Question

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我一直在尝试使用ggplot2在单个图中以图形方式表示许多曲线（大约12个）。我最初将数据收集在Excel工作表中并在R中进行传输。每条曲线的数据量不同，每条曲线的x值也不同。因此，数据不能被视为矩阵或数据集。我想表示曲线而不分别在两列中提取数据以表示相应的曲线。

我尝试了许多版本的代码，例如以下代表前两条曲线（没有结果）：

library("ggplot2")
g <- ggplot(D, aes(x=V1))
k <- g + geom_line(aes(y=V2), colour="red")
s <- k + geom_line(aes(x=V5))
h <- s + geom_line(aes(y=V6), colour="green")

我以后会显示大量数据的最小版本。即使它看起来很大，即使它只有8行8列。我为此道歉。为了一个简单的例子，我删除了许多列和行。因此，要表示的曲线总共为4：（V1，V2），（V5，V6），（V11，V12）和（V15，V16），其中第一个坐标是x，第二个坐标是每个4例。我将非常感谢你的帮助。

> dput(D)
 structure(list(V1 = structure(c(85L, 86L, 87L, 88L, 89L, 90L, 
 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "0", "0.005966", "0.011966", 
 "0.017966", "0.023966", "0.029966", "0.035966", "0.041966", "0.047966", 
 "0.053966", "0.059966", "0.065966", "0.071966", "0.077966", "0.083966", 
 "0.089966", "0.092265", "0.098408", "0.105918", "0.113602", "0.120645", 
 "0.130484", "0.137735", "0.148359", "0.154359", "0.165272", "0.171272", 
 "0.18083", "0.18683", "0.19283", "0.19883", "0.20483", "0.21083", 
 "0.21683", "0.22283", "0.22883", "0.23483", "0.24083", "0.252113", 
 "0.258113", "0.264113", "0.270113", "0.276113", "0.282113", "0.288113", 
 "0.294113", "0.300113", "0.306113", "0.312113", "0.318113", "0.324113", 
 "0.330113", "0.336113", "0.342113", "0.348113", "0.354113", "0.363916", 
 "0.375691", "0.381691", "0.393053", "0.399053", "0.405053", "0.411053", 
 "0.417053", "0.426986", "0.432986", "0.438986", "0.448759", "0.458853", 
 "0.464853", "0.470853", "0.481612", "0.487612", "0.497969", "0.503969", 
 "0.509969", "0.515969", "0.521969", "0.527969", "0.533969", "0.539969", 
 "0.551301", "0.557301", "0.562965", "0.568965", "0.574965", "0.580965", 
 "0.586965", "0.592965", "0.598965", "0.599966", "Displ.", "M11 (10-BF)"
  ), class = "factor"), V2 = structure(c(88L, 89L, 90L, 91L, 92L, 
 85L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "0", "112.369", 
 "149.825", "187.282", "224.738", "262.194", "299.651", "337.107", 
 "37.456", "374.564", "412.02", "449.476", "486.933", "524.389", 
 "561.845", "576.195", "605.792", "629.753", "648.093", "658.487", 
 "670.233", "677.776", "687.528", "692.703", "701.893", "706.104", 
 "712.587", "716.571", "720.277", "723.983", "727.688", "731.394", 
 "735.1", "738.806", "74.913", "742.512", "746.217", "749.923", 
 "756.33", "757.954", "759.576", "761.199", "762.82", "764.441", 
 "766.062", "767.654", "769.246", "770.837", "772.428", "774.018", 
 "775.572", "777.125", "778.678", "780.231", "781.783", "783.334", 
 "785.664", "788.255", "789.526", "791.883", "792.981", "793.987", 
 "794.895", "795.803", "796.996", "797.655", "798.313", "799.259", 
 "800.029", "800.407", "800.745", "801.259", "801.505", "801.915", 
 "802.145", "802.375", "802.604", "802.76", "802.915", "803.07", 
 "803.179", "803.188", "803.199", "803.322", "803.373", "803.413", 
 "803.438", "803.44", "803.441", "803.443", "803.444", "BaseFor."
 ), class = "factor"), V5 = structure(c(85L, 86L, 87L, 88L, 1L, 
 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "0", "0.005941", 
 "0.011941", "0.017941", "0.023941", "0.029941", "0.035941", "0.041941", 
 "0.047941", "0.053941", "0.059941", "0.065941", "0.071941", "0.077941", 
 "0.083941", "0.089941", "0.095941", "0.101941", "0.103817", "0.110449", 
 "0.118017", "0.125068", "0.13262", "0.143702", "0.152147", "0.15839", 
 "0.16439", "0.17039", "0.17639", "0.182967", "0.191488", "0.202601", 
 "0.208601", "0.214601", "0.223557", "0.229557", "0.235557", "0.241557", 
