E1 Extras

Analysis

Published

July 2, 2024

plot_violin_condit <- function(fcondit, fvb, pvar) {

  full_range <- testAvg |> pull({{pvar}}) |> range(na.rm=TRUE)
  testAvg |>
    filter(condit == fcondit, vb==fvb) |> 
    ggplot(aes(.data[[pvar]])) +
    geom_density(fill = 'dodgerblue4') +
    theme_void() +
    coord_cartesian(xlim = full_range)
}

unique_combinations = testAvg |> ungroup() |>
  distinct(condit, vb)

testAvg |> group_by(condit,vb) |> 
  summarize(mean=mean(dist),sd=sd(dist)) |> 
  mutate(Distribution=vb) |> 
  gt() |>
  tab_spanner(
    label = 'Training Condit',
    columns = -condit
  ) |> 
  cols_label_with(fn = str_to_title) |> 
  fmt_number(decimals = 2) |> 
  cols_align('left', columns = condit) |> 
  text_transform(
    locations = cells_body(columns = 'Distribution'),
    fn = function(column) {
      map2(unique_combinations$condit, unique_combinations$vb,~plot_violin_condit(.x,.y,'vx')) |>
        ggplot_image(height = px(50), aspect_ratio = 3)
    }
  )

Training Condit
Vb	Mean	Sd	Distribution
Constant
100-300	252.99	219.63
350-550	191.58	159.52
600-800	150.40	110.83
800-1000	188.91	160.60
1000-1200	240.57	173.05
1200-1400	295.42	186.14
Varied
100-300	387.25	343.45
350-550	289.01	272.48
600-800	236.17	188.89
800-1000	224.16	145.95
1000-1200	209.20	130.32
1200-1400	242.13	136.30

vars_to_plot <- c('dist', 'vx')

tables <- map(vars_to_plot, function(pvar) {
  testAvg |>
    group_by(condit, vb) |>
    summarize(mean = mean(.data[[pvar]]), sd = sd(.data[[pvar]])) |>
    mutate(Distribution = vb) |>
    gt() |>
    tab_spanner(
      label = 'Training Condit',
      columns = -condit
    ) |>
    cols_label_with(fn = str_to_title) |>
    fmt_number(decimals = 2) |>
    cols_align('left', columns = condit) |>
    text_transform(
      locations = cells_body(columns = 'Distribution'),
      fn = function(column) {
        map2(unique_combinations$condit, unique_combinations$vb, ~plot_violin_condit(.x, .y, pvar)) |>
          ggplot_image(height = px(50), aspect_ratio = 3)
      }
    )
})
print(tables[[1]])

