From b34ff71099e9a8a2298a1ff16e3173d586ea8796 Mon Sep 17 00:00:00 2001
From: Cameron Craddock <cameron.craddock@gmail.com>
Date: Wed, 1 Feb 2017 13:10:23 -0500
Subject: [PATCH] added colinearity and icc

---
 data_analysis/ABIDE_CORR_colinearity.ipynb    |   3 +-
 .../ABIDE_measures_and_ratings.R.ipynb        |   2 +-
 data_analysis/CORR_ICC.ipynb                  | 764 ++++++++++++++++++
 3 files changed, 767 insertions(+), 2 deletions(-)
 create mode 100644 data_analysis/CORR_ICC.ipynb

diff --git a/data_analysis/ABIDE_CORR_colinearity.ipynb b/data_analysis/ABIDE_CORR_colinearity.ipynb
index 90ff265..1dd7e44 100644
--- a/data_analysis/ABIDE_CORR_colinearity.ipynb
+++ b/data_analysis/ABIDE_CORR_colinearity.ipynb
@@ -206,6 +206,7 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "R",
    "language": "R",
@@ -217,7 +218,7 @@
    "mimetype": "text/x-r-source",
    "name": "R",
    "pygments_lexer": "r",
-   "version": "3.3.1"
+   "version": "3.3.2"
   }
  },
  "nbformat": 4,
diff --git a/data_analysis/ABIDE_measures_and_ratings.R.ipynb b/data_analysis/ABIDE_measures_and_ratings.R.ipynb
index 931afce..68c22ba 100644
--- a/data_analysis/ABIDE_measures_and_ratings.R.ipynb
+++ b/data_analysis/ABIDE_measures_and_ratings.R.ipynb
@@ -1233,7 +1233,7 @@
    "mimetype": "text/x-r-source",
    "name": "R",
    "pygments_lexer": "r",
-   "version": "3.3.1"
+   "version": "3.3.2"
   }
  },
  "nbformat": 4,
diff --git a/data_analysis/CORR_ICC.ipynb b/data_analysis/CORR_ICC.ipynb
new file mode 100644
index 0000000..afcd3c4
--- /dev/null
+++ b/data_analysis/CORR_ICC.ipynb
@@ -0,0 +1,764 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate ICC of QAP measures from CORR data\n",
+    "Code to calculate the modern ICC version (using linear mixed-effects model).\n",
+    "A lot is borrowed from AFNI's 3dICC_REML.R. Big shout out to the AFNI folks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Attaching package: ‘gridExtra’\n",
+      "\n",
+      "The following object is masked from ‘package:dplyr’:\n",
+      "\n",
+      "    combine\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "library(lme4)\n",
+    "library(dplyr)\n",
+    "library(reshape2)\n",
+    "library(tidyr)\n",
+    "library(ggplot2)\n",
+    "library(grid)\n",
+    "library(gridExtra)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read and tidy the CORR anatomical QAP measures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# read the data\n",
+    "corr_anat_qa<-read.csv(\"2016_05_CORR_qap_anatomical_spatial.csv\")\n",
+    "\n",
+    "# reduce to the columns that we are interested in\n",
+    "id.vars=c('Participant','Site','Session','Series')\n",
+    "measure.vars=c('CNR','Cortical.Contrast','EFC','FBER','FWHM','Qi1','SNR')\n",
+    "\n",
+    "corr_anat_qa<-corr_anat_qa[c(id.vars,measure.vars)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Filter 1\n",
+    "# Use only the first scan from sessions 1 & 2\n",
+    "corr_anat_qa <-  corr_anat_qa %>% \n",
+    "                  filter(Series == 'anat_1') %>% \n",
+    "                   filter(Session %in% c('session_1','session_2'))\n",
+    "corr_anat_qa$Series <- droplevels(corr_anat_qa$Series)\n",
+    "corr_anat_qa$Session <- droplevels(corr_anat_qa$Session)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Filter 2 \n",
+    "# remove participants that do not have two sessions of data\n",
+    "pts_keep <- (corr_anat_qa %>% \n",
+    "             group_by(Participant) %>% \n",
+    "              summarise(N=n()) %>%\n",
+    "               filter(N>1))$Participant\n",
