A simple Notebook with R kernel on IBM Watson Studio.

R_Notebook_is_awesome_watson_studio.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# R Notebook is Awesome #\n\nThe primary goal of this **Jupyter Notebook** is to contribute to the dissemination of the **R language**.\n\nAnd to explore some resources of the  in a light and fun way using **IBM Cloud services - Watson Studio** environment.\n\nBelow, we will find varied samples of code, using vectors and R built-in data sets and different forms of data visualization.\n\n\nAuthor [mxeliezer](https://github.com/mxeliezer), R Enthusiast and quantitative researcher.\n\nStudent at [DEP - UA](https://www.ua.pt/pt/dep)."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## 1. From a vector to a plot ##\n\nHow about we start by creating a simple vector with the ```seq()``` function. \n\nAfter that, we can use the ```plot()``` function to create our first plot. "
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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iABIQESEFLY2/Hx5jzVM9gfIYW5ZckiQlSY7e+aTPhBSOFtXuQ9P+ftfKLMnaoH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\njcXPtqEAAAAASUVORK5CYII=",
                        "text/plain": "plot without title"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "x<-seq(0,2*pi,length.out=50)\nplot(x,sin(x))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### 1.1. R is full of magic ###\n\nWhat if we try something more striking?  We can create a new vector, now using the ```rnorm()``` function. \n\nWe can use the ```hist()``` function to create a **histogram**. Then we can plot it by specifying some parameters."
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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2pnOnfu7OPjo1ceAJXIYDKZ9M4AAHCc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\noNgBAAAogmIHAACgCIodAACAIv4PnIKom5l27L4AAAAASUVORK5CYII=",
                        "text/plain": "Plot with title \u201cHistogram of x\u201d"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "x <- rnorm(1000)\nhx <- hist(x, breaks=100, plot=FALSE)\nplot(hx, col=ifelse(abs(hx$breaks) < 1.669, 4, 2))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## 2. Analysing data ##\n\n\n### 2.1. R bult-in data set ###\n\nThis time we will use a R bult-in data set: **mtcars**. And through the **ggplot2** package, we will create a **scatter plot** in the best possible way. Let's do it?\n\nWe can learn a little more about our data set, **mtcars**, through the ```help()``` function, according to the code below. This function is one of the simplest ways to access basic R documentation.\n\nWe can visualize our date set in several ways. Below we will see the upper part of it, with the function ```head()```.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": "help(mtcars)"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<table>\n<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n<tbody>\n\t<tr><th scope=row>Mazda RX4</th><td>21.0 </td><td>6    </td><td>160  </td><td>110  </td><td>3.90 </td><td>2.620</td><td>16.46</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n\t<tr><th scope=row>Mazda RX4 Wag</th><td>21.0 </td><td>6    </td><td>160  </td><td>110  </td><td>3.90 </td><td>2.875</td><td>17.02</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n\t<tr><th scope=row>Datsun 710</th><td>22.8 </td><td>4    </td><td>108  </td><td> 93  </td><td>3.85 </td><td>2.320</td><td>18.61</td><td>1    </td><td>1    </td><td>4    </td><td>1    </td></tr>\n\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4 </td><td>6    </td><td>258  </td><td>110  </td><td>3.08 </td><td>3.215</td><td>19.44</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n\t<tr><th scope=row>Hornet Sportabout</th><td>18.7 </td><td>8    </td><td>360  </td><td>175  </td><td>3.15 </td><td>3.440</td><td>17.02</td><td>0    </td><td>0    </td><td>3    </td><td>2    </td></tr>\n\t<tr><th scope=row>Valiant</th><td>18.1 </td><td>6    </td><td>225  </td><td>105  </td><td>2.76 </td><td>3.460</td><td>20.22</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n</tbody>\n</table>\n",