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</style>
  <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
  <thead>
    <tr class="gt_col_headings gt_spanner_row">
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="4" scope="colgroup" id="Training Condit">
        <span class="gt_column_spanner">Training Condit</span>
      </th>
    </tr>
    <tr class="gt_col_headings">
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="Vb">Vb</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Mean">Mean</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Sd">Sd</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="Distribution">Distribution</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr class="gt_group_heading_row">
      <th colspan="4" class="gt_group_heading" scope="colgroup" id="Constant">Constant</th>
    </tr>
    <tr class="gt_row_group_first"><td headers="Constant  vb" class="gt_row gt_center">100-300</td>
<td headers="Constant  mean" class="gt_row gt_right">252.99</td>
<td headers="Constant  sd" class="gt_row gt_right">219.63</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">350-550</td>
<td headers="Constant  mean" class="gt_row gt_right">191.58</td>
<td headers="Constant  sd" class="gt_row gt_right">159.52</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">600-800</td>
<td headers="Constant  mean" class="gt_row gt_right">150.40</td>
<td headers="Constant  sd" class="gt_row gt_right">110.83</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">800-1000</td>
<td headers="Constant  mean" class="gt_row gt_right">188.91</td>
<td headers="Constant  sd" class="gt_row gt_right">160.60</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">1000-1200</td>
<td headers="Constant  mean" class="gt_row gt_right">240.57</td>
<td headers="Constant  sd" class="gt_row gt_right">173.05</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">1200-1400</td>
<td headers="Constant  mean" class="gt_row gt_right">295.42</td>
<td headers="Constant  sd" class="gt_row gt_right">186.14</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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bOwp3YLa6mUzN38WAZ7ru6T8v4kr66dBAAAAMDmZHBno7gz/eTq7sg5L0vdjJo287e+rbTbl0tK+b+SvLh2EgAAAACbj8GdjeKdZTAaDxdP1u6gkjIYZeH2dzftwr42pfxIkrtqNwEAAACwuRjc2Qi2pJQ3dAdOt6Ud1m6hotLNZeHO97TN/O4upfybJHfWbgIAAABg8zC4sxE8mr6f7Q7dVruDdaCMFrLlrve1zfzuUUr5t0nuq90EAAAAwOZgcGf6lfKudsv+cbt9uXYJ60QZbcmWu76+bRf2jVLKv05yf+0mAAAAADY+gzvT7rr0/e3d4dvaxPtS+SNltJCFu97XttsODJLyfyd5c+0mAAAAADa2tnYAXKFvS2numj/9hlLarnYL60xph+mWby7jT3+kTP7gkw8mmST5ydpdAAAAAGxMBnem2SCl+d7h/hsXukO31m5hnSrNIN3yzWXy1Gcy/sxvvTjJVUn+RZILldMAAAAA2GCclGGavTz9ZK+xnefVtJm7+ZHM3nB/kjyeUn4qyaHKVQAAAABsMAZ3ptkTpZsfD/dcU7uDqVAyuuq+zJ97Z0rb3ZzS/HySV9euAgAAAGDjcFKGabUzKX9vdOSOwXDfdbVbmCLtwu50y6eaC5/4cNd/4bNvTrItyU/EiRkAAAAArpAn3JlWjyX9oDt4pnYHU6iZ25Ut9/yJZnTsniT5k1982v3OylkAAAAATDlPuDOdSvlb7dbFxZlrX15qpzClSpPh3msz2HU8Fz7+oW39hafemWR/kn+f5MnKdQAAAABMIYM70+jaJH9l5uqXlMGOw7VbmHLN3M50R8416Sdl/KnfOJNS3pf0TyX5z0nGtfsAAAAAmB4Gd6bRt6Y0d8/f8lgpg652CxtAadoM91yT4dKp0n/+E93k8x9/ZUrz9i8O7/817rsDAAAAcAkM7kybJqX5h8N9123tDp+r3cIG04zm0x04XQa7jmfy+d9bmDz56dekNO9N+vkkH0ry+7UbAQAAAFi/3L9m2rwkyY/On31Lhksna7ewwV345K/mCx/68Zz/7Q/2SZ+U8qPp+3+c5IeTfKJSVkmyO8lyLt6c35dkV5IdSbYmmUvSffHfGyd5Kslnk3wyyceS/HqSDyf5aJJ+jdsBAAAANjSDO9PmH5TB6C1bX/kdbWkGtVvYJCZPfjpPf+QDefo3PjCe/MEn2iR9SvmP6ft/m+RnkvynJB9foQ/XJFlMcjjJkS/752hKczzpl9P3X31LqTR9GXST0nZ9SpOUJukn6ccXkvEXmn58vvmKf/9zSf+B9P3PJPmxJD+Zi+M8AAAAAJfJ4M40mUspv9sdOjc/d+qh2i1sSn3Gn/lYzv/OB3Phd395cuFTHynpJxe/j5bmd9P3H0z6Lz09/ntJPp3k80meTjLJxTF9Nsl8Lj6NvjMXn1bfl5TllHI4fb8/6f/YnyaV0cKFdm5X28ztLM3c9pSZ7Wlmt6UZbU2Z2ZLSzae0w+dOn1zI5KnPZvLkpzL53O9l/Psfy/jTH5lc+PRHL/4/lPJU+v5fJ/m+JD8U4zsAAADAC2ZwZ5q8Icn3L9z19RnsOlq7BdKPz2f8mY9m/OmPZvz7H8vkcx/vJ3/wifHkqc8OLvVaSxnOjstoIc3s9raZ2ZZmdvvFf+Z2pJnbmWZ2e7KKf5ujH5/P+JO/mvO/88s5/1s/P5489Zk2pflM+snfS/I3k/zaqn1wAAAAgA3G4M4UKT/SzG59xdaX/dk2xacu61jfpz//ZPoLT6a/8HTST5K+T0pJmkHKoEsZzKQMRhdPv6wXfZ8Ln/iVPP0b/zFP/+Z/6dNP+iT/KMlfyMWXxgIAAADwHKyWTIs9SX575pqXNjPXvbJ2C2x4k6d+P1/4lZ/M07/605N+fD5J/naS/yUXX74KAAAAwDNoawfAJXpHkvtnTz6cZjRfuwU2vDIYZbjnmoyO3F4yGZfxpz9ya0rzdUn/sSQ/X7sPAAAAYD0yuDMdSvnudtvy4sw1L/W3MmANlbbLcO+1GS6dLJPP/NbM5MlPvz6l3Jrk3yX5XO0+AAAAgPXEeMk0uCrJf5+96XUZHbundgtsXn2fL/z6f8hTv/hDk76ffCr95PEk/7p2FgAAAMB64Ql3psE3JuW+uVseK2Uwqt0Cm1cpGWw/mG7pVLnwiQ/P9F/43JuTNEl+IklfuQ4AAACgOoM7611Jaf7+YM9VO0ZH7vQ3MmAdKN18ukO3lv4Lnyvjz/zmvUk5keSfJzlfuw0AAACgpqZ2ADyPM+knx7oDp43tsI6UZpC5Uw9n9sQDSfL6lPLjSfZUzgIAAACoyuDOevemNO1kuHhT7Q7gGYyO3pX5c+8opbSnU5qfSXKwdhMAAABALQZ31rM2pXnTcP+NTRnM1G4BnsVw33WZv+u9TWm7oynNTyc5UrsJAAAAoAaDO+vZi9JP9nXLt9TuAJ7HYMfhLNz9vrYMRkspzU/F6A4AAABsQgZ31rM3lcFoPNh3Xe0O4BK0W5eycNf72jIY7U9pfiLJUu0mAAAAgLVkcGe9GqU0jw6XTralGdRuAS5Ru3UxC3e+py3tYDml+X+SbK/dBAAAALBWDO6sV69IP9nqnAxMn3bbcubPvbNJKdcm5YeTeAkDAAAAsCkY3Fmv3li6+fFg9/HaHcBlGOw6lvkzjzdJf3eSf5Ck1G4CAAAAWG1t7QB4BvMp5e+PDt3aDfddX7sFuEztln0pg5lc+L3/dlOScZKfqN0EAAAAsJo84c569Nr0/cxw+ebaHcAVGh2/J92hW5PkO5O8rnIOAAAAwKoyuLMOlTc2M9vGgx2Ha4cAV6xk7uSDaXccmqSU70tybe0iAAAAgNVicGe92Z7kVcMDt7QpTj7DhtAMMn/r25oynJtJaf5ZkvnaSQAAAACrweDOevNA0g8752RgQ2lmtmb+1re26ftrk3x37R4AAACA1eClqawz5a81c7uOzN7wqibxhDtsJM3cjqSUcuHjH745yYeT/HztJgAAAICV5Al31pPdSf+y7sAtrbEdNqaZq1+Swa7jfUr5O0mO1+4BAAAAWEkGd9aTh5I0Q+dkYOMqTebOvLGUwcwopXx/kkHtJAAAAICVYnBn/Sjlje3C3nG7ZV/tEmAVNTPbMnfzI236/tYk3167BwAAAGClGNxZL/an7180PHCL9wrAJjBcPJHuwOkk5TuTnKzdAwAAALASDO6sFw8lKd3SqdodwBqZPfFAymi+pJR/mGRYuwcAAADgShncWSfKG9uti5NmYU/tEGCNlOFs5k490qbvTyX5tto9AAAAAFfK4M56sJz0dw6Xb/H5CJvMcP8N6ZZvSUr580murd0DAAAAcCUMnKwHF8/JLDvjDJvR7ImvTRnMNCnl7yYptXsAAAAALpfBnfpKeWO7bXnSzO2qXQJUULr5zN742jZ9/6Ikb6ndAwAAAHC5DO7UdjB9f3u3fLPPRdjEukNnM9h5ZJLS/I0kO2r3AAAAAFwOIye1PZwkw6VTtTuAqkpmTz3c5OLY/pdq1wAAAABcDoM7dZXyhnb7gUkz54FW2OzaLfsyOv6ikuS9SU7X7gEAAAB4oQzu1HQofX+bczLAl8xc87KUbn6SUr47XqAKAAAATBlDJzVdPCezeLJ2B7BOlMEosze+pk3f35Hksdo9AAAAAC+EwZ16SnnMORngK3UHzqTdtjxJab4ryWztHgAAAIBLZXCnloPp+9u6pVM+B4E/rpTMnnigST9ZTvKttXMAAAAALpWxk1ounpNZck4G+GqDnUcufn8o5c8k2Ve7BwAAAOBSGNypo5TH2m3Lk2ZuZ+0SYJ2avf7VScpskj9XuwUAAADgUhjcqeFg+v5ct3yzzz/gWTXzuzI6eldJ8p4k19TuAQAAAHg+Bk9qeDBJhksnancA69zMNS9NGXRJyl+u3QIAAADwfAzurL0/PCezq3YJsM6Vbj6jq17SJP1DSW6t3QMAAADwXAzurLXl9P0d3fIpn3vAJRkdvyelmx+nlL9auwUAAADguRg9WWuvT5LhonMywKUpbZeZa1/epu9fkuSltXsAAAAAno3BnTVWHm23Lk6a+d21Q4ApMjp8Ls3stnFK+YtJSu0eAAAAgGdicGct7Uv6u4fOyQAvVNN+6Sn325O8onYOAAAAwDMxfLKWXp+kdIsna3cAU6g7cCbN3I5xSvkL8ZQ7AAAAsA4Z3Fk7pTzabtk3bhb21C4BptEfPeV+NsmraucAAAAAfCWDO2tlT/r+3uHSqbZ2CDC9ugOn08ztHKeU74yn3AEAAIB1xuDOWvnaJM1wyTkZ4AqUJjPXfk2bvj+T5OW1cwAAAAC+nMGdtVHKI8387nG7ZW/tEmDKdcu3pJndMU4pfz6ecgcAAADWEYM7a2Fn+ry0Wz7V2saAK9a0mbnmpW36/lySF9fOAQAAAPgSgztr4XVJ3w4XnZMBVkZ38Gyama3jlPI/124BAAAA+BKDO2ugPNzM7hi32xZrhwAbRdNmdPVL2vT9fUnuqJ0DAAAAkBjcWX1bk7x86JwMsMK6Q7eldHPjpPyZ2i0AAAAAicGd1feapB92iydqdwAbTGmHGR2/t0361yTxTQYAAACozuDOanu4mdk6bnccrN0BbECjI3emDLpJkj9duwUAAADA4M5qmk8prx4uOScDrI4ynEl35M4myRuTHK7dAwAAAGxuBndW0yvT96OhczLAKhoduycpTZJ8S+0WAAAAYHMzuLOaHird/Hiw00OnwOppZramO3imSSnvSbKjdg8AAACweRncWS2jlPK13dLJ9otPngKsmtHx+5K+n03yvtotAAAAwOZlCWW1fE36fs45GWAttFv2Zrjv+j6l+ZNJZmr3AAAAAJuTwZ3V8lAZzowHu47V7gA2idFV95X0k925+AJVAAAAgDVncGc1DFOa1w8XT7Rp2totwCYx2HU07balSUrz7UlK7R4AAABg8zG4sxruTT/Z5pwMsLZKRsfva9JPrk/y8to1AAAAwOZjcGc1PFjabjLcc3XtDmCT6ZZOppnZOk4pf7J2CwAAALD5GNxZaU1K8/Bw/41NmkHtFmCzadqMjt3dpu9fkeT62jkAAADA5mJwZ6XdmX6yZ7h4U+0OYJPqDp1LaQaTJN9SuwUAAADYXAzurLQH0wwmg33X1e4ANqnSzaU7dGuTUt6WZFftHgAAAGDzMLizkkpK88hw33VNabvaLcAm1h29O+n7UZJ3124BAAAANg+DOyvpTPrJgeHiidodwCbXbtmbwZ5r+pTmm5J4oQQAAACwJgzurKQHU5p+uM97CoH6RsfuKekni0keqN0CAAAAbA4Gd1ZKSWkeHe65OmU4W7sFIMO916aZ2zlOyjfXbgEAAAA2B4M7K+WG9JPjw8UTpXYIQJKklIyO3d0m/d1JTtbOAQAAADY+gzsr5cGk9MPFm2p3APyh7uCtKe1wkuQba7cAAAAAG5/BnZVRyqODXUdTuvnaJQB/qAxn0h0826SUtybZWbsHAAAA2NgM7qyE4+n7m4ZLJ52TAdad7uhdSd+Pkry9dgsAAACwsRncWQmvT5LhfudkgPWn3bIvg13H+pTmT8TPPQAAAGAVGR64cqU83G4/OGlmt9UuAXhGo6N3lfSTI0leXrsFAAAA2LgM7lyp5fT9uW7phM8lYN0a7r8xZbRlnJRvqN0CAAAAbFxGUq7UA0kyXDxRuwPg2TVtRkfuaJP+/iSHa+cAAAAAG5PBnStTysPtln3jZn537RKA59QdPpeUkiRfV7sFAAAA2JgM7lyJXen7Fw2XTra1QwCeTzOzNcP9N5WU5j1Juto9AAAAwMZjcOdKvC5J45wMMC1GR+9I+smuJK+v3QIAAABsPAZ3rkB5qJnbOW637q8dAnBJBruvSjO/a5yUb6zdAgAAAGw8Bncu19YkL794TqbUbgG4RCWjI3e2SX93khtq1wAAAAAbi8Gdy/XqpB92zskAU6Y7eDZp2km8PBUAAABYYQZ3LteDzczWcbv9YO0OgBekdHPplk41KeUdSWZr9wAAAAAbh8GdyzGbUl4zXDzRpjgnA0yf7sgdSd9vSfJo7RYAAPm/q00AACAASURBVABg4zC4czm+Jn0/O3ROBphSg52H0y7sHaeU99VuAQAAADYOgzuX46EynB0Pdh2t3QFwmUq6o3e26ftzSfzpIQAAALAiDO68UMOU5vUXz8n49AGmV3fgdNIMJkneU7sFAAAA2BgsprxQ96WfbBkueSAUmG5lOJtu+eYmpbwtyVztHgAAAGD6Gdx5oR4sg2483H1V7Q6AK9YdPpf0/UKSR2q3AAAAANPP4M4L0aY0Dw/33dimGdRuAbhiX/by1PfWbgEAAACmn8GdF+KO9JPdzskAG0dJd+T2Nn1/e5Iba9cAAAAA083gzgvxYJrBZLD32todACumO3AmadpJknfVbgEAAACmm8GdS1VSmkeG+65rStvVbgFYMaWbS7d0sklpnkgyU7sHAAAAmF4Gdy7V6fSTA8PFm2p3AKy47tC5pJ9sS/JA7RYAAABgehncuVQPpTT9cN8NtTsAVtxg97E0czvHKeU9tVsAAACA6WVw51KUlObR4Z6rU4aztVsAVkHJ6OLLU+9Lcrx2DQAAADCdDO5cihvST44PF0+U2iEAq6U7eDYpTZ/kHbVbAAAAgOlkcOdSPJSUfrj/xtodAKumjLZkuP+GktK8K8mgdg8AAAAwfQzuPL/SPDLYdTRltFC7BGBVffHlqXuTvLp2CwAAADB9DO48n+PpJzcNl046JwNseMO916SMtoyT8q7aLQAAAMD0MbjzfB5KkuHiTbU7AFZfaTI6fK5N+vuTLNbOAQAAAKaLwZ3nVsrD7faDk2ZmW+0SgDXRHTqbXPz5+LbKKQAAAMCUMbjzXA6k72/tlk/5PAE2jWZuVwa7r+pTmq9L4pwWAAAAcMkMqTyXBxPnZIDNpzt8W0k/OZrkntotAAAAwPQwuPMcysPt1sVJM7erdgjAmhounkgZzIyTvLN2CwAAADA9DO48m71Jf/dw6aTPEWDTKc0g3cEzbUp5LImXWAAAAACXxJjKs3kgSRkunqzdAVBFd+jWpO9HSd5QuwUAAACYDgZ3nlkpDzcLe8btlr21SwCqaLctp926OEkp767dAgAAAEwHgzvPZEf6vKRbOtnWDgGoqTt8e5O+P5PkRO0WAAAAYP0zuPNMXpf0rXMywGbXHbgladpJknfUbgEAAADWP4M7z6A80szuGLfbFmuHAFRVhrPpFk82Kc3bk3S1ewAAAID1zeDOV9qa5OXD5VNtUmq3AFR38eWpk+1JXle7BQAAAFjfDO58pfuTftgtOlcMkCSD3Velmd02Tso7a7cAAAAA65vBna/0cDOzddxuP1i7A2B9KCXdoXNt0r8iyYHaOQAAAMD6ZXDny82nlPuHS6faFOdkAL6kO3Q2uXhn662VUwAAAIB1zODOl3tl+n40XHJOBuDLNbM7MthzdZ/SvDtecAEAAAA8C4M7X+7h0s2PBzuO1O4AWHe6Q7eV9JMjSe6p3QIAAACsTwZ3vmQmpbyuc04G4BkNF29KGYzGSd5RuwUAAABYnwzufMnL0/dzw6WTtTsA1qXSDNIdPNOmlDck2Vq7BwAAAFh/DO58ycOlmxsPdh2t3QGwbnWHbkv6fpTksdotAAAAwPpjcCdJRinN64eLJ9oUnxIAz6bdtpx26+IkpbyrdgsAAACw/lhXSZKXpp8sdM7JADyv7vC5Jn1/W5IbarcAAAAA64vBnSR5uAxnxoNdx2t3AKx73fItSWn6JE/UbgEAAADWF4M7w5TmoeHiiTZNW7sFYN0r3VyGiydKSvNEkmHtHgAAAGD9MLjzkvSTrUPnZAAu2ejQrUk/2ZXk/totAAAAwPphcOeRMhiNh7uvrt0BMDUGe65OM7N1nJR31G4BAAAA1g+D++Y2TGkedk4G4AUqTbpDt7VJf3+S/bVzAAAAgPXB4L653Zd+ss05GYAXrjt4Nrn4c/StlVMAAACAdcLgvrk9UgbdZLjHORmAF6qZ35XBrmN9SvPuJKV2DwAAAFCfwX3zGqQ0Dw/3n2jSDGq3AEyl7vC5kn5yVZI7arcAAAAA9RncN6970092OCcDcPmGiydSBt0kyTtrtwAAAAD1Gdw3r0dK202Ge6+p3QEwtUo7zPDA6SalvDHJQu0eAAAAoC6D++Y0SGkeGS7e5JwMwBUaHbot6fvZJI/UbgEAAADqMrhvTvemn+x0TgbgyrXbD6Tdsm+cUt5duwUAAACoy+C+OT1SBt14sPfa2h0AG0BJd/hcm76/I8l1tWsAAACAegzum88gpXl0uP+mtjgnA7AiugOnk9L0Sd5RuwUAAACox+C++dyXfrJjuHSqdgfAhlG6+QwXbyopzTuSDGv3AAAAAHUY3DefR8qgGw/3XlO7A2BDufjy1MmuJPfXbgEAAADqMLhvLoOU5pHh4ok2zskArKjBnqvTzGwbJ+VdtVsAAACAOgzum8uLnZMBWCWlSXf4tjbpX51kuXYOAAAAsPYM7pvLY2UwGg/3OCcDsBq6g7cmSUnytsopAAAAQAUG981jmNI8Mlw62aZpa7cAbEjN3I4M9lzdpzRfFz9jAQAAYNMxBmweL0s/2eqcDMDqGh0+V9JPDie5t3YLAAAAsLYM7pvHY2U4Mx7uvqp2B8CGNtx/Y8pwdpzEy1MBAABgkzG4bw6jlOah4dIp52QAVlszSHfwbJtSHkmys3YOAAAAsHYM7pvDy9NPFjrnZADWRHf4XNL3wySP124BAAAA1o7BfXN4rHRz48Hu47U7ADaFdsu+tDsOTVKa9yYptXsAAACAtWFw3/hmU8rru6VTbYpfboC1Mjp8e5N+ckOS22q3AAAAAGvDArvxvTp9Pzdcvrl2B8CmMlw6mdJ2k3h5KgAAAGwaBveN7w1ltGU82Hm0dgfAplIGowwPnm5SyuNJttTuAQAAAFafwX1j25JSXtst39ymOCEMsNZGh84lfT+b5A21WwAAAIDVZ3Df2F6bvh91zskAVNFuP5B26+IkpbyndgsAAACw+gzuG1p5UzO7fdzuOFg7BGDT6o7c0aTvzyS5pXYLAAAAsLoM7hvXjiSv6A6cbhPnZABq6Q7cktIMJkm+rnYLAAAAsLoM7hvXQ0k/GDonA1BVGcxkeOB0k1LekmShdg8AAACwegzuG1Upb2oW9ozbrftrlwBseqMjtyd9P5/ksdotAAAAwOoxuG9M+9P39zknA7A+fNnLU99XuwUAAABYPQb3jenRJKVzTgZgnShengoAAACbgMF9Iyrl8Xbb8qSZ3127BIAv6g7cktIOJ0neU7sFAAAAWB0G943nWPr+tu7Aab+2AOvIxZennvnSy1O31O4BAAAAVp5RduN5Q5IMnZMBWHe++PLUuSRvrt0CAAAArDyD+8ZSUpq3DnYd65uZrbVbAPgK7bbltNsPTFLKN8RbrQEAAGDDMbhvLCfST67tDpw24gCsU6Mjdzbp+xuT3F67BQAAAFhZBveN5fGUZjJcOlm7A4BnMVy+OWUwGif5+totAAAAwMoyuG8cTUrz5uG+65synK3dAsCzKO0w3aHb2qQ8lmR37R4AAABg5RjcN46700+WugOna3cA8DxGR+5I0g+TPFG7BQAAAFg5BveN4/HSdpPBvutrdwDwPJqFPRnsPt6nNN8QP4sBAABgw/Cb/I2hS2neMFw62ZR2WLsFgEswOnpXST85nOSVtVsAAACAlWFw3xhelX6ytTtwS+0OAC7RcP+NaWa2jpPyjbVbAAAAgJVhcN8Y3ly6+fFg91W1OwC4VKVJd+SONulfmeR47RwAAADgyhncp9+2lPK13cEzbYpfToBpMjp8Ll/83v2+2i0AAADAlbPQTr+H0/fD7sDp2h0AvEBltCXd8qmSUr4uyVztHgAAAODKGNynXSlvbRb2jNttS7VLALgM3dG7kr7fkuTx2i0AAADAlTG4T7dD6fsXdQfPtkmp3QLAZRjsOJR22/IkpfmW+GYOAAAAU83gPt3elCTOyQBMs5LRsXua9JMbkryodg0AAABw+Qzu06ukNG8f7DrWN7Pba7cAcAW65VMp3dw4Kd9cuwUAAAC4fAb36XVL+sm13cGzzg8ATLtmkNGRO9ukfyDJkdo5AAAAwOUxuE+vt6ZpJ8OlE7U7AFgB3ZE7ktIkyTfUbgEAAAAuj8F9Og1Tmrd0iyeaMpip3QLACmhmtqZbvrmklPcmWajdAwAAALxwBvfp9Ir0k53Dg2drdwCwgkbH7kn6fiHJ22q3AAAAAC+cwX06vb108+PhnqtrdwCwgtrtBzLYeWSS0nxr/IwGAACAqeM389NnR1Je1x08237x1i8AG8jo+Iua9JNjSe6v3QIAAAC8MBbb6fNY0g+7g2dqdwCwCob7b0wzu32clG+v3QIAAAC8MAb3aVPKE+3WxUm7dbF2CQCroTQZHb+3Tfp7kvjTVQAAAJgiBvfpcm36/rbu0K1+3QA2sO7QrSmD0TjJn6rdAgAAAFw6w+10eVtK03fLt9TuAGAVlcEoo6N3tkkeS3Kodg8AAABwaQzu06NNaZ4Y7ru+lNFC7RYAVll39O6kNCXJt9RuAQAAAC6NwX16vCz9ZH936NbaHQCsgWZma7oDp0tKeW+SHbV7AAAAgOdncJ8eT5Rubjzce13tDgDWyOiqFyd9P5vk62u3AAAAAM/P4D4ddqSUB7uDZ9s0be0WANZIu2Vvhvtv6FOab00yW7sHAAAAeG4G9+nwxvT9sDt4tnYHAGtsdPVLSvrJziRP1G4BAAAAnpvBfRqU8s522/Kk3bpYuwSANTbYcTiDnUf7lOZ/SDKo3QMAAAA8O4P7+ncyfX+6O3ybXyuATWp0zUtK+snBJG+o3QIAAAA8OyPu+vdEmnbSLd9SuwOASoZ7r027dXGS0vxP8bMbAAAA1i2/aV/fRinN27ulk00ZelcewOZVMnPNy5r0k2uTPFC7BgAAAHhmBvf17XXpJ9u7Q7fV7gCgsuHiTWkW9oxTmj+XpNTuAQAAAL6awX09K+XdzeyO8WDX8dolANRWmsxc87I2/eRkktfUzgEAAAC+msF9/Tqcvn9Zd/hcm+JBRgCSbvnmNHO7xinlO+IpdwAAAFh3DO7r1xNJSXfobO0OANaL0mTm2q9p0/enk7yqdg4A8P+3d6dBlp2Fecef95x7b6+zaqRZNZs0aEbSaB0tMyMJY2NhICJsxb4KS0hgsxW2CYnLlRjLBFJlh6pUuUJSCYmTEBswleDEBkMQYItNYTOLxKYFkLCEQBpp1r735EMPBmIbadDtPre7f7/PU9P/7vkw9z793vcAAPwkg/toqlOqa7prd6YaX9F2CwAjpLfp/FSTq/sp5Z/HKXcAAAAYKQb30XRlmsGG3pZLDCkA/KQfnXLfk+TJbecAAAAAP2JwH0nl2tKb6ndP2dl2CAAjqLfpglRTa/op1Q3xfzkAAACMDG/SR8/6pHnK2JZL61R12y0AjKJSZXznE+o0g91Jnt52DgAAADDL4D56Xpqk6m25uO0OAEZYb8O5qZet++Epd7+hBQAAgBFgcB8tVUp1XefkxzTV5Oq2WwAYZaVkfNcT6zSDHUle2HYOAAAAYHAfNVemGZw6tvVSD0sF4GF11+1KvWrzIKV6U5LxtnsAAABgqTO4j5RyXelN9btrz2w7BIAFoWTizCdXaQYbk1zfdg0AAAAsdQb30bEhaa7ysFQATkTnpO3prt3VpFS/lWRl2z0AAACwlBncR8cvJ6l6Wy5puwOABWb8zCeXNM3yJG9suwUAAACWMoP7aOikVNd31+5qqslVbbcAsMDUy9amt+XikpTXJtnadg8AAAAsVQb30fCP0gzW9bbu9bBUAH4m42dcmVJ3qiRvbrsFAAAAliqD+ygo5RXVxIp+95Qz2i4BYIGqxpdn7PTHVUmenWRf2z0AAACwFBnc23d6muYXe1v31yn+OQD42Y2d/nOpxlf0U8q/if/jAQAAYN55M96+61KqZmzzRW13ALDAlbqbibOfUqdpzkvysrZ7AAAAYKkxuLdrMqW6prfx3FLGpttuAWAR6G7Ync6a05qU6i1JVrfdAwAAAEuJwb1dz0kzWN7btr/tDgAWjZKJ3U8rSbMiyZvargEAAIClxODenpJSXlUvXz/orNrcdgsAi0i9bG3Gtl9RklyXZE/bPQAAALBUGNzbszdNc+7Y9suqpLTdAsAiM37GL6aMLRuklLcn6bTdAwAAAEuBwb09ryrd8X534/ltdwCwCJXOWCbPffoPH6D6q233AAAAwFJgcG/H+qQ8s7fl0rrU3bZbAFikuuvOTnf92UkpNyTZ1nYPAAAALHYG93Zcl6Qa27qv7Q4AFrmJ3U9LqXvd41fLuMMMAAAA5lDddsASNJZSvbO7/qzJsa2Xtt0CwCJXOmOpelPVsbu/tD3JnUk+03YTAAAALFZOuM+/Z6UZnDS27bK2OwBYInpbLk7n5B1NSnlbkq1t9wAAAMBiZXCfXyWlen29bN2gs2Z72y0ALBklk+c9u5S6N5aU/xSfcAMAAIA5YXCfX5elGZwzdtoVlWt0AZhP1cSKTJzzjDppLk/yurZ7AAAAYDEyuM+r8trSm+x3N53fdggAS1Bv03npbjw3SbkhyYVt9wAAAMBiY3CfP9uT5qlj2/bXpeq03QLAklQyec4zUk0sLynVHydZ3nYRAAAALCYG9/nzqpQqY1v3tt0BwBJWuhOZ3PPCOrMPT3173HEGAAAAQ+OhafNjZUr5L71NF/Z6my5ouwWAJa6aWJnSGSsz99x6VpLvJflk200AAACwGDjhPj+uSdNMjp12RdsdAJAkGTvt8nTXn52k/F6SfW33AAAAwGLgY+Rzr5tS3d5Zc/q66b3X+HkDMDKamcM5cOPv9wcHv//9NIMLk9zRdtMiNZHktCTbkpyaZF2SVZm9Q38ss6/H+kkOJbk/yb1J7kryjSRfS/KdJM28VwMAAHDCPL1z7j07zWD9+OmPbbsDAH5C6Yxn+pKX1Qc+8q9XNf2jf5qm2Zvkwba7FrjxJBdl9lMDe1KqPWkGW/LjhxxK1ZTO+KB0xppSd0uSNM2gyeBYmmOHSzNz5Cev/CvVgaS5OU3z8SR/meTGJAfm6fsBAADgBDhxPbdKSvlCPb1217LHva7y4wZgFM3c+9U8eNO/a9I0f5o0T0sy03bTAlKSnJPkSSnll9Jkb9J0k6SaOqlfr9xU18vXp546OdXUSbP35/cm81NfEwz6GRw5kMFD38vgwXvSP3B3Zn5w56B//7eTQb9KSj/JXyXNe5O8O8ntc/9tAgAA8EhYgOfWlUn+fPKC56S36cK2WwDgH3T09k/k4OfelST/Nsl1cYXJT1Ml2Z/kmSnVM9IMNiZJvXLToLNmR9VZsz2dVVtSuhPD/aqDmcz84M7M/M2tOfbdLw36939n9lk8pdyUpvmPSd6Z5IHhflEAAABOhMF9LpXywWps2WOXP/6Ndar64f88ALTo8C3vz+FbPpAkNyT5py3njKLdSV6UUr0gzWBdqTqDztpdVXf9WemesjOlNzWvMYND38+x73w+R+/49KB/4O4qpRxM07wjyduSfGVeYwAAAEhicJ9L5yf5vxNnXZWx065ouwUAHoEmh77w3hz55l8ls4P7DS0HjYIVSZ6fUq5N05ybUjXddWeW3sbz01m7M6Xutd2XJOnf/+0cue2mHL3z5kEGM1VS/mfSvCnJJ9tuAwAAWEoM7nPnnaUz9szlV/5mXTpjbbcAwCPTNDn42T/K0Ts/nSS/keQtLRe1ZU+S61PK89I04/WKjYPelkuq3oZzj9/BPpqaowdz5LabcuTrN/abY4fqlPJnaZp/luTmttsAAACWAoP73Dg9ya3jO36hjO/6pbZbAODENIMc/Mwf5ei3bk6S307yW1kad7qPJXlWSnl1mubCUvcGvVMvrHpbLk29YkPbbSek6R/N0dtuyuGvfqjfHD1YZ/Z+9zfEA1YBAADmlIvF58bvpqovmNrzglI6o/FRcwB4xEpJd91ZaY4+mP4PvvXYJCcn+fMs3tF9XZJfS6nemTTPr6dPWTu+8wnV5AXPKd31u1ONL2u774SVqk5n9daMbd1XlapO//t3nJmm+ZXMvvb7RJKZlhMBAAAWJSfch29DUm4b27avO7H7qW23AMCj0OTwV96fw7f+RZLyvqR5XpIDbVcN0QVJXp2U5yWpu+vOLGPbL09nzfYstpdIg8MP5PCX/1eO3nlzUqrb0wxentlfogAAADBEi+vd5Gj4VynV65Y//g2lmljVdgsAPGpHb/94Dn7+T5okt6QZXJXka203PQp1kqck5XVJc1np9Aa9LXursW37Uk2ubrttzs1875s59Ll39fsP/k2d5L8meVWS77WcBQAAsGgY3IdrdUr5Vm/ThROT5z+77RYAGJqZe7+ehz71jn4zc/hQmubFSd7TdtMJWpnk6pTqNWkGp1aTq/tj2y+ve5svypJ7uPmgn8Nf+3AO3/L+Jsn30gxeluR/tJ0FAACwGLjDfbjekOTxk3uen6o31XYLAAxNNbk6vU3nVzP33dZpDt//nCSbk9yY5EjLaQ/n7CT/IqX85yRP7qw5bfnE2U8pk7ufWnVWb0mpOm33zb9SpXPS9nQ3nFP699023hw58LzM/nt+KMnRlusAAAAWNCfch2dFSnVnd/3uZVN7XtB2CwDMjUE/h7/6oRy+9QNNUu5OM7g+s6ejR+mBquNJnpGU65Nmf6k6g+6pe6qx7ftTL1vXdttoaQY5fOsHc/iWDzQp5ZtpBs9KcnPbWQAAAAuVwX143pjkd5b93GtTL9/QdgsAzKn+/d/Owc/890H/gbuqlPLBNM3rk3y2xaSS5PwkL0mpXpRmsKKaWtMf27av7p26J6U70WLa6Ju577YcvPkP+4NDDzRJ8/okb8to/RIFAABgQTC4D8eylOrO7tpdK6YufknbLQAwP5pBjtz+iRz+yp/1m6MH6yTvTvLmJJ+ex4rTkjzr+Mi+M1U96G04t+ptuSSdk7bFS51Hrjl2KAc/+8c5dtcXkpR3J83VSR5ouwsAAGAh8S50OH4jyZuXPfY1qVdsbLsFAOZVM3M4R77xsRz52of7zcyROqV8PE3zB0n+JMMfbOskFyZ5ckp5eprm7CTpnLS96W26oHQ3nJvSHR/yl1xKmhz5xsdy6Ivva5J8Pc3gHyf5UttVAAAAC4XB/dGbTqnu6J5yxsqpS6728wRgyWpmjuTonZ/OkW/+ZX/w4D11SjmWJh9Imv+d2QesfilJ/wT/2unMXhVzSZL9KdXPpxksTylNZ/W2dDecU7rrz041vmLI383SNnPfbXnoU+/oN0cfOpKmeW5m7+kHAADgYRiIH71fS/KWZVe8OvXKTW23AMAIaNL/wbdz9Dufy7G7/ro/eOjeOklSyqEkX0jTfC3Jt5Lcm+RAkpnMviZZlmRVknVJ2ZZSdqYZ/O1Hx6qpk/qdNTvq7sk70jl5h3vZ59jg8AN56FPvGPS/f0dJ8ptJboh73QEAAH4qg/ujM51S3d495YxVTrcDwN9vcOgHmbnvtvR/8K30H7grg4fu7TeHHyjNYKb6O3+41E3Vm+xXk6vranpNqabXprNiQ+qVp6b0JluoX+IGMzn4+ffk6B2fSpL/luTqJIfbjQIAABhdRuJH558kucHpdgA4cc1gJpk5kqYZzL4g6Yyl1N14eTJqmhz5+kdy6IvvS0r5RJrmqiT3tF0FAAAwiryj/dmtSCl3dNeeuXzq4pe03QIAMKeO3f3FHPz0Hw6aZnBnmsETktzSdhMAAMCo+bsf5eaRek2aZvn4zivb7gAAmHPddWdl+rJXVqU7sSmlfDLJvrabAAAARk3ddsACtTqlvKu78dze2Lb9bbcAAMyLanx5ehvPq45998ud5tihFyX56yRfabsLAABgVDjh/rP59TSZHD/D6XYAYGmpJlZm2WW/UndWbekkeU+Sl7fdBAAAMCqccD9x61PKO3ub93TGNl/cdgsAwLwrdTfdjeeXwYG7M3jwnquSDJJ8tO0uAACAthncT9xbU6qLpy5+cSndibZbAABaUao6vQ3nlMGRA+nf/+3HJVmZ5P1JmpbTAAAAWmNwPzE7kvyHse2XVb2N57XdAgDQrlLSXbcrTX8m/ftuuzTJtiTvy+yJdwAAgCXH4H5i/qDUvTOnLn5xKXWv7RYAgBFQ0j15R0rdy8w9Xz03KeckeW+SfttlAAAA883g/shdlOT3x894fOmesrPtFgCAkdJZvTVlbFlmvvvlnSnl0iTvTnKs7S4AAID5VLUdsECUlPLW0pvqj512RdstAAAjaWzr3kxe8Nwk+YWU8v4ky1pOAgAAmFcG90fmiWmax47vfELtKhkAgH9Yb9MFmdrzwpKUfSnlg5l9mCoAAMCSYHB/eJ2U6veqqTX9sS2XtN0CADDyuut3Z+ril5SU6sKU8uEkJ7XdBAAAMB8M7g/vl9MMHjNx9lPqFD8uAIBHort2V6YvubpKqXenVDcmOaXtJgAAgLlmQf7plqdUv9NZc3rTXetBqQAAJ6Jz8mMyvfeaqlT1rpTykSTr2m4CAACYSwb3n+6iUtXLJ86+qiSl7RYAgAWnc9L2TO29pipVZ0dK9ZEk69tuAgAAmCtW5Iex4km/fXvpjG9uuwMAYCGbue+2PHTT2wfNYOYbaQZXJLmr7SYAAIBhc8L9YZTOxKDtBgCAha6zemum9l1blbqzPaX6aJINbTcBAAAMm8EdAIB50Vm1JdN7r6tK3d16fHTf2HYTAADAMBncAQCYN/WqUzO97+V1qbtbjt/pbnQHAAAWDYM7AADzql75d0Z318sAAACLgsEdAIB5V688NdN7/3Z0d6c7AACwKBjcAQBoRb3qh6N7Z6uT7gAAwGJgcAcAoDWzo/t1Vak7246P7uvbbgIAAPhZGdwBAGjV8ZPu1Y+ddDe6AwAAC5LBHQCA1tWrNmdq77V1qTrbU6obk6xruwkAAOBEGdwBABgJnVVbMrXv2qpU9WnHR/e1bTcBAACc/8ihmQAABU5JREFUCIM7AAAjo7NqS6b2XluVqj79+PUyRncAAGDBMLgDADBSOqu3/vjo7qQ7AACwYBjcAQAYObOj+zVVqeodRncAAGChMLgDADCSOqu3Gd0BAIAFxeAOAMDIMroDAAALicEdAICRNju6X/vD0f0jSda13QQAAPD3MbgDADDyfvQg1c7pKdVHk6xvuwkAAOD/Z3AHAGBB6Kzemql911al7mw7PrpvbLsJAADgxxncAQBYMDqrtmR678vrHxvdT227CQAA4IcM7gAALCj1qs2Z3nd9Veru5pTqY0m2tt0EAACQGNwBAFiA6pWbMr3/+rp0xjYeH91Pb7sJAADA4A4AwIJUr9g4O7p3x9cdH913tt0EAAAsbQZ3AAAWrHr5+kzvf2VdepNrjo/uu9tuAgAAli6DOwAAC1q97JQsu+yVdTW2bOXxB6nuabsJAABYmgzuAAAseNXUmkxf/sq6mlg5nVI+nOTytpsAAIClx+AOAMCiUE2syvRlr6zrqZPHU8oHkjyh7SYAAGBpMbgDALBoVOPLM33ZK+p6+YZuUt6X5OltNwEAAEuHwR0AgEWl9KYyvf+6qrN6a5XkXUmubrsJAABYGgzuAAAsOqUznqm911TdtTuT5N8neX3LSQAAwBJgcAcAYFEqdTdTF72k9DZdkCRvTfIvk5R2qwAAgMXM4A4AwOJV1Zk8/zkZ2355kvx6Zk+7d9qNAgAAFiuDOwAAi1spmTj7qozvemKSvDSlvDfJZMtVAADAImRwBwBgCSgZ3/HzmTzvWUnypJTyf5KsaTkKAABYZAzuAAAsGb3NF2Xq4peWUuo9KdUnk5zWdhMAALB4GNwBAFhSumt3ZXr/K6rSHd98fHS/pO0mAABgcTC4AwCw5NSrTs2yy19VVxOrVqSUG5M8re0mAABg4TO4AwCwJFVTJ2XZFb9ad1Zt7iZ5d5LXJyktZwEAAAuYwR0AgCWr9KYyte+6qrfp/JLkrUn9orabAACAhavTdgAAALSpVJ1MXvDcdNbsSLXmMZ988C/e1HYSAACwQBncAQAgJb3NF2WQ+ljbJQAAwMLlShkAAAAAABgCgzsAAAAAAAyBwR0AAAAAAIbA4A4AAAAAAENgcAcAAAAAgCEwuAMAAAAAwBAY3AEAAAAAYAgM7gAAAAAAMAQGdwAAAAAAGAKDOwAAAAAADIHBHQAAAAAAhsDgDgAAAAAAQ2BwBwAAAACAITC4AwAAAADAEBjcAQAAAABgCAzuAAAAAAAwBAZ3AAAAAAAYAoM7AAAAAAAMgcEdAAAAAACGwOAOAAAAAABDYHAHAAAAAIAhMLgDAAAAAMAQGNwBAAAAAGAIDO4AAAAAADAEBncAAAAAABgCgzsAAAAAAAyBwR0AAAAAAIbA4A4AAAAAAENgcAcAAAAAgCEwuAMAAAAAwBAY3AEAAAAAYAgM7gAAAAAAMAQGdwAAAAAAGAKDOwAAAAAADIHBHQAAAAAAhsDgDgAAAAAAQ2BwBwAAAACAITC4AwAAAADAEBjcAQAAAABgCAzuAAAAAAAwBAZ3AAAAAAAYAoM7AAAAAAAMgcEdAAAAAACGwOAOAAAAAABDYHAHAAAAAIAh6LQdMOpKGbxoMMhE2x0AAMy96TzwnQfajgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5tn/A6JdP1Wb8V2uAAAAAElFTkSuQmCC" style="height:50px;"></td></tr>
    <tr class="gt_group_heading_row">
      <th colspan="4" class="gt_group_heading" scope="colgroup" id="Varied">Varied</th>
    </tr>
    <tr class="gt_row_group_first"><td headers="Varied  vb" class="gt_row gt_center">100-300</td>
<td headers="Varied  mean" class="gt_row gt_right">387.25</td>
<td headers="Varied  sd" class="gt_row gt_right">343.45</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">350-550</td>
<td headers="Varied  mean" class="gt_row gt_right">289.01</td>
<td headers="Varied  sd" class="gt_row gt_right">272.48</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">600-800</td>
<td headers="Varied  mean" class="gt_row gt_right">236.17</td>
<td headers="Varied  sd" class="gt_row gt_right">188.89</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">800-1000</td>
<td headers="Varied  mean" class="gt_row gt_right">224.16</td>
<td headers="Varied  sd" class="gt_row gt_right">145.95</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">1000-1200</td>
<td headers="Varied  mean" class="gt_row gt_right">209.20</td>
<td headers="Varied  sd" class="gt_row gt_right">130.32</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">1200-1400</td>
<td headers="Varied  mean" class="gt_row gt_right">242.13</td>
<td headers="Varied  sd" class="gt_row gt_right">136.30</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
  </tbody>
  
