{ "cells": [ { "cell_type": "markdown", "id": "8d42fc52", "metadata": {}, "source": [ "#### Installation of R packages" ] }, { "cell_type": "code", "execution_count": 1, "id": "52ac0f54", "metadata": {}, "outputs": [], "source": [ "#install.packages(\"ISwR\")" ] }, { "cell_type": "markdown", "id": "3a957ba3", "metadata": {}, "source": [ "#### Package loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "de33e724", "metadata": {}, "outputs": [], "source": [ "library(ISwR)" ] }, { "cell_type": "markdown", "id": "99bcc6a7", "metadata": {}, "source": [ "#### Variable definition and assignment" ] }, { "cell_type": "code", "execution_count": 3, "id": "1d0c4a11", "metadata": {}, "outputs": [], "source": [ "weight <- 60\n", "height = 1.75\n", "subject <- \"A\"\n", "healthy <- TRUE" ] }, { "cell_type": "markdown", "id": "bcee452b", "metadata": {}, "source": [ "#### Variable evaluation" ] }, { "cell_type": "code", "execution_count": 4, "id": "ad938581", "metadata": {}, "outputs": [ { "data": { "text/html": [ "60" ], "text/latex": [ "60" ], "text/markdown": [ "60" ], "text/plain": [ "[1] 60" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight" ] }, { "cell_type": "markdown", "id": "57dd208e", "metadata": {}, "source": [ "#### Functions for type checking" ] }, { "cell_type": "code", "execution_count": 5, "id": "1778ae3b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "FALSE" ], "text/latex": [ "FALSE" ], "text/markdown": [ "FALSE" ], "text/plain": [ "[1] FALSE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "is.numeric(weight) # variable \n", "is.double(weight)\n", "is.integer(weight)\n", "is.character(subject)" ] }, { "cell_type": "markdown", "id": "5dcfe56a", "metadata": {}, "source": [ "#### Functions for variable conversion" ] }, { "cell_type": "code", "execution_count": 6, "id": "9de5f0fd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight <- as.integer(weight)\n", "is.integer(weight)" ] }, { "cell_type": "markdown", "id": "ecb65b7e", "metadata": {}, "source": [ "#### Computing the body mass index (BMI) from the weight and height" ] }, { "cell_type": "code", "execution_count": 7, "id": "5bb310e3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "19.5918367346939" ], "text/latex": [ "19.5918367346939" ], "text/markdown": [ "19.5918367346939" ], "text/plain": [ "[1] 19.59184" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Body mass index (BMI)\n", "bmi <- weight/height^2 \n", "bmi " ] }, { "cell_type": "markdown", "id": "1d6b02ea", "metadata": {}, "source": [ "#### Functions for string manipulation" ] }, { "cell_type": "code", "execution_count": 8, "id": "2a5c0c64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"19.6\"\n" ] } ], "source": [ "message <- sprintf(\"%.1f\", bmi)\n", "print(message)" ] }, { "cell_type": "markdown", "id": "f465aa65", "metadata": {}, "source": [ "#### Vector definition" ] }, { "cell_type": "code", "execution_count": 9, "id": "f5792a41", "metadata": {}, "outputs": [], "source": [ "weight <- c(60, 72, 57, 90, 95, 72) \n", "height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)\n", "subject <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\")\n" ] }, { "cell_type": "markdown", "id": "1c86ae38", "metadata": {}, "source": [ "#### Vector evaluation" ] }, { "cell_type": "code", "execution_count": 10, "id": "ea73ae27", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 60
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  1. 1.75
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1.75\n", "\\item 1.8\n", "\\item 1.65\n", "\\item 1.9\n", "\\item 1.74\n", "\\item 1.91\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1.75\n", "2. 1.8\n", "3. 1.65\n", "4. 1.9\n", "5. 1.74\n", "6. 1.91\n", "\n", "\n" ], "text/plain": [ "[1] 1.75 1.80 1.65 1.90 1.74 1.91" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
  1. 'A'
  2. 'B'
  3. 'C'
  4. 'D'
  5. 'E'
  6. 'F'
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'A'\n", "\\item 'B'\n", "\\item 'C'\n", "\\item 'D'\n", "\\item 'E'\n", "\\item 'F'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'A'\n", "2. 'B'\n", "3. 'C'\n", "4. 'D'\n", "5. 'E'\n", "6. 'F'\n", "\n", "\n" ], "text/plain": [ "[1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight\n", "height\n", "subject" ] }, { "cell_type": "markdown", "id": "5502a330", "metadata": {}, "source": [ "#### Creating a vector with a particular size" ] }, { "cell_type": "code", "execution_count": 11, "id": "4e59d217", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 0
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0\n", "2. 0\n", "3. 0\n", "4. 0\n", "5. 0\n", "6. 0\n", "7. 0\n", "8. 0\n", "9. 0\n", "10. 0\n", "\n", "\n" ], "text/plain": [ " [1] 0 0 0 0 0 0 0 0 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vec <- rep(0, 10)\n", "vec" ] }, { "cell_type": "markdown", "id": "6689bea8", "metadata": {}, "source": [ "#### Vector length" ] }, { "cell_type": "code", "execution_count": 12, "id": "8b9a291b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "6" ], "text/latex": [ "6" ], "text/markdown": [ "6" ], "text/plain": [ "[1] 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "length(weight)\n" ] }, { "cell_type": "markdown", "id": "e2bfeacd", "metadata": {}, "source": [ "#### Vector indexes: from one to the length of the vector" ] }, { "cell_type": "code", "execution_count": 13, "id": "be37ba67", "metadata": {}, "outputs": [ { "data": { "text/html": [ "60" ], "text/latex": [ "60" ], "text/markdown": [ "60" ], "text/plain": [ "[1] 60" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "72" ], "text/latex": [ "72" ], "text/markdown": [ "72" ], "text/plain": [ "[1] 72" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight[1]\n", "weight[length(weight)]" ] }, { "cell_type": "markdown", "id": "8e052e4d", "metadata": {}, "source": [ "#### Iteration: for loop\n", "from one to the length of weight" ] }, { "cell_type": "code", "execution_count": 14, "id": "d89d1ae9", "metadata": {}, "outputs": [], "source": [ "bmi <- 0\n", "for (i in 1:length(weight)) {\n", " bmi[i] <- weight[i]/height[i]^2\n", "}\n" ] }, { "cell_type": "markdown", "id": "44062e04", "metadata": {}, "source": [ "evaluation of the bmi vector" ] }, { "cell_type": "code", "execution_count": 15, "id": "3bc6e592", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 19.5918367346939
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "markdown", "id": "78af2d5a", "metadata": {}, "source": [ "#### Iteration: while loop\n", "run while i is below or equal to the length of weight" ] }, { "cell_type": "code", "execution_count": 16, "id": "708de5df", "metadata": {}, "outputs": [], "source": [ "bmi <- 0\n", "i <- 1\n", "while (i <= length(weight)) {\n", " bmi[i] <- weight[i]/height[i]^2\n", " i <- i + 1\n", "}\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "7449e07e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 19.5918367346939
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "markdown", "id": "9394fcf5", "metadata": {}, "source": [ "#### Remove a variable" ] }, { "cell_type": "code", "execution_count": 18, "id": "2c181a87", "metadata": {}, "outputs": [ { "data": { "text/html": [ "FALSE" ], "text/latex": [ "FALSE" ], "text/markdown": [ "FALSE" ], "text/plain": [ "[1] FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rm(bmi)\n", "exists(\"bmi\")" ] }, { "cell_type": "markdown", "id": "6203ac32", "metadata": {}, "source": [ "#### Right way of manipulating vectors: assigning at once" ] }, { "cell_type": "code", "execution_count": 19, "id": "6e6712a2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 19.5918367346939
