Avoiding redundancy when selecting rows in a data frame

Question

My code is littered with statements of the following taste:

selected <- long_data_frame_name[long_data_frame_name$col1 == "condition1" & 
long_data_frame_name$col2 == "condition2" & !is.na(long_data_frame_name$col3),
selected_columns]

The repetition of the data frame name is tedious and error-prone. Is there a way to avoid it?

Answer 1

You can use with

For instance

sel.ID <- with(long_data_frame_name, col1==2 & col2<0.5 & col3>0.2)
selected <- long_data_frame_name[sel.ID, selected_columns]

Answer 2

Several ways come to mind.

If you think about it, you are subsetting your data. Hence use the subset function (base package):

your_subset <- subset(long_data_frame_name,
                      col1 == "cond1" & "cond2" == "cond2" & !is.na(col3),
                      select = selected_columns)

This is in my opinion the most "talking" code to accomplish your task.

Answer 3

Use data tables.

library(data.table)
long_data_table_name = data.table(long_data_frame_name, key="col1,col2,col3")
selected <- long_data_table_name[col1 == "condition1" & 
                                 col2 == "condition2" & 
                                 !is.na(col3),
                                 list(col4,col5,col6,col7)]

You don't have to set the key in the data.table(...) call, but if you have a large dataset, this will be much faster. Either way it will be much faster than using data frames. Finally, using J(...) , as below, does require a keyed data.table, but is even faster.

selected <- long_data_table_name[J("condition1","condition2",NA),
                                 list(col4,col5,col6,col7)]

Answer 4

You have several possibilities:

• attach which adds the variables of the data.frame to the search path just below the global environment. Very useful for code demonstrations but I warn you not to do that programmatically.

• with which creates a whole new environment temporarilly.

In very limited cases you want to use other options such as within .

df = data.frame(random=runif(100))
df1 = with(df,log(random))
df2 = within(df,logRandom <- log(random))

within will examine the created environment after evaluation and add the modifications to the data. Check the help of with to see more examples. with will just evaluate you expression.