  
</table>
</div>

print(tables[[2]])

<div id="vbmxqasdhh" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
  <style>#vbmxqasdhh table {
  font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}

#vbmxqasdhh thead, #vbmxqasdhh tbody, #vbmxqasdhh tfoot, #vbmxqasdhh tr, #vbmxqasdhh td, #vbmxqasdhh th {
  border-style: none;
}

#vbmxqasdhh p {
  margin: 0;
  padding: 0;
}

#vbmxqasdhh .gt_table {
  display: table;
  border-collapse: collapse;
  line-height: normal;
  margin-left: auto;
  margin-right: auto;
  color: #333333;
  font-size: 16px;
  font-weight: normal;
  font-style: normal;
  background-color: #FFFFFF;
  width: auto;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #A8A8A8;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #A8A8A8;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
}

#vbmxqasdhh .gt_caption {
  padding-top: 4px;
  padding-bottom: 4px;
}

#vbmxqasdhh .gt_title {
  color: #333333;
  font-size: 125%;
  font-weight: initial;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-color: #FFFFFF;
  border-bottom-width: 0;
}

#vbmxqasdhh .gt_subtitle {
  color: #333333;
  font-size: 85%;
  font-weight: initial;
  padding-top: 3px;
  padding-bottom: 5px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-color: #FFFFFF;
  border-top-width: 0;
}

#vbmxqasdhh .gt_heading {
  background-color: #FFFFFF;
  text-align: center;
  border-bottom-color: #FFFFFF;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#vbmxqasdhh .gt_bottom_border {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#vbmxqasdhh .gt_col_headings {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
}

#vbmxqasdhh .gt_col_heading {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 6px;
  padding-left: 5px;
  padding-right: 5px;
  overflow-x: hidden;
}

#vbmxqasdhh .gt_column_spanner_outer {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: normal;
  text-transform: inherit;
  padding-top: 0;
  padding-bottom: 0;
  padding-left: 4px;
  padding-right: 4px;
}

#vbmxqasdhh .gt_column_spanner_outer:first-child {
  padding-left: 0;
}

#vbmxqasdhh .gt_column_spanner_outer:last-child {
  padding-right: 0;
}

#vbmxqasdhh .gt_column_spanner {
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: bottom;
  padding-top: 5px;
  padding-bottom: 5px;
  overflow-x: hidden;
  display: inline-block;
  width: 100%;
}

#vbmxqasdhh .gt_spanner_row {
  border-bottom-style: hidden;
}

#vbmxqasdhh .gt_group_heading {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  text-align: left;
}

#vbmxqasdhh .gt_empty_group_heading {
  padding: 0.5px;
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  vertical-align: middle;
}

#vbmxqasdhh .gt_from_md > :first-child {
  margin-top: 0;
}

#vbmxqasdhh .gt_from_md > :last-child {
  margin-bottom: 0;
}

#vbmxqasdhh .gt_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  margin: 10px;
  border-top-style: solid;
  border-top-width: 1px;
  border-top-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 1px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 1px;
  border-right-color: #D3D3D3;
  vertical-align: middle;
  overflow-x: hidden;
}

#vbmxqasdhh .gt_stub {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
}

#vbmxqasdhh .gt_stub_row_group {
  color: #333333;
  background-color: #FFFFFF;
  font-size: 100%;
  font-weight: initial;
  text-transform: inherit;
  border-right-style: solid;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
  padding-left: 5px;
  padding-right: 5px;
  vertical-align: top;
}

#vbmxqasdhh .gt_row_group_first td {
  border-top-width: 2px;
}

#vbmxqasdhh .gt_row_group_first th {
  border-top-width: 2px;
}

#vbmxqasdhh .gt_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#vbmxqasdhh .gt_first_summary_row {
  border-top-style: solid;
  border-top-color: #D3D3D3;
}

#vbmxqasdhh .gt_first_summary_row.thick {
  border-top-width: 2px;
}

#vbmxqasdhh .gt_last_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#vbmxqasdhh .gt_grand_summary_row {
  color: #333333;
  background-color: #FFFFFF;
  text-transform: inherit;
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
}

#vbmxqasdhh .gt_first_grand_summary_row {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-top-style: double;
  border-top-width: 6px;
  border-top-color: #D3D3D3;
}

#vbmxqasdhh .gt_last_grand_summary_row_top {
  padding-top: 8px;
  padding-bottom: 8px;
  padding-left: 5px;
  padding-right: 5px;
  border-bottom-style: double;
  border-bottom-width: 6px;
  border-bottom-color: #D3D3D3;
}

#vbmxqasdhh .gt_striped {
  background-color: rgba(128, 128, 128, 0.05);
}

#vbmxqasdhh .gt_table_body {
  border-top-style: solid;
  border-top-width: 2px;
  border-top-color: #D3D3D3;
  border-bottom-style: solid;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
}

#vbmxqasdhh .gt_footnotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#vbmxqasdhh .gt_footnote {
  margin: 0px;
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#vbmxqasdhh .gt_sourcenotes {
  color: #333333;
  background-color: #FFFFFF;
  border-bottom-style: none;
  border-bottom-width: 2px;
  border-bottom-color: #D3D3D3;
  border-left-style: none;
  border-left-width: 2px;
  border-left-color: #D3D3D3;
  border-right-style: none;
  border-right-width: 2px;
  border-right-color: #D3D3D3;
}

#vbmxqasdhh .gt_sourcenote {
  font-size: 90%;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 5px;
  padding-right: 5px;
}

#vbmxqasdhh .gt_left {
  text-align: left;
}

#vbmxqasdhh .gt_center {
  text-align: center;
}

#vbmxqasdhh .gt_right {
  text-align: right;
  font-variant-numeric: tabular-nums;
}

#vbmxqasdhh .gt_font_normal {
  font-weight: normal;
}

#vbmxqasdhh .gt_font_bold {
  font-weight: bold;
}

#vbmxqasdhh .gt_font_italic {
  font-style: italic;
}

#vbmxqasdhh .gt_super {
  font-size: 65%;
}

#vbmxqasdhh .gt_footnote_marks {
  font-size: 75%;
  vertical-align: 0.4em;
  position: initial;
}

