如何在R中自动生成折线图

Question

全部 我有以下R脚本。为了表示赞赏，它基于我从http://www.statmethods.net/graphs/line.html获得的脚本，并应用于我自己的数据。也许你可以告诉我我是R的新手。下面的脚本生成自动图例并将其标记为1,2,3。相反，我希望脚本打印“样本名称作为文本”而不​​是数字。我想知道是否有人可以帮助我。谢谢。

# Create Line Chart
fd<- read.csv ("indata", header=TRUE)

## convert factor to numeric for convenience 
fd$sampleN <- as.numeric(fd$sampleN)
nfd <- max(fd$sampleN)

# get the range for the x and y axis 
xrange <- range(fd$gc)

#yrange <- range(fd$coverage)
yrange <- range(0,2) #you can customize this one.

# set up the plot 
plot(xrange, yrange, type="n", xlab="gc", ylab="nc" ) 
colors <- rainbow(nfd) 
linetype <- c(1:nfd) 
plotchar <- seq(18,18+nfd,1)

# add lines 
for (i in 1:nfd) { 
  tree <- subset(fd, sampleN==i) 
  lines(tree$gc, tree$coverage, type="b", lwd=1.5,
    lty=linetype[i], col=colors[i], pch=plotchar[i]) 

} 

# add a title and subtitle 
title("metrics", "")

# add a legend 
legend(xrange[1], yrange[2], 1:nfd, cex=0.8, col=colors,
     pch=plotchar, lty=linetype, title="Samples")

