eHDPrep (version 1.3.3)

completeness_heatmap: Completeness Heatmap

Description

Produces a heatmap visualising completeness across a dataset.

Usage

completeness_heatmap(
  data,
  id_var,
  annotation_tbl = NULL,
  method = 1,
  show_rownames = FALSE,
  ...
)

Value

completeness heatmap

Arguments

data

Data frame to be analysed.

id_var

Character constant of row identifier variable name.

annotation_tbl

Data frame containing variable annotation data. Column 1 should contain variable names, column 2 should contain an annotation label.

method

Integer between 1 and 3. Default: 1. See Details for more information.

show_rownames

Boolean. Should rownames be shown. Default: False.

...

Parameters to be passed to pheatmap.

Details

• Method 1: Missing values are numerically encoded with a highly negative number, numerically distant from all values in data, using distant_neg_val. Values in categorical variables are replaced with the number of unique values in the variable. Clustering uses these values. Cells are coloured by presence (yellow = missing; blue = present).

• Method 2: Same as Method 1 but cells are coloured by values used to cluster.

• Method 3: Values in data are encoded as Boolean values for clustering (present values = 1; missing values = 0). Cells are coloured by presence (yellow = missing; blue = present).

References

Kolde R (2019). _pheatmap: Pretty Heatmaps_. R package version 1.0.12, <https://CRAN.R-project.org/package=pheatmap>.

See Also

pheatmap

Other measures of completeness: assess_completeness(), compare_completeness(), plot_completeness(), row_completeness(), variable_completeness()

Examples

data(example_data)

# heatmap without variable category annotations:
hm <- completeness_heatmap(example_data,patient_id)
plot.new() # ensure new plot is created
hm


# heatmap with variable category annotations:
## create a dataframe containing variable annotations
tibble::tribble(~"var", ~"datatype",
"patient_id", "id",
"tumoursize", "numeric",
"t_stage", "ordinal_tstage",
"n_stage", "ordinal_nstage",
"diabetes", "factor",
"diabetes_type", "ordinal",
"hypertension", "factor",
"rural_urban", "factor",
"marital_status", "factor",
"SNP_a", "genotype",
"SNP_b", "genotype",
"free_text", "freetext") -> data_types

hm <- completeness_heatmap(example_data,patient_id, annotation_tbl = data_types)
plot.new() # ensure new plot is created
hm