#vbmxqasdhh .gt_asterisk {
  font-size: 100%;
  vertical-align: 0;
}

#vbmxqasdhh .gt_indent_1 {
  text-indent: 5px;
}

#vbmxqasdhh .gt_indent_2 {
  text-indent: 10px;
}

#vbmxqasdhh .gt_indent_3 {
  text-indent: 15px;
}

#vbmxqasdhh .gt_indent_4 {
  text-indent: 20px;
}

#vbmxqasdhh .gt_indent_5 {
  text-indent: 25px;
}
</style>
  <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
  <thead>
    <tr class="gt_col_headings gt_spanner_row">
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="4" scope="colgroup" id="Training Condit">
        <span class="gt_column_spanner">Training Condit</span>
      </th>
    </tr>
    <tr class="gt_col_headings">
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="Vb">Vb</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Mean">Mean</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Sd">Sd</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="Distribution">Distribution</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr class="gt_group_heading_row">
      <th colspan="4" class="gt_group_heading" scope="colgroup" id="Constant">Constant</th>
    </tr>
    <tr class="gt_row_group_first"><td headers="Constant  vb" class="gt_row gt_center">100-300</td>
<td headers="Constant  mean" class="gt_row gt_right">522.83</td>
<td headers="Constant  sd" class="gt_row gt_right">245.10</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img src="data:image/png;base64,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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">350-550</td>
<td headers="Constant  mean" class="gt_row gt_right">660.61</td>
<td headers="Constant  sd" class="gt_row gt_right">224.30</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">600-800</td>
<td headers="Constant  mean" class="gt_row gt_right">771.75</td>
<td headers="Constant  sd" class="gt_row gt_right">210.50</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">800-1000</td>
<td headers="Constant  mean" class="gt_row gt_right">1,008.76</td>
<td headers="Constant  sd" class="gt_row gt_right">254.58</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">1000-1200</td>
<td headers="Constant  mean" class="gt_row gt_right">1,173.42</td>
<td headers="Constant  sd" class="gt_row gt_right">307.89</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Constant  vb" class="gt_row gt_center">1200-1400</td>
<td headers="Constant  mean" class="gt_row gt_right">1,306.99</td>
<td headers="Constant  sd" class="gt_row gt_right">355.81</td>
<td headers="Constant  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr class="gt_group_heading_row">
      <th colspan="4" class="gt_group_heading" scope="colgroup" id="Varied">Varied</th>
    </tr>
    <tr class="gt_row_group_first"><td headers="Varied  vb" class="gt_row gt_center">100-300</td>
<td headers="Varied  mean" class="gt_row gt_right">665.49</td>
<td headers="Varied  sd" class="gt_row gt_right">365.02</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">350-550</td>
<td headers="Varied  mean" class="gt_row gt_right">772.47</td>
<td headers="Varied  sd" class="gt_row gt_right">321.45</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">600-800</td>
<td headers="Varied  mean" class="gt_row gt_right">879.54</td>
<td headers="Varied  sd" class="gt_row gt_right">291.57</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">800-1000</td>
<td headers="Varied  mean" class="gt_row gt_right">1,070.25</td>
<td headers="Varied  sd" class="gt_row gt_right">248.79</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">1000-1200</td>
<td headers="Varied  mean" class="gt_row gt_right">1,180.70</td>
<td headers="Varied  sd" class="gt_row gt_right">265.92</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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NnPfOiCJH+T5P21mwAAWBy8UgYA4MQYJnl2kh9splZNps55bNtbsbV2Eywek9nc8Rc/P5n9/K2fSzc5kOTW2kkAACx8BncAgOPvtJTyO+m6A8PtF2e0//qUtl+7CRadyRc+lTte+6JJNznyZ+m6a5JMajcBALCweaUMAMDxU5I8LSl/UIbTa2bO//ZmuP3ilMZHLqihDKbSjJaWw7e9e0eSzyR5U+0mAAAWNhfuAADHx6qkvDTpbuiv399NnfktpQymazcB6fKFv/6NHL7t3YfSdWcneXftIgAAFi6DOwDAsbs4pXlZSlk3PnBDM9x+YXzMgrmjO/SFfP7PXjDbHf7i36WbnJvkUO0mAAAWJn++GQDggStJfjDJbzXTq6ZnLvpXbX/9/hjbYW4p7SDt0vXN4Y/8zbocfQb6s9pNAAAsTAZ3AIAHZllSfjvJ9/Q3nVFmLviXTTNeXrsJ+Bra6dWZ3HNHZj/7kQcn+dMkt9RuAgBg4XF+BQBw/52W0rwiyfbxaTeU4faL4mMVzH3dkXtyx2tfNDv54mdvTjc5PcldtZsAAFhYXLgDANw/N6aUV5bBzKqZC7+zGWw4PcZ2mB9K00u7bHNz6MNvXpFkJsmrajcBALCwGNwBAO6bJskzk/zn3spt7czF3922M2srJwH3VzO1It2RezJ7+80PSvLaJDdXTgIAYAFxjgUA8I3NJOU3k+6Rg60PytTpj0wadwswX3Wzh3PHa39mdnLX7R9JNzmQ5Au1mwAAWBg8KQIAfH2bU5o/S8ml44OPKuO91ySlqd0EHIPStGmXb24OffjNy5MsTfLK2k0AACwMBncAgK/tzJTmdaXtb5254DuawaYza/cAx0kzXp7uyKHM3n7z+UleE6+WAQDgOHCeBQBw765LKa9vRktXz1zyb9reml21e4DjbLT3mjRTq2ZTmv+WZKp2DwAA858LdwCAf+5fJvntdtmm3pKLn9o2Uytr9wAnQGnatMs2NYc+/OYVSUZJXl27CQCA+c3gDgDwj0qSH0/ys/11ezP9oKc0pe/oFRayZmpFukN3Zfazt1yY5E+SfKx2EwAA85dXygAAHNUk+bkkzxqccn6mz39yKe2gdhNwEoz2XZdmtHTypVfL+BcfAIAHzIU7AEDST/IbSZ4y2nVFxqd/U1LcJcBiUZpemiXrmsMf+Zs1Se5J8he1mwAAmJ8M7gDAYjdKyv9KcuP4wMMz2n1Vjr5ZBlhM2unVmXzh05n9/G2XJHlZkk/VbgIAYP5xugUALGbTKeWPk+7hU2d+c4Y7L6vdA1Q0Pu2GlP6oSSm/Es9KAAA8AC7cAYDFaiYpr0zJZVNnP64MtpxbuweorLSDNOOlzeFb33VKko8k+ZvaTQAAzC+uNgCAxWhJSvk/KeXB0+c8oQw2n1W7B5gjBpvPTm/1qV1KeVGSdbV7AACYX1y4AwCLzdGxPeXC6fOeVPobTq/dA8wpJb2V28o9H3pDP123Ocn/ql0EAMD8YXAHABaTma8a29cfqN0DzEFlMJUk5cinPnh6kr9K8g91iwAAmC+8UgYAWCzGKeWPjo7tTzS2A1/X6NTL00yvnk1pfinJuHYPAADzgwt3AGAxGKSUl6fLQ6fPfXzpbzhYuweY60qTdtnG5tCH/3pFktkkr61cBADAPGBwBwAWul5SfifpHjF11reWweaza/cA80QztSKTu27P7OdvvTjJ7yb5dO0mAADmNq+UAQAWspLkl5Pu0eODj8pgy7m1e4B5Znzg4Sm9YUkp/yVH/5sCAABfkwt3AGAhe36Sp432XZvRzstqtwDzUGkHaQZTzeHb/m57kvckeXftJgAA5i4X7gDAQvUDSf7dcMclGe16SO0WYB4bnHJB2uWbJynNzyVZUrsHAIC5y4U7ALAQPT7JfxlsPjtTB29MirdAAMeglPSWbS6Hbn7TdJJhklfXTgIAYG4yuAMAC81Dk/J7vTW7yvS5Tyxp/IE+4Ng1o6XpDt1ZZj97y4OS/H6ST9RuAgBg7vEECgAsJAdTyivapeub6fO+vaRxWwAcP6O916YMprqk/GJ8gSoAAPfCUygAsFBsTGle1wyXLpu5+KltM5iu3QMsMKXtpxkuaQ7f9q5Tkrw/yTtrNwEAMLe4cAcAFoLplPLHpe2tnb7wKW0zWlq7B1igBpvPSbvilElK86L4AlUAAP4JF+4AwHzXJOV/puSymfOf3PRWbqvdAyxkpaS3bFM5dPMbZ3L0eer/1k4CAGDuMLgDAPPdTyf5l+ODjy6DTWfWbgEWgWa0NJO778js5z56QZL/meTTtZsAAJgbvFIGAJjPnpjkh4fbL85w24W1W4BFZLzv2pTeoKSUn4svUAUA4EtcuAMA89X5KeUVvTW7mumzH1tS7F3AyVPaQUpv2Bz5+HtPTfLWHP0SVQAAFjkX7gDAfLQhpfnDZryimT73iSXFRxrg5BtuuzDtkvWzKc1/SjKs3QMAQH0u3AGA+WaYUl5Zmt7umYuf2jbj5bV7gMWqlLRL1jWHbvnr5UnuTPL62kkAANTlHAwAmG9enK570NTZj2vaJetqtwCLXG/1zvQ3np6U8hNJNtbuAQCgLoM7ADCffEeS7x7tvjL9DafVbgFIkowPPCIpzSjJc2q3AABQl1fKAADzxdkp5eX9tXuaqTMe40tSgTmj9MfJZLYc+fQ/nJnklUk+WrsJAIA6XLgDAPPBipTmD5rR0mbq7Mf5klRgzhnuuiJluGQ2pfx8Er8RBABYpFy4AwBzXUnK76aU82Yu/FdNM72qdg/AP1OaNs1oSXP41nduSvL+JO+s3QQAwMnnPAwAmOt+IOkeMT79kaVdvrl2C8DXNNh0VtrlWyYpzQuTTNXuAQDg5HPhDgDMZRcn+a3BprPKeP918ZYGYE4rJe3S9eXQzW9akuRwkj+vnQQAwMnlwh0AmKtWpjT/s5le1Y3PuDHGdmA+6K3YmsHms5JSnpFkU+0eAABOLoM7ADAXlaT8WkpZP33uk9rSG9buAbjPRvselpRmkOQ5tVsAADi5DO4AwFz0r5PuhvGBRzTtso21WwDul2a8PKNTr2iSPCnJubV7AAA4eQzuAMBcczCl/Gx//YFuuP2i2i0AD8jw1MtTBjOzKeVn451YAACLhi9NBQDmknFK8//KYGbVzIXf2ZR2ULsH4AEpTS/NYLo5fNu7T0ny9iTvrd0EAMCJ58IdAJhLXphusmf6nMe3ZTBVuwXgmAy2nJN26fpJSvOiJH6DCACwCLhwBwDmiocn+dnRrisy2HpB7RaAY1dK2pk15dAtb1me5PYkb6ydBADAieXCHQCYC9amNP+tXbZpMtpzTe0WgOOmt3pX+uv2dSnNM5OsrN0DAMCJZXAHAGorSfmVlLJi6pzHNWn8ATxgYRntf3hJuiVJfrx2CwAAJ5bBHQCo7clJ94jxgUc07cza2i0Ax127ZG2GWx9UkvL0JDtr9wAAcOIY3AGAmranlJ/vrdnVDbddVLsF4IQZ7bk6pe03SXlu7RYAAE4cf2YbAKilScorSm+wY+bC72pKf1S7B+CEKb1BUlKOfOoD+5O8OslHajcBAHD8uXAHAGp5etJdMj746KYZL6vdAnDCDXdcmma0dDalvChJqd0DAMDx58IdAKhhV0r5g/76A73xvmtjdwIWg9K0KYPp5vCt79qc5B1J3lO7CQCA48uFOwBwsrUp5ddLb9SbOuPGGNuBxWSw+ey0S9dPUpoXJhnU7gEA4PgyuAMAJ9vT03UXjg8+ui3DJbVbAE6u0mR84IYm3WR7ku+qnQMAwPFlcAcATqadKeV5/fWndYNNZ9RuAaiit2ZXemt2dynNs5Isrd0DAMDxY3AHAE6WJqW8tLTD3viMRxevkgEWs/GB60u6yYokP1y7BQCA48fgDgCcLN+Vrrt0fPoj28arZIBFrl26MYMt5ySl/ECSTbV7AAA4PtraAQDAorA5pfxhf+2e/vjA9a7bAZK0yzfnnpte36Trlif5w9o9AAAcOxfuAMCJVpLyi6XpjcdnPMbYDvAlzXh5hjsuaZJ8R5IDtXsAADh2BncA4ER7TNI9fLT/+qYZL6/dAjCnjHY9JKU/miTlebVbAAA4dl4pAwCcSCtSmle1yzePps64saS4bgf4SqXtpzS95sgn37c7yWuS3Fy7CQCAB86FOwBwIj03yaqpM7+5SfGxA+DeDLZflGa0bDalvDDeuwUAMK+5cAcATpRLkrxktOuKMth8Vu0WgDmrlCZlON0cvvVdm5K8K8nf1W4CAOCBcWoGAJwIg5TmV5qpFbPD3VfWbgGY8wabzk67dP0kpXlukn7tHgAAHhiDOwBwIvxgusnuqTMe05bWbgTwDZWS0f6HN+kmO5N8R+0cAAAeGIM7AHC87UgpPznYdGZ6a3bXbgGYN/prd6e3ameX0jw7yXTtHgAA7j+DOwBwPJWU8pLS9nuj026o3QIwz5SMD1xf0k3WJPne2jUAANx/BncA4Hh6VLruutG+hzXNcEntFoB5p12+Jf2NB5NSfiTJ6to9AADcP23tAABgwZhJaf6kXbZheuqMx5SUUrsHYF7qLduUe276q37SDZO8qnYPAAD3nQt3AOB4+fF0k41TBx/TpPiIAfBANdOrM9x6QUnK05Jsq90DAMB952kYADgeDiTlB4bbLky7YkvtFoB5b7jnqpSmbZI8q3YLAAD3ncEdADhWJaX859IfZbT32totAAtCM1yS4amXNUmekORg7R4AAO4bgzsAcKy+OV132fjAw9symKrdArBgDHdentIfT5Ly3NotAADcN740FQA4FtMpzZ+0yzdNT53+aF+UCnAclbaX0rTNkU++b1eS1yS5uXYTAABfnwt3AOBY/Gi6yYap0x/dGNsBjr/B9ovSjJbOppQXJPEfWgCAOc6FOwDwQJ2alN8anHJeM9x+Ue0WgAWplCZlMN0cvvVdm5L8bZL31W4CAOBrc+EOADxA5cWlNyjjfQ+rHQKwoA02n512Zu1sSnleHE0BAMxpBncA4IG4LumuH+29pi3DmdotAAtbaTLa/7A2XbcnyZNq5wAA8LUZ3AGA+2uQ0vx8M7NmdrjNq2QATob++v1pV2ydpDT/Icmodg8AAPfO4A4A3F9PTzfZOXX6I9s03mwAcHKUjPdf36SbbEzy1No1AADcO4M7AHB/rE0pz+qv29/11uyu3QKwqPRWbU9/3d4upfmJJEtr9wAA8M8Z3AGA++PZSZkan/aIUjsEYDEa7XtYSTdZnuQHa7cAAPDPGdwBgPvqjCTfOdxxSWmmV9duAViU2qUbMth8dlLKDyZZW7sHAICvZnAHAO6LklJ+rgymJqPdV9ZuAVjURnuvTlJGSX6sdgsAAF/N4A4A3Bc3pOsuG+97WFv6o9otAItaM7Uqw20PKkl5apLttXsAAPhHBncA4BsZpDQ/2y5ZPxmccl7tFgCSDHdfmdK0TZKfqt0CAMA/MrgDAN/I09JNto9Pv6FJ8dEBYC5ohksyPPWyJskTkpxeuwcAgKM8NQMAX8/KlOaZ/XX7ut7qXbVbAPgKw52Xp/RHk6T8dO0WAACOMrgDAF/PTyTdktGBh5faIQB8tdIfZbT7yjbprk/y4No9AAAY3AGAr21XUp4+3HZhaWfW1m4B4F4Mtl2UZrR0NqU8P4lfjgIAVGZwBwC+hvK80uuX0Z6ra4cA8DWUtp/R3mvadN2FSa6v3QMAsNgZ3AGAe/PgpHvUaPdVTRlM124B4OsYbDk3zfTq2ZTmefGMBwBQVVs7AACYc0pK+f1mvGzd1DmPa0qx3QDMaaWkGS9rDn/0bWuS/H2Sd9ZOAgBYrDxBAwD/1Lek684Z7XtYW5pe7RYA7oP+htPSLts0SWmek2RQuwcAYLFy4Q4AfKVhSvOH7bINS6ZOf1RJ8f17APNDSTuzqhy65S3Lknw8yV/XLgIAWIxcuAMAX+mp6SanjA/c0BjbAeaX3upd6a3Z1aU0z0wyU7sHAGAxcuEOAHzZ8pTm5f11e4aj3Q+1tgPMQ+2SdeXQzW+cSvLFJK+r3QMAsNi4cAcAvuwZ6bqlo/3XG9sB5ql2+eb0Nx5MSn4NvIMAACAASURBVHlGklW1ewAAFhsX7gBAkmxJyu8MTjm3HW69oHYLAMegt3Rj7vnQX/WT9JK8unYPAMBi4sIdAEiSZ6Vp29Hea2p3AHCMmpk1GZxyQUkp35NkS+0eAIDFxOAOABxM8u2jnZc2zWhZ7RYAjoPRniuT0rRJnlm7BQBgMTG4A8BiV8pzS380GZ56Re0SAI6TZrQswx2XNEmenGRf7R4AgMXC4A4Ai9tl6brrRruvakt/VLsFgONodOoVKb3hJCnPqd0CALBY+NJUAFi8Skr53Wa8bP302Y9tUvweHmAhKW0/SWmOfOrv9yZ5VZKPVk4CAFjwPFkDwOL1yHTd+aO917VperVbADgBhjsenDKcmU0pz09SavcAACx0LtwBYHHqpTQvb5esXTF18NElxQYDsBCVpk3TGzWHP/53W5O8MckHajcBACxkLtwBYHH69nSTXaP913uVDMACNzjlvDRTq2ZTygviGRAA4IRy4Q4Ai884pXl5b+XW6fH+64o3DAAscKVJM1raHP7Y29cmeX+Sd9ZOAgBYqFw3AMDi87R0kw2j/dc3xnaAxaG/8fS0yzZNUprnJBnU7gEAWKhcuAPA4rI8pfxBf93+wWjXFdZ2gEWjpJ1eVQ7d8pblST6R5M21iwAAFiIX7gCwuPxgum7paN+1xnaARaa3Zld6a3Z1Kc0zk8zU7gEAWIhcuAPA4rEupfzuYPPZ/eG2C2u3AFBBu3R9OfShN0wluSfJn9fuAQBYaFy4A8Di8aNJGY32Xl27A4BK2mWbMth0ZlLKDydZW7sHAGChMbgDwOKwLSlPHW59UGmmVtVuAaCi0d5rk5Rxkh+t3QIAsNAY3AFgcXhmmrYZ7n5o7Q4AKmumV2W47cKSlH+dZEftHgCAhcTgDgAL3/4kTxrtvKRpRktrtwAwB4x2X5nS9pok/7F2CwDAQmJwB4AFr/yH0htOhqdeUTsEgDmiDGcyPPXyJsm3JTm7dg8AwEJhcAeAhe28pHvUcNcVbemPa7cAMIcMd16WMpiaTSnPr90CALBQGNwBYCEr5TllMDU73HFJ7RIA5pjSG2a055o2XffQJFfV7gEAWAgM7gCwcF2WrrtytOfqtrSD2i0AzEHDrRekmVo5m9K8MJ4PAQCOWVs7AAA4IUpK+e1mtGzD9Nnf1qTYUAC4F6VJM1raHP7Y29cleX+Sd9ZOAgCYzzx9A8DCdF267kGjvde0aXq1WwCYw/obD6ZdvnmS0jw3ybB2DwDAfObCHQAWnial+b1meuXqqTO/uUkptXsAmNNK2uk15dAtf70syWeSvLF2EQDAfOXCHQAWnhvTTU4f7b229SoZAO6L3uqd6a/d26U0P5lkee0eAID5yoU7ACwsbUrz++2SdSumTn9Ucd0OwH3VLttYDt30hi+/Uub/VY0BAJinnL0BwMLyhHSTXaN913mVDAD3S7tkfQannFtSyvcn2VK7BwBgPjK4A8DCMUhpntUu3zLpr99XuwWAeWi055qkNL0kz6rdAgAwHxncAWDh+I50k1PG+69rEtftANx/zXhZRjsva5J8e5KDtXsAAOYbgzsALAzjlOaZvVU7Jr3Vp9ZuAWAeG556RUp/PEkpL6zdAgAw3xjcAWBh+O50k3Wjfa7bATg2pT/KaO81bbruqiRX1e4BAJhPDO4AMP/NpDQ/1l+7p+ut3Fa7BYAFYLj1QWmmVs6mlBclaWv3AADMFwZ3AJj/vifdZOVo77VO2wE4Ppo24wMPb9N1pyV5Yu0cAID5wuAOAPPb8pTyjP6G09Iu31y7BYAFpL/htLQrtk5Smp9OMlW7BwBgPjC4A8D89v3puiWjPVfX7gBgwSkZn/aIJt1kfZLvrV0DADAfGNwBYP5anVJ+YLD5rLRLN9RuAWAB6q3Ymv7GM5JSfjTJuto9AABzncEdAOavH0oyHu2+qnYHAAvYeP91Sco4yTMrpwAAzHm+bR4A5qf1KeW3B1vO7Q1OOb92CwALWOlPpTtyd5m9/eZzkrwsySdrNwEAzFUu3AFgfvqRpAxctwNwMox2X5nSH01SygtrtwAAzGUu3AFg/tmSlP8+3HZhO9h8Vu0WABaB0vZT2n5z5BPv25Xk9Un+oXYTAMBc5MIdAOafH0vTNMPdD6ndAcAiMtx2UZqplbMp5cVxvAUAcK98SAKA+WVnUl463PHgdrDxYO0WABaT0qQZr2gOf/Rta5N8OMnf1k4CAJhrXLgDwPzyE6XtldGpV9TuAGAR6m84kN6qHV1K89NJltTuAQCYawzuADB/7E3yxOGOS5oynKndAsCiVDI+7YaSbrImyQ/XrgEAmGsM7gAwf/xU6Q264c7LancAsIi1yzZlsOXcpJQfSrK1dg8AwFxicAeA+eFgkm8Z7ry8KYOp2i0ALHKjfdelNL02yfNqtwAAzCUGdwCYF8qzS380O9x5Se0QAEgzWprhroc0Sb41yUW1ewAA5gqDOwDMfecl3Q2jXQ9pS29UuwUAkiTDnZelGS2dTSn/KZ4tAQCSJG3tAADgGyjl18pgatvUOY9vSuNHNwBzQ2nalNHS5vDH3rkxyQeTvKN2EwBAba4QAGBuuyRdd/Vo95VtaQe1WwDgqww2nZl2xdZJSvOCJDO1ewAAanMmBwBzV0nKb5bhks1T5zyuKcXvyQGYa0p6yzaUQze/cSbJJMlrahcBANTkyR0A5q6HJN0loz1XtaXp1W4BgHvVLt+SwZZzk1J+KMm22j0AADUZ3AFgbiop5TnNePns8JTza7cAwNc12nddStPrJeUFtVsAAGoyuAPA3PSwdN35o73XtPFFqQDMcc1oaYa7r2qS7jFJLq/dAwBQi8EdAOaeJqV5TjO9anaw+ezaLQBwn4x2XpJmauVsSvOSJN6FBgAsSgZ3AJh7HpVucnC099