R 在ggplot上用平滑线覆盖多个数据点

我正在从一个包含多个单独受试者的多个基因表达值的数据集开始工作，并尝试使用ggplot用各自的平滑线绘制每个基因的数据点

到目前为止，我已经能够为每个基因创建一个单独的图表：

ggplot(data=data1, aes(x=factor(X, levels=unique(X)), y=ALDOA, group=1))+
+ geom_point()+
+ geom_smooth()

如何将所有基因的数据与同一图表上的线条叠加

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我是新的R和ggplot，所以任何帮助将不胜感激

您的数据当前格式较宽

ggplot

需要长格式，因此请使用

tidyr:：gather（）

：

库（dplyr）
图书馆（供猫用）
图书馆（GG2）
图书馆（tidyr）
tbl_宽%
read_csv（）
tbl_long%
聚集（基因，表达，-X）
tbl_long%>%
ggplot（aes（x=fct_顺序（x），y=expression，color=gene，group=gene））+
几何点（）
geom_光滑（方法=“黄土”，se=假）+
主题（axis.title.x=element\u blank（））

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欢迎来到stack。请注意，您应该注意使用图像文件来显示您的数据集，因为我们无法使用它来重现您的问题。请看这里：这能帮到你吗？这里有一个关于提问可重复问题的很好的指南：你显示的数据集是你想要绘制的图像吗？根据列名，我认为这不是你用来制作示例图的列名。您能否在问题中澄清这一点，并添加用于操作数据集的任何代码？这将是迈向可复制示例的一步，但一定要查看上面评论中的链接以获得更多提示。：）@好问题，我想这是他想要的情节。@osmith你是对的，我发布了一个我实际使用的表格的转置版本。现在editedMatthew看起来很棒，但数据点似乎与我的数据集中的数据点不匹配。如何将数据集中的数据绘制到此图中？您只提供了一小部分数据作为图片。所以我不得不补上一些。您的数据由上述代码中的

tbl\u wide

表示。从第11行开始，使用您的数据对象代替

tbl_wide

。当然，数据点不匹配，您上传了一张图像，并根据评论员的建议将其更改为另一张图像。这很有效！谢谢你对新手的耐心，马修！

library(dplyr)
library(forcats)
library(ggplot2)
library(tidyr)

tbl_wide <- "X,ALDOA,ALDOC,GPI,GAPDHS,LDHA,PGK1,PKLR
C1,-0.643185598,-0.645053078,-0.087097464,-0.343085671,-0.770712771,0.004189881,0.088937264
C2,-0.167424935,-0.414607255,0.049551335,-0.405339423,-0.182211808,-0.127414498,-0.313125427
C3,-0.81858642,-0.938110755,-1.141371324,-0.212165875,-0.582733509,-0.299505078,-0.417053296
C4,-0.83403929,-0.36359332,-0.731276681,-1.173581357,-0.42953985,-0.14434282,-0.861271021
C5,-0.689384044,-0.833311409,-0.622961915,-1.13983245,0.479864518,-0.353765462,-0.787467172
C6,-0.465153207,-0.740128773,-0.05430084,0.499455778,-0.692945684,-0.215067456,-0.460695935
S2,0.099525323,0.327565645,-0.315537278,0.065457821,0.78394394,0.189251447,0.11684847
S3,0.33216583,0.190001824,0.749459725,0.224739679,-0.138610536,-0.420150288,0.919318891
S4,0.522281547,0.278411886,1.715325626,0.534957031,1.130054777,-0.129296273,1.803756399
S5,0.691225088,0.665540011,1.661124529,0.662320212,0.267803229,0.853683613,1.105808889
S6,1.269616976,1.86390714,2.069219749,1.312324149,1.498836807,1.794147633,0.842335285
S7,1.254166133,1.819075004,0.44893804,0.438435159,0.482694339,0.446939822,0.802671992
S8,0.751743085,0.702057721,0.657752337,1.668582798,-0.186354601,1.214976683,0.287904556
S9,0.091028475,-0.214746307,0.037471169,-0.90747123,-0.172209571,0.062382102,0.136354703
S10,1.5792826,1.736452158,0.194961866,0.706323594,1.396245579,0.208168636,0.883114282
R2,-0.36289097,-0.252649755,0.026497148,-0.026676693,-0.720750516,-0.087657548,0.390400605
R3,0.106992251,0.290831853,-0.815393104,-0.020562949,-0.579128953,-0.222087138,0.603723294
R4,0.208230649,0.533552023,-0.116632671,1.126588341,-0.09646495,0.157577458,-0.402493353
R5,-0.10781116,0.436174594,-0.969979695,-1.298192703,0.541570124,-0.07591813,-0.704663307
R6,-0.282867322,-0.960902616,0.184185506,-1.215118472,0.856165556,-0.256458847,-1.528611038
R7,-0.300331377,-0.918484952,0.191947526,-0.895049036,1.200294702,0.7120941,-0.047383224
R8,0.278804568,-0.07335879,0.300083636,0.37631121,-0.288228181,0.427576413,0.631281194
R9,0.393632652,0.228379711,-0.201269856,1.731887958,0.141541807,0.242716283,0.154875397
R10,0.731821818,0.058779515,-0.310899832,0.578285435,-0.474621274,0.126920851,0.017104493" %>% 
  read_csv()

tbl_long <- tbl_wide %>% 
  gather(gene, expression, -X)

tbl_long %>% 
  ggplot(aes(x = fct_inorder(X), y = expression, color = gene, group = gene)) + 
  geom_point() + 
  geom_smooth(method = "loess", se = FALSE) + 
  theme(axis.title.x = element_blank())