用ggplot2-R绘制数据框 [英] Plot dataframe with ggplot2 - R

问题描述

我正在尝试使用以下数据框创建图。它有10个观察值和25个变量（其中之一 - ID - 仅仅是不同观测值的ID列）

 'data。框架'：25个变量中的10个：
 $ NDVI_mean：num 0.0607 0.0552 0.5811 0.7676 0.0328 ... 
 $ NDVI_sd：num 0.0881 0.0298 0.1644 0.0937 0.0292 ... 
 $ NDVI_mean.1 ：num 0.0211 0.0549 0.1375 0.1207 0.024 ... 
 $ NDVI_sd.1：num 0.0111 0.0195 0.0227 0.0701 0.0197 ... 
 $ NDVI_mean.2：num 0.0703 0.0715 0.6832 0.769 0.0418 ... 
 $ NDVI_sd.2：num 0.0938 0.0298 0.1601 0.0674 0.0402 ... 
 $ NDVI_mean.3：num 0.0636 0.0552 0.6829 0.732 0.0292 ... 
 $ NDVI_sd.3：num 0.0912 0.0222 0.1613 0.1102 0.0355 ... 
 $ NDVI_mean.4：num 0.092 0.0781 0.6947 0.5256 0.056 ... 
 $ NDVI_sd.4：num 0.0879 0.0211 0.158 0.0686 0.0328 ... 
 $ NDVI_mean.5：num 0.1047 0.091 0.4251 0.3573 0.0722 ... 
 $ NDVI_sd.5：num 0.0441 0.013 0.0585 0.0368 0.0156 ... 
 $ NDVI_mean.6：num 0。 0547 0.0654 0.5912 0.6098 0.0404 ... 
 $ NDVI_sd.6：num 0.0874 0.0195 0.2143 0.0975 0.0287 ... 
 $ NDVI_mean.7：num 0.1047 0.0882 0.6914 0.6532 0.0689 ... 
 $ NDVI_sd .7：num 0.0843 0.0177 0.1553 0.0653 0.0299 ... 
 $ NDVI_mean.8：num 0.0859 0.071 0.6905 0.6866 0.0556 ... 
 $ NDVI_sd.8：num 0.0809 0.018 0.1624 0.0866 0.0311 ... 
 $ NDVI_mean.9：num 0.0949 0.1204 0.1434 0.2849 0.1231 ... 
 $ NDVI_sd.9：num 0.00951 0.00719 0.01228 0.03483 0.01023 ... 
 $ NDVI_mean.10：num 0.0854 0.0752 0.6712 0.7326 0.0628。 .. 
 $ NDVI_sd.10：num 0.0789 0.0212 0.1471 0.0951 0.0326 ... 
 $ NDVI_mean.11：num 0.0942 0.0986 0.6434 0.7741 0.0899 ... 
 $ NDVI_sd.11：num 0.0735 0.0188 0.1299 0.0765 0.0277 ... 
 $ ID：int 1 2 3 4 5 6 7 8 9 10 
  

