我有四个数据框cloud，prec，rhum和temp，分别包含云量，降水量，相对湿度和气温的观测值和模型输出值。
每个数据框列指的是：1）日期（此处未使用），2）CRU（观察）和3:17是15个模型的输出。
我正在尝试为每个数据框创建泰勒图（显示模型和参考之间的对应关系），而不必复制代码。
为了创建这个图，我需要对第一个模型绘制观察图，然后使用add=T参数绘制与其他模型相同的图。
我目前正在做的是：
# create letters for legend
leg <- LETTERS[1:15]
# first plot - ref data and one model
taylor.diagram(cloud[,2], cloud[,3], pch=leg[1], col='black', pcex=1, normalize=T)
# add remaining models
for (i in 3:dim(cloud)[2]) {
model.wanted <- cloud[,i]
taylor.diagram(cloud$CRU, model.wanted, add=T, col='black', pch=leg[i-2], pcex=1, normalize=T)
}
请注意，我只使用数据框cloud来创建上面的图。我的问题是：我如何循环这4个数据帧以产生各自的图，而不必为每个数据帧复制相同的代码？
以下是重现我的问题的数据：
require(plotrix)
require(colorspace)
cloud <- structure(list(date = structure(c(0, 31, 59, 90, 120, 151), class = "Date"),
CRU = c(67.6552579213225, 64.8199295882443, 67.6882136843717,
64.4932343486913, 62.0941221490892, 59.9333537425379), CanESM2 = c(66.972917385659,
71.4248852462099, 65.1381412601471, 67.1597958635029, 62.9675122718923,
65.6251207369531), `GFDL-ESM2M` = c(62.5805390942166, 62.8894567998808,
60.5108804320452, 64.7156850235086, 61.0681295548824, 64.5205937542971
), `GISS-E2-H` = c(61.6562727435151, 63.6979377938432, 61.934720028102,
62.9147267351652, 62.9400740659167, 64.0185837954248), `GISS-E2-R-CC` = c(63.0442187081722,
60.0853062955399, 60.9436761706614, 60.8772572772824, 61.7701362701327,
59.043906825997), `GISS-E2-R` = c(59.6711430384541, 62.8334974371481,
63.0131761539749, 61.7294481121309, 62.2555191908942, 61.5088982333356
), `HadGEM2-AO` = c(54.154605609548, 53.5172215357719, 54.4719170376292,
50.5503675739528, 51.8468629765371, 56.0233048048772), `HadGEM2-CC` = c(55.2767160056209,
55.4545621321912, 48.5067733614208, 49.7513141190709, 52.4741278958181,
49.7424716037677), `IPSL-CM5A-MR` = c(66.7466522042235, 63.6054491778145,
61.935661862022, 61.245452060337, 58.9980974818949, 60.1933125803763
), `IPSL-CM5B-LR` = c(61.5338846590087, 65.7333456200048,
67.3005378140901, 61.2494067126826, 62.0015259754588, 65.9300494528375
), MIROC4h = c(56.6392694529316, 56.7074476707051, 51.0562354028364,
51.0794469888308, 52.1698227633231, 54.4719042536529), `MRI-CGCM3` = c(63.6631087715584,
61.2187020278953, 59.3379579304255, 56.2363349944667, 56.8857660830788,
55.78558572393), `NorESM1-M` = c(65.0169360325908, 62.0288071442766,
57.6896775396247, 54.8514090107477, 55.3626083968536, 54.2549283168748
), `bcc-csm1-1-m` = c(56.3135573432878, 53.4686209116205,
56.6030430885226, 55.732017360598, 51.6051707954295, 53.9728077055279
), `bcc-csm1-1` = c(58.3639905689753, 59.9757869293257, 59.9353082093579,
58.3831427846875, 58.2989891237962, 61.5507059627109), inmcm4 = c(70.8259646403441,
69.1040767391383, 64.5515271993269, 64.6499744101575, 67.712215046297,
67.1942041779122)), .Names = c("date", "CRU", "CanESM2",
"GFDL-ESM2M", "GISS-E2-H", "GISS-E2-R-CC", "GISS-E2-R", "HadGEM2-AO",