+    "corr_anat_qa <-  corr_anat_qa %>% \n",
+    "                  filter(Participant %in% pts_keep)\n",
+    "corr_anat_qa$Participant <- droplevels(corr_anat_qa$Participant)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Filter 3 \n",
+    "# remove Sites that have fewer that 10 pts\n",
+    "sites_keep <- (corr_anat_qa %>% \n",
+    "                 group_by(Site) %>% \n",
+    "                   summarise(N=n()) %>%\n",
+    "                     filter(N>10))$Site\n",
+    "corr_anat_qa <-  corr_anat_qa %>% \n",
+    "                  filter(Site %in% sites_keep)\n",
+    "corr_anat_qa$Site <- droplevels(corr_anat_qa$Site)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "         \n",
+       "          session_1 session_2\n",
+       "  BNU_1          50        50\n",
+       "  BNU_2          61        61\n",
+       "  HNU_1          30        30\n",
+       "  IACAS          27        27\n",
+       "  IBA_TRT        14        14\n",
+       "  IPCAS_1        30        30\n",
+       "  IPCAS_2        35        35\n",
+       "  IPCAS_8        13        13\n",
+       "  LMU_3          25        25\n",
+       "  MRN            48        48\n",
+       "  NYU_1          25        25\n",
+       "  NYU_2          62        62\n",
+       "  SWU_1          19        19\n",
+       "  SWU_3          24        24\n",
+       "  SWU_4         234       234\n",
+       "  UM             80        80\n",
+       "  UPSM_1         99        99\n",
+       "  Utah_1         19        19\n",
+       "  UWM            25        25\n",
+       "  XHCUMS         24        24"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table(corr_anat_qa$Site, corr_anat_qa$Session)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>Participant</th><th scope=col>Site</th><th scope=col>Series</th><th scope=col>Measure</th><th scope=col>session_1</th><th scope=col>session_2</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>25000            </td><td>NYU_2            </td><td>anat_1           </td><td>CNR              </td><td> 11.98814305     </td><td> 12.56221793     </td></tr>\n",
+       "\t<tr><td>25000            </td><td>NYU_2            </td><td>anat_1           </td><td>Cortical.Contrast</td><td>  0.37586680     </td><td>  0.42267393     </td></tr>\n",
+       "\t<tr><td>25000            </td><td>NYU_2            </td><td>anat_1           </td><td>EFC              </td><td>  0.39672353     </td><td>  0.39002321     </td></tr>\n",
+       "\t<tr><td>25000            </td><td>NYU_2            </td><td>anat_1           </td><td>FBER             </td><td>398.24758483     </td><td>344.16062441     </td></tr>\n",
+       "\t<tr><td>25000            </td><td>NYU_2            </td><td>anat_1           </td><td>FWHM             </td><td>  3.28540315     </td><td>  3.31394664     </td></tr>\n",
+       "\t<tr><td>25000            </td><td>NYU_2            </td><td>anat_1           </td><td>Qi1              </td><td>  0.05368263     </td><td>  0.05388648     </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllll}\n",
+       " Participant & Site & Series & Measure & session\\_1 & session\\_2\\\\\n",
+       "\\hline\n",
+       "\t 25000             & NYU\\_2           & anat\\_1          & CNR               &  11.98814305      &  12.56221793     \\\\\n",
+       "\t 25000             & NYU\\_2           & anat\\_1          & Cortical.Contrast &   0.37586680      &   0.42267393     \\\\\n",
+       "\t 25000             & NYU\\_2           & anat\\_1          & EFC               &   0.39672353      &   0.39002321     \\\\\n",