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi <- weight/height^2 \n", "bmi " ] }, { "cell_type": "markdown", "id": "f654b0ff", "metadata": {}, "source": [ "#### Creating a function\n", "name <- function(parameters) { body }" ] }, { "cell_type": "code", "execution_count": 20, "id": "682911c9", "metadata": {}, "outputs": [], "source": [ "compute_bmi <- function(weight, height) {\n", " bmi <- weight/height^2 \n", " return(bmi)\n", "}" ] }, { "cell_type": "markdown", "id": "4cd72edd", "metadata": {}, "source": [ "#### Using a function with scalars" ] }, { "cell_type": "code", "execution_count": 21, "id": "abd58d44", "metadata": {}, "outputs": [ { "data": { "text/html": [ "19.5918367346939" ], "text/latex": [ "19.5918367346939" ], "text/markdown": [ "19.5918367346939" ], "text/plain": [ "[1] 19.59184" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "bmi <- compute_bmi(60, 1.75)\n", "bmi\n" ] }, { "cell_type": "markdown", "id": "9df8b472", "metadata": {}, "source": [ "#### Using the same function with vectors" ] }, { "cell_type": "code", "execution_count": 22, "id": "90db70cb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 19.5918367346939
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi <- compute_bmi(weight, height)\n", "bmi" ] }, { "cell_type": "markdown", "id": "c7e11f53", "metadata": {}, "source": [ "#### Example of a function to compute the average\n", "(iterating in all elements of the vector)" ] }, { "cell_type": "code", "execution_count": 23, "id": "67374bc5", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " s <- 0\n", " n <- length(vec)\n", " for (x in vec) {\n", " s <- s + x \n", " }\n", " return(s/n)\n", "}" ] }, { "cell_type": "markdown", "id": "b683d41d", "metadata": {}, "source": [ "invoking the function" ] }, { "cell_type": "code", "execution_count": 24, "id": "8af439bb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "7925f58c", "metadata": {}, "source": [ "#### Example of a function to compute the average\n", "(manipulating vectors at once)" ] }, { "cell_type": "code", "execution_count": 25, "id": "d81ade80", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " s <- sum(vec)\n", " n <- length(vec)\n", " return(s/n)\n", "}" ] }, { "cell_type": "markdown", "id": "e85fe370", "metadata": {}, "source": [ "invoking the function" ] }, { "cell_type": "code", "execution_count": 26, "id": "acd8835e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "29d0264b", "metadata": {}, "source": [ "#### Average function using mean function\n", "Major statistical functions are available in R" ] }, { "cell_type": "code", "execution_count": 27, "id": "f46e574c", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " return(mean(vec))\n", "}" ] }, { "cell_type": "markdown", "id": "0a6609d2", "metadata": {}, "source": [ "invoking the function" ] }, { "cell_type": "code", "execution_count": 28, "id": "82116583", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "cd0eded4", "metadata": {}, "source": [ "#### Working with vectors with NA\n", "Operations with NA lead to NA." ] }, { "cell_type": "code", "execution_count": 29, "id": "a76dcffa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<NA>" ], "text/latex": [ "" ], "text/markdown": [ "<NA>" ], "text/plain": [ "[1] NA" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- c(10, NA, 13)\n", "y <- average(x)\n", "y" ] }, { "cell_type": "markdown", "id": "ad2e1300", "metadata": {}, "source": [ "#### addressing NA with na.rm=TRUE" ] }, { "cell_type": "code", "execution_count": 30, "id": "38a57461", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " return(mean(vec, na.rm=TRUE))\n", "}" ] }, { "cell_type": "code", "execution_count": 31, "id": "6fc66146", "metadata": {}, "outputs": [ { "data": { "text/html": [ "11.5" ], "text/latex": [ "11.5" ], "text/markdown": [ "11.5" ], "text/plain": [ "[1] 11.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- c(10, NA, 13)\n", "y <- average(x)\n", "y" ] }, { "cell_type": "markdown", "id": "5143eaa8", "metadata": {}, "source": [ "#### Plotting graphics\n", "scatter plots" ] }, { "cell_type": "code", "execution_count": 32, "id": "5bc25389", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "plot(height, weight)" ] }, { "cell_type": "markdown", "id": "f6c2f13d", "metadata": {}, "source": [ "#### Most functions contain many default parameters" ] }, { "cell_type": "code", "execution_count": 33, "id": "de2fc532", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "plot(height, weight, pch=2)" ] }, { "cell_type": "markdown", "id": "3beca799", "metadata": {}, "source": [ "#### Default function arguments can be shown with args" ] }, { "cell_type": "code", "execution_count": 34, "id": "a03fe7dc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n",
       "    log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n",
       "    ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n",
       "    panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n",
       "    ...) \n",
       "NULL
" ], "text/latex": [ "\\begin{minted}{r}\n", "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL\n", "\\end{minted}" ], "text/markdown": [ "```r\n", "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL\n", "```" ], "text/plain": [ "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "args(plot.default)" ] }, { "cell_type": "markdown", "id": "87d4c7ee", "metadata": {}, "source": [ "#### All functions in R that belongs to packages have help with examples" ] }, { "cell_type": "code", "execution_count": 35, "id": "6226b10f", "metadata": {}, "outputs": [], "source": [ "?base::plot" ] }, { "cell_type": "markdown", "id": "8a6491dd", "metadata": {}, "source": [ "#### Canvas for plotting is still active until a new plot" ] }, { "cell_type": "code", "execution_count": 36, "id": "c37b7038", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "plot(height, weight)\n", "hh = c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90)\n", "lines(hh, 22.5 * hh^2)" ] }, { "cell_type": "markdown", "id": "caf65b7c", "metadata": {}, "source": [ "#### Factors\n", "Factors are used to handle categorical data." ] }, { "cell_type": "code", "execution_count": 37, "id": "38fa7999", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 0
  2. 3
  3. 2
  4. 2
  5. 1
\n", "\n", "
\n", "\t\n", "\t\tLevels:\n", "\t\n", "\t\n", "\t
  1. '0'
  2. '1'
  3. '2'
  4. '3'
\n", "
" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0\n", "\\item 3\n", "\\item 2\n", "\\item 2\n", "\\item 1\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item '0'\n", "\\item '1'\n", "\\item '2'\n", "\\item '3'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0\n", "2. 3\n", "3. 2\n", "4. 2\n", "5. 1\n", "\n", "\n", "\n", "**Levels**: 1. '0'\n", "2. '1'\n", "3. '2'\n", "4. '3'\n", "\n", "\n" ], "text/plain": [ "[1] 0 3 2 2 1\n", "Levels: 0 < 1 < 2 < 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pain <- c(0,3,2,2,1)\n", "fpain <- factor(pain,levels=0:3, ordered=TRUE)\n", "fpain" ] }, { "cell_type": "markdown", "id": "300d0d78", "metadata": {}, "source": [ "#### Levels provide correspondence between numerical values and categorical labels" ] }, { "cell_type": "code", "execution_count": 38, "id": "9a683e5e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. none
  2. severe
  3. medium
  4. medium
  5. mild
\n", "\n", "
\n", "\t\n", "\t\tLevels:\n", "\t\n", "\t\n", "\t
  1. 'none'
  2. 'mild'
  3. 'medium'
  4. 'severe'
\n", "
" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item none\n", "\\item severe\n", "\\item medium\n", "\\item medium\n", "\\item mild\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'none'\n", "\\item 'mild'\n", "\\item 'medium'\n", "\\item 'severe'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. none\n", "2. severe\n", "3. medium\n", "4. medium\n", "5. mild\n", "\n", "\n", "\n", "**Levels**: 1. 'none'\n", "2. 'mild'\n", "3. 'medium'\n", "4. 'severe'\n", "\n", "\n" ], "text/plain": [ "[1] none severe medium medium mild \n", "Levels: none < mild < medium < severe" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "levels(fpain) <- c(\"none\",\"mild\",\"medium\",\"severe\")\n", "fpain" ] }, { "cell_type": "markdown", "id": "d019846e", "metadata": {}, "source": [ "#### Convert height to factor\n", "Levels: small, medium, high" ] }, { "cell_type": "markdown", "id": "f1dfffae", "metadata": {}, "source": [ "#### coding setting element by element" ] }, { "cell_type": "code", "execution_count": 39, "id": "07d36349", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. medium
  2. medium
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  4. tall
  5. medium
  6. tall
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item medium\n", "\\item medium\n", "\\item short\n", "\\item tall\n", "\\item medium\n", "\\item tall\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'medium'\n", "\\item 'short'\n", "\\item 'tall'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. medium\n", "2. medium\n", "3. short\n", "4. tall\n", "5. medium\n", "6. tall\n", "\n", "\n", "\n", "**Levels**: 1. 'medium'\n", "2. 'short'\n", "3. 'tall'\n", "\n", "\n" ], "text/plain": [ "[1] medium medium short tall medium tall \n", "Levels: medium short tall" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lev <- rep(\"\", length(height))\n", "\n", "for (i in 1:length(height)) {\n", " if (height[i] < 1.7)\n", " lev[i] <- \"short\"\n", " else if (height[i] < 1.9)\n", " lev[i] <- \"medium\"\n", " else \n", " lev[i] <- \"tall\"\n", "}\n", "lev <- as.factor(lev)\n", "lev" ] }, { "cell_type": "markdown", "id": "569660bc", "metadata": {}, "source": [ "#### coding setting the vector at once\n", "It uses the cut function." ] }, { "cell_type": "code", "execution_count": 40, "id": "d81b0ae6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. (1.7,1.9]
  2. (1.7,1.9]
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  1. '(0,1.7]'
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  1. medium
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  5. medium
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" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item medium\n", "\\item medium\n", "\\item short\n", "\\item medium\n", "\\item medium\n", "\\item tall\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'short'\n", "\\item 'medium'\n", "\\item 'tall'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. medium\n", "2. medium\n", "3. short\n", "4. medium\n", "5. medium\n", "6. tall\n", "\n", "\n", "\n", "**Levels**: 1. 'short'\n", "2. 'medium'\n", "3. 'tall'\n", "\n", "\n" ], "text/plain": [ "[1] medium medium short medium medium tall \n", "Levels: short < medium < tall" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lev <- cut(height, breaks=c(0, 1.7, 1.9, .Machine$double.xmax), ordered=TRUE)\n", "lev\n", "levels(lev) <- c(\"short\", \"medium\", \"tall\")\n", "lev" ] }, { "cell_type": "markdown", "id": "a7b75411", "metadata": {}, "source": [ "#### Matrix\n", "Matrices can be filled from vectors or data frames. " ] }, { "cell_type": "code", "execution_count": 41, "id": "d900a570", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\item 5\n", "\\item 6\n", "\\item 7\n", "\\item 8\n", "\\item 9\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 3\n", "4. 4\n", "5. 5\n", "6. 6\n", "7. 7\n", "8. 8\n", "9. 9\n", "\n", "\n" ], "text/plain": [ "[1] 1 2 3 4 5 6 7 8 9" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- 1:9\n", "x" ] }, { "cell_type": "markdown", "id": "fab3bf6e", "metadata": {}, "source": [ "#### Converting a vector to matrix" ] }, { "cell_type": "code", "execution_count": 42, "id": "36dd40b8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 3 × 3 of type int
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\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 4 & 7\\\\\n", "\t 2 & 5 & 8\\\\\n", "\t 3 & 6 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "| 1 | 4 | 7 |\n", "| 2 | 5 | 8 |\n", "| 3 | 6 | 9 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 4 7 \n", "[2,] 2 5 8 \n", "[3,] 3 6 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(x) <- c(3,3)\n", "x" ] }, { "cell_type": "markdown", "id": "013a9fa5", "metadata": {}, "source": [ "#### accessing elements from a matrix" ] }, { "cell_type": "code", "execution_count": 43, "id": "31aad9ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 1\n", "[1] 4\n", "[1] 7\n", "[1] 2\n", "[1] 5\n", "[1] 8\n", "[1] 3\n", "[1] 6\n", "[1] 9\n" ] } ], "source": [ "for (i in 1:nrow(x)) \n", " for (j in 1:ncol(x))\n", " print(x[i,j])\n", " \n" ] }, { "cell_type": "markdown", "id": "582a8811", "metadata": {}, "source": [ "#### Iterating and assigning values to each element" ] }, { "cell_type": "code", "execution_count": 44, "id": "ad2239bd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 3 × 3 of type dbl
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\n" ], "text/latex": [ "A matrix: 3 × 3 of type dbl\n", "\\begin{tabular}{lll}\n", "\t 3 & 12 & 21\\\\\n", "\t 6 & 15 & 24\\\\\n", "\t 9 & 18 & 27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type dbl\n", "\n", "| 3 | 12 | 21 |\n", "| 6 | 15 | 24 |\n", "| 9 | 18 | 27 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 3 12 21 \n", "[2,] 6 15 24 \n", "[3,] 9 18 27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y <- x\n", "for (i in 1:nrow(y)) \n", " for (j in 1:ncol(y))\n", " y[i,j] <- 3 * y[i, j]\n", " \n", "y" ] }, { "cell_type": "markdown", "id": "ea7e0255", "metadata": {}, "source": [ "#### Assigning the values of a matrix at once" ] }, { "cell_type": "code", "execution_count": 45, "id": "a6c41f00", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 3 × 3 of type dbl
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\n" ], "text/latex": [ "A matrix: 3 × 3 of type dbl\n", "\\begin{tabular}{lll}\n", "\t 3 & 12 & 21\\\\\n", "\t 6 & 15 & 24\\\\\n", "\t 9 & 18 & 27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type dbl\n", "\n", "| 3 | 12 | 21 |\n", "| 6 | 15 | 24 |\n", "| 9 | 18 | 27 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 3 12 21 \n", "[2,] 6 15 24 \n", "[3,] 9 18 27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y <- 3*x\n", "y" ] }, { "cell_type": "markdown", "id": "e4503b07", "metadata": {}, "source": [ "#### Converting a vector to a matrix by row" ] }, { "cell_type": "code", "execution_count": 46, "id": "6c1a2c28", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 3 × 3 of type int
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\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 2 & 3\\\\\n", "\t 4 & 5 & 6\\\\\n", "\t 7 & 8 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "| 1 | 2 | 3 |\n", "| 4 | 5 | 6 |\n", "| 7 | 8 | 9 |\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 2 3 \n", "[2,] 4 5 6 \n", "[3,] 7 8 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- matrix(1:9,nrow=3,byrow=T)\n", "x" ] }, { "cell_type": "markdown", "id": "2f8b5706", "metadata": {}, "source": [ "#### transposing a matrix" ] }, { "cell_type": "code", "execution_count": 47, "id": "2b2d54df", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A matrix: 3 × 3 of type int
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A data.frame: 6 × 2
AB
<dbl><dbl>
152603910
254704220
356403885
461805160
563905645
665154680