"HadGEM2-CC", "IPSL-CM5A-MR", "IPSL-CM5B-LR", "MIROC4h", "MRI-CGCM3",
"NorESM1-M", "bcc-csm1-1-m", "bcc-csm1-1", "inmcm4"), row.names = 1441:1446, class = "data.frame")
rhum <- structure(list(date = structure(c(0, 31, 59, 90, 120, 151), class = "Date"),
CRU = c(73.071142795189, 72.116517171492, 75.4633207613727,
74.9045111133869, 74.5648160247136, 75.5390179521703), CanESM2 = c(82.1738954084519,
84.2968531301844, 83.2241528402853, 82.4713266361527, 81.9882784900331,
82.2132649960434), `GFDL-ESM2M` = c(76.1144124999242, 75.513665275741,
73.7942696812278, 76.1981389223065, 77.0376873674448, 77.2976291681033
), `GISS-E2-H` = c(73.1633360881694, 73.7535375028465, 73.723200571049,
72.6744510703059, 72.5031851158365, 72.5837847137451), `GISS-E2-R-CC` = c(71.1623239923221,
71.5630121183116, 71.9035651911908, 71.8883493576273, 72.1201485964011,
71.100014834153), `GISS-E2-R` = c(72.9110190015648, 73.3857936309234,
72.300836242765, 73.3048428362573, 72.5734859763251, 73.6193392333092
), `HadGEM2-AO` = c(71.7564950985936, 72.6083887837505, 72.7375342461659,
72.740344318256, 71.66267194681, 73.9705993920042), `HadGEM2-CC` = c(71.7338096720155,
73.4017465092843, 72.2130968345954, 69.8187574099937, 71.6096575044331,
70.2428292344746), `IPSL-CM5A-MR` = c(78.288059507225, 77.7982102562531,
77.9391756146972, 77.3570118881806, 76.9254102912144, 78.1113354206643
), `IPSL-CM5B-LR` = c(75.3990921536942, 77.3814960362618,
77.868640870546, 75.6332361338431, 75.5473474519295, 76.3470846818623
), MIROC4h = c(79.4069241143388, 80.0844909618891, 79.6346667043229,
79.0549592401828, 78.8346751581159, 81.0161486809714), `MRI-CGCM3` = c(81.8501322783085,
81.2568695034897, 80.4855355377643, 78.7182931088007, 78.8133439352917,
79.2912475172121), `NorESM1-M` = c(78.3120701427348, 77.4754017090379,
78.7131021740562, 78.1530052865458, 77.5852423941183, 78.1157005267673
), `bcc-csm1-1-m` = c(70.2950344625551, 69.4160485183984,
71.8456119878669, 70.8761688262538, 69.9851922515958, 70.2416348498607
), `bcc-csm1-1` = c(70.8614835346512, 70.4910287544741, 71.8480513221339,
70.8844927347194, 68.8084697254917, 71.9636850577907), inmcm4 = c(78.5251270969012,
78.1160855633474, 77.6402386801424, 78.1458952445315, 80.520200454645,
81.1088210504097)), .Names = c("date", "CRU", "CanESM2",
"GFDL-ESM2M", "GISS-E2-H", "GISS-E2-R-CC", "GISS-E2-R", "HadGEM2-AO",
"HadGEM2-CC", "IPSL-CM5A-MR", "IPSL-CM5B-LR", "MIROC4h", "MRI-CGCM3",
"NorESM1-M", "bcc-csm1-1-m", "bcc-csm1-1", "inmcm4"), row.names = 1441:1446, class = "data.frame")
prec <- structure(list(date = structure(c(0, 31, 59, 90, 120, 151), class = "Date"),
CRU = c(5.49765705623559, 4.77359806046721, 5.62158322758403,
4.67963243680965, 4.2224734968374, 3.36206455563072), CanESM2 = c(4.79841013514418e-05,
5.37084366300876e-05, 4.72436063370894e-05, 5.07240040760987e-05,
