Question

我正在测试对数据集的情绪进行分析。在这里，我试图看看消息量和嗡嗡声，消息量和分数之间是否有任何有趣的观察...

我的数据集看起来像是：

> str(data)
'data.frame':   40 obs. of  11 variables:
 $ Date Time   : POSIXct, format: "2015-07-08 09:10:00" "2015-07-08 09:10:00" ...
 $ Subject     : chr  "MMM" "ACE" "AES" "AFL" ...
 $ Sscore      : chr  "-0.2280" "-0.4415" "1.9821" "-2.9335" ...
 $ Smean       : chr  "0.2593" "0.3521" "0.0233" "0.0035" ...
 $ Svscore     : chr  "-0.2795" "-0.0374" "1.1743" "-0.2975" ...
 $ Sdispersion : chr  "0.375" "0.500" "1.000" "1.000" ...
 $ Svolume     : num  8 4 1 1 5 3 2 1 1 2 ...
 $ Sbuzz       : chr  "0.6026" "0.7200" "1.9445" "0.8321" ...
 $ Last close  : chr  "155.430000000" "104.460000000" "13.200000000" "61.960000000" ...
 $ Company name: chr  "3M Company" "ACE Limited" "The AES Corporation" "AFLAC Inc." ...
 $ Date        : Date, format: "2015-07-08" "2015-07-08" ...

我考虑过线性回归，所以我想使用ggplot，但我使用这个代码，我觉得我错了，因为我没有出现的回归线...是因为回归是为了弱？我帮助了以下代码：code of topchef

我的是：

library(ggplot2)
require(ggplot2)
library("reshape2")
require(reshape2)
data.2 = melt(data[3:9], id.vars='Svolume')
ggplot(data.2) +
  geom_jitter(aes(value,Svolume, colour=variable),) + geom_smooth(aes(value,Svolume, colour=variable), method=lm, se=FALSE) +
  facet_wrap(~variable, scales="free_x") +
  labs(x = "Variables", y = "Svolumes")

但我可能会错过一些理解，因为我没有得到我想要的东西。我对R很新，所以我希望有人帮助我。

My results

我有这个错误：

    geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?

最后你认为不同的主题可以有不同的颜色而不是每个变量一种颜色吗？我可以在每张图上添加回归线吗？

感谢您的帮助。

示例数据：

       Date Time Subject  Sscore  Smean Svscore Sdispersion Svolume  Sbuzz    Last close        Company name       Date
1  2015-07-08 09:10:00     MMM -0.2280 0.2593 -0.2795       0.375       8 0.6026 155.430000000          3M Company 2015-07-08
2  2015-07-08 09:10:00     ACE -0.4415 0.3521 -0.0374       0.500       4 0.7200 104.460000000         ACE Limited 2015-07-08
3  2015-07-07 09:10:00     AES  1.9821 0.0233  1.1743       1.000       1 1.9445  13.200000000 The AES Corporation 2015-07-07
4  2015-07-04 09:10:00     AFL -2.9335 0.0035 -0.2975       1.000       1 0.8321  61.960000000          AFLAC Inc. 2015-07-04
5  2015-07-07 09:10:00     MMM  0.2977 0.2713 -0.7436       0.400       5 0.4895 155.080000000          3M Company 2015-07-07
6  2015-07-07 09:10:00     ACE -0.2331 0.3519 -0.1118       1.000       3 0.7196 103.330000000         ACE Limited 2015-07-07
7  2015-06-28 09:10:00     AES  1.8721 0.0609  1.9100       0.500       2 2.4319  13.460000000 The AES Corporation 2015-06-28
8  2015-07-03 09:10:00     AFL  0.6024 0.0330 -0.2663       1.000       1 0.6822  61.960000000          AFLAC Inc. 2015-07-03
9  2015-07-06 09:10:00     MMM -1.0057 0.2579 -1.3796       1.000       1 0.4531 155.380000000          3M Company 2015-07-06
10 2015-07-06 09:10:00     ACE -0.0263 0.3435 -0.1904       1.000       2 1.3536 103.740000000         ACE Limited 2015-07-06
11 2015-06-19 09:10:00     AES -1.1981 0.1517  1.2063       1.000       2 1.9427  13.850000000 The AES Corporation 2015-06-19
12 2015-07-02 09:10:00     AFL -0.8247 0.0269  1.8635       1.000       5 2.2454  62.430000000          AFLAC Inc. 2015-07-02
13 2015-07-05 09:10:00     MMM -0.4272 0.3107 -0.7970       0.167       6 0.6003 155.380000000          3M Company 2015-07-05
14 2015-07-04 09:10:00     ACE  0.0642 0.3274 -0.0975       0.667       3 1.2932 103.740000000         ACE Limited 2015-07-04
15 2015-06-17 09:10:00     AES  0.1627 0.1839  1.3141       0.500       2 1.9578  13.580000000 The AES Corporation 2015-06-17
16 2015-07-01 09:10:00     AFL -0.7419 0.0316  1.5699       0.250       4 2.0988  62.200000000          AFLAC Inc. 2015-07-01
17 2015-07-04 09:10:00     MMM -0.5962 0.3484 -1.2481       0.667       3 0.4496 155.380000000          3M Company 2015-07-04
18 2015-07-03 09:10:00     ACE  0.8527 0.3085  0.1944       0.833       6 1.3656 103.740000000         ACE Limited 2015-07-03
19 2015-06-15 09:10:00     AES  0.8145 0.1725  0.2939       1.000       1 1.6121  13.350000000 The AES Corporation 2015-06-15
20 2015-06-30 09:10:00     AFL  0.3076 0.0538 -0.0938       1.000       1 0.7071  61.440000000          AFLAC Inc. 2015-06-30

