ggplot2：如何定制点的颜色和形状？

本文介绍了ggplot2：如何定制点的颜色和形状？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 29岁程序员，3月因学历无情被辞！ 我正在尝试使用 ggplot2 创建一个带状图。以下是 tbl 的子集，它包含我正在使用的相关列，以及 dput 。 > tbl [，c（'Study_ID'，'Probe_ID'，'Group1'，'Group2'，'LogFC'，'adj_P_Value'，'P_Value'，'CI_L'，'CI_R'，'Disease'）] Study_ID Probe_ID Group1 Group2 LogFC adj_P_Value P_Value CI_L CI_R 1 GSE2461 220307_at男女-0.09017596 1.000000e + 00 5.662047e-01 -0.43955752 0.25920561 2 GSE2461 220307_at溃疡性结肠炎过敏性肠综合征0.08704844 1.000000e + 00 5.784053e -01 -0.26134341 0.43544028 3 GSE27887 220307_at nonlesional skin lesional skin -0.03501474 1.000000e + 00 4.409881e-01 -0.12677636 0.05674688 4 GSE27887 220307_at pretreatment posttreatment 0.01096914 1.000000e + 00 8.080366e-01 -0.08064105 0.10257932 5 GSE42296 7921677 Infliximab治疗前-0.03707265 1.000000e + 00 3.979403e-01 -0.12407201 0.04992672 6 GSE42296 7921677 Re sponder无反应者0.07644834 1.000000e + 00 1.505444e-01 -0.02849309 0.18138977 7 GSE42296 7921677类风湿性关节炎克罗恩病0.42318863 3.960125e-06 1.989713e-10 0.31076269 0.53561457 8 GSE58558 220307_at MF -0.11881801 1.000000e + 00 1.130180e-01 -0.26629675 0.02866072 9 GSE58558 220307_at非皮损皮损-0.18914128 1.000000e + 00 3.696739e-03 -0.31525660 -0.06302596 10 GSE58558 220307_at无响应者-0.14470319 1.000000e + 00 2.328062e -01 -0.38396386 0.09455748 11 GSE58558 220307_at本周12天1 -0.39619004 4.311942e-01 2.215798e-05 -0.57226227 -0.22011781 12 GSE58558 220307_本周2天1 -0.28765455 1.000000e + 00 8.753977e- 04 -0.45375957 -0.12154953 13 GSE59294 220307_at C Dupilumab 300 mg B Dupilumab 150 mg 0.16853309 1.000000 e + 00 1.140155e-01 -0.04273877 0.37980494 14 GSE59294 220307_at D安慰剂B Dupilumab 150 mg -0.18995566 1.000000e + 00 2.264691e-01 -0.50367856 0.12376724 15 GSE59294 220307_at NL皮肤LS皮肤0.01376129 1.000000e +00 9.041383e-01 -0.21711706 0.24463964 16 GSE59294 220307_at Pre Post 0.02234607 1.000000e + 00 8.069367e-01 -0.16235054 0.20704268 疾病 1肠易激综合征;溃疡性结肠炎 2肠易激综合征;溃疡性结肠炎 3特应性皮炎 4特应性皮炎 5克罗恩氏病;类风湿性关节炎 6克罗恩病;类风湿性关节炎 7克罗恩病;类风湿性关节炎 8特应性皮炎 9特应性皮炎 10特应性皮炎 11特应性皮炎 12特应性皮炎 13特应性皮炎 14特应性皮炎 15特应性皮炎 16特应性皮炎 以下是 dput ： >输入（小滴（tbl [，c（'Study_ID'，'Probe_ID'，'Group1'，'Group2'，'LogFC'，'adj_P_Value'，'P_Value'，'CI_L'，'CI_R'，'疾病'）] ））结构（列表（Study_ID = c（GSE2461，GSE2461，GSE27887，GSE27887，GSE42296，GSE42296，GSE42296，GSE58558 GSE58558，GSE58558，GSE58558，GSE58558，GSE59294，GSE59294，GSE59294，GSE59294），Probe_ID = c（220307_at， 220307_at，220307_at，220307_at，7921677，7921677，7921677，220307_at，220307_at，220307_at，220307_at220307_at ，220307_at，220307_at，220307_at，220307_at），Group1 = c（男性，溃疡性结肠炎，非病变性皮肤，预处理 ，响应者，类风湿性关节炎，M，非皮损皮肤，响应者，第12周，第2周，C Dupilumab 300mg， D安慰剂，NL皮肤，Pre），组2 = c（女性，肠易激综合征，损伤皮肤，后处理治疗前，无应答者，克罗恩病，F，损伤皮肤，无反应者，第1天，第1天，B Dupilumab 150 mg Logford = c（-0.0901759558643281， 0.0870484364429408，-0.0350147376937934,0.0109691380052655， -0.0370726462749328，0.0764483363743359,0.423188628619509，-0.118818013184408，-0.118818013184408， -0.189141277685995，-0.144703191279992，-0.396190039768736，-0.28765454670704， 