Kaggle Data science Survey.ipynb

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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "* [Scenario](#chapter1)\n",
    "* [About the project](#chapter2)\n",
    "\n",
    "* [1.Data Inspection](#chapter3)\n",
    "    * [1.1 Data Understanding](#section_3_1)\n",
    "    * [1.2 Checking the data types](#section_3_2)\n",
    "    * [1.3 Checking the missing values](#section_3_3)\n",
    "    \n",
    "* [2.Exploratory Data Analysis](#chapter4)\n",
    "    * [1.Users of each Tool](#section_4_1)\n",
    "    * [2.Counting the users of each tool](#section_4_2)\n",
    "    * [3.Plotting the most popular tools](#section_4_3)\n",
    "    * [4.R vs Python Debate](#section_4_4)\n",
    "    * [5.Plotting R vs Python users](#section_4_5)\n",
    "    * [6.Language recommendations](#section_4_6)\n",
    "    * [7.Most Recommended language by preferred language](#section_4_7)\n",
    "    * [8.Number of survey respondents by Employer Industry](#section_4_8)\n",
    "    * [9.Top 5 used Algorithms](#section_4_9)\n",
    "    * [10.Top 5 used Algorithms by R lovers](#section_4_10)\n",
    "    * [11.Top 5 used Algorithms by Python lovers](#section_4_11)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7287548b",
   "metadata": {},
   "source": [
    "## 👀 Scenario <a class=\"anchor\" id=\"chapter1\">\n",
    "Throughout the world of data science, there are many languages and tools that can be used to complete a given task.Although , we are able to use the tool we prefer, it is often important to work with similar platforms so that we can share our code with one another."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1138fa89",
   "metadata": {},
   "source": [
    "## ❓  About the project <a class=\"anchor\" id=\"chapter2\">\n",
    "In this project , we will be analyzing a survey dataset from kaggle about which tools and languages professionals use in their day-to-day work. The dataset includes responses from over 10,000 people that write code to analyze data in their daily work."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2784b7ba",
   "metadata": {},
   "source": [
    "## 🔎 1. Data Inspection <a class=\"anchor\" id=\"chapter3\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99e2e40f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'tidyverse' was built under R version 3.6.3\"\n",
      "-- \u001b[1mAttaching packages\u001b[22m ------------------------------------------------------------------------------- tidyverse 1.3.1 --\n",
      "\n",
      "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.6     \u001b[32mv\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
      "\u001b[32mv\u001b[39m \u001b[34mtibble \u001b[39m 3.1.1     \u001b[32mv\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.6\n",
      "\u001b[32mv\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.3     \u001b[32mv\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32mv\u001b[39m \u001b[34mreadr  \u001b[39m 1.4.0     \u001b[32mv\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n",
      "\n",
      "Warning message:\n",
      "\"package 'tibble' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'tidyr' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'readr' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'purrr' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'dplyr' was built under R version 3.6.3\"\n",
      "Warning message:\n",
      "\"package 'forcats' was built under R version 3.6.3\"\n",
      "-- \u001b[1mConflicts\u001b[22m ---------------------------------------------------------------------------------- tidyverse_conflicts() --\n",
      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Loading the libraries \n",
    "library(tidyverse)\n",
    "library(tidyr)\n",
    "library(dplyr)\n",
    "\n",
    "# Loading the data \n",
    "responses <- read.csv(\"C:/Users/Atolb/Downloads/kagglesurvey.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae29fe43",
   "metadata": {},
   "source": [
    "#### 1. Data Understanding <a class=\"anchor\" id=\"section_3_1\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b28ff8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 10,153\n",
      "Columns: 5\n",
      "$ Respondent                   \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13~\n",
      "$ WorkToolsSelect              \u001b[3m\u001b[90m<fct>\u001b[39m\u001b[23m \"Amazon Web services,Oracle Data Mining/ ~\n",
      "$ LanguageRecommendationSelect \u001b[3m\u001b[90m<fct>\u001b[39m\u001b[23m F#, Python, Python, Python, R, Python, Py~\n",
      "$ EmployerIndustry             \u001b[3m\u001b[90m<fct>\u001b[39m\u001b[23m Internet-based, Mix of fields, Technology~\n",
      "$ WorkAlgorithmsSelect         \u001b[3m\u001b[90m<fct>\u001b[39m\u001b[23m \"Neural Networks,Random Forests,RNNs\", \"B~\n"
     ]
    }
   ],
   "source": [
    "# getting a glimpse about the data \n",
    "glimpse(responses)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a5d357",
   "metadata": {},
   "source": [
    " <div align=\"center\">\n",
    "\n",
    "| Variable              | Description                                                                                   |\n",
    "|:----------------------|:----------------------------------------------------------------------------------------------|\n",
    "| Respondent            |  Responder Id  |\n",
    "| WorkToolsSelect              | The tools that the respondent use.              |\n",
    "| LanguageRecommendationSelect                   | The language recommended by the survey respondent                                  |\n",
    "| EmployerIndustry\t     |  The industry in which the employer operates.  |\n",
    "| WorkAlgorithmSelect   | The Algorithm chosen by the survey respondent |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a20e89b",
   "metadata": {},
   "source": [
    "#### 2. Checking the data types <a class=\"anchor\" id=\"section_3_2\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c495913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Respondent</dt>\n",
       "\t\t<dd>'integer'</dd>\n",
       "\t<dt>WorkToolsSelect</dt>\n",
       "\t\t<dd>'integer'</dd>\n",
       "\t<dt>LanguageRecommendationSelect</dt>\n",
       "\t\t<dd>'integer'</dd>\n",
       "\t<dt>EmployerIndustry</dt>\n",
       "\t\t<dd>'integer'</dd>\n",
