figure 14: Static circle correlation plot.

In Figure 15, we set `type = 'text'`. The text

    corrGrid(mat = cm[1:7,1:7],
             type = 'text')

Figure 15: Static text correlation plot.

# Correlation Pie Plot

In Figure 16, we set `type = 'pie'`. This plot shows pie charts where the filled in percentage represents the correlation value.

    corrGrid(mat = cm[1:7,1:7],
             type = 'pie')

Figure 16: Static pie-chart correlation plot.

# Static Solar Plot

Figure 17 shows a static version of the solar system correlation plot with `mpg` selected as the dependent variable via the `sun` argument. Planet variables are coloured either red (positive correlation) or blue (negative correlation).

    corrSolar(mat = cm,
              sun = 'mpg')

Figure 17: Solar system correlation plot with the variable `mpg` as the sun.

# Chord plot

Figure 18 shows a chord plot displaying correlations. This type of plot can quickly become overloaded with information, so it is recommended to only use this plot when thresholding correlation values. In Figure 18, `threshold = 0.8`, which filters any correlations from the visualization an absolute value lower than selected value. The width of the chords corresponds to the magnitude of the correlation with their colours indicating positive (red) or negative correlations (blue). The additional arguments are:

- `threshold`: Filter correlations with an absolute value lower than selected value.
- `circle`: If TRUE then plot is displayed as a circle.

    corrChord(mat = cm,
              circle = FALSE,
              threshold = 0.8)

Figure 18: Chord plot displaying correlation values with a magnitude above 0.8.

Figure 19, displays the same information as Figure 18, but uses a circular layout, via the `circle` argument.

    corrChord(mat = cm,
              circle = TRUE,
              threshold = 0.8)

Figure 19: Circular chord plot displaying correlation values with a magnitude above 0.8.

# Circular correlation plot

Figure 20 shows a circular correlation plot. This is similar to Figure 19 but places the variable names on the outside of a neater circle. As with Figures 18 and 19, the width of the chords in Figure 20 corresponds to the magnitude of the correlation with their colours indicating positive (red) or negative correlations (blue). The additional arguments are: * `ticks`: If TRUE, then axis ticks are displayed on the circle. * `threshold`: Filter correlations with an absolute value lower than selected value.

    corrCircle(mat = cm,
               threshold = 0,
               ticks = TRUE)

Figure 20: Circular correlation plot displaying correlation values.

In Figure 21, we filter the visualization to display correlations with an absolute value greater than 0.8. In this case the axis ticks are removed.

    corrCircle(mat = cm,
               ticks = FALSE,
               threshold = 0.8)

Figure 21: Circular correlation plot displaying correlation values with a magnitude above 0.8.

# Coverting a correlation matrix to data frame

Often it can be useful to convert a matrix to a long-format data frame. The `martrix2long` function provides just that. The only input is a matrix. Below a demonstartion is shown for the first four colummns of the `mtcars` data.

    corr_matrix <- cor(mtcars[1:4])
    long_data_frame <- matrix2long(mat = corr_matrix)
    long_data_frame
    #>    row_name col_name      value id
    #> 1       mpg      mpg  1.0000000  1
    #> 2       cyl      mpg -0.8521620  1
    #> 3      disp      mpg -0.8475514  1
    #> 4        hp      mpg -0.7761684  1
    #> 5       mpg      cyl -0.8521620  2
    #> 6       cyl      cyl  1.0000000  2
    #> 7      disp      cyl  0.9020329  2
    #> 8        hp      cyl  0.8324475  2
    #> 9       mpg     disp -0.8475514  3
    #> 10      cyl     disp  0.9020329  3
    #> 11     disp     disp  1.0000000  3
    #> 12       hp     disp  0.7909486  3
    #> 13      mpg       hp -0.7761684  4
    #> 14      cyl       hp  0.8324475  4
    #> 15     disp       hp  0.7909486  4
    #> 16       hp       hp  1.0000000  4

# Conclusion

The `corrViz` package provides a convenient and powerful way to visualize correlations between variables in your dataset. With its variety of visualization methods and customization options, you can gain valuable insights into the relationships within your data and communicate these findings effectively.

1. C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.↩︎

2. Almende B.V. and Contributors, Thieurmel B (2022). visNetwork: Network Visualization using ‘vis.js’ Library. R package version 2.1.2, https://CRAN.R-project.org/package=visNetwork↩︎

3. Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. https://igraph.org↩︎