ggplot2 multiple stat_smooth：改变颜色&线型 [英] ggplot2 multiple stat_smooth: change color & linetype

问题描述

我无法使用多个更平滑的（ stat_smooth（））来更改当前绘图的颜色和线型。

的数据结构：

  serviceInstanceName时间戳值
 1 DE1Service-utilityPredicted 2014-02-22 10.000000 
 2 SE1Service-utilityPredicted 2014-02-22 4.385694 
 3 DE2Service-utilityPredicted 2014-02-22 0.000000 
 4 US1Service-utilityPredicted 2014-02-22 2.230000 
 5 DE1Service-utilityActual 2014-02- 22 10.000000 
 6 SE1Service-utilityActual 2014-02-22 8.011919 
 7 DE2Service-utilityActual 2014-02-22 3.000000 
 8 US1Service-utilityActual 2014-02-22 1.325191 
 .. 。
  

根据时间戳（y轴）和值（x轴），有八个独特的服务实例。

这里的代码：

  ggplot（rmm，aes（x =恬estamp，y = value，color = serviceInstanceName，group = serviceInstanceName））
 + stat_smooth（size = 1.5，method =loess，level = 0.95，fullrange = TRUE，se = FALSE）
 + scale_x_datetime （break = date_breaks（1 day），labels = date_format（％a /％m））
 + theme（axis.text.x = element_text（angle = 90，hjust = 1））+ xlab （Day）
 + ylab（Utility）+ ggtitle（Utility Trend）
  

这里的情节：

我想要的：

=>手动更改每个唯一* serviceInstanceName属性值的线型和颜色。我尝试了很多来自 scale_color_manual（）的东西，直到提取平滑器的值..但实际上无法解决这个问题。

任何帮助表示赞赏。谢谢！

解决方案

好吧，您的数据并不适合重新创建剧情，所以我创建了一个不同的示例数据集 p>

  rmm <-data.frame（
 timestamp = as.POSIXct（rep（seq（as.Date（2014- ），
 as.Date（2014-01-10），by =1 day），5）），
 serviceInstanceName = rep（letters [1：5]，每个= 10），
值= cumsum（rnorm（50））
）
  

我不确定你试过的是什么，但是 scale_color_manual 应该可以工作。如果您想更改线型，您需要在 aes（）中设置该线型
$ b

  library（ggplot2）
 library（scales）
 
 ggplot（rmm，aes（x = timestamp，y = value，
 color = serviceInstanceName，linetype = serviceInstanceName））+ 
 stat_smooth（size = 1.5，method =loess，level = 0.95，
 fullrange = TRUE，se = FALSE）+ 
 scale_x_datetime（breaks = date_breaks（1 day ），
 labels = date_format（％a /％m））+ 
 theme（axis.text.x = element_text（angle = 90，hjust = 1））+ xlab（Day ）+ 
 ylab（Utility）+ ggtitle（效用趋势）+ 
 scale_color_manual（值= c（a =orange，b =yellow，
c =red ，d =sienna，e =cornsilk））
  

I am not able to change the colors and linetypes of my current plot with multiple smoother ( stat_smooth())

Here an overview of the data structure:

     serviceInstanceName            timestamp     value
1    DE1Service-utilityPredicted    2014-02-22    10.000000
2    SE1Service-utilityPredicted    2014-02-22     4.385694
3    DE2Service-utilityPredicted    2014-02-22     0.000000
4    US1Service-utilityPredicted    2014-02-22     2.230000
5    DE1Service-utilityActual       2014-02-22    10.000000
6    SE1Service-utilityActual       2014-02-22     8.011919
7    DE2Service-utilityActual       2014-02-22     3.000000
8    US1Service-utilityActual       2014-02-22     1.325191
...

There are eight unique service instances with according timestamp (y axis) and value (x axis).

Here the code:

ggplot(rmm, aes(x=timestamp, y=value, color=serviceInstanceName, group=serviceInstanceName)) 
+ stat_smooth(size=1.5, method = "loess", level = 0.95, fullrange = TRUE, se = FALSE)
+ scale_x_datetime(breaks = date_breaks("1 day"), labels = date_format("%a/%m"))
+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) + xlab("Day") 
+ ylab("Utility") + ggtitle("Utility Trend")

Here the plot:

What I want:

=> To manually change the linetypes and colors of each unique *serviceInstanceName' attribute value. I tried many things from scale_color_manual() .. until extracting the value of the smoother.. but really could not solve this.

Any help is appreciated. Thanks!

解决方案

Well, your data wasn't that hlpeful for recreating the plot so i created a different sample data set

rmm<-data.frame(
    timestamp = as.POSIXct(rep(seq(as.Date("2014-01-01"), 
        as.Date("2014-01-10"), by="1 day"),5)),
    serviceInstanceName = rep(letters[1:5], each=10),
    value = cumsum(rnorm(50))
)

And i'm not sure exactly what you tried, but scale_color_manual should have worked. And if you want to change the line type you need to set that in the aes()

library(ggplot2)
library(scales)

ggplot(rmm, aes(x=timestamp, y=value, 
    color=serviceInstanceName, linetype=serviceInstanceName)) +
stat_smooth(size=1.5, method = "loess", level = 0.95, 
    fullrange = TRUE, se = FALSE) + 
scale_x_datetime(breaks = date_breaks("1 day"), 
    labels = date_format("%a/%m")) + 
theme(axis.text.x = element_text(angle = 90, hjust = 1)) + xlab("Day")  + 
ylab("Utility") + ggtitle("Utility Trend") + 
scale_color_manual(values=c(a="orange",b="yellow",
    c="red", d="sienna",e="cornsilk"))

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