o2vigVgPmi6WV82g1tusmBJN9ZOwcAoAZP8QAwt7QpzX9sl6ybDDaeUbsFAO6X/vr96a3Z1aU0P51kVe0eAICTzeAOAHPLY9NN9oz2XduklNotAHA/lYxPe2RJsjTJs2vXAACcbL6FDQDmjn5K8/J22cal49MeURKDOwDzTzOcTnfk7jJ7+83nJnlFkttqNwEAnCwu3AFg7vgX6SZbx/uua4ztAMxnoz1XpQymJynlF+KHGgCwiLhwB4C5YZTSvLy3cuv0aN+1rtsBmNdK00szWtIcvvVdW5J8IMk7ajcBAJwMLtwBYG74V+kmG0au2wFYIAabz067cuskpXlRjr7THQBgwXPhDgD1zaQ0f9Bbs2s82n2ltR2ABaKkt2xTOfShN04lGSZ5de0iAIATzYU7ANT3b9JNVo33XWdsB2BBaZdtynDbhSXJ9yY5ULsHAOBEM7gDQF3LU8qP9Declnb55totAHDcjfZdm9Ifd75AFQBYDAzuAFDXD6Trloz2Xlu7AwBOiNIfZ3zg4W267tIkj63dAwBwIhncAaCeNSnl+webz067ZF3tFgA4YQZbzku74pRJSvPiJMtq9wAAnCgGdwCo50eSMh7tuap2BwCcWKVk6uCNTbpudZKfqp0DAHCitLUDAGCR2pJS/sdg6wXtYMu5tVsA4IRrRkvSHb6rzN7+4QuSvCLJbbWbAACONxfuAFDHj6c07Wj3lbU7AOCkGe29JmU4M0kpvxTPowDAAuTCHQBOvl1JXjrccUkz2HiwdgsAnDSl6aUZL28Of+wdm5J8LMlbazcBABxPLgoA4OR7Vmn7Ge16SO0OADjpBpvOSG/Nri6leUGStbV7AACOJ4M7AJxcZyT5tuGplzdlMF27BQAqKJk6+OiSUmaS/EztGgCA48krZQDgZCrlpaU/3jF97hOa0vRq1wBAFWUwlXRdOfLpDx5M8pdJbqrdBABwPLhwB4CT5+J03cNGux/alt6odgsAVDXadUWa6dWzKc0vJ/GDEQBYEFy4A8DJUVLKb5Xhkk1TZz+uKY0fwQAscqVJu2xjc+jDf70iSZfkNbWTAACOlQt3ADg5rk3XXTzee01b2n7tFgCYE3qrdmRwynlJyo8k2V+7BwDgWBncAeDEa1LK85vpVbNHRwUA4MvG+x+eMhiXlPIr8YwKAMxzPswAwIn3bem608b7Htam+NELAF+pDKYyPu2RbbruwiTfWbsHAOBYeOoHgBNrkNI8p122adLfeHrtFgCYkwabz0xvze4upfxMkk21ewAAHiiDOwCcWE9JN9k63n99k5TaLQAwR5VMnfGYUko7TsovxA9NAGCeamsHAMACNpPSvLy3eud4tPdqwwEAfB2lP07pDcuRT7xvT5J3Jfm72k0AAPeXC3cAOHH+bbrJ6vH+643tAHAfDLdfnHb5lklK84tJVtbuAQC4vwzuAHBirE4pP9LfeDDt8s21WwBgfihNps76libJqiQ/UzsHAOD+MrgDwInxo0mmxvuuq90BAPNKu2R9RrsfWpL8iyTXVM4BALhfDO4AcPxtS8rTh1sfVJrp1bVbAGDeGe16aNol6yYpzUuTLK3dAwBwXxncAeD4+w+laZvh7itrdwDA/NS0mTrr25qk25Dk+bVzAADuq7Z2AAAsMGcmeclo10Oa/voDtVsAYN5qRkuTyZFy5DM3nZvkL5LcVLsJAOAbceEOAMdTKS8og6nJ8NTLa5cAwLw33HNVmpk1synNf0syU7sHAOAbMbgDwPFzVbruytGeq9vSG9ZuAYB5rzS9TJ/12DZdtzleLQMAzANeKQMAx0eT0vx+M7Vi9fRZ39qk+J02ABwPzXhZMjlcjnzmQ+cleX2Sf6jdBADwtVgDAOD4eGy6ycHx/uvbNH6fDQDH02jP1Wln1s6mNL+eZFntHgCAr8UiAADHbpTS/O92+aaZ8Wk3lKTU7gGAhaU06a3Y2hz68Jtnkm5DklfUTgIAuDcGdwA4dt+XdDdOn/OE0kytqN0CAAtSM1qaJOXIpz94ZpK3JXlf3SIAgH/OK2UA4NisSik/0V9/IL1V22u3AMCCNtr1kLTLNk1Sml9NsqZ2DwDAP2VwB4Bj8+NJpkf7H1a7AwAWvqbN1NmPa1LKiqT8crzHDQCYY7xSBgAeuF1J+Y3h9gubwZZza7cAwKLQDKdTesNy5BPv25vk5hx9vQwAwJzgwh0AHrDyvNL2y2jP1bVDAGBRGW5/cHqrT+1SykuS7KjdAwDwZQZ3AHhgLkm6Rw13X9mUwXTtFgBYXErJ1FnfVko7HKaU/5GkVzsJACDxShkAeCCalPL7zXjZ2qlzHt+U4vfXAHCylf4o7fTq5vDH3r45yWySP6/dBABgIQCA+++x6bqzR/sf3pbGQR0A1NLfeDCDU85Lkp9McmHlHAAAgzsA3E9TKc0L2uVbJoNNZ9RuAYBFb3zaN6WZWtmlNL+TZFntHgBgcTO4A8D98wPpJhvGp39Tk5TaLQCw6JXeMNPnPKFNsiXJL9TuAQAWN+9wB4D7bmNK+b3BprN6wx2X1G4BAL6kGS9Lml458qm/Pz3JTUneXrsJAFicXLgDwH33nJRmMNr//9u782A7z8K+47/nPefce+6ixVq9SN5tSV7xvqYBAl6a0kCAYLNjEzAhG5l2krQhIZNJOtMUOunQhJI0LUOaTMCJSdghJGwmbAYbGxwbsGXj3ZZkS5audO8979s/pBAIxng50nuXz2fmjDTS1cz3/nPO6KdHz3tp2x0AwL/SP/aZ6a46tkkp70iyoe0eAGBxMrgDwBNzZpJX9Y99VlWNHdR2CwDwr5WS8dMvL6U3NpJSrk7SbzsJAFh8XCkDAD9aSSlXl9HJQyfOfHlVqm7bPQDAYyjd0XSXHlpN3/WVNUkOSvKhtpsAgMXF4A4AP9rlSX55/JQXVN2DDm+7BQB4HNXEqqSezezWzWcnuSnJzW03AQCLhytlAODxTaRUb+0sX1ePrDuj7RYA4Anob7g4nYOOqFPKu5Ic03YPALB4GNwB4PH9apr6kPGTX1CllLZbAIAnoupk4syXV6Xb76dUfx33uQMAB4grZQDghzsqpfzlyPozOqNHXdB2CwDwJJReP51lh1Yzd123NsmaJB9ouwkAWPgM7gDwQ5U/LZ3epomzX1NKd7TtGADgSepMrEqaJrNbbjsjye1Jbmi7CQBY2FwpAwCP7TlJ84L+xouqqr+07RYA4Cnqb3huuquPa1LKO5Oc0nYPALCwGdwB4AeNpFR/WE2sGowedWHbLQDA01GqTJzxslKNLummVH+TZHnbSQDAwmVwB4Af9Atp6uPGT3lBJ5Xb1wBgvisjE5k469WdJIcn5d3xd2EAYD+xIgDA9zs0pVzTO+SkXv+4n2i7BQAYkmpsWar+kjJz/zeOT9Ik+VTbTQDAwmNwB4Dv985U3VMnz7milN5Y2y0AwBB1lq9LvXt7Bo/c/cwkX01yS8tJAMAC47/RAcC/eFaSy/rHP6eqxg9quwUA2A/GT35+OsvX1ynlz5NsarsHAFhYDO4AsNdISvWOanzFoH/sj7fdAgDsL1U3E2e/uiojE/2U6gNJ/Cs7ADA0BncA2OuX09THj5/y051U3bZbAID9qOovzcTZr+4kOSqlvCeJD38AYCjc4Q4AyfqU8te9Q0/uelAqACwO1djyVOMrysy9Nx2dZFmSj7TdBADMfwZ3AEh5V+l0T5g458pSev22YwCAA6Sz7NA0g5kMtm4+N8m9Sa5ruwkAmN9cKQPAYve8pHl+f+MlVTW2rO0WAOAAG9t0aXoHn9Ak5Q+TPLvtHgBgfjO4A7CYTaZUf9RZekg9evSFbbcAAG0oVcZPf2npLF1bUqr3JdnQdhIAMH8Z3AFYzN6Spj5s/NQXVSk+EgFgsSrd0Uycc2VVemPjKdVHkqxquwkAmJ+sCwAsVqcledPoURekc9DhbbcAAC2rxpZn8tzXdkqpDk8pH0gy1nYTADD/eGgqAItRJ6W8v4wuWTtx9quqUnXb7gEA5oCqvzSdpYeWmbuvPywpJyW5OknTdhcAMH8Y3AFYjH4pyWsmTr+s6iw7rO0WAGAO6UyuThmdLLP337wxyYokH2m7CQCYPwzuACw2R6SUa3oHn9jrb7y47RYAYA7qLl+f1LOZ3br57CS7klzbdhMAMD8Y3AFYTEpK+ctS9Y6fOO+1pXT7bfcAAHNUd/WxqXduzWD7vc9NsjnJDS0nAQDzgIemArCYvDxNc9HYic+rqv6ytlsAgDmtZPwZL05vzYYmyZ8m+XdtFwEAc58T7gAsFmtTqg91Vx41Onby80tKabsHAJjrSpXeISeV2Qe/2TR7drw4yaeS3Nl2FgAwdznhDsAiUd6eUpaMP+PFxnYA4AkrnZFMnvvaqjO5ppNSPpzk9LabAIC5y+AOwGLw00nzorFNl1bVxKq2WwCAeaaMjGfivNd1qv6yfkr1d0k2tt0EAMxNBncAFrqVKdX/6ixfV48e/WNttwAA81TVX5rJC67qlJHxpSnVJ5Mc03YTADD3GNwBWOjenmTl+GmXVSk+9gCAp64aX5nJ89/QKb3+qpTqU0mOaLsJAJhbLA8ALGQvTHLZ2KZLSmfJ2rZbAIAFoLNkTSbPv6pTuiMH7xvd17fdBADMHQZ3ABaqNSnVOzvL19ejx/x42y0AwALSWXrI3tG9M7Iupfp0knVtNwEAc4PBHYCFqCTlHSll+cTpl7tKBgAYus6ywzJ5/us7pdNbn1J9Jk66AwAxuAOwML0saV4wdsJPVtXk6rZbAIAFqrN83T+fdF+fUn02yZFtNwEA7TK4A7DQHJ5S/qi78uh69KgL224BABa4zvJ1mbzgqk7pjh6WUl2b5Li2mwCA9hjcAVhIqpTyrtLpjY+fflmVUtruAQAWgc6ywzJ54c91Sq+/dt/ofmLbTQBAOwzuACwkb0rTPHPs5BdU1dhBbbcAAItIZ8nBWXLhz3eq0ckV+66XObPtJgDgwOu0HQAAQ3JqUt7TO/SUamzTJUmcbgcADqwyMpHeIadUM/fd1Gtmd78iyReS3N52FwBw4BjcAVgIxlKqvyujkysnz31tVTojbfcAAItU6Y1l5LBnVLMP3Npp9ux8WZJbkny97S4A4MAwuAOwEPxB0lw6ec4VVWfJ2rZbAIBFrnRHM3LYaWWwbXPqqW0vTrI1yRfb7gIA9j+DOwDz3fOSvG302Gdl9Ihz2m4BAEiSlE43I+tOK4NHHyz1jvsvTTKZ5BNJmpbTAID9yOAOwHy2PqX6eGf5YSMTp7+0pHgWOAAwh5QqI4eckmawJ4Ntd5yflBOTfCDJbNtpAMD+YXAHYL7qppQPlE7v6MnzX98pIxNt9wAA/KBS0luzIWVkIrMP3LIppVyc5P1JHm07DQAYPkcBAZivfitNc8H4qT/TqcZXtt0CAPC4Ro+6IBPnvKaUqntGSnVdklPbbgIAhs8JdwDmo+ck+eORI88t/eOe1XYLAMAT0plcnd7BJ5TZ+74+3gymr0hyS5JvtN0FAAyPwR2A+ebQlOoTnSVr+xNnvbKUykcZADB/VKNLMrLu9GqwdXNVTz38kiSjST4ZD1MFgAXBSgHAfLL33vaqd/zk+a+vqv6StnsAAJ600h3JyLozSjOzK4OHv/NjKeW8JB9KMtV2GwDw9BjcAZhP/kuSyyfOuLx0Vx7ddgsAwFNXqvTWbko1viIz9998VFJenjSfSXJP22kAwFNncAdgvnhhkj8YPeqCjB77zLZbAACGorPs0PTWnlBm7/+niWZ2z5VJtif5YttdAMBTY3AHYD7YmFI+3FlxRHfijJeVlKrtHgCAoan6SzJy+FlVvevBqt7xwCVJOSPJx5PsarsNAHhyDO4AzHVLUqpPlt746snzr+qU3ljbPQAAQ1c63YwcekrK6JLMPnjrsUl5VdJcn+T2ttsAgCfO4A7AXFYl5S9SyvmT57226iw5uO0eAID9qKS7fH16B59YBg99a6yZ3vnqJMuSfDrJbLttAMATYXAHYC57c5I3jJ38U2Xk0FPabgEAOCCq0SUZOeKcKrPTGWy787yU6sVJ84V4oCoAzHkGdwDmqhckecfIEedkbOPFSUrbPQAAB0wpVXprNqS78pjMPnTr8mZ2z+uS9JNcG6fdAWDOMrgDMBedmlI+1FlxRGfyzFd4SCoAsGhV4yv2nnafmSqDh++6MKV6adLcmGRz220AwA8yuAMw16xJqT5djS5dtuT8qzql12+7BwCgVaXqprd2U7qrjs3sltuWNjNTr0lyVPaedt/Vch4A8D0M7gDMJWMp5WOl6m6YvOD1nWpiZds9AABzRjV+UEaOPLcqpcrs1s2nppSrkmZHkq8mqdvuAwAM7gDMHVWSdye5eOKsV5XuyqPb7gEAmHNKqdJddUxGDntGGTz6wEi9a8tP7nuo6j8lub3tPgBY7AzuAMwVv5vkqrETn5eRw89suwUAYE4rIxMZWX966Sw7LINtd6xoZqZenZSzsve0+0Mt5wHAomVwB2Au+Nkk/3X0qAvS33hRktJ2DwDAPFDSmVyT0SPPq0pvPINtm49JPfj5JEckuT7JIy0HAsCiY3AHoG3/Nsmf9w4+IeOnvaSkVG33AADML6VKd8URGT3i3CopZfDwXaemaX4hydokX0uyveVCAFg0DO4AtOnMlPLhzvL13YlzrqxK1W27BwBg3iqdXnqrj8vI4WeX1DPV4JG7z0ryi0nWJbk5ybZ2CwFg4TO4A9CWDSnVp6qxg8aXXPCGTun12+4BAFgQSnc0vbWb9g7vzaAabL/ntDTNLyY5McnmJPe0WwgAC5fBHYA2rEupPlN6YyuXXPjGTtVf1nYPAMCCU3r99NZuzOgR55bS6ZbBI/duTD37+pRySZKdSW5NMmg5EwAWFE+lA+BAW5VSXVs6vWMmL3xjp7P0kLZ7AAAWhWYwnek7v5w9t31mUO98qJNSPZSm/uMkf5Lktrb7AGAhMLgDcCAtTSmfTOmcOnn+66vuiiPb7gEAWISazD74rezZ/LnM3Pf1Jk1TkvKZpPk/Sf4qHrIKAE+ZwR2AA2U8pXwsKedNnnNF1V2zoe0eAIBFr96zIzPfuS577vzioH70wU5K2ZOmeX+S9yb5YPZePQMAPEEGdwAOhLGU8oEkz5o485Wld8hJbfcAAPB9mgwevjvTd30l03dfP2j27OiklOk0zYeT/G32ju/3txwJAHOewR2A/a2fUt6XJheNn3F5GTnstLZ7AAB4PE2T2W13ZObeGzNzz9cG9dTDnSRJqW5IU38wyceTfD7J7jYzAWAuMrgDsD/1U8o1aZpLxk+7LCPrz2i7BwCAJ6XJYMcDmb3/5sw88E/N7Jbbk6YuKWUmTb6QNJ9M8rkkX0iytd1WAGifwR2A/eV7xvaXZGT9mW33AADwNDWD6cxuuT2zD30rgy231bMP31XS1Hu3hVJtTlN/Psl1SW5IcmP2XkPTtFcMAAeWwR2A/WE8pfxtmuYnjO0AAAtXM5jJ4JG7Mth2Z2a33ZnBtu8M6qltne9+Qam2pWluSpqbk9ya5FtJvp3k9nggKwALkMEdgGFbkpQPJblg/PTLy8g6d7YDACwmzcxUBtvvy2DHvam335fBjvubwY7762Z6Z+f7vrBU25LcmabenOTuJPckuTd7T8Xfn+TBJA8l2RWn5AGYJwzuAAzTipTysaScPnHmy0vvkJPb7gEAYI5oZvek3vlQ6l1bU+/cknrq4b2vXVvrevf2upne2X3MP1jKdFK2JdmWpnkoabYmeTjJI/te27/nxx/2mt3v3yAAxOAOwPAcklJ9IqVsmDjr1VVv7ca2ewAAmE/qQerpR9PseTT17h1ppnd+91XP7EozvSvNzO400zvrZmaqbmamSjO7p/ruHfKPp5TdSdmR5JGk2Zqm2ZpkW/Y+6HVL9p6k35K9p+q/94R9vb++XQAWJoM7AMNwXEr18VJ110+ce2XVXXl02z0AACwKTZp6kMxMpZnds3eQn92TZnb3vte+X5vZnczu3vfzqTQzu+p673CfZmaqk6Z5rH2kTqkeTJq70jR3JLkzd9AZPwAADOJJREFUyR1JNie5LXvvoncPPQDfx+AOwNN1Zkr10dLrL5s873WdzrLD2u4BAIAnoUkzsyfN9KOpp3fuO2G/Pc2eHXt/3L099a6tg3rq4TSze/71PfQPpGm+kTTfSPL1JDfuez3cwjcCwBxgcAfg6bg0pfxVNXbQyOR5r+tUEyvb7gEAgP2mmdmdemrfHfQ7H8rg0YdSP/pAM9hxX93M7P6XMb5Ud6epv5Dky0m+uO+1o6VsAA4ggzsAT9XPJnlHZ/m6TJ5zZVVGJ9vuAQCAljSp9zyaevu9GWy/L4NH7s7stjsH9c6HOt/9glJ9PU39ySSfSvLpJA+0lgvAfmNwB+DJqpL8XpJf7a3d1Iyf+fJSOiNtNwEAwJzTzO7O4OG7Mrv1jgy23p7ZLbfXzWC6SpKU6qY09YeTfDTJZ5PsabMVgOEwuAPwZEwk5c+S5vmjR1+YsROfl5Sq7SYAAJgfmjqDR+7J7JZvZ+aBW5vZLd9uUg+qlLIrTfOxJNck+UCSrS2XAvAUGdwBeKIOT6nenzQnj538/DJ65Plt9wAAwLzWDGYyu+W2zN5/c2buu2lQTz3SSVKnlH9I07wnydUxvgPMKwZ3AJ6IC1Kq95VO76CJs17Z6a4+vu0eAABYYJoMHrk3M/fdlOm7rx/Ujz7YScpsko8kzbuT/G2S3S1HAvAjGNwBeDwlyRuS8j+qiZWZPOeKTjW5uu0mAABY8AY77svMXV/N9F3X7T35Xqodaep3J/mTJF9tuw+Ax2ZwB+CHGUvyR0le1Tv4xGb89MtK6fbbbgIAgMWlaTK79fZM3/mlzNx9fd3Us1VK+Uqa5g+T/EWSXW0nAvAvDO4APJZjU8o1aZqT+psuSf/YZyfFRwYAALSpmdmd6bu/munNn6sH2++rUqrtaep3JPmfSe5suw8AgzsAP+iFKeVdpdvvT5z5cve1AwDAnNNkdtudmb7ts5m+52tNmrpJ8t4kv5/kupbjABY1gzsA/6yf5K1Jfq674oh6/IxXVNXYsrabAACAx1FPPZLpzddmz+2fGzSzezop5R/SNL+b5O+TNG33ASw2BncAkmRTSvXeNPWJ/eOenf7Gi5NStd0EAAA8Qc3snkzf8YXs+fanBvXu7Z2U8qU0zVuSfDiGd4ADxuAOsLiVJFellP9eeuPdiTNe6goZAACYz+pBpu+6Lrtv/cSg3rW1k1K+nKb5jSQfi+EdYL8zuAMsXgenlD9N01zaW7upGX/Gz5QyOtl2EwAAMAxNnem7vpLdt3xsUO/a1kkp16Zpfi3JZ9tOA1jIDO4Ai9OLUqp3llItGzvpp6qRI8+JjwQAAFiA6kGmv/PlvcP77u2dpHwwaX41ydfbTgNYiKwrAIvLqiRvT/KSzvL19cQZL62qiVVtNwEAAPtZU89m+vZrs/uWvxs0s7urJP87yW8mubflNIAFpdN2AAAHzAtTqo+mlDPHTri0jD/jZ0oZmWi7CQAAOABKqdJdcWRGjzyvSpoy2HbnaSl5477f/nKS2Tb7ABYKJ9wBFr6Dk/L2pHlhZ/m6evy0y6rOkrVtNwEAAC2qd23L1Dc+mJl7bkhK9Z009ZuS/HU8WBXgaTG4AyxcVZLXppS3plTjY5surUaP/rGkVG13AQAAc8TsltszdeM19WD7vVVK+Yc0zRuT3Nx2F8B8ZXAHWJhOSinvTNOc1119XDN+ygtLNbGy7SYAAGAuaupM3/HFTN38wUEzsydJ89+S/E6SnS2XAcw7BneAhWUyyW8m5VfKyFjGTnp+Z2TdM+LtHgAA+FGa6V2ZuvnDmb7j80mp7k5TvyHJ+9vuAphPLDAAC0NJ8qKU6g/S1IeMHnle+hsvSRkZb7sLAACYZ2a33ZGpG66uB9vvq5JyTdL8QpK72+4CmA8M7gDz34kp5e1pmmd2lh1Wj5/6wqqzfH3bTQAAwHzW1Nlz22ey++aP1E0zmErT/Ick70xSt50GMJcZ3AHmr5VJ3pLk50pvrBk74Sc7I4efnRRv7QAAwHDUu7Zl1w1XN7MP3lqScm3SXJHk1ra7AOYqqwzA/NNL8oaU6neSLBk96oLS3/DclN5Y210AAMCC1GT6rq9m6sb3DZrZ3YM0zW8keVuSQdtlAHONwR1g/ihJnpdSvS1NfUxvzYamf9K/L53JNW13AQAAi0Cz59HsuvGazNzztaSUL6VpXpXk5ra7AOYSgzvA/HBWUt6WNBd2lqwdjJ34vE53zYa2mwAAgEVo5t4bs+uGqwfNzFS977T7W+O0O0ASgzvAXHdMkt9N8pIyMjkY23RJZ+Tws5JStd0FAAAsYs30zuz62jWZueeGpJTPp2lemeSbbXcBtM3gDjA3rU3y5qRcVTrdMnrcs6vRY/5NSmek7S4AAIDvmrn7huz62tWDZnbPTJrmV5K8I0nTdhdAWwzuAHPLsiT/MaX8SlL6o0eeV/rHPydldLLtLgAAgMdU796eqRuubmbuv7mklI+maa5Ick/bXQBtMLgDzA0TSX4+pfr1NPWykXWnp7/x4lTjK9ruAgAAeAKaTN/xxUzd9Dd1Uw92pKmvTPJXbVcBHGgGd4B2jSZ5XUr15jT16t7BJzb9jReXztJD2u4CAAB40uqdW7LzK39eD7bdWSX5v0l+Kcn2dqsADhyDO0A7eklek1L9Vpr60O7q45qxjZeWzkHr2+4CAAB4epo6u7/599l9y8eapHwnTX1Zkn9sOwvgQDC4AxxY3SSvSKl+O029vrviyLq/6dKqu/LotrsAAACGarDtO9l53Z8N6l1bS5LfTvJ7SWZbzgLYrwzuAAdGN8lLU6q3pKmP6ixfX49tuqTqrj4u3ooBAICFqpndk6mb/ibTd34pKeUf0zSXJ7mj7S6A/cXKA7B/dZNcvm9oP7qzfF3d33hx1VuzId6CAQCAxWLmnhuy6/r3DprB9FSa5sok72m7CWB/sPYA7B/dJJftG9qP6Sw7bO/QvnZjvPUCAACLUT31cHZd9//qwa6t32x2bz8hSd12E8CwWX0Ahuufr475rTT10YZ2AACA79HUGex+5OodH/+9F7edArA/dNsOAFggeklellL9Zpr6qM7SQ+r+xovTW7uxMrQDAADsU6p0xpbvaTsDYH8xuAM8Pb0kr9g3tB/RWXZo3d9wcXprNxjaAQAAABYZgzvAUzOS5FUp1ZvT1Ov3XR2T3prjDe0AAAAAi5TBHeDJGU3y6pTqN9LU6zrL19djGy9Kd/VxhnYAAACARc7gDvDE9JNcsW9oP6R70OF1f+NF6a461tAOAAAAQBKDO8CPMpbktSnVf05Tr+2uOLLub7go3VXHVG2HAQAAADC3GNwBHtt4ktelVP8pTb26u/Lopr/huemuPNrQDgAAAMBjMrgDfL/xJFelVL+epl7VXXVM099wUborjnRvDAAAAACPy+AOsNdEkjekVL+Wpl7ZXXVs0994UboHHWFoBwAAAOAJMbgDJKnGll9ZTz38+73VxzX9DRelc9DhhnYAAAAAnhSDO0CSyR9/0y3Nri3pLF9vaAcAAADgKTG4AyQp3cnZavl42xkAAAAAzGNV2wEAAAAAALAQGNwBAAAAAGAIDO4AAAAAADAEBncAAAAAABgCgzsAAAAAAAyBwR0AAAAAAIbA4A4AAAAAAENgcAcAAAAAgCEwuAMAAAAAwBAY3AEAAAAAYAgM7gAAAAAAMAQGdwAAAAAAGAKDOwAAAAAADIHBHQAAAAAAhsDgDgAAAAAAQ2BwBwAAAACAITC4AwAAAADAEBjcAQAAAABgCAzuAAAAAAAwBAZ3AAAAAAAYAoM7AAAAAAAMgcEdAAAAAACGwOAOAAAAAABDYHAHAAAAAIAhMLgDAAAAAMAQGNwBAAAAAGAIDO4AAAAAADAEBncAAAAAABgCgzsAAAAAAAyBwR0AAAAAAIbA4A4AAAAAAENgcAcAAAAAgCEwuAMAAAAAwBAY3AEAAAAAYAgM7gAAAAAAMAQGdwAAAAAAGAKDOwAAAAAADIHBHQAAAAAAhsDgDgAAAAAAQ2BwBwAAAACAITC4AwAAAADAEBjcAQAAAABgCAzuAAAAAAAwBAZ3AAAAAAAYAoM7AAAAAAAMgcEdAAAAAACGwOAOAAAAAABDYHAHAAAAAIAhMLgDAAAAAMAQGNwBAAAAAGAIDO4AAAAAADAEBncAAAAAABgCgzsAAAAAAAyBwR0AAAAAAIag23YAwFxQ9/ZcX812L267AwAAYKGrqu69bTcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB8v/8PQPSU4hUqNkYAAAAASUVORK5CYII=" style="height:50px;"></td></tr>
    <tr><td headers="Varied  vb" class="gt_row gt_center">1200-1400</td>
<td headers="Varied  mean" class="gt_row gt_right">1,269.34</td>
<td headers="Varied  sd" class="gt_row gt_right">291.65</td>
<td headers="Varied  Distribution" class="gt_row gt_center"><img 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" style="height:50px;"></td></tr>
  </tbody>
  
