Chapter 3 R.ipynb

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    "## 3.12\n",
    "\n",
    "governor <- 0\n",
    "win <- 0\n",
    "president <- 0\n",
    "\n",
    "data <- read.csv('candidates_tweets.txt', header = FALSE, sep = ' ' )\n",
    "\n",
    "for( rn in 1:nrow(data) ) {\n",
    "    line = tolower( data[ rn, 1:ncol(data) ] )\n",
    "    line = gsub( ',', '', line )  ## removing dots, periods to clean up the data a bit, but this is not extensive\n",
    "    line = gsub( '\\\\.', '', line )\n",
    "    \n",
    "    for( word in line ) {\n",
    "        if( word == \"governor\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            governor <- governor + 1\n",
    "        }\n",
    "        \n",
    "        if( word == \"win\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            win <- win + 1\n",
    "        }\n",
    "        \n",
    "        if( word == \"president\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            president <- president + 1\n",
    "        }\n",
    "    }\n",
    "    \n",
    "}\n",
    "            \n",
    "print( paste( governor, win, president ) )"
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    "## 3.13\n",
    "\n",
    "data <- read.csv('candidates_tweets.txt', header = FALSE, sep = ' ' )\n",
    "\n",
    "for( rn in 1:nrow(data) ) {\n",
    "    line = tolower( data[ rn, 1:ncol(data) ] )\n",
    "    line = gsub( ',', '', line )  ## removing dots, periods to clean up the data a bit, but this is not extensive\n",
    "    line = gsub( '\\\\.', '', line )\n",
    "    \n",
    "    governor <- 0\n",
    "    win <- 0\n",
    "    president <- 0\n",
    "    \n",
    "    for( word in line ) {\n",
    "        if( word == \"governor\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            governor <- governor + 1\n",
    "        }\n",
    "        \n",
    "        if( word == \"win\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            win <- win + 1\n",
    "        }\n",
    "        \n",
    "        if( word == \"president\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            president <- president + 1\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    print( paste( \"governor\", governor, \"times,\", \"win\", win, \"times,\", \"president\", president, \"times\" ) )\n",
    "    \n",
    "}"
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    "## 3.14\n",
    "\n",
    "suitable_cases <- 0\n",
    "\n",
    "data <- read.csv('candidates_tweets.txt', header = FALSE, sep = ' ' )\n",
    "\n",
    "for( rn in 1:nrow(data) ) {\n",
    "    line = tolower( data[ rn, 1:ncol(data) ] )\n",
    "    line = gsub( ',', '', line )  ## removing dots, periods to clean up the data a bit, but this is not extensive\n",
    "    line = gsub( '\\\\.', '', line )\n",
    "    \n",
    "    governor <- 0\n",
    "    win <- 0\n",
    "    president <- 0\n",
    "    \n",
    "    for( word in line ) {\n",
    "        if( word == \"governor\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            governor <- governor + 1\n",
    "        }\n",
    "        \n",
    "        if( word == \"win\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            win <- win + 1\n",
    "        }\n",
    "        \n",
    "        if( word == \"president\" ) { ## depending how much is required, this part may look different. now we look for an exact match\n",
    "            president <- president + 1\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    if( governor >= 1 && president >= 1 ) {\n",
    "        suitable_cases <- suitable_cases + 1\n",
    "        print( data[ rn, 1:ncol(data) ] )\n",
    "    }\n",
    "}\n",
    "            \n",
    "        \n",
    "print( paste(\"Total of\", suitable_cases ) )"
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    "## 3.15\n",
    "\n",
    "candidate_comments <- 0 ## two gatherer variables\n",
    "other_comments <- 0\n",
    "\n",
    "data <- read.csv('twitter-conversation.txt', header = FALSE )\n",
    "\n",
    "for( rn in 1:nrow( data ) ) {\n",
    "    day <- data[ rn, 1]\n",
    "    day <- gsub('Day', '', day )\n",
    "    day <- as.integer( day )\n",
    "    \n",
    "    username <- data[rn, 2]\n",
    "\n",
    "    if( day < 30 ) {\n",
    "        if( username == 'hansjohansson' || username == 'evapersson' ) {\n",
    "            candidate_comments <- candidate_comments + 1\n",
    "        } else {\n",
    "            other_comments <- other_comments + 1\n",
    "        }\n",
    "    } \n",
    "}\n",
    "       \n",
    "if( candidate_comments > 2 && other_comments > 1 ) {\n",
    "    print(\"Discussion qualifies\")\n",
    "}"
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    "## 3.16\n",
    "\n",
    "data <- read.csv('racism-slur.txt', header = FALSE )\n",
    "\n",
    "for( rn in 1:nrow(data) ) {\n",
    "    offensive_behaviour <- data[rn, 2]\n",
    "    white_men <- data[rn, 3]\n",
    "    friends <- data[rn, 4]\n",
    "    \n",
    "    if( offensive_behaviour > 0 ) { ## the person posting the message had a history of o ensive behaviour\n",
    "        if( white_men == \"True\" ) { ## its poster was a white man\n",
    "            if( friends == \"False\" ) { ## the poster and the target of the racist slur were not friends\n",
    "                if( grepl(\"n\\\\*\\\\*\\\\*\\\\*r\", data[rn, 1] ) ) { ## the message contained a racist slur\n",
    "                    if( grepl(\"@\", data[rn, 1] ) ) { ## the message was a reply to another user\n",
    "                        print( data[ rn, 1 ])\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }                     \n",
    "}"
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