如何在R中聚集热图中的值？
使用以下数据：如何在R中聚集热图中的值？,r,casting,dataframe,heatmap,xtable,R,Casting,Dataframe,Heatmap,Xtable,使用以下数据： > mytable<-read.delim("mytable.csv",sep=",",header=T) > class(mytable) [1] "data.frame" > mytable count lang1 lang2 1 908446 ar ar 2 96 ar bg 3 73 ar bo 4
> mytable<-read.delim("mytable.csv",sep=",",header=T)
> class(mytable)
[1] "data.frame"
> mytable
count lang1 lang2
1 908446 ar ar
2 96 ar bg
3 73 ar bo
4 2 ar chr
5 61 ar da
6 1282 ar de
7 84 ar el
8 28067 ar en
9 1178 ar es
10 962 ar et
11 25945 ar fa
12 100 ar fi
13 765 ar fr
14 18 ar he
15 1 ar hi
16 1036 ar ht
17 267 ar hu
18 17 ar hy
19 3306 ar id
20 23 ar is
21 262 ar it
22 1 ar iu
23 265 ar ja
24 46 ar ka
25 400 ar ko
26 43 ar lt
27 160 ar lv
28 1 ar my
29 1539 ar nl
30 28 ar no
31 558362 ar none
32 507 ar pl
33 847 ar pt
34 577 ar ru
35 369 ar sk
36 309 ar sl
37 127 ar sv
38 1 ar ta
39 9 ar th
40 911 ar tl
41 585 ar tr
42 3 ar uk
43 46861 ar und
44 6499 ar ur
45 2245 ar vi
46 17 ar zh
47 13 ca ar
48 1 ca bg
49 27 ca da
50 100 ca de
51 946 ca en
52 8840 ca es
53 56 ca et
54 15 ca fi
55 912 ca fr
56 97 ca ht
57 64 ca hu
58 96 ca id
59 8 ca is
60 556 ca it
61 12 ca ja
62 2 ca ko
63 13 ca lt
64 58 ca lv
65 47 ca nl
66 6 ca no
67 7729 ca none
68 26 ca pl
69 1032 ca pt
70 10 ca ru
71 62 ca sk
72 57 ca sl
73 32 ca sv
74 93 ca tl
75 39 ca tr
76 275 ca und
77 53 ca vi
78 14 cs ar
79 33 cs bg
80 1 cs da
81 64 cs de
82 1729 cs en
83 162 cs es
84 47 cs et
85 6 cs fi
86 39 cs fr
87 27 cs ht
88 28 cs hu
89 30 cs id
90 2 cs is
91 30 cs it
92 5 cs ja
93 12 cs lt
94 26 cs lv
95 18 cs nl
96 790 cs none
97 77 cs pl
98 86 cs pt
99 366 cs ru
100 1497 cs sk
101 83 cs sl
102 2 cs sv
103 26 cs tl
104 16 cs tr
105 1 cs uk
106 186 cs und
107 60 cs vi
108 3 cs zh
>mytable类（mytable）
[1] “数据帧”
>我的桌子
计数lang1 lang2
1908446 ar
296ArBG
3 73阿宝
4.2 ar-chr
561阿尔达
61282阿尔德
7 84阿雷尔
828067 ar en
9 1178 ar es
10962 ar et
1125945 ar fa
12 100 ar fi
13765 ar fr
14.18何志平
15 1 ar hi
16 1036 ar ht
17267Ar hu
18 17 ar hy
19 3306 ar id
20 23 ar是
21262 ar it
22 1 ar iu
23265 ar ja
2446阿尔卡
25400阿尔科
26 43 ar lt
27 160 ar低压
28.1我的
291539AR荷兰
30 28 ar号
31 558362 ar无
32507Ar pl
33 847 ar pt
34577Ar-ru
35369AR sk
36 309 ar sl
37 127 ar sv
381Ar ta
39日9时
40 911 ar tl
41585 ar tr
42 3 ar uk
4346861美元
44 6499欧元
45 2245 ar vi
46 17 ar zh
4713CA-ar
48 1 ca bg
49 27加拿大
50 100卡德
51946ca-en
528840加利福尼亚州
53 56加拿大东部
54 15 ca-fi
55 912加拿大联邦储备银行
56 97卡热处理
57 64卡胡
58 96 ca id
59 8 ca是
60 556 ca it
61 12卡贾
62 2卡高
63 13钙
64 58 ca lv
65 47加拿大国家图书馆
66 6 ca号
677729无
68 26加利福尼亚州
69 1032钙铂
70 10卡鲁
7162CA sk
72 57卡sl
73 32卡西弗
74 93钙铊
75 39 ca tr
76 275加元
77 53 ca vi
78 14 cs ar
79 33 cs背景
80 1 cs da
81 64 cs de
82 1729 cs en
83 162 cs es
84 47 cs et
85 6 cs fi
86 39 cs fr
87 27 cs ht
88 28 cs胡
89 30 cs id
90 2 cs是
91 30 cs it
92 5 cs ja
93 12 cs lt
94 26 cs lv
95 18 cs nl
96 790 cs无
97 77 cs pl
98 86 cs pt
99 366 cs ru
100 1497 cs sk
101 83 cs sl
102 2 cs sv
103 26 cs tl
104 16 cs tr
105 1 cs英国
106 186加元
107 60 cs vi
108 3 cs zh
> Xmytable<-xtabs(mytable$count ~ mytable$lang1 + mytable$lang2, mytable)
> heatmap(Xmytable)
>Xmytable热图（Xmytable）
library(ggplot2)
ggplot(x, aes(x = lang1, y = lang2, fill = count)) + geom_bin2d()
< >添加树状图，考虑这个线程和/或发布另一个问题。 这是迄今为止我发现的最好的选择：
