{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Review: Solutions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Installed Rcpp (0.12.12) different from Rcpp used to build dplyr (0.12.11).\n", "Please reinstall dplyr to avoid random crashes or undefined behavior.”Warning message:\n", "“package ‘dplyr’ was built under R version 3.4.1”" ] } ], "source": [ "suppressPackageStartupMessages(library(tidyverse))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**1**. Generate a sequence of the numbers `10,9,8,7,6,5,4,3,2,1`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 10\n", "\\item 9\n", "\\item 8\n", "\\item 7\n", "\\item 6\n", "\\item 5\n", "\\item 4\n", "\\item 3\n", "\\item 2\n", "\\item 1\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 10\n", "2. 9\n", "3. 8\n", "4. 7\n", "5. 6\n", "6. 5\n", "7. 4\n", "8. 3\n", "9. 2\n", "10. 1\n", "\n", "\n" ], "text/plain": [ " [1] 10 9 8 7 6 5 4 3 2 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- 10:1\n", "x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2**. Extract only numbers divisible by 3 from the sequence generated in Q1." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 9\n", "\\item 6\n", "\\item 3\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 9\n", "2. 6\n", "3. 3\n", "\n", "\n" ], "text/plain": [ "[1] 9 6 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x[x %% 3 == 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3**. Generate the sequence `1,2,3,4,1,2,3,4,1,2,3,4`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 3\n", "4. 4\n", "5. 1\n", "6. 2\n", "7. 3\n", "8. 4\n", "9. 1\n", "10. 2\n", "11. 3\n", "12. 4\n", "\n", "\n" ], "text/plain": [ " [1] 1 2 3 4 1 2 3 4 1 2 3 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rep(1:4, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4**. Generate the sequence `1,1,1,1,2,2,2,2,3,3,3,3`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 2\n", "\\item 2\n", "\\item 2\n", "\\item 3\n", "\\item 3\n", "\\item 3\n", "\\item 4\n", "\\item 4\n", "\\item 4\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 1\n", "3. 1\n", "4. 2\n", "5. 2\n", "6. 2\n", "7. 3\n", "8. 3\n", "9. 3\n", "10. 4\n", "11. 4\n", "12. 4\n", "\n", "\n" ], "text/plain": [ " [1] 1 1 1 2 2 2 3 3 3 4 4 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rep(1:4, each=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**5**. Replace all odd numbers in Q1 with their square and leave the even numbers the same" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 10\n", "\\item 81\n", "\\item 8\n", "\\item 49\n", "\\item 6\n", "\\item 25\n", "\\item 4\n", "\\item 9\n", "\\item 2\n", "\\item 1\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 10\n", "2. 81\n", "3. 8\n", "4. 49\n", "5. 6\n", "6. 25\n", "7. 4\n", "8. 9\n", "9. 2\n", "10. 1\n", "\n", "\n" ], "text/plain": [ " [1] 10 81 8 49 6 25 4 9 2 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ifelse(x %% 2 == 1, x^2, x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**6**. Generate a matrix containing all sliding windows of length 4 from the sequence in Q1. The first row is `10,9,8,7` and the last one is `4,3,2,1`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
109 8 7
98 7 6
87 6 5
76 5 4
65 4 3
54 3 2
43 2 1
