如何使用 R（最好是 ggplot2）生成堆叠平面或重叠菱形的图形？
How to produce a graphic of stacked planes or overlapping diamonds using R (and ideally ggplot2)?
在提高自己技能的同时，我看到了非常出色的 ggplot2 workshop 通过了解它在基本层面上的工作原理，让自己更好地使用这个包。
作为该研讨会的一部分，我对研讨会中使用的一种可视化效果感到震惊，因为它对于解释分层的依赖关系特别有用，我想弄清楚如何生成这样的图片（最好使用 R）。
这两张图片显示了我试图重现的可视化的两个部分：
带标签的堆叠平面：
堆叠平面，大部分为透明胶片和标签（适当突出显示）：
我已经能够使用 rgl 生成类似的东西，但它远没有那么好。鉴于我正在尝试提高 ggplot2 的技能，我希望能够使用 ggplot2（或其扩展之一）来制作它，因为这将使我能够更轻松地控制图形的一些“细节”。
是否可以使用 ggplot2 或扩展包？
在rgl中生成它的代码是：
library(rgl)
# Create some dummy data
dat <- replicate(2, 1:3)
# Initialize the scene, no data plotted
# hardcoded user matrix of a particular view (so I can go straight to that view each time)
userMatrix_orig <- matrix(c(-0.7069399, -0.2729415, 0.6524867, 0.0000000, 0.7072651, -0.2773000, 0.6502926, 0.0000000, 0.003442926, 0.921199083, 0.389076293, 0.000000000, 0, 0, 0, 1), nrow = 4 )
plot3d(dat, type = 'n', xlim = c(-1, 1), ylim = c(-1, 1), zlim = c(-10, 10),
xlab = '', ylab = '', zlab = '', axes=FALSE)
view3d(userMatrix=userMatrix_orig)
material3d(alpha=1.0)
# Add planes
planes3d(1, 1, 1, -2, col = 'paleturquoise', alpha = 0.8, name="hello")
planes3d(1, 1, 1, -4, col = 'palegreen', alpha = 0.8)
planes3d(1, 1, 1, -6, col = 'palevioletred', alpha = 0.8)
planes3d(1, 1, 1, -8, col = 'midnightblue', alpha = 0.8)
planes3d(1, 1, 1, 0, col = 'red', alpha = 0.8)
planes3d(1, 1, 1, 2, col = 'green', alpha = 0.8)
planes3d(1, 1, 1, 4, col = 'orange', alpha = 0.8)
planes3d(1, 1, 1, 6, col = 'blue', alpha = 0.8)
# Label the planes
family_val <- c("sans")
adj_val <- 1
cex_val <- 2.5
text3d(x=1, y =-1, z = -6, texts="data", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = -4, texts="mapping", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = -2, texts="statistics", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 0, texts="scales", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 2, texts="geometries", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 4, texts="facets", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 6, texts="coordinates", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 8, texts="theme", adj = adj_val, family = family_val, cex = cex_val )
我用它制作的图形是：
我将使用如下函数在 ggplot 中重新创建图像：
make_graphic <- function(highlight = 1:8) {
library(ggplot2)
alpha_vals <- c(0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2)
alpha_vals[highlight] <- 1
df <- data.frame(x = rep(c(0.5, 0.75, 1, 0.75, 0.5), 8),
y = rep(c(0.5, 0, 0.5, 1, 0.5), 8) + rep(0:7, each = 5)/2,
z = rep(LETTERS[1:8], each = 5))
ggplot(df, aes(x, y)) +
geom_polygon(aes(fill = z, alpha = z)) +
geom_text(data = data.frame(x = 0.48, y = rev(0.5 + (0:7)/2),
z = rev(LETTERS[1:8]),
a = c("THEME", "COORDINATES", "FACETS",
"GEOMETRIES", "SCALES", "STATISTICS",
"MAPPING", "DATA")), fontface = 2,