       "\t<dt>WorkAlgorithmsSelect</dt>\n",
       "\t\t<dd>'integer'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Respondent] 'integer'\n",
       "\\item[WorkToolsSelect] 'integer'\n",
       "\\item[LanguageRecommendationSelect] 'integer'\n",
       "\\item[EmployerIndustry] 'integer'\n",
       "\\item[WorkAlgorithmsSelect] 'integer'\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Respondent\n",
       ":   'integer'WorkToolsSelect\n",
       ":   'integer'LanguageRecommendationSelect\n",
       ":   'integer'EmployerIndustry\n",
       ":   'integer'WorkAlgorithmsSelect\n",
       ":   'integer'\n",
       "\n"
      ],
      "text/plain": [
       "                  Respondent              WorkToolsSelect \n",
       "                   \"integer\"                    \"integer\" \n",
       "LanguageRecommendationSelect             EmployerIndustry \n",
       "                   \"integer\"                    \"integer\" \n",
       "        WorkAlgorithmsSelect \n",
       "                   \"integer\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sapply(responses,typeof)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a4ba5d",
   "metadata": {},
   "source": [
    "We can see that 4 out of the 5 columns have an incorrect data type. \n",
    "These columns are : \n",
    "* WorkToolsSelect \n",
    "* LanguageRecommendationSelect \n",
    "* EmployerIndustry \n",
    "* WorkAlgorithmSelect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6bc5d429",
   "metadata": {},
   "outputs": [],
   "source": [
    "responses$WorkToolsSelect <- as.character(responses$WorkToolsSelect)\n",
    "responses$LanguageRecommendationSelect <- as.character(responses$LanguageRecommendationSelect)\n",
    "responses$EmployerIndustry  <- as.character(responses$EmployerIndustry)\n",
    "responses$WorkAlgorithmsSelect <- as.character(responses$WorkAlgorithmsSelect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5192782",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t10153 obs. of  5 variables:\n",
      " $ Respondent                  : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ WorkToolsSelect             : chr  \"Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl\" \"Amazon Machine Learning,Amazon Web services,Cloudera,Hadoop/Hive/Pig,Impala,Java,Mathematica,MATLAB/Octave,Micr\"| __truncated__ \"C/C++,Jupyter notebooks,MATLAB/Octave,Python,R,TensorFlow\" \"Jupyter notebooks,Python,SQL,TensorFlow\" ...\n",
      " $ LanguageRecommendationSelect: chr  \"F#\" \"Python\" \"Python\" \"Python\" ...\n",
      " $ EmployerIndustry            : chr  \"Internet-based\" \"Mix of fields\" \"Technology\" \"Academic\" ...\n",
      " $ WorkAlgorithmsSelect        : chr  \"Neural Networks,Random Forests,RNNs\" \"Bayesian Techniques,Decision Trees,Random Forests,Regression/Logistic Regression\" \"Bayesian Techniques,CNNs,Ensemble Methods,Neural Networks,Regression/Logistic Regression,SVMs\" \"Bayesian Techniques,CNNs,Decision Trees,Gradient Boosted Machines,Neural Networks,Random Forests,Regression/Log\"| __truncated__ ...\n"
     ]
    }
   ],
   "source": [
    "str(responses)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "477c147c",
   "metadata": {},
   "source": [
    "It seems that the columns data types are now correct."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18acd8eb",
   "metadata": {},
   "source": [
    "#### 3. Checking the missing values <a class=\"anchor\" id=\"section_3_3\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6392b792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0"
      ],
      "text/latex": [
       "0"
      ],
      "text/markdown": [
       "0"
      ],
      "text/plain": [
       "[1] 0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(is.na(responses))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a880b325",
   "metadata": {},
   "source": [
    "There are no missing values in the dataset :) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "469dcde6",
   "metadata": {},
   "source": [
    "## ⚙️ 2. Exploratory Data Analysis <a class=\"anchor\" id=\"chapter4\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3192390a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Respondent</th><th scope=col>WorkToolsSelect</th><th scope=col>LanguageRecommendationSelect</th><th scope=col>EmployerIndustry</th><th scope=col>WorkAlgorithmsSelect</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                                                                                                                                                                                                                  </td><td>Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl                                                                                                                                                   </td><td>F#                                                                                                                                                                                                                 </td><td>Internet-based                                                                                                                                                                                                     </td><td>Neural Networks,Random Forests,RNNs                                                                                                                                                                                </td></tr>\n",
       "\t<tr><td>2                                                                                                                                                                                                                  </td><td>Amazon Machine Learning,Amazon Web services,Cloudera,Hadoop/Hive/Pig,Impala,Java,Mathematica,MATLAB/Octave,Microsoft Excel Data Mining,Microsoft SQL Server Data Mining,NoSQL,Python,R,SAS Base,SAS JMP,SQL,Tableau</td><td>Python                                                                                                                                                                                                             </td><td>Mix of fields                                                                                                                                                                                                      </td><td>Bayesian Techniques,Decision Trees,Random Forests,Regression/Logistic Regression                                                                                                                                   </td></tr>\n",
