两个不同的Y轴的比例不同-R中的ggplot2不会改变

如何解决两个不同的Y轴的比例不同-R中的ggplot2不会改变

我正在尝试将温度和降水数据显示在同一张图上。我已经绘制了它，但是我无法使轴适当改变。我已经附上了我使用的代码。

已编辑以添加我的数据。希望这会有所帮助！非常感谢！

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p <- ggplot(Complete_Seasonality_Data,aes(x = NewMonths2))
p <- p + geom_point(aes(y = PRCP,colour = "PRCP"))
p <- p + geom_point(aes(y = Temp_C/1,colour = "Temp"))
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*.1,name = "Temp (in)"))
p <- p + scale_colour_manual(values = c("blue","red"))
p <- p + labs(y = "PRCP (C)",x = "Month",colour = "Parameter")
p <- p + theme(legend.position = c(0.65,0.9))
p

这是它要推出的情节。除了右边的Y轴发生变化之外，降水线不会发生变化。

第二次编辑以显示Duck新代码。

structure(list(Group.1 = c("April","August","December","February","January","July","June","March","May","November","October","September"),Season = c(NA_real_,NA_real_,NA_real_),Month = c(NA_real_,Year = c(2017.05882352941,2016.6,2017.6,2017.05882352941,2017,2016.6),Date = structure(c(1494315952.94118,1490691600,1500316560,1506183120,1504163520,1487501280,1499108611.76471,1489840800,1496798682.35294,1498314240,1496087280,1493421840
),class = c("POSIXct","POSIXt")),Site = c(8.17647058823529,8.125,7.775,6.375,8.20588235294118,6.80555555555556,6.55882352941176,8.1,6.375),PercentCover = c(0.765882352941176,0.7125,0.7505,0.7775,0.8625,0.867,0.763529411764706,0.83,0.850588235294118,0.848,0.7065,0.834),AveHt = c(60.1684438927086,50.2311192279942,58.9048701298701,57.3448097041847,55.2253291847042,64.6965656565657,57.9602622867329,56.672138047138,64.4076426024955,57.1465322871573,54.3781565656566,58.3185831529582),SE = c(7.07246013321596,7.79305525403115,7.00224498332823,6.46671176266333,6.32495719718401,7.04611575726224,8.09695750051648,5.65899377193264,7.28959135811987,6.24571692582705,7.32819802238581,7.05669314452393),MaxHt = c(88.3823529411765,81.625,87.75,85,85.875,96.425,92.9117647058823,82.5,98.6764705882353,88.125,79.75,89.65),green = c(0.350962665193537,0.278211058736042,0.183934291894458,0.197711422851132,0.179043270311077,0.335751664926552,0.186533536107468,0.256634190010066,0.319397625619223,0.204519948331115,0.249063275007846,0.277894684744482),yellow = c(0.556643767952726,0.569690303836593,0.686152813243381,0.654331042886853,0.594548585049017,0.554485584960289,0.581008683220038,0.609988063809375,0.594827659217835,0.620510694031593,0.633793562346056,0.600527348262596),brown = c(0.0923935668537371,0.14983619398845,0.122185622134889,0.145933312808728,0.226114026992848,0.10976275011316,0.229212761734686,0.132653108499399,0.0857747151629417,0.174675239990233,0.114398064606882,0.121577966992922),Temp = c(78.4411764705882,82.975,75.65,74.75,74.3,82.2051282051282,81.0882352941177,75.8333333333333,79.8823529411765,78.6,80.1944444444444,83
),Temp_C = c(25.8006535947712,28.3194444444444,24.25,23.75,23.5,27.8917378917379,27.2712418300654,24.3518518518519,26.6013071895425,25.8888888888889,26.7746913580247,28.3333333333333),Vis = c(1.98823529411765,2.12820512820513,2.2125,2.07,2.1625,2.07179487179487,2.05,2.02777777777778,2.11764705882353,2.205,2.11,2.17375),Nests = c(12.4117647058824,17.1,7.1,6.275,4,8.9,13.8787878787879,4.88888888888889,7.38235294117647,2.8,13.025,5.6),SickorDeadFish = c(0.0882352941176471,0.2,0.175,0.075,0.05,0.117647058823529,0.0882352941176471,0.166666666666667,0.0294117647058824,0.25,0.333333333333333,0.275),Cladophora = c(0.0866666666666667,0.0492857142857143,0.0471428571428571,0.0907142857142857,0.0264285714285714,0.0154545454545455,0.0380952380952381,0.0295238095238095,0.0161904761904762,0.0178571428571429,0.0407142857142857,0.03),Comments = c(NaN,NaN,NaN),STATION = c(NA_real_,NAME = c(NA_real_,DATE = c(NA_real_,MONTH = c(NA_real_,AWND = c(6.52626966292135,5.97866090712743,5.85811926605505,6.31656097560976,6.181,6.1103908045977,6.23947727272727,6.5154211663067,6.0985313174946,5.64997635933806,5.43263157894737,5.54940639269406
),FMTM = c(1412.13333333333,1431.1935483871,1411.77419354839,1535.16666666667,1339.24137931034,1439.77419354839,1378.3,1398.8064516129,1353.12903225806,1362.96666666667,1408.45161290323,1381.46666666667),PGTM = c(1394.1095890411,1394.96774193548,1306.83333333333,1412.0511627907,1327.90350877193,1435.51769911504,1372.37674418605,1389.12328767123,1376.75576036866,1373.45341614907,1346.2774566474,1396),PRCP = c(0.0205869074492099,0.0248701298701299,0.0663425925925926,0.0481472684085511,0.0360991379310345,0.0101144164759725,0.00790067720090293,0.0762693156732892,0.0298491379310345,0.0472985781990521,0.034965034965035,0.0243778801843318),SNOW = c(0,0),SNWD = c(0,TAVG = c(78.5333333333333,61.1052631578947,68.6333333333333,80.2903225806452,79.4,72.5161290322581,77.8709677419355,TMAX = c(83.6826484018265,88.8509719222462,81.4940617577197,80.6938271604938,80.8072562358277,88.1520737327189,86.8795454545455,81.3290043290043,84.6048034934498,83.8289786223278,86.3615560640732,88.1009174311927),TMIN = c(67.5423340961098,72.5917926565875,66.4394299287411,64.9283950617284,64.5600907029478,71.9654377880184,70.6772727272727,65.7597402597403,68.6527472527472,68.9643705463183,70.558352402746,71.7821100917431),TSUN = c(NaN,WDF2 = c(115.538116591928,100.905172413793,133.577981651376,143.965936739659,149.438444924406,91.141876430206,99.5022624434389,131.612903225806,124.279569892473,109.693396226415,119.450800915332,115.068493150685),WDF5 = c(107.545045045045,97.6077586206897,124.528735632184,133.031784841076,140.826086956522,82.5229357798165,90.972850678733,120.634573304158,115.714285714286,103.720379146919,109.266055045872,104.736842105263),WSF2 = c(15.2026905829596,14.8530172413793,14.6919724770642,15.4111922141119,15.1332613390929,14.9070938215103,15.083257918552,15.4161290322581,14.8625806451613,14.322641509434,14.3432494279176,14.5600456621005),WSF5 = c(22.1105855855856,21.9961206896552,20.8029885057471,20.8081145584726,20.4824675324675,22.4052752293578,22.2158371040724,21.9317286652079,21.130303030303,20.8722748815166,20.493119266055,21.0052511415525),WT01 = c(1,1,1),WT02 = c(NaN,WT08 = c(1,WT10 = c(NaN,NaN)),row.names = c(NA,-12L),class = "data.frame")

解决方法

也许这就是您想要的：

library(tidyverse)
Complete_Seasonality_Data$NewMonths2 <- as.Date(Complete_Seasonality_Data$Date)
#Code
scalefactor <- max(Complete_Seasonality_Data$PRCP)/max(Complete_Seasonality_Data$Temp_C)
p <- ggplot(Complete_Seasonality_Data,aes(x = NewMonths2))
p <- p + geom_point(aes(y = PRCP,colour = "PRCP"))
p <- p + geom_point(aes(y = Temp_C*scalefactor,colour = "Temp"))
p <- p + scale_y_continuous(sec.axis = sec_axis(~./scalefactor,name = "Temp (in)"))
p <- p + scale_colour_manual(values = c("blue","red"))
p <- p + labs(y = "PRCP (C)",x = "Month",colour = "Parameter")
p <- p + theme(legend.position = c(0.65,0.9))
p

输出：

更新：要包含行，您可以尝试以下操作：

p <- ggplot(Complete_Seasonality_Data,colour = "PRCP"))
p <- p + geom_line(aes(y = PRCP,colour = "Temp"))
p <- p + geom_line(aes(y = Temp_C*scalefactor,0.9))
p

输出：