eog_plot.R

library(ggplot2)
library(dplyr)
library(tidyr)
theme_set(cowplot::theme_cowplot())
eog <- read.csv('eog_cal/eeg_data_20200921.csv')

subjData <- 
    eog %>%
    unite(ld8, LunaID, ScanDate) %>% # remove LunaID and scanDate columns. replace with ld8
    mutate_at(vars(matches('Error')), abs) %>%  # abs  delta columns
    # made the data long. column meas has calR2, {Position,Displacement}Error, {mgs,vgs}Latency
    gather(meas, v, -ld8, -Trial, -Delay) %>% 
    filter(!is.na(v)) %>%
    # group
    group_by(ld8, meas, Delay) %>%
    summarise(subj_mean=mean(v),
              subj_sd=sd(v),
              subj_n=n(),
              subj_se=subj_sd/sqrt(subj_n))

groupData <- subjData %>% group_by(meas, Delay) %>%
    mutate(subj_mean=abs(subj_mean)) %>% 
    summarize(delay_mean=mean(subj_mean),
              delay_sd=sd(subj_mean),
              delay_se=delay_sd/n())

groupData %>%
    mutate(Delay=as.factor(Delay),
           mtype=gsub("^[A-Z]?[a-z]*"," ", meas)) %>% 
    ggplot() +
    aes(x=meas, fill=Delay, y=delay_mean) +
    geom_bar(stat='identity',position='dodge') +
    geom_errorbar(position='dodge',
                  aes(ymax=delay_mean+delay_se,ymin=delay_mean-delay_se)) +
    facet_wrap(~mtype, scales="free")