family = "opencondensed",
aes(label = a, alpha = z), colour = "white", size = 10, hjust = 1) +
scale_x_continuous(limits = c(0.2, 1)) +
scale_fill_manual(values = c("#a6aaa9", "#ef4e47", "#34a5da", "#ff9d35",
"#8abe5e", "#ffe989", "#c52060", "#3f969a")) +
scale_alpha_manual(values = alpha_vals) +
theme_void() +
theme(legend.position = "none",
plot.background = element_rect(fill = "#222222"))
}
这样可以通过以下操作轻松地重新创建图形：
make_graphic()
如果你只想突出显示第二个底部项目，你可以这样做：
make_graphic(2)
这是一个尝试。
数据
library(dplyr)
mydata <- data.frame(
label = c("THEME", "COORDINATES", "FACETS", "GEOMETRIES", "SCALES", "STATISTICS", "MAPPING", "DATA"),
ybase = 8:1,
color = c("#3f969a", "#c52060", "#ffe989", "#8abe5e", "#ff9d35", "#34a5da", "#ef4e47", "#a6aaa9")
) %>%
rowwise() %>%
mutate(
xs = list(c(0, 2, 0, -2)),
ys = lapply(ybase, `+`, c(1.1, 0, -1.1, 0)),
ord = list(1:4)
) %>%
ungroup() %>%
tidyr::unnest(c(xs, ys, ord)) %>%
arrange(ybase, ord)
spldata <- split(mydata, mydata$label)
spldata <- spldata[order(sapply(spldata, function(z) z$ybase[1]))]
我创建 spldata 的原因是因为 ggplot2 不允许 (afaik) 轻松设置 z-order，所以我将诉诸（下一个块）迭代地绘制多边形。
剧情，无亮点
library(ggplot2)
ggplot(mydata, aes(xs, ys, group = label)) +
lapply(spldata, function(dat) {
geom_polygon(aes(fill = I(color)), data = dat)
}) +
geom_text(aes(x = -2.2, y = ybase, label = label),
hjust = 1, color = "white", size = 7,
data = ~ filter(., ord == 1)) +
guides(fill = "none", color = "none", alpha = "none") +
scale_x_continuous(expand = expansion(add = c(2.5, 0.2))) +
theme(
plot.background = element_rect(colour = "black", fill = "black"),
panel.background = element_rect(colour = "black", fill = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(), axis.ticks = element_blank()
)
情节，高亮显示
这里的变化：
- 将
alpha = if ... 添加到 geom_polygons
- 将
geom_text 分成两个调用，因为我不想在多边形和文本之间发现 colour= 美学
this <- c("THEME", "MAPPING")
ggplot(mydata, aes(xs, ys, group = label)) +
lapply(spldata, function(dat) {
geom_polygon(aes(fill = I(color)),
alpha = if (dat$label[1] %in% this) 1 else 0.2,
data = dat)
}) +
{
if (any(!mydata$label %in% this))
geom_text(aes(x = -2.2, y = ybase, label = label),
hjust = 1, color = "gray50", size = 7,
data = ~ filter(., ord == 1, !label %in% this))
} +
{
if (any(this %in% mydata$label))
geom_text(aes(x = -2.2, y = ybase, label = label),
hjust = 1, color = "white", size = 7,
data = ~ filter(., ord == 1, label %in% this))
} +
guides(fill = "none", color = "none", alpha = "none") +
scale_x_continuous(expand = expansion(add = c(2.5, 0.2))) +
theme(
plot.background = element_rect(colour = "#222222", fill = "#222222"),
panel.background = element_rect(colour = "#222222", fill = "#222222"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(), axis.ticks = element_blank()
)
（我从 AllanCameron 那里借用了 “一个或多个” 的想法 this 以便能够突出显示多个（或者 none).