+       "\t 25000             & NYU\\_2           & anat\\_1          & FBER              & 398.24758483      & 344.16062441     \\\\\n",
+       "\t 25000             & NYU\\_2           & anat\\_1          & FWHM              &   3.28540315      &   3.31394664     \\\\\n",
+       "\t 25000             & NYU\\_2           & anat\\_1          & Qi1               &   0.05368263      &   0.05388648     \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "Participant | Site | Series | Measure | session_1 | session_2 | \n",
+       "|---|---|---|---|---|---|\n",
+       "| 25000             | NYU_2             | anat_1            | CNR               |  11.98814305      |  12.56221793      | \n",
+       "| 25000             | NYU_2             | anat_1            | Cortical.Contrast |   0.37586680      |   0.42267393      | \n",
+       "| 25000             | NYU_2             | anat_1            | EFC               |   0.39672353      |   0.39002321      | \n",
+       "| 25000             | NYU_2             | anat_1            | FBER              | 398.24758483      | 344.16062441      | \n",
+       "| 25000             | NYU_2             | anat_1            | FWHM              |   3.28540315      |   3.31394664      | \n",
+       "| 25000             | NYU_2             | anat_1            | Qi1               |   0.05368263      |   0.05388648      | \n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  Participant Site  Series Measure           session_1    session_2   \n",
+       "1 25000       NYU_2 anat_1 CNR                11.98814305  12.56221793\n",
+       "2 25000       NYU_2 anat_1 Cortical.Contrast   0.37586680   0.42267393\n",
+       "3 25000       NYU_2 anat_1 EFC                 0.39672353   0.39002321\n",
+       "4 25000       NYU_2 anat_1 FBER              398.24758483 344.16062441\n",
+       "5 25000       NYU_2 anat_1 FWHM                3.28540315   3.31394664\n",
+       "6 25000       NYU_2 anat_1 Qi1                 0.05368263   0.05388648"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## make table of session1 vs. session2 scatter plots\n",
+    "df<-melt(corr_anat_qa,\n",
+    "         id.vars=id.vars,\n",
+    "         measure.vars=measure.vars,\n",
+    "         variable.name=\"Measure\")\n",
+    "\n",
+    "df <- df %>% spread(Session, value)\n",
+    "head(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# QC hand ratings\n",
+    "scatter_plots<-function(df,x_var,y_var,axis_strings,title_strings,pdf_fname,fig_size_w,fig_size_h,ncol,nrow){\n",
+    "    options(repr.plot.width=fig_size_w, repr.plot.height=fig_size_h)\n",
+    "    \n",
+    "#     pdf(pdf_fname, width = fig_size_w, height = fig_size_h, family=\"ArialMT\")\n",
+    "    \n",
+    "    grid.newpage()\n",
+    "\n",
+    "    pushViewport(viewport(layout = grid.layout(nrow, ncol)))\n",
+    "    \n",
+    "    vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)\n",
+    "\n",
+    "    pval_thresh=c(0.05,0.01,0.001,0.0001)\n",
+    "    pval_indicators=c(\"*\",\"**\",\"***\",\"****\")\n",
+    "        \n",
+    "    measure.vars = unique(df$Measure)\n",
+    "\n",
+    "    for (i in seq(1,length(measure.vars)))\n",
+    "    {\n",
+    "        # get only the rows corresponding to this measure\n",
+    "        sdf<- df %>% filter(Measure == measure.vars[i])\n",
+    "        scale_min<-min(sdf[x_var],sdf[y_var])\n",
+    "        scale_max<-max(sdf[x_var],sdf[y_var])\n",
+    "        \n",