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & A & B\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 5260 & 3910\\\\\n", "\t2 & 5470 & 4220\\\\\n", "\t3 & 5640 & 3885\\\\\n", "\t4 & 6180 & 5160\\\\\n", "\t5 & 6390 & 5645\\\\\n", "\t6 & 6515 & 4680\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | A <dbl> | B <dbl> |\n", "|---|---|---|\n", "| 1 | 5260 | 3910 |\n", "| 2 | 5470 | 4220 |\n", "| 3 | 5640 | 3885 |\n", "| 4 | 6180 | 5160 |\n", "| 5 | 6390 | 5645 |\n", "| 6 | 6515 | 4680 |\n", "\n" ], "text/plain": [ " A B \n", "1 5260 3910\n", "2 5470 4220\n", "3 5640 3885\n", "4 6180 5160\n", "5 6390 5645\n", "6 6515 4680" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d <- data.frame(A=a, B=b)\n", "head(d)" ] }, { "cell_type": "markdown", "id": "cf05166f", "metadata": {}, "source": [ "#### Adding a column in the data frame " ] }, { "cell_type": "code", "execution_count": 59, "id": "8d526668", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 3
ABc
<dbl><dbl><dbl>
152603910 9170
254704220 9690
356403885 9525
46180516011340
56390564512035
66515468011195
\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & A & B & c\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & 5260 & 3910 & 9170\\\\\n", "\t2 & 5470 & 4220 & 9690\\\\\n", "\t3 & 5640 & 3885 & 9525\\\\\n", "\t4 & 6180 & 5160 & 11340\\\\\n", "\t5 & 6390 & 5645 & 12035\\\\\n", "\t6 & 6515 & 4680 & 11195\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "| | A <dbl> | B <dbl> | c <dbl> |\n", "|---|---|---|---|\n", "| 1 | 5260 | 3910 | 9170 |\n", "| 2 | 5470 | 4220 | 9690 |\n", "| 3 | 5640 | 3885 | 9525 |\n", "| 4 | 6180 | 5160 | 11340 |\n", "| 5 | 6390 | 5645 | 12035 |\n", "| 6 | 6515 | 4680 | 11195 |\n", "\n" ], "text/plain": [ " A B c \n", "1 5260 3910 9170\n", "2 5470 4220 9690\n", "3 5640 3885 9525\n", "4 6180 5160 11340\n", "5 6390 5645 12035\n", "6 6515 4680 11195" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d$c <- d$A + d$B\n", "head(d)" ] }, { "cell_type": "code", "execution_count": 60, "id": "ed285c21", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
Bc
<dbl><dbl>
13910 9170
24220 9690
33885 9525
4516011340
5564512035
6468011195
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & B & c\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 3910 & 9170\\\\\n", "\t2 & 4220 & 9690\\\\\n", "\t3 & 3885 & 9525\\\\\n", "\t4 & 5160 & 11340\\\\\n", "\t5 & 5645 & 12035\\\\\n", "\t6 & 4680 & 11195\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | B <dbl> | c <dbl> |\n", "|---|---|---|\n", "| 1 | 3910 | 9170 |\n", "| 2 | 4220 | 9690 |\n", "| 3 | 3885 | 9525 |\n", "| 4 | 5160 | 11340 |\n", "| 5 | 5645 | 12035 |\n", "| 6 | 4680 | 11195 |\n", "\n" ], "text/plain": [ " B c \n", "1 3910 9170\n", "2 4220 9690\n", "3 3885 9525\n", "4 5160 11340\n", "5 5645 12035\n", "6 4680 11195" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d$A <- NULL\n", "head(d)" ] }, { "cell_type": "markdown", "id": "b3c2ac80", "metadata": {}, "source": [ "#### Reading csv file\n", "There are many functions for reading CSV, Excel, and RData formats." ] }, { "cell_type": "code", "execution_count": 61, "id": "cb4df166", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 14
TypeAlcoholMalicAshAlcalinityMagnesiumPhenolsFlavanoidsNonflavanoidsProanthocyaninsColorHueDilutionProline
<int><dbl><dbl><dbl><dbl><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><int>
1113.201.782.1411.21002.652.760.261.284.381.053.401050
2113.162.362.6718.61012.803.240.302.815.681.033.171185
3114.371.952.5016.81133.853.490.242.187.800.863.451480
4113.242.592.8721.01182.802.690.391.824.321.042.93 735
5114.201.762.4515.21123.273.390.341.976.751.052.851450
6114.391.872.4514.6 962.502.520.301.985.251.023.581290
\n" ], "text/latex": [ "A data.frame: 6 × 14\n", "\\begin{tabular}{r|llllllllllllll}\n", " & Type & Alcohol & Malic & Ash & Alcalinity & Magnesium & Phenols & Flavanoids & Nonflavanoids & Proanthocyanins & Color & Hue & Dilution & Proline\\\\\n", " & & & & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n", "\t2 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n", "\t3 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n", "\t4 & 1 & 13.24 & 2.59 & 2.87 & 21.0 & 118 & 2.80 & 2.69 & 0.39 & 1.82 & 4.32 & 1.04 & 2.93 & 735\\\\\n", "\t5 & 1 & 14.20 & 1.76 & 2.45 & 15.2 & 112 & 3.27 & 3.39 & 0.34 & 1.97 & 6.75 & 1.05 & 2.85 & 1450\\\\\n", "\t6 & 1 & 14.39 & 1.87 & 2.45 & 14.6 & 96 & 2.50 & 2.52 & 0.30 & 1.98 & 5.25 & 1.02 & 3.58 & 1290\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 14\n", "\n", "| | Type <int> | Alcohol <dbl> | Malic <dbl> | Ash <dbl> | Alcalinity <dbl> | Magnesium <int> | Phenols <dbl> | Flavanoids <dbl> | Nonflavanoids <dbl> | Proanthocyanins <dbl> | Color <dbl> | Hue <dbl> | Dilution <dbl> | Proline <int> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 |\n", "| 2 | 1 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 |\n", "| 3 | 1 | 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 |\n", "| 4 | 1 | 13.24 | 2.59 | 2.87 | 21.0 | 118 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735 |\n", "| 5 | 1 | 14.20 | 1.76 | 2.45 | 15.2 | 112 | 3.27 | 3.39 | 0.34 | 1.97 | 6.75 | 1.05 | 2.85 | 1450 |\n", "| 6 | 1 | 14.39 | 1.87 | 2.45 | 14.6 | 96 | 2.50 | 2.52 | 0.30 | 1.98 | 5.25 | 1.02 | 3.58 | 1290 |\n", "\n" ], "text/plain": [ " Type Alcohol Malic Ash Alcalinity Magnesium Phenols Flavanoids Nonflavanoids\n", "1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 \n", "2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 \n", "3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 \n", "4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 \n", "5 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 \n", "6 1 14.39 1.87 2.45 14.6 96 2.50 2.52 0.30 \n", " Proanthocyanins Color Hue Dilution Proline\n", "1 1.28 4.38 1.05 3.40 1050 \n", "2 2.81 5.68 1.03 3.17 1185 \n", "3 2.18 7.80 0.86 3.45 1480 \n", "4 1.82 4.32 1.04 2.93 735 \n", "5 1.97 6.75 1.05 2.85 1450 \n", "6 1.98 5.25 1.02 3.58 1290 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wine = read.table(\n", " \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", \n", " header = TRUE, sep = \",\")\n", " colnames(wine) <- c('Type', 'Alcohol', 'Malic', 'Ash', \n", " 'Alcalinity', 'Magnesium', 'Phenols', \n", " 'Flavanoids', 'Nonflavanoids',\n", " 'Proanthocyanins', 'Color', 'Hue', \n", " 'Dilution', 'Proline')\n", "head(wine)" ] }, { "cell_type": "markdown", "id": "11892ffc", "metadata": {}, "source": [ "#### saving in binary format " ] }, { "cell_type": "code", "execution_count": 62, "id": "2143afee", "metadata": {}, "outputs": [], "source": [ "save(wine, file=\"wine.RData\")" ] }, { "cell_type": "markdown", "id": "837380f2", "metadata": {}, "source": [ "#### removing data frame from memory" ] }, { "cell_type": "code", "execution_count": 63, "id": "cd5bdb2d", "metadata": {}, "outputs": [], "source": [ "rm(wine)" ] }, { "cell_type": "markdown", "id": "c5b982a0", "metadata": {}, "source": [ "#### load binary format" ] }, { "cell_type": "code", "execution_count": 64, "id": "4bb77c1f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 14
TypeAlcoholMalicAshAlcalinityMagnesiumPhenolsFlavanoidsNonflavanoidsProanthocyaninsColorHueDilutionProline
<int><dbl><dbl><dbl><dbl><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><int>
1113.201.782.1411.21002.652.760.261.284.381.053.401050
2113.162.362.6718.61012.803.240.302.815.681.033.171185
3114.371.952.5016.81133.853.490.242.187.800.863.451480
