Nick George
{:title "SQLite3 for data analysis and inter-language exchange" :date "2020-10-14" :updated "{{{time(%Y-%m-%d %a)}}}" :tags ["python" "R" "database" "data analysis" "data"]}

SQLite3 for data analysis and inter-language exchange

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First published: 2020-10-14 Wed
Last updated: 2020-10-14 Wed

Language agnostic data storage

After taking Gary Bernhardt's excellent course on SQL I've started using SQLite3 to store and organize data from my patch clamp experiments (described here). One of the best parts of using SQL is the fact that the data is stored in an organized, structured, and language agnostic way. I use Python and R extensively for my analysis and data processing, and typically I have stored temporary analysis files as simple CSVs or JSON files. While this works (and also uses language agnostic text files), I typically end up with a directory full of temporary files that I have to read in batch and re-structure into a usable format when I read it back in for further work. I typically do this in R, but it leads to an unpleasant amount of boilerplate code and bespoke commands I look up and paste from older scripts – not a pleasant process.

Pre-processing and cleaning data

I recently began analyzing data from whole cell patch clamp experiments. I do initial data analysis (counting action potentials and recording action potential spike times) using Python, and in this case I wrote this data out to a bunch of JSON files for further analysis using R (note: I should have added it to the database directly, but I was pressed for time). After the Python step, one of my data directories looks like this:

├── spike_times_sweep_0.json
├── spike_times_sweep_1.json
├── spike_times_sweep_10.json

From there, I typically use R to read all these files into memory and perform some re-naming, grouping, and formatting work before proceeding with my whatever analysis I am doing. The processing code in R can be extensive, and while it doesn't take very long it does take up a lot of space at the top of my scripts just to end up with a well structured Dataframe. I'd like to avoid reading all the files from disc every time I do an analysis.


data_paths <- list.files("/path/to/data/", pattern = "*.json", full.names = T)

spikes_table <- bind_rows(purrr::map(data_paths, fromJSON)) %>%
    uid = paste(fname, mouse_id, cell_side, cell_n, treatment, sep = "_"),
    group_id = paste(mouse_id, cell_side, cell_n, treatment, sep = "_"),
    treatment = case_when(
      treatment == "sham" ~ "Control",
      treatment == "occl" ~ "Naris Occlusion",
      TRUE ~ "Unknown"
    cell_side = case_when(
      cell_side == "left" ~ "Left",
      cell_side == "right" ~ "Right",
      cell_side == "open" ~ "Open",
      cell_side == "occl" ~ "Occluded",
      TRUE ~ "Unknown"
# ... and on and on

Typically, I would write this out to a CSV for further analysis or paste this code at the top of every script. This just adds yet another (large) text file I have to keep track of, and someone is bound to open this with Excel, with possibly horrible consequences. I already have a database with my patch clamp metadata (described here), so why not add this preprocessed data to that database as a table?

Adding pre-processed data to a database with R

R has excellent database support, here I will use the DBI package and RSQLite, which provide a nice interface for managing databases. One of the coolest parts is that they allow me to connect to my database and add a table with the correct types without even defining it first. For example, I'll use the DBI::dbWriteTable() command, with default values and row.names = FALSE to add my processed table to the database (docs).

con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") # create a connection, this time to an in-memory SQLite DB, but normally I'd add the path to my database.
DBI::dbWriteTable(con, "steps_table", spikes_table, row.names = F) # write the `spikes_table` to the database
DBI::dbDisconnect(con) # disconnect 

And that's it. You can now read the data in like so:

con <- DBI::dbConnect(RSQLite::SQLite(), "/path/to/data.db")
spikes_table <- DBI::dbReadTable(con, "steps_table")
DBI::dbDisconnect(con) # disconnect 

There are many options for read/write table available as well.

Now I can access this table from whatever language I happen to be using by connecting to the database and selecting what I need.