我自己研究了一段时间后，想出了以下函数：
library(ggplot2)
generate_layer_diagram <- function(highlight_layers = "all", num_layers = 8,
overwrite_layer_labels = c('DATA','MAPPING','SCALES','STATISTICS','GEOMETRIES','FACETS','COORDINATES','THEMES'),
overwrite_colours = c('grey','blue','red','orange','paleturquoise','palegreen','palevioletred','midnightblue'),
base_colour_set_name="Set3",
base_num_colours=12L,
save_path="",
transparent_background=FALSE) {
base_image_height <- 20.32
base_image_width <- 21.77
scaling_factor <- 0.69
alpha_highlight <- 1.0
alpha_mute <- 0.2
font_size <- 8*scaling_factor
font_weight <- "bold"
if(transparent_background) {
font_colour <- "black"
background_color <- "transparent"
} else {
font_colour <- "white"
background_color <- "black"
}
diamond <- function(side_length, centre) {
base <- matrix(c(1, 0, 0, 1, -1, 0, 0, -1), nrow = 2) * sqrt(2) / 2
trans <- (base * side_length) + centre
as.data.frame(t(trans))
}
if(is.character(highlight_layers) && highlight_layers == "all") {
highlight_layers = c(1:num_layers)
}
highlights <- c(rep(FALSE,num_layers))
highlights[highlight_layers] <- TRUE
layer_labels <- paste0(c("layer_"),c(1:num_layers)) %>% data.table
layer_labels[,labels:=.][,.:=NULL]
if(length(overwrite_layer_labels) > num_layers) {
overwrite_layer_labels <- overwrite_layer_labels[1:num_layers]
}
layer_labels[1:length(overwrite_layer_labels),labels:=overwrite_layer_labels]
base_colour_set <- RColorBrewer::brewer.pal(base_num_colours,base_colour_set_name) %>% data.table()
base_colour_set <- base_colour_set[,colours:=.][,.:=NULL]
if(num_layers > base_num_colours) {
base_colour_set <- base_colour_set[rep(seq_len(nrow(base_colour_set)), ceiling(num_layers/base_num_colours)), ]
}
base_colour_set <- base_colour_set[1:num_layers]
colour_set <- base_colour_set
if(length(overwrite_colours) > num_layers) {
overwrite_colours <- overwrite_colours[1:num_layers]
}
colour_set[1:length(overwrite_colours),colours:=overwrite_colours]
dt <- data.table(side_lengths = rep(c(2),num_layers),
centres = matrix(c(1 + rep(0,num_layers),2 + 0:(num_layers-1)),nrow=num_layers),
colours = colour_set,
labels = layer_labels,
highlights = highlights)
dt[,alphas:=(as.numeric(highlights)*alpha_highlight + as.numeric(!highlights)*alpha_mute)]
myplot <- ggplot() + lapply(c(1:num_layers),function(x) {geom_polygon(data = diamond(dt$side_lengths[x], c(dt$centres.V1[x],dt$centres.V2[x])), mapping = aes(x = V1, y = V2), fill = dt$colours[x], alpha = dt$alphas[x])}) +
lapply(c(1:num_layers),function(z) {annotate("text", x = -(dt$centres.V1[z]/2)*1.1, y = dt$centres.V2[z], label = dt$labels[z], alpha = dt$alphas[z], size=font_size,
fontface = font_weight, hjust=1, colour=font_colour)}) +
coord_cartesian(xlim = c(-2,3), ylim =c(-1, (num_layers+4) )) +
theme_void() +
#theme_classic() + # gets rid of the ugly bounding box
theme( plot.background = element_rect(fill = background_color)
,axis.line = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank()
#,plot.margin = element_blank()
,panel.grid = element_blank()
,panel.grid.major = element_blank()
,panel.grid.minor = element_blank()
,panel.background = element_rect(fill=NA)
,panel.border = element_rect(fill=NA)
