我可以在 plotly 中重新创建这个极坐标蜘蛛图吗？
Can I recreate this polar coordinate spider chart in plotly?
我在弄清楚如何使用 plotly 重新创建蜘蛛图（或雷达图）的以下图形时遇到了一些困难。实际上，我什至无法在最新版本的 ggplot2 中重新创建它，因为自 1.0.1 以来已经发生了重大变化。
这是一个示例图形：
这是构建它的原始函数：
http://pcwww.liv.ac.uk/~william/Geodemographic%20Classifiability/func%20CreateRadialPlot.r
这是原始函数如何工作的示例：
http://rstudio-pubs-static.s3.amazonaws.com/5795_e6e6411731bb4f1b9cc7eb49499c2082.html
这里有一些不那么虚假的数据：
d <- structure(list(Year = rep(c("2015","2016"),each=24),
Response = structure(rep(1L:24L,2),
.Label = c("Trustworthy", "Supportive", "Leading",
"Strong", "Dependable", "Consultative",
"Knowledgeable", "Sensible",
"Intelligent", "Consistent", "Stable",
"Innovative", "Aggressive",
"Conservative", "Visionary",
"Arrogant", "Professional",
"Responsive", "Confident", "Accessible",
"Timely", "Focused", "Niche", "None"),
class = "factor"),
Proportion = c(0.54, 0.48, 0.33, 0.35, 0.47, 0.3, 0.43, 0.29, 0.36,
0.38, 0.45, 0.32, 0.27, 0.22, 0.26,0.95, 0.57, 0.42,
0.38, 0.5, 0.31, 0.31, 0.12, 0.88, 0.55, 0.55, 0.31,
0.4, 0.5, 0.34, 0.53, 0.3, 0.41, 0.41, 0.46, 0.34,
0.22, 0.17, 0.28, 0.94, 0.62, 0.46, 0.41, 0.53, 0.34,
0.36, 0.1, 0.84), n = rep(c(240L,258L),each=24)),
.Names = c("Year", "Response", "Proportion", "n"),
row.names = c(NA, -48L), class = c("tbl_df", "tbl", "data.frame"))
这是我的尝试（不是很好）
plot_ly(d, r = Proportion, t = Response, x = Response,
color = factor(Year), mode = "markers") %>%
layout(margin = list(l=50,r=0,b=0,t=0,pad = 4), showlegend = TRUE)
关于如何使用 plotly 重新创建它有什么想法吗？
据我所知，您已经使用 ggplot2 获得了绘图（示例图片）。如果这是真的，最简单的想法就是在你的 ggplot 对象上 运行 ggplotly() 添加 plotly 功能，如下例所示：
install.packages(c("ggplot2","plotly"))
library(ggplot2)
library(plotly)
plot <- ggplot(data =mtcars, aes(x = mpg, y = cyl))+
geom_point()
ggplotly (plot)
这将导致以下互动情节：
通过伪造，我在这方面取得了一些进展。极坐标，好像就是讨厌我：
数据：
df <- d <- structure(list(Year = c("2015", "2015", "2015", "2015", "2015",
"2015", "2015", "2015", "2015", "2015", "2015", "2015", "2015",
"2015", "2015", "2015", "2015", "2015", "2015", "2015", "2015",
"2015", "2015", "2015", "2016", "2016", "2016", "2016", "2016",
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016",
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016",
"2016", "2016", "2016"), Response = structure(c(1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L), .Label = c("Trustworthy", "Supportive", "Leading",
"Strong", "Dependable", "Consultative", "Knowledgeable", "Sensible",
"Intelligent", "Consistent", "Stable", "Innovative", "Aggressive",
"Conservative", "Visionary", "Arrogant", "Professional", "Responsive",
"Confident", "Accessible", "Timely", "Focused", "Niche", "None"
), class = "factor"), Proportion = c(0.54, 0.48, 0.33, 0.35,