\n" ], "text/latex": [ "A data.frame: 3 × 14\n", "\\begin{tabular}{r|llllllllllllll}\n", " & Type & Alcohol & Malic & Ash & Alcalinity & Magnesium & Phenols & Flavanoids & Nonflavanoids & Proanthocyanins & Color & Hue & Dilution & Proline\\\\\n", " & & & & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n", "\t2 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n", "\t3 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 14\n", "\n", "| | Type <int> | Alcohol <dbl> | Malic <dbl> | Ash <dbl> | Alcalinity <dbl> | Magnesium <int> | Phenols <dbl> | Flavanoids <dbl> | Nonflavanoids <dbl> | Proanthocyanins <dbl> | Color <dbl> | Hue <dbl> | Dilution <dbl> | Proline <int> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050 |\n", "| 2 | 1 | 13.16 | 2.36 | 2.67 | 18.6 | 101 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185 |\n", "| 3 | 1 | 14.37 | 1.95 | 2.50 | 16.8 | 113 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480 |\n", "\n" ], "text/plain": [ " Type Alcohol Malic Ash Alcalinity Magnesium Phenols Flavanoids Nonflavanoids\n", "1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 \n", "2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 \n", "3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 \n", " Proanthocyanins Color Hue Dilution Proline\n", "1 1.28 4.38 1.05 3.40 1050 \n", "2 2.81 5.68 1.03 3.17 1185 \n", "3 2.18 7.80 0.86 3.45 1480 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "load(\"wine.RData\")\n", "head(wine, 3)" ] }, { "cell_type": "markdown", "id": "dfd0d49f", "metadata": {}, "source": [ "#### exporting data.frame into csv file" ] }, { "cell_type": "code", "execution_count": 65, "id": "99c07fd0", "metadata": {}, "outputs": [], "source": [ "write.table(wine, file=\"wine.csv\", row.names=FALSE, quote = FALSE, sep = \",\")" ] }, { "cell_type": "markdown", "id": "f5147833", "metadata": {}, "source": [ "#### filtering vectors" ] }, { "cell_type": "code", "execution_count": 66, "id": "82575667", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. FALSE
  2. FALSE
  3. FALSE
  4. FALSE
  5. FALSE
  6. FALSE
  7. FALSE
  8. TRUE
  9. TRUE
  10. TRUE
  11. TRUE
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item TRUE\n", "\\item TRUE\n", "\\item TRUE\n", "\\item TRUE\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. FALSE\n", "2. FALSE\n", "3. FALSE\n", "4. FALSE\n", "5. FALSE\n", "6. FALSE\n", "7. FALSE\n", "8. TRUE\n", "9. TRUE\n", "10. TRUE\n", "11. TRUE\n", "\n", "\n" ], "text/plain": [ " [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a <- c(5260,5470,5640,6180,6390,6515,6805,7515,7515,8230,8770)\n", "b <- c(3910,4220,3885,5160,5645,4680,5265,5975,6790,6900,7335)\n", "\n", "# logical vector\n", "bool <- (a > 7000)\n", "bool" ] }, { "cell_type": "code", "execution_count": 67, "id": "a03b1408", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 7515
  2. 7515
  3. 8230
  4. 8770
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 7515\n", "2. 7515\n", "3. 8230\n", "4. 8770\n", "\n", "\n" ], "text/plain": [ "[1] 7515 7515 8230 8770" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# selecting elements from positions that are true\n", "a[bool] " ] }, { "cell_type": "code", "execution_count": 68, "id": "20ba5879", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 3910
  2. 4220
  3. 3885
  4. 5975
  5. 6790
  6. 6900
  7. 7335
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5975\n", "\\item 6790\n", "\\item 6900\n", "\\item 7335\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5975\n", "5. 6790\n", "6. 6900\n", "7. 7335\n", "\n", "\n" ], "text/plain": [ "[1] 3910 4220 3885 5975 6790 6900 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# filtering with logical expressions\n", "b[a < 6000 | a > 7000]" ] }, { "cell_type": "code", "execution_count": 69, "id": "d976d8c5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 5160
  2. 5645
  3. 4680
  4. 5265
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 5160\n", "\\item 5645\n", "\\item 4680\n", "\\item 5265\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 5160\n", "2. 5645\n", "3. 4680\n", "4. 5265\n", "\n", "\n" ], "text/plain": [ "[1] 5160 5645 4680 5265" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b[6000 <= a & a <= 7000]" ] }, { "cell_type": "markdown", "id": "8c969809", "metadata": {}, "source": [ "#### filtering data frames" ] }, { "cell_type": "code", "execution_count": 70, "id": "f0175992", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 11 × 3
abc
<dbl><dbl><dbl>
1526039101350
2547042201250
3564038851755
4618051601020
563905645 745
6651546801835
7680552651540
8751559751540
975156790 725
10823069001330
11877073351435
\n" ], "text/latex": [ "A data.frame: 11 × 3\n", "\\begin{tabular}{r|lll}\n", " & a & b & c\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & 5260 & 3910 & 1350\\\\\n", "\t2 & 5470 & 4220 & 1250\\\\\n", "\t3 & 5640 & 3885 & 1755\\\\\n", "\t4 & 6180 & 5160 & 1020\\\\\n", "\t5 & 6390 & 5645 & 745\\\\\n", "\t6 & 6515 & 4680 & 1835\\\\\n", "\t7 & 6805 & 5265 & 1540\\\\\n", "\t8 & 7515 & 5975 & 1540\\\\\n", "\t9 & 7515 & 6790 & 725\\\\\n", "\t10 & 8230 & 6900 & 1330\\\\\n", "\t11 & 8770 & 7335 & 1435\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 11 × 3\n", "\n", "| | a <dbl> | b <dbl> | c <dbl> |\n", "|---|---|---|---|\n", "| 1 | 5260 | 3910 | 1350 |\n", "| 2 | 5470 | 4220 | 1250 |\n", "| 3 | 5640 | 3885 | 1755 |\n", "| 4 | 6180 | 5160 | 1020 |\n", "| 5 | 6390 | 5645 | 745 |\n", "| 6 | 6515 | 4680 | 1835 |\n", "| 7 | 6805 | 5265 | 1540 |\n", "| 8 | 7515 | 5975 | 1540 |\n", "| 9 | 7515 | 6790 | 725 |\n", "| 10 | 8230 | 6900 | 1330 |\n", "| 11 | 8770 | 7335 | 1435 |\n", "\n" ], "text/plain": [ " a b c \n", "1 5260 3910 1350\n", "2 5470 4220 1250\n", "3 5640 3885 1755\n", "4 6180 5160 1020\n", "5 6390 5645 745\n", "6 6515 4680 1835\n", "7 6805 5265 1540\n", "8 7515 5975 1540\n", "9 7515 6790 725\n", "10 8230 6900 1330\n", "11 8770 7335 1435" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data <- data.frame(a=a, b=b)\n", "data$c <- data$a - data$b\n", "head(data, nrow(data))" ] }, { "cell_type": "code", "execution_count": 71, "id": "3f08fc76", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 4 × 3
abc
<dbl><dbl><dbl>
8751559751540
975156790 725
10823069001330
11877073351435
\n" ], "text/latex": [ "A data.frame: 4 × 3\n", "\\begin{tabular}{r|lll}\n", " & a & b & c\\\\\n", " & & & \\\\\n", "\\hline\n", "\t8 & 7515 & 5975 & 1540\\\\\n", "\t9 & 7515 & 6790 & 725\\\\\n", "\t10 & 8230 & 6900 & 1330\\\\\n", "\t11 & 8770 & 7335 & 1435\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 3\n", "\n", "| | a <dbl> | b <dbl> | c <dbl> |\n", "|---|---|---|---|\n", "| 8 | 7515 | 5975 | 1540 |\n", "| 9 | 7515 | 6790 | 725 |\n", "| 10 | 8230 | 6900 | 1330 |\n", "| 11 | 8770 | 7335 | 1435 |\n", "\n" ], "text/plain": [ " a b c \n", "8 7515 5975 1540\n", "9 7515 6790 725\n", "10 8230 6900 1330\n", "11 8770 7335 1435" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(data[data$a > 7000,])" ] }, { "cell_type": "code", "execution_count": 72, "id": "9bbfd456", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 4 × 2
ab
<dbl><dbl>
875155975
975156790
1082306900
1187707335