0.168533085440721，-0.189955660434197,0.0137612879743023,0.0223460675171673 ），adj_P_Value = c（1,1,1,1,1,1,1， 1，1，1，1，1），P_Value = c（0.566204678925109， 0.578405275354266,0.440988072013756,0.808036622723435,0.397940346528484， 0.150544373610059，1.98971262936634e-10,0.11301796668591,0.00369673863311212， 0.232806229179741,2.21579776371792e-05,0.000875397680320129， 0.114015475901252,0.226469133014055,0.904138332714553,0.806936684043 586 ），CI_L = c（-0.439557521861354，-0.261343410788222，-0.12677635951562， -0.0806410486876688，-0.124072011981945，-0.0284930943795223， 0.310762687356251，-0.26629674914578，-0.315256597358499，-0.383963864121397， -0.57226227039893，-0.453759565458485，-0.0427387734415052，-0.503678563834605， -0.217117064412363，-0.162350541147386），CI_R = C（0.259205610132698， 0.435440283674103，0.0567468841280329，0.1025793246982，0.0499267194320791， 0.181389767128194，0.535614569882768 ，0.0286607227769647，-0.0630259580134921， 0.0945574815614131，-0.220117809138542，-0.121549527955595,0.379804944322947， 0.12376724296621,0.244639640360967,0.207042676181721），疾病= c（过敏性肠综合征;溃疡性结肠炎，过敏性肠综合征;溃疡性结肠炎，特应性皮炎，特应性皮炎，克罗恩病;类风湿性关节炎，克罗恩氏病;类风湿性关节炎，克罗恩氏病;特应性皮炎，特应性皮炎，特应性皮炎，特应性皮炎，特应性皮炎，特应性皮炎 ，特应性皮炎，特应性皮炎）），.Names = c（Study_ID，Probe_ID，Group1，Group2，LogFC，adj_P_Value， P_Value，CI_L，CI_R，疾病），row.names = c（NA， -16L），class =data.frame） $ b $ b code> #test使用ggplot2 maxFC = max（as.numeric（as.character（tbl $ LogFC））） minFC = min（as.numeric（as.character（tbl $ LogFC ））） datasetList = tbl $ Study_ID hLines =（which（duplicated（datasetList）== FALSE） - 0.5） tbl $ ylab ，tbl $ Group1，sep =） p geom_vline（xintercept = log（0.5,2），size = 0.2）+ geom_vline（xintercept = log（2 / 3,2），size = 0.2）+ geom_vline（xintercept = log（1.5,2），size = 0.2）+ geom_vline（xintercept = log（2,2），size = 0.2）+ labs（title = tbl $ gene，y =Contrasts，x = bquote（〜Log [2]〜'（Fold Change）'））+ geom_errorbarh（aes（x = LogFC，xmin = CI_L，xmax = CI_R），height = .1）+ geom_point（aes（color = cut（adj_P_Value，c（-Inf，0.01，0.05，Inf ）））+ scale_color_manual（name =P Value， values = c（（ - Inf，0.01）=red，（0.01,0.05）） =橙，（0.05，Inf）=黑），标签= c（ scale_shape_manual（values = c（4,15,19））+ coord_cartesian（xlim = c（min（-2，minFC），max（2，maxFC）））+ theme（axis.text.y = element_blank（），strip.text.y = element_text（angle = 180），＃panel.grid.major = element_blank（），＃panel.grid.minor = element_blank（）， axis.line.y = element_blank（）， axis.line.x = element_blank（），＃panel.background = element_rect（fill ='white'，color ='white'），＃panel.grid = element_blank（）， panel.spacing.y =单位（0.5，'lines'）， axis.ticks.y = element_blank（））+ facet_grid（Study_ID + ylab〜。，scales ='free'，space ='free'，switch ='both'） p 基本上，点的实际位置是通过它们的 LogFC 值，但具有 adj_P_Value 的点应显示为红色圆圈，介于0.01和0.0之间5作为橙色方块，并且> = 0.05 作为黑色十字（即，我提供的数据不应该显示任何方块）。我的尝试是在 geom_point 中使用 cut ，但这似乎不起作用。颜色显示正确，但形状不正确。这一直在困扰我一段时间。如果有任何我打破的约定或标准（我可能是），请让我知道并提出一些可以实现我已经完成的事情。谢谢！ 更新： #test使用ggplot2 maxFC = max（as.numeric（as.character（tbl $ LogFC））） minFC = min（as.numeric（as.character（tbl $ LogFC））） datasetList = tbl $ Study_ID hLines =（which（duplicated（datasetList）== FALSE） - 0.5） tbl $ ylab ，tbl $ Group1，sep =） tbl ％ mutate（colourgroup = case_when （。