+    "#         formula=sprintf(\"value ~ as.factor(%s)\",qc_var)\n",
+    "#         # get the lable and annotate to indicate statistical significance of the difference\n",
+    "#         pval<-wilcox.test(as.formula(formula),data=sdf)$p.value\n",
+    "\n",
+    "#         pval_string=\"ns\"\n",
+    "#         for (t in seq(1,length(pval_thresh)))\n",
+    "#         {\n",
+    "#             if(pval < pval_thresh[t])\n",
+    "#             {\n",
+    "#                 pval_string=pval_indicators[t]\n",
+    "#             }\n",
+    "#         }\n",
+    "\n",
+    "#         print(paste(measure.vars[i],label_strings[[measure.vars[i]]],pval_string,sprintf('pval %e',pval)))\n",
+    "        \n",
+    "        p <- ggplot(sdf, aes_string(x=x_var, y=y_var, fill=measure.vars[i])) + \n",
+    "            geom_point(size=.5) + \n",
+    "            theme_bw() +\n",
+    "            ggtitle(label_strings[[measure.vars[i]]])+\n",
+    "            xlab(axis_strings[[x_var]])+\n",
+    "            ylab(axis_strings[[y_var]])+\n",
+    "            theme(legend.position = \"none\",\n",
+    "                  plot.title = element_text(family = \"ArialMT\",\n",
+    "                                             face = \"plain\",\n",
+    "                                             size = 10,\n",
+    "                                             vjust = 0,\n",
+    "                                             hjust = 0.5),\n",
+    "                  axis.text.x = element_text(family = \"ArialMT\",\n",
+    "                                             face = \"plain\",\n",
+    "                                             size = 8, \n",
+    "                                             angle = 0, \n",
+    "                                             hjust = 0.5),\n",
+    "                  axis.text.y = element_text(family = \"ArialMT\", \n",
+    "                                             face = \"plain\", \n",
+    "                                             size = 8, \n",
+    "                                             angle = 90, \n",
+    "                                             hjust = 0.5),\n",
+    "                  axis.title.y = element_text(family = \"ArialMT\", \n",
+    "                                              face = \"plain\", \n",
+    "                                              size = 10, \n",
+    "                                              angle = 90, \n",
+    "                                              vjust = 0.9),\n",
+    "                  axis.title.x = element_text(family = \"ArialMT\", \n",
+    "                                              face = \"plain\", \n",
+    "                                              size = 10, \n",
+    "                                              angle = 0, \n",
+    "                                              vjust = 0.9),\n",
+    "                  plot.margin = unit(c(.25, .25, 0.25, .25), \n",
+    "                                     \"lines\")) +\n",
+    "            coord_fixed(ylim=c(scale_min,scale_max), xlim=c(scale_min,scale_max))\n",
+    "#         +\n",
+    "\n",
+    "#             # reduce the range to between the 2% and 98% quartiles to make the plot\n",
+    "#             # range more readible, do this without censoring so that the bars will go\n",
+    "#             # out of frame     \n",
+    "#             coord_cartesian(ylim=with(sdf, val_range))\n",
+    "\n",
+    "\n",
+    "        # get the location of this plot in the grid\n",
+    "        x=ceiling(i/ncol)\n",
+    "        y=((i-1) %% ncol)+1\n",
