As a Stata user, I’ve often relied on the collapse command to summarize data at a higher level. But what if you’re working in R? Is there an equivalent command to simplify your data analysis?
The short answer is yes. If you have budget data by line item and department, and you want to sum at the department level, you can use the aggregate function in R. This function allows you to group your data by one or more variables and perform aggregation operations like sum, mean, or count.
For example, if your data is in a dataframe called ‘df’ and you want to sum the budget by department, you can use the following code:
`aggregate(df$budget, by = list(df$department), FUN = sum)`
This will give you a summary of the budget by department, which is similar to what the collapse command does in Stata.
But that’s not all. R also has other functions like dplyr’s group_by and summarise, or data.table’s setkey and by, that can help you achieve similar results. These functions offer more flexibility and power, especially when working with large datasets.
So, if you’re transitioning from Stata to R or just looking for ways to simplify your data analysis, it’s worth exploring these alternatives to the collapse command.