r - 为什么这个 ggplot 上的颜色是错误的？

Question

我是 ggplot2 的新手，所以请怜悯我。

我的第一次尝试产生了一个奇怪的结果(至少对我来说很奇怪)。我可重现的 R 代码是:

library(ggplot2)
iterations = 7
variables = 14
data <- matrix(ncol=variables, nrow=iterations)

data[1,] = c(0,0,0,0,0,0,0,0,10134,10234,10234,10634,12395,12395)
data[2,] = c(18596,18596,18596,18596,19265,19265,19390,19962,19962,19962,19962,20856,20856,21756)
data[3,] = c(7912,11502,12141,12531,12718,12968,13386,17998,19996,20226,20388,20583,20879,21367)
data[4,] = c(0,0,0,0,0,0,0,43300,43500,44700,45100,45100,45200,45200)
data[5,] = c(11909,11909,12802,12802,12802,13202,13307,13808,21508,21508,21508,22008,22008,22608)
data[6,] = c(11622,11622,11622,13802,14002,15203,15437,15437,15437,15437,15554,15554,15755,16955)
data[7,] = c(8626,8626,8626,9158,9158,9158,9458,9458,9458,9458,9458,9458,9558,11438)

df <- data.frame(data)
n_data_rows = nrow(df)

previous_volumes = df[1:(n_data_rows-1),]/1000
todays_volume    = df[n_data_rows,]/1000

time = seq(ncol(df))/6
min_y = min(previous_volumes, todays_volume)
max_y = max(previous_volumes, todays_volume)
ylimit = c(min_y, max_y)
x = seq(nrow(previous_volumes))

# This gives a plot with 6 gray lines and one red line, but no Ledgend

p = ggplot()

for (row in x) {
  y1 = as.integer(previous_volumes[row,])
  dd = data.frame(time, y1)
  p = p + geom_line(data=dd, aes(x=time, y=y1, group="1"), color="gray")
}
p 

这段代码产生了一个正确的情节……但没有图例。情节如下:


如果我将“颜色”移到“aes”中，我现在会得到一个图例……但是颜色是错误的。
例如，代码:

p = ggplot()

for (row in x) {
  y1 = as.integer(previous_volumes[row,])
  dd = data.frame(time, y1)
  p = p + geom_line(data=dd, aes(x=time, y=y1, group="1", color="gray"))
}

y2 = as.integer(todays_volume[1,])
dd = data.frame(time, y2)
p = p + geom_line(data=dd, aes(x=time, y=y2, group="2", colour="red"))
p

产生:



为什么线条颜色不对？

查尔斯

Answer 1

可以在单个图层的基础上控制颜色(即颜色 = XYZ)变量，但是，这些不会出现在任何图例中。当您将美学(即在本例中为颜色美学)映射到数据中的变量时会生成图例，在这种情况下，您需要指示如何表示该特定映射。如果您没有明确指定，ggplot2 将尝试做出最佳猜测(比如因子数据与数字数据的离散映射和连续映射之间的差异)。这里有很多可用的选项，包括(但不限于):scale_colour_continuous , scale_colour_discrete , scale_colour_brewer , scale_colour_manual .
通过它的声音，scale_colour_manual可能是您所追求的，请注意，在下面我已将数据中的“变量”列映射到颜色美学，而在“变量”数据中，离散值 [PREV-A 到 PREV-F，今天]存在，所以现在我们需要指示实际颜色 'PREV-A'、'PREV-B'、...'PREV-F' 和 'Today' 代表什么。
或者，如果变量列包含“实际”颜色(即十六进制 '#FF0000' 或名称 'red')，那么您可以使用 scale_colour_identity .我们还可以创建另一列类别 ('Previous','Today') 使事情更容易一些，在这种情况下，一定要引入 'group' 美学映射以防止具有相同颜色的系列(实际上是不同的)系列)在它们之间连续。
首先准备数据，然后通过一些不同的方法来分配颜色。

# Put data as points 1 per row, series as columns, start with 
# previous days
df.new  = as.data.frame(t(previous_volumes))

#Rename the series, for colour mapping
colnames(df.new) = sprintf("PREV-%s",LETTERS[1:ncol(df.new)])

#Add the times for each point.
df.new$Times     = seq(0,1,length.out = nrow(df.new))

#Add the Todays Volume
df.new$Today = as.numeric(todays_volume)

#Put in long format, to enable mapping of the 'variable' to colour.
df.new.melt       = reshape2::melt(df.new,'Times')

#Create some colour mappings for use later
df.new.melt$color_group    = sapply(as.character(df.new.melt$variable),
                                    function(x)switch(x,'Today'='Today','Previous'))
df.new.melt$color_identity = sapply(as.character(df.new.melt$variable),
                                    function(x)switch(x,'Today'='red','grey'))

这里有几种不同的处理颜色的方法:

#1. Base plot + color mapped to variable
plot1 = base + geom_path(aes(color=variable)) + 
  ggtitle("Plot #1")

#2. Base plot + color mapped to variable, Manual scale for Each of the previous days and today
colors = setNames(c(rep('gray',nrow(previous_volumes)),'red'),
                                 unique(df.new.melt$variable))
plot2 = plot1 + scale_color_manual(values = colors) + 
  ggtitle("Plot #2")

#3. Base plot + color mapped to color group
plot3 = base + geom_path(aes(color = color_group,group=variable)) + 
  ggtitle("Plot #3")

#4. Base plot + color mapped to color group, Manual scale for each of the groups
plot4 = plot3 + scale_color_manual(values = c('Previous'='gray','Today'='red')) +
  ggtitle("Plot #4")

#5. Base plot + color mapped to color identity
plot5 = base + geom_path(aes(color = color_identity,group=variable))
plot5a = plot5 + scale_color_identity() +  #Identity not usually in legend
  ggtitle("Plot #5a")
plot5b = plot5 + scale_color_identity(guide='legend') + #Identity forced into legend
  ggtitle("Plot #5b")

gridExtra::grid.arrange(plot1,plot2,plot3,plot4,
                        plot5a,plot5b,ncol=2,
                        top="Various Outputs")

因此，鉴于您的问题，#2 或 #4 可能是您所追求的，使用 #2，我们可以添加另一个图层来渲染最后一个点的值:

#Additionally, add label of the last point in each series.
df.new.melt.labs = plyr::ddply(df.new.melt,'variable',function(df){ 
  df       = tail(df,1) #Last Point
  df$label = sprintf("%.2f",df$value)
  df
})
baseWithLabels = base +   
  geom_path(aes(color=variable)) +
  geom_label(data = df.new.melt.labs,aes(label=label,color=variable),
             position = position_nudge(y=1.5),size=3,show.legend = FALSE) +
  scale_color_manual(values=colors)
print(baseWithLabels)

如果你想能够区分各种'PREV-X'行，那么你也可以映射 linetype对此变量和/或使标签几何更具描述性，下面演示了两种修改:

#Add labels of the last point in each series, include series info:
df.new.melt.labs2 = plyr::ddply(df.new.melt,'variable',function(df){ 
  df       = tail(df,1) #Last Point
  df$label = sprintf("%s: %.2f",df$variable,df$value)
  df
})
baseWithLabelsAndLines = base +   
  geom_path(aes(color=variable,linetype=variable)) +
  geom_label(data = df.new.melt.labs2,aes(label=label,color=variable),
             position = position_nudge(y=1.5),hjust=1,size=3,show.legend = FALSE) +
  scale_color_manual(values=colors) +
  labs(linetype = 'Series')
print(baseWithLabelsAndLines)