我有一个包含多个元素的图,如下所示:
其代码为
df2 <- data %>% group_by(presence,class,Wolf) %>% summarise(roaddensity_mean=mean(roaddens),dens_se_group=standard_error(roaddens))
df3 <- df2 %>% group_by(presence,class) %>% summarise(roaddensity_mean=mean(roaddensity_mean),dens_se_group=mean(dens_se_group))
p1 <- ggplot(df1,aes(x=class,y=roaddensity_mean,color=presence)) +
geom_errorbar(data=df3,aes(ymin=roaddensity_mean-1.96*dens_se_group, ymax=roaddensity_mean+1.96*dens_se_group,color=presence),
width = 0.4,size=1,position=position_dodge(width=0.5))+
labs(title='',y="Road density/km²",x="") +
geom_point(data=df2,stat='identity',position=position_dodge(width=0.5),size=1.25,alpha=0.4) + ylim(0,2) +
scale_color_manual(values=c("#40B0A6","#E1BE6A"))+
theme(axis.text.x=element_blank(),legend.direction = "horizontal",plot.margin = unit(c(0.35, 0.5, 0, 0.5), "cm"),
axis.title = element_text(size=11),axis.text=element_text(size=10)) + guides(color=guide_legend(title="")) +
theme(panel.background = element_rect(fill = 'white'),panel.grid.major = element_line(color = 'gray'), panel.grid.minor = element_line(color = 'gray'))
我所要做的就是用黑色绘制几何点,但是当我尝试这样做时,它也会将它们的位置更改为中间,因为它不再通过“存在”来分隔它们。
所以我试图找到一个轻微的代码调整,只是有黑色的几何点,并保持一切相同的原始图,但还没有找到一个解决方案
DF1的输入
structure(list(presence = structure(c(3L, 3L, 3L, 4L, 4L, 4L), levels = c("0",
"1", "Used", "Available"), class = "factor"), class = structure(c(1L,
2L, 3L, 1L, 2L, 3L), levels = c("Resident", "Floater", "Disperser"
), class = "factor"), roaddensity_mean = c(0.644340794888985,
0.688824654672777, 0.564575889060891, 0.912037176999315, 0.838949052923906,
0.802653338954428), dens_se = c(0.00844381665550316, 0.0148649612280198,
0.0248181112925504, 0.00602487059941883, 0.010051939708459, 0.0188940407637902
)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), row.names = c(NA,
-6L), groups = structure(list(presence = structure(3:4, levels = c("0",
"1", "Used", "Available"), class = "factor"), .rows = structure(list(
1:3, 4:6), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -2L), .drop = TRUE))
DF2
structure(list(presence = structure(c(3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L), levels = c("0", "1", "Used", "Available"), class = "factor"),
class = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), levels = c("Resident",
"Floater", "Disperser"), class = "factor"), Wolf = structure(c(2L,
3L, 5L, 7L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 28L, 29L, 30L, 31L,
32L, 33L, 34L, 36L, 37L, 39L, 40L, 41L, 42L, 44L, 45L, 47L,
48L, 52L, 53L, 54L, 55L, 56L, 57L, 60L, 62L, 63L, 64L, 65L,
66L, 68L, 69L, 70L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
84L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 95L, 96L, 1L,
4L, 8L, 27L, 35L, 38L, 43L, 44L, 46L, 47L, 50L, 51L, 58L,
59L, 60L, 61L, 67L, 71L, 72L, 81L, 82L, 83L, 85L, 94L, 2L,
3L, 6L, 9L, 15L, 21L, 26L, 37L, 40L, 44L, 47L, 49L, 56L,
64L, 65L, 77L, 78L, 88L, 93L, 96L, 2L, 3L, 5L, 7L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 36L, 37L,
39L, 40L, 41L, 42L, 44L, 45L, 47L, 48L, 52L, 53L, 54L, 55L,
56L, 57L, 60L, 62L, 63L, 64L, 65L, 66L, 68L, 69L, 70L, 73L,
74L, 75L, 76L, 77L, 78L, 79L, 80L, 84L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 95L, 96L, 1L, 4L, 8L, 27L, 35L, 38L,
43L, 44L, 46L, 47L, 50L, 51L, 58L, 59L, 60L, 61L, 67L, 71L,
72L, 81L, 82L, 83L, 85L, 94L, 2L, 3L, 6L, 9L, 15L, 21L, 26L,
37L, 40L, 44L, 47L, 49L, 56L, 64L, 65L, 77L, 78L, 88L, 93L,