,validate=TRUE) # sets the background and removes the various axes
option_markers <- c(rep(0,num_layers))
option_markers[highlight_layers] <- 1
suffix <- paste0(option_markers,collapse = "_")
if(save_path != ""){
ggsave(paste0(save_path,"\pic",suffix,".png"), myplot, height=base_image_height*scaling_factor, width=base_image_width*scaling_factor, units = "cm")
} else {
myplot
}
return(myplot)
}
highlight_layers = "all"
num_layers <- 5
overwrite_layer_labels <- c('DATA','MAPPING','SCALES','STATISTICS','GEOMETRIES','FACETS','COORDINATES','THEMES')
overwrite_colours <- c('grey','blue','red','orange','paleturquoise','palegreen','palevioletred','midnightblue')
Ans 各种示例函数调用产生：
generate_layer_diagram()
generate_layer_diagram(c(1:3),num_layers = num_layers)
generate_layer_diagram(1)
generate_layer_diagram(2)
# Data Mapping and Geometries
generate_layer_diagram(c(1,2,5))
产生：
感谢@AllenCameron 的 传递矢量以一次突出显示多个图层的灵感。
在提高自己技能的同时，我看到了非常出色的 ggplot2 workshop 通过了解它在基本层面上的工作原理，让自己更好地使用这个包。
作为该研讨会的一部分，我对研讨会中使用的一种可视化效果感到震惊，因为它对于解释分层的依赖关系特别有用，我想弄清楚如何生成这样的图片（最好使用 R）。
这两张图片显示了我试图重现的可视化的两个部分：
带标签的堆叠平面：
堆叠平面，大部分为透明胶片和标签（适当突出显示）：
我已经能够使用 rgl 生成类似的东西，但它远没有那么好。鉴于我正在尝试提高 ggplot2 的技能，我希望能够使用 ggplot2（或其扩展之一）来制作它，因为这将使我能够更轻松地控制图形的一些“细节”。
是否可以使用 ggplot2 或扩展包？
在rgl中生成它的代码是：
library(rgl)
# Create some dummy data
dat <- replicate(2, 1:3)
# Initialize the scene, no data plotted
# hardcoded user matrix of a particular view (so I can go straight to that view each time)
userMatrix_orig <- matrix(c(-0.7069399, -0.2729415, 0.6524867, 0.0000000, 0.7072651, -0.2773000, 0.6502926, 0.0000000, 0.003442926, 0.921199083, 0.389076293, 0.000000000, 0, 0, 0, 1), nrow = 4 )
plot3d(dat, type = 'n', xlim = c(-1, 1), ylim = c(-1, 1), zlim = c(-10, 10),
xlab = '', ylab = '', zlab = '', axes=FALSE)
view3d(userMatrix=userMatrix_orig)
material3d(alpha=1.0)
# Add planes
planes3d(1, 1, 1, -2, col = 'paleturquoise', alpha = 0.8, name="hello")
planes3d(1, 1, 1, -4, col = 'palegreen', alpha = 0.8)
planes3d(1, 1, 1, -6, col = 'palevioletred', alpha = 0.8)
planes3d(1, 1, 1, -8, col = 'midnightblue', alpha = 0.8)
planes3d(1, 1, 1, 0, col = 'red', alpha = 0.8)
planes3d(1, 1, 1, 2, col = 'green', alpha = 0.8)
planes3d(1, 1, 1, 4, col = 'orange', alpha = 0.8)
planes3d(1, 1, 1, 6, col = 'blue', alpha = 0.8)
# Label the planes
family_val <- c("sans")
adj_val <- 1
cex_val <- 2.5
text3d(x=1, y =-1, z = -6, texts="data", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = -4, texts="mapping", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = -2, texts="statistics", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 0, texts="scales", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 2, texts="geometries", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 4, texts="facets", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 6, texts="coordinates", adj = adj_val, family = family_val, cex = cex_val )
text3d(x=1, y =-1, z = 8, texts="theme", adj = adj_val, family = family_val, cex = cex_val )
我用它制作的图形是：
我将使用如下函数在 ggplot 中重新创建图像：
make_graphic <- function(highlight = 1:8) {
library(ggplot2)
alpha_vals <- c(0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2)
alpha_vals[highlight] <- 1
