Package: cptcity 1.1.0
cptcity: 'cpt-city' Colour Gradients
Incorporates colour gradients from the 'cpt-city' web archive available at <http://soliton.vm.bytemark.co.uk/pub/cpt-city/>.
Authors:
cptcity_1.1.0.tar.gz
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cptcity.pdf |cptcity.html✨
cptcity/json (API)
NEWS
| # Install 'cptcity' in R: |
| install.packages('cptcity', repos = c('https://ibarraespinosa.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ibarraespinosa/cptcity/issues
Datasets:
- cpt_names - Names of the 7140 color gradients of cptcity R Package
On CRAN:cptcity-1.1.1(2024-12-02)
colorcolorscolourcolour-palettecolourscpt-citycptcitygradient
Last updated 2 years agofrom:749106f1a8. Checks:8 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 18 2025 |
| R-4.5-win | OK | Feb 18 2025 |
| R-4.5-mac | OK | Feb 18 2025 |
| R-4.5-linux | OK | Feb 18 2025 |
| R-4.4-win | OK | Feb 18 2025 |
| R-4.4-mac | OK | Feb 18 2025 |
| R-4.3-win | OK | Feb 18 2025 |
| R-4.3-mac | OK | Feb 18 2025 |
Exports:cptfind_cptluckyshow_cpt
Dependencies:
Citation
If you want to cite the cptcity R package, please use:
Sergio Ibarra-Espinosa (2018) cptcity: incorporating the cpt-city archive into R. R package version 1.0.0. https://CRAN.R-project.org/package=cptcity
Corresponding BibTeX entry:
@Manual{,
title = {cptcity: incorporating the cpt-city archive into R},
author = {Sergio Ibarra-Espinosa},
year = {2017},
note = {Colour gradients from
http://soliton.vm.bytemark.co.uk/pub/cpt-city/},
url = {https://CRAN.R-project.org/package=vein},
}
Readme and manuals
cptcity package
Sergio Ibarra-Espinosa 2023-08-13
A package to return colour gradients from CPTCITY
Colour palettes comes from http://soliton.vm.bytemark.co.uk/pub/cpt-city/index.html
Currently there are 7140 colour gradients. To se the names type
cpt_names.
There are three functions:
-
find_cpt: To search the name of the colour gradient.
Example:
library(cptcity)
find_cpt("radar")
## [1] "idv_radar_dbz_nws" "idv_radar_dbz" "idv_radar_reflect_24"
## [4] "idv_radar_topo_mdr" "ncl_radar_1" "ncl_radar"
-
show_cpt: Visually selectyour palette
pals <- find_cpt("radar")
show_cpt(pals)
## Creating 2 3 matrix
-
cpt: To return the colour gradient.
cptcity::cpt(pal = "ncl_radar_1", n = 100)