 "0.251764", "0.257764", "0.263764", "0.273723", "0.279723", "0.285723", 
 "0.296481", "0.302481", "0.308481", "0.314481", "0.320481", "0.329858", 
 "0.335858", "0.341858", "0.347858", "0.353858", "0.359858", "0.365858", 
 "0.371858", "0.38087", "0.38687", "0.39287", "0.404708", "0.415154", 
 "0.421154", "0.4287", "0.4347", "0.4407", "0.451398", "0.457398", 
 "0.463398", "0.469398", "0.475398", "0.487014", "0.497525", "0.509064", 
 "0.515064", "0.521064", "0.527064", "0.533064", "0.543151", "0.549151", 
 "0.555151", "0.566361", "0.57723", "0.58323", "0.58923", "0.59523", 
 "0.599941", "Displ.", "M13 (10-BF_M)"), class = "factor"), V6 =  structure    (c     (84L, 
  85L, 86L, 87L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", 
  "0", "112.442", "140.553", "168.663", "196.774", "224.885", "252.995", 
  "28.111", "281.106", "309.216", "337.327", "365.437", "393.548", 
  "421.659", "449.769", "477.598", "486.301", "515.282", "544.842", 
  "56.221", "567.028", "588.112", "612.031", "627.001", "636.278", 
  "644.516", "652.395", "660.274", "668.094", "676.388", "686.223", 
  "691.258", "696.203", "702.797", "706.954", "710.844", "714.734", 
  "721.266", "725.069", "728.873", "734.733", "738.113", "741.493", 
  "747.304", "750.435", "753.566", "756.618", "759.67", "763.8", 
  "765.277", "766.747", "768.217", "769.687", "771.156", "772.625", 
  "774.093", "776.263", "777.617", "778.97", "781.541", "783.744", 
  "784.896", "786.257", "787.267", "788.276", "789.981", "790.847", 
  "791.661", "792.411", "793.16", "794.53", "795.617", "796.748", 
  "797.29", "797.732", "798.143", "798.555", "799.151", "799.467", 
  "799.753", "800.244", "800.621", "800.772", "800.923", "801.074", 
  "801.193", "84.332", "BaseFor."), class = "factor"), V11 = structure(c     (85L, 86L, 87L, 88L, 89L, 90L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", 
"0", "0.003903", "0.009903", "0.015903", "0.021903", "0.027903", 
 "0.033903", "0.039903", "0.045903", "0.051903", "0.057903", "0.063903", 
 "0.069903", "0.075903", "0.077429", "0.08433", "0.093127", "0.101114", 
 "0.108712", "0.11453", "0.12053", "0.124929", "0.130929", "0.136267", 
 "0.142267", "0.152885", "0.158885", "0.164885", "0.170885", "0.180633", 
 "0.190768", "0.196768", "0.202768", "0.208768", "0.214768", "0.22325", 
 "0.231018", "0.240961", "0.247414", "0.253414", "0.262807", "0.264757", 
 "0.270757", "0.276757", "0.284065", "0.29092", "0.293955", "0.296581", 
 "0.303881", "0.309881", "0.317746", "0.323746", "0.329746", "0.335746", 
 "0.341746", "0.347746", "0.353746", "0.359746", "0.365746", "0.371746", 
 "0.377746", "0.383746", "0.389746", "0.401176", "0.407176", "0.413936", 
 "0.421828", "0.427828", "0.433828", "0.439828", "0.445828", "0.451828", 
 "0.457828", "0.463828", "0.469828", "0.478943", "0.485564", "0.491564", 
  "0.497564", "0.503564", "0.509564", "0.515564", "0.521564", "0.527564", 
 "0.538766", "0.544766", "0.550766", "0.556766", "0.562766", "0.568766", 
 "0.574766", "0.580766", "0.586766", "0.592766", "0.597903", "Displ.", 
 "M15 (10-INF)"), class = "factor"), V12 = structure(c(64L, 63L, 
  62L, 61L, 60L, 59L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "0", 
 "1005.726", "1009.623", "1011.811", "1017.902", "1025.83", "1031.746", 
 "1038.527", "1039.66", "1042.112", "1056.988", "1067.679", "1071.904", 
 "1081.668", "1084.051", "1096.224", "1097.858", "1106.559", "1118.378", 
 "1125.618", "1135", "1140.472", "1141.291", "1148.964", "1156.559", 