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", "\t 10 & 9 & 8 & 7 \\\\\n", "\t 9 & 8 & 7 & 6 \\\\\n", "\t 8 & 7 & 6 & 5 \\\\\n", "\t 7 & 6 & 5 & 4 \\\\\n", "\t 6 & 5 & 4 & 3 \\\\\n", "\t 5 & 4 & 3 & 2 \\\\\n", "\t 4 & 3 & 2 & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 10 | 9 | 8 | 7 | \n", "| 9 | 8 | 7 | 6 | \n", "| 8 | 7 | 6 | 5 | \n", "| 7 | 6 | 5 | 4 | \n", "| 6 | 5 | 4 | 3 | \n", "| 5 | 4 | 3 | 2 | \n", "| 4 | 3 | 2 | 1 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4]\n", "[1,] 10 9 8 7 \n", "[2,] 9 8 7 6 \n", "[3,] 8 7 6 5 \n", "[4,] 7 6 5 4 \n", "[5,] 6 5 4 3 \n", "[6,] 5 4 3 2 \n", "[7,] 4 3 2 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n <- length(x)\n", "width <- 4\n", "nrows <- n - width + 1\n", "m <- matrix(NA, nrows, width)\n", "for (i in 1:nrows) {\n", " m[i,] = x[i:(i+width-1)]\n", "}\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**7**. Generate the matrix shown below and find the row and column sums\n", "\n", "| | | | |\n", "|-|-|-|-|\n", "|1|2|3|4|\n", "|5|1|7|8|\n", "|9|10|NA|12|\n", "|13|14|15|1|" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
1 2 3 4
5 1 7 8
910NA12
131415 1
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", "\t 1 & 2 & 3 & 4\\\\\n", "\t 5 & 1 & 7 & 8\\\\\n", "\t 9 & 10 & NA & 12\\\\\n", "\t 13 & 14 & 15 & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| 1 | 2 | 3 | 4 | \n", "| 5 | 1 | 7 | 8 | \n", "| 9 | 10 | NA | 12 | \n", "| 13 | 14 | 15 | 1 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4]\n", "[1,] 1 2 3 4 \n", "[2,] 5 1 7 8 \n", "[3,] 9 10 NA 12 \n", "[4,] 13 14 15 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m <- matrix(1:16, byrow=T, nrow=4)\n", "diag(m) <- 1\n", "m[3,3] = NA\n", "m" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 10\n", "\\item 21\n", "\\item \n", "\\item 43\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 10\n", "2. 21\n", "3. <NA>\n", "4. 43\n", "\n", "\n" ], "text/plain": [ "[1] 10 21 NA 43" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "apply(m, 1, sum)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 28\n", "\\item 27\n", "\\item \n", "\\item 25\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 28\n", "2. 27\n", "3. <NA>\n", "4. 25\n", "\n", "\n" ], "text/plain": [ "[1] 28 27 NA 25" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "apply(m, 2, sum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**8**. Scale the matrix from Q7 so each **row** has mean of 0 and standard deviation of 1. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
-1.16189500-0.3872983 0.3872983 1.1618950
-0.08075729-1.3728739 0.5653010 0.8883301
-0.87287156-0.2182179 NA 1.0910895
0.34345475 0.4961013 0.6487479 -1.4883039
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", "\t -1.16189500 & -0.3872983 & 0.3872983 & 1.1618950 \\\\\n", "\t -0.08075729 & -1.3728739 & 0.5653010 & 0.8883301 \\\\\n", "\t -0.87287156 & -0.2182179 & NA & 1.0910895 \\\\\n", "\t 0.34345475 & 0.4961013 & 0.6487479 & -1.4883039 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| -1.16189500 | -0.3872983 | 0.3872983 | 1.1618950 | \n", "| -0.08075729 | -1.3728739 | 0.5653010 | 0.8883301 | \n", "| -0.87287156 | -0.2182179 | NA | 1.0910895 | \n", "| 0.34345475 | 0.4961013 | 0.6487479 | -1.4883039 | \n", "\n", "\n" ], "text/plain": [ " [,1] [,2] [,3] [,4] \n", "[1,] -1.16189500 -0.3872983 0.3872983 1.1618950\n", "[2,] -0.08075729 -1.3728739 0.5653010 0.8883301\n", "[3,] -0.87287156 -0.2182179 NA 1.0910895\n", "[4,] 0.34345475 0.4961013 0.6487479 -1.4883039" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sm <- t(scale(t(m)))\n", "sm" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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\n" ], "text/latex": [ "\\begin{enumerate*}\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", "\n", "\n" ], "text/plain": [ "[1] 0 0 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "round(apply(sm, 1, mean, na.rm=T), 2)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\item 1\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 1\n", "2. 