\n" ], "text/latex": [ "A data.frame: 4 × 2\n", "\\begin{tabular}{r|ll}\n", " & a & b\\\\\n", " & & \\\\\n", "\\hline\n", "\t8 & 7515 & 5975\\\\\n", "\t9 & 7515 & 6790\\\\\n", "\t10 & 8230 & 6900\\\\\n", "\t11 & 8770 & 7335\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 2\n", "\n", "| | a <dbl> | b <dbl> |\n", "|---|---|---|\n", "| 8 | 7515 | 5975 |\n", "| 9 | 7515 | 6790 |\n", "| 10 | 8230 | 6900 |\n", "| 11 | 8770 | 7335 |\n", "\n" ], "text/plain": [ " a b \n", "8 7515 5975\n", "9 7515 6790\n", "10 8230 6900\n", "11 8770 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(data[data$a > 7000,c(1,2)])" ] }, { "cell_type": "markdown", "id": "a5454520", "metadata": {}, "source": [ "#### performance with matrix and data frames" ] }, { "cell_type": "code", "execution_count": 73, "id": "cdc8c347", "metadata": {}, "outputs": [], "source": [ "rheight <- rnorm(100000, 1.8, sd=0.2)\n", "rweight <- rnorm(100000, 72, sd=15)" ] }, { "cell_type": "markdown", "id": "f228be75", "metadata": {}, "source": [ "#### computing a entire column at once" ] }, { "cell_type": "code", "execution_count": 74, "id": "dae10733", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 0.002118826 secs" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "2400984 bytes" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "hw$bmi <- hw$weight/hw$height^2\n", "end_time <- Sys.time()\n", "end_time - start_time\n", "object.size(hw)" ] }, { "cell_type": "markdown", "id": "740b7c7c", "metadata": {}, "source": [ "#### processing cell by cell" ] }, { "cell_type": "code", "execution_count": 75, "id": "f0590ce1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 4.172815 secs" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "for (i in 1:nrow(hw)) {\n", " hw$bmi[i] <- hw$weight[i]/hw$height[i]^2\n", "}\n", "end_time <- Sys.time()\n", "end_time - start_time" ] }, { "cell_type": "markdown", "id": "feadffd7", "metadata": {}, "source": [ "#### convert the entire column" ] }, { "cell_type": "code", "execution_count": 76, "id": "b5e4b09a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 0.14888 secs" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "hw <- as.matrix(hw)\n", "hw <- cbind(hw, 0)\n", "for (i in 1:nrow(hw)) {\n", " hw[i,3] <- hw[i,2]/hw[i,1]^2\n", "}\n", "end_time <- Sys.time()\n", "end_time - start_time\n" ] }, { "cell_type": "markdown", "id": "8314bae7", "metadata": {}, "source": [ "#### apply family\n", "\n", "apply functions can be applied for all rows or columns. \n", "\n", "The first character of the function name establishes the return type (s: simple, l: list)." ] }, { "cell_type": "code", "execution_count": 77, "id": "fa3b939a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
blood.glucoseshort.velocity
<dbl><dbl>
115.31.76
210.81.34
3 8.11.27
419.51.47
5 7.21.27
6 5.31.49
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 15.3 & 1.76\\\\\n", "\t2 & 10.8 & 1.34\\\\\n", "\t3 & 8.1 & 1.27\\\\\n", "\t4 & 19.5 & 1.47\\\\\n", "\t5 & 7.2 & 1.27\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | blood.glucose <dbl> | short.velocity <dbl> |\n", "|---|---|---|\n", "| 1 | 15.3 | 1.76 |\n", "| 2 | 10.8 | 1.34 |\n", "| 3 | 8.1 | 1.27 |\n", "| 4 | 19.5 | 1.47 |\n", "| 5 | 7.2 | 1.27 |\n", "| 6 | 5.3 | 1.49 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "1 15.3 1.76 \n", "2 10.8 1.34 \n", "3 8.1 1.27 \n", "4 19.5 1.47 \n", "5 7.2 1.27 \n", "6 5.3 1.49 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(ISwR)\n", "data(thuesen)\n", "head(thuesen)" ] }, { "cell_type": "code", "execution_count": 78, "id": "0a409147", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
$blood.glucose
\n", "\t\t
10.3
\n", "\t
$short.velocity
\n", "\t\t
1.32565217391304
\n", "
\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$blood.glucose] 10.3\n", "\\item[\\$short.velocity] 1.32565217391304\n", "\\end{description}\n" ], "text/markdown": [ "$blood.glucose\n", ": 10.3\n", "$short.velocity\n", ": 1.32565217391304\n", "\n", "\n" ], "text/plain": [ "$blood.glucose\n", "[1] 10.3\n", "\n", "$short.velocity\n", "[1] 1.325652\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#lapply returns a list\n", "lapply(thuesen, mean, na.rm=T)" ] }, { "cell_type": "code", "execution_count": 79, "id": "37e7c1b7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
blood.glucose
10.3
short.velocity
1.32565217391304
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[blood.glucose] 10.3\n", "\\item[short.velocity] 1.32565217391304\n", "\\end{description*}\n" ], "text/markdown": [ "blood.glucose\n", ": 10.3short.velocity\n", ": 1.32565217391304\n", "\n" ], "text/plain": [ " blood.glucose short.velocity \n", " 10.300000 1.325652 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#sapply returns a vector\n", "sapply(thuesen, mean, na.rm=T)" ] }, { "cell_type": "code", "execution_count": 80, "id": "a91b1d25", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 1.76
  2. 1.34
  3. 1.27
  4. 1.47
  5. 1.27
  6. 1.49
  7. 1.31
  8. 1.09
  9. 1.18
  10. 1.22
  11. 1.25
  12. 1.19
  13. 1.95
  14. 1.28
  15. 1.52
  16. 8.6
  17. 1.12
  18. 1.37
  19. 1.19
  20. 1.05
  21. 1.32
  22. 1.03
  23. 1.12
  24. 1.7
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1.76\n", "\\item 1.34\n", "\\item 1.27\n", "\\item 1.47\n", "\\item 1.27\n", "\\item 1.49\n", "\\item 1.31\n", "\\item 1.09\n", "\\item 1.18\n", "\\item 1.22\n", "\\item 1.25\n", "\\item 1.19\n", "\\item 1.95\n", "\\item 1.28\n", "\\item 1.52\n", "\\item 8.6\n", "\\item 1.12\n", "\\item 1.37\n", "\\item 1.19\n", "\\item 1.05\n", "\\item 1.32\n", "\\item 1.03\n", "\\item 1.12\n", "\\item 1.7\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1.76\n", "2. 1.34\n", "3. 1.27\n", "4. 1.47\n", "5. 1.27\n", "6. 1.49\n", "7. 1.31\n", "8. 1.09\n", "9. 1.18\n", "10. 1.22\n", "11. 1.25\n", "12. 1.19\n", "13. 1.95\n", "14. 1.28\n", "15. 1.52\n", "16. 8.6\n", "17. 1.12\n", "18. 1.37\n", "19. 1.19\n", "20. 1.05\n", "21. 1.32\n", "22. 1.03\n", "23. 1.12\n", "24. 1.7\n", "\n", "\n" ], "text/plain": [ " [1] 1.76 1.34 1.27 1.47 1.27 1.49 1.31 1.09 1.18 1.22 1.25 1.19 1.95 1.28 1.52\n", "[16] 8.60 1.12 1.37 1.19 1.05 1.32 1.03 1.12 1.70" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
blood.glucose
4.2
short.velocity
1.03
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[blood.glucose] 4.2\n", "\\item[short.velocity] 1.03\n", "\\end{description*}\n" ], "text/markdown": [ "blood.glucose\n", ": 4.2short.velocity\n", ": 1.03\n", "\n" ], "text/plain": [ " blood.glucose short.velocity \n", " 4.20 1.03 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# apply - second parameter (1: by rows, 2: by columns)\n", "m <- as.matrix(thuesen)\n", "apply(m, 1, min, na.rm=TRUE)\n", "apply(m, 2, min, na.rm=TRUE)" ] }, { "cell_type": "markdown", "id": "331fe9d8", "metadata": {}, "source": [ "### sort and order" ] }, { "cell_type": "code", "execution_count": 81, "id": "7be5fb8f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
blood.glucoseshort.velocity
<dbl><dbl>
115.31.76
210.81.34
3 8.11.27
419.51.47
5 7.21.27
6 5.31.49
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & 15.3 & 1.76\\\\\n", "\t2 & 10.8 & 1.34\\\\\n", "\t3 & 8.1 & 1.27\\\\\n", "\t4 & 19.5 & 1.47\\\\\n", "\t5 & 7.2 & 1.27\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | blood.glucose <dbl> | short.velocity <dbl> |\n", "|---|---|---|\n", "| 1 | 15.3 | 1.76 |\n", "| 2 | 10.8 | 1.34 |\n", "| 3 | 8.1 | 1.27 |\n", "| 4 | 19.5 | 1.47 |\n", "| 5 | 7.2 | 1.27 |\n", "| 6 | 5.3 | 1.49 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "1 15.3 1.76 \n", "2 10.8 1.34 \n", "3 8.1 1.27 \n", "4 19.5 1.47 \n", "5 7.2 1.27 \n", "6 5.3 1.49 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(ISwR)\n", "data(thuesen)\n", "head(thuesen)" ] }, { "cell_type": "code", "execution_count": 82, "id": "f7f7f7a1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 4.2
  2. 4.9
  3. 5.2
  4. 5.3
  5. 6.7
  6. 6.7
  7. 7.2
  8. 7.5
  9. 8.1
  10. 8.6
  11. 8.8
  12. 9.3
  13. 9.5
  14. 10.3
  15. 10.8
  16. 11.1
  17. 12.2
  18. 12.5
  19. 13.3
  20. 15.1
  21. 15.3
  22. 16.1
  23. 19
  24. 19.5
