R_LAB_UNIT_5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNjo5iB5wfLMl7Mw99MOrP5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/d0k7/R-Programming-Language-Lab-Code/blob/main/R_LAB_UNIT_5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "1fMaZOu1_daC",
        "outputId": "71242733-1a2d-4a9a-8686-88c595c6d70b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “Weight vs. Height”"
            ],
            "image/png": 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fKpRX/8Sc/SC8s3rehzYl3yH8BKSEhN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\nCggJUEBIgAJCAhQQEqCAkAAFhAQo+P9QWeJ27NTsPgAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ],
      "source": [
        "#Q1) Create a database with the fields of weight,height and sex then create a plot of weight on the x-axis and height on the y-axis. Use different point characters or colors\n",
        "#     to distinguish between males and females and provide a matching legend. Label the axes and give the plot a title.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Create the database\n",
        "my_data <- data.frame(\n",
        "  weight = c(60, 70, 55, 65, 75, 68),\n",
        "  height = c(160, 170, 155, 165, 175, 168),\n",
        "  sex = c(\"Male\", \"Male\", \"Female\", \"Female\", \"Male\", \"Female\")\n",
        ")\n",
        "\n",
        "# Plot using base R graphics\n",
        "plot(my_data$weight, my_data$height, col = ifelse(my_data$sex == \"Male\", \"blue\", \"red\"),\n",
        "     pch = ifelse(my_data$sex == \"Male\", 16, 17), xlab = \"Weight\", ylab = \"Height\",\n",
        "     main = \"Weight vs. Height\", xlim = c(50, 80), ylim = c(150, 180))\n",
        "legend(\"topleft\", legend = c(\"Male\", \"Female\"), col = c(\"blue\", \"red\"), pch = c(16, 17))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Q2) create a plot using ggplot2 for the same database consists of weight on the x-axis and height on the y- axis. Use different point characters or colors to\n",
        "#    distinguish between males and females and provide a matching legend. Label the axes and give the plot a title.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Load ggplot2 library\n",
        "library(ggplot2)\n",
        "\n",
        "# Plot using ggplot2\n",
        "ggplot(data = my_data, aes(x = weight, y = height, color = sex, shape = sex)) +\n",
        "  geom_point(size = 4) +\n",
        "  labs(x = \"Weight\", y = \"Height\", title = \"Weight vs. Height\") +\n",
        "  scale_color_manual(values = c(\"blue\", \"red\")) +\n",
        "  scale_shape_manual(values = c(16, 17)) +\n",
        "  theme_minimal()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "_PuA4FCQ_3Ie",
        "outputId": "f0032887-0ae4-43de-8093-f5f8ff7665dc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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EwQkoKQBIRkg5AUhBQTIYUhJAEhmSAk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\n8aXL05Ckv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    },
    {
      "cell_type": "code",
      "source": [
        "#Q3) Write R code that will plot education on the x-axis and income on the y-axis, with both x- and y-axis limits fixed to be [0;100]. Provide appropriate axis labels.\n",
        "#    For jobs with a prestige value of less than or equal to 80, use a black * as the point character. For jobs with prestige greater than 80, use a blue @.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Create the database\n",
        "my_data <- data.frame(\n",
        "  education = c(80, 90, 60, 70, 75, 85),\n",
        "  income = c(50, 70, 40, 60, 55, 80),\n",
        "  prestige = c(90, 100, 70, 80, 75, 95)\n",
        ")\n",
        "\n",
        "# Plot with different point characters based on prestige values\n",
        "plot(my_data$education, my_data$income, col = ifelse(my_data$prestige <= 80, \"black\", \"blue\"),\n",
        "     pch = ifelse(my_data$prestige <= 80, \"*\", \"@\"), xlab = \"Education\", ylab = \"Income\",\n",
        "     main = \"Education vs. Income\", xlim = c(0, 100), ylim = c(0, 100))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "Sckfa5yP_9nt",
        "outputId": "c8f21446-6982-44c7-a565-23927755a8a2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “Education vs. Income”"
            ],
            "image/png": 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SVLlgQHBzdp0qRhw4atWrUy7SasUqdO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}