0.47, 0.3, 0.43, 0.29, 0.36, 0.38, 0.45, 0.32, 0.27, 0.22, 0.26,
0.95, 0.57, 0.42, 0.38, 0.5, 0.31, 0.31, 0.12, 0.88, 0.55, 0.55,
0.31, 0.4, 0.5, 0.34, 0.53, 0.3, 0.41, 0.41, 0.46, 0.34, 0.22,
0.17, 0.28, 0.94, 0.62, 0.46, 0.41, 0.53, 0.34, 0.36, 0.1, 0.84
), n = c(240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L,
240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L,
240L, 240L, 240L, 240L, 258L, 258L, 258L, 258L, 258L, 258L, 258L,
258L, 258L, 258L, 258L, 258L, 258L, 258L, 258L, 258L, 258L, 258L,
258L, 258L, 258L, 258L, 258L, 258L)), .Names = c("Year", "Response",
"Proportion", "n"), row.names = c(NA, -48L), class = c("tbl_df",
"tbl", "data.frame"))
使用基础知识在散点图上创建圆形映射：
df$degree <- seq(0,345,15) # 24 responses, equals 15 degrees per response
df$o <- df$Proportion * sin(df$degree * pi / 180) # SOH
df$a <- df$Proportion * cos(df$degree * pi / 180) # CAH
df$o100 <- 1 * sin(df$degree * pi / 180) # Outer ring x
df$a100 <- 1 * cos(df$degree * pi / 180) # Outer ring y
df$a75 <- 0.75 * cos(df$degree * pi / 180) # 75% ring y
df$o75 <- 0.75 * sin(df$degree * pi / 180) # 75% ring x
df$o50 <- 0.5 * sin(df$degree * pi / 180) # 50% ring x
df$a50 <- 0.5 * cos(df$degree * pi / 180) # 50% ring y
和情节。我在这里作弊，通过再次双重绘制第 1 行和第 25 行，让它们连接到最后一个位置：
p = plot_ly()
for(i in 1:24) {
p <- add_trace(
p,
x = c(d$o100[i],0),
y = c(d$a100[i],0),
evaluate = TRUE,
line = list(color = "#d3d3d3", dash = "3px"),
showlegend = FALSE
)
}
p %>%
add_trace(data = d[c(1:48,1,25),], x = o, y = a, color = Year,
mode = "lines+markers",
hoverinfo = "text",
text = paste(Year, Response,round(Proportion * 100), "%")) %>%
add_trace(data = d, x = o100, y = a100,
text = Response,
hoverinfo = "none",
textposition = "top middle", mode = "lines+text",
line = list(color = "#d3d3d3", dash = "3px", shape = "spline"),
showlegend = FALSE) %>%
add_trace(data = d, x = o50, y = a50, mode = "lines",
line = list(color = "#d3d3d3", dash = "3px", shape = "spline"),
hoverinfo = "none",
showlegend = FALSE) %>%
add_trace(data = d, x = o75, y = a75, mode = "lines",
line = list(color = "#d3d3d3", dash = "3px", shape = "spline"),
hoverinfo = "none",
showlegend = FALSE) %>%
layout(
autosize = FALSE,
hovermode = "closest",
autoscale = TRUE,
width = 800,
height = 800,
xaxis = list(range = c(-1.25,1.25), showticklabels = FALSE, zeroline = FALSE, showgrid = FALSE),
yaxis = list(range = c(-1.25,1.25), showticklabels = FALSE, zeroline = FALSE, showgrid = FALSE))
如您所见，除了 最后一条连接线和 从原点到文本的线外，我已经得到它的响应。
极坐标图可用的选项仍然有限。据我所知，没有任何方法可以将文本添加到圆周周围类别标签的极坐标图中。目前，文本散点、注释和刻度标签（四分之一点处除外）均不与 plotly 中的极坐标兼容。
所以，我们需要有点创意。
一种很好用的极坐标系是使用方位角投影的球面地球投影图。下面是一个演示，说明您可以如何调整它来解决这个问题。
首先，将要绘制的值转换为以南极为中心的纬度和经度：
scale <- 10 # multiply latitudes by a factor of 10 to scale plot to good size in initial view
