# ggplot2

## ERP visualization: Basic Shiny Demo

Shiny app In an unusual fit of enthusiasm, I decided to have to go at writing a little app in Shiny, a simple programming framework to make web-based apps using R. So, as usual, all programmed using RStudio, the devs who also make Shiny and various fantastic R packages such as dplyr and ggplot2. It turned out to be pretty simple. I’m planning to add various additional functions as I get time to work on my blog posts, like allowing people to use their own data, for example.

## ERP Visualization: Within-subject confidence intervals

As I mentioned in a previous post, between-subject confidence intervals/standard errors are not necessarily all that useful when your data is within-subjects. What you’re interested in is the not really the between-subject variability but the variability of the differences between your conditions within subjects. I’m going to use here the command summarySEwithin from the package Rmisc. This removes between-subject variability for within-subject variables, returning corrected standard deviations, standard errors, and confidence intervals.

## ERP Visualization: timepoint-by-timepoint tests

Running statistical tests using “purrr” Something which puzzled me for a while was how to efficiently perform running (i.e. timepoint-by-timepoint) statistical tests on ERP/EEG in R. That was solved for me when I discovered the purrr package, another of ggplot2 author Hadley Wickham’s projects. Using the split command, you can easily split a data frame into multiple frames by one of its variables. In the EEG/ERP case, that means I can easily split the data into separate data frames for each timepoint and run my test of choice on each timepoint independently using the map command.

## ERP Visualization Part 1: Comparing two conditions

ERP visualization is harder than people think. Often people take the path of least resistance, plotting grand average ERP data as simple traces representing condition means, with no information regarding variability around these means. There are a couple of variations on this simple theme which show regions of significance, but it’s extremely rare to show anything else. A new editorial letter by Rousselet, Foxe, and Bolam in the European Journal of Neuroscience offers some useful guidelines, and Ana Todorovic’s recent post on adding scatterplots to time-series data is also great.