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Getting started with prakriti

What is prakriti?

prakriti (Sanskrit for nature) gives you 30 color palettes pulled from Indian landscapes. Each one is built for a specific job - sequential for ordered data, diverging for data with a midpoint, qualitative for categories - and they all plug straight into ggplot2.

library(prakriti)
library(ggplot2)

Finding your way around

prakriti_names() lists every palette. prakriti_info() gives you the full picture - name, type, number of colors, and what landscape inspired it.

prakriti_names()
#>  [1] "himalaya"          "thar"              "backwaters"       
#>  [4] "western_ghats"     "rann"              "valley_of_flowers"
#>  [7] "andaman"           "nilgiri"           "spiti"            
#> [10] "kaziranga"         "chilika"           "mehrangarh"       
#> [13] "pangong"           "sundarbans"        "hampi"            
#> [16] "gulmarg"           "loktak"            "kaas"             
#> [19] "darjeeling"        "chinar"            "ganges"           
#> [22] "coorg"             "kutch_textile"     "jaisalmer"        
#> [25] "munnar"            "ladakh_monastery"  "chambal_ravines"  
#> [28] "nocturn"           "konkan"            "corbett"
prakriti_info()
#>                 name        type n
#> 1           himalaya  sequential 6
#> 2               thar  sequential 6
#> 3         backwaters  sequential 5
#> 4      western_ghats qualitative 6
#> 5               rann   diverging 6
#> 6  valley_of_flowers qualitative 7
#> 7            andaman qualitative 6
#> 8            nilgiri  sequential 6
#> 9              spiti   diverging 6
#> 10         kaziranga qualitative 6
#> 11           chilika  sequential 6
#> 12        mehrangarh   diverging 6
#> 13           pangong  sequential 6
#> 14        sundarbans qualitative 6
#> 15             hampi  sequential 6
#> 16           gulmarg qualitative 6
#> 17            loktak qualitative 6
#> 18              kaas qualitative 7
#> 19        darjeeling   diverging 6
#> 20            chinar  sequential 6
#> 21            ganges  sequential 6
#> 22             coorg qualitative 6
#> 23     kutch_textile qualitative 7
#> 24         jaisalmer   diverging 6
#> 25            munnar  sequential 6
#> 26  ladakh_monastery qualitative 6
#> 27   chambal_ravines  sequential 6
#> 28           nocturn  sequential 6
#> 29            konkan qualitative 6
#> 30           corbett   diverging 6
#>                                                             inspiration
#> 1            Blinding snow, glacial turquoise, bottomless Himalayan sky
#> 2               Blazing Rajasthan dunes, saffron sunset, scorched earth
#> 3                      Luminous Kerala palms reflected in emerald water
#> 4                  Monsoon: orchids, laterite, kingfishers, butterflies
#> 5           Infinite white salt flats, flamingo shock-pink, violet dusk
#> 6         Carpets of alpine wildflowers - every color screaming at once
#> 7                Electric turquoise shallows, fire coral, bleached sand
#> 8                   Blue-green mountains disappearing into monsoon mist
#> 9        Stark indigo night sky crashing into sun-scorched ochre cliffs
#> 10           Golden elephant grass, rhino armor, river mud, tiger flash
#> 11                    Flamingo clouds over pewter lagoon at first light
#> 12             Jodhpur's electric blue houses blazing under golden hour
#> 13            Pangong Tso shifting from turquoise to ultramarine to ink
#> 14           Neon mangrove canopy, dark tidal roots, tiger-flame ambush
#> 15     Rose-gold boulders catching sunset fire, fading to magenta night
#> 16  Blinding snow, vivid meadow, deodar silhouettes against indigo dusk
#> 17                Amber dawn, floating green phumdis on deep teal water
#> 18    Explosive wildflower carpets - hot pink, violet, acid green, gold
#> 19 Kanchenjunga on fire at sunrise, plunging into deep tea-estate green
#> 20     Kashmir's chinar ablaze - gold to vermilion to smoldering embers
#> 21        Sacred river at dawn - silt gold, monsoon green, deep current
#> 22          Coffee blossoms, red laterite, rain-soaked plantation green
#> 23       Rann at festival - mirrorwork silver, indigo, turmeric, madder
#> 24           Sandstone fort glowing at noon, cooling into blue twilight
#> 25                  Rolling tea carpets from bright flush to deep shade
#> 26         Whitewashed walls, prayer-flag primaries against barren rock
#> 27         Eroded badlands - bone white, khaki, terracotta, deep shadow
#> 28             Bioluminescent shores of Havelock - ink sky to starlight
#> 29     Laterite cliffs, coconut spray, Arabian Sea teal, monsoon violet
#> 30         Sal forest dawn - gold mist, tiger-stripe amber, deep canopy