数据框的图片

我想用图创建一个图X轴应该是不同的NDVI_mean变量（NDVI_mean - NDVI_mean.1 - NDVI_mean.2 - 等等）。

Y轴应该是这些变量的值

+ p

我会比如图中包含10条与10条观察值相对应的行

我对ggplot2是全新的。我已经设法使用此代码创建一些图表，但它不是我想要的

  ggplot2（NDVIdf）+ geom_lines（aes（ x = NDVI_mean，y = ID））
  

编辑

dput的输出（NDVIdf）

 >输入（NDVIdf）
结构（列表（NDVI_mean = c（0.0607135413903215,0.05151773354119158，
 0.58106114381679,0.7767559372904067,0.0327779986531678,0.0178615320775541，
 0.242088217197272,0.2019285277774,0.0393074640533382,0.362654323805063 
），NDVI_sd = C（0.0881409040764672，0.0297587817960566，0.164350402694459，
 0.0937447350009958，0.0291673504162979，0.0328954778667684，0.154674728930805，
 0.143054126101431，0.0394783704067313，0.0311605700206721），NDVI_mean.1 = C（0.0210982854397687，
 0.0549092171312182，0.137518549485032，0.120670383289592， 0.0240424367577284，
 0.0159096452554129，0.0672761385275565，0.0341803495552938，-0.00134448575083377，
 0.016580205828644），NDVI_sd.1 = C（0.0110723260269658，0.01951851232517，
 0.0227065359549684，0.0700670943356275，0.0196608837546022，0.0121199724795787，
 0.0585473350749026， 0.0227914450038557,0.0135058392129466,0.0150912377421709 
），NDVI_mean.2 = c（0.0703180934388137,0.0714783174472453 ，0.683213190781725，
 0.769036956136717，0.0418112830812162，0.0284743048998433，0.23348292946483，
 0.235929665998861，0.0450296798850473，0.193100322654342），NDVI_sd.2 = C（0.0937618859311873，
 0.0298498793436821，0.160085159223464，0.0673810570033997，0.0402149180186587，
 0.0397066821267195，0.150683537602667，0.145471088294412，0.0437922991992655，
 0.0141486994532874），NDVI_mean.3 = C（0.063601350404069，0.0551904437586304，
 0.682930851671194，0.731967082842268，0.0291583347580934，0.0193111448443503，
 0.319631300233593，0.166050320085929，0.0366014763086276，0.221872499210234 
），NDVI_sd.3 = c（0.0912293427166813,0.0222453701937956,0.11322107465979，
 0.110207844254988,0.0355049011856384,0.0349930810428516,0.2227276210965238，
 0.140438890801978,0.0376830428032925,0.0220584182188743），NDVI_mean.4 = c（0.0919804842383724，
 0.0781265501499422,0.694671427954745,0.525632176936541,0.055980386796576，
 0.0444207693277835,0.42 6953378337129，0.160783372015251，0.0609390942283722，
 0.280378773041507），NDVI_sd.4 = C（0.0879414801936151，0.0210552190044792，
 0.158033594194682，0.0685669288657517，0.0327713833848639，0.0354769286383367，
 0.252469606866754，0.12982572565032，0.036836867665617，0.0411465416161875 
 ），NDVI_mean.5 = C（0.104738543138272，0.0909713368375085，0.42508525657118，
 0.357320549164012，0.0721572385527876，0.0663794698314188，0.23911562990616，
 0.142111328436142，0.0838823297267412，0.251654432686439），NDVI_sd.5 = C（0.0441048632295888，
 0.0130326877444498 ，0.0585279766430101，0.0368348303398042，0.0155510862094617，
 0.0192137216652464，0.0585475549304422，0.0736886597614494，0.0212806524351524，
 0.0259938407933158），NDVI_mean.6 = C（0.054670992223019，0.065381296775636，
 0.591168574495215，0.609806323819807，0.0403625648315437，0.00811946347995523，
 0.310916693477462,0.158498269033413,0.0443372830506701,0.371097291211943 
），NDVI_sd.6 = C（0.0874297350407085，0.0195485598323798，0.214314782421285，
 0.0974716364190341，0.0286726835375469，0.0464844141552075，0.124111018323546，
 0.128024962302557，0.0389891785245309，0.0776972260415738），NDVI_mean.7 = C（0.104681150177595，
 0.0882044567680745，0.691354972003269，0.65319672247453， 0.0689026139683328，
 0.0564115948034715，0.345466555679804，0.189094867024672，0.0757218905916805，
 0.473698360613464），NDVI_sd.7 = C（0.0842570243914433，0.0176701260206802，
 0.15529028462675，0.0653161775993753，0.0298643217871498，0.0350802264835342，
 0.179839784256719，0.123083052319927 ，0.0381348337459403，0.0504351117371588 
）中，NDVI_mean.8 = C（0.08591308296691，0.0710169977816541，0.69050244219096，
 0.686550053201553，0.05559868259279，0.0386856009179078，0.385427380396624，
 0.191494000375466，0.0582605982908748，0.634092671211804），NDVI_sd.8 = C （0.0809198942299164，
 0.0179761950231585，0.162375086927031，0.0865933396475938，0.03110691 09690036，
 0.0341123644056808，0.221678551331174，0.123510636808576，0.0352156193862569，
 0.037441682380018），NDVI_mean.9 = C（0.0948832729278301，0.120400127877444，
 0.143375746425582，0.284877572639076，0.123096886134381，0.111171634746743，
 0.223262233590715，0.120538120679937 ，0.0971369124338333，0.233815280325772 
）中，NDVI_sd.9 = C（0.00951402820331221，0.00718755312976778，0.0122787887859583，
 0.0348298462083254，0.0102326571562652，0.00707392292527096，
 0.047660749828529，0.0127667426992843，0.00615732059786784，0.0341929453840942 
）中，NDVI_mean 0.10 = C（0.0853653180560701，0.0751638478379656，0.671240397847597，
 0.732629951796317，0.0628200256114987，0.0389376153602489，0.261580310922298，
 0.237551383820751，0.0543488352606212，0.729810364384283），NDVI_sd.10 = C（0.0788860954632659，
 0.0212295674726634，0.147125862744127， 0.0951195938807548，0.0325819971840338，
 0.0313179762667036,0.149062631594425,0.123975319460501,0.029547 9331978356，
 0.0347570926406439），NDVI_mean.11 = C（0.0941656718689444，0.0985743153705462，
 0.643407964040386，0.774084527469533，0.0899420980061257，0.0535166413991826，
 0.303595683796766，0.245633779631581，0.0643377575135575，0.697236179516483 
）中，NDVI_sd。 11 = c（0.0735030069394199,0.0187835191570716,0.129850840148202，
 0.0764938134743573,0.0276954256603995,0.0260888900038652,0.12217568202193，
 0.0934074608484564,0.0272831553843282,0.026010072370012），ID = 1:10），.Names = c（NDVI_mean，
NDVI_sd，NDVI_mean.1，NDVI_sd.1，NDVI_mean.2，NDVI_sd.2，
NDVI_mean.3，NDVI_sd.3，NDVI_mean。 4，NDVI_sd.4，NDVI_mean.5，
NDVI_sd.5，NDVI_mean.6，NDVI_sd.6，NDVI_mean.7，NDVI_sd.7，
NDVI_mean.8，NDVI_sd.8，NDVI_mean.9，NDVI_sd.9，NDVI_mean.10，
NDVI_sd.10，NDVI_mean.11， ，ID），row.names = c（1，
2，3，4，5，6，7，8 ，9，10），class =data.frame）
  