       "\t<tr><td>3                                                                                                                                                                                                                  </td><td>C/C++,Jupyter notebooks,MATLAB/Octave,Python,R,TensorFlow                                                                                                                                                          </td><td>Python                                                                                                                                                                                                             </td><td>Technology                                                                                                                                                                                                         </td><td>Bayesian Techniques,CNNs,Ensemble Methods,Neural Networks,Regression/Logistic Regression,SVMs                                                                                                                      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Respondent & WorkToolsSelect & LanguageRecommendationSelect & EmployerIndustry & WorkAlgorithmsSelect\\\\\n",
       "\\hline\n",
       "\t 1                                                                                                                                                                                                                   & Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl                                                                                                                                                    & F\\#                                                                                                                                                                                                                & Internet-based                                                                                                                                                                                                      & Neural Networks,Random Forests,RNNs                                                                                                                                                                                \\\\\n",
       "\t 2                                                                                                                                                                                                                   & Amazon Machine Learning,Amazon Web services,Cloudera,Hadoop/Hive/Pig,Impala,Java,Mathematica,MATLAB/Octave,Microsoft Excel Data Mining,Microsoft SQL Server Data Mining,NoSQL,Python,R,SAS Base,SAS JMP,SQL,Tableau & Python                                                                                                                                                                                                              & Mix of fields                                                                                                                                                                                                       & Bayesian Techniques,Decision Trees,Random Forests,Regression/Logistic Regression                                                                                                                                   \\\\\n",
       "\t 3                                                                                                                                                                                                                   & C/C++,Jupyter notebooks,MATLAB/Octave,Python,R,TensorFlow                                                                                                                                                           & Python                                                                                                                                                                                                              & Technology                                                                                                                                                                                                          & Bayesian Techniques,CNNs,Ensemble Methods,Neural Networks,Regression/Logistic Regression,SVMs                                                                                                                      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Respondent | WorkToolsSelect | LanguageRecommendationSelect | EmployerIndustry | WorkAlgorithmsSelect |\n",
       "|---|---|---|---|---|\n",
       "| 1                                                                                                                                                                                                                   | Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl                                                                                                                                                    | F#                                                                                                                                                                                                                  | Internet-based                                                                                                                                                                                                      | Neural Networks,Random Forests,RNNs                                                                                                                                                                                 |\n",