 "1166.651", "1176.709", "1186.523", "1198.38", "1202.793", "1217.696", 
 "1226.19", "1234.685", "1240.749", "1242.795", "1256.85", "1268.252", 
 "1269.925", "1272.089", "1275.215", "1275.357", "1276.389", "166.25", 
 "254.359", "343.708", "433.057", "522.87", "612.683", "702.496", 
 "79.716", "792.309", "858.234", "859.779", "861.582", "863.381", 
 "865.178", "866.972", "868.763", "870.552", "872.337", "874.12", 
 "875.901", "878.915", "880.338", "881.758", "882.122", "883.176", 
 "884.591", "886.003", "887.412", "888.819", "889.813", "890.896", 
 "891.464", "893.109", "895.73", "899.729", "903.725", "907.718", 
 "911.709", "915.696", "921.024", "926.016", "932.761", "944.564", 
 "949.074", "950.715", "956.855", "962.992", "969.127", "975.258", 
"981.357", "987.454", "993.547", "999.638", "BaseFor."), class = "factor"), 
V15 = structure(c(85L, 86L, 87L, 88L, 89L, 90L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("", "0", "0.000278", "0.005722", 
"0.011722", "0.017722", "0.023722", "0.029722", "0.035722", 
"0.041722", "0.047722", "0.053722", "0.059722", "0.065722", 
"0.071722", "0.077722", "0.083722", "0.089722", "0.095722", 
"0.101722", "0.107722", "0.113722", "0.117013", "0.123013", 
"0.129013", "0.138671", "0.14632", "0.156907", "0.163297", 
"0.165095", "0.171095", "0.181276", "0.185661", "0.191661", 
"0.197661", "0.20741", "0.219165", "0.227842", "0.233842", 
"0.239842", "0.245842", "0.251842", "0.257842", "0.265518", 
"0.277034", "0.287175", "0.293925", "0.298905", "0.304905", 
"0.310905", "0.316905", "0.319905", "0.327", "0.337938", 
"0.345053", "0.353392", "0.359392", "0.365392", "0.373443", 
"0.381492", "0.390686", "0.398531", "0.406132", "0.412132", 
"0.418132", "0.424132", "0.430132", "0.436132", "0.442132", 
"0.450659", "0.456659", "0.462659", "0.468659", "0.477793", 
"0.483793", "0.489793", "0.495793", "0.501793", "0.507793", 
"0.513793", "0.519793", "0.525793", "0.531793", "0.537793", 
"0.543793", "0.549793", "0.555793", "0.561793", "0.567793", 
"0.573793", "0.579793", "0.585793", "0.591793", "0.593722", 
"Displ.", "M17 (10-INF_M)"), class = "factor"), V16 = structure(c(66L, 
65L, 64L, 63L, 62L, 61L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", 
"0", "1001.042", "1007.585", "1013.736", "1018.478", "1022.144", 
"1030.544", "1043.215", "1054.922", "1055.09", "1073.135", 
"1088.127", "1092.718", "1101.899", "1107.55", "1112.331", 
"1122.695", "1127.945", "1135.753", "1145.092", "1147.475", 
"1161.206", "1173.141", "1183.647", "1189.412", "1194.152", 
"1204.658", "1212.448", "1214.9", "1218.199", "1224.255", 
"1229.838", "1235.349", "1245.109", "1247.205", "1248.478", 
"1251.639", "1251.741", "133.508", "182.716", "232.96", "283.203", 
"333.447", "383.69", "39.235", "433.934", "484.177", "534.421", 
"584.777", "635.134", "685.49", "735.847", "785.196", "81.948", 
"831.454", "849.509", "850.124", "852.032", "854.335", "856.635", 
"858.931", "861.223", "863.514", "866.744", "870.24", "873.733", 
"875.898", "877.221", "881.135", "885.044", "888.948", "893.188", 
"895.326", "900.137", "905.603", "911.296", "916.983", "918.404", 
"926.137", "932.075", "938.009", "943.939", "951.689", "956.848", 
"958.382", "962.005", "967.053", "972.099", "977.142", "980.212", 
"981.722", "986.714", "993.275", "BaseFor."), class                = "factor")),    .Names  =     c("V1", 
 "V2", "V5", "V6", "V11", "V12", "V15", "V16"), row.names = c(3L, 
 4L, 5L, 6L, 7L, 8L, 12L, 13L, 14L, 15L, 16L, 17L), class = "data.frame")