+    "\n",
+    "        # add the plot to the grid\n",
+    "        print(p, vp = vplayout(x, y))\n",
+    "    }\n",
+    "#     dev.off()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# plots\n",
+    "label_strings=c(CNR='CNR',\n",
+    "                Cortical.Contrast='Cortical Contrast',\n",
+    "                EFC='EFC',\n",
+    "                FBER='FBER',\n",
+    "                FWHM='Smoothness (FWHM)',\n",
+    "                Qi1='Fraction of Artifact Voxels',\n",
+    "                SNR='SNR')\n",
+    "\n",
+    "axis_strings=list(session_1 = \"Session 1\", session_2 = \"Session 2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAANSCAYAAADf5jGoAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA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\n",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_plots(df,'session_1','session_2',axis_strings, label_strings, 'none',90.0/25.4,2*90.0/25.4,2,4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1815.0123550613"
+      ],
+      "text/latex": [
+       "1815.0123550613"
+      ],
+      "text/markdown": [
+       "1815.0123550613"
+      ],
+      "text/plain": [
+       "[1] 1815.012"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max(df$session_1,df$session_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>session_1</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td> 11.98814305</td></tr>\n",
+       "\t<tr><td>  0.37586680</td></tr>\n",
+       "\t<tr><td>  0.39672353</td></tr>\n",
+       "\t<tr><td>398.24758483</td></tr>\n",
+       "\t<tr><td>  3.28540315</td></tr>\n",
+       "\t<tr><td>  0.05368263</td></tr>\n",
+       "\t<tr><td> 20.87262329</td></tr>\n",
+       "\t<tr><td> 11.32466900</td></tr>\n",
+       "\t<tr><td>  0.39896631</td></tr>\n",
+       "\t<tr><td>  0.38589012</td></tr>\n",
+       "\t<tr><td>336.95657337</td></tr>\n",
+       "\t<tr><td>  3.03384026</td></tr>\n",
+       "\t<tr><td>  0.07022690</td></tr>\n",
+       "\t<tr><td> 19.16839313</td></tr>\n",
+       "\t<tr><td> 15.65142881</td></tr>\n",
+       "\t<tr><td>  0.45218389</td></tr>\n",
+       "\t<tr><td>  0.34866897</td></tr>\n",
+       "\t<tr><td>738.30316254</td></tr>\n",
+       "\t<tr><td>  2.98765598</td></tr>\n",
+       "\t<tr><td>  0.04234216</td></tr>\n",
+       "\t<tr><td> 24.18005577</td></tr>\n",
+       "\t<tr><td> 10.86934234</td></tr>\n",
+       "\t<tr><td>  0.40164913</td></tr>\n",
+       "\t<tr><td>  0.38296196</td></tr>\n",
+       "\t<tr><td>428.70516481</td></tr>\n",
+       "\t<tr><td>  3.27693739</td></tr>\n",
+       "\t<tr><td>  0.10234573</td></tr>\n",
+       "\t<tr><td> 17.88846782</td></tr>\n",
+       "\t<tr><td> 10.42933618</td></tr>\n",
+       "\t<tr><td>  0.36575632</td></tr>\n",
+       "\t<tr><td>⋮</td></tr>\n",
+       "\t<tr><td>  0.1167270</td></tr>\n",
+       "\t<tr><td> 23.0993404</td></tr>\n",
+       "\t<tr><td> 10.1684486</td></tr>\n",
+       "\t<tr><td>  0.3060326</td></tr>\n",
+       "\t<tr><td>  0.3754514</td></tr>\n",
+       "\t<tr><td>488.3824279</td></tr>\n",
+       "\t<tr><td>  4.5356700</td></tr>\n",
+       "\t<tr><td>  0.1075467</td></tr>\n",
+       "\t<tr><td> 22.4778855</td></tr>\n",
+       "\t<tr><td>  9.1958760</td></tr>\n",
+       "\t<tr><td>  0.2958868</td></tr>\n",
+       "\t<tr><td>  0.4774488</td></tr>\n",
+       "\t<tr><td>433.6678712</td></tr>\n",
+       "\t<tr><td>  4.1290600</td></tr>\n",
+       "\t<tr><td>  0.2212622</td></tr>\n",
+       "\t<tr><td> 20.8463544</td></tr>\n",
+       "\t<tr><td>  8.8056281</td></tr>\n",