输入数据如下：

    sampleN,gc,coverage
sample_metrics1,0,0.24558
sample_metrics1,1,0.05663
sample_metrics1,2,0.088432
sample_metrics1,3,0.117296
sample_metrics1,4,0.169752
sample_metrics1,5,0.228159
sample_metrics1,6,0.333096
sample_metrics1,7,0.427725
sample_metrics1,8,0.428772
sample_metrics1,9,0.502811
sample_metrics1,10,0.580475
sample_metrics1,11,0.649858
sample_metrics1,12,0.686928
sample_metrics1,13,0.76773
sample_metrics1,14,0.817875
sample_metrics1,15,0.862198
sample_metrics1,16,0.878292
sample_metrics1,17,0.90871
sample_metrics1,18,0.910914
sample_metrics1,19,0.949483
sample_metrics1,20,0.931209
sample_metrics1,21,0.935704
sample_metrics1,22,0.945239
sample_metrics1,23,0.927157
sample_metrics1,24,0.930656
sample_metrics1,25,0.935901
sample_metrics1,26,0.932245
sample_metrics1,27,0.934365
sample_metrics1,28,0.937328
sample_metrics1,29,0.94063
sample_metrics1,30,0.943312
sample_metrics1,31,0.950184
sample_metrics1,32,0.963963
sample_metrics1,33,0.984982
sample_metrics1,34,1.003258
sample_metrics1,35,1.023331
sample_metrics1,36,1.045045
sample_metrics1,37,1.057649
sample_metrics1,38,1.067116
sample_metrics1,39,1.067653
sample_metrics1,40,1.063026
sample_metrics1,41,1.052287
sample_metrics1,42,1.040282
sample_metrics1,43,1.020074
sample_metrics1,44,1.000212
sample_metrics1,45,0.9896
sample_metrics1,46,0.985244
sample_metrics1,47,0.985526
sample_metrics1,48,0.982893
sample_metrics1,49,0.981362
sample_metrics1,50,0.979265
sample_metrics1,51,0.979916
sample_metrics1,52,0.979846
sample_metrics1,53,0.984885
sample_metrics1,54,0.986803
sample_metrics1,55,0.994042
sample_metrics1,56,0.996345
sample_metrics1,57,1.007551
sample_metrics1,58,1.003795
sample_metrics1,59,1.008586
sample_metrics1,60,1.01787
sample_metrics1,61,1.02482
sample_metrics1,62,1.020601
sample_metrics1,63,1.018051
sample_metrics1,64,1.035102
sample_metrics1,65,1.025948
sample_metrics1,66,1.03562
sample_metrics1,67,1.040274
sample_metrics1,68,1.036387
sample_metrics1,69,1.058105
sample_metrics1,70,1.06795
sample_metrics1,71,1.064344
sample_metrics1,72,1.049132
sample_metrics1,73,1.071662
sample_metrics1,74,1.08023
sample_metrics1,75,1.09954
sample_metrics1,76,1.14631
sample_metrics1,77,1.172438
sample_metrics1,78,1.175826
sample_metrics1,79,1.211766
sample_metrics1,80,1.208507
sample_metrics1,81,1.217303
sample_metrics1,82,1.262392
sample_metrics1,83,1.331984
sample_metrics1,84,1.383047
sample_metrics1,85,1.416259
sample_metrics1,86,1.583698
sample_metrics1,87,1.785849
sample_metrics1,88,1.857123
sample_metrics1,89,2.114804
sample_metrics1,90,1.991428
sample_metrics1,91,1.976769
sample_metrics1,92,2.216659
sample_metrics1,93,1.606086
sample_metrics1,94,1.821604
sample_metrics1,95,5.19378
sample_metrics1,96,2.46372
sample_metrics1,97,9.844896
sample_metrics1,98,17.281094
sample_metrics1,99,10.147151
sample_metrics1,100,0
sample_metrics2,0,0.27566
sample_metrics2,1,0.052443
sample_metrics2,2,0.060661
sample_metrics2,3,0.142456
sample_metrics2,4,0.110315
sample_metrics2,5,0.195035
sample_metrics2,6,0.266574
sample_metrics2,7,0.256115
sample_metrics2,8,0.332823
sample_metrics2,9,0.341872
sample_metrics2,10,0.335247
sample_metrics2,11,0.480877
sample_metrics2,12,0.40355
sample_metrics2,13,0.452441
sample_metrics2,14,0.522209
sample_metrics2,15,0.489945
sample_metrics2,16,0.51272
sample_metrics2,17,0.538931
sample_metrics2,18,0.58408
sample_metrics2,19,0.600836
sample_metrics2,20,0.615026
sample_metrics2,21,0.631172
sample_metrics2,22,0.651189
sample_metrics2,23,0.653251
sample_metrics2,24,0.679981
sample_metrics2,25,0.69569
sample_metrics2,26,0.71077
sample_metrics2,27,0.735305
sample_metrics2,28,0.754265
sample_metrics2,29,0.778111
sample_metrics2,30,0.804297
sample_metrics2,31,0.831779
sample_metrics2,32,0.863024
sample_metrics2,33,0.894018
sample_metrics2,34,0.937245
sample_metrics2,35,0.985043
sample_metrics2,36,1.029299
sample_metrics2,37,1.069061
sample_metrics2,38,1.094257
sample_metrics2,39,1.102138
sample_metrics2,40,1.114504
sample_metrics2,41,1.11484
sample_metrics2,42,1.111952
sample_metrics2,43,1.108918
sample_metrics2,44,1.090665
sample_metrics2,45,1.082678
sample_metrics2,46,1.076516
sample_metrics2,47,1.08798
sample_metrics2,48,1.093462
sample_metrics2,49,1.08452
sample_metrics2,50,1.090786
sample_metrics2,51,1.093115
sample_metrics2,52,1.101703
sample_metrics2,53,1.107301
sample_metrics2,54,1.11793
sample_metrics2,55,1.130198