1\n", "3. 1\n", "4. 1\n", "\n", "\n" ], "text/plain": [ "[1] 1 1 1 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "apply(sm, 1, sd, na.rm=T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**9**. Generate and assign row names `pt-1, pt-2, pt-3, pt-4` and column names `gene.1, gene.2, gene.3, gene.4` to the matrix from Q7." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
gene.1gene.2gene.3gene.4
pt-1 1 2 3 4
pt-2 5 1 7 8
pt-3 910NA12
pt-4131415 1
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & gene.1 & gene.2 & gene.3 & gene.4\\\\\n", "\\hline\n", "\tpt-1 & 1 & 2 & 3 & 4\\\\\n", "\tpt-2 & 5 & 1 & 7 & 8\\\\\n", "\tpt-3 & 9 & 10 & NA & 12\\\\\n", "\tpt-4 & 13 & 14 & 15 & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | gene.1 | gene.2 | gene.3 | gene.4 | \n", "|---|---|---|---|\n", "| pt-1 | 1 | 2 | 3 | 4 | \n", "| pt-2 | 5 | 1 | 7 | 8 | \n", "| pt-3 | 9 | 10 | NA | 12 | \n", "| pt-4 | 13 | 14 | 15 | 1 | \n", "\n", "\n" ], "text/plain": [ " gene.1 gene.2 gene.3 gene.4\n", "pt-1 1 2 3 4 \n", "pt-2 5 1 7 8 \n", "pt-3 9 10 NA 12 \n", "pt-4 13 14 15 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rownames(m) = paste(\"pt\", 1:4, sep=\"-\")\n", "colnames(m) = paste(\"gene\", 1:4, sep=\".\")\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**10**. Convert the matrix from Q7 to a `data.frame` and add a column `group` with values `A,B,A,B`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
gene.1gene.2gene.3gene.4group
pt-1 1 2 3 4A
pt-2 5 1 7 8B
pt-3 910NA12A
pt-4131415 1B
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & gene.1 & gene.2 & gene.3 & gene.4 & group\\\\\n", "\\hline\n", "\tpt-1 & 1 & 2 & 3 & 4 & A \\\\\n", "\tpt-2 & 5 & 1 & 7 & 8 & B \\\\\n", "\tpt-3 & 9 & 10 & NA & 12 & A \\\\\n", "\tpt-4 & 13 & 14 & 15 & 1 & B \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | gene.1 | gene.2 | gene.3 | gene.4 | group | \n", "|---|---|---|---|\n", "| pt-1 | 1 | 2 | 3 | 4 | A | \n", "| pt-2 | 5 | 1 | 7 | 8 | B | \n", "| pt-3 | 9 | 10 | NA | 12 | A | \n", "| pt-4 | 13 | 14 | 15 | 1 | B | \n", "\n", "\n" ], "text/plain": [ " gene.1 gene.2 gene.3 gene.4 group\n", "pt-1 1 2 3 4 A \n", "pt-2 5 1 7 8 B \n", "pt-3 9 10 NA 12 A \n", "pt-4 13 14 15 1 B " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df <- data.frame(m, group=c('A', 'B', 'A', 'B'))\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**11**. Remove the row with a missing value (NA) from the `data.frame` in Q10." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
gene.1gene.2gene.3gene.4group
pt-1 1 2 34 A
pt-2 5 1 78 B
pt-41314151 B
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & gene.1 & gene.2 & gene.3 & gene.4 & group\\\\\n", "\\hline\n", "\tpt-1 & 1 & 2 & 3 & 4 & A \\\\\n", "\tpt-2 & 5 & 1 & 7 & 8 & B \\\\\n", "\tpt-4 & 13 & 14 & 15 & 1 & B \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | gene.1 | gene.2 | gene.3 | gene.4 | group | \n", "|---|---|---|\n", "| pt-1 | 1 | 2 | 3 | 4 | A | \n", "| pt-2 | 5 | 1 | 7 | 8 | B | \n", "| pt-4 | 13 | 14 | 15 | 1 | B | \n", "\n", "\n" ], "text/plain": [ " gene.1 gene.2 gene.3 gene.4 group\n", "pt-1 1 2 3 4 A \n", "pt-2 5 1 7 8 B \n", "pt-4 13 14 15 1 B " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1 <- df %>% drop_na()\n", "df1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**12**. Find the average value of each gene by group using the `data.frame` from Q11." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