                        "text/latex": "\\begin{tabular}{r|lllllllllll}\n  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n\\hline\n\tMazda RX4 & 21.0  & 6     & 160   & 110   & 3.90  & 2.620 & 16.46 & 0     & 1     & 4     & 4    \\\\\n\tMazda RX4 Wag & 21.0  & 6     & 160   & 110   & 3.90  & 2.875 & 17.02 & 0     & 1     & 4     & 4    \\\\\n\tDatsun 710 & 22.8  & 4     & 108   &  93   & 3.85  & 2.320 & 18.61 & 1     & 1     & 4     & 1    \\\\\n\tHornet 4 Drive & 21.4  & 6     & 258   & 110   & 3.08  & 3.215 & 19.44 & 1     & 0     & 3     & 1    \\\\\n\tHornet Sportabout & 18.7  & 8     & 360   & 175   & 3.15  & 3.440 & 17.02 & 0     & 0     & 3     & 2    \\\\\n\tValiant & 18.1  & 6     & 225   & 105   & 2.76  & 3.460 & 20.22 & 1     & 0     & 3     & 1    \\\\\n\\end{tabular}\n",
                        "text/markdown": "\n| <!--/--> | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb |\n|---|---|---|---|---|---|---|---|---|---|---|---|\n| Mazda RX4 | 21.0  | 6     | 160   | 110   | 3.90  | 2.620 | 16.46 | 0     | 1     | 4     | 4     |\n| Mazda RX4 Wag | 21.0  | 6     | 160   | 110   | 3.90  | 2.875 | 17.02 | 0     | 1     | 4     | 4     |\n| Datsun 710 | 22.8  | 4     | 108   |  93   | 3.85  | 2.320 | 18.61 | 1     | 1     | 4     | 1     |\n| Hornet 4 Drive | 21.4  | 6     | 258   | 110   | 3.08  | 3.215 | 19.44 | 1     | 0     | 3     | 1     |\n| Hornet Sportabout | 18.7  | 8     | 360   | 175   | 3.15  | 3.440 | 17.02 | 0     | 0     | 3     | 2     |\n| Valiant | 18.1  | 6     | 225   | 105   | 2.76  | 3.460 | 20.22 | 1     | 0     | 3     | 1     |\n\n",
                        "text/plain": "                  mpg  cyl disp hp  drat wt    qsec  vs am gear carb\nMazda RX4         21.0 6   160  110 3.90 2.620 16.46 0  1  4    4   \nMazda RX4 Wag     21.0 6   160  110 3.90 2.875 17.02 0  1  4    4   \nDatsun 710        22.8 4   108   93 3.85 2.320 18.61 1  1  4    1   \nHornet 4 Drive    21.4 6   258  110 3.08 3.215 19.44 1  0  3    1   \nHornet Sportabout 18.7 8   360  175 3.15 3.440 17.02 0  0  3    2   \nValiant           18.1 6   225  105 2.76 3.460 20.22 1  0  3    1   "
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "head(mtcars)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### 2.2. Installing  a new package ###\n\nNow let's install the **ggplot2** package, using the ```install.pakages()``` function. \n\nAnd then activate it with the ```library()``` function."
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "also installing the dependencies \u2018processx\u2019, \u2018diffobj\u2019, \u2018rematch2\u2019, \u2018brio\u2019, \u2018callr\u2019, \u2018cli\u2019, \u2018lifecycle\u2019, \u2018praise\u2019, \u2018ps\u2019, \u2018rlang\u2019, \u2018waldo\u2019, \u2018withr\u2019, \u2018testthat\u2019, \u2018isoband\u2019\n\nUpdating HTML index of packages in '.Library'\nMaking 'packages.html' ... done\n"
                }
            ],
            "source": "install.packages(\"ggplot2\")"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [],
            "source": "library(ggplot2)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can find out more about the **ggplot2** package through the ```help()``` function, from before.\n\nTake a good look at the attributes that compose each command function. In this case, we must especially assign the data and the values of the X and Y axes."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Let's try a simple **scatter plot**, relating the variables **mpg** (miles per gallon) and **wt** (weight), from **mtcars** data set.\n\nThe ```geom_point()``` command allows us to customize some aspects of our scatter plot, as in this case, the format of the dots."
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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eFHk6YCKQCSGJB2adJNDUCKApIUkJq3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\nTkSuQmCC",
                        "text/plain": "plot without title"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "ggplot(mtcars,aes(x=mpg,y=wt)) + geom_point(shape=1)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### 2.3. Get more form the data ###\n\nWe will introduce a third variable to our **scatter plot**, in addition to **mpg** and **wt**. The automobiles will be categorized by the number of cylinders, so we're going to use the shape of each point to represent this.\n\n#### Step one: ####\n\nWe'll create a factor from the ***cyl*** (cylinder) variable."