+       "\t<tr><td>  0.3086438</td></tr>\n",
+       "\t<tr><td>  0.5085318</td></tr>\n",
+       "\t<tr><td>417.4817263</td></tr>\n",
+       "\t<tr><td>  3.8107502</td></tr>\n",
+       "\t<tr><td>  0.2712270</td></tr>\n",
+       "\t<tr><td> 20.9941339</td></tr>\n",
+       "\t<tr><td>  9.2096447</td></tr>\n",
+       "\t<tr><td>  0.2946456</td></tr>\n",
+       "\t<tr><td>  0.4485918</td></tr>\n",
+       "\t<tr><td>385.5859471</td></tr>\n",
+       "\t<tr><td>  4.1956003</td></tr>\n",
+       "\t<tr><td>  0.2580485</td></tr>\n",
+       "\t<tr><td> 21.7159774</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|l}\n",
+       " session\\_1\\\\\n",
+       "\\hline\n",
+       "\t  11.98814305\\\\\n",
+       "\t   0.37586680\\\\\n",
+       "\t   0.39672353\\\\\n",
+       "\t 398.24758483\\\\\n",
+       "\t   3.28540315\\\\\n",
+       "\t   0.05368263\\\\\n",
+       "\t  20.87262329\\\\\n",
+       "\t  11.32466900\\\\\n",
+       "\t   0.39896631\\\\\n",
+       "\t   0.38589012\\\\\n",
+       "\t 336.95657337\\\\\n",
+       "\t   3.03384026\\\\\n",
+       "\t   0.07022690\\\\\n",
+       "\t  19.16839313\\\\\n",
+       "\t  15.65142881\\\\\n",
+       "\t   0.45218389\\\\\n",
+       "\t   0.34866897\\\\\n",
+       "\t 738.30316254\\\\\n",
+       "\t   2.98765598\\\\\n",
+       "\t   0.04234216\\\\\n",
+       "\t  24.18005577\\\\\n",
+       "\t  10.86934234\\\\\n",
+       "\t   0.40164913\\\\\n",
+       "\t   0.38296196\\\\\n",
+       "\t 428.70516481\\\\\n",
+       "\t   3.27693739\\\\\n",
+       "\t   0.10234573\\\\\n",
+       "\t  17.88846782\\\\\n",
+       "\t  10.42933618\\\\\n",
+       "\t   0.36575632\\\\\n",
+       "\t ⋮\\\\\n",
+       "\t   0.1167270\\\\\n",
+       "\t  23.0993404\\\\\n",
+       "\t  10.1684486\\\\\n",
+       "\t   0.3060326\\\\\n",
+       "\t   0.3754514\\\\\n",
+       "\t 488.3824279\\\\\n",
+       "\t   4.5356700\\\\\n",
+       "\t   0.1075467\\\\\n",
+       "\t  22.4778855\\\\\n",
+       "\t   9.1958760\\\\\n",
+       "\t   0.2958868\\\\\n",
+       "\t   0.4774488\\\\\n",
+       "\t 433.6678712\\\\\n",
+       "\t   4.1290600\\\\\n",
+       "\t   0.2212622\\\\\n",
+       "\t  20.8463544\\\\\n",
+       "\t   8.8056281\\\\\n",
+       "\t   0.3086438\\\\\n",
+       "\t   0.5085318\\\\\n",
+       "\t 417.4817263\\\\\n",
+       "\t   3.8107502\\\\\n",
+       "\t   0.2712270\\\\\n",
+       "\t  20.9941339\\\\\n",
+       "\t   9.2096447\\\\\n",
+       "\t   0.2946456\\\\\n",
+       "\t   0.4485918\\\\\n",
+       "\t 385.5859471\\\\\n",
+       "\t   4.1956003\\\\\n",
+       "\t   0.2580485\\\\\n",
+       "\t  21.7159774\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "session_1 | \n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|  11.98814305 | \n",
+       "|   0.37586680 | \n",
+       "|   0.39672353 | \n",
+       "| 398.24758483 | \n",
+       "|   3.28540315 | \n",
+       "|   0.05368263 | \n",
+       "|  20.87262329 | \n",
+       "|  11.32466900 | \n",
+       "|   0.39896631 | \n",
+       "|   0.38589012 | \n",
+       "| 336.95657337 | \n",
+       "|   3.03384026 | \n",
+       "|   0.07022690 | \n",
+       "|  19.16839313 | \n",
+       "|  15.65142881 | \n",
+       "|   0.45218389 | \n",
+       "|   0.34866897 | \n",
+       "| 738.30316254 | \n",
+       "|   2.98765598 | \n",
+       "|   0.04234216 | \n",
+       "|  24.18005577 | \n",
+       "|  10.86934234 | \n",
+       "|   0.40164913 | \n",
+       "|   0.38296196 | \n",
+       "| 428.70516481 | \n",
+       "|   3.27693739 | \n",
