Prepare a Named Colour Palette for Data Columns

colour_prep is a helper function designed to work in tandem with colour_pal. It prepares a colour palette for visualising data by assigning colours to unique values across specified columns in a data frame. This function is particularly useful for creating consistent colour mappings in visualisations such as plots and diagrams, where colours are associated with specific data values.

For political colour palettes (identified by the "pol" prefix in pal_name), colour_prep attempts to match political party names from the data frame with corresponding colours from the palette. For example, if a data column contains "Scottish National Party (SNP)", the function will try to match it with the "SNP" entry in the colour palette and assign the appropriate hexadecimal colour, e.g., "#FFF95D" (light yellow).

Values that do not match are assigned a default grey colour ("#cccccc"). Users can manually amend these default assignments by modifying the returned list, e.g., your_saved_colour_palette$default_value <- "#34134a".

Usage

colour_prep(data, columns, pal_name = "catExtended")

Arguments

data

A data frame containing the data for which colours are to be prepared.

columns

A character vector specifying the names of the columns in data for which unique values are to be extracted and assigned colours.

pal_name

The name of the colour palette to use, defaulting to "catExtended". This refers to an extended categorical colour palette. Other palettes from colour_pal can also be specified.

Value

A named list of hexadecimal colour codes, where each name corresponds to a unique value found in the specified columns of the data frame. This list can be directly used for colour mapping in data visualisations.

See also

colour_pal()

Examples

# Example data frame
df <- data.frame(
  Category1 = c("Scottish National Party (SNP)", "Labour", "Conservative", "Other"),
  Category2 = c("An independent candidate", "Invalid_Party_Name", "Other", "Conservative")
)

# Prepare a colour palette for the unique values in Category1 and Category2
colour_prep(df, c("Category1", "Category2"))
#> $`An independent candidate`
#> [1] "#478c5b"
#> 
#> $Conservative
#> [1] "#374e8e"
#> 
#> $Invalid_Party_Name
#> [1] "#df7c18"
#> 
#> $Labour
#> [1] "#ac004f"
#> 
#> $Other
#> [1] "#4fbbae"
#> 
#> $`Scottish National Party (SNP)`
#> [1] "#ce4631"
#> 

# See political party colours and compare with the full palette to see how the two names are matched
actual <- colour_prep(df, c("Category1", "Category2"), pal_name = "polUK")
actual <- stack(actual)
expected <- colour_pal("polUK")
expected <- stack(expected)
colours <- dplyr::left_join(actual, expected, by="values")
colours <- stats::setNames(colours, c("colours", "df_name", "palette_name"))
print(colours)
#>   colours                       df_name       palette_name
#> 1 #DCDCDC      An independent candidate        Independent
#> 2 #2e84c6                  Conservative Conservative Party
#> 3 #cccccc            Invalid_Party_Name               <NA>
#> 4 #d42027                        Labour       Labour Party
#> 5 #cdcdd1                         Other              Other
#> 6 #FFF95D Scottish National Party (SNP)                SNP