df <- data.frame(x = rep(c(0.5, 0.75, 1, 0.75, 0.5), 8),
y = rep(c(0.5, 0, 0.5, 1, 0.5), 8) + rep(0:7, each = 5)/2,
z = rep(LETTERS[1:8], each = 5))
ggplot(df, aes(x, y)) +
geom_polygon(aes(fill = z, alpha = z)) +
geom_text(data = data.frame(x = 0.48, y = rev(0.5 + (0:7)/2),
z = rev(LETTERS[1:8]),
a = c("THEME", "COORDINATES", "FACETS",
"GEOMETRIES", "SCALES", "STATISTICS",
"MAPPING", "DATA")), fontface = 2,
family = "opencondensed",
aes(label = a, alpha = z), colour = "white", size = 10, hjust = 1) +
scale_x_continuous(limits = c(0.2, 1)) +
scale_fill_manual(values = c("#a6aaa9", "#ef4e47", "#34a5da", "#ff9d35",
"#8abe5e", "#ffe989", "#c52060", "#3f969a")) +
scale_alpha_manual(values = alpha_vals) +
theme_void() +
theme(legend.position = "none",
plot.background = element_rect(fill = "#222222"))
}
这样可以通过以下操作轻松地重新创建图形：
make_graphic()
如果你只想突出显示第二个底部项目，你可以这样做：
make_graphic(2)
这是一个尝试。
数据
library(dplyr)
mydata <- data.frame(
label = c("THEME", "COORDINATES", "FACETS", "GEOMETRIES", "SCALES", "STATISTICS", "MAPPING", "DATA"),
ybase = 8:1,
color = c("#3f969a", "#c52060", "#ffe989", "#8abe5e", "#ff9d35", "#34a5da", "#ef4e47", "#a6aaa9")
) %>%
rowwise() %>%
mutate(
xs = list(c(0, 2, 0, -2)),
ys = lapply(ybase, `+`, c(1.1, 0, -1.1, 0)),
ord = list(1:4)
) %>%
ungroup() %>%
tidyr::unnest(c(xs, ys, ord)) %>%
arrange(ybase, ord)
spldata <- split(mydata, mydata$label)
spldata <- spldata[order(sapply(spldata, function(z) z$ybase[1]))]
我创建 spldata 的原因是因为 ggplot2 不允许 (afaik) 轻松设置 z-order，所以我将诉诸（下一个块）迭代地绘制多边形。
剧情，无亮点
library(ggplot2)
ggplot(mydata, aes(xs, ys, group = label)) +
lapply(spldata, function(dat) {
geom_polygon(aes(fill = I(color)), data = dat)
}) +
geom_text(aes(x = -2.2, y = ybase, label = label),
hjust = 1, color = "white", size = 7,
data = ~ filter(., ord == 1)) +
guides(fill = "none", color = "none", alpha = "none") +
scale_x_continuous(expand = expansion(add = c(2.5, 0.2))) +
theme(
plot.background = element_rect(colour = "black", fill = "black"),
panel.background = element_rect(colour = "black", fill = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(), axis.ticks = element_blank()
)
情节，高亮显示
这里的变化：
- 将
alpha = if ...添加到geom_polygons - 将
geom_text分成两个调用，因为我不想在多边形和文本之间发现colour=美学
this <- c("THEME", "MAPPING")
ggplot(mydata, aes(xs, ys, group = label)) +
lapply(spldata, function(dat) {
geom_polygon(aes(fill = I(color)),
alpha = if (dat$label[1] %in% this) 1 else 0.2,
data = dat)
}) +
{
if (any(!mydata$label %in% this))
geom_text(aes(x = -2.2, y = ybase, label = label),
hjust = 1, color = "gray50", size = 7,
data = ~ filter(., ord == 1, !label %in% this))
} +
{
if (any(this %in% mydata$label))
geom_text(aes(x = -2.2, y = ybase, label = label),
hjust = 1, color = "white", size = 7,
data = ~ filter(., ord == 1, label %in% this))
} +
guides(fill = "none", color = "none", alpha = "none") +
scale_x_continuous(expand = expansion(add = c(2.5, 0.2))) +
theme(
plot.background = element_rect(colour = "#222222", fill = "#222222"),
panel.background = element_rect(colour = "#222222", fill = "#222222"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(), axis.ticks = element_blank()
)
（我从 AllanCameron 那里借用了 “一个或多个” 的想法 this 以便能够突出显示多个（或者 none).
