r - R ggplot2 线图在我不想要时添加填充
问题描述
我有一个包含一些速度数据的数据集。我在这个数据集中有一个额外的列，称为followtime，它对应于速度的某些值，并将它们突出显示为一个因素（即，试验1中的某些值将在followtime中突出显示为1，其余为0，试验2中的某些值将用 2 突出显示，其余为 0 等）。见下文（这个例子有随机速度，但followtime看起来很像我自己的数据集）。
trial <- c(rep(1,25), rep(2, 25), rep(3, 25))
minitime <- c(1:25)
time <- c(rep(minitime, 3))
totalsmooth_velocity <- runif(75, min=-3, max=2)
followtime <- c(rep(0, 10), rep(1, 10), rep(0,5), rep(0, 5), rep(2, 5), rep(0, 15), rep(0, 15), rep(3, 10))
df <- cbind(trial, time, totalsmooth_velocity, followtime)
df <- as.data.frame(df)
df$time <- as.integer(df$time)
我想用 ggplot2 制作一个折线图，用不同的颜色对每个后续时间进行颜色编码。这是我用散点图绘制的数据（由于某种原因，这没有问题）：
对应的代码：
stim1bfollows<- ggplot()+
geom_point(data=df, aes(x=time, y=totalsmooth_velocity, color = as.factor(followtime)), size = 1.0)+
geom_hline(yintercept=c(0, -0.16))
stim1bfollows
但是，当我尝试将其编码为折线图时，它看起来像这样：
对应的代码：
stim1bfollows<- ggplot()+
geom_line(data=df, aes(x=time, y=totalsmooth_velocity, color = as.factor(followtime)), size = 1.0)+
geom_hline(yintercept=c(0, -0.16))
stim1bfollows
我不要那个填充物！我不确定出了什么问题，我尝试了一些使用“闪避”的更改，并将颜色作为一个因素和数字处理，但如果有人能指出我正确的方向，我将非常感激。谢谢！
编辑
即使与小组有这个问题！图片：
stim1bfollows<- ggplot()+
geom_line(data=follows, aes(x=time, y=totalsmooth_velocity, group = as.factor(followtime), color = as.factor(followtime)), size = 1.0)+
geom_hline(yintercept=c(0, -0.16))
stim1bfollows
也供参考，我的原始数据集的 str(follows)：
str(follows)
'data.frame': 750 obs. of 13 variables:
$ bartrial : int 9 9 9 9 9 9 9 9 9 9 ...
$ trial : int 1 1 1 1 1 1 1 1 1 1 ...
$ time : int 17026 17027 17028 17029 17030 17031 17032 17033 17034 17035 ...
$ X : num 158 158 158 158 158 ...
$ Y : num 64.5 64.6 64.6 64.5 64.5 ...
$ velocity : num 0.05766 -0.0266 -0.05106 -0.00543 0.04506 ...
$ barvelocity : num -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 -0.16 ...
$ index : num -0.3604 0.1663 0.3191 0.0339 -0.2816 ...
$ veldiff : num 0.218 0.133 0.109 0.155 0.205 ...
$ direction : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ response : int 0 1 1 1 0 0 0 0 1 0 ...
$ totalsmooth_velocity: num 0.0173 0.0185 0.0202 0.0233 0.0272 ...
$ followtime : num 0 0 0 0 0 0 0 0 0 0 ...
