usydColours

Jazmin Ozsvar 04/06/2020

The University of Sydney colour palettes

The usydColours package is intended to provide a set of colour palettes for use with ggplot2 and plot graphs. The palettes have been derived from The University of Sydney Branding Document V2.0, and include the primary branding palette as well as some novelty palettes that capture the spirit and landmarks of the University.

For more information on University branding, refer to the following links:
* https://intranet.sydney.edu.au/services/marketing-communications/our-brand.html
* https://intranet.sydney.edu.au/content/dam/intranet/documents/services/marketing-communications/brand-documents/LR_Brand_Guidelines_V2.1_General.pdf

The functionality of usydColours is based on the wonderful wesanderson package (https://github.com/karthik/wesanderson).

Installation

library(devtools)

install_github("Sydney-Informatics-Hub/usydColours")

Usage

Below is a list of the currently available palettes.

##  [1] "primary"               "modern_diverging"      "extended"             
##  [4] "secondary"             "pastel"                "complementary_ReGr"   
##  [7] "complementary_ReBl"    "bright"                "muted"                
## [10] "trafficlight"          "heatmap"               "flametree"            
## [13] "jacaranda"             "harbour"               "sandstone"            
## [16] "ochre"                 "greyscale"             "BlGrYe"               
## [19] "BlOr"                  "diverging_blue_red"    "diverging_blue_orange"

Qualitative palettes

# Primary
util_visualise_pal(usyd_palettes[["primary"]])

# Extended
util_visualise_pal(usyd_palettes[["extended"]])

# Secondary
util_visualise_pal(usyd_palettes[["secondary"]])

# Pastel
util_visualise_pal(usyd_palettes[["pastel"]])

# Bright
util_visualise_pal(usyd_palettes[["bright"]])

Sequential palettes

# Flame tree
util_visualise_pal(usyd_palettes[["flametree"]])

# Jacaranda
util_visualise_pal(usyd_palettes[["jacaranda"]])

# Sandstone
util_visualise_pal(usyd_palettes[["sandstone"]])

# Harbour
util_visualise_pal(usyd_palettes[["harbour"]])

# Ochre
util_visualise_pal(usyd_palettes[["ochre"]])

# Greyscale
util_visualise_pal(usyd_palettes[["greyscale"]])

Diverging palettes

# Modern (red/green)
util_visualise_pal(usyd_palettes[["modern_diverging"]])

# Complementary (red/green)
util_visualise_pal(usyd_palettes[["complementary_ReGr"]])

# Complementary (red/blue)
util_visualise_pal(usyd_palettes[["complementary_ReBl"]])

# Muted
util_visualise_pal(usyd_palettes[["muted"]])

# Traffic light
util_visualise_pal(usyd_palettes[["trafficlight"]])

# Diverging (blue/red)
util_visualise_pal(usyd_palettes[["diverging_blue_red"]])

# Diverging (blue/orange)
util_visualise_pal(usyd_palettes[["diverging_blue_orange"]])

Use with ggplot2

Below are some examples of usydColours in action.

Categorical data

# Bar graph with the primary palette
diamonds %>%
  filter(price < 1000) %>%
  ggplot(aes(price, fill = cut)) +
    geom_histogram(colour = "black", position = "dodge", binwidth = 250) +
    theme_bw() +
    scale_fill_manual(values = usyd_palette("primary"))

# Density graph with the muted palette
iris %>%
  ggplot(aes(x = Sepal.Length, fill = Species)) + 
    geom_density() +
    theme_bw() +
    facet_wrap(. ~ Species) +
    scale_fill_manual(values = usyd_palette("muted")) 

# Scatter plot using the jacaranda palette
diamonds %>%
  filter(row_number() %% 5 == 1) %>%
  filter(carat < 3) %>%
  filter(clarity %in% c("SI2", "SI1", "VS2", "VVS2")) %>%
  ggplot(aes(carat, price, colour = clarity)) +
    geom_jitter(alpha = 0.7) +
    theme_bw() +
    scale_colour_manual(values = usyd_palette("jacaranda"))

Continuous data

Use usyd_palette to interpolate colours between the discrete colours of the available palettes for continuous data. Remember to supply values for n and set type = "continuous".

Tip: you can also use usyd_palette to generate more colours of your favourite palette for categorical plots too!