$adj_P_Value≤0.01〜1，。$ adj_P_Value> 0.01&。$ adj_P_Value 。$ adj_P_Value> = 0.05〜3 $ bp #geom_point + geom_vline（xintercept = log（0.5,2），size = 0.2）+ geom_vline（xintercept = log（2 / 3,2），size = 0.2）+ geom_vline = log（1.5,2），size = 0.2）+ geom_vline（xintercept = log（2,2），size = 0.2）+ labs （标题= tbl $基因，y =对比度，x = bquote（〜Log [2]〜'（倍数变化）'））+ geom_errorbarh（aes（x = LogFC，xmin = CI_L，xmax = CI_R），height = .1）+ geom_point（）+ scale_color_manual（c（'red'，'orange'，'black'））+ scale_shape_manual（c ，4））+ coord_cartesian（xlim = c（min（-2，minFC），max（2，maxFC）））+ theme（axis.text.y = element_blank（），strip。 text.y = element_text（angle = 180），＃panel.grid.major = element_blank（），＃panel.grid.minor = element_blank（）， axis.line.y = element_blank（）， axis.line.x = element_blank（），＃panel.background = element_rect（fill ='white'，color ='white'）， #panel。 grid = element_blank（）， panel.spacing.y = unit（0.5，'lines'）， axis.ticks.y = element_blank（））+ facet_grid（Study_ID + ylab〜 。，scale ='free'，space ='free'，switch ='both'） p 解决方案添加你想要的因子列 library （dplyr） tbl< - tbl％>％ mutate（colourgroup = case_when（ adj_P_Value< = 0.01〜1， adj_P_Value> 0.01& adj_P_Value< 0.05〜2， adj_P_Value> = 0.05〜3）） p> aes（x = LogFC，y = Probe_ID，group = Study_ID） $ b $ p $ b $ $ $ $ $ $ $ $ $ aes（x = LogFC，y = Probe_ID， color = factor（colourgroup），shape = factor（colourgroup）） 和 scale_color_manual（values = c（red，orange，black））+ scale_shape_manual（values = c（1， 2,3）） 最小示例 这个最简单的 ggplot 命令适用于我。注意我有意地切换了 x 和 y 值， red 和 orange 可能难以区分 ggplot（df2， aes（x = Probe_ID，y = LogFC，color = factor（colourgroup），shape = factor（colourgroup）））+ geom_point（）+ scale_color_manual（values = c（red，orange ，black））+ scale_shape_manual（values = c（1,2,3）） I am trying to create a stripchart using ggplot2. Below is a subset of tbl which has the relevant columns I am using, along with a dput.> tbl[,c('Study_ID', 'Probe_ID', 'Group1','Group2','LogFC', 'adj_P_Value', 'P_Value', 'CI_L','CI_R','Disease')] Study_ID Probe_ID Group1 Group2 LogFC adj_P_Value P_Value CI_L CI_R1 GSE2461 220307_at Male Female -0.09017596 1.000000e+00 5.662047e-01 -0.43955752 0.259205612 GSE2461 220307_at ulcerative colitis irritable bowel syndrome 0.08704844 1.000000e+00 5.784053e-01 -0.26134341 0.435440283 GSE27887 220307_at nonlesional skin lesional skin -0.03501474 1.000000e+00 4.409881e-01 -0.12677636 0.056746884 GSE27887 220307_at pretreatment posttreatment 0.01096914 1.000000e+00 8.080366e-01 -0.08064105 0.102579325 GSE42296 7921677 Infliximab Before treatment -0.03707265 1.000000e+00 3.979403e-01 -0.12407201 0.049926726 GSE42296 7921677 Responder Nonresponder 0.07644834 1.000000e+00 1.505444e-01 -0.02849309 0.181389777 GSE42296 7921677 Rheumatoid Arthritis Crohn's Disease 0.42318863 3.960125e-06 1.989713e-10 0.31076269 0.535614578 GSE58558 220307_at M F -0.11881801 1.000000e+00 1.130180e-01 -0.26629675 0.028660729 