       "| 2                                                                                                                                                                                                                   | Amazon Machine Learning,Amazon Web services,Cloudera,Hadoop/Hive/Pig,Impala,Java,Mathematica,MATLAB/Octave,Microsoft Excel Data Mining,Microsoft SQL Server Data Mining,NoSQL,Python,R,SAS Base,SAS JMP,SQL,Tableau | Python                                                                                                                                                                                                              | Mix of fields                                                                                                                                                                                                       | Bayesian Techniques,Decision Trees,Random Forests,Regression/Logistic Regression                                                                                                                                    |\n",
       "| 3                                                                                                                                                                                                                   | C/C++,Jupyter notebooks,MATLAB/Octave,Python,R,TensorFlow                                                                                                                                                           | Python                                                                                                                                                                                                              | Technology                                                                                                                                                                                                          | Bayesian Techniques,CNNs,Ensemble Methods,Neural Networks,Regression/Logistic Regression,SVMs                                                                                                                       |\n",
       "\n"
      ],
      "text/plain": [
       "  Respondent\n",
       "1 1         \n",
       "2 2         \n",
       "3 3         \n",
       "  WorkToolsSelect                                                                                                                                                                                                    \n",
       "1 Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl                                                                                                                                                   \n",
       "2 Amazon Machine Learning,Amazon Web services,Cloudera,Hadoop/Hive/Pig,Impala,Java,Mathematica,MATLAB/Octave,Microsoft Excel Data Mining,Microsoft SQL Server Data Mining,NoSQL,Python,R,SAS Base,SAS JMP,SQL,Tableau\n",
       "3 C/C++,Jupyter notebooks,MATLAB/Octave,Python,R,TensorFlow                                                                                                                                                          \n",
       "  LanguageRecommendationSelect EmployerIndustry\n",
       "1 F#                           Internet-based  \n",
       "2 Python                       Mix of fields   \n",
       "3 Python                       Technology      \n",
       "  WorkAlgorithmsSelect                                                                         \n",
       "1 Neural Networks,Random Forests,RNNs                                                          \n",
       "2 Bayesian Techniques,Decision Trees,Random Forests,Regression/Logistic Regression             \n",
       "3 Bayesian Techniques,CNNs,Ensemble Methods,Neural Networks,Regression/Logistic Regression,SVMs"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(responses,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effcfca5",
   "metadata": {},
   "source": [
    "#### 1.Users of each Tool <a class=\"anchor\" id=\"section_4_1\">\n",
    "We are interested in knowing how many people use each tool that of the tools that were listed in the column WorkToolsSelect. As we can see from the dataframe subset we have above , the respondents listed multiple tools that are each separated by a comma.\n",
    "Therefore , we need to separate out all of the tools used by each individual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46b0b3f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tools <- responses %>% \n",
    "mutate(work_tools = str_split(WorkToolsSelect,\",\")) %>% \n",
    "unnest(work_tools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5456490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Respondent</th><th scope=col>WorkToolsSelect</th><th scope=col>LanguageRecommendationSelect</th><th scope=col>EmployerIndustry</th><th scope=col>WorkAlgorithmsSelect</th><th scope=col>work_tools</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1                                                               </td><td>Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl</td><td>F#                                                              </td><td>Internet-based                                                  </td><td>Neural Networks,Random Forests,RNNs                             </td><td>Amazon Web services                                             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       " Respondent & WorkToolsSelect & LanguageRecommendationSelect & EmployerIndustry & WorkAlgorithmsSelect & work\\_tools\\\\\n",
       "\\hline\n",
       "\t 1                                                                & Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl & F\\#                                                             & Internet-based                                                   & Neural Networks,Random Forests,RNNs                              & Amazon Web services                                             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Respondent | WorkToolsSelect | LanguageRecommendationSelect | EmployerIndustry | WorkAlgorithmsSelect | work_tools |\n",
       "|---|---|---|---|---|---|\n",
       "| 1                                                                | Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl | F#                                                               | Internet-based                                                   | Neural Networks,Random Forests,RNNs                              | Amazon Web services                                              |\n",
       "\n"
      ],
      "text/plain": [