groupgene.1gene.2gene.3gene.4
A 1 2.0 3 4.0
B 9 7.511 4.5
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " group & gene.1 & gene.2 & gene.3 & gene.4\\\\\n", "\\hline\n", "\t A & 1 & 2.0 & 3 & 4.0\\\\\n", "\t B & 9 & 7.5 & 11 & 4.5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "group | gene.1 | gene.2 | gene.3 | gene.4 | \n", "|---|---|\n", "| A | 1 | 2.0 | 3 | 4.0 | \n", "| B | 9 | 7.5 | 11 | 4.5 | \n", "\n", "\n" ], "text/plain": [ " group gene.1 gene.2 gene.3 gene.4\n", "1 A 1 2.0 3 4.0 \n", "2 B 9 7.5 11 4.5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1 %>% group_by(group) %>%\n", "summarise_all(mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**13**. Reshape the `data.frame` from Q10 to have only 3 columns `group`, `gene`, `value`. The `geen` column should have entries such as `gene:1`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\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", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
groupgenevalue
A gene.1 1
B gene.1 5
A gene.1 9
B gene.113
A gene.2 2
B gene.2 1
A gene.210
B gene.214
A gene.3 3
B gene.3 7
A gene.3NA
B gene.315
A gene.4 4
B gene.4 8
A gene.412
B gene.4 1
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " group & gene & value\\\\\n", "\\hline\n", "\t A & gene.1 & 1 \\\\\n", "\t B & gene.1 & 5 \\\\\n", "\t A & gene.1 & 9 \\\\\n", "\t B & gene.1 & 13 \\\\\n", "\t A & gene.2 & 2 \\\\\n", "\t B & gene.2 & 1 \\\\\n", "\t A & gene.2 & 10 \\\\\n", "\t B & gene.2 & 14 \\\\\n", "\t A & gene.3 & 3 \\\\\n", "\t B & gene.3 & 7 \\\\\n", "\t A & gene.3 & NA \\\\\n", "\t B & gene.3 & 15 \\\\\n", "\t A & gene.4 & 4 \\\\\n", "\t B & gene.4 & 8 \\\\\n", "\t A & gene.4 & 12 \\\\\n", "\t B & gene.4 & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "group | gene | value | \n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| A | gene.1 | 1 | \n", "| B | gene.1 | 5 | \n", "| A | gene.1 | 9 | \n", "| B | gene.1 | 13 | \n", "| A | gene.2 | 2 | \n", "| B | gene.2 | 1 | \n", "| A | gene.2 | 10 | \n", "| B | gene.2 | 14 | \n", "| A | gene.3 | 3 | \n", "| B | gene.3 | 7 | \n", "| A | gene.3 | NA | \n", "| B | gene.3 | 15 | \n", "| A | gene.4 | 4 | \n", "| B | gene.4 | 8 | \n", "| A | gene.4 | 12 | \n", "| B | gene.4 | 1 | \n", "\n", "\n" ], "text/plain": [ " group gene value\n", "1 A gene.1 1 \n", "2 B gene.1 5 \n", "3 A gene.1 9 \n", "4 B gene.1 13 \n", "5 A gene.2 2 \n", "6 B gene.2 1 \n", "7 A gene.2 10 \n", "8 B gene.2 14 \n", "9 A gene.3 3 \n", "10 B gene.3 7 \n", "11 A gene.3 NA \n", "12 B gene.3 15 \n", "13 A gene.4 4 \n", "14 B gene.4 8 \n", "15 A gene.4 12 \n", "16 B gene.4 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df %>% gather(gene, value, -group)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**14**. Sort the `data.frame` from Q11 in decreasing order of `gene:1`." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
gene.1gene.2gene.3gene.4group
1314151 B
5 1 78 B
1 2 34 A
