decast id.var measure. Var data splitting and merging

R And data.table -melt/dcast( Data splitting and merging )

 Write it at the front ： The process of data shaping is indeed somewhat similar to kneading dough , First pass the data through melt() The function crumples the data , And then through dcast() Function to reshape the data into the desired shape 

reshape2 package ：

melt- Convert wide format data into long format .

cast- Convert long format data into wide format .（dcast- Return a data frame when outputting .acast- Returns a vector when outputting / matrix / Array .）

 notes ：melt It means data fusion , What it does is actually transfer the data from “ wide ” turn “ Long ”.

cast  In addition to restoring data , You can also integrate data .

dcast  Output data frame . Each variable on the left side of the formula will be used as a column in the result , The variable on the right is treated as a factor type , Each level produces a column in the result .

tidyr package ：

gather- Convert wider data into a longer form , It is analogous to from reshape2 Function of fusion function in package 

spread- Convert long data into a wider form , It is analogous to from reshape2 Function of casting function in package .

data.table package ：

data.table Function of melt  and dcast  It's an enhancement package reshape2 The extension of the function with the same name in 

library(data.table)

ID <- c(NA,1,2,2)

Time <- c(1,2,NA,1)

X1 <- c(5,3,NA,2)

X2 <- c(NA,5,1,4)

mydata <- data.table(ID,Time,X1,X2) 

mydata

## ID Time X1 X2

## 1: NA 1 5 NA

## 2: 1 2 3 5

## 3: 2 NA NA 1

## 4: 2 1 2 4

md <- melt(mydata, id=c("ID","Time")) #or md <- melt(mydata, id=1:2)

#melt So that each line is a unique identifier - Variable combinations 

md     # Take the first column as id Column , All other columns can be merged 

## 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

 Put the variable "variable", and "value" Put it together , The result is two new columns , One column is the variable variable, Refers to which blend variable , The other column is the value value, That is, the value corresponding to the variable . We also call this row by row arrangement as long data format 

 

melt: The fusion of data sets is to reconstruct it into such a format ： One line per measurement variable , The row contains the identifier variable needed to uniquely determine this measurement .



 

str(mydata)

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

## $ ID : num NA 1 2 2

## $ Time: num 1 2 NA 1

## $ X1 : num 5 3 NA 2

## $ X2 : num NA 5 1 4

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

str(md)

## 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")) ##setcolorder() It can be used to modify the order of columns .

md

## 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

mdr <- melt(mydata, id=c("ID","Time"),variable.name="Xzl",value.name="Vzl",na.rm = TRUE) #variable.name Define variable names 

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 Screening 

mdr1

## ID Time Xzl Vzl

## 1: NA 1 X1 5

## 2: 1 2 X1 3

## 3: 2 1 X1 2

md[Time==1]

## ID variable Time value

## 1: NA X1 1 5

## 2: 2 X1 1 2

## 3: NA X2 1 NA

## 4: 2 X2 1 4

md[Time==2]

## ID variable Time value

## 1: 1 X1 2 3

## 2: 1 X2 2 5

# Executive integration 

# rowvar1 + rowvar2 + ... ~ colvar1 + colvar2 + ...

#  In this formula ,rowvar1 + rowvar2 + ...  The set of variables to be defined is crossed out , To determine the content of each line , and colvar1 + colvar2 + ...  Defines what to cross out 、 Determine the variable set of each column .

newmd<- dcast(md, ID~variable, mean)

newmd

## 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  Use Time Yes (ID,variable) grouping  Time:1,2,NA  similar excel Data dialysis 

 

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

newmd3         #variable:X1,X2 Time:1,2,NA  similar excel Data dialysis 

## 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

 Even if it's just a small dust in the world , Fate should also be controlled by yourself , Like a sunflower , Welcome to the sunshine 、 Brave bloom