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Feature Plots

Download a copy of the vignette to follow along here: feature_plots.Rmd

Given a cluster solution formatted as a row of a solutions data frame (or extended solutions data frame) and a data list containing features to plot, the auto_plot() function can automatically generate ggplot-based bar and jitter plots showing how that particular feature was divided across clusters.

library(metasnf)

dl <- data_list(
    list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
    list(income, "household_income", "demographics", "continuous"),
    list(fav_colour, "favourite_colour", "misc", "categorical"),
    list(pubertal, "pubertal_status", "demographics", "continuous"),
    list(anxiety, "anxiety", "behaviour", "ordinal"),
    list(depress, "depressed", "behaviour", "ordinal"),
    uid = "unique_id"
)

## ℹ 188 observations dropped due to incomplete data.

# Build space of settings to cluster over
set.seed(42)
sc <- snf_config(
    dl = dl,
    n_solutions = 2,
    min_k = 20,
    max_k = 50
)

## ℹ No distance functions specified. Using defaults.

## ℹ No clustering functions specified. Using defaults.

# Clustering
sol_df <- batch_snf(dl, sc)

sol_df_row <- sol_df[1, ]

The row you pick could come from a solutions_df or ext_solutions_df class object.

plot_list <- auto_plot(
    sol_df_row = sol_df_row,
    dl = dl,
    verbose = TRUE
)

## Generating plot 1/35: smri_vol_scs_cbwmatterlh
## Generating plot 2/35: smri_vol_scs_ltventriclelh
## Generating plot 3/35: smri_vol_scs_inflatventlh
## Generating plot 4/35: smri_vol_scs_crbwmatterlh
## Generating plot 5/35: smri_vol_scs_crbcortexlh
## Generating plot 6/35: smri_vol_scs_tplh
## Generating plot 7/35: smri_vol_scs_caudatelh
## Generating plot 8/35: smri_vol_scs_putamenlh
## Generating plot 9/35: smri_vol_scs_pallidumlh
## Generating plot 10/35: smri_vol_scs_3rdventricle
## Generating plot 11/35: smri_vol_scs_4thventricle
## Generating plot 12/35: smri_vol_scs_bstem
## Generating plot 13/35: smri_vol_scs_hpuslh
## Generating plot 14/35: smri_vol_scs_amygdalalh
## Generating plot 15/35: smri_vol_scs_csf
## Generating plot 16/35: smri_vol_scs_aal
## Generating plot 17/35: smri_vol_scs_vedclh
## Generating plot 18/35: smri_vol_scs_cbwmatterrh
## Generating plot 19/35: smri_vol_scs_ltventriclerh
## Generating plot 20/35: smri_vol_scs_inflatventrh
## Generating plot 21/35: smri_vol_scs_crbwmatterrh
## Generating plot 22/35: smri_vol_scs_crbcortexrh
## Generating plot 23/35: smri_vol_scs_tprh
## Generating plot 24/35: smri_vol_scs_caudaterh
## Generating plot 25/35: smri_vol_scs_putamenrh
## Generating plot 26/35: smri_vol_scs_pallidumrh
## Generating plot 27/35: smri_vol_scs_hpusrh
## Generating plot 28/35: smri_vol_scs_amygdalarh
## Generating plot 29/35: smri_vol_scs_aar
## Generating plot 30/35: smri_vol_scs_vedcrh
## Generating plot 31/35: household_income
## Generating plot 32/35: colour
## Generating plot 33/35: pubertal_status
## Generating plot 34/35: cbcl_anxiety_r
## Generating plot 35/35: cbcl_depress_r

plot_list$"household_income"

plot_list$"smri_vol_scs_csf"

plot_list$"colour"

If there’s something you’d like to change about the plot, you can always tack on ggplot2 elements to build from the skeleton provided by auto_plot:

plot_list$"colour" +
    ggplot2::labs(
        fill = "Favourite Colour",
        x = "Cluster",
        title = "Favourite Colour by Cluster"
    ) +
    ggplot2::scale_fill_manual(
        values = c(
            "green" = "forestgreen",
            "red" = "firebrick3",
            "yellow" = "darkgoldenrod1"
        )
    )

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.