sample_metrics2,56,1.141167
sample_metrics2,57,1.155742
sample_metrics2,58,1.165464
sample_metrics2,59,1.157386
sample_metrics2,60,1.167818
sample_metrics2,61,1.166827
sample_metrics2,62,1.135574
sample_metrics2,63,1.128703
sample_metrics2,64,1.130197
sample_metrics2,65,1.089628
sample_metrics2,66,1.048679
sample_metrics2,67,1.024862
sample_metrics2,68,0.951014
sample_metrics2,69,0.863408
sample_metrics2,70,0.741022
sample_metrics2,71,0.661516
sample_metrics2,72,0.545033
sample_metrics2,73,0.433509
sample_metrics2,74,0.367888
sample_metrics2,75,0.275933
sample_metrics2,76,0.148513
sample_metrics2,77,0.104557
sample_metrics2,78,0.09074
sample_metrics2,79,0.035399
sample_metrics2,80,0.038729
sample_metrics2,81,0.013173
sample_metrics2,82,0.011767
sample_metrics2,83,0.019057
sample_metrics2,84,0.012294
sample_metrics2,85,0
sample_metrics2,86,0
sample_metrics2,87,0
sample_metrics2,88,0
sample_metrics2,89,0
sample_metrics2,90,0
sample_metrics2,91,0
sample_metrics2,92,0
sample_metrics2,93,0
sample_metrics2,94,0
sample_metrics2,95,6.012146
sample_metrics2,96,0
sample_metrics2,97,10.129887
sample_metrics2,98,5.080385
sample_metrics2,99,12.529071
sample_metrics2,100,0
genome_windows,0,0.000831175
genome_windows,1,0.000594994
genome_windows,2,0.0006862
genome_windows,3,0.000876388
genome_windows,4,0.000942013
genome_windows,5,0.000958863
genome_windows,6,0.001091706
genome_windows,7,0.001176513
genome_windows,8,0.001343131
genome_windows,9,0.001520906
genome_windows,10,0.001799756
genome_windows,11,0.002206363
genome_windows,12,0.002886519
genome_windows,13,0.003815775
genome_windows,14,0.005416344
genome_windows,15,0.007978863
genome_windows,16,0.011943919
genome_windows,17,0.017942331
genome_windows,18,0.026916138
genome_windows,19,0.039731306
genome_windows,20,0.057394938
genome_windows,21,0.080734088
genome_windows,22,0.110251175
genome_windows,23,0.146365406
genome_windows,24,0.188943563
genome_windows,25,0.236974131
genome_windows,26,0.288641038
genome_windows,27,0.342599325
genome_windows,28,0.397027756
genome_windows,29,0.449998694
genome_windows,30,0.500295781
genome_windows,31,0.546689806
genome_windows,32,0.587357375
genome_windows,33,0.621426481
genome_windows,34,0.647914206
genome_windows,35,0.66795535
genome_windows,36,0.680710806
genome_windows,37,0.684866219
genome_windows,38,0.678578188
genome_windows,39,0.663729056
genome_windows,40,0.642785275
genome_windows,41,0.617580269
genome_windows,42,0.5895275
genome_windows,43,0.561500825
genome_windows,44,0.536200638
genome_windows,45,0.514647113
genome_windows,46,0.496522031
genome_windows,47,0.481198119
genome_windows,48,0.465125163
genome_windows,49,0.445097106
genome_windows,50,0.419522256
genome_windows,51,0.389562975
genome_windows,52,0.358433044
genome_windows,53,0.329269363
genome_windows,54,0.30327115
genome_windows,55,0.280368106
genome_windows,56,0.258316269
genome_windows,57,0.234869275
genome_windows,58,0.209039444
genome_windows,59,0.181009944
genome_windows,60,0.152880644
genome_windows,61,0.126921669
genome_windows,62,0.104752206
genome_windows,63,0.086465069
genome_windows,64,0.071383019
genome_windows,65,0.058826631
genome_windows,66,0.048428619
genome_windows,67,0.039328369
genome_windows,68,0.031444125
genome_windows,69,0.025158775
genome_windows,70,0.020178806
genome_windows,71,0.01616445
genome_windows,72,0.013079681
genome_windows,73,0.0106774
genome_windows,74,0.008807444
genome_windows,75,0.007423456
genome_windows,76,0.006410344
genome_windows,77,0.005637675
genome_windows,78,0.005014725
genome_windows,79,0.0043783
genome_windows,80,0.003736631
genome_windows,81,0.003142294
genome_windows,82,0.002639056
genome_windows,83,0.002170913
genome_windows,84,0.001683113
genome_windows,85,0.001218638
genome_windows,86,0.000809938
genome_windows,87,0.000524731
genome_windows,88,0.000361769
genome_windows,89,0.000234463
genome_windows,90,0.000153681
genome_windows,91,9.75E-05
genome_windows,92,0.000057575
genome_windows,93,3.61E-05
genome_windows,94,0.00002095
genome_windows,95,1.03E-05
genome_windows,96,6.16E-06
genome_windows,97,4.11E-06
genome_windows,98,0.00000205
genome_windows,99,8.31E-07
genome_windows,100,2.12E-06

Answer 1

从?legend，您可以看到第三个参数（当前为1:nfd）是图例上显示的文本。如果您想使用标签A，B和C，则应将1:nfd替换为c("A", "B", "C")。

Answer 2

我会使用ggplot2：

library(ggplot2)
ggplot(fd, aes(x = gc, y = coverage, color = sampleN)) + 
   geom_line()

这会使用图例中的因子名称，例如sample_metrics1等