96L), levels = c("1", "3", "4", "5", "7", "8", "10", "11",
"14", "15", "16", "17", "42", "46", "50", "51", "52", "54",
"55", "56", "60", "62", "63", "64", "65", "66", "71", "74",
"76", "86", "88", "90", "91", "94", "95", "98", "100", "102",
"103", "104", "134", "135", "146", "147", "149", "150", "153",
"158", "160", "162", "163", "164", "165", "166", "169", "174",
"175", "176", "178", "179", "180", "181", "183", "190", "193",
"196", "197", "198", "199", "200", "202", "204", "206", "211",
"213", "214", "215", "218", "219", "220", "222", "224", "225",
"228", "263", "326", "330", "333", "334", "344", "347", "349",
"376", "377", "378", "380"), class = "factor"), roaddensity_mean = c(1.01589281216281,
0.594955804269291, 0.820048833717715, 0.55543689630627, 0.735175877030779,
0.703294463723088, 0.14717971660192, 1.15301514573886, 1.26623189978458,
0.549081708609647, 0.418795585546052, 0.403465922430855,
0.552670369363893, 0.90712539802107, 0.219708511564515, 0.621113503325107,
0.289661842043862, 0.684319651528335, 0.897937723875893,
0.628718060718654, 0.704773689632962, 0.513728168496973,
0.605063942092509, 0.473228704909802, 0.684494478365975,
0.454758349651386, 0.610760625350926, 1.40638806315728, 0.680758272259927,
0.677653081229325, 0.699041874218408, 0.305608323414585,
0.58758979574581, 0.280901220315442, 1.14351465273216, 0.822815118325133,
1.26293996527918, 0.313897865894127, 0.325471806914118, 0.438288001966973,
0.512769336298429, 0.159304099098402, 0.917618444896924,
0.499845431978498, 0.27858449166646, 0.431125027741936, 0.514363695505618,
0.37592877, 0.777263062833333, 0.678623857172131, 0.403902668145695,
0.615586223930578, 0.32515041596, 1.22008627690865, 0.917855678814433,
0.619433429458484, 0.669039640526316, 0.569446113653137,
0.641389031019802, 0.584311186444444, 0.799582387179487,
0.921402927603306, 0.598648375609756, 0.638776112987013,
1.72535020486111, 0.423487118478261, 1.44174572, 1.45103335671429,
0.952027997281553, 0.566182278703704, 0.601641233340541,
0.4561838955, 0.297159788337017, 0.575043946292997, 0.784421925196698,
1.0756901502733, 0.242521787293987, 0.958427589800432, 0.963409657878619,
0.330738935942967, 0.470192939148807, 0.305637823709164,
0.83217025639387, 0.794780621642058, 0.52595742248153, 0.579742455788221,
0.59310353713696, 0.684860984038462, 0.562176052525252, 0.935382558741497,
0.274828115645412, 2.56176947173913, 1.4703750421365, 0.867103765254237,
0.605866101496881, 0.757209240576923, 0.466572599359278,
0.560883359323986, 0.506589530136473, 0.53688361106288, 0.725607777428679,
0.484256128060912, 1.1897541538805, 0.53071304289513, 0.585206982563745,
0.686393561991724, 0.0986971871707839, 0.325040857634391,
0.54632444986142, 1.15860202083333, 0.83763202, 0.53068827127907,
0.502528869591837, 1.0580214262069, 0.901614713888889, 1.24116076666667,
0.803489099571755, 1.05253806888083, 1.42030557630271, 1.16279895348698,
1.26506284609121, 0.836165797338182, 0.374642676177875, 1.43523645983108,
1.27373342071722, 0.821745618034996, 1.23245880134607, 0.806206836738491,
1.01923115149509, 1.14286271550372, 0.557842426118368, 1.23451094279995,
0.643190695812293, 1.08011156449041, 0.767787000552506, 1.36752377218757,
1.12935034976995, 0.779484544217097, 0.827342934648422, 0.442502099863712,
0.767283284013841, 0.550664964493328, 0.917612235251008,
