1. Data Management Module: Concatenating, Stacking, Merging and Recoding
Programming in R
Data Management Module: Concatenating, Stacking, Merging and Recoding

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
Using SQL in R
Concatenating, Stacking and Merging
Replacing values

3. Data Management: SAS Files in R
You have likely noticed that SAS files have a .sas7bdat extension.
To get these files into R, you must:
Convert them into xport files in SAS.
Install the hmisc package in R (or the foreign package).
Use the sasxport.get function to “write” the SAS data into an R dataframe.

4. Data Management: 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.

5. Data Management: 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.

6. Data Management: 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.

7. Data Management: Recoding values
Frequently when you receive data either a) the values are incorrect or b) they are “coded” with designated values – like 999. These values need to be replaced. This process is called recoding or imputation – depending on the logic behind the replacement. These notes will cover recoding (simply replacing one value with another). Later notes will cover statistical imputation methods.

8. Data Management: Recoding 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)