GSE58558 220307_at non lesional skin lesional skin -0.18914128 1.000000e+00 3.696739e-03 -0.31525660 -0.0630259610 GSE58558 220307_at responder nonresponder -0.14470319 1.000000e+00 2.328062e-01 -0.38396386 0.0945574811 GSE58558 220307_at week 12 day 1 -0.39619004 4.311942e-01 2.215798e-05 -0.57226227 -0.2201178112 GSE58558 220307_at week 2 day 1 -0.28765455 1.000000e+00 8.753977e-04 -0.45375957 -0.1215495313 GSE59294 220307_at C Dupilumab 300 mg B Dupilumab 150 mg 0.16853309 1.000000e+00 1.140155e-01 -0.04273877 0.3798049414 GSE59294 220307_at D Placebo B Dupilumab 150 mg -0.18995566 1.000000e+00 2.264691e-01 -0.50367856 0.1237672415 GSE59294 220307_at NL skin LS skin 0.01376129 1.000000e+00 9.041383e-01 -0.21711706 0.2446396416 GSE59294 220307_at Pre Post 0.02234607 1.000000e+00 8.069367e-01 -0.16235054 0.20704268 Disease1 irritable bowel syndrome; ulcerative colitis2 irritable bowel syndrome; ulcerative colitis3 atopic Dermatitis4 atopic Dermatitis5 Crohn's Disease; Rheumatoid Arthritis6 Crohn's Disease; Rheumatoid Arthritis7 Crohn's Disease; Rheumatoid Arthritis8 Atopic Dermatitis9 Atopic Dermatitis10 Atopic Dermatitis11 Atopic Dermatitis12 Atopic Dermatitis13 atopic Dermatitis14 atopic Dermatitis15 atopic Dermatitis16 atopic DermatitisHere is the dput :> dput(droplevels(tbl[,c('Study_ID', 'Probe_ID', 'Group1','Group2','LogFC', 'adj_P_Value', 'P_Value', 'CI_L','CI_R','Disease')]))structure(list(Study_ID = c("GSE2461", "GSE2461", "GSE27887","GSE27887", "GSE42296", "GSE42296", "GSE42296", "GSE58558", "GSE58558","GSE58558", "GSE58558", "GSE58558", "GSE59294", "GSE59294", "GSE59294","GSE59294"), Probe_ID = c("220307_at", "220307_at", "220307_at","220307_at", "7921677", "7921677", "7921677", "220307_at", "220307_at","220307_at", "220307_at", "220307_at", "220307_at", "220307_at","220307_at", "220307_at"), Group1 = c("Male", "ulcerative colitis","nonlesional skin", "pretreatment", "Infliximab", "Responder","Rheumatoid Arthritis", "M", "non lesional skin", "responder","week 12", "week 2", "C Dupilumab 300 mg", "D Placebo", "NL skin","Pre"), Group2 = c("Female", "irritable bowel syndrome", "lesional skin","posttreatment", "Before treatment", "Nonresponder", "Crohn's Disease","F", "lesional skin", "nonresponder", "day 1", "day 1", "B Dupilumab 150 mg","B Dupilumab 150 mg", "LS skin", "Post"), LogFC = c(-0.0901759558643281,0.0870484364429408, -0.0350147376937934, 0.0109691380052655,-0.0370726462749328, 0.0764483363743359, 0.423188628619509, -0.118818013184408,-0.189141277685995, -0.144703191279992, -0.396190039768736, -0.28765454670704,0.168533085440721, -0.189955660434197, 0.0137612879743023, 0.0223460675171673), adj_P_Value = c(1, 1, 1, 1, 1, 1, 3.96012504622782e-06, 1,1, 1, 0.431194244819507, 1, 1, 1, 1, 1), P_Value = c(0.566204678925109,0.578405275354266, 0.440988072013756, 0.808036622723435, 0.397940346528484,0.150544373610059, 1.98971262936634e-10, 0.11301796668591, 0.00369673863311212,0.232806229179741, 2.21579776371792e-05, 