解决方案

您的数据格式很宽，但是 ggplot 将其转换为长格式。也就是说，每个测量值应该有一行。在你的情况下，你应该有一个12 * 2 * 10行的数据框，以及三列是：观察值（1-10），统计值（mean.1，sd.2，...）和值。

您可以使用 tidyr 的 gather 函数轻松地重新格式化数据转换为长格式：

  library（tidyr）
 
 NDVIdf_forplot<  -  gather（NDVIdf，key =统计，值=值，-ID）
 ggplot（NDVIdf_forplot，aes（x =统计量，y =值）+ geom_line（）
  

$ b $ gather 函数有两个参数：key和value，它们为新的长格式数据框中的相关字段设置列名。在这种情况下，key是赋予新列的名称，用于对以前的列标题（mean，sd，...）进行编码并将值赋予先前的值（0.0607，...）。

表达式 -id 告诉聚集不删除 ID 列，所以新数据框中的每条记录仍然与th关联正确的ID。这样，你可以用它来分隔 ggplot 中的不同行，如下所示：

  ggplot（NDVIdf_forplot，aes（x = statistic，y = value，group = ID））+ geom_line（）
  

而且，如果您想以不同的颜色着色不同的颜色并包含图例，则可以使用颜色美学：

  ggplot（NDVIdf_forplot，aes（x = statistic，y = value，group = ID，color = ID））+ 
 geom_line（）
  

