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

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

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

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

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

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