## [1] "#B2F8FF" "#B2F8FF" "#B2F8FF" "#B2DCFF" "#B2BEFF" "#B2B8FF" "#B2B8FF"
## [8] "#A088EE" "#8742D7" "#7D25CD" "#7D25CD" "#7322BE" "#601D9E" "#541A8B"
## [15] "#541A8B" "#66328A" "#AF9387" "#EDE685" "#EDE685" "#ECE584" "#DDD67C"
## [22] "#CDC673" "#CDC673" "#CDC673" "#B7B380" "#9D9C91" "#969696" "#969696"
## [29] "#B4B4B4" "#E6E6E6" "#FFFFFF" "#FFFFFF" "#EEFFFF" "#C6FFFF" "#AAFFFF"
## [36] "#AAFFFF" "#A2F6FF" "#79C9FF" "#55A0FF" "#55A0FF" "#55A0FF" "#3A55FF"
## [43] "#2009FF" "#1D00FF" "#1D00FF" "#4053C3" "#6EBF75" "#7EE55B" "#7EE55B"
## [50] "#71DE54" "#5AD249" "#4ECC43" "#4ECC43" "#48C741" "#39BB3C" "#2EB239"
## [57] "#2EB239" "#2DB039" "#25A43B" "#1E993D" "#1E993D" "#1E993D" "#7FC54E"
## [64] "#EAF562" "#FFFF66" "#FFFF66" "#FFED66" "#FFD566" "#FFCC66" "#FFCC66"
## [71] "#FFBC5F" "#FF9B53" "#FF884C" "#FF884C" "#FF7945" "#FF442D" "#FF1919"
## [78] "#FF1919" "#FD1A1A" "#E52B2B" "#CD3C3C" "#CC3D3D" "#CC3D3D" "#BC3838"
## [85] "#A93232" "#A53131" "#A53131" "#BA2269" "#DD0AC3" "#ED00ED" "#ED00ED"
## [92] "#D814E6" "#A945D7" "#8967CD" "#8967CD" "#9474CF" "#CAB5DB" "#FAF0E6"
## [99] "#FAF0E6" "#FAF0E6"
-
lucky: To choose a random colour gradient
cptcity::lucky(n = 100)
## Colour gradient: nd_basic_Blue_White_Red, number: 5194
## [1] "#0000FF" "#0505FF" "#0A0AFF" "#0F0FFF" "#1414FF" "#1919FF" "#1F1FFF"
## [8] "#2424FF" "#2929FF" "#2E2EFF" "#3333FF" "#3838FF" "#3E3EFF" "#4343FF"
## [15] "#4848FF" "#4D4DFF" "#5252FF" "#5757FF" "#5D5DFF" "#6262FF" "#6767FF"
## [22] "#6C6CFF" "#7171FF" "#7676FF" "#7C7CFF" "#8181FF" "#8686FF" "#8B8BFF"
## [29] "#9090FF" "#9595FF" "#9A9AFF" "#A0A0FF" "#A5A5FF" "#AAAAFF" "#AFAFFF"
## [36] "#B4B4FF" "#B9B9FF" "#BEBEFF" "#C3C3FF" "#C9C9FF" "#CECEFF" "#D3D3FF"
## [43] "#D8D8FF" "#DDDDFF" "#E2E2FF" "#E7E7FF" "#EDEDFF" "#F2F2FF" "#F7F7FF"
## [50] "#FCFCFF" "#FFFCFC" "#FFF7F7" "#FFF2F2" "#FFEDED" "#FFE7E7" "#FFE2E2"
## [57] "#FFDDDD" "#FFD8D8" "#FFD3D3" "#FFCECE" "#FFC9C9" "#FFC3C3" "#FFBEBE"
## [64] "#FFB9B9" "#FFB4B4" "#FFAFAF" "#FFAAAA" "#FFA5A5" "#FFA0A0" "#FF9A9A"
## [71] "#FF9595" "#FF9090" "#FF8B8B" "#FF8686" "#FF8181" "#FF7C7C" "#FF7676"
## [78] "#FF7171" "#FF6C6C" "#FF6767" "#FF6262" "#FF5D5D" "#FF5757" "#FF5252"
## [85] "#FF4D4D" "#FF4848" "#FF4343" "#FF3E3E" "#FF3838" "#FF3333" "#FF2E2E"
## [92] "#FF2929" "#FF2424" "#FF1F1F" "#FF1919" "#FF1414" "#FF0F0F" "#FF0A0A"
## [99] "#FF0505" "#FF0000"
Install and use it!
# install.packages("cptcity") 1.0.6
# # remotes::install_github("ibarraespinosa/cptcity")
install.packages("cptcity", repos = c('https://ibarraespinosa.r-universe.dev', 'https://cloud.r-project.org'))
packageVersion("cptcity")
## [1] '1.1.0'
library(cptcity)
image(matrix(1:100), col = cpt(pal = "mpl_inferno"))
ggplot2
And you can use it ggplot2
library(cptcity)
library(ggplot2)
ggplot(faithfuld, aes(waiting, eruptions)) +
geom_raster(aes(fill = density))
ggplot(faithfuld, aes(waiting, eruptions)) +
geom_raster(aes(fill = density)) +
scale_fill_gradientn(colours = cpt())
raster
library(cptcity)
library(raster)
## Loading required package: sp
## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
## (status 2 uses the sf package in place of rgdal)
f <- system.file("external/test.grd", package="raster")
r <- raster(f)
spplot(r, scales = list(draw = T))
spplot(r, scales = list(draw = T),
col.regions = rev(cpt(n = 100, pal = "colo_Skyblue2u_Gentle_Waves")))
sf
library(cptcity)
library(sf)
## Linking to GEOS 3.11.1, GDAL 3.6.2, PROJ 9.1.1; sf_use_s2() is TRUE
nc = st_read(system.file("shape/nc.shp", package="sf"))
## Reading layer `nc' from data source
## `/home/sergio/R/x86_64-pc-linux-gnu-library/4.2/sf/shape/nc.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 100 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS: NAD27
plot(nc["AREA"], pal = cpt(colorRampPalette = T))
mapview
library(cptcity)
library(mapview)
library(sf)
nc = st_read(system.file("shape/nc.shp", package="sf"))
mapview(nc, zcol = "AREA", col.regions = cpt(colorRampPalette = T), legend = T)
stars
library(cptcity)
library(stars)
## Loading required package: abind
tif = system.file("tif/L7_ETMs.tif", package = "stars")
(x1 = read_stars(tif))
## stars object with 3 dimensions and 1 attribute
## attribute(s):
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## L7_ETMs.tif 1 54 69 68.91242 86 255
## dimension(s):
## from to offset delta refsys point x/y
## x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band 1 6 NA NA NA NA
br <- classInt::classIntervals(var = x1$L7_ETMs.tif,
n = 100,
style = "sd")
plot(x1,
axes = TRUE,
lty = 0,
col = cptcity::cpt(rev = TRUE,
pal = 819,
n = length(br$brks) - 1),
key.pos = 2,
breaks = br$brks
)
terra
library(cptcity)
library(terra)
## terra 1.7.37
f <- system.file("ex/elev.tif", package="terra")
r <- rast(f)
image(r, col = cptcity::lucky())
## Colour gradient: gacruxa_calamba_calamba_02, number: 2252
# Colour gradient: dca_alarm.p4.0.1, number: 1115
I included a random colour gradient function!I included a random colour gradient function!
# install.packages("cptcity")
# # devtools::install_github("ibarraespinosa/cptcity")
install.packages("cptcity", repos = c('https://ibarraespinosa.r-universe.dev', 'https://cloud.r-project.org'))
library(cptcity)
image(matrix(1:100), col = lucky())
COPYING
Each palette has a COPYING.XML files available at ins/extdata. To see each file do:
library(cptcity)
files <- list.files(system.file("extdata",
package = "cptcity"),
pattern = ".xml",
recursive = T,
full.names = T)
# For instance, the COPYING files of "arendal" is
files[1]
# and the file
readLines(files[1])
1] "/usr/local/lib/R/site-library/cptcity/extdata/arendal/COPYING.xml"
[1] "<?xml version=\"1.0\"?>"
[2] "<copying>"
[3] " <authors>"
[4] " <author href=\"http://www.grida.no/graphicslib/\">"
[5] " <name>GRID-Arendal</name>"
[6] " </author>"
[7] " </authors>"
[8] " <license>"
[9] " <informal>"
[10] " Attribution and linkback requested."
[11] " </informal>"
[12] " <year>2014</year>"
[13] " <text>"
[14] " Using this graphic and referring to it is encouraged, "
[15] " and please use it in presentations, web pages, newspapers, "
[16] " blogs and reports."
[17] ""
[18] " For any form of publication, please include the link to "
[19] " this page and give the cartographer/designer credit."
[20] " </text>"
[21] " </license>"
[22] " <src>"
[23] " <format>PNG images</format>"
[24] " <link href=\"http://www.grida.no/graphicslib/detail/mean-surface-temperature_c7e4\""
[25] "\t >Temperature</link>"
[26] " <link href=\"http://www.grida.no/graphicslib/detail/arctic-topography-and-bathymetry_5645\""
[27] "\t >Arctic bathymetry</link>"
[28] " <link href=\"http://www.grida.no/graphicslib/detail/map-of-zimbabwe_36a5\""
[29] "\t >Zambezi</link>"
[30] " </src>"
[31] " <distribute>"
[32] " <qgis distribute=\"yes\" license=\"other\"/>"
[33] " </distribute>"
[34] "</copying>"