       "  Respondent WorkToolsSelect                                                 \n",
       "1 1          Amazon Web services,Oracle Data Mining/ Oracle R Enterprise,Perl\n",
       "  LanguageRecommendationSelect EmployerIndustry\n",
       "1 F#                           Internet-based  \n",
       "  WorkAlgorithmsSelect                work_tools         \n",
       "1 Neural Networks,Random Forests,RNNs Amazon Web services"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(tools,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30494fb3",
   "metadata": {},
   "source": [
    "#### 2.Counting the users of each tool <a class=\"anchor\" id=\"section_4_2\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4df281ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>work_tools</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Python           </td><td>6073             </td></tr>\n",
       "\t<tr><td>R                </td><td>4708             </td></tr>\n",
       "\t<tr><td>SQL              </td><td>4261             </td></tr>\n",
       "\t<tr><td>Jupyter notebooks</td><td>3206             </td></tr>\n",
       "\t<tr><td>TensorFlow       </td><td>2256             </td></tr>\n",
       "\t<tr><td>                 </td><td>2198             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " work\\_tools & count\\\\\n",
       "\\hline\n",
       "\t Python            & 6073             \\\\\n",
       "\t R                 & 4708             \\\\\n",
       "\t SQL               & 4261             \\\\\n",
       "\t Jupyter notebooks & 3206             \\\\\n",
       "\t TensorFlow        & 2256             \\\\\n",
       "\t                   & 2198             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| work_tools | count |\n",
       "|---|---|\n",
       "| Python            | 6073              |\n",
       "| R                 | 4708              |\n",
       "| SQL               | 4261              |\n",
       "| Jupyter notebooks | 3206              |\n",
       "| TensorFlow        | 2256              |\n",
       "|                   | 2198              |\n",
       "\n"
      ],
      "text/plain": [
       "  work_tools        count\n",
       "1 Python            6073 \n",
       "2 R                 4708 \n",
       "3 SQL               4261 \n",
       "4 Jupyter notebooks 3206 \n",
       "5 TensorFlow        2256 \n",
       "6                   2198 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tool_count <- tools %>%\n",
    "group_by(work_tools) %>%\n",
    "summarise(count=n()) %>%\n",
    "arrange(desc(count))\n",
    "# printing the top 5 tools \n",
    "head(tool_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5f34ef",
   "metadata": {},
   "source": [
    "#### 3.Plotting the most popular tools <a class=\"anchor\" id=\"section_4_3\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71e67e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FY3ArvV1dX5+fmHH3648RVjzIEDB+yv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\nBw+2vAqSW0k70dmX8oZ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zH2DxVywOTK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\nwA4AAMAR/w8KlJxQ46FgQAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(tool_count,aes(x=work_tools,y=count)) + \n",
    "    geom_bar(stat=\"identity\") +\n",
    "    theme(axis.text.x = element_text(angle = 90, \n",
    "                 vjust = 0.5,\n",
    "                 hjust = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a16a918",
   "metadata": {},
   "source": [
    "We can see from the figure above that R and Python are the two most commonly used languages."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7547c876",
   "metadata": {},
   "source": [
    "#### 4. R vs Python Debate <a class=\"anchor\" id=\"section_4_4\">\n",
    "Although R and Python are the most commonly used languages , it's possible that many respondents use both R and Python. \n",
    "Let's look at how many people use R , Python and both tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ef52f923",
   "metadata": {},
   "outputs": [],
   "source": [
    "debate_tools <- responses %>%\n",
    "mutate(language_preference = case_when(\n",
    "         str_detect(WorkToolsSelect,\"R\") & ! str_detect(WorkToolsSelect , \"Python\") ~ \"R\", \n",
    "         str_detect(WorkToolsSelect,\"Python\") & ! str_detect(WorkToolsSelect,\"R\") ~ \"Python\", \n",
    "         str_detect(WorkToolsSelect,\"R\") & str_detect(WorkToolsSelect , \"Python\") ~ \"Both\",\n",
    "    TRUE ~ \"neither\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6f0e6719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>language_preference</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Both   </td><td>3660   </td></tr>\n",
       "\t<tr><td>neither</td><td>2860   </td></tr>\n",
       "\t<tr><td>Python </td><td>2413   </td></tr>\n",
       "\t<tr><td>R      </td><td>1220   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " language\\_preference & count\\\\\n",
       "\\hline\n",
       "\t Both    & 3660   \\\\\n",
       "\t neither & 2860   \\\\\n",
       "\t Python  & 2413   \\\\\n",
       "\t R       & 1220   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| language_preference | count |\n",
       "|---|---|\n",
       "| Both    | 3660    |\n",
       "| neither | 2860    |\n",
       "| Python  | 2413    |\n",
       "| R       | 1220    |\n",
       "\n"
      ],
      "text/plain": [
       "  language_preference count\n",
       "1 Both                3660 \n",
       "2 neither             2860 \n",
       "3 Python              2413 \n",
       "4 R                   1220 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "debate_tools %>%\n",
    "group_by(language_preference) %>%\n",
    "summarise(count=n()) %>%\n",
    "arrange(desc(count))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "221e912c",
   "metadata": {},
   "source": [
    "13% of the respondents use R while around 24% use Python which indicates that Python is preferred over R. However around 37% of the respondents use both tools."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5ec249",
   "metadata": {},
   "source": [
    "#### 5. Plotting R vs Python users <a class=\"anchor\" id=\"section_4_5\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "59ecf102",
   "metadata": {},