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [],
            "source": "mtcars$cylFactor<-factor(mtcars$cyl)"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<ol class=list-inline>\n\t<li>6</li>\n\t<li>6</li>\n\t<li>4</li>\n\t<li>6</li>\n\t<li>8</li>\n\t<li>6</li>\n\t<li>8</li>\n\t<li>4</li>\n\t<li>4</li>\n\t<li>6</li>\n\t<li>6</li>\n\t<li>8</li>\n\t<li>8</li>\n\t<li>8</li>\n\t<li>8</li>\n\t<li>8</li>\n\t<li>8</li>\n\t<li>4</li>\n\t<li>4</li>\n\t<li>4</li>\n\t<li>4</li>\n\t<li>8</li>\n\t<li>8</li>\n\t<li>8</li>\n\t<li>8</li>\n\t<li>4</li>\n\t<li>4</li>\n\t<li>4</li>\n\t<li>8</li>\n\t<li>6</li>\n\t<li>8</li>\n\t<li>4</li>\n</ol>\n\n<details>\n\t<summary style=display:list-item;cursor:pointer>\n\t\t<strong>Levels</strong>:\n\t</summary>\n\t<ol class=list-inline>\n\t\t<li>'4'</li>\n\t\t<li>'6'</li>\n\t\t<li>'8'</li>\n\t</ol>\n</details>",
                        "text/latex": "\\begin{enumerate*}\n\\item 6\n\\item 6\n\\item 4\n\\item 6\n\\item 8\n\\item 6\n\\item 8\n\\item 4\n\\item 4\n\\item 6\n\\item 6\n\\item 8\n\\item 8\n\\item 8\n\\item 8\n\\item 8\n\\item 8\n\\item 4\n\\item 4\n\\item 4\n\\item 4\n\\item 8\n\\item 8\n\\item 8\n\\item 8\n\\item 4\n\\item 4\n\\item 4\n\\item 8\n\\item 6\n\\item 8\n\\item 4\n\\end{enumerate*}\n\n\\emph{Levels}: \\begin{enumerate*}\n\\item '4'\n\\item '6'\n\\item '8'\n\\end{enumerate*}\n",
                        "text/markdown": "1. 6\n2. 6\n3. 4\n4. 6\n5. 8\n6. 6\n7. 8\n8. 4\n9. 4\n10. 6\n11. 6\n12. 8\n13. 8\n14. 8\n15. 8\n16. 8\n17. 8\n18. 4\n19. 4\n20. 4\n21. 4\n22. 8\n23. 8\n24. 8\n25. 8\n26. 4\n27. 4\n28. 4\n29. 8\n30. 6\n31. 8\n32. 4\n\n\n\n**Levels**: 1. '4'\n2. '6'\n3. '8'\n\n\n",
                        "text/plain": " [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4\nLevels: 4 6 8"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "mtcars$cylFactor"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Step two: ####\n\nUsing the ```ggplot()``` function to create the scatter plot. Let's take a look at the output.\n\nAt this point, we will give our scatter plot a greater number of details. Take a good look at each one."
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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Bg0upZoKIqiKIrP5zO6kGhIkuR2u4PB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\nEhQ7AAAASfx/hofXMaWx8DEAAAAASUVORK5CYII=",
                        "text/plain": "plot without title"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "ggplot(mtcars,aes(x=mpg,y=wt, color= cylFactor)) + geom_point(shape = 19) + labs(colour=\"Cylinders\") + xlab(\"Miles per Gallon\") + ylab(\"Weight\") + ggtitle(\"Scatterplot: mtcars dataset\")"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "What we did? We add the colors according to the types of cylinders (```color= cylFactor```), we change the shape of the points, with ```geom_point()```, we change the name of the legend with the function ```labs()``` and the names of the X and Y axes, with ```xlab()``` and ```ylab()```. And finally, we add the title with ```ggtitle()```."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "I hope it has been fruitful for you and until next time."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Disclaimer ####\n\nThis Notebbok was adapted from many other sources. I only as a learning and knowledge sharing exercise. There is no intention of commercial use.\n\nAdditional contacts, suggestions and criticism can be made through this [e-mail](mailto:araujo@tuta.io) \n\nThank you very much."
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "R 3.6",
            "language": "R",
            "name": "r-3.6"
        },
        "language_info": {
            "codemirror_mode": "r",
            "file_extension": ".r",
            "mimetype": "text/x-r-source",
            "name": "R",
            "pygments_lexer": "r",
            "version": "3.6.1"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 1
}

Test_R_kernel_watson_studio.ipynb