# Heatmap
mpg %>%
  count(class, drv) %>%
  complete(class, drv, fill = list(n = 0L)) %>%
  ggplot(aes(x = class, y = drv)) +
  geom_tile(mapping = aes(fill = n)) +
  theme_bw() +
  scale_fill_gradientn(colours = usyd_palette("flametree", 100, type = "continuous"))

library(maps)
library(mapproj)

# Chloropleth
# This example is from: https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html
unemp <- read.csv("http://datasets.flowingdata.com/unemployment09.csv",
                  header = FALSE, stringsAsFactors = FALSE)
names(unemp) <- c("id", "state_fips", "county_fips", "name", "year",
                  "?", "?", "?", "rate")
unemp$county <- tolower(gsub(" County, [A-Z]{2}", "", unemp$name))
unemp$county <- gsub("^(.*) parish, ..$","\\1", unemp$county)
unemp$state <- gsub("^.*([A-Z]{2}).*$", "\\1", unemp$name)

county_df <- map_data("county", projection = "albers", parameters = c(39, 45))
names(county_df) <- c("long", "lat", "group", "order", "state_name", "county")
county_df$state <- state.abb[match(county_df$state_name, tolower(state.name))]
county_df$state_name <- NULL

state_df <- map_data("state", projection = "albers", parameters = c(39, 45))

choropleth <- merge(county_df, unemp, by = c("state", "county"))
choropleth <- choropleth[order(choropleth$order), ]


ggplot(choropleth, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = rate), colour = alpha("white", 1 / 2), size = 0.2) +
  geom_polygon(data = state_df, colour = "white", fill = NA) +
  coord_fixed() +
  theme_void() +
  ggtitle("US unemployment rate by county") +
  theme(axis.line = element_blank(), axis.text = element_blank(),
        axis.ticks = element_blank(), axis.title = element_blank()) +
  scale_fill_gradientn(colours = usyd_palette("muted", 100, type = "continuous"))

Defining custom palettes

Want to make your own palette? No problem! You can also define your own palette by selecting colours of your choice with usyd_pal_gen.

Below is the full list of colour names contained in usydColours.

After you’ve decided on what colours you want, use usyd_pal_gen as shown below to make a custom palette.

# Define a new palette
my_palette <- usyd_pal_gen(
  "UpdatedOchre", 
  "LightOchre", 
  "NeutralGrey", 
  "Eucalypt", 
  "DarkEucalypt") 

# Use the new palette
diamonds %>%
  filter(price < 1000) %>%
  ggplot(aes(price, fill = cut)) +
  geom_histogram(colour = "black", position = "dodge", binwidth = 250) +
  theme_bw() +
  scale_fill_manual(values = my_palette)

RGB values and hex codes of colours

You can also refer to this table for RGB values and hex codes if you’d like to apply them in Photoshop or any other code.

colourName	R	G	B	hex
AccentBlue	1	72	164	#0148A4
AccentGrey	241	241	241	#F1F1F1
AccentYellow	255	184	0	#FFB800
MasterbrandBlack	10	10	10	#0A0A0A
MasterbrandCharcoal	66	66	66	#424242
MasterbrandOchre	230	70	38	#E64626
MasterbrandWhite	255	255	255	#FFFFFF
SecondaryBeige	253	202	144	#FDCA90
SecondaryBlue	78	152	211	#4E98D3
SecondaryDarkGreen	0	126	59	#007E3B
SecondaryDarkSeafoam	0	164	133	#00A485
SecondaryIvory	248	239	221	#F8EFDD
SecondaryLemon	251	243	141	#FBF38D
SecondaryLightBlue	145	189	229	#91BDE5
SecondaryLightGreen	189	220	150	#BDDC96
SecondaryLightPink	248	185	204	#F8B9CC
SecondaryLightSeafoam	104	198	182	#68C6B6
SecondaryLilac	184	150	198	#B896C6
SecondaryMaroon	122	32	0	#7A2000
SecondaryOrange	249	161	52	#F9A134
SecondaryPeach	247	156	114	#F79C72
SecondaryPink	214	81	157	#D6519D
SecondaryPurple	127	63	152	#7F3F98
DarkEucalypt	37	88	77	#25584D
Eucalypt	113	164	153	#71A499
NeutralGrey	224	224	224	#E0E0E0
LightOchre	255	173	140	#FFAD8C
UpdatedOchre	231	71	38	#E74726