我自己研究了一段时间后，想出了以下函数：
library(ggplot2)
generate_layer_diagram <- function(highlight_layers = "all", num_layers = 8,
overwrite_layer_labels = c('DATA','MAPPING','SCALES','STATISTICS','GEOMETRIES','FACETS','COORDINATES','THEMES'),
overwrite_colours = c('grey','blue','red','orange','paleturquoise','palegreen','palevioletred','midnightblue'),
base_colour_set_name="Set3",
base_num_colours=12L,
save_path="",
transparent_background=FALSE) {
base_image_height <- 20.32
base_image_width <- 21.77
scaling_factor <- 0.69
alpha_highlight <- 1.0
alpha_mute <- 0.2
font_size <- 8*scaling_factor
font_weight <- "bold"
if(transparent_background) {
font_colour <- "black"
background_color <- "transparent"
} else {
font_colour <- "white"
background_color <- "black"
}
diamond <- function(side_length, centre) {
base <- matrix(c(1, 0, 0, 1, -1, 0, 0, -1), nrow = 2) * sqrt(2) / 2
trans <- (base * side_length) + centre
as.data.frame(t(trans))
}
if(is.character(highlight_layers) && highlight_layers == "all") {
highlight_layers = c(1:num_layers)
}
highlights <- c(rep(FALSE,num_layers))
highlights[highlight_layers] <- TRUE
layer_labels <- paste0(c("layer_"),c(1:num_layers)) %>% data.table
layer_labels[,labels:=.][,.:=NULL]
if(length(overwrite_layer_labels) > num_layers) {
overwrite_layer_labels <- overwrite_layer_labels[1:num_layers]
}
layer_labels[1:length(overwrite_layer_labels),labels:=overwrite_layer_labels]
base_colour_set <- RColorBrewer::brewer.pal(base_num_colours,base_colour_set_name) %>% data.table()
base_colour_set <- base_colour_set[,colours:=.][,.:=NULL]
if(num_layers > base_num_colours) {
base_colour_set <- base_colour_set[rep(seq_len(nrow(base_colour_set)), ceiling(num_layers/base_num_colours)), ]
}
base_colour_set <- base_colour_set[1:num_layers]
colour_set <- base_colour_set
if(length(overwrite_colours) > num_layers) {
overwrite_colours <- overwrite_colours[1:num_layers]
}
colour_set[1:length(overwrite_colours),colours:=overwrite_colours]
dt <- data.table(side_lengths = rep(c(2),num_layers),
centres = matrix(c(1 + rep(0,num_layers),2 + 0:(num_layers-1)),nrow=num_layers),
colours = colour_set,
labels = layer_labels,
highlights = highlights)
dt[,alphas:=(as.numeric(highlights)*alpha_highlight + as.numeric(!highlights)*alpha_mute)]
myplot <- ggplot() + lapply(c(1:num_layers),function(x) {geom_polygon(data = diamond(dt$side_lengths[x], c(dt$centres.V1[x],dt$centres.V2[x])), mapping = aes(x = V1, y = V2), fill = dt$colours[x], alpha = dt$alphas[x])}) +
lapply(c(1:num_layers),function(z) {annotate("text", x = -(dt$centres.V1[z]/2)*1.1, y = dt$centres.V2[z], label = dt$labels[z], alpha = dt$alphas[z], size=font_size,
fontface = font_weight, hjust=1, colour=font_colour)}) +
coord_cartesian(xlim = c(-2,3), ylim =c(-1, (num_layers+4) )) +
theme_void() +
#theme_classic() + # gets rid of the ugly bounding box
theme( plot.background = element_rect(fill = background_color)
,axis.line = element_blank(), axis.ticks = element_blank(), axis.title = element_blank(), axis.text = element_blank()
#,plot.margin = element_blank()
,panel.grid = element_blank()
,panel.grid.major = element_blank()
,panel.grid.minor = element_blank()
,panel.background = element_rect(fill=NA)
,panel.border = element_rect(fill=NA)
,validate=TRUE) # sets the background and removes the various axes
option_markers <- c(rep(0,num_layers))
option_markers[highlight_layers] <- 1
suffix <- paste0(option_markers,collapse = "_")
if(save_path != ""){
ggsave(paste0(save_path,"\pic",suffix,".png"), myplot, height=base_image_height*scaling_factor, width=base_image_width*scaling_factor, units = "cm")
} else {
myplot
}
return(myplot)
}
highlight_layers = "all"
num_layers <- 5
overwrite_layer_labels <- c('DATA','MAPPING','SCALES','STATISTICS','GEOMETRIES','FACETS','COORDINATES','THEMES')
overwrite_colours <- c('grey','blue','red','orange','paleturquoise','palegreen','palevioletred','midnightblue')
Ans 各种示例函数调用产生：
generate_layer_diagram()
generate_layer_diagram(c(1:3),num_layers = num_layers)
generate_layer_diagram(1)
generate_layer_diagram(2)
# Data Mapping and Geometries
generate_layer_diagram(c(1,2,5))
产生：
感谢@AllenCameron 的