还有头：
head(follows)
bartrial trial time X Y velocity barvelocity index veldiff direction response
2001 9 1 17026 158.2507 64.52043 0.057657143 -0.16 -0.36035714 0.2176571 TRUE 0
2002 9 1 17027 158.1855 64.57809 -0.026600000 -0.16 0.16625000 0.1334000 TRUE 1
2003 9 1 17028 158.2674 64.55149 -0.051057143 -0.16 0.31910714 0.1089429 TRUE 1
2004 9 1 17029 158.2733 64.50043 -0.005428571 -0.16 0.03392857 0.1545714 TRUE 1
2005 9 1 17030 158.2763 64.49500 0.045057143 -0.16 -0.28160714 0.2050571 TRUE 0
2006 9 1 17031 158.2363 64.54006 0.028971429 -0.16 -0.18107143 0.1889714 TRUE 0
totalsmooth_velocity followtime
2001 0.01732903 0
2002 0.01852428 0
2003 0.02024635 0
2004 0.02326663 0
2005 0.02719260 0
2006 0.03045590 0
添加一个小子集：
dput(subset(follows, time %in% 17100:17130))
structure(list(bartrial = c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L), trial = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L), time = c(17100L,
17101L, 17102L, 17103L, 17104L, 17105L, 17106L, 17107L, 17108L,
17109L, 17110L, 17111L, 17112L, 17113L, 17114L, 17115L, 17116L,
17117L, 17118L, 17119L, 17120L, 17121L, 17122L, 17123L, 17124L,
17125L, 17126L, 17127L, 17128L, 17129L, 17130L, 17100L, 17101L,
17102L, 17103L, 17104L, 17105L, 17106L, 17107L, 17108L, 17109L,
17110L, 17111L, 17112L, 17113L, 17114L, 17115L, 17116L, 17117L,
17118L, 17119L, 17120L, 17121L, 17122L, 17123L, 17124L, 17125L,
17126L, 17127L, 17128L, 17129L, 17130L, 17100L, 17101L, 17102L,
17103L, 17104L, 17105L, 17106L, 17107L, 17108L, 17109L, 17110L,
17111L, 17112L, 17113L, 17114L, 17115L, 17116L, 17117L, 17118L,
17119L, 17120L, 17121L, 17122L, 17123L, 17124L, 17125L, 17126L,
17127L, 17128L, 17129L, 17130L), X = c(158.554971428571, 158.561857142857,
158.545942857143, 158.442742857143, 158.463457142857, 158.447628571429,
158.4628, 158.426028571429, 158.3998, 158.355114285714, 158.339971428571,
158.318457142857, 158.339657142857, 158.298142857143, 158.241714285714,
158.2846, 158.311114285714, 158.324142857143, 158.307228571429,
158.255428571429, 158.271914285714, 158.308028571429, 158.2894,
158.301342857143, 158.308428571429, 158.227228571429, 158.250057142857,
158.226771428571, 158.174914285714, 158.200485714286, 158.213085714286,
98.11471429, 99.706, 101.0531143, 102.1066286, 103.2292, 103.9274,
104.7769429, 105.7868571, 106.5872857, 107.3484286, 108.6168571,
109.3342286, 110.2153714, 111.2181714, 112.8689429, 114.7111143,
116.7568571, 118.523, 119.8732857, 121.4064, 122.5118286, 123.6406286,
124.6844, 125.5278286, 126.3410286, 128.1753143, 129.8935429,
131.1022857, 132.2688286, 133.3624571, 133.9324286, 96.6617714285714,
96.6587142857143, 96.1991428571429, 94.7394285714286, 92.9670285714286,
90.7313428571429, 88.4762, 85.5486571428571, 82.0275142857143,
79.3814, 77.4777428571429, 75.5628857142857, 72.7176, 69.8095142857143,
66.4666571428571, 61.6254, 57.966, 54.3682571428571, 51.6569428571429,
50.0204, 48.7530285714286, 47.9830571428571, 46.7885142857143,
45.3995428571429, 43.5878, 41.7556285714286, 39.6544571428571,
37.6700285714286, 35.0251714285714, 32.6265714285714, 29.3777142857143
), Y = c(57.2500571428571, 57.0420857142857, 56.7889714285714,
56.6496857142857, 56.5398571428571, 56.4711714285714, 56.2698285714286,
56.1793428571429, 56.0277714285714, 56.0177142857143, 55.7782857142857,
55.6382, 55.7419714285714, 55.6086857142857, 55.5973142857143,
55.6697428571429, 55.5047714285714, 55.4622571428571, 55.1674285714286,
55.1252857142857, 54.8758857142857, 54.8422571428571, 54.7050857142857,
54.6246571428571, 54.6242285714286, 54.5318, 54.3272285714286,
54.1329428571429, 54.1315428571429, 54.0057142857143, 53.9648857142857,
189.6266286, 189.5072571, 189.4862571, 189.3138, 189.2692571,
189.2216857, 189.1862857, 189.1338, 189.0529143, 188.9495429,
188.8440286, 188.7655143, 188.7224286, 188.5904857, 188.5451429,
188.5732286, 188.3531429, 188.1283429, 187.9746571, 187.5346,
187.2002857, 186.9196571, 186.6167429, 186.5036857, 186.2668,
185.873, 185.4187714, 185.1869429, 184.8553429, 184.6551429,
184.5514857, 10.31026, 10.3998028571429, 9.98941714285714, 9.93686571428571,