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 4.2\n", "\\item 4.9\n", "\\item 5.2\n", "\\item 5.3\n", "\\item 6.7\n", "\\item 6.7\n", "\\item 7.2\n", "\\item 7.5\n", "\\item 8.1\n", "\\item 8.6\n", "\\item 8.8\n", "\\item 9.3\n", "\\item 9.5\n", "\\item 10.3\n", "\\item 10.8\n", "\\item 11.1\n", "\\item 12.2\n", "\\item 12.5\n", "\\item 13.3\n", "\\item 15.1\n", "\\item 15.3\n", "\\item 16.1\n", "\\item 19\n", "\\item 19.5\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 4.2\n", "2. 4.9\n", "3. 5.2\n", "4. 5.3\n", "5. 6.7\n", "6. 6.7\n", "7. 7.2\n", "8. 7.5\n", "9. 8.1\n", "10. 8.6\n", "11. 8.8\n", "12. 9.3\n", "13. 9.5\n", "14. 10.3\n", "15. 10.8\n", "16. 11.1\n", "17. 12.2\n", "18. 12.5\n", "19. 13.3\n", "20. 15.1\n", "21. 15.3\n", "22. 16.1\n", "23. 19\n", "24. 19.5\n", "\n", "\n" ], "text/plain": [ " [1] 4.2 4.9 5.2 5.3 6.7 6.7 7.2 7.5 8.1 8.6 8.8 9.3 9.5 10.3 10.8\n", "[16] 11.1 12.2 12.5 13.3 15.1 15.3 16.1 19.0 19.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sort(thuesen$blood.glucose)" ] }, { "cell_type": "code", "execution_count": 83, "id": "fa5a3d0e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
  1. 17
  2. 22
  3. 12
  4. 6
  5. 11
  6. 15
  7. 5
  8. 9
  9. 3
  10. 16
  11. 23
  12. 7
  13. 24
  14. 18
  15. 2
  16. 8
  17. 10
  18. 19
  19. 21
  20. 14
  21. 1
  22. 20
  23. 13
  24. 4
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 17\n", "\\item 22\n", "\\item 12\n", "\\item 6\n", "\\item 11\n", "\\item 15\n", "\\item 5\n", "\\item 9\n", "\\item 3\n", "\\item 16\n", "\\item 23\n", "\\item 7\n", "\\item 24\n", "\\item 18\n", "\\item 2\n", "\\item 8\n", "\\item 10\n", "\\item 19\n", "\\item 21\n", "\\item 14\n", "\\item 1\n", "\\item 20\n", "\\item 13\n", "\\item 4\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 17\n", "2. 22\n", "3. 12\n", "4. 6\n", "5. 11\n", "6. 15\n", "7. 5\n", "8. 9\n", "9. 3\n", "10. 16\n", "11. 23\n", "12. 7\n", "13. 24\n", "14. 18\n", "15. 2\n", "16. 8\n", "17. 10\n", "18. 19\n", "19. 21\n", "20. 14\n", "21. 1\n", "22. 20\n", "23. 13\n", "24. 4\n", "\n", "\n" ], "text/plain": [ " [1] 17 22 12 6 11 15 5 9 3 16 23 7 24 18 2 8 10 19 21 14 1 20 13 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "order(thuesen$blood.glucose)" ] }, { "cell_type": "code", "execution_count": 84, "id": "ca65c09c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 2
blood.glucoseshort.velocity
<dbl><dbl>
174.21.12
224.91.03
125.21.19
65.31.49
116.71.25
156.71.52
\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & & \\\\\n", "\\hline\n", "\t17 & 4.2 & 1.12\\\\\n", "\t22 & 4.9 & 1.03\\\\\n", "\t12 & 5.2 & 1.19\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\t11 & 6.7 & 1.25\\\\\n", "\t15 & 6.7 & 1.52\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| | blood.glucose <dbl> | short.velocity <dbl> |\n", "|---|---|---|\n", "| 17 | 4.2 | 1.12 |\n", "| 22 | 4.9 | 1.03 |\n", "| 12 | 5.2 | 1.19 |\n", "| 6 | 5.3 | 1.49 |\n", "| 11 | 6.7 | 1.25 |\n", "| 15 | 6.7 | 1.52 |\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "17 4.2 1.12 \n", "22 4.9 1.03 \n", "12 5.2 1.19 \n", "6 5.3 1.49 \n", "11 6.7 1.25 \n", "15 6.7 1.52 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "o <- order(thuesen$blood.glucose)\n", "sorted <- thuesen[o,]\n", "head(sorted)" ] }, { "cell_type": "markdown", "id": "b73fab4d", "metadata": {}, "source": [ "#### Pipelines\n", "The operator $\\%$>$\\%$ creates a pipeline. \n", "\n", "The first parameter of the next invoked function receives the data from the pipeline. \n", "\n", "Library $dplyr$ contains a set of functions that support relational algebra operations." ] }, { "cell_type": "code", "execution_count": 85, "id": "30b5e039", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
YearQuarterFlightsDelays
<int><int><int><int>
120161116
220162125
320163133
420164125
520171104
620172 93
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & Year & Quarter & Flights & Delays\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11 & 6\\\\\n", "\t2 & 2016 & 2 & 12 & 5\\\\\n", "\t3 & 2016 & 3 & 13 & 3\\\\\n", "\t4 & 2016 & 4 & 12 & 5\\\\\n", "\t5 & 2017 & 1 & 10 & 4\\\\\n", "\t6 & 2017 & 2 & 9 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | Year <int> | Quarter <int> | Flights <int> | Delays <int> |\n", "|---|---|---|---|---|\n", "| 1 | 2016 | 1 | 11 | 6 |\n", "| 2 | 2016 | 2 | 12 | 5 |\n", "| 3 | 2016 | 3 | 13 | 3 |\n", "| 4 | 2016 | 4 | 12 | 5 |\n", "| 5 | 2017 | 1 | 10 | 4 |\n", "| 6 | 2017 | 2 | 9 | 3 |\n", "\n" ], "text/plain": [ " Year Quarter Flights Delays\n", "1 2016 1 11 6 \n", "2 2016 2 12 5 \n", "3 2016 3 13 3 \n", "4 2016 4 12 5 \n", "5 2017 1 10 4 \n", "6 2017 2 9 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flight_data <- read.table(text = \"Year Quarter Flights Delays\n", " 2016 1 11 6\n", " 2016 2 12 5\n", " 2016 3 13 3\n", " 2016 4 12 5\n", " 2017 1 10 4\n", " 2017 2 9 3\n", " 2017 3 11 4\n", " 2017 4 25 15\n", " 2018 1 14 3\n", " 2018 2 12 5\n", " 2018 3 13 3\n", " 2018 4 15 4\",\n", " header = TRUE,sep = \"\") \n", "head(flight_data)" ] }, { "cell_type": "code", "execution_count": 86, "id": "5b900bf8", "metadata": {}, "outputs": [], "source": [ "#install.packages(\"dplyr\")" ] }, { "cell_type": "code", "execution_count": 87, "id": "b25f0481", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Attaching package: ‘dplyr’\n", "\n", "\n", "The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A data.frame: 2 × 3
YearQuarterFlights
<int><int><int>
12016111
22017425
\n" ], "text/latex": [ "A data.frame: 2 × 3\n", "\\begin{tabular}{r|lll}\n", " & Year & Quarter & Flights\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11\\\\\n", "\t2 & 2017 & 4 & 25\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 2 × 3\n", "\n", "| | Year <int> | Quarter <int> | Flights <int> |\n", "|---|---|---|---|\n", "| 1 | 2016 | 1 | 11 |\n", "| 2 | 2017 | 4 | 25 |\n", "\n" ], "text/plain": [ " Year Quarter Flights\n", "1 2016 1 11 \n", "2 2017 4 25 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(dplyr)\n", "result <- flight_data %>% \n", " filter(Delays > 5) %>% \n", " select(Year, Quarter, Flights)\n", "head(result)" ] }, { "cell_type": "code", "execution_count": 88, "id": "eddf7163", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 3 × 3
Yearmeansd
<int><dbl><dbl>
201612.000.8164966
201713.757.5443135
201813.501.2909944
\n" ], "text/latex": [ "A tibble: 3 × 3\n", "\\begin{tabular}{lll}\n", " Year & mean & sd\\\\\n", " & & \\\\\n", "\\hline\n", "\t 2016 & 12.00 & 0.8164966\\\\\n", "\t 2017 & 13.75 & 7.5443135\\\\\n", "\t 2018 & 13.50 & 1.2909944\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 3\n", "\n", "| Year <int> | mean <dbl> | sd <dbl> |\n", "|---|---|---|\n", "| 2016 | 12.00 | 0.8164966 |\n", "| 2017 | 13.75 | 7.5443135 |\n", "| 2018 | 13.50 | 1.2909944 |\n", "\n" ], "text/plain": [ " Year mean sd \n", "1 2016 12.00 0.8164966\n", "2 2017 13.75 7.5443135\n", "3 2018 13.50 1.2909944" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(dplyr)\n", "result <- flight_data %>% \n", " group_by(Year) %>% \n", " summarize(mean = mean(Flights), sd = sd(Flights))\n", "head(result)" ] }, { "cell_type": "code", "execution_count": 89, "id": "a3ac3f6e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "12" ], "text/latex": [ "12" ], "text/markdown": [ "12" ], "text/plain": [ "[1] 12" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
YearQuarterFlightsDelays
<int><int><int><int>
120161116
220162125
320163133
420164125
520171104
620172 93
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & Year & Quarter & Flights & Delays\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11 & 6\\\\\n", "\t2 & 2016 & 2 & 12 & 5\\\\\n", "\t3 & 2016 & 3 & 13 & 3\\\\\n", "\t4 & 2016 & 4 & 12 & 5\\\\\n", "\t5 & 2017 & 1 & 10 & 4\\\\\n", "\t6 & 2017 & 2 & 9 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | Year <int> | Quarter <int> | Flights <int> | Delays <int> |\n", "|---|---|---|---|---|\n", "| 1 | 2016 | 1 | 11 | 6 |\n", "| 2 | 2016 | 2 | 12 | 5 |\n", "| 3 | 2016 | 3 | 13 | 3 |\n", "| 4 | 2016 | 4 | 12 | 5 |\n", "| 5 | 2017 | 1 | 10 | 4 |\n", "| 6 | 2017 | 2 | 9 | 3 |\n", "\n" ], "text/plain": [ " Year Quarter Flights Delays\n", "1 2016 1 11 6 \n", "2 2016 2 12 5 \n", "3 2016 3 13 3 \n", "4 2016 4 12 5 \n", "5 2017 1 10 4 \n", "6 2017 2 9 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nrow(flight_data)\n", "head(flight_data)" ] }, { "cell_type": "code", "execution_count": 90, "id": "f644b850", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Attaching package: ‘reshape’\n", "\n", "\n", "The following object is masked from ‘package:dplyr’:\n", "\n", " rename\n", "\n", "\n" ] }, { "data": { "text/html": [ "24" ], "text/latex": [ "24" ], "text/markdown": [ "24" ], "text/plain": [ "[1] 24" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
