1. DATA MANAGEMENT MODULE: Concatenating, Stacking and Merging
STAT 4030 – Programming in R
DATA MANAGEMENT MODULE:
Concatenating, Stacking and Merging
Jennifer Lewis Priestley, Ph.D.
Kennesaw State University 1

2. DATA MANAGEMENT MODULE
Importing and Exporting
Imputting data directly into R
Creating, Adding and Dropping Variables
Assigning objects
Subsetting and Formatting
Working with SAS Files
Merging, Stacking and Recoding
Using SQL in R 2 2 2

3. Data Management Module: Concatenating
To “concatenate” basically means to bring together columns (vectors) of data. In R, this is accomplished through the function cbind:
Newdata <- cbind(data1, data2)
This will create as many columns are in the sum of data1 and data2.
Note that a “matchkey” is not needed. 3

4. Data Management Module: Stacking
To “stack” basically means to bring together rows of data. In R, this is accomplished through the function rbind:
Newdata <- rbind(data1, data2)
This will create as many rows are in the sum of data1 and data2.
Note that there MUST be the same column names in data1 and data2.
Note that a “matchkey” is not needed. 4

5. Data Management Module: Merging
To “Merge” basically means to bring together dataframes. In R, this is accomplished through the function merge:
Newdata <- merge (data1, data2, by="PrimaryKey", all="TRUE")
Note that all = TRUE will include all rows and columns for both data1 and data2 – essentially an outer join. all=FALSE will include only rows and columns that are present in both data1 and data2 – essentially an inner join.
Note that a “matchkey” IS needed. 5

6. Data Management Module: Missing Values
At this point, lets recode values using the same logic you would use in Excel:
IF(Condition, value if true, value if false)
In R:
newvariable<-ifelse(oldvariable test, value if true, value if false) 6