d$lat <- scale*d$Proportion - 90
d$long <- (as.numeric(d$Response)-1) * 360/24
使用方位角等距投影绘图
p <- plot_ly(d[c(1:24,1,25:48,25),], lat=lat, lon=long, color = factor(Year), colors=c('#F8756B','#00BDC2'),
type = 'scattergeo', mode = 'lines+markers', showlegend=T) %>%
layout(geo = list(scope='world', showland=F, showcoastlines=F, showframe=F,
projection = list(type = 'azimuthal equidistant', rotation=list(lat=-90), scale=5)),
legend=list(x=0.7,y=0.85))
贴上一些标签
p %<>% add_trace(type="scattergeo", mode = "text", lat=rep(scale*1.1-90,24), lon=long,
text=Response, showlegend=F, textfont=list(size=10)) %>%
add_trace(type="scattergeo", mode = "text", showlegend=F, textfont=list(size=12),
lat=seq(-90, -90+scale,length.out = 5), lon=rep(0,5),
text=c("","25%","50%","75%","100%"))
最后，添加网格线
l1 <- list(width = 0.5, color = rgb(.5,.5,.5), dash = "3px")
l2 <- list(width = 0.5, color = rgb(.5,.5,.5))
for (i in c(0.1, 0.25, 0.5, 0.75, 1))
p <- add_trace(lat=rep(-90, 100)-scale*i, lon=seq(0,360, length.out=100), type='scattergeo', mode='lines', line=l1, showlegend=F, evaluate=T)
for (i in 1:24)
p <- add_trace(p,lat=c(-90+scale*0.1,-90+scale), lon=rep(i*360/24,2), type='scattergeo', mode='lines', line=l2, showlegend=F, evaluate=T)
剧情版本更新4.x
更新中的重大更改意味着如果不进行一些修改以使其保持最新状态，原始版本将不再有效。这是一个更新版本：
library(data.table)
gridlines1 = data.table(lat = -90 + scale*(c(0.1, 0.25, 0.5, 0.75, 1)))
gridlines1 = gridlines1[, .(long = c(seq(0,360, length.out=100), NA)), by = lat]
gridlines1[is.na(long), lat := NA]
gridlines2 = data.table(long = seq(0,360, length.out=25)[-1])
gridlines2 = gridlines2[, .(lat = c(NA, -90, -90+scale, NA)), by = long]
gridlines2[is.na(lat), long := NA]
text.labels = data.table(
lat=seq(-90, -90+scale,length.out = 5),
long = 0,
text=c("","25%","50%","75%","100%"))
p = plot_ly() %>%
add_trace(type="scattergeo", data = d[c(1:24, 1, 25:48, 25),],
lat=~lat, lon=~long,
color = factor(d[c(1:24, 1, 25:48, 25),]$Year),
mode = 'lines+markers')%>%
layout(geo = list(scope='world', showland=F, showcoastlines=F, showframe=F,
projection = list(type = 'azimuthal equidistant', rotation=list(lat=-90), scale=5)),
legend = list(x=0.7, y=0.85)) %>%
add_trace(data = gridlines1, lat=~lat, lon=~long,
type='scattergeo', mode='lines', line=l1,
showlegend=F, inherit = F) %>%
add_trace(data = gridlines2, lat=~lat, lon=~long,
type='scattergeo', mode='lines', line=l2, showlegend=F) %>%
add_trace(data = text.labels, lat=~lat, lon=~long,
type="scattergeo", mode = "text", text=~text, textfont = list(size = 12),
showlegend=F, inherit=F) %>%
add_trace(data = d, lat=-90+scale*1.2, lon=~long,
type="scattergeo", mode = "text", text=~Response, textfont = list(size = 10),