   "outputs": [],
   "source": [
    "debate_plot <- debate_tools %>%\n",
    "group_by(language_preference) %>%\n",
    "summarise(count=n()) %>%\n",
    "filter(language_preference != \"neither\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9a3ab2ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wCAILYQdtmVTZ5p78YHAECFKj0UOzQ0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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(debate_plot , aes(x=language_preference,y=count)) + \n",
    "geom_bar(stat=\"identity\",fill=\"grey\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e9b9977",
   "metadata": {},
   "source": [
    "#### 6.Language recommendations <a class=\"anchor\" id=\"section_4_6\">\n",
    "As we have seen before , the largest group of professionals program in both Python and R. But which language do they recommend to new learners. Do R lovers always recommend R?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bfc363d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`summarise()` has grouped output by 'language_preference'. You can override using the `.groups` argument.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>language_preference</th><th scope=col>LanguageRecommendationSelect</th><th scope=col>count</th><th scope=col>row</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Both   </td><td>Python </td><td>1917   </td><td>1      </td></tr>\n",
       "\t<tr><td>Both   </td><td>R      </td><td> 912   </td><td>2      </td></tr>\n",
       "\t<tr><td>Both   </td><td>       </td><td> 591   </td><td>3      </td></tr>\n",
       "\t<tr><td>Both   </td><td>SQL    </td><td> 108   </td><td>4      </td></tr>\n",
       "\t<tr><td>neither</td><td>       </td><td>2348   </td><td>1      </td></tr>\n",
       "\t<tr><td>neither</td><td>Python </td><td> 196   </td><td>2      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " language\\_preference & LanguageRecommendationSelect & count & row\\\\\n",
       "\\hline\n",
       "\t Both    & Python  & 1917    & 1      \\\\\n",
       "\t Both    & R       &  912    & 2      \\\\\n",
       "\t Both    &         &  591    & 3      \\\\\n",
       "\t Both    & SQL     &  108    & 4      \\\\\n",
       "\t neither &         & 2348    & 1      \\\\\n",
       "\t neither & Python  &  196    & 2      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| language_preference | LanguageRecommendationSelect | count | row |\n",
       "|---|---|---|---|\n",
       "| Both    | Python  | 1917    | 1       |\n",
       "| Both    | R       |  912    | 2       |\n",
       "| Both    |         |  591    | 3       |\n",
       "| Both    | SQL     |  108    | 4       |\n",
       "| neither |         | 2348    | 1       |\n",
       "| neither | Python  |  196    | 2       |\n",
       "\n"
      ],
      "text/plain": [
       "  language_preference LanguageRecommendationSelect count row\n",
       "1 Both                Python                       1917  1  \n",
       "2 Both                R                             912  2  \n",
       "3 Both                                              591  3  \n",
       "4 Both                SQL                           108  4  \n",
       "5 neither                                          2348  1  \n",
       "6 neither             Python                        196  2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recommendations <- debate_tools %>%\n",
    "group_by(language_preference , LanguageRecommendationSelect) %>%\n",
    "summarise(count=n()) %>%\n",
    "arrange(language_preference,desc(count)) %>%\n",
    "mutate(row = row_number()) %>% \n",
    "filter(row <= 4)\n",
    "head(recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d08ce3",
   "metadata": {},
   "source": [
    "#### 7.Most Recommended language by preferred language <a class=\"anchor\" id=\"section_4_7\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b4d8fbfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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peXl0e/uF87AAAhnWC3vr6+uLh45cqV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\nBDp2AAAAABbxf/BAQPAHmtkQAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(recommendations,aes(x=LanguageRecommendationSelect,y=count)) + \n",
    "geom_bar(stat = \"identity\") + \n",
    "facet_wrap(~language_preference) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d7a358",
   "metadata": {},
   "source": [
    "As we can see from the figure above , Python users highly recommend that new learners learn python. The same happens with R users they highly recommend R to new learners. However, programmers that use both languages tend to recommend Python to new learners."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e7cb0d",
   "metadata": {},
   "source": [
    "#### 8. Number of survey respondents by Employer Industry <a class=\"anchor\" id=\"section_4_8\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c5cbbcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>EmployerIndustry</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Technology                      </td><td>1839                            </td></tr>\n",
       "\t<tr><td>Academic                        </td><td>1450                            </td></tr>\n",
       "\t<tr><td>                                </td><td>1155                            </td></tr>\n",
       "\t<tr><td>Financial                       </td><td> 965                            </td></tr>\n",
       "\t<tr><td>Mix of fields                   </td><td> 825                            </td></tr>\n",
       "\t<tr><td>Other                           </td><td> 808                            </td></tr>\n",
       "\t<tr><td>Internet-based                  </td><td> 633                            </td></tr>\n",
       "\t<tr><td>Government                      </td><td> 474                            </td></tr>\n",
       "\t<tr><td>CRM/Marketing                   </td><td> 343                            </td></tr>\n",
       "\t<tr><td>Telecommunications              </td><td> 308                            </td></tr>\n",
       "\t<tr><td>Manufacturing                   </td><td> 293                            </td></tr>\n",
       "\t<tr><td>Insurance                       </td><td> 266                            </td></tr>\n",
       "\t<tr><td>Retail                          </td><td> 256                            </td></tr>\n",