Answer 1

这是我认为我应该合作的数据集。

> D

        V1      V2       V5      V6      V11     V12      V15     V16
3 0.562965 803.438  0.58323 800.772 0.527564 878.915 0.543793  870.24
4 0.568965  803.44  0.58923 800.923 0.538766 875.901 0.549793 866.744
5 0.574965 803.441  0.59523 801.074 0.544766  874.12 0.555793 863.514
6 0.580965 803.443 0.599941 801.193 0.550766 872.337 0.561793 861.223
7 0.586965 803.444                  0.556766 870.552 0.567793 858.931
8 0.592965 803.322                  0.562766 868.763 0.573793 856.635

你所包含的结构是一团糟 - 值存储为因子，而不是数字。所以我在这里整理它们（烦人地你必须转换为字符，然后转换为数字）。之后，我将列聚集到值和变量列中。

library(tidyverse)

D_long <- D %>% 
  dplyr::mutate_all(as.character) %>% 
  dplyr::mutate_all(as.numeric) %>% 
  tidyr::gather(variable, value, V2:V16) %>% 
  dplyr::filter(!is.na(value))

D_long

输出

         V1 variable      value
1  0.562965       V2 803.438000
2  0.568965       V2 803.440000
3  0.574965       V2 803.441000
4  0.580965       V2 803.443000
5  0.586965       V2 803.444000
6  0.592965       V2 803.322000
7  0.562965       V5   0.583230
8  0.568965       V5   0.589230
9  0.574965       V5   0.595230
10 0.580965       V5   0.599941
11 0.562965       V6 800.772000
12 0.568965       V6 800.923000
13 0.574965       V6 801.074000
14 0.580965       V6 801.193000
15 0.562965      V11   0.527564
16 0.568965      V11   0.538766
17 0.574965      V11   0.544766
18 0.580965      V11   0.550766
19 0.586965      V11   0.556766
20 0.592965      V11   0.562766
21 0.562965      V12 878.915000
22 0.568965      V12 875.901000
23 0.574965      V12 874.120000
24 0.580965      V12 872.337000
25 0.586965      V12 870.552000
26 0.592965      V12 868.763000
27 0.562965      V15   0.543793
28 0.568965      V15   0.549793
29 0.574965      V15   0.555793
30 0.580965      V15   0.561793
31 0.586965      V15   0.567793
32 0.592965      V15   0.573793
33 0.562965      V16 870.240000
34 0.568965      V16 866.744000
35 0.574965      V16 863.514000
36 0.580965      V16 861.223000
37 0.586965      V16 858.931000
38 0.592965      V16 856.63500

然后将列映射到美学，并绘制线图层：

ggplot(D_long, aes(x = V1, y = value, color = variable)) +
  geom_line()

输出

Answer 2

Considering what you need, you should have arranged your data in your csv file like this

library(magrittr)
library(ggplot2)

D <- structure(list(X = c(0.562965, 0.568965, 0.574965, 0.580965, 
    0.586965, 0.592965, 0.58323, 0.58923, 0.59523, 0.599941, 0.527564, 
    0.538766, 0.544766, 0.550766, 0.556766, 0.562766, 0.543793, 0.549793, 
    0.555793, 0.561793, 0.567793, 0.573793), Y = c(803.438, 803.44, 
    803.441, 803.443, 803.444, 803.322, 800.772, 800.923, 801.074, 
    801.193, 878.915, 875.901, 874.12, 872.337, 870.552, 868.763, 
    870.24, 866.744, 863.514, 861.223, 858.931, 856.635), Group = c("V1_V2", 
    "V1_V2", "V1_V2", "V1_V2", "V1_V2", "V1_V2", "V5_V6", "V5_V6", 
    "V5_V6", "V5_V6", "V11_V12", "V11_V12", "V11_V12", "V11_V12", 
    "V11_V12", "V11_V12", "V15_V16", "V15_V16", "V15_V16", "V15_V16", 
    "V15_V16", "V15_V16")), .Names = c("X", "Y", "Group"), row.names = c(NA, 
    -22L), class = c("tbl_df", "tbl", "data.frame"), spec = structure(list(
    cols = structure(list(X = structure(list(), class = c("collector_double", 
    "collector")), Y = structure(list(), class = c("collector_double", 
    "collector")), Group = structure(list(), class = c("collector_character", 
    "collector"))), .Names = c("X", "Y", "Group")), default = structure(list(), 
    class = c("collector_guess", 
    "collector"))), .Names = c("cols", "default"), class = "col_spec"))
head(D)