showlegend=F, inherit=F)
p
我在弄清楚如何使用 plotly 重新创建蜘蛛图（或雷达图）的以下图形时遇到了一些困难。实际上，我什至无法在最新版本的 ggplot2 中重新创建它，因为自 1.0.1 以来已经发生了重大变化。
这是一个示例图形：
这是构建它的原始函数：
http://pcwww.liv.ac.uk/~william/Geodemographic%20Classifiability/func%20CreateRadialPlot.r
这是原始函数如何工作的示例：
http://rstudio-pubs-static.s3.amazonaws.com/5795_e6e6411731bb4f1b9cc7eb49499c2082.html
这里有一些不那么虚假的数据：
d <- structure(list(Year = rep(c("2015","2016"),each=24),
Response = structure(rep(1L:24L,2),
.Label = c("Trustworthy", "Supportive", "Leading",
"Strong", "Dependable", "Consultative",
"Knowledgeable", "Sensible",
"Intelligent", "Consistent", "Stable",
"Innovative", "Aggressive",
"Conservative", "Visionary",
"Arrogant", "Professional",
"Responsive", "Confident", "Accessible",
"Timely", "Focused", "Niche", "None"),
class = "factor"),
Proportion = c(0.54, 0.48, 0.33, 0.35, 0.47, 0.3, 0.43, 0.29, 0.36,
0.38, 0.45, 0.32, 0.27, 0.22, 0.26,0.95, 0.57, 0.42,
0.38, 0.5, 0.31, 0.31, 0.12, 0.88, 0.55, 0.55, 0.31,
0.4, 0.5, 0.34, 0.53, 0.3, 0.41, 0.41, 0.46, 0.34,
0.22, 0.17, 0.28, 0.94, 0.62, 0.46, 0.41, 0.53, 0.34,
0.36, 0.1, 0.84), n = rep(c(240L,258L),each=24)),
.Names = c("Year", "Response", "Proportion", "n"),
row.names = c(NA, -48L), class = c("tbl_df", "tbl", "data.frame"))
这是我的尝试（不是很好）
plot_ly(d, r = Proportion, t = Response, x = Response,
color = factor(Year), mode = "markers") %>%
layout(margin = list(l=50,r=0,b=0,t=0,pad = 4), showlegend = TRUE)
关于如何使用 plotly 重新创建它有什么想法吗？
据我所知，您已经使用 ggplot2 获得了绘图（示例图片）。如果这是真的，最简单的想法就是在你的 ggplot 对象上 运行 ggplotly() 添加 plotly 功能，如下例所示：
install.packages(c("ggplot2","plotly"))
library(ggplot2)
library(plotly)
plot <- ggplot(data =mtcars, aes(x = mpg, y = cyl))+
geom_point()
ggplotly (plot)
这将导致以下互动情节：
通过伪造，我在这方面取得了一些进展。极坐标，好像就是讨厌我：
数据：
df <- d <- structure(list(Year = c("2015", "2015", "2015", "2015", "2015",
"2015", "2015", "2015", "2015", "2015", "2015", "2015", "2015",
"2015", "2015", "2015", "2015", "2015", "2015", "2015", "2015",
"2015", "2015", "2015", "2016", "2016", "2016", "2016", "2016",
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016",
"2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016",
"2016", "2016", "2016"), Response = structure(c(1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L), .Label = c("Trustworthy", "Supportive", "Leading",
"Strong", "Dependable", "Consultative", "Knowledgeable", "Sensible",
"Intelligent", "Consistent", "Stable", "Innovative", "Aggressive",
"Conservative", "Visionary", "Arrogant", "Professional", "Responsive",
"Confident", "Accessible", "Timely", "Focused", "Niche", "None"
), class = "factor"), Proportion = c(0.54, 0.48, 0.33, 0.35,
0.47, 0.3, 0.43, 0.29, 0.36, 0.38, 0.45, 0.32, 0.27, 0.22, 0.26,
0.95, 0.57, 0.42, 0.38, 0.5, 0.31, 0.31, 0.12, 0.88, 0.55, 0.55,
0.31, 0.4, 0.5, 0.34, 0.53, 0.3, 0.41, 0.41, 0.46, 0.34, 0.22,
0.17, 0.28, 0.94, 0.62, 0.46, 0.41, 0.53, 0.34, 0.36, 0.1, 0.84
), n = c(240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L,
240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L, 240L,
240L, 240L, 240L, 240L, 258L, 258L, 258L, 258L, 258L, 258L, 258L,
258L, 258L, 258L, 258L, 258L, 258L, 258L, 258L, 258L, 258L, 258L,
258L, 258L, 258L, 258L, 258L, 258L)), .Names = c("Year", "Response",
"Proportion", "n"), row.names = c(NA, -48L), class = c("tbl_df",
"tbl", "data.frame"))
使用基础知识在散点图上创建圆形映射：
df$degree <- seq(0,345,15) # 24 responses, equals 15 degrees per response
df$o <- df$Proportion * sin(df$degree * pi / 180) # SOH
df$a <- df$Proportion * cos(df$degree * pi / 180) # CAH
df$o100 <- 1 * sin(df$degree * pi / 180) # Outer ring x
df$a100 <- 1 * cos(df$degree * pi / 180) # Outer ring y
df$a75 <- 0.75 * cos(df$degree * pi / 180) # 75% ring y
df$o75 <- 0.75 * sin(df$degree * pi / 180) # 75% ring x
df$o50 <- 0.5 * sin(df$degree * pi / 180) # 50% ring x
df$a50 <- 0.5 * cos(df$degree * pi / 180) # 50% ring y
和情节。我在这里作弊，通过再次双重绘制第 1 行和第 25 行，让它们连接到最后一个位置：
p = plot_ly()
for(i in 1:24) {
p <- add_trace(
p,
x = c(d$o100[i],0),
y = c(d$a100[i],0),
evaluate = TRUE,
line = list(color = "#d3d3d3", dash = "3px"),
showlegend = FALSE
)
}
p %>%
add_trace(data = d[c(1:48,1,25),], x = o, y = a, color = Year,
mode = "lines+markers",
hoverinfo = "text",
text = paste(Year, Response,round(Proportion * 100), "%")) %>%
add_trace(data = d, x = o100, y = a100,
text = Response,
hoverinfo = "none",
textposition = "top middle", mode = "lines+text",
line = list(color = "#d3d3d3", dash = "3px", shape = "spline"),
showlegend = FALSE) %>%
add_trace(data = d, x = o50, y = a50, mode = "lines",
line = list(color = "#d3d3d3", dash = "3px", shape = "spline"),
hoverinfo = "none",
showlegend = FALSE) %>%
add_trace(data = d, x = o75, y = a75, mode = "lines",
line = list(color = "#d3d3d3", dash = "3px", shape = "spline"),
hoverinfo = "none",
showlegend = FALSE) %>%
layout(
autosize = FALSE,
hovermode = "closest",
autoscale = TRUE,
width = 800,
height = 800,
xaxis = list(range = c(-1.25,1.25), showticklabels = FALSE, zeroline = FALSE, showgrid = FALSE),
yaxis = list(range = c(-1.25,1.25), showticklabels = FALSE, zeroline = FALSE, showgrid = FALSE))
如您所见，除了 最后一条连接线和 从原点到文本的线外，我已经得到它的响应。
极坐标图可用的选项仍然有限。据我所知，没有任何方法可以将文本添加到圆周周围类别标签的极坐标图中。目前，文本散点、注释和刻度标签（四分之一点处除外）均不与 plotly 中的极坐标兼容。
所以，我们需要有点创意。
一种很好用的极坐标系是使用方位角投影的球面地球投影图。下面是一个演示，说明您可以如何调整它来解决这个问题。
首先，将要绘制的值转换为以南极为中心的纬度和经度：
scale <- 10 # multiply latitudes by a factor of 10 to scale plot to good size in initial view