  
</table>
</div>

library(gtExtras)
gt_two_column_layout(tables)

Training Condit
Vb	Mean	Sd	Distribution
Constant
100-300	252.99	219.63
350-550	191.58	159.52
600-800	150.40	110.83
800-1000	188.91	160.60
1000-1200	240.57	173.05
1200-1400	295.42	186.14
Varied
100-300	387.25	343.45
350-550	289.01	272.48
600-800	236.17	188.89
800-1000	224.16	145.95
1000-1200	209.20	130.32
1200-1400	242.13	136.30

Training Condit
Vb	Mean	Sd	Distribution
Constant
100-300	522.83	245.10
350-550	660.61	224.30
600-800	771.75	210.50
800-1000	1,008.76	254.58
1000-1200	1,173.42	307.89
1200-1400	1,306.99	355.81
Varied
100-300	665.49	365.02
350-550	772.47	321.45
600-800	879.54	291.57
800-1000	1,070.25	248.79
1000-1200	1,180.70	265.92
1200-1400	1,269.34	291.65

#https://modelsummary.com/articles/datasummary.html

datasummary(vx*vb ~ Mean + SD + Histogram, data = testAvg)

tinytable_juwvhm9rd06xjvj0682o

	vb	Mean	SD	Histogram
vx	100-300	592.33	316.56	▂▆▇▄▂▃▂▁▁
	350-550	715.10	280.69	▁▅▇▇▅▃▂▁▁▁
	600-800	824.26	258.15	▁▅▅▇▅▃▂▁▁▁
	800-1000	1038.72	252.85	▁▃▆▆▇▃▂▁▁
	1000-1200	1176.96	287.31	▁▂▄▆▇▅▃▂▁▁
	1200-1400	1288.65	325.64	▂▄▅▇▅▃▁▂▁

datasummary(vx*vb*condit ~ Mean + SD + Histogram, data = testAvg)

tinytable_it4r53z7k2cw2072wg20

	vb	condit	Mean	SD	Histogram
vx	100-300	Constant	522.83	245.10	▁▅▅▇▄▃▂▁▂▁
		Varied	665.49	365.02	▄▇▇▂▁▅▃▂▂▁
	350-550	Constant	660.61	224.30	▁▃▅▇▇▃▂▂ ▁
		Varied	772.47	321.45	▄▄▇▄▃▂▁▂▁▂
	600-800	Constant	771.75	210.50	▁▄▆▃▇▄▂▁▂▁
		Varied	879.54	291.57	▃▄▄▇▇▄▂▁▂▂
	800-1000	Constant	1008.76	254.58	▂▄▇▆▅▂▁▁
		Varied	1070.25	248.79	▁▂▅▃▅▇▄▃▁▂
	1000-1200	Constant	1173.42	307.89	▁▂▅▆▇▆▃▂ ▃
		Varied	1180.70	265.92	▁▂▄▆▇▆▃▃▁▃
	1200-1400	Constant	1306.99	355.81	▂▄▆▇▄▃▁▃▁
		Varied	1269.34	291.65	▂▄▂▅▆▇▄▂▁

datasummary(vx*vb*condit ~ Mean + SD + Histogram, data = test)

tinytable_n5imsh2pumt8xwvn58xl

	vb	condit	Mean	SD	Histogram
vx	100-300	Constant	524.28	326.84	▅▇▆▃▂▁▁
		Varied	663.96	448.23	▅▇▄▃▂▂▁
	350-550	Constant	658.76	302.96	▁▅▇▆▄▂▁
		Varied	767.77	401.84	▂▇▆▄▃▂▁▁
	600-800	Constant	770.39	299.50	▁▄▇▆▄▃▁
		Varied	876.49	389.91	▁▆▇▆▄▃▂▁
	800-1000	Constant	1000.54	356.85	▃▇▆▃▂▁
		Varied	1063.94	369.78	▂▅▆▇▄▃▁
	1000-1200	Constant	1166.65	429.75	▁▂▅▇▄▂▁
		Varied	1180.43	372.22	▁▄▇▇▅▂▁
	1200-1400	Constant	1282.55	482.56	▁▂▄▇▆▃▂▂▁
		Varied	1264.68	411.92	▁▂▄▇▆▄▃▁

datasummary_crosstab(vb ~ condit * tOrder, data = test)

tinytable_ktm80sa3y7w1aw9muitx

		Constant		Varied
vb		testFirst	trainFirst	testFirst	trainFirst	All
100-300	N	671	493	564	544	2272
	% row	29.5	21.7	24.8	23.9	100.0
350-550	N	679	499	573	543	2294
	% row	29.6	21.8	25.0	23.7	100.0
600-800	N	673	497	573	543	2286
	% row	29.4	21.7	25.1	23.8	100.0
800-1000	N	265	197	224	214	900
	% row	29.4	21.9	24.9	23.8	100.0
1000-1200	N	250	190	228	206	874
	% row	28.6	21.7	26.1	23.6	100.0
1200-1400	N	244	187	226	208	865
	% row	28.2	21.6	26.1	24.0	100.0
All	N	2782	2063	2388	2258	9491
	% row	29.3	21.7	25.2	23.8	100.0

datasummary_crosstab(result ~ condit,
                     statistic = 1 ~ Percent("col"),
                     data = test)

tinytable_imhrdydx53crw1625a6w

result		Constant	Varied
Hit	% col	25.0	21.4
Over	% col	52.5	59.0
Under	% col	22.6	19.7
All	% col	100.0	100.0

datasummary_crosstab(result ~ condit*vb,
                     statistic = 1 ~ Percent("col"),
                     data = test)

tinytable_pqwb8xznacswtlbqq0q3

		Constant						Varied
result		100-300	350-550	600-800	800-1000	1000-1200	1200-1400	100-300	350-550	600-800	800-1000	1000-1200	1200-1400
Hit	% col	25.3	24.7	28.1	28.1	21.8	16.2	20.1	24.7	20.7	19.4	21.0	19.8
Over	% col	71.6	60.1	40.4	42.4	41.6	34.6	77.9	63.7	51.8	53.9	47.2	33.9
Under	% col	3.2	15.2	31.5	29.4	36.6	49.2	2.0	11.6	27.5	26.7	31.8	46.3
All	% col	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0

datasummary(vx+dist+vy ~ mean *condit*vb,
            data = e1)

tinytable_y4170hvkvcrdvo3kq58z

	Constant						Varied
	100-300	350-550	600-800	800-1000	1000-1200	1200-1400	100-300	350-550	600-800	800-1000	1000-1200	1200-1400
vx	509.05	644.81	760.25	927.21	1166.65	1282.55	611.22	733.03	848.60	1012.53	1103.13	1185.13
dist	240.32	178.74	145.34	137.17	232.80	287.09	338.27	258.83	215.48	204.68	204.51	260.39
vy	81.42	115.76	157.74	206.72	244.71	276.07	92.91	126.21	158.92	189.22	209.89	230.23

datasummary(Heading("X Velocity")*vx+Heading("Abs. Deviation")*dist+vy ~ mean *condit*vb,
            data = e1)

tinytable_4non5ske3hez49i0roj6

	Constant						Varied
	100-300	350-550	600-800	800-1000	1000-1200	1200-1400	100-300	350-550	600-800	800-1000	1000-1200	1200-1400
X Velocity	509.05	644.81	760.25	927.21	1166.65	1282.55	611.22	733.03	848.60	1012.53	1103.13	1185.13
Abs. Deviation	240.32	178.74	145.34	137.17	232.80	287.09	338.27	258.83	215.48	204.68	204.51	260.39
vy	81.42	115.76	157.74	206.72	244.71	276.07	92.91	126.21	158.92	189.22	209.89	230.23

datasummary(vx + dist ~ Factor(condit) * (mean + sd),
            data = test)

tinytable_4z0355xhpo0drg1ofci0

	Constant		Varied
	mean	sd	mean	sd
vx	787.61	423.16	882.02	452.04
dist	207.76	254.08	279.52	329.31

datasummary(vx + dist ~ Factor(condit)*vb * (mean + sd),
            data = test)

tinytable_8i9iur34tx3jctr69hbn

	Constant												Varied
	100-300		350-550		600-800		800-1000		1000-1200		1200-1400		100-300		350-550		600-800		800-1000		1000-1200		1200-1400
	mean	sd	mean	sd	mean	sd	mean	sd	mean	sd	mean	sd	mean	sd	mean	sd	mean	sd	mean	sd	mean	sd	mean	sd
vx	524.28	326.84	658.76	302.96	770.39	299.50	1000.54	356.85	1166.65	429.75	1282.55	482.56	663.96	448.23	767.77	401.84	876.49	389.91	1063.94	369.78	1180.43	372.22	1264.68	411.92
dist	253.85	297.56	190.57	229.23	150.01	184.41	183.91	241.99	232.80	282.14	287.09	290.25	385.65	426.23	285.04	340.41	234.14	269.93	220.87	248.39	207.87	226.43	241.93	234.68

datasummary(vb*Factor(condit) * (mean + sd) ~ vx + dist,
            data = test)

tinytable_gw4ts13t5p1p7bid90b1

vb	condit		vx	dist
100-300	Constant	mean	524.28	253.85
		sd	326.84	297.56
	Varied	mean	663.96	385.65
		sd	448.23	426.23
350-550	Constant	mean	658.76	190.57
		sd	302.96	229.23
	Varied	mean	767.77	285.04
		sd	401.84	340.41
600-800	Constant	mean	770.39	150.01
		sd	299.50	184.41
	Varied	mean	876.49	234.14
		sd	389.91	269.93
800-1000	Constant	mean	1000.54	183.91
		sd	356.85	241.99
	Varied	mean	1063.94	220.87
		sd	369.78	248.39
1000-1200	Constant	mean	1166.65	232.80
		sd	429.75	282.14
	Varied	mean	1180.43	207.87
		sd	372.22	226.43
1200-1400	Constant	mean	1282.55	287.09
		sd	482.56	290.25
	Varied	mean	1264.68	241.93
		sd	411.92	234.68

tmp <- mtcars[, c("mpg", "hp")]

# create a list with individual variables
# remove missing and rescale
tmp_list <- lapply(tmp, na.omit)
tmp_list <- lapply(tmp_list, scale)

# create a table with `datasummary`
# add a histogram with column_spec and spec_hist
# add a boxplot with colun_spec and spec_box
emptycol = function(x) " "
datasummary(mpg + hp ~ Mean + SD + Heading("Boxplot") * emptycol + Heading("Histogram") * emptycol,
    output = "kableExtra",
    data = tmp) %>%
    kableExtra::column_spec(column = 4, image = spec_boxplot(tmp_list)) %>%
    kableExtra::column_spec(column = 5, image = spec_hist(tmp_list))

	Mean	SD	Boxplot	Histogram
mpg	20.09	6.03
hp	146.69	68.56

tmp_list <- lapply(test |> ungroup() |> select(vx,vb,condit), na.omit)
# scale only numeric columns - not all are numeric so can't just lapply

tmp_list <- map(tmp_list, ~if(is.numeric(.x)) scale(.x) else .x)

ts=test |> ungroup() |> select(vx,vb,condit) |> mutate(vxScale=scale(vx)[,1])

 datasummary(vxScale*vb ~  Mean + Histogram,
            data=ts, output = "kableExtra")  %>%
    kableExtra::column_spec(column = 4, image = spec_boxplot(ts))

Warning in ensure_len_html(image, nrows, "image"): The number of provided
values in image does not equal to the number of rows.

	vb	Mean	Histogram
vxScale	100-300	−0.55	▆▇▄▂▁▁
	350-550	−0.28	▂▇▇▅▃▁▁
	600-800	−0.03	▁▅▇▅▃▂▁
	800-1000	0.45	▁▃▇▇▄▂▁
	1000-1200	0.77	▂▅▇▅▂▁
	1200-1400	1.00	▂▄▇▆▄▂▁

datasummary(vx*vb ~  Mean + Median+ SD + Histogram,
            data=test, output = "markdown")

tinytable_jn1ng5p3qezj9w6ckgnw

	vb	Mean	Median	SD	Histogram
vx	100-300	592.40	477.71	396.88	▆▇▄▂▁▁
	350-550	711.79	646.42	358.61	▂▇▇▅▃▁▁
	600-800	822.19	764.23	350.56	▁▅▇▅▃▂▁
	800-1000	1031.39	988.06	364.38	▁▃▇▇▄▂▁
	1000-1200	1173.49	1144.20	402.04	▂▅▇▅▂▁
	1200-1400	1273.58	1237.95	448.34	▂▄▇▆▄▂▁

datasummary(dist*vb ~  Mean + Median+ SD + Histogram,
            data=test, output = "markdown")

tinytable_sllabca9k765gtga8x6k

	vb	Mean	Median	SD	Histogram
dist	100-300	318.12	177.71	371.81	▇▂▁▁▁
	350-550	236.53	126.28	292.49	▇▂▁▁
	600-800	191.08	111.93	233.93	▇▃▁▁
	800-1000	201.90	131.15	245.69	▇▃▁▁
	1000-1200	220.42	149.37	256.15	▇▃▁▁
	1200-1400	264.43	197.72	264.65	▇▄▂▂▁▁

datasummary(vx*vb*condit ~  Mean + Histogram,
            data=test, output = "kableExtra")  %>%
    kableExtra::column_spec(column = 5, image = spec_boxplot(tmp_list))

Warning in ensure_len_html(image, nrows, "image"): The number of provided
values in image does not equal to the number of rows.

	vb	condit	Mean	Histogram
vx	100-300	Constant	524.28	▅▇▆▃▂▁▁
		Varied	663.96	▅▇▄▃▂▂▁
	350-550	Constant	658.76	▁▅▇▆▄▂▁
		Varied	767.77	▂▇▆▄▃▂▁▁
	600-800	Constant	770.39	▁▄▇▆▄▃▁
		Varied	876.49	▁▆▇▆▄▃▂▁
	800-1000	Constant	1000.54	▃▇▆▃▂▁
		Varied	1063.94	▂▅▆▇▄▃▁
	1000-1200	Constant	1166.65	▁▂▅▇▄▂▁
		Varied	1180.43	▁▄▇▇▅▂▁
	1200-1400	Constant	1282.55	▁▂▄▇▆▃▂▂▁
		Varied	1264.68	▁▂▄▇▆▄▃▁

cap <- "Testing - No Feedback"
f <- (`Condit` = condit) ~ (` ` = vx) * (`Distribution` = Histogram) + vx * vb * ((`Avg.` = Mean)*Arguments(fmt='%.0f') + (`SD` = SD)*Arguments(fmt='%.0f'))

datasummary(f,
            data = test,
            output = 'gt',
            title = cap,
            notes = 'Artwork by @Thomas',
            sparse_header = TRUE)

Testing - No Feedback
Condit	Distribution	100-300		350-550		600-800		800-1000		1000-1200		1200-1400
Condit	Distribution	Avg.	SD	Avg.	SD	Avg.	SD	Avg.	SD	Avg.	SD	Avg.	SD
Constant	▃▇▇▄▂▁	524	327	659	303	770	300	1001	357	1167	430	1283	483
Varied	▃▇▇▆▄▃▁	664	448	768	402	876	390	1064	370	1180	372	1265	412
Artwork by @Thomas

f <- (`Band` = vb) ~ (` ` = vx) * (`Distribution` = Histogram) + vx * condit * ((`Avg.` = Mean)*Arguments(fmt='%.0f') + (`SD` = SD)*Arguments(fmt='%.0f'))

datasummary(f,
            data = test,
            output = 'gt',
            title = cap,
            notes = 'Artwork by @Thomas',
            sparse_header = TRUE)

