README.md

In russHyde/flogr: An R Package for Functional Logging in Data Analysis Pipelines

flogr: A lightweight, functional, logging tool for use in R-based data-analysis pipelines

You're analysing data in R: this involves piping together a bunch of functions and writing a report. At the end, you've got access to the input-to and the output-from that pipeline of functions. So, you can't readily peak at what the data looked like during the intermediate processing steps anymore. To add those intermediate results to your report you could either

1. add side-effects to the functions in your pipeline, eg to print/plot results out during processing;
2. use the %T>% pass-through pipe to print-out intermediate results without modifying your functions;
3. save intermediate results to variables and print/plot them out within your report
4. use flog

If your pipeline looks like this:

result <- input %>% f1 %>% f2 %>% f3

Then you can rewrite the same pipeline using flogr as follows:

result <- input %>% flog(modifiers = c(f1, f2, f3), null_logger) %>% get_dataset

but this just gives you the same information.

Suppose you wanted to make a graph based on f1(input), but otherwise discard the results of f1(input)

result_tuple <- input %>%
  flog(modifier = f1,
       logger = function(post) ggplot(post, aes(x = x, y = y)) + geom_line()
       ) %>%
  flog(c(f2, f3), null_logger)

result2 <- get_dataset(result_tuple) # identical to `result` above

Then you could print out that graph after the pipeline has ran, rather than printing it as a side-effect:

# print out the graph
get_logdata(result_tuple)[[1]]

That is, flog just provides a wrapper around functions, where you can save intermediate results / figures and access them later, without having to save those things as a side-effect.

TODO: flog_filter_df

russHyde/flogr documentation built on May 29, 2019, 9:10 a.m.