d$lat <- scale*d$Proportion - 90
d$long <- (as.numeric(d$Response)-1) * 360/24
使用方位角等距投影绘图
p <- plot_ly(d[c(1:24,1,25:48,25),], lat=lat, lon=long, color = factor(Year), colors=c('#F8756B','#00BDC2'),
type = 'scattergeo', mode = 'lines+markers', showlegend=T) %>%
layout(geo = list(scope='world', showland=F, showcoastlines=F, showframe=F,
projection = list(type = 'azimuthal equidistant', rotation=list(lat=-90), scale=5)),
legend=list(x=0.7,y=0.85))
贴上一些标签
p %<>% add_trace(type="scattergeo", mode = "text", lat=rep(scale*1.1-90,24), lon=long,
text=Response, showlegend=F, textfont=list(size=10)) %>%
add_trace(type="scattergeo", mode = "text", showlegend=F, textfont=list(size=12),
lat=seq(-90, -90+scale,length.out = 5), lon=rep(0,5),
text=c("","25%","50%","75%","100%"))
最后，添加网格线
l1 <- list(width = 0.5, color = rgb(.5,.5,.5), dash = "3px")
l2 <- list(width = 0.5, color = rgb(.5,.5,.5))
for (i in c(0.1, 0.25, 0.5, 0.75, 1))
p <- add_trace(lat=rep(-90, 100)-scale*i, lon=seq(0,360, length.out=100), type='scattergeo', mode='lines', line=l1, showlegend=F, evaluate=T)
for (i in 1:24)
p <- add_trace(p,lat=c(-90+scale*0.1,-90+scale), lon=rep(i*360/24,2), type='scattergeo', mode='lines', line=l2, showlegend=F, evaluate=T)
剧情版本更新4.x
更新中的重大更改意味着如果不进行一些修改以使其保持最新状态，原始版本将不再有效。这是一个更新版本：
library(data.table)
gridlines1 = data.table(lat = -90 + scale*(c(0.1, 0.25, 0.5, 0.75, 1)))
gridlines1 = gridlines1[, .(long = c(seq(0,360, length.out=100), NA)), by = lat]
gridlines1[is.na(long), lat := NA]
gridlines2 = data.table(long = seq(0,360, length.out=25)[-1])
gridlines2 = gridlines2[, .(lat = c(NA, -90, -90+scale, NA)), by = long]
gridlines2[is.na(lat), long := NA]
text.labels = data.table(
lat=seq(-90, -90+scale,length.out = 5),
long = 0,
text=c("","25%","50%","75%","100%"))
p = plot_ly() %>%
add_trace(type="scattergeo", data = d[c(1:24, 1, 25:48, 25),],
lat=~lat, lon=~long,
color = factor(d[c(1:24, 1, 25:48, 25),]$Year),
mode = 'lines+markers')%>%
layout(geo = list(scope='world', showland=F, showcoastlines=F, showframe=F,
projection = list(type = 'azimuthal equidistant', rotation=list(lat=-90), scale=5)),
legend = list(x=0.7, y=0.85)) %>%
add_trace(data = gridlines1, lat=~lat, lon=~long,
type='scattergeo', mode='lines', line=l1,
showlegend=F, inherit = F) %>%
add_trace(data = gridlines2, lat=~lat, lon=~long,
type='scattergeo', mode='lines', line=l2, showlegend=F) %>%
add_trace(data = text.labels, lat=~lat, lon=~long,
type="scattergeo", mode = "text", text=~text, textfont = list(size = 12),
showlegend=F, inherit=F) %>%
add_trace(data = d, lat=-90+scale*1.2, lon=~long,
type="scattergeo", mode = "text", text=~Response, textfont = list(size = 10),
showlegend=F, inherit=F)
p