Emerging technologies in the experimental sciences have opened the way for large-scale experiments. Such experiments generate ever growing amounts of data from which researchers need to extract relevant pieces for subsequent analysis. R offers a great environment for statistical analysis. However, due to the diversity of possible data sources and formats, data preprocessing and import can be time consuming especially with data that require user interaction such as editing, filtering or formatting. Writing a code for these tasks can be time-consuming, error prone and rather complex. We present speedR, an R-package for interactive data import, filtering and code generation in order to address these needs. Using speedR, researchers can import new data, make basic corrections, examine current R session objects, open them in the speedR environment for filtering (subsetting), put the filtered data back into R, and even create new R functions with applied import and filtering constraints to speed up their productivity.