+       "|   0.10234573 | \n",
+       "|  17.88846782 | \n",
+       "|  10.42933618 | \n",
+       "|   0.36575632 | \n",
+       "| ⋮ | \n",
+       "|   0.1167270 | \n",
+       "|  23.0993404 | \n",
+       "|  10.1684486 | \n",
+       "|   0.3060326 | \n",
+       "|   0.3754514 | \n",
+       "| 488.3824279 | \n",
+       "|   4.5356700 | \n",
+       "|   0.1075467 | \n",
+       "|  22.4778855 | \n",
+       "|   9.1958760 | \n",
+       "|   0.2958868 | \n",
+       "|   0.4774488 | \n",
+       "| 433.6678712 | \n",
+       "|   4.1290600 | \n",
+       "|   0.2212622 | \n",
+       "|  20.8463544 | \n",
+       "|   8.8056281 | \n",
+       "|   0.3086438 | \n",
+       "|   0.5085318 | \n",
+       "| 417.4817263 | \n",
+       "|   3.8107502 | \n",
+       "|   0.2712270 | \n",
+       "|  20.9941339 | \n",
+       "|   9.2096447 | \n",
+       "|   0.2946456 | \n",
+       "|   0.4485918 | \n",
+       "| 385.5859471 | \n",
+       "|   4.1956003 | \n",
+       "|   0.2580485 | \n",
+       "|  21.7159774 | \n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     session_1   \n",
+       "1     11.98814305\n",
+       "2      0.37586680\n",
+       "3      0.39672353\n",
+       "4    398.24758483\n",
+       "5      3.28540315\n",
+       "6      0.05368263\n",
+       "7     20.87262329\n",
+       "8     11.32466900\n",
+       "9      0.39896631\n",
+       "10     0.38589012\n",
+       "11   336.95657337\n",
+       "12     3.03384026\n",
+       "13     0.07022690\n",
+       "14    19.16839313\n",
+       "15    15.65142881\n",
+       "16     0.45218389\n",
+       "17     0.34866897\n",
+       "18   738.30316254\n",
+       "19     2.98765598\n",
+       "20     0.04234216\n",
+       "21    24.18005577\n",
+       "22    10.86934234\n",
+       "23     0.40164913\n",
+       "24     0.38296196\n",
+       "25   428.70516481\n",
+       "26     3.27693739\n",
+       "27     0.10234573\n",
+       "28    17.88846782\n",
+       "29    10.42933618\n",
+       "30     0.36575632\n",
+       "⋮    ⋮           \n",
+       "6579   0.1167270 \n",
+       "6580  23.0993404 \n",
+       "6581  10.1684486 \n",
+       "6582   0.3060326 \n",
+       "6583   0.3754514 \n",
+       "6584 488.3824279 \n",
+       "6585   4.5356700 \n",
+       "6586   0.1075467 \n",
+       "6587  22.4778855 \n",
+       "6588   9.1958760 \n",
+       "6589   0.2958868 \n",
+       "6590   0.4774488 \n",
+       "6591 433.6678712 \n",
+       "6592   4.1290600 \n",
+       "6593   0.2212622 \n",
+       "6594  20.8463544 \n",
+       "6595   8.8056281 \n",
+       "6596   0.3086438 \n",
+       "6597   0.5085318 \n",
+       "6598 417.4817263 \n",
+       "6599   3.8107502 \n",
+       "6600   0.2712270 \n",
+       "6601  20.9941339 \n",
+       "6602   9.2096447 \n",
+       "6603   0.2946456 \n",
+       "6604   0.4485918 \n",
+       "6605 385.5859471 \n",
+       "6606   4.1956003 \n",
+       "6607   0.2580485 \n",
+       "6608  21.7159774 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"session_1\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1477.7613533892"
+      ],
+      "text/latex": [
+       "1477.7613533892"
+      ],
+      "text/markdown": [
+       "1477.7613533892"
+      ],
+      "text/plain": [
+       "[1] 1477.761"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max(df$session_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "?get"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.3.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}