I am trying the create a graph using the following dataframe. It has 10 observations and 25 variables (one of them - ID - is just an ID column for the different observations

'data.frame':    10 obs. of  25 variables:
 $ NDVI_mean   : num  0.0607 0.0552 0.5811 0.7676 0.0328 ...
 $ NDVI_sd     : num  0.0881 0.0298 0.1644 0.0937 0.0292 ...
 $ NDVI_mean.1 : num  0.0211 0.0549 0.1375 0.1207 0.024 ...
 $ NDVI_sd.1   : num  0.0111 0.0195 0.0227 0.0701 0.0197 ...
 $ NDVI_mean.2 : num  0.0703 0.0715 0.6832 0.769 0.0418 ...
 $ NDVI_sd.2   : num  0.0938 0.0298 0.1601 0.0674 0.0402 ...
 $ NDVI_mean.3 : num  0.0636 0.0552 0.6829 0.732 0.0292 ...
 $ NDVI_sd.3   : num  0.0912 0.0222 0.1613 0.1102 0.0355 ...
 $ NDVI_mean.4 : num  0.092 0.0781 0.6947 0.5256 0.056 ...
 $ NDVI_sd.4   : num  0.0879 0.0211 0.158 0.0686 0.0328 ...
 $ NDVI_mean.5 : num  0.1047 0.091 0.4251 0.3573 0.0722 ...
 $ NDVI_sd.5   : num  0.0441 0.013 0.0585 0.0368 0.0156 ...
 $ NDVI_mean.6 : num  0.0547 0.0654 0.5912 0.6098 0.0404 ...
 $ NDVI_sd.6   : num  0.0874 0.0195 0.2143 0.0975 0.0287 ...
 $ NDVI_mean.7 : num  0.1047 0.0882 0.6914 0.6532 0.0689 ...
 $ NDVI_sd.7   : num  0.0843 0.0177 0.1553 0.0653 0.0299 ...
 $ NDVI_mean.8 : num  0.0859 0.071 0.6905 0.6866 0.0556 ...
 $ NDVI_sd.8   : num  0.0809 0.018 0.1624 0.0866 0.0311 ...
 $ NDVI_mean.9 : num  0.0949 0.1204 0.1434 0.2849 0.1231 ...
 $ NDVI_sd.9   : num  0.00951 0.00719 0.01228 0.03483 0.01023 ...
 $ NDVI_mean.10: num  0.0854 0.0752 0.6712 0.7326 0.0628 ...
 $ NDVI_sd.10  : num  0.0789 0.0212 0.1471 0.0951 0.0326 ...
 $ NDVI_mean.11: num  0.0942 0.0986 0.6434 0.7741 0.0899 ...
 $ NDVI_sd.11  : num  0.0735 0.0188 0.1299 0.0765 0.0277 ...
 $ ID          : int  1 2 3 4 5 6 7 8 9 10

Image of the dataframe

I would like to create a graph with the following characteristics:

The X axes should be the different NDVI_mean variables (NDVI_mean - NDVI_mean.1 - NDVI_mean.2 - etc.)

The Y axes should be the values of those variables

+

I would like the graph to contain 10 lines which correspond to the 10 observations

I am completely new with ggplot2. I have manage to create some graphs using this code but it is not what I want

ggplot2(NDVIdf)+geom_lines(aes(x=NDVI_mean, y=ID))

Edit

Output of dput(NDVIdf)