Filter by type if you know what kind of data you’re working with:

info <- prakriti_info()
info[info$type == "diverging", ]
#>          name      type n
#> 5        rann diverging 6
#> 9       spiti diverging 6
#> 12 mehrangarh diverging 6
#> 19 darjeeling diverging 6
#> 24  jaisalmer diverging 6
#> 30    corbett diverging 6
#>                                                             inspiration
#> 5           Infinite white salt flats, flamingo shock-pink, violet dusk
#> 9        Stark indigo night sky crashing into sun-scorched ochre cliffs
#> 12             Jodhpur's electric blue houses blazing under golden hour
#> 19 Kanchenjunga on fire at sunrise, plunging into deep tea-estate green
#> 24           Sandstone fort glowing at noon, cooling into blue twilight
#> 30         Sal forest dawn - gold mist, tiger-stripe amber, deep canopy

Pulling colors

prakriti_palette() returns a character vector of hex codes. By default you get the full palette. Pass n to grab a subset.

prakriti_palette("thar")
#> [1] "#FFF0A3" "#FFB727" "#F57D15" "#D94701" "#8B1A04" "#3D0C02"
#> attr(,"name")
#> [1] "thar"
#> attr(,"type")
#> [1] "sequential"
prakriti_palette("himalaya", n = 3)
#> [1] "#FCFEFF" "#A8D8EA" "#3D9BE9"
#> attr(,"name")
#> [1] "himalaya"
#> attr(,"type")
#> [1] "sequential"

Reverse any palette with direction = -1:

prakriti_palette("chinar", direction = -1)
#> [1] "#260000" "#7F0000" "#D50000" "#FF6F00" "#FFB300" "#FFECB3"
#> attr(,"name")
#> [1] "chinar"
#> attr(,"type")
#> [1] "sequential"

Need more colors than the palette has? Interpolate smoothly:

prakriti_palette("nilgiri", n = 15, type = "continuous")
#>  [1] "#E0F2E9" "#B0E2CE" "#81D2B4" "#57C19C" "#43AC89" "#2E9776" "#1E8667"
#>  [8] "#167C5F" "#0E7257" "#09654F" "#085748" "#064941" "#043D36" "#03302B"
#> [15] "#022420"
#> attr(,"name")
#> [1] "nilgiri"
#> attr(,"type")
#> [1] "sequential"

Viewing palettes

Single palette:

display_prakriti("valley_of_flowers")

The whole collection (make your plot pane tall):

display_prakriti()

Using with ggplot2

Qualitative palettes default to discrete scales. Sequential and diverging default to continuous. You can override with discrete = TRUE or FALSE.

ggplot(iris, aes(Sepal.Length, Petal.Length,
                 color = Species, shape = Species)) +
  geom_point(size = 3, alpha = 0.85) +
  scale_color_prakriti("valley_of_flowers") +
  labs(title = "Iris measurements",
       x = "Sepal length (cm)", y = "Petal length (cm)") +
  theme_minimal()

ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_raster(interpolate = TRUE) +
  scale_fill_prakriti("himalaya") +
  coord_cartesian(expand = FALSE) +
  labs(title = "Old Faithful eruption density") +
  theme_minimal()

ggplot(mtcars, aes(factor(cyl), mpg, fill = factor(cyl))) +
  geom_boxplot() +
  scale_fill_prakriti("thar", discrete = TRUE) +
  labs(title = "MPG by cylinder count", x = "Cylinders", y = "MPG") +
  theme_minimal() +
  theme(legend.position = "none")

What’s next

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.