0.000875397680320129,0.114015475901252, 0.226469133014055, 0.904138332714553, 0.806936684043586), CI_L = c(-0.439557521861354, -0.261343410788222, -0.12677635951562,-0.0806410486876688, -0.124072011981945, -0.0284930943795223,0.310762687356251, -0.26629674914578, -0.315256597358499, -0.383963864121397,-0.57226227039893, -0.453759565458485, -0.0427387734415052, -0.503678563834605,-0.217117064412363, -0.162350541147386), CI_R = c(0.259205610132698,0.435440283674103, 0.0567468841280329, 0.1025793246982, 0.0499267194320791,0.181389767128194, 0.535614569882768, 0.0286607227769647, -0.0630259580134921,0.0945574815614131, -0.220117809138542, -0.121549527955595, 0.379804944322947,0.12376724296621, 0.244639640360967, 0.207042676181721), Disease = c("irritable bowel syndrome; ulcerative colitis","irritable bowel syndrome; ulcerative colitis", "atopic Dermatitis","atopic Dermatitis", "Crohn's Disease; Rheumatoid Arthritis","Crohn's Disease; Rheumatoid Arthritis", "Crohn's Disease; Rheumatoid Arthritis","Atopic Dermatitis", "Atopic Dermatitis", "Atopic Dermatitis","Atopic Dermatitis", "Atopic Dermatitis", "atopic Dermatitis","atopic Dermatitis", "atopic Dermatitis", "atopic Dermatitis")), .Names = c("Study_ID", "Probe_ID", "Group1", "Group2", "LogFC","adj_P_Value", "P_Value", "CI_L", "CI_R", "Disease"), row.names = c(NA,-16L), class = "data.frame")Finally, here the code that I have so far.#test using ggplot2maxFC = max(as.numeric(as.character(tbl$LogFC)))minFC = min(as.numeric(as.character(tbl$LogFC)))datasetList = tbl$Study_IDhLines =(which(duplicated(datasetList) == FALSE) - 0.5)tbl$ylab <- paste(tbl$Group2," \U2192 ","\n", tbl$Group1, sep = "")p <- ggplot(data = tbl, aes(x = LogFC, y = Probe_ID, group = Study_ID)) + geom_vline(xintercept = log(0.5,2), size = 0.2) + geom_vline(xintercept = log(2/3,2), size = 0.2) + geom_vline(xintercept = log(1.5,2), size = 0.2) + geom_vline(xintercept = log(2,2), size = 0.2) + labs(title = tbl$gene, y = "Contrasts", x = bquote(~Log[2]~'(Fold Change)')) + geom_errorbarh(aes(x = LogFC, xmin = CI_L, xmax = CI_R), height = .1) + geom_point(aes(colour = cut(adj_P_Value, c(-Inf, 0.01, 0.05, Inf)))) + scale_color_manual(name = "P Value", values = c("(-Inf,0.01]" = "red", "(0.01,0.05)" = "orange", "(0.05, Inf]" = "black"), labels = c("<= 0.01", "0.01 < P Value <= 0.05", "> 0.05")) + scale_shape_manual(values = c( 4,15,19)) + coord_cartesian(xlim = c(min(-2,minFC),max(2,maxFC))) + theme(axis.text.y = element_blank(), strip.text.y = element_text(angle = 180), #panel.grid.major = element_blank(), #panel.grid.minor = element_blank(), axis.line.y = element_blank(), axis.line.x = element_blank(), #panel.background = element_rect(fill = 'white', colour = 'white'), #panel.grid = element_blank(), panel.spacing.y = unit(0.5,'lines'), axis.ticks.y = element_blank()) + facet_grid(Study_ID+ylab~ ., scales = 'free', space = 'free', switch = 'both')pBasically, the points actual position is determined via