mytable<-read.delim("mytable.csv",sep=",",header=T)
mytable$ln<-log(mytable$count)
mytable#count<-NULL
mytable
"bio","twit","ln"
"ar","ar",13.7194907264167
"ar","bg",4.56434819146784
"ar","bo",4.29045944114839
"ar","chr",0.693147180559945
"ar","da",4.11087386417331
"ar","de",7.15617663748062
"ar","el",4.43081679884331
"ar","en",10.2423497879763
"ar","es",7.07157336421153
"ar","et",6.86901445066571
"ar","fa",10.1637341918018
"ar","fi",4.60517018598809
"ar","fr",6.63987583382654
"ar","he",2.89037175789616
"ar","hi",0
"ar","ht",6.94312242281943
"ar","hu",5.58724865840025
"ar","hy",2.83321334405622
"ar","id",8.10349427838097
"ar","is",3.13549421592915
"ar","it",5.5683445037611
"ar","iu",0
"ar","ja",5.57972982598622
"ar","ka",3.8286413964891
"ar","ko",5.99146454710798
"ar","lt",3.76120011569356
"ar","lv",5.07517381523383
"ar","my",0
"ar","nl",7.33888813383888
"ar","no",3.3322045101752
"ar","NONE",13.2327627765388
"ar","pl",6.22851100359118
"ar","pt",6.74170069465205
"ar","ru",6.3578422665081
"ar","sk",5.91079664404053
"ar","sl",5.73334127689775
"ar","sv",4.84418708645859
"ar","ta",0
"ar","th",2.19722457733622
"ar","tl",6.81454289725996
"ar","tr",6.37161184723186
"ar","uk",1.09861228866811
"ar","und",10.7549410519963
"ar","ur",8.77940359789435
"ar","vi",7.71646080017636
"ar","zh",2.83321334405622
"ca","ar",2.56494935746154
"ca","bg",0
"ca","da",3.29583686600433
"ca","de",4.60517018598809
"ca","en",6.85224256905188
"ca","es",9.08704215563169
"ca","et",4.02535169073515
"ca","fi",2.70805020110221
"ca","fr",6.81563999007433
"ca","ht",4.57471097850338
"ca","hu",4.15888308335967
"ca","id",4.56434819146784
"ca","is",2.07944154167984
"ca","it",6.32076829425058
"ca","ja",2.484906649788
"ca","ko",0.693147180559945
"ca","lt",2.56494935746154
"ca","lv",4.06044301054642
"ca","nl",3.85014760171006
"ca","no",1.79175946922805
"ca","NONE",8.95273476710687
"ca","pl",3.25809653802148
"ca","pt",6.93925394604151
"ca","ru",2.30258509299405
"ca","sk",4.12713438504509
"ca","sl",4.04305126783455
"ca","sv",3.46573590279973
"ca","tl",4.53259949315326
"ca","tr",3.66356164612965
"ca","und",5.61677109766657
"ca","vi",3.97029191355212
"cs","ar",2.63905732961526
"cs","bg",3.49650756146648
"cs","da",0
"cs","de",4.15888308335967
"cs","en",7.45529848568329
"cs","es",5.08759633523238
"cs","et",3.85014760171006
"cs","fi",1.79175946922805
"cs","fr",3.66356164612965
"cs","ht",3.29583686600433
"cs","hu",3.3322045101752
"cs","id",3.40119738166216
"cs","is",0.693147180559945
"cs","it",3.40119738166216
"cs","ja",1.6094379124341
"cs","lt",2.484906649788
"cs","lv",3.25809653802148
"cs","nl",2.89037175789616
"cs","NONE",6.67203294546107
"cs","pl",4.34380542185368
"cs","pt",4.45434729625351
"cs","ru",5.90263333340137
"cs","sk",7.31121838441963
"cs","sl",4.4188406077966
"cs","sv",0.693147180559945
"cs","tl",3.25809653802148
"cs","tr",2.77258872223978
"cs","uk",0
"cs","und",5.2257466737132
"cs","vi",4.0943445622221
"cs","zh",1.09861228866811
Xmytable<-xtabs(mytable$ln ~ mytable$lang1 + mytable$lang2, mytable)
library(pheatmap)
pheatmap(Xmytable, cluster_rows=T)
MyTable这将是一个奇怪的热图，3个单元格的高度/宽度？谢谢链接，我的朋友！希望这个链接能够更好地理解我在下面发布的集群问题。geom_bin2d是一个相当弱的热图形式，因为它为零的任何值留下空白，并且没有聚类，但它肯定是朝着正确方向迈出的一步！非常感谢。