Testing - No Feedback
Band	Distribution	Constant		Varied
Band	Distribution	Avg.	SD	Avg.	SD
100-300	▆▇▄▂▁▁	524	327	664	448
350-550	▂▇▇▅▃▁▁	659	303	768	402
600-800	▁▅▇▅▃▂▁	770	300	876	390
800-1000	▁▃▇▇▄▂▁	1001	357	1064	370
1000-1200	▂▅▇▅▂▁	1167	430	1180	372
1200-1400	▂▄▇▆▄▂▁	1283	483	1265	412
Artwork by @Thomas

f <- (`Band` = vb) ~ (` ` = vx) * (`Distribution` = Histogram) + vx * condit * expMode2 * ((`Avg.` = Mean)*Arguments(fmt='%.0f') + (`SD` = SD)*Arguments(fmt='%.0f'))

datasummary(f,
            data = e1,
            output = 'gt',
            sparse_header = TRUE)

		Constant								Varied
		Train		Train-Nf		Test		Test-Fb		Train		Train-Nf		Test		Test-Fb
Band	Distribution	Avg.	SD	Avg.	SD	Avg.	SD	Avg.	SD	Avg.	SD	Avg.	SD	Avg.	SD	Avg.	SD
100-300	▇▇▄▂▁▁			599	368	524	327	395	265			652	413	664	448	484	406
350-550	▂▇▅▂▁▁			714	332	659	303	554	265			754	395	768	402	655	379
600-800	▁▅▇▄▂▁			816	321	770	300	688	267			882	393	876	390	770	354
800-1000	▂▇▄▁	922	298			1001	357			1002	369			1064	370
1000-1200	▂▆▇▃▁					1167	430			1087	383			1180	372
1200-1400	▁▂▅▇▅▂▁					1283	483			1169	430			1265	412

f <- (`Band` = vb)* expMode2 ~ vx * condit  * ((`Avg.` = Mean)*Arguments(fmt='%.0f') + (`SD` = SD)*Arguments(fmt='%.0f'))

datasummary(f,
            data = e1,
            output = 'gt',
            sparse_header = TRUE)

Band	expMode2	Constant		Varied
Band	expMode2	Avg.	SD	Avg.	SD
100-300	Train
	Train-Nf	599	368	652	413
	Test	524	327	664	448
	Test-Fb	395	265	484	406
350-550	Train
	Train-Nf	714	332	754	395
	Test	659	303	768	402
	Test-Fb	554	265	655	379
600-800	Train
	Train-Nf	816	321	882	393
	Test	770	300	876	390
	Test-Fb	688	267	770	354
800-1000	Train	922	298	1002	369
	Train-Nf
	Test	1001	357	1064	370
	Test-Fb
1000-1200	Train			1087	383
	Train-Nf
	Test	1167	430	1180	372
	Test-Fb
1200-1400	Train			1169	430
	Train-Nf
	Test	1283	483	1265	412
	Test-Fb

f <- (`Band` = vb)* expMode2 ~ dist * condit  * ((`Avg.` = Mean)*Arguments(fmt='%.0f') )

datasummary(f,
            data = e1,
            output = 'gt',
            sparse_header = TRUE)

Band	expMode2	Constant	Varied
Band	expMode2	Avg.	Avg.
100-300	Train
	Train-Nf	321	374
	Test	254	386
	Test-Fb	138	225
350-550	Train
	Train-Nf	229	281
	Test	191	285
	Test-Fb	110	194
600-800	Train
	Train-Nf	169	231
	Test	150	234
	Test-Fb	115	169
800-1000	Train	134	201
	Train-Nf
	Test	184	221
	Test-Fb
1000-1200	Train		204
	Train-Nf
	Test	233	208
	Test-Fb
1200-1400	Train		264
	Train-Nf
	Test	287	242
	Test-Fb

```{r}
#| label: test vx table
#| tbl-cap: "Testing - No Feedback"
#| tbl-subcap: ["Constant Testing - X Velocity", "Varied Testing - X Velocity"]
#| layout-ncol: 2

result <- test_summary_table(test, "vx", mfun = list(mean = mean, median = median, sd = sd))
result$constant #|> kable_styling(full_width = F)
result$varied #|> kable_styling(full_width = F)
```

# A tibble: 6 × 5
  Band      `Band Type`    Mean Median    Sd
  <fct>     <fct>         <dbl>  <dbl> <dbl>
1 100-300   Extrapolation   524    448   327
2 350-550   Extrapolation   659    624   303
3 600-800   Extrapolation   770    724   300
4 800-1000  Trained        1001    940   357
5 1000-1200 Extrapolation  1167   1104   430
6 1200-1400 Extrapolation  1283   1225   483
# A tibble: 6 × 5
  Band      `Band Type`    Mean Median    Sd
  <fct>     <fct>         <dbl>  <dbl> <dbl>
1 100-300   Extrapolation   664    533   448
2 350-550   Extrapolation   768    677   402
3 600-800   Extrapolation   876    813   390
4 800-1000  Trained        1064   1029   370
5 1000-1200 Trained        1180   1179   372
6 1200-1400 Trained        1265   1249   412

nb=5
vt1=e1 |> filter(expMode=="train") |>
  learn_curve_table(gt.train,vx,gw=Trial_Bin,groupVec=c(id,vb,condit),nbins=nb,prefix="Block_") |>
  rename("Band"=vb,"Group"=condit)
  
vt1 %>% kable() %>% add_header_above(c(" "=2, "Training Block "=1," " = ncol(vt1)-3))

Mean Vx over blocks. Mean (Standard Error)
		Training Block
Band	Group	Block_1	Block_2	Block_3	Block_4	Block_5
800-1000	Constant	921 (11)	941 (8)	912 (7)	907 (7)	931 (7)
800-1000	Varied	970 (20)	975 (17)	1017 (18)	1020 (17)	1029 (16)
1000-1200	Varied	1068 (22)	1100 (19)	1080 (19)	1110 (17)	1080 (15)
1200-1400	Varied	1109 (24)	1183 (19)	1160 (23)	1198 (20)	1200 (18)

# vt1 %>% gt() %>% tab_options(column_labels.background.color = "#176940",
#                 table.font.size = px(14))

Training Performance

# vpt1=plotWithTable(vp1,vt1,arrange="V")
nb=5
vp1=e1 |> filter(expMode=="train") |> learn_curve_plot(gt.train,vx,condit,facet_var=vb,groupVec=c(gt.train,condit,tOrder,id,vb),nbins=nb)
vp1

#vp1 / gridExtra::tableGrob(vt1)
#vp1 / gt_temp(vt1)

# vt2=e1 |> filter(expMode=="train") |> 
#   learn_curve_table(gt.train,dist,gw=condit,groupVec=c(id,condit,vb),nbins=nb) %>%
#   rename("Block"=Trial_Bin)

vt2=e1 |> filter(expMode=="train") |> 
  learn_curve_table(gt.train,dist,gw=Trial_Bin,groupVec=c(id,condit,vb),nbins=nb,prefix="Block_") %>%
  rename("Band"=vb,"Group"=condit)

vp2=e1 |> filter(expMode=="train") |> 
  learn_curve_plot(gt.train,dist,condit,facet_var=vb,groupVec=c(gt.train,condit,tOrder,id,vb),nbins=nb) 
vp2

vt3=e1 |> filter(expMode=="train") |> 
  learn_curve_table(gt.train,sdist,gw=Trial_Bin,groupVec=c(id,vb,condit),nbins=nb,prefix="Block_") |>
  rename("Band"=vb,"Group"=condit) 
vt3 %>% kable(format = "html",escape = FALSE)

Signed Deviation over blocks. Mean (Standard Error)
Band	Group	Block_1	Block_2	Block_3	Block_4	Block_5
800-1000	Constant	24 (9)	34 (6)	13 (6)	10 (5)	28 (5)
800-1000	Varied	64 (17)	61 (13)	96 (15)	102 (14)	104 (13)
1000-1200	Varied	-14 (19)	10 (15)	-11 (15)	18 (13)	-11 (12)
1200-1400	Varied	-155 (21)	-85 (16)	-108 (19)	-78 (16)	-76 (14)

vp3=e1 |> filter(expMode=="train") |> learn_curve_plot(gt.train,sdist,condit,facet_var=vb,groupVec=c(gt.train,condit,tOrder,id,vb),nbins=nb) 
vp3

vp4 <- e1 |> filter(expMode=="train") |> group_by(id) |>
  mutate(Trial_Bin = cut(gt.train,breaks = nb,include.lowest=TRUE,labels=FALSE)) |>
  group_by(id,Trial_Bin,condit, vb) |> summarise(nHits=sum(dist==0),n=n(),Percent_Hit=nHits/n) %>%
  learn_curve_plot2(Trial_Bin,Percent_Hit,color_var=condit,facet_var=vb,groupVec=c(id,vb,condit))

22

```{r}
#| layout: [[65, 65],[-5], [1,550, 550]]
#| label: train tables2
#| tbl-subcap:
#|   - "Vx"
#|   - "Deviation from target"
#| eval: false

vt1 %>% kable(format = "html",escape = FALSE)
vt2 %>% kable(format = "html",escape = FALSE)

vp3+big_text()
vp4+big_text()
```

33

```{r}
#| layout: [[65, 65],[-5], [1,350, 350,10]]
#| label: train tables
#| tbl-subcap:
#|   - "Vx"
#|   - "Deviation from target"
#| column: screen-inset-right
#| eval: false
vt1 %>% kable(format = "html",escape = FALSE)
vt2 %>% kable(format = "html",escape = FALSE)

vp3
vp4
```

((vp1+big_text()+vp2+big_text())/(vp3+big_text()+vp4+big_text())) + plot_layout(guides = "collect")

Testing

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |>  
  ggplot(aes(x = vb, y = dist,fill=condit)) +
    stat_summary(geom = "bar", position=position_dodge(), fun = mean) +
    stat_summary(geom = "errorbar", position=position_dodge(.9), fun.data = mean_se, width = .4, alpha = .7)

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |>  ggplot(aes(x = vb, y = dist,fill=condit)) +
    stat_summary(geom = "bar", position=position_dodge(), fun = mean) +
    stat_summary(geom = "errorbar", position=position_dodge(.9), fun.data = mean_se, width = .4, alpha = .7)

# create a kable table to mirror plot of distance effects for vb and condit 


e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |> group_by(vb,condit) %>% 
  summarise(distMean=mean(dist),distSd=sd(dist)) %>% 
  mutate(meanLab=paste0("Mean=",round(distMean,0)),sdLab=paste0("Sd=",round(distSd,0))) %>% 
  mutate(sumStatLab=paste0(meanLab,"\n",sdLab)) %>% 
  select(vb,condit,sumStatLab) %>% 
  spread(condit,sumStatLab) %>% 
  kable(format = "html",escape = FALSE) %>% 
  kable_styling(font_size = 10)

vb	Constant	Varied
100-300	Mean=254 Sd=298	Mean=386 Sd=426
350-550	Mean=191 Sd=229	Mean=285 Sd=340
600-800	Mean=150 Sd=184	Mean=234 Sd=270
800-1000	Mean=184 Sd=242	Mean=221 Sd=248
1000-1200	Mean=233 Sd=282	Mean=208 Sd=226
1200-1400	Mean=287 Sd=290	Mean=242 Sd=235

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |>  ggplot(aes(x = vb, y = vx,fill=condit)) +
    stat_summary(geom = "bar", position=position_dodge(), fun = mean) +
    stat_summary(geom = "errorbar", position=position_dodge(.9), fun.data = mean_se, width = .4, alpha = .7)

testAvg |>  ggplot(aes(x = vb, y = vx,fill=condit)) +
    stat_summary(geom = "bar", position=position_dodge(), fun = mean) +
    stat_summary(geom = "errorbar", position=position_dodge(.9), fun.data = mean_se, width = .4, alpha = .7)

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |> group_by(vb,condit) %>% 
  summarise(vxMean=mean(vx),vxSd=sd(vx)) %>% 
  mutate(meanLab=paste0("Mean=",round(vxMean,0)),sdLab=paste0("Sd=",round(vxSd,0))) %>% 
  mutate(sumStatLab=paste0(meanLab,"\n",sdLab)) %>% 
  select(vb,condit,sumStatLab) %>% 
  spread(condit,sumStatLab) %>% 
  kable(format = "html",escape = FALSE) %>% 
  kable_styling(font_size = 10)

vb	Constant	Varied
100-300	Mean=524 Sd=327	Mean=664 Sd=448
350-550	Mean=659 Sd=303	Mean=768 Sd=402
600-800	Mean=770 Sd=300	Mean=876 Sd=390
800-1000	Mean=1001 Sd=357	Mean=1064 Sd=370
1000-1200	Mean=1167 Sd=430	Mean=1180 Sd=372
1200-1400	Mean=1283 Sd=483	Mean=1265 Sd=412

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |>  ggplot(aes(x = vb, y = sdist,fill=condit)) +
    stat_summary(geom = "bar", position=position_dodge(), fun = mean) +
    stat_summary(geom = "errorbar", position=position_dodge(.9), fun.data = mean_se, width = .4, alpha = .7)

testAvg |>  ggplot(aes(x = vb, y = sdist,fill=condit)) +
    stat_summary(geom = "bar", position=position_dodge(), fun = mean) +
    stat_summary(geom = "errorbar", position=position_dodge(.9), fun.data = mean_se, width = .4, alpha = .7)

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |> group_by(vb,condit) %>% 
  summarise(sdistMean=mean(sdist),sdistSd=sd(sdist)) %>% 
  mutate(meanLab=paste0("Mean=",round(sdistMean,0)),sdLab=paste0("Sd=",round(sdistSd,0))) %>% 
  mutate(sumStatLab=paste0(meanLab,"\n",sdLab)) %>% 
  select(vb,condit,sumStatLab) %>% 
  spread(condit,sumStatLab) %>% 
  kable(format = "html",escape = FALSE) %>% 
  kable_styling(font_size = 10)

vb	Constant	Varied
100-300	Mean=252 Sd=299	Mean=385 Sd=427
350-550	Mean=163 Sd=250	Mean=265 Sd=356
600-800	Mean=62 Sd=229	Mean=152 Sd=323
800-1000	Mean=89 Sd=291	Mean=136 Sd=303
1000-1200	Mean=64 Sd=360	Mean=66 Sd=300
1200-1400	Mean=-3 Sd=408	Mean=-25 Sd=336

testAvg |>  ggplot(aes(x = vb, y = Percent_Hit,fill=condit)) +
    stat_summary(geom = "bar", position=position_dodge(), fun = mean) +
    stat_summary(geom = "errorbar", position=position_dodge(.9), fun.data = mean_se, width = .4, alpha = .7)

testAvg |> group_by(vb,condit) %>% 
  summarise(Percent_HitMean=mean(Percent_Hit),Percent_HitSd=sd(Percent_Hit)) %>% 
  mutate(meanLab=paste0("Mean=",round(Percent_HitMean,3)),sdLab=paste0("Sd=",round(Percent_HitSd,2))) %>% 
  mutate(sumStatLab=paste0(meanLab,"\n",sdLab)) %>% 
  select(vb,condit,sumStatLab) %>% 
  spread(condit,sumStatLab) %>% 
  kable(format = "html",escape = FALSE) %>% 
  kable_styling(font_size = 10)

vb	Constant	Varied
100-300	Mean=0.252 Sd=0.26	Mean=0.201 Sd=0.26
350-550	Mean=0.246 Sd=0.19	Mean=0.245 Sd=0.21
600-800	Mean=0.281 Sd=0.17	Mean=0.206 Sd=0.16
800-1000	Mean=0.278 Sd=0.23	Mean=0.19 Sd=0.19
1000-1200	Mean=0.21 Sd=0.2	Mean=0.206 Sd=0.2
1200-1400	Mean=0.161 Sd=0.17	Mean=0.2 Sd=0.19

BandType

create_table(test, "vx")

Summary of vx

Band	Band Type	Constant	Band Type	Varied
100-300	Extrapolation	Mean=523 SD=245	Extrapolation	Mean=665 SD=365
350-550	Extrapolation	Mean=661 SD=224	Extrapolation	Mean=772 SD=321
600-800	Extrapolation	Mean=772 SD=210	Extrapolation	Mean=880 SD=292
800-1000	Trained	Mean=1009 SD=255	Trained	Mean=1070 SD=249
1000-1200	Extrapolation	Mean=1173 SD=308	Trained	Mean=1181 SD=266
1200-1400	Extrapolation	Mean=1307 SD=356	Trained	Mean=1269 SD=292

create_table(test, "Percent_Hit")

Summary of Percent_Hit

Band	Band Type	Constant	Band Type	Varied
100-300	Extrapolation	Mean=0.252 SD=0.26	Extrapolation	Mean=0.201 SD=0.26
350-550	Extrapolation	Mean=0.246 SD=0.19	Extrapolation	Mean=0.245 SD=0.21
600-800	Extrapolation	Mean=0.281 SD=0.17	Extrapolation	Mean=0.206 SD=0.16
800-1000	Trained	Mean=0.278 SD=0.23	Trained	Mean=0.19 SD=0.19
1000-1200	Extrapolation	Mean=0.21 SD=0.2	Trained	Mean=0.206 SD=0.2
1200-1400	Extrapolation	Mean=0.161 SD=0.17	Trained	Mean=0.2 SD=0.19

Aggregation

e1 %>%
  filter(expMode %in% c("test-Nf","test-train-nf")) %>%
  group_by(vb, condit, bandType) %>%
  summarise(distMean = mean(dist), distSd = sd(dist)) %>%
  mutate(
    meanLab = paste0("Mean=", round(distMean, 0)),
    sdLab = paste0("Sd=", round(distSd, 0))
  ) %>%
  mutate(sumStatLab = paste0(meanLab, "\n", sdLab)) %>%
  pivot_wider(
    id_cols = c(vb, bandType),
    names_from = condit,
    values_from = sumStatLab
  ) %>%
  arrange(vb, bandType) %>%
  kable(format = "html", escape = FALSE)

vb	bandType	Constant	Varied
100-300	Extrapolation	Mean=254 Sd=298	Mean=386 Sd=426
350-550	Extrapolation	Mean=191 Sd=229	Mean=285 Sd=340
600-800	Extrapolation	Mean=150 Sd=184	Mean=234 Sd=270
800-1000	Trained	Mean=184 Sd=242	Mean=221 Sd=248
1000-1200	Trained	NA	Mean=208 Sd=226
1000-1200	Extrapolation	Mean=233 Sd=282	NA
1200-1400	Trained	NA	Mean=242 Sd=235
1200-1400	Extrapolation	Mean=287 Sd=290	NA

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |> group_by(vb,condit) %>% 
  summarise(vxMean=mean(vx),vxSd=sd(vx)) %>% 
  mutate(meanLab=paste0("Mean=",round(vxMean,0)),sdLab=paste0("Sd=",round(vxSd,0))) %>% 
  mutate(sumStatLab=paste0(meanLab,"\n",sdLab)) %>% 
  select(vb,condit,sumStatLab) %>% 
  spread(condit,sumStatLab) %>% 
  kable(format = "html",escape = FALSE,caption = "Vx Mean") %>% 
  kable_styling(font_size = 10)

Vx Mean
vb	Constant	Varied
100-300	Mean=524 Sd=327	Mean=664 Sd=448
350-550	Mean=659 Sd=303	Mean=768 Sd=402
600-800	Mean=770 Sd=300	Mean=876 Sd=390
800-1000	Mean=1001 Sd=357	Mean=1064 Sd=370
1000-1200	Mean=1167 Sd=430	Mean=1180 Sd=372
1200-1400	Mean=1283 Sd=483	Mean=1265 Sd=412

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |> group_by(vb,condit) %>% 
  summarise(sdistMean=mean(sdist),sdistSd=sd(sdist)) %>% 
  mutate(meanLab=paste0("Mean=",round(sdistMean,0)),sdLab=paste0("Sd=",round(sdistSd,0))) %>% 
  mutate(sumStatLab=paste0(meanLab,"\n",sdLab)) %>% 
  select(vb,condit,sumStatLab) %>% 
  spread(condit,sumStatLab) %>% 
  kable(format = "html",escape = FALSE,caption = "Signed Deviation Mean") %>% 
  kable_styling(font_size = 10)

Signed Deviation Mean
vb	Constant	Varied
100-300	Mean=252 Sd=299	Mean=385 Sd=427
350-550	Mean=163 Sd=250	Mean=265 Sd=356
600-800	Mean=62 Sd=229	Mean=152 Sd=323
800-1000	Mean=89 Sd=291	Mean=136 Sd=303
1000-1200	Mean=64 Sd=360	Mean=66 Sd=300
1200-1400	Mean=-3 Sd=408	Mean=-25 Sd=336

testAvg |> group_by(vb,condit) %>% 
  summarise(Percent_HitMean=mean(Percent_Hit),Percent_HitSd=sd(Percent_Hit)) %>% 
  mutate(meanLab=paste0("Mean=",round(Percent_HitMean,3)),sdLab=paste0("Sd=",round(Percent_HitSd,2))) %>% 
  mutate(sumStatLab=paste0(meanLab,"\n",sdLab)) %>% 
  select(vb,condit,sumStatLab) %>% 
  spread(condit,sumStatLab) %>% 
  kable(format = "html",escape = FALSE,caption = "Mean % Hit") %>% 
  kable_styling(font_size = 10)