       "\t<tr><td>Pharmaceutical                  </td><td> 161                            </td></tr>\n",
       "\t<tr><td>Non-profit                      </td><td> 140                            </td></tr>\n",
       "\t<tr><td>Hospitality/Entertainment/Sports</td><td> 124                            </td></tr>\n",
       "\t<tr><td>Military/Security               </td><td> 113                            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " EmployerIndustry & count\\\\\n",
       "\\hline\n",
       "\t Technology                       & 1839                            \\\\\n",
       "\t Academic                         & 1450                            \\\\\n",
       "\t                                  & 1155                            \\\\\n",
       "\t Financial                        &  965                            \\\\\n",
       "\t Mix of fields                    &  825                            \\\\\n",
       "\t Other                            &  808                            \\\\\n",
       "\t Internet-based                   &  633                            \\\\\n",
       "\t Government                       &  474                            \\\\\n",
       "\t CRM/Marketing                    &  343                            \\\\\n",
       "\t Telecommunications               &  308                            \\\\\n",
       "\t Manufacturing                    &  293                            \\\\\n",
       "\t Insurance                        &  266                            \\\\\n",
       "\t Retail                           &  256                            \\\\\n",
       "\t Pharmaceutical                   &  161                            \\\\\n",
       "\t Non-profit                       &  140                            \\\\\n",
       "\t Hospitality/Entertainment/Sports &  124                            \\\\\n",
       "\t Military/Security                &  113                            \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| EmployerIndustry | count |\n",
       "|---|---|\n",
       "| Technology                       | 1839                             |\n",
       "| Academic                         | 1450                             |\n",
       "|                                  | 1155                             |\n",
       "| Financial                        |  965                             |\n",
       "| Mix of fields                    |  825                             |\n",
       "| Other                            |  808                             |\n",
       "| Internet-based                   |  633                             |\n",
       "| Government                       |  474                             |\n",
       "| CRM/Marketing                    |  343                             |\n",
       "| Telecommunications               |  308                             |\n",
       "| Manufacturing                    |  293                             |\n",
       "| Insurance                        |  266                             |\n",
       "| Retail                           |  256                             |\n",
       "| Pharmaceutical                   |  161                             |\n",
       "| Non-profit                       |  140                             |\n",
       "| Hospitality/Entertainment/Sports |  124                             |\n",
       "| Military/Security                |  113                             |\n",
       "\n"
      ],
      "text/plain": [
       "   EmployerIndustry                 count\n",
       "1  Technology                       1839 \n",
       "2  Academic                         1450 \n",
       "3                                   1155 \n",
       "4  Financial                         965 \n",
       "5  Mix of fields                     825 \n",
       "6  Other                             808 \n",
       "7  Internet-based                    633 \n",
       "8  Government                        474 \n",
       "9  CRM/Marketing                     343 \n",
       "10 Telecommunications                308 \n",
       "11 Manufacturing                     293 \n",
       "12 Insurance                         266 \n",
       "13 Retail                            256 \n",
       "14 Pharmaceutical                    161 \n",
       "15 Non-profit                        140 \n",
       "16 Hospitality/Entertainment/Sports  124 \n",
       "17 Military/Security                 113 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "responses %>%\n",
    "group_by(EmployerIndustry) %>%\n",
    "summarise(count=n()) %>%\n",
    "arrange(desc(count))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11dce709",
   "metadata": {},
   "source": [
    "Most of the survey respondents work in the Technology industry followed by the Academic industry."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a75137",
   "metadata": {},
   "source": [
    "#### 9.Top 5 used Algorithms <a class=\"anchor\" id=\"section_4_9\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5de7c719",
   "metadata": {},
   "outputs": [],
   "source": [
    "Algorithms <- debate_tools %>% \n",
    "mutate(algorithm = str_split(WorkAlgorithmsSelect,\",\")) %>% \n",
    "unnest(algorithm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c98a4c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>algorithm</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Regression/Logistic Regression</td><td>4636                          </td></tr>\n",
       "\t<tr><td>Decision Trees                </td><td>3640                          </td></tr>\n",
       "\t<tr><td>Random Forests                </td><td>3378                          </td></tr>\n",
       "\t<tr><td>                              </td><td>2852                          </td></tr>\n",
       "\t<tr><td>Neural Networks               </td><td>2743                          </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " algorithm & count\\\\\n",
       "\\hline\n",
       "\t Regression/Logistic Regression & 4636                          \\\\\n",
       "\t Decision Trees                 & 3640                          \\\\\n",
       "\t Random Forests                 & 3378                          \\\\\n",