> dput(NDVIdf)
structure(list(NDVI_mean = c(0.0607135413903215, 0.0551773354119158, 
0.58106114381679, 0.767559372904067, 0.0327779986531678, 0.0178615320775541, 
0.242088217197272, 0.20999285277774, 0.0393074640533382, 0.362654323805063
), NDVI_sd = c(0.0881409040764672, 0.0297587817960566, 0.164350402694459, 
0.0937447350009958, 0.0291673504162979, 0.0328954778667684, 0.154674728930805, 
0.143054126101431, 0.0394783704067313, 0.0311605700206721), NDVI_mean.1 = c(0.0210982854397687, 
0.0549092171312182, 0.137518549485032, 0.120670383289592, 0.0240424367577284, 
0.0159096452554129, 0.0672761385275565, 0.0341803495552938, -0.00134448575083377, 
0.016580205828644), NDVI_sd.1 = c(0.0110723260269658, 0.01951851232517, 
0.0227065359549684, 0.0700670943356275, 0.0196608837546022, 0.0121199724795787, 
0.0585473350749026, 0.0227914450038557, 0.0135058392129466, 0.0150912377421709
), NDVI_mean.2 = c(0.0703180934388137, 0.0714783174472453, 0.683213190781725, 
0.769036956136717, 0.0418112830812162, 0.0284743048998433, 0.23348292946483, 
0.235929665998861, 0.0450296798850473, 0.193100322654342), NDVI_sd.2 = c(0.0937618859311873, 
0.0298498793436821, 0.160085159223464, 0.0673810570033997, 0.0402149180186587, 
0.0397066821267195, 0.150683537602667, 0.145471088294412, 0.0437922991992655, 
0.0141486994532874), NDVI_mean.3 = c(0.063601350404069, 0.0551904437586304, 
0.682930851671194, 0.731967082842268, 0.0291583347580934, 0.0193111448443503, 
0.319631300233593, 0.166050320085929, 0.0366014763086276, 0.221872499210234
), NDVI_sd.3 = c(0.0912293427166813, 0.0222453701937956, 0.161322107465979, 
0.110207844254988, 0.0355049011856384, 0.0349930810428516, 0.226276210965238, 
0.140438890801978, 0.0376830428032925, 0.0220584182188743), NDVI_mean.4 = c(0.0919804842383724, 
0.0781265501499422, 0.694671427954745, 0.525632176936541, 0.055980386796576, 
0.0444207693277835, 0.426953378337129, 0.160783372015251, 0.0609390942283722, 
0.280378773041507), NDVI_sd.4 = c(0.0879414801936151, 0.0210552190044792, 
0.158033594194682, 0.0685669288657517, 0.0327713833848639, 0.0354769286383367, 
0.252469606866754, 0.12982572565032, 0.036836867665617, 0.0411465416161875
), NDVI_mean.5 = c(0.104738543138272, 0.0909713368375085, 0.42508525657118, 
0.357320549164012, 0.0721572385527876, 0.0663794698314188, 0.23911562990616, 
0.142111328436142, 0.0838823297267412, 0.251654432686439), NDVI_sd.5 = c(0.0441048632295888, 
0.0130326877444498, 0.0585279766430101, 0.0368348303398042, 0.0155510862094617, 
0.0192137216652464, 0.0585475549304422, 0.0736886597614494, 0.0212806524351524, 
0.0259938407933158), NDVI_mean.6 = c(0.054670992223019, 0.065381296775636, 
0.591168574495215, 0.609806323819807, 0.0403625648315437, 0.00811946347995523, 
0.310916693477462, 0.158498269033413, 0.0443372830506701, 0.371097291211943
), NDVI_sd.6 = c(0.0874297350407085, 0.0195485598323798, 0.214314782421285, 
0.0974716364190341, 0.0286726835375469, 0.0464844141552075, 0.124111018323546, 
0.128024962302557, 0.0389891785245309, 0.0776972260415738), NDVI_mean.7 = c(0.104681150177595, 
0.0882044567680745, 0.691354972003269, 0.65319672247453, 0.0689026139683328, 
0.0564115948034715, 0.345466555679804, 0.189094867024672, 0.0757218905916805, 
0.473698360613464), NDVI_sd.7 = c(0.0842570243914433, 0.0176701260206802, 
0.15529028462675, 0.0653161775993753, 0.0298643217871498, 0.0350802264835342, 
0.179839784256719, 0.123083052319927, 0.0381348337459403, 0.0504351117371588
), NDVI_mean.8 = c(0.08591308296691, 0.0710169977816541, 0.69050244219096, 