Mean % Hit
vb	Constant	Varied
100-300	Mean=0.252 Sd=0.26	Mean=0.201 Sd=0.26
350-550	Mean=0.246 Sd=0.19	Mean=0.245 Sd=0.21
600-800	Mean=0.281 Sd=0.17	Mean=0.206 Sd=0.16
800-1000	Mean=0.278 Sd=0.23	Mean=0.19 Sd=0.19
1000-1200	Mean=0.21 Sd=0.2	Mean=0.206 Sd=0.2
1200-1400	Mean=0.161 Sd=0.17	Mean=0.2 Sd=0.19

Tables Aggregating by Id first

e1 %>% group_by(id, vb, condit) %>%
  summarise(dist = mean(dist), .groups = 'drop') %>%
  group_by(vb, condit) %>%
  summarise(distMean = mean(dist), distSd = sd(dist), .groups = 'drop') %>%
  mutate(
    meanLab = paste0("Mean=", round(distMean, 0)),
    sdLab = paste0("Sd=", round(distSd, 0))
  ) %>%
  mutate(sumStatLab = paste0(meanLab, "\n", sdLab)) %>%
  select(vb, condit, sumStatLab) %>%
  spread(condit, sumStatLab) %>%
  kable(format = "html", escape = FALSE, caption = "Deviation Mean - Aggregate by Id") %>%
  kable_styling(font_size = 11)

Deviation Mean - Aggregate by Id
vb	Constant	Varied
100-300	Mean=241 Sd=174	Mean=340 Sd=264
350-550	Mean=180 Sd=121	Mean=261 Sd=202
600-800	Mean=146 Sd=76	Mean=218 Sd=145
800-1000	Mean=138 Sd=57	Mean=206 Sd=86
1000-1200	Mean=241 Sd=173	Mean=206 Sd=74
1200-1400	Mean=295 Sd=186	Mean=261 Sd=70

e1 %>%
  filter(expMode %in% c("test-Nf", "test-train-nf")) %>%
  group_by(id, vb, condit) %>%
  summarise(vx = mean(vx), .groups = 'drop') %>%
  group_by(vb, condit) %>%
  summarise(vxMean = mean(vx), vxSd = sd(vx), .groups = 'drop') %>%
  mutate(
    meanLab = paste0("Mean=", round(vxMean, 0)),
    sdLab = paste0("Sd=", round(vxSd, 0))
  ) %>%
  mutate(sumStatLab = paste0(meanLab, "\n", sdLab)) %>%
  select(vb, condit, sumStatLab) %>%
  spread(condit, sumStatLab) %>%
  kable(format = "html", escape = FALSE, caption = "Vx Mean - Aggregate by Id") %>%
  kable_styling(font_size = 11)

Vx Mean - Aggregate by Id
vb	Constant	Varied
100-300	Mean=523 Sd=245	Mean=665 Sd=365
350-550	Mean=661 Sd=224	Mean=772 Sd=321
600-800	Mean=772 Sd=210	Mean=880 Sd=292
800-1000	Mean=1009 Sd=255	Mean=1070 Sd=249
1000-1200	Mean=1173 Sd=308	Mean=1181 Sd=266
1200-1400	Mean=1307 Sd=356	Mean=1269 Sd=292

e1 %>%
  filter(expMode %in% c("test-Nf", "test-train-nf")) %>%
  group_by(id, vb, condit) %>%
  summarise(sdist = mean(sdist), .groups = 'drop') %>%
  group_by(vb, condit) %>%
  summarise(sdistMean = mean(sdist), sdistSd = sd(sdist), .groups = 'drop') %>%
  mutate(
    meanLab = paste0("Mean=", round(sdistMean, 0)),
    sdLab = paste0("Sd=", round(sdistSd, 0))
  ) %>%
  mutate(sumStatLab = paste0(meanLab, "\n", sdLab)) %>%
  select(vb, condit, sumStatLab) %>%
  spread(condit, sumStatLab) %>%
  kable(format = "html", escape = FALSE, caption = "Signed Distance Mean - Aggregate by Id") %>%
  kable_styling(font_size = 11)

Signed Distance Mean - Aggregate by Id
vb	Constant	Varied
100-300	Mean=251 Sd=221	Mean=386 Sd=344
350-550	Mean=164 Sd=181	Mean=270 Sd=286
600-800	Mean=63 Sd=156	Mean=155 Sd=241
800-1000	Mean=96 Sd=206	Mean=141 Sd=196
1000-1200	Mean=70 Sd=253	Mean=66 Sd=209
1200-1400	Mean=17 Sd=297	Mean=-20 Sd=232

testAvg %>%
  group_by(id, vb, condit) %>%
  summarise(Percent_Hit = mean(Percent_Hit), .groups = 'drop') %>%
  group_by(vb, condit) %>%
  summarise(
    Percent_HitMean = mean(Percent_Hit),
    Percent_HitSd = sd(Percent_Hit),
    .groups = 'drop'
  ) %>%
  mutate(
    meanLab = paste0("Mean=", round(Percent_HitMean, 3)),
    sdLab = paste0("Sd=", round(Percent_HitSd, 2))
  ) %>%
  mutate(sumStatLab = paste0(meanLab, "\n", sdLab)) %>%
  select(vb, condit, sumStatLab) %>%
  spread(condit, sumStatLab) %>%
  kable(format = "html", escape = FALSE, caption = "Mean % Hit - Aggregate by Id") %>%
  kable_styling(font_size = 10)

Mean % Hit - Aggregate by Id
vb	Constant	Varied
100-300	Mean=0.252 Sd=0.26	Mean=0.201 Sd=0.26
350-550	Mean=0.246 Sd=0.19	Mean=0.245 Sd=0.21
600-800	Mean=0.281 Sd=0.17	Mean=0.206 Sd=0.16
800-1000	Mean=0.278 Sd=0.23	Mean=0.19 Sd=0.19
1000-1200	Mean=0.21 Sd=0.2	Mean=0.206 Sd=0.2
1200-1400	Mean=0.161 Sd=0.17	Mean=0.2 Sd=0.19

Tables that also indicate bandType

# Create the Constant table
constant_table <- e1 %>%
  filter(expMode %in% c("test-Nf", "test-train-nf")) %>%
  group_by(vb, bandType, condit) %>%
  summarise(distMean = mean(dist), distSd = sd(dist)) %>%
  filter(condit == "Constant") %>%
  mutate(
    meanLab = paste0("Mean=", round(distMean, 0)),
    sdLab = paste0("Sd=", round(distSd, 0)),
    sumStatLab = paste0(meanLab, "\n", sdLab)
  ) %>%
  select(vb, bandType, sumStatLab) %>%
  rename(Constant = sumStatLab)

# Create the Varied table
varied_table <- e1 %>%
  filter(expMode %in% c("test-Nf", "test-train-nf")) %>%
  group_by(vb, bandType, condit) %>%
  summarise(distMean = mean(dist), distSd = sd(dist)) %>%
  filter(condit == "Varied") %>%
  mutate(
    meanLab = paste0("Mean=", round(distMean, 0)),
    sdLab = paste0("Sd=", round(distSd, 0)),
    sumStatLab = paste0(meanLab, "\n", sdLab)
  ) %>%
  select(vb, bandType, sumStatLab) %>%
  rename(Varied = sumStatLab)

# Merge tables
final_table <- full_join(constant_table, varied_table, by = "vb")

# Create the table
final_table %>%
  kbl(digits = c(0, 0, 0, 0, 0),
      caption = "Data summary") %>%
  kable_minimal(full_width = FALSE,
      position = "left") %>%
  add_header_above(c("vb" = 1, "Constant" = 2, "Varied" = 2))

Data summary
vb	Constant		Varied
vb	bandType.x	Constant	bandType.y	Varied
100-300	Extrapolation	Mean=254 Sd=298	Extrapolation	Mean=386 Sd=426
350-550	Extrapolation	Mean=191 Sd=229	Extrapolation	Mean=285 Sd=340
600-800	Extrapolation	Mean=150 Sd=184	Extrapolation	Mean=234 Sd=270
800-1000	Trained	Mean=184 Sd=242	Trained	Mean=221 Sd=248
1000-1200	Extrapolation	Mean=233 Sd=282	Trained	Mean=208 Sd=226
1200-1400	Extrapolation	Mean=287 Sd=290	Trained	Mean=242 Sd=235

rectWidth=.4
vbRect<- e1 %>% group_by(vb) %>% 
  summarise(lowBound=first(bandInt),highBound=first(highBound)) %>% mutate(vbn=as.numeric(vb),
                  vbLag=vbn-rectWidth,vbLead=vbn+rectWidth)

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |>  ggplot(aes(x = condit, y = vx,fill=vb)) + 
  ggdist::stat_halfeye()


vbRect<- e1 |> group_by(vb) |>
  summarise(lowBound=first(bandInt),highBound=first(highBound)) |> 
  mutate(vbn=as.numeric(vb), rectWidth=.2,
                  vbLag=vbn-rectWidth,vbLead=vbn+rectWidth)

e1 |> filter(expMode %in% c("test-Nf","test-train-nf")) |>  
  ggplot(aes(x = vb, y = vx,fill=vb)) + 
  ggdist::stat_halfeye(alpha=.5) + 
  geom_rect(data=vbRect,aes(xmin=vbLag,xmax=vbLead,ymin=lowBound,ymax=highBound,fill=vb),alpha=.3,inherit.aes = FALSE)+
  scale_y_continuous(expand=expansion(add=100),breaks=round(seq(0,2400,by=200),2)) +
  geom_segment(data=vbRect, aes(x=vbLag,xend=vbLead,y=highBound,yend=highBound),alpha=1,linetype="dashed")+
  geom_segment(data=vbRect, aes(x=vbLag,xend=vbLead,y=lowBound,yend=lowBound),alpha=1,linetype="dashed")+
  geom_text(data=vbRect,aes(x=vbLag-.03,y=lowBound+100,label=vb),angle=90,size=3.5,fontface="bold")


testAvg |>  
  ggplot(aes(x = vb, y = vx,fill=vb)) + 
  ggdist::stat_halfeye(alpha=.5,width=.7) + 
  geom_rect(data=vbRect,aes(xmin=vbLag,xmax=vbLead,ymin=lowBound,ymax=highBound,fill=vb),alpha=.3,inherit.aes = FALSE)+
  facet_wrap(~condit,ncol=1)+
  scale_y_continuous(expand=expansion(add=100),breaks=round(seq(0,2000,by=200),2)) +
  geom_segment(data=vbRect, aes(x=vbLag,xend=vbLead,y=highBound,yend=highBound),alpha=1,linetype="dashed",inherit.aes = FALSE)+
  geom_segment(data=vbRect, aes(x=vbLag,xend=vbLead,y=lowBound,yend=lowBound),alpha=1,linetype="dashed",inherit.aes = FALSE)+
  geom_text(data=vbRect,aes(x=vbLag-.03,y=lowBound+100,label=vb),angle=90,size=5.5,fontface="bold",inherit.aes = FALSE)  
  


# testAvg |>  
#   ggplot(aes(x = vb, y = sdist,fill=vb)) + 
#   ggdist::stat_halfeye(alpha=.5,width=.7) + 
#   facet_wrap(~condit,ncol=1)
# 
# 
# testAvg |>  
#   ggplot(aes(x = vb, y = dist,fill=vb)) + 
#   ggdist::stat_halfeye(alpha=.5,width=.7) + 
#   facet_wrap(~condit,ncol=1)
# 
#

sumStats2 = test %>% group_by(id,condit,vb) %>%
  summarise(distMean=mean(dist),distMedian=median(dist),distSd=sd(dist)) %>%group_by(condit,vb) %>%
  summarise(groupMean=round(mean(distMean),0),groupMedian=round(mean(distMedian),0),groupSd=round(mean(distSd),0)) %>%
  mutate(meanLab=paste0("Mean=",groupMean),medianLab=paste0("Median=",groupMedian),sdLab=paste0("Sd=",groupSd)) %>%
  mutate(sumStatLab=paste0(meanLab,"\n",medianLab,"\n",sdLab))

sumStats = test %>% group_by(id,condit,vb) %>%
  summarise(distMean=mean(vx),distMedian=median(vx),distSd=sd(vx)) %>%group_by(condit,vb) %>%
  summarise(groupMean=round(mean(distMean),0),groupMedian=round(mean(distMedian),0),groupSd=round(mean(distSd),0)) %>%
  mutate(meanLab=paste0("Mean=",groupMean),medianLab=paste0("Median=",groupMedian),sdLab=paste0("Sd=",groupSd)) %>%
  mutate(sumStatLab=paste0(meanLab,"\n",medianLab,"\n",sdLab))

bandLines4 <- list(geom_segment(data=vbRect,aes(x=vbLag,xend=vbLead,y=highBound,yend=highBound),alpha=1,linetype="dashed"),
                   geom_segment(data=vbRect,aes(x=vbLag,xend=vbLead,y=lowBound,yend=lowBound),alpha=1,linetype="dashed"),
                   geom_text(data=vbRect,aes(x=vbLag-.03,y=lowBound+100,label=vb),angle=90,size=2.5,fontface="bold") )    


test %>% group_by(id,vb,condit) %>% 
  summarise(vxMean=mean(vx)) %>%
  ggplot(aes(x=vb,y=vxMean,fill=vb))+
  gghalves::geom_half_violin(color=NA)+ # remove border color
  gghalves::geom_half_boxplot(position=position_nudge(x=-0.05),side="r",outlier.shape = NA,center=TRUE, 
                    errorbar.draw = FALSE,width=.25)+
  gghalves::geom_half_point(transformation = position_jitter(width = 0.05, height = 0.05),size=.3,aes(color=vb))+
  facet_wrap(~condit,scale="free_x")+
  geom_rect(data=vbRect,aes(xmin=vbLag,xmax=vbLead,ymin=lowBound,ymax=highBound,fill=vb),alpha=.3,inherit.aes = FALSE)+
  bandLines4+
  #geom_text(data=sumStats,aes(x=vb,y=2100,label = groupMean),size=2, vjust = -0.5)+
  scale_y_continuous(expand=expansion(add=100),breaks=round(seq(0,2000,by=200),2))+
  theme(legend.position='none',
        plot.title=element_text(face="bold"),
        axis.title.x=element_text(face="bold"),
        axis.title.y=element_text(face="bold"),
        axis.text.x = element_text(size = 7.5))+
  ylab("Mean X Velocity")+xlab("Target Velocity Band") + 
  ggtitle("Testing Performance (no-feedback) - X-Velocity Per Band")+ 
  geom_text(data=sumStats2,aes(y=2090,label = sumStatLab),size=1.9)


#test %>% group_by(id,vb,condit) |> plot_distByCondit()


test %>% group_by(id,vb,condit) %>% 
  summarise(distMean=mean(dist)) %>% 
  ggplot(aes(x=vb,y=distMean,fill=vb))+
  geom_half_violin(color=NA)+ # remove border color
  geom_half_boxplot(position=position_nudge(x=-0.05),side="r",outlier.shape = NA,center=TRUE, 
                    errorbar.draw = FALSE,width=.25)+
  geom_half_point(transformation = position_jitter(width = 0.05, height = 0.05),size=.3,aes(color=vb))+
  facet_wrap(~condit,scale="free_x")+
  scale_y_continuous(expand=expansion(add=100),breaks=round(seq(0,2000,by=200),2))+
  theme(legend.position='none',
        plot.title=element_text(face="bold"),
        axis.title.x=element_text(face="bold"),
        axis.title.y=element_text(face="bold"),
        axis.text.x = element_text(size = 9.0))+
  ylab("Mean Distance From Boundary")+xlab("Target Velocity Band") + 
  ggtitle("Testing Performance (no-feedback) - Absolute Distance from Boundary")+ 
  geom_text(data=sumStats2,aes(y=1200,label = sumStatLab),size=3,fontface="bold")

test %>% group_by(sbjCode,condit,vb) %>% 
  summarise(distMean=mean(dist)) %>% 
  ggplot(aes(x=condit,y=distMean,fill=condit))+
  stat_summary(geom="bar",fun=mean,position=position_nudge(x=.21),alpha=.7,width=.2)+
   stat_summary(geom="errorbar",fun.data=mean_se,position=position_nudge(x=.21),alpha=.7,width=.1,)+
  geom_half_violin(position=position_dodge(.5),alpha=.55,color=NA)+ # remove border color
  geom_half_boxplot(position=position_dodge(.5),side="r",outlier.shape = NA,center=TRUE, 
                    errorbar.draw = FALSE,width=.25,alpha=.55)+
  geom_half_point(transformation = position_jitter(width = 0.05, height = 0.05),alpha=.2,size=.3)+
  facet_wrap(~vb,scale="free_x")+
  scale_y_continuous(expand=expansion(add=200),breaks=round(seq(0,2000,by=200),2))+
  theme(legend.position='none',
        plot.title=element_text(face="bold"),
        axis.title.x=element_text(face="bold"),
        axis.title.y=element_text(face="bold"),
        axis.text.x = element_text(size = 9.0))+
  ylab("Mean Distance From Boundary")+xlab("Training Condition") + 
  ggtitle("Testing Performance (no-feedback) - Absolute Distance from Boundary")+ 
  geom_text(data=sumStats2,aes(y=1390,label = sumStatLab),position=position_dodge(.5),size=3,fontface="bold")

library(ggforce)

testAvg %>% ggplot(aes(x=vx,y=condit,col=condit))+ ggdist::stat_halfeye()+facet_col(facets="vb")

testAvg %>% ggplot(aes(x=sdist,y=condit,col=condit))+ geom_boxplot()+facet_col(facets="vb")+geom_vline(xintercept=0)+theme_classic()

library(ggh4x)
library(ggdist)

testAvg %>% ggplot(aes(x=vx,y=vb,col=bandType)) +  ggdist::stat_halfeye(normalize="groups")+ facet_nested_wrap(vars(condit,tOrder),nrow=2)+theme_classic()

testAvg %>% ggplot(aes(x=vx,y=vb,col=bandType)) +  ggdist::stat_histinterval(normalize="groups")+ facet_nested_wrap(vars(condit,tOrder),nrow=2)+theme_classic()

testAvg %>% ggplot(aes(x=vx,y=vb,col=bandType)) +  ggdist::stat_histinterval(normalize="groups")+ facet_nested_wrap(vars(condit,tOrder),nrow=2)+theme_classic()

e1 |> group_by(id, condit, vb, bandInt,bandType,tOrder,expMode2) %>%
  summarise(vx=mean(vx),dist=mean(dist),sdist=mean(sdist)) %>%
  ggplot(aes(x=vx,y=condit,col=condit)) +  ggdist::stat_histinterval(normalize="groups")+ facet_nested_wrap(vars(vb,expMode2),nrow=6)+theme_classic()

e1 |> group_by(id, condit, vb, bandInt,bandType,tOrder,expMode2) %>%
  summarise(vx=mean(vx),dist=mean(dist),sdist=mean(sdist)) %>% ggplot(aes(x=sdist,y=vb,col=condit))+ stat_histinterval(normalize="groups",position=position_dodge())+facet_col(facets="expMode2")+geom_vline(xintercept=0)+theme_classic()

Dictionary

Variable Name	Variable Levels	Description
condit	Constant, Varied	Condition of the experiment: constant or varied
tOrder	Test First, Train First	Order of testing and training stages: test first or train first
expMode	train, train-Nf, test-Nf, etc.	Mode of the experiment: train, train-Nf, test-Nf, etc.
trainStage	Beginning, Middle, End, Test	Stage of the training: beginning, middle, end, or test
expStage	TrainStart, intTest1, etc.	Stage of the experiment: TrainStart, intTest1, TrainMid1, etc.
band	1, 2, 3, 4, 5, 6	Band number
vb	100-300, 350-550, etc.	Velocity band range
lowBound	100, 350, 600, etc.	Lower bound of the velocity band range
feedback	0, 1	Feedback type: 0 (no feedback), 1 (feedback)
stage	1, 2, 3, etc.	Stage number of the experiment