their LogFC value, but the points that have an adj_P_Value <= 0.01 should be displayed as red circles, in between 0.01 and 0.05 as orange squares, and >= 0.05 as black crosses (i.e. the data that I've provided shouldn't show any squares). My attempt as this was using cut in the geom_pointbut this doesn't seem to work. The colors are showing up correct, but the shapes are not. This has been bugging me for a while. If there's any conventions or standards that I'm breaking (which I probably am), please let me know and suggest something that would achieve exactly what I have already. Thanks!Update:#test using ggplot2maxFC = max(as.numeric(as.character(tbl$LogFC)))minFC = min(as.numeric(as.character(tbl$LogFC)))datasetList = tbl$Study_IDhLines =(which(duplicated(datasetList) == FALSE) - 0.5)tbl$ylab <- paste(tbl$Group2," \U2192 ","\n", tbl$Group1, sep = "")tbl <- tbl %>% mutate(colourgroup = case_when( .$adj_P_Value <= 0.01 ~ 1, .$adj_P_Value > 0.01 & .$adj_P_Value < 0.05 ~ 2, .$adj_P_Value >= 0.05 ~ 3 ) )p <- ggplot(data = tbl, aes(x = LogFC, y = Probe_ID, colour = colourgroup, shape = colourgroup)) + #geom_point() + geom_vline(xintercept = log(0.5,2), size = 0.2) + geom_vline(xintercept = log(2/3,2), size = 0.2) + geom_vline(xintercept = log(1.5,2), size = 0.2) + geom_vline(xintercept = log(2,2), size = 0.2) + labs(title = tbl$gene, y = "Contrasts", x = bquote(~Log[2]~'(Fold Change)')) + geom_errorbarh(aes(x = LogFC, xmin = CI_L, xmax = CI_R), height = .1) + geom_point() + scale_color_manual(c('red','orange','black')) + scale_shape_manual(c(19,15,4))+ coord_cartesian(xlim = c(min(-2,minFC),max(2,maxFC))) + theme(axis.text.y = element_blank(), strip.text.y = element_text(angle = 180), #panel.grid.major = element_blank(), #panel.grid.minor = element_blank(), axis.line.y = element_blank(), axis.line.x = element_blank(), #panel.background = element_rect(fill = 'white', colour = 'white'), #panel.grid = element_blank(), panel.spacing.y = unit(0.5,'lines'), axis.ticks.y = element_blank()) + facet_grid(Study_ID+ylab~ ., scales = 'free', space = 'free', switch = 'both')p 解决方案 Add a column of the factor you wantlibrary(dplyr)tbl <- tbl %>% mutate(colourgroup = case_when( adj_P_Value <= 0.01 ~ 1, adj_P_Value > 0.01 & adj_P_Value < 0.05 ~ 2, adj_P_Value >= 0.05 ~ 3 ))Then change aes(x = LogFC, y = Probe_ID, group = Study_ID)To aes(x = LogFC, y = Probe_ID, colour = factor(colourgroup), shape = factor(colourgroup))And scale_color_manual(values=c("red","orange","black")) +scale_shape_manual(values=c(1,2,3))MINIMAL EXAMPLEThis minimal ggplot command worked for me. Note I switched the x and y values on purpose, and red and orange can be difficult to distinguishggplot(df2, aes(x = Probe_ID, y=LogFC, colour=factor(colourgroup), shape=factor(colourgroup))) + geom_point() + scale_color_manual(values=c("red","orange","black")) + scale_shape_manual(values=c(1,2,3)) 这篇关于ggplot2：如何定制点的颜色和形状？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！