0.686550053201553, 0.05559868259279, 0.0386856009179078, 0.385427380396624, 
0.191494000375466, 0.0582605982908748, 0.634092671211804), NDVI_sd.8 = c(0.0809198942299164, 
0.0179761950231585, 0.162375086927031, 0.0865933396475938, 0.0311069109690036, 
0.0341123644056808, 0.221678551331174, 0.123510636808576, 0.0352156193862569, 
0.037441682380018), NDVI_mean.9 = c(0.0948832729278301, 0.120400127877444, 
0.143375746425582, 0.284877572639076, 0.123096886134381, 0.111171634746743, 
0.223262233590715, 0.120538120679937, 0.0971369124338333, 0.233815280325772
), NDVI_sd.9 = c(0.00951402820331221, 0.00718755312976778, 0.0122787887859583, 
0.0348298462083254, 0.0102326571562652, 0.00707392292527096, 
0.047660749828529, 0.0127667426992843, 0.00615732059786784, 0.0341929453840942
), NDVI_mean.10 = c(0.0853653180560701, 0.0751638478379656, 0.671240397847597, 
0.732629951796317, 0.0628200256114987, 0.0389376153602489, 0.261580310922298, 
0.237551383820751, 0.0543488352606212, 0.729810364384283), NDVI_sd.10 = c(0.0788860954632659, 
0.0212295674726634, 0.147125862744127, 0.0951195938807548, 0.0325819971840338, 
0.0313179762667036, 0.149062631594425, 0.123975319460501, 0.0295479331978356, 
0.0347570926406439), NDVI_mean.11 = c(0.0941656718689444, 0.0985743153705462, 
0.643407964040386, 0.774084527469533, 0.0899420980061257, 0.0535166413991826, 
0.303595683796766, 0.245633779631581, 0.0643377575135575, 0.697236179516483
), NDVI_sd.11 = c(0.0735030069394199, 0.0187835191570716, 0.129850840148202, 
0.0764938134743573, 0.0276954256603995, 0.0260888900038652, 0.122217568202193, 
0.0934074608484564, 0.0272831553843282, 0.026010072370012), ID = 1:10), .Names = c("NDVI_mean", 
"NDVI_sd", "NDVI_mean.1", "NDVI_sd.1", "NDVI_mean.2", "NDVI_sd.2", 
"NDVI_mean.3", "NDVI_sd.3", "NDVI_mean.4", "NDVI_sd.4", "NDVI_mean.5", 
"NDVI_sd.5", "NDVI_mean.6", "NDVI_sd.6", "NDVI_mean.7", "NDVI_sd.7", 
"NDVI_mean.8", "NDVI_sd.8", "NDVI_mean.9", "NDVI_sd.9", "NDVI_mean.10", 
"NDVI_sd.10", "NDVI_mean.11", "NDVI_sd.11", "ID"), row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10"), class = "data.frame")

解决方案

Your data is in wide format but ggplot requires that you first convert it to long format. That is, you should have one row per observation per measurement. In your case, you should have a dataframe with 12*2*10 rows, and three columns being: observation (1-10), statistic (mean.1, sd.2, ...) and value.

You can use tidyr's gather function to easily reformat the data into long format:

library(tidyr)

NDVIdf_forplot <- gather(NDVIdf, key = statistic, value = value, -ID)
ggplot(NDVIdf_forplot, aes(x = statistic, y = value) + geom_line()

The gather function takes two arguments: key and value, that set the column names for the relevant fields in the new long format dataframe. In this case, key is the name given to the new column that encodes the previous column headings (mean, sd, ...) and value to the previous values (0.0607,...).

The expression -id tells gather not to remove the ID column, so each record in the new dataframe is still associated with the correct id. That way you can use it to separate the different lines in the ggplot call like this:

ggplot(NDVIdf_forplot, aes(x = statistic, y= value, group = ID)) + geom_line()

And, if you want to colour the different observations differently and include a legend, you can use the colour aesthetic:

ggplot(NDVIdf_forplot, aes(x = statistic, y = value, group = ID, colour = ID)) + 
    geom_line()

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