dput

data <- structure(list(`Date Time` = structure(c(1436361000, 1436361000, 
1436274600, 1436015400, 1436274600, 1436274600, 1435497000, 1435929000, 
1436188200, 1436188200, 1434719400, 1435842600, 1436101800, 1436015400, 
1434546600, 1435756200, 1436015400, 1435929000, 1434373800, 1435669800
), class = c("POSIXct", "POSIXt"), tzone = ""), Subject = c("MMM", 
"ACE", "AES", "AFL", "MMM", "ACE", "AES", "AFL", "MMM", "ACE", 
"AES", "AFL", "MMM", "ACE", "AES", "AFL", "MMM", "ACE", "AES", 
"AFL"), Sscore = c(-0.228, -0.4415, 1.9821, -2.9335, 0.2977, 
-0.2331, 1.8721, 0.6024, -1.0057, -0.0263, -1.1981, -0.8247, 
-0.4272, 0.0642, 0.1627, -0.7419, -0.5962, 0.8527, 0.8145, 0.3076
), Smean = c(0.2593, 0.3521, 0.0233, 0.0035, 0.2713, 0.3519, 
0.0609, 0.033, 0.2579, 0.3435, 0.1517, 0.0269, 0.3107, 0.3274, 
0.1839, 0.0316, 0.3484, 0.3085, 0.1725, 0.0538), Svscore = c(-0.2795, 
-0.0374, 1.1743, -0.2975, -0.7436, -0.1118, 1.91, -0.2663, -1.3796, 
-0.1904, 1.2063, 1.8635, -0.797, -0.0975, 1.3141, 1.5699, -1.2481, 
0.1944, 0.2939, -0.0938), Sdispersion = c(0.375, 0.5, 1, 1, 0.4, 
1, 0.5, 1, 1, 1, 1, 1, 0.167, 0.667, 0.5, 0.25, 0.667, 0.833, 
1, 1), Svolume = c(8L, 4L, 1L, 1L, 5L, 3L, 2L, 1L, 1L, 2L, 2L, 
5L, 6L, 3L, 2L, 4L, 3L, 6L, 1L, 1L), Sbuzz = c(0.6026, 0.72, 
1.9445, 0.8321, 0.4895, 0.7196, 2.4319, 0.6822, 0.4531, 1.3536, 
1.9427, 2.2454, 0.6003, 1.2932, 1.9578, 2.0988, 0.4496, 1.3656, 
1.6121, 0.7071), `Last close` = c(155.43, 104.46, 13.2, 61.96, 
155.08, 103.33, 13.46, 61.96, 155.38, 103.74, 13.85, 62.43, 155.38, 
103.74, 13.58, 62.2, 155.38, 103.74, 13.35, 61.44), `Company name` = c("3M Company", 
"ACE Limited", "The AES Corporation", "AFLAC Inc.", "3M Company", 
"ACE Limited", "The AES Corporation", "AFLAC Inc.", "3M Company", 
"ACE Limited", "The AES Corporation", "AFLAC Inc.", "3M Company", 
"ACE Limited", "The AES Corporation", "AFLAC Inc.", "3M Company", 
"ACE Limited", "The AES Corporation", "AFLAC Inc."), Date = structure(c(16624, 
16624, 16623, 16620, 16623, 16623, 16614, 16619, 16622, 16622, 
16605, 16618, 16621, 16620, 16603, 16617, 16620, 16619, 16601, 
16616), class = "Date")), .Names = c("Date Time", "Subject", 
"Sscore", "Smean", "Svscore", "Sdispersion", "Svolume", "Sbuzz", 
"Last close", "Company name", "Date"), row.names = c("1", "2", 
"3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", 
"15", "16", "17", "18", "19", "20"), class = "data.frame")

Answer 1

请注意警告Maybe you want aes(group = 1)。我所做的就是为group = 1添加aes到geom_smooth。

ggplot(data.2) +
  geom_jitter(aes(value,Svolume, colour=variable),) + 
  geom_smooth(aes(value,Svolume, colour=variable, group = 1), method=lm, se=FALSE) +
  facet_wrap(~variable, scales="free_x") +
  labs(x = "Variables", y = "Svolumes")

一些未经请求的建议

您无需使用require和library，也可以使用其中一种。
您只需要aes一次
您的示例数据不起作用 - 我不得不用它来阅读它。有关建议，请参阅How to make a great R reproducible example?。

以下是我将如何编写ggplot代码：

library(ggplot2)
require(reshape2)

data.2 = melt(data[3:9], id.vars='Svolume')

ggplot(data.2) +
  aes(x = value, y = Svolume, colour = variable) +
  geom_jitter() +
  geom_smooth(method=lm, se=FALSE, aes(group = 1)) +
  facet_wrap(~variable, scales="free_x") +
  labs(x = "Variables", y = "Svolumes")

使用ggplot2对R中的数据集进行多元线性回归

1 个答案: