R中数据拆分和整合

library(data.table)
ID <- c(NA,1,2,2)
ID
Time <- c(1,2,NA,1)
Time
X1 <- c(5,3,NA,2)
X1
X2 <- c(NA,5,1,4)
X2
mydata <- data.table(ID,Time,X1,X2)
mydata

``````````````````````````

ID Time X1 X2
1: NA 1 c NA
2: 1 2 a 5
3: 2 NA NA 1
4: 2 1 b 4

md <- melt(mydata, id=c("ID","Time"))
##melt以使每一行都是一个唯一的标识符-变量组合
md

````````````````````````````

##    ID Time variable value

## 1: NA    1       X1     5

## 2:  1    2       X1     3

## 3:  2   NA       X1    NA

## 4:  2    1       X1     2

## 5: NA    1       X2    NA

## 6:  1    2       X2     5

## 7:  2   NA       X2     1

## 8:  2    1       X2     4

``````````````````````````````

str(md)
str(mydata)

## Classes 'data.table' and 'data.frame':   8 obs. of  4 variables:

##  $ ID      : num  NA 1 2 2 NA 1 2 2

##  $ Time    : num  1 2 NA 1 1 2 NA 1

##  $ variable: Factor w/ 2 levels "X1","X2": 1 1 1 1 2 2 2 2

##  $ value   : num  5 3 NA 2 NA 5 1 4

##  - attr(*, ".internal.selfref")=<externalptr>

setcolorder(md,c("ID","variable","Time","value"))

##    ID variable Time value

## 1: NA       X1    1     5

## 2:  1       X1    2     3

## 3:  2       X1   NA    NA

## 4:  2       X1    1     2

## 5: NA       X2    1    NA

## 6:  1       X2    2     5

## 7:  2       X2   NA     1

## 8:  2       X2    1     4

##setcolorder()可以用来修改列的顺序。
mdr <- melt(mydata, id=c("ID","Time"),variable.name="Xzl",value.name="Vzl",na.rm = TRUE)
#variable.name定义变量名
mdr

##    ID Time Xzl Vzl

## 1: NA    1  X1   5

## 2:  1    2  X1   3

## 3:  2    1  X1   2

## 4:  1    2  X2   5

## 5:  2   NA  X2   1

## 6:  2    1  X2   4

mdr1 <- melt(mydata, id=c("ID","Time"),variable.name="Xzl",value.name="Vzl",measure.vars=c("X1"),na.rm = TRUE)
#measure.vars筛选

mdr1

##    ID Time Xzl Vzl

## 1: NA    1  X1   5

## 2:  1    2  X1   3

## 3:  2    1  X1   2

#执行整合
newmd<- dcast(md, ID~variable, mean)
#value为数值型

##    ID X1  X2

## 1:  1  3 5.0

## 2:  2 NA 2.5

## 3: NA  5  NA

newmd2<- dcast(md, ID+variable~Time)
newmd2

##    ID variable  1  2 NA

## 1:  1       X1 NA  3 NA

## 2:  1       X2 NA  5 NA

## 3:  2       X1  2 NA NA

## 4:  2       X2  4 NA  1

## 5: NA       X1  5 NA NA

## 6: NA       X2 NA NA NA

#ID+variable~Time 使用Time对(ID，variable)分组 Time:1,2,NA 类似excel的数据透析

newmd3<- dcast(md, ID~variable+Time)

newmd3
#variable:X1,X2 Time:1,2,NA 类似excel的数据透析

##    ID X1_1 X1_2 X1_NA X2_1 X2_2 X2_NA

## 1:  1   NA    3    NA   NA    5    NA

## 2:  2    2   NA    NA    4   NA     1

## 3: NA    5   NA    NA   NA   NA    NA

##实例

data <- read.table("data.txt",header = T)

##

Hugo_Symbol Variant_Classification Tumor_ICGC_Barcode
1 ERBB2 Missense_Mutation ICGC_05_11926
2 EGFR Missense_Mutation ICGC_06_09859
3 EGFR Missense_Mutation ICGC_08_00984
4 EGF Missense_Mutation ICGC_08_14667
5 CTNN Missense_Mutation ICGC_09_02266
6 MET Missense_Mutation ICGC_09_02266
7 MET Missense_Mutation ICGC_09_06938
8 CCNE1 Missense_Mutation ICGC_09_06938
9 CTNN Missense_Mutation ICGC_09_07343

str(data)
data2 <- dcast(data, Hugo_Symbol ~ Tumor_ICGC_Barcode,
fun.aggregate = function(x) {ifelse(test = length(as.character(x))>1 ,
no = as.character(x), yes = vcr(x, gis = FALSE))
},
value.var = 'Variant_Classification', fill = '')

vcr = function(x, gis = FALSE) {
x = as.character(x)
x = strsplit(x = x, split = ';', fixed = TRUE)[[1]]
x = unique(x)
xad = x[x %in% c('Amp', 'Del')]
xvc = x[!x %in% c('Amp', 'Del')]

if(gis){
x = ifelse(test = length(xad) > 1, no = xad, yes = 'Complex')
}else{
if(length(xvc)>0){
xvc = ifelse(test = length(xvc) > 1, yes = 'Multi_Hit', no = xvc)
}
x = ifelse(test = length(xad) == 1, yes = paste(xvc, xad, sep = ';'), no = xvc)
}

return(x)
}

#data2 即将数据转换为透视表格式