YearQuartervariablevalue
<int><int><fct><int>
120161Flights11
220162Flights12
320163Flights13
1720171Delays 4
1820172Delays 3
1920173Delays 4
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & Year & Quarter & variable & value\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 2016 & 1 & Flights & 11\\\\\n", "\t2 & 2016 & 2 & Flights & 12\\\\\n", "\t3 & 2016 & 3 & Flights & 13\\\\\n", "\t17 & 2017 & 1 & Delays & 4\\\\\n", "\t18 & 2017 & 2 & Delays & 3\\\\\n", "\t19 & 2017 & 3 & Delays & 4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | Year <int> | Quarter <int> | variable <fct> | value <int> |\n", "|---|---|---|---|---|\n", "| 1 | 2016 | 1 | Flights | 11 |\n", "| 2 | 2016 | 2 | Flights | 12 |\n", "| 3 | 2016 | 3 | Flights | 13 |\n", "| 17 | 2017 | 1 | Delays | 4 |\n", "| 18 | 2017 | 2 | Delays | 3 |\n", "| 19 | 2017 | 3 | Delays | 4 |\n", "\n" ], "text/plain": [ " Year Quarter variable value\n", "1 2016 1 Flights 11 \n", "2 2016 2 Flights 12 \n", "3 2016 3 Flights 13 \n", "17 2017 1 Delays 4 \n", "18 2017 2 Delays 3 \n", "19 2017 3 Delays 4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#install.packages(reshape)\n", "library(reshape)\n", "result <- melt(flight_data[,c('Year', 'Quarter', 'Flights', 'Delays')], \n", " id.vars = c(1,2))\n", "nrow(result)\n", "head(result[c(1:3,17:19), ])" ] }, { "cell_type": "markdown", "id": "9bfa9ee8", "metadata": {}, "source": [ "#### merge\n", "\n", "The function $merge$ can be used to join data frames. It can be used to produce inner, left, right, and outer joins. " ] }, { "cell_type": "code", "execution_count": 91, "id": "156b7407", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 5 × 2
cityvalue
<chr><dbl>
1Rio de Janeiro10
2Sao Paulo 12
3Paris 20
4New York 25
5Tokyo 18
\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{r|ll}\n", " & city & value\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & Rio de Janeiro & 10\\\\\n", "\t2 & Sao Paulo & 12\\\\\n", "\t3 & Paris & 20\\\\\n", "\t4 & New York & 25\\\\\n", "\t5 & Tokyo & 18\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "| | city <chr> | value <dbl> |\n", "|---|---|---|\n", "| 1 | Rio de Janeiro | 10 |\n", "| 2 | Sao Paulo | 12 |\n", "| 3 | Paris | 20 |\n", "| 4 | New York | 25 |\n", "| 5 | Tokyo | 18 |\n", "\n" ], "text/plain": [ " city value\n", "1 Rio de Janeiro 10 \n", "2 Sao Paulo 12 \n", "3 Paris 20 \n", "4 New York 25 \n", "5 Tokyo 18 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stores <- data.frame(\n", " city = c(\"Rio de Janeiro\", \"Sao Paulo\", \"Paris\", \"New York\", \"Tokyo\"),\n", " value = c(10, 12, 20, 25, 18))\n", "head(stores)\n" ] }, { "cell_type": "code", "execution_count": 92, "id": "96d229c2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 5 × 2
citycountry
<chr><chr>
1Rio de JaneiroBrazil
2Sao Paulo Brazil
3Paris France
4New York US
5Tokyo Japan
\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{r|ll}\n", " & city & country\\\\\n", " & & \\\\\n", "\\hline\n", "\t1 & Rio de Janeiro & Brazil\\\\\n", "\t2 & Sao Paulo & Brazil\\\\\n", "\t3 & Paris & France\\\\\n", "\t4 & New York & US \\\\\n", "\t5 & Tokyo & Japan \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "| | city <chr> | country <chr> |\n", "|---|---|---|\n", "| 1 | Rio de Janeiro | Brazil |\n", "| 2 | Sao Paulo | Brazil |\n", "| 3 | Paris | France |\n", "| 4 | New York | US |\n", "| 5 | Tokyo | Japan |\n", "\n" ], "text/plain": [ " city country\n", "1 Rio de Janeiro Brazil \n", "2 Sao Paulo Brazil \n", "3 Paris France \n", "4 New York US \n", "5 Tokyo Japan " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "divisions <- data.frame(\n", " city = c(\"Rio de Janeiro\", \"Sao Paulo\", \"Paris\", \"New York\", \"Tokyo\"),\n", " country = c(\"Brazil\", \"Brazil\", \"France\", \"US\", \"Japan\"))\n", "head(divisions)" ] }, { "cell_type": "code", "execution_count": 93, "id": "f60cefb0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 5 × 3
cityvaluecountry
<chr><dbl><chr>
1New York 25US
2Paris 20France
3Rio de Janeiro10Brazil
4Sao Paulo 12Brazil
5Tokyo 18Japan
\n" ], "text/latex": [ "A data.frame: 5 × 3\n", "\\begin{tabular}{r|lll}\n", " & city & value & country\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & New York & 25 & US \\\\\n", "\t2 & Paris & 20 & France\\\\\n", "\t3 & Rio de Janeiro & 10 & Brazil\\\\\n", "\t4 & Sao Paulo & 12 & Brazil\\\\\n", "\t5 & Tokyo & 18 & Japan \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 3\n", "\n", "| | city <chr> | value <dbl> | country <chr> |\n", "|---|---|---|---|\n", "| 1 | New York | 25 | US |\n", "| 2 | Paris | 20 | France |\n", "| 3 | Rio de Janeiro | 10 | Brazil |\n", "| 4 | Sao Paulo | 12 | Brazil |\n", "| 5 | Tokyo | 18 | Japan |\n", "\n" ], "text/plain": [ " city value country\n", "1 New York 25 US \n", "2 Paris 20 France \n", "3 Rio de Janeiro 10 Brazil \n", "4 Sao Paulo 12 Brazil \n", "5 Tokyo 18 Japan " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stdiv <- merge(stores, divisions, by.x=\"city\", by.y=\"city\")\n", "head(stdiv)" ] }, { "cell_type": "code", "execution_count": 94, "id": "4cb961bd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A tibble: 4 × 3
countrycountamount
<chr><int><dbl>
Brazil222
France120
Japan 118
US 125
\n" ], "text/latex": [ "A tibble: 4 × 3\n", "\\begin{tabular}{lll}\n", " country & count & amount\\\\\n", " & & \\\\\n", "\\hline\n", "\t Brazil & 2 & 22\\\\\n", "\t France & 1 & 20\\\\\n", "\t Japan & 1 & 18\\\\\n", "\t US & 1 & 25\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 4 × 3\n", "\n", "| country <chr> | count <int> | amount <dbl> |\n", "|---|---|---|\n", "| Brazil | 2 | 22 |\n", "| France | 1 | 20 |\n", "| Japan | 1 | 18 |\n", "| US | 1 | 25 |\n", "\n" ], "text/plain": [ " country count amount\n", "1 Brazil 2 22 \n", "2 France 1 20 \n", "3 Japan 1 18 \n", "4 US 1 25 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result <- stdiv %>% group_by(country) %>% \n", " summarize(count = n(), amount = sum(value))\n", "head(result)" ] }, { "cell_type": "markdown", "id": "d2e3a3a0", "metadata": {}, "source": [ "#### statistical tests: t-test\n", "There are many statistical tests in R.\n", "One of the most used is the t-test. It checks if the mean of observations is not different from a theoretical value." ] }, { "cell_type": "code", "execution_count": 95, "id": "580b4fab", "metadata": {}, "outputs": [], "source": [ "weight <- c(60, 72, 57, 90, 95, 72) \n", "height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)\n", "bmi <- weight/height^2 " ] }, { "cell_type": "code", "execution_count": 96, "id": "0d0b8c52", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\tOne Sample t-test\n", "\n", "data: bmi\n", "t = 0.34488, df = 5, p-value = 0.7442\n", "alternative hypothesis: true mean is not equal to 22.5\n", "95 percent confidence interval:\n", " 18.41734 27.84791\n", "sample estimates:\n", "mean of x \n", " 23.13262 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t.test(bmi, mu=22.5)" ] }, { "cell_type": "code", "execution_count": null, "id": "115d05d0", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "4.3.1" } }, "nbformat": 4, "nbformat_minor": 5 }