       "\t                                & 2852                          \\\\\n",
       "\t Neural Networks                & 2743                          \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| algorithm | count |\n",
       "|---|---|\n",
       "| Regression/Logistic Regression | 4636                           |\n",
       "| Decision Trees                 | 3640                           |\n",
       "| Random Forests                 | 3378                           |\n",
       "|                                | 2852                           |\n",
       "| Neural Networks                | 2743                           |\n",
       "\n"
      ],
      "text/plain": [
       "  algorithm                      count\n",
       "1 Regression/Logistic Regression 4636 \n",
       "2 Decision Trees                 3640 \n",
       "3 Random Forests                 3378 \n",
       "4                                2852 \n",
       "5 Neural Networks                2743 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Algorithms %>%\n",
    "group_by(algorithm) %>%\n",
    "summarise(count=n()) %>%\n",
    "arrange(desc(count)) %>%\n",
    "head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c345d9",
   "metadata": {},
   "source": [
    "#### 10.Top 5 used Algorithms by R lovers <a class=\"anchor\" id=\"section_4_10\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "56acdf41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>algorithm</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Regression/Logistic Regression</td><td>786                           </td></tr>\n",
       "\t<tr><td>Decision Trees                </td><td>590                           </td></tr>\n",
       "\t<tr><td>Random Forests                </td><td>447                           </td></tr>\n",
       "\t<tr><td>Bayesian Techniques           </td><td>289                           </td></tr>\n",
       "\t<tr><td>Neural Networks               </td><td>229                           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " algorithm & count\\\\\n",
       "\\hline\n",
       "\t Regression/Logistic Regression & 786                           \\\\\n",
       "\t Decision Trees                 & 590                           \\\\\n",
       "\t Random Forests                 & 447                           \\\\\n",
       "\t Bayesian Techniques            & 289                           \\\\\n",
       "\t Neural Networks                & 229                           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| algorithm | count |\n",
       "|---|---|\n",
       "| Regression/Logistic Regression | 786                            |\n",
       "| Decision Trees                 | 590                            |\n",
       "| Random Forests                 | 447                            |\n",
       "| Bayesian Techniques            | 289                            |\n",
       "| Neural Networks                | 229                            |\n",
       "\n"
      ],
      "text/plain": [
       "  algorithm                      count\n",
       "1 Regression/Logistic Regression 786  \n",
       "2 Decision Trees                 590  \n",
       "3 Random Forests                 447  \n",
       "4 Bayesian Techniques            289  \n",
       "5 Neural Networks                229  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# which algorithm does R lovers use the most \n",
    "Algorithms %>%\n",
    "filter(language_preference == \"R\") %>%\n",
    "group_by(algorithm) %>%\n",
    "summarise(count=n()) %>% \n",
    "arrange(desc(count)) %>%\n",
    "head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c961336",
   "metadata": {},
   "source": [
    "#### 11.Top 5 used Algorithms by Python lovers <a class=\"anchor\" id=\"section_4_11\">\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "429a6d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>algorithm</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Regression/Logistic Regression</td><td>1108                          </td></tr>\n",
       "\t<tr><td>Neural Networks               </td><td>1057                          </td></tr>\n",
       "\t<tr><td>Decision Trees                </td><td> 824                          </td></tr>\n",
       "\t<tr><td>Random Forests                </td><td> 814                          </td></tr>\n",
       "\t<tr><td>CNNs                          </td><td> 684                          </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " algorithm & count\\\\\n",
       "\\hline\n",
       "\t Regression/Logistic Regression & 1108                          \\\\\n",
       "\t Neural Networks                & 1057                          \\\\\n",
       "\t Decision Trees                 &  824                          \\\\\n",
       "\t Random Forests                 &  814                          \\\\\n",
       "\t CNNs                           &  684                          \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| algorithm | count |\n",
       "|---|---|\n",
       "| Regression/Logistic Regression | 1108                           |\n",
       "| Neural Networks                | 1057                           |\n",
       "| Decision Trees                 |  824                           |\n",
       "| Random Forests                 |  814                           |\n",
       "| CNNs                           |  684                           |\n",
       "\n"
      ],
      "text/plain": [
       "  algorithm                      count\n",
       "1 Regression/Logistic Regression 1108 \n",
       "2 Neural Networks                1057 \n",
       "3 Decision Trees                  824 \n",
       "4 Random Forests                  814 \n",
       "5 CNNs                            684 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# which algorithm does Python lovers use the most \n",
    "Algorithms %>%\n",
    "filter(language_preference == \"Python\") %>%\n",
    "group_by(algorithm) %>%\n",
    "summarise(count=n()) %>% \n",
    "arrange(desc(count)) %>%\n",
    "head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe4012b",
   "metadata": {},
   "source": [
    "It seems that Both R lovers and python lovers mostly  use the Regression Algorithm in their machine learning projects."
   ]
  }
 ],
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