1.75336960875976, 0.964158644548036, 1.24157398007387, 1.02960117665735,
0.794035347313041, 0.692222899809873, 0.649439241638017,
1.10690712323597, 0.735583967860472, 1.40255056638556, 0.391800241516151,
0.603378941412544, 0.718763503232408, 0.278994314091257,
0.752677874538609, 1.18614248824571, 0.799841732520478, 0.421251035696545,
0.94776770311828, 0.837913398089888, 0.880360207777778, 1.16061023714583,
0.876386522486339, 0.69635604207064, 1.11581526020661, 0.504473584586667,
1.06860284314845, 0.936200598075601, 0.764524808580024, 0.737221095559211,
0.781386104526445, 1.10480912293894, 1.24657842874074, 0.92387933017094,
0.986628866198347, 0.955723391829268, 0.838097012008658,
1.63504397134722, 0.668303167344203, 1.3680033925669, 1.09550073003061,
1.17894855346278, 1.05314801953704, 0.539820512702703, 1.45826308247619,
1.18209949158435, 0.864129982277444, 1.17323282656875, 1.24788692091735,
0.671439761816646, 1.24308202079898, 1.12835015705314, 0.539495887609586,
1.07620662385206, 0.727363152918715, 0.685308526395294, 0.726248872844029,
0.885994242446795, 0.868293179888237, 0.866858170202046,
0.816519305432692, 1.04323416645791, 1.13800795826531, 0.322286807734578,
1.70188114821087, 0.994621354803165, 0.805079977231638, 0.549211849882883,
0.801160731602564, 0.865038681796107, 1.22574923104707, 0.715635927837216,
0.605409770365431, 1.01340565731717, 1.12639180035336, 1.34325851267476,
3.12012260886868, 0.538180722953443, 0.953902615287807, 1.10997799360896,
0.312698965003995, 0.828883656105216, 0.778786083611111,
1.08046257082667, 0.809625998178295, 1.27607253170068, 1.30289346011494,
0.907333581481481, 1.13577995905983), dens_se_group = c(0.121252630289307,
0.06307522686269, 0.0420148859360373, 0.0623425234164211,
0.0722047815095388, 0.0722190238317927, 0.034165011272962,
0.0936790859814306, 0.0714356396741611, 0.0570436810778115,
0.0788899930828033, 0.0475608654800484, 0.0598258300666842,
0.130259750986686, 0.0334983354274925, 0.0761826236952826,
0.0453655791167425, 0.106781997714366, 0.040836244508566,
0.0588199042229267, 0.0918996412489127, 0.059357285207517,
0.0627450154452284, 0.0931376976250813, 0.084292452116752,
0.0411589571700968, 0.0450637305076403, 0.143482758772818,
0.0590540839343392, 0.102088012024323, 0.105528090687969,
0.0411070052757566, 0.0590357031703664, 0.0413723778927877,
0.0939695841562126, 0.113708033475645, 0.158663845039196,
0.0301738069049785, 0.0481176785452355, 0.0475410335753984,
0.0485730649599259, 0.0337475523712995, 0.0931442960086653,
0.0438977172489599, 0.0424171746665752, 0.0738349440137023,
0.0913272748385275, 0.0627775731632729, 0.0750793002689392,
0.0644990405314962, 0.0681619458121342, 0.0641801830023756,
0.0438174706972614, 0.0576257130602113, 0.139342575103628,
0.0663789070872911, 0.0863145096716977, 0.0600985558206582,
0.0764272563148116, 0.169826259735112, 0.184240678059479,
0.114217210628983, 0.077105857002114, 0.0917979995962206,
0.130375538952498, 0.0831714673344984, 0.155637910054504,
0.182334782866416, 0.121781647284439, 0.0905464615847127,
0.0794797909580277, 0.0794262146070196, 0.0690763715519387,
0.054177580544246, 0.133886131515446, 0.0975198400831905,
0.060948263509353, 0.0592085516548547, 0.0964065218316166,
0.0566068972354325, 0.0705188476471073, 0.059090116217133,
0.0704901062387426, 0.0772605951309048, 0.079904976311246,
0.0341781918365715, 0.0693474430214774, 0.0736837558846294,