10.0085457142857, 10.1963542857143, 10.3918028571429, 10.4927542857143,
10.7132285714286, 10.9808628571429, 11.2396571428571, 11.2958885714286,
11.4141742857143, 11.5568771428571, 11.7057142857143, 12.0326514285714,
12.2709742857143, 12.49016, 12.7785485714286, 13.0098257142857,
13.2676714285714, 13.3456028571429, 13.3960371428571, 13.4590771428571,
13.46362, 13.5725371428571, 13.756, 13.8612657142857, 13.9868371428571,
14.1618342857143, 14.4414857142857), velocity = c(-0.207971428571433,
-0.253114285714283, -0.139285714285712, -0.109828571428572, -0.0686857142857136,
-0.201342857142862, -0.0904857142857125, -0.15157142857143, -0.0100571428571357,
-0.239428571428583, -0.140085714285711, 0.103771428571434, -0.133285714285712,
-0.0113714285714366, 0.0724285714285742, -0.164971428571434,
-0.0425142857142831, -0.294828571428567, -0.0421428571428564,
-0.249400000000009, -0.0336285714285651, -0.137171428571428,
-0.0804285714285768, -0.000428571428571445, -0.0924285714285702,
-0.204571428571427, -0.194285714285712, -0.00140000000000384,
-0.125828571428563, -0.0408285714285697, -0.104885714285722,
-0.119371429, -0.021, -0.172457143, -0.044542857, -0.047571429,
-0.0354, -0.052485714, -0.080885714, -0.103371429, -0.105514286,
-0.078514286, -0.043085714, -0.131942857, -0.045342857, 0.028085714,
-0.220085714, -0.2248, -0.153685714, -0.440057143, -0.334314286,
-0.280628571, -0.302914286, -0.113057143, -0.236885714, -0.3938,
-0.454228571, -0.231828571, -0.3316, -0.2002, -0.103657143, -0.136857143,
0.0895428571428578, -0.410385714285713, -0.0525514285714301,
0.0716800000000006, 0.187808571428572, 0.195448571428571, 0.100951428571427,
0.220474285714285, 0.267634285714285, 0.258794285714286, 0.0562314285714294,
0.118285714285713, 0.142702857142858, 0.148837142857142, 0.326937142857144,
0.238322857142858, 0.219185714285715, 0.28838857142857, 0.231277142857143,
0.257845714285715, 0.0779314285714268, 0.050434285714287, 0.0630399999999991,
0.00454285714285874, 0.108917142857143, 0.183462857142857, 0.105265714285713,
0.12557142857143, 0.174997142857144, 0.279651428571428, 0.17196
), barvelocity = c(-0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16, -0.16,
-0.16, -0.16, -0.16, -0.16, -0.16, -0.16), index = c(1.29982142857146,
1.58196428571427, 0.870535714285703, 0.686428571428577, 0.42928571428571,
1.25839285714289, 0.565535714285703, 0.947321428571435, 0.0628571428570979,
1.49642857142864, 0.875535714285691, -0.648571428571465, 0.833035714285701,
0.0710714285714786, -0.452678571428589, 1.03107142857146, 0.265714285714269,
1.84267857142855, 0.263392857142852, 1.55875000000005, 0.210178571428532,
0.857321428571423, 0.502678571428605, 0.00267857142857153, 0.577678571428564,
1.27857142857142, 1.2142857142857, 0.00875000000002402, 0.786428571428521,
0.25517857142856, 0.65553571428576, 0.746071429, 0.13125, 1.077857143,
0.278392857, 0.297321429, 0.22125, 0.328035714, 0.505535714,
0.646071429, 0.659464286, 0.490714286, 0.269285714, 0.824642857,
0.283392857, -0.175535714, 1.375535714, 1.405, 0.960535714, 2.750357143,
2.089464286, 1.753928571, 1.893214286, 0.706607143, 1.480535714,
2.46125, 2.838928571, 1.448928571, 2.0725, 1.25125, 0.647857143,
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-0.784821428571436, -1.09373214285715, -1.74782142857143, -1.07475
), veldiff = c(-0.0479714285714331, -0.0931142857142825, 0.0207142857142875,
0.0501714285714277, 0.0913142857142864, -0.0413428571428619,
0.0695142857142875, 0.00842857142857042, 0.149942857142864, -0.0794285714285829,
0.0199142857142894, 0.263771428571434, 0.0267142857142878, 0.148628571428563,
0.232428571428574, -0.00497142857143387, 0.117485714285717, -0.134828571428567,
0.117857142857144, -0.0894000000000085, 0.126371428571435, 0.0228285714285724,
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-0.0342857142857122, 0.158599999999996, 0.0341714285714366, 0.11917142857143,
0.0551142857142784, 0.040628571, 0.139, -0.012457143, 0.115457143,
0.112428571, 0.1246, 0.107514286, 0.079114286, 0.056628571, 0.054485714,