4.37060840171321e-05, 4.41549152620003e-05), `GFDL-ESM2M` = c(6.3110190671931e-05,
5.65601672228163e-05, 5.67905323120218e-05, 5.58747174119601e-05,
5.10299194571122e-05, 5.14087758533192e-05), `GISS-E2-H` = c(4.40334130183662e-05,
5.78179278927125e-05, 5.76373298150557e-05, 5.13821649194105e-05,
5.06616003234137e-05, 4.60405440060584e-05), `GISS-E2-R-CC` = c(4.61461863558762e-05,
4.68618486326754e-05, 4.74821135265079e-05, 4.96532111588744e-05,
4.66607892112438e-05, 4.53600483364368e-05), `GISS-E2-R` = c(4.2961224350566e-05,
4.73954128346998e-05, 4.55096010495126e-05, 4.86006751348426e-05,
4.62571152892935e-05, 4.80067136455394e-05), `HadGEM2-AO` = c(4.50663401267986e-05,
4.40587125943942e-05, 5.41502610411833e-05, 4.86846159373331e-05,
4.85895249255265e-05, 4.72307926738793e-05), `HadGEM2-CC` = c(4.95938421491449e-05,
5.13035752101009e-05, 3.91581380304816e-05, 4.72639322614053e-05,
4.97923195981998e-05, 4.55380785145821e-05), `IPSL-CM5A-MR` = c(4.53951482623454e-05,
4.92054317849646e-05, 4.50621633685784e-05, 4.64624592856241e-05,
4.61646753878609e-05, 4.31003875327151e-05), `IPSL-CM5B-LR` = c(4.17308168575824e-05,
5.44154765751375e-05, 6.03962372881687e-05, 4.75167315044657e-05,
4.53351027522876e-05, 4.82428198335856e-05), MIROC4h = c(5.41267226016149e-05,
5.48366948503037e-05, 3.91468390362469e-05, 4.19957443512316e-05,
4.23787540871146e-05, 4.59629308941648e-05), `MRI-CGCM3` = c(5.67073841622448e-05,
6.00310012922815e-05, 5.07071712055857e-05, 4.64848152199045e-05,
4.70707970739826e-05, 4.64690827108151e-05), `NorESM1-M` = c(5.75674811670015e-05,
4.47625188467447e-05, 4.30881349345795e-05, 4.36944636932066e-05,
3.76844750636054e-05, 4.15962233582706e-05), `bcc-csm1-1-m` = c(4.39661514721367e-05,
3.6267821524052e-05, 4.96688538581108e-05, 4.90777169791919e-05,
4.50199953206488e-05, 5.09378733154497e-05), `bcc-csm1-1` = c(4.36386251524885e-05,
4.26126923570228e-05, 4.66884100523015e-05, 4.91951448592167e-05,
4.72643404938543e-05, 4.92076636119263e-05), inmcm4 = c(4.7871848561418e-05,
4.65707714989636e-05, 4.22968050234147e-05, 4.66198387367896e-05,
4.72156627251913e-05, 4.97391754662504e-05)), .Names = c("date",
"CRU", "CanESM2", "GFDL-ESM2M", "GISS-E2-H", "GISS-E2-R-CC",
"GISS-E2-R", "HadGEM2-AO", "HadGEM2-CC", "IPSL-CM5A-MR", "IPSL-CM5B-LR",
"MIROC4h", "MRI-CGCM3", "NorESM1-M", "bcc-csm1-1-m", "bcc-csm1-1",
"inmcm4"), row.names = 1441:1446, class = "data.frame")
temp <- structure(list(date = structure(c(0, 31, 59, 90, 120, 151), class = "Date"),
CRU = c(296.434223174652, 296.551152609827, 295.79685902342,
295.167607530997, 293.372857798867, 292.000823511404), CanESM2 = c(294.090026903654,
294.048913615266, 293.927266376339, 292.923982638532, 291.957568370083,
290.557879524454), `GFDL-ESM2M` = c(294.587617285656, 295.170026368258,
294.699185428731, 293.39897071883, 291.958153620491, 290.823281496282
), `GISS-E2-H` = c(296.045575347365, 295.993069888109, 295.901643246656,
294.701849734658, 292.999441203513, 291.91942751522), `GISS-E2-R-CC` = c(295.225379535162,
295.585000105295, 295.330113525391, 294.238700231362, 292.708886065567,
291.756369528966), `GISS-E2-R` = c(294.794358359889, 295.510733017949,
295.185885248909, 294.214823331777, 292.880296472025, 291.535325179965
), `HadGEM2-AO` = c(294.945626152886, 295.335533520436, 294.719626134683,
294.040903554102, 292.615202968664, 291.228220812703), `HadGEM2-CC` = c(294.22197507981,
294.441414809199, 294.401702411495, 293.49032785516, 292.14932653059,
290.87190355089), `IPSL-CM5A-MR` = c(293.578417786715, 293.78668991892,
293.352080970452, 292.406195291441, 290.799876584059, 289.835797813371
), `IPSL-CM5B-LR` = c(294.73205410606, 295.119550463582,
294.817129682574, 293.711843713236, 292.056669504266, 290.864620461269
), MIROC4h = c(294.70635495013, 294.670434784471, 294.147371413917,
293.527049790767, 292.226820807206, 291.332287333528), `MRI-CGCM3` = c(294.933276968616,
294.879510125054, 294.846681027775, 293.857122733133, 292.572892091985,
290.789874549553), `NorESM1-M` = c(293.444156356722, 293.137026285093,
293.051839199847, 292.416654266893, 291.405479215209, 290.793948976394
), `bcc-csm1-1-m` = c(295.051305850737, 295.442825940227,
295.005397121139, 294.329125025342, 293.283857271964, 292.067603042335
), `bcc-csm1-1` = c(294.754791763038, 295.030755277935, 294.58344368204,
293.739068664194, 292.679743609512, 291.334636694479), inmcm4 = c(293.541987832945,
293.698833764506, 293.124162988495, 292.419409972074, 290.939433379592,
290.38564940157)), .Names = c("date", "CRU", "CanESM2", "GFDL-ESM2M",
"GISS-E2-H", "GISS-E2-R-CC", "GISS-E2-R", "HadGEM2-AO", "HadGEM2-CC",
"IPSL-CM5A-MR", "IPSL-CM5B-LR", "MIROC4h", "MRI-CGCM3", "NorESM1-M",
"bcc-csm1-1-m", "bcc-csm1-1", "inmcm4"), row.names = 1441:1446, class = "data.frame")
答案 0 :(得分：1)
这有用吗？
# create letters for legend
leg <- LETTERS[1:15]
for (nm in c('cloud', 'prec', 'rhum', 'temp') {
dat <- get(nm)
# first plot - ref data and one model
taylor.diagram(dat[,2], dat[,3], pch=leg[1], col='black', pcex=1, normalize=T)
# add remaining models
for (i in 3:dim(dat)[2]) {
model.wanted <- dat[,i]
taylor.diagram(dat$CRU, model.wanted, add=T, col='black', pch=leg[i-2], pcex=1, normalize=T)
}
}
编辑： 另一种方法，可以更轻松地收集/编辑数据：
alldat <- list(cloud, prec, rhum, temp)
lapply(alldat, function(dat) {
# first plot - ref data and one model
taylor.diagram(dat[,2], dat[,3], pch=leg[1], col='black', pcex=1, normalize=T)
# add remaining models
for (i in 3:dim(dat)[2]) {
model.wanted <- dat[,i]
taylor.diagram(dat$CRU, model.wanted, add=T, col='black', pch=leg[i-2], pcex=1, normalize=T)
}
})
通过这种方式，您可以通过以下方式引入数据：
alldat <- lapply(list.files(pattern="*.csv"), function(fn) {
dat <- read.csv(fn, header=TRUE)
## clean/process dat
## ...
dat
})
这使得从 4 数据集转移到 40 相对无痛。