0.0909932562984428, 0.0770188131260058, 0.0321224404695654,
0.426352360863202, 0.0860904764680956, 0.117344075287764,
0.047135117351449, 0.087796458703135, 0.195647691770468,
0.107645404295871, 0.0638257581164984, 0.0574054873999136,
0.130632708387773, 0.15445538200891, 0.420782131758654, 0.53071304289513,
0.129449647950927, 0.0966976588064522, 0.0478244103175173,
0.0500911026711741, 0.0758923191462454, 0.266146674296548,
0.201048417318756, 0.100258152229103, 0.143653740787346,
0.242629918781001, 0.243947309121107, 0.209007706870181,
0.0678651740892763, 0.0500516714724084, 0.031780642986091,
0.0537257936991515, 0.0653501464247892, 0.0493304633238825,
0.0336688977463781, 0.0612308683973206, 0.0455720034294281,
0.0436259344304239, 0.0943512454837203, 0.0410030689363069,
0.0451293352510801, 0.0896445586179648, 0.0307747235605067,
0.0734208222538278, 0.0394913939343875, 0.0765409225429585,
0.025374441063364, 0.0444649470099386, 0.0730495689397327,
0.0459619709615132, 0.0433532922641296, 0.053082585844426,
0.0504231803684409, 0.029436186635978, 0.0315384102925965,
0.100155129745926, 0.0425515004455708, 0.0809255503046289,
0.076741229624734, 0.0404474839627097, 0.0502691651068222,
0.0367618437292748, 0.0543715894162467, 0.0644420393285048,
0.102853262043496, 0.0209757541932988, 0.0379067741150279,
0.0388374571177147, 0.0224026507951449, 0.0488595669578494,
0.0607984484981257, 0.0308073909184775, 0.0355765592047844,
0.0672714122816897, 0.0678947295042004, 0.0724137869735462,
0.0498994605948105, 0.0429375623756504, 0.0508074557193626,
0.0503967777899689, 0.0359045343803192, 0.0313416900900793,
0.074412171894048, 0.0444861195437179, 0.0524949430539524,
0.0433144020179389, 0.0669415347792242, 0.139758451102717,
0.126410741700456, 0.0740585372205941, 0.0633466036963939,
0.0540614327666959, 0.0846801874393233, 0.0735049005380994,
0.0850690151236804, 0.0847073151505744, 0.0734745870049583,
0.0729637582416377, 0.0420837212484981, 0.0831133972681497,
0.0704433708286561, 0.0407420105367455, 0.0935582039895798,
0.0651900689745068, 0.0684379765984847, 0.0432801023022752,
0.067455128176762, 0.045395354824592, 0.0713098680424737,
0.054211706995267, 0.0410346211598702, 0.0431101126046647,
0.0594260657636561, 0.0252432542452958, 0.0515760061224177,
0.0523099371769159, 0.0780564045285394, 0.0550242094269161,
0.0212900616785109, 0.198414884937002, 0.0460065121485867,
0.0680413344006127, 0.0367297232554462, 0.0565383472751982,
0.15930206668028, 0.11106227940454, 0.0473257701721107, 0.0363120820477224,
0.0959890189790112, 0.132682010408609, 0.180161941009971,
0.678985467450568, 0.0890767154343322, 0.0663485795199558,
0.145044628112251, 0.0299757777722962, 0.0566222512010285,
0.147162248659636, 0.160968063668646, 0.082616400974738,
0.154240136781489, 0.170831374701335, 0.118322836278145,
0.139302355825633)), class = c("grouped_df", "tbl_df", "tbl",
"data.frame"), row.names = c(NA, -232L), groups = structure(list(
presence = structure(c(3L, 3L, 3L, 4L, 4L, 4L), levels = c("0",
"1", "Used", "Available"), class = "factor"), class = structure(c(1L,
2L, 3L, 1L, 2L, 3L), levels = c("Resident", "Floater", "Disperser"
), class = "factor"), .rows = structure(list(1:72, 73:96,
97:116, 117:188, 189:212, 213:232), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -6L), .drop = TRUE))
1条答案
按热度按时间1aaf6o9v1#
您需要添加一个
group = presence
来保持分组:由reprex package(v1.0.0)于2023年3月17日创建