OJS data exploration

pacman::p_load(tidyverse,here)

d <- readRDS(here("data/dPrune-01-19-23.rds"))

# Prepare the data for analysis
dtest <- d %>%
    filter(expMode %in% c("test-Nf", "test-train-nf")) %>%
    group_by(id, lowBound) %>%
    mutate(nBand = n(), band = bandInt, id = factor(id)) %>%
    group_by(id) %>%
    mutate(nd = n_distinct(lowBound))
dtest <- dtest %>%
    group_by(id, lowBound) %>%
    filter(nBand >= 5 & nd == 6)
dtest <- dtest %>%
    group_by(id) %>%
    filter(!id %in% unique(dtest$id[dtest$nBand < 5]))

dtestAgg <- dtest %>%
    group_by(id, condit, catOrder, feedbackType, vb, band, lowBound, highBound, input) %>%
    mutate(vxCapped = ifelse(vx > 1600, 1600, vx)) %>%
    summarise(
        vxMean = mean(vx), devMean = mean(dist), vxMed = median(vx), devMed = median(dist),
        vxMeanCap = mean(vxCapped), .groups = "keep"
    )
ds1 <- d %>%
    filter(expMode %in% c("train", "train-Nf", "test-Nf", "test-train-nf")) %>%
    filter(!id %in% unique(dtest$id[dtest$nBand < 5]), vx<1500) %>%
    select(id, condit, catOrder, feedbackType, expMode, trial, gt.train, vb, band, bandInt, lowBound, highBound, input, vx, dist, vxb)


ojs_define(dso=ds1)

import { aq, op } from "@uwdata/arquero"

//data = FileAttachment("palmer-penguins.csv").csv({ typed: true })


//ds1=transpose(ds1)

ds=transpose(dso)

Plot.plot({
  facet: {
    data: ds,
    x: "condit",
    y: "bandInt",
    marginRight: 80
  },
  marks: [
    Plot.frame(),
    Plot.rectY(ds, 
      Plot.binX(
        {y: "count"}, 
        {x: "vx", thresholds: 50, fill: "bandInt"} // thresholds = number of bins
      )
    ),
    Plot.tickX(ds, 
      Plot.groupZ(
        {x: "count"}, 
        {x: "vx",
         z: d => ds.condit+ ds.bandInt,
         stroke: "#333",
         strokeWidth: 2
        }
      )
    )
  ],
    x: {label: "Vx", domain: [0, 1800],grid: true},
})

Plot.plot({
  marks: [
    Plot.line(ds, {
      x: "gt.train",      // feature for the x channel
      y: "vx",     // feature for the y channel
      fill:"condit",
      stroke: "vb",     
    }),
  ],
  x: {label: "Trial Number"},
  y: {label: "Vx", domain: [0, 1800],grid: true},
  color: {legend: true, scheme: "Turbo",type: "categorical"},
  width: 400,
  height: 400
});

Plot.plot({
  grid: true,
  marks: [
    Plot.rectY(ds, Plot.binX({y: "count"}, {x: "vx", fill: "condit", fy: "condit"})),
    Plot.ruleY([0])
  ]
})

Plot.rectY(ds, Plot.binX({y: "count"}, {x: "condit", fill: "condit"})).plot()

Plot.line(ds, {x: "gt.train", y: "vx",fill:"condit"}).plot({y: {grid: true}})

d = aq.from(ds)
d
  .groupby("condit", "vb", "feedbackType","expMode","catOrder")
  .rollup({mean_vx: d => op.mean(d.vx)}, {mean_dist: d => op.mean(d.dist)})
  .view(15)

dt = aq.from(ds)
dtAgg = dt
  .filter(dt => dt.expMode === "train")
  .groupby("condit", "vb","gt.train")
  .rollup({mean_vx: dt => op.mean(dt.vx)}, {mean_dist: dt => op.mean(dt.dist)})


Plot.line(dtAgg, {x: "gt.train", y: "mean_vx",fill:"vb"}).plot({y: {grid: true}})

Plot.plot({ 
    grid: true, 
    marginRight: 60,
    facet: {
        data: dtAgg,
        x: "condit",
        y: "vb",
        marginRight: 80
    },
    marks: [
        Plot.frame(),
        Plot.lineY(dtAgg, {
            x: "gt.train", 
            y: "mean_vx",
            fill:"vb"
            })
    ]
})

dtAgg
  .view(25)