#> # A tibble: 6 x 3
#>       X     Y Group
#>   <dbl> <dbl> <chr>
#> 1 0.563  803. V1_V2
#> 2 0.569  803. V1_V2
#> 3 0.575  803. V1_V2
#> 4 0.581  803. V1_V2
#> 5 0.587  803. V1_V2
#> 6 0.593  803. V1_V2

ggplot(D, aes(x = X, y = Y, color = Group, group = Group)) +
  geom_line()

# or
D %>% 
  ggplot(., aes(x = X, y = Y, color = Group, group = Group)) +
  geom_line()

Edit: to create the data frame D automatically from OP's original data
Credit to this answer

D1 <- structure(list(V1 = c(0.562965, 0.568965, 0.574965, 0.580965, 
        0.586965, 0.592965), V2 = c(803.438, 803.44, 803.441, 803.443, 
        803.444, 803.322), V5 = c(0.58323, 0.58923, 0.59523, 0.599941, 
        NA, NA), V6 = c(800.772, 800.923, 801.074, 801.193, NA, NA), 
            V11 = c(0.527564, 0.538766, 0.544766, 0.550766, 0.556766, 
            0.562766), V12 = c(878.915, 875.901, 874.12, 872.337, 870.552, 
            868.763), V15 = c(0.543793, 0.549793, 0.555793, 0.561793, 
            0.567793, 0.573793), V16 = c(870.24, 866.744, 863.514, 861.223, 
            858.931, 856.635)), .Names = c("V1", "V2", "V5", "V6", "V11", 
        "V12", "V15", "V16"), row.names = c(NA, -6L), class = c("tbl_df", 
        "tbl", "data.frame"), spec = structure(list(cols = structure(list(
            V1 = structure(list(), class = c("collector_double", "collector"
            )), V2 = structure(list(), class = c("collector_double", 
            "collector")), V5 = structure(list(), class = c("collector_double", 
            "collector")), V6 = structure(list(), class = c("collector_double", 
            "collector")), V11 = structure(list(), class = c("collector_double", 
            "collector")), V12 = structure(list(), class = c("collector_double", 
            "collector")), V15 = structure(list(), class = c("collector_double", 
            "collector")), V16 = structure(list(), class = c("collector_double", 
            "collector"))), .Names = c("V1", "V2", "V5", "V6", "V11", 
        "V12", "V15", "V16")), default = structure(list(), class = c("collector_guess", 
        "collector"))), .Names = c("cols", "default"), class = "col_spec"))

# make group names which are the combination of every 2 column names 
groupName <- paste0(names(D1)[c(TRUE, FALSE)], names(D1)[c(FALSE, TRUE)])
groupName
#> [1] "V1V2"   "V5V6"   "V11V12" "V15V16"

# next we split the data into a list of groups of 2 columns, 
# then change the names of the list with setNames and 
# rbind the list elements to a single data.table using rbindlist 
# and specifying the idcol as 'Group'
library(data.table)
lst <- split.default(D1, cumsum(rep(c(TRUE, FALSE), ncol(D1)/2)))
D <- rbindlist(setNames(lst, groupName), idcol = "Group")

D %>% 
  ggplot(., aes(x = V1, y = V2, color = Group, group = Group)) +
  xlab("X") + ylab("Y") +
  geom_line()

Other tip: use read_csv from readr package to read data into R as it has stringsAsFactors = FALSE by default and is much faster than base R read.csv. Read more about it here and here.

Created on 2018-03-25 by the reprex package (v0.2.0).

使用ggplot2在单个图中以图形方式表示许多曲线

2 个答案: