如何在GGally中使用自己的密度函数创建低密度图 [英] How to create lower density plot using your own density function in GGally
本文介绍了如何在GGally中使用自己的密度函数创建低密度图的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
library(GGally)
library(tidyverse)
library viridis)
dat < - iris%>>%select( - 物种)
my_fn< - function(data,mapping,...) {
#使用默认的ggplot密度函数
p < - ggplot(data = data,mapping = mapping)+
stat_density2d(aes(fill = .. density ..), geom =tile,contour = FALSE)+
scale_fill_gradientn(colors = viridis :: viridis(100,option =viridis))
p
}
ggpairs(dat,lower = list(continuous = my_fn))+
theme_void()
我可以创建这个图:
我的问题是我怎样才能用下面的方案来改变GGally密度较低的图:
library(MASS)
#获取2维点的密度。
#@param x数字向量。
#@param y一个数字向量。
#@param n用n个网格创建一个正方形来计算密度。
#@return每个方块内的密度。
get_density < - 函数(x,y,n = 100){
dens <-MASS :: kde2d(x = x,y = y,n = n)
ix< ; - findInterval(x,dens $ x)
iy < - findInterval(y,dens $ y)
ii < - cbind(ix,iy)
return(dens $ z [ )
}
#数据处理方法2 ---------------------- ----------------------------
theme_set(theme_bw(base_size = 16))
tbl< - as .tibble(虹膜)%>%
select( - 物种)
#tbl
dens_wrapper< - 函数(tbl = NULL,var1 = NULL, var2 = NULL){
tbl_pair< - tbl%>%
select_(var1,var2)
x< - tbl_pair%>%pull(var1)
y< - tbl_pair%>%pull(var2)
tbl_pair $ density< - get_density(x,y)
tbl_pair
}
feature1 =Sepal.Length
feature2 =Petal.Length
tbl_pair1< - dens_wrapper(tbl = tbl,var1 = feature1,var2 = feature2)
ggplot(tbl_pair1)+
geom_point(aes_string (feature1,feature2,color ='de nsity'))+
scale_color_viridis()
产生这种结果的是:
解决方案
使用与
With the following code:
library(GGally)
library(tidyverse)
library(viridis)
dat <- iris %>% select(-Species)
my_fn <- function(data, mapping, ...){
# Using default ggplot density function
p <- ggplot(data = data, mapping = mapping) +
stat_density2d(aes(fill=..density..), geom="tile", contour = FALSE) +
scale_fill_gradientn(colours=viridis::viridis(100, option="viridis"))
p
}
ggpairs(dat, lower=list(continuous=my_fn)) +
theme_void()
I can create this plot:
My question is how can I change the GGally lower density plot with the following scheme:
library(MASS)
# Get density of points in 2 dimensions.
# @param x A numeric vector.
# @param y A numeric vector.
# @param n Create a square n by n grid to compute density.
# @return The density within each square.
get_density <- function(x, y, n = 100) {
dens <- MASS::kde2d(x = x, y = y, n = n)
ix <- findInterval(x, dens$x)
iy <- findInterval(y, dens$y)
ii <- cbind(ix, iy)
return(dens$z[ii])
}
# Data wrangling method2 --------------------------------------------------
theme_set(theme_bw(base_size = 16))
tbl <- as.tibble(iris) %>%
select(-Species)
# tbl
dens_wrapper <- function (tbl=NULL, var1=NULL, var2=NULL) {
tbl_pair <- tbl %>%
select_(var1, var2)
x <- tbl_pair %>% pull(var1)
y <- tbl_pair %>% pull(var2)
tbl_pair$density <- get_density(x,y)
tbl_pair
}
feature1 = "Sepal.Length"
feature2 = "Petal.Length"
tbl_pair1 <- dens_wrapper(tbl=tbl, var1=feature1, var2=feature2)
ggplot(tbl_pair1) +
geom_point(aes_string(feature1, feature2, color = 'density')) +
scale_color_viridis()
Which produce this:
解决方案
Using a similar idea as from Change colors in ggpairs now that params is deprecated , you can just add the calculations in to your own defined function.
my_fn <- function(data, mapping, N=100, ...){
get_density <- function(x, y, n ) {
dens <- MASS::kde2d(x = x, y = y, n = n)
ix <- findInterval(x, dens$x)
iy <- findInterval(y, dens$y)
ii <- cbind(ix, iy)
return(dens$z[ii])
}
X <- data[,as.character(mapping$x)]
Y <- data[,as.character(mapping$y)]
data$density <- get_density(x=X, y=Y, n=N)
p <- ggplot(data, mapping) +
geom_point(aes(colour=density), ...) +
scale_color_viridis()
p
}
ggpairs(dat, lower=list(continuous=my_fn)) +
theme_bw()
Produces:
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