0.081485714, 0.116914286, 0.028057143, 0.114657143, 0.188085714,
-0.060085714, -0.0648, 0.006314286, -0.280057143, -0.174314286,
-0.120628571, -0.142914286, 0.046942857, -0.076885714, -0.2338,
-0.294228571, -0.071828571, -0.1716, -0.0402, 0.056342857, 0.023142857,
0.249542857142858, -0.250385714285713, 0.10744857142857, 0.231680000000001,
0.347808571428572, 0.355448571428571, 0.260951428571428, 0.380474285714285,
0.427634285714285, 0.418794285714286, 0.216231428571429, 0.278285714285713,
0.302702857142858, 0.308837142857142, 0.486937142857144, 0.398322857142858,
0.379185714285715, 0.44838857142857, 0.391277142857143, 0.417845714285715,
0.237931428571427, 0.210434285714287, 0.223039999999999, 0.164542857142859,
0.268917142857143, 0.343462857142857, 0.265265714285713, 0.28557142857143,
0.334997142857144, 0.439651428571428, 0.33196), direction = c(TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE), response = c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L), totalsmooth_velocity = c(-0.185715482616494,
-0.169799554542362, -0.159003977144789, -0.148207077837678, -0.137191691887319,
-0.128149664364088, -0.119572837388017, -0.109953053079138, -0.0972978435598013,
-0.0831080759384155, -0.071115594814103, -0.0650522447859859,
-0.0666755590279073, -0.0730178458107374, -0.0807231123791393,
-0.0864353659777761, -0.093731176054967, -0.104374785327469,
-0.112291710942601, -0.11532488840979, -0.115998870083849, -0.115261598380177,
-0.114061015714174, -0.112121981406158, -0.109279350709846, -0.106646059940738,
-0.105335045414329, -0.10502031419553, -0.104660283980211, -0.104294507030314,
-0.103962535607781, -0.0544997541294192, -0.0759672933472838,
-0.0928318384423578, -0.0995235408007862, -0.0921496441281631,
-0.0808943187101956, -0.075941734832591, -0.0721387042961063,
-0.0644417616889991, -0.0631996614782276, -0.0701231877757797,
-0.080159555991031, -0.0935121675370214, -0.110384423826791,
-0.134341886011727, -0.164771274930442, -0.19560773152681, -0.220786396744707,
-0.24470800956836, -0.271709665272387, -0.295631039236247, -0.3103118068394,
-0.311136008145884, -0.303141587685218, -0.294649077535086, -0.287800911512628,
-0.278977437979406, -0.26785606082973, -0.254114183957905, -0.228672234833075,
-0.193945588974598, -0.0248176049007914, -0.0280154922261918,
-0.00509363044425982, 0.0359756870492962, 0.0802930373308052,
0.112958997476596, 0.13498042401608, 0.156286940578198, 0.175363274888579,
0.190694154672854, 0.197511866511148, 0.197482141367721, 0.198739873544398,
0.206075849950098, 0.215615753800187, 0.222496823856169, 0.221856298879546,
0.209154962712217, 0.189688538371653, 0.173346673382715, 0.154502814473438,
0.127758414088336, 0.103345128326991, 0.091494613288984, 0.0964662157108718,
0.11027334789746, 0.124778764300703, 0.146992697502439, 0.180840414940991,
0.213557223353035, 0.232378429475247), followtime = c(0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3)), row.names = c("2075", "2076", "2077", "2078",
"2079", "2080", "2081", "2082", "2083", "2084", "2085", "2086",
"2087", "2088", "2089", "2090", "2091", "2092", "2093", "2094",
"2095", "2096", "2097", "2098", "2099", "2100", "2101", "2102",
"2103", "2104", "2105", "20751", "20761", "20771", "20781", "20791",
"20801", "20811", "20821", "20831", "20841", "20851", "20861",
"20871", "20881", "20891", "20901", "20911", "20921", "20931",
"20941", "20951", "20961", "20971", "20981", "20991", "21001",
"21011", "21021", "21031", "21041", "21051", "20752", "20762",
"20772", "20782", "20792", "20802", "20812", "20822", "20832",
"20842", "20852", "20862", "20872", "20882", "20892", "20902",
"20912", "20922", "20932", "20942", "20952", "20962", "20972",
"20982", "20992", "21002", "21012", "21022", "21032", "21042",
"21052"), class = "data.frame")
解决方案
您需要group在您的aesfor中使用geom_line：
ggplot(data=df, aes(x=time, y=totalsmooth_velocity, group = as.factor(followtime), color = as.factor(followtime)))+
geom_point( size = 1.0)+
geom_hline(yintercept=c(0, -0.16), color = "black")+
geom_line()
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