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " gene.1 & gene.2 & gene.3 & gene.4 & group\\\\\n", "\\hline\n", "\t 13 & 14 & 15 & 1 & B \\\\\n", "\t 5 & 1 & 7 & 8 & B \\\\\n", "\t 1 & 2 & 3 & 4 & A \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "gene.1 | gene.2 | gene.3 | gene.4 | group | \n", "|---|---|---|\n", "| 13 | 14 | 15 | 1 | B | \n", "| 5 | 1 | 7 | 8 | B | \n", "| 1 | 2 | 3 | 4 | A | \n", "\n", "\n" ], "text/plain": [ " gene.1 gene.2 gene.3 gene.4 group\n", "1 13 14 15 1 B \n", "2 5 1 7 8 B \n", "3 1 2 3 4 A " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1 %>% arrange(desc(gene.1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**15**. Replace all missing value with the column mean for the `data.frame` from Q10. group. Create a new data.frame that contains only the `log` values for all genes and the `group` column." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
gene.1gene.2gene.3gene.4group
0.000000 0.69314721.098612 1.386294 A
1.609438 0.00000001.945910 2.079442 B
2.197225 2.30258511.945910 2.484907 A
2.564949 2.63905732.708050 0.000000 B
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " gene.1 & gene.2 & gene.3 & gene.4 & group\\\\\n", "\\hline\n", "\t 0.000000 & 0.6931472 & 1.098612 & 1.386294 & A \\\\\n", "\t 1.609438 & 0.0000000 & 1.945910 & 2.079442 & B \\\\\n", "\t 2.197225 & 2.3025851 & 1.945910 & 2.484907 & A \\\\\n", "\t 2.564949 & 2.6390573 & 2.708050 & 0.000000 & B \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "gene.1 | gene.2 | gene.3 | gene.4 | group | \n", "|---|---|---|---|\n", "| 0.000000 | 0.6931472 | 1.098612 | 1.386294 | A | \n", "| 1.609438 | 0.0000000 | 1.945910 | 2.079442 | B | \n", "| 2.197225 | 2.3025851 | 1.945910 | 2.484907 | A | \n", "| 2.564949 | 2.6390573 | 2.708050 | 0.000000 | B | \n", "\n", "\n" ], "text/plain": [ " gene.1 gene.2 gene.3 gene.4 group\n", "1 0.000000 0.6931472 1.098612 1.386294 A \n", "2 1.609438 0.0000000 1.945910 2.079442 B \n", "3 2.197225 2.3025851 1.945910 2.484907 A \n", "4 2.564949 2.6390573 2.708050 0.000000 B " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[is.na(df)] <- mean(df$gene.1)\n", "df2 <- df %>% mutate_if(is.numeric, log)\n", "df2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**16**. create a new `data.frame` with columns for `genes.5`, `genes.6`, rows for `pt-2, pt-3, pt-1, pt-4` (in this order) and values drawn from a Poisson distribution with rate 10. Merge this with the `data.frame` from Q10 to get a new `data.frame` with 4 rows and 7 columns." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
gene.5gene.6
pt-2 716
pt-31011
pt-1 3 5
pt-4 8 5
\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & gene.5 & gene.6\\\\\n", "\\hline\n", "\tpt-2 & 7 & 16\\\\\n", "\tpt-3 & 10 & 11\\\\\n", "\tpt-1 & 3 & 5\\\\\n", "\tpt-4 & 8 & 5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | gene.5 | gene.6 | \n", "|---|---|---|---|\n", "| pt-2 | 7 | 16 | \n", "| pt-3 | 10 | 11 | \n", "| pt-1 | 3 | 5 | \n", "| pt-4 | 8 | 5 | \n", "\n", "\n" ], "text/plain": [ " gene.5 gene.6\n", "pt-2 7 16 \n", "pt-3 10 11 \n", "pt-1 3 5 \n", "pt-4 8 5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m <- matrix(rpois(8, 10), 4, 2)\n", "rownames(m) <- paste('pt', c(2,3,1,4), sep='-')\n", "colnames(m) <- paste('gene', 5:6, sep=',')\n", "df3 <- data.frame(m)\n", "df3" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
gene.1gene.2gene.3gene.4groupgene.5gene.6
pt-1 1 2 3 4A 3 5
pt-2 5 1 7 8B 716
pt-3 910 712A 1011
pt-4131415 1B 8 5
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllll}\n", " & gene.1 & gene.2 & gene.3 & gene.4 & group & gene.5 & gene.6\\\\\n", "\\hline\n", "\tpt-1 & 1 & 2 & 3 & 4 & A & 3 & 5\\\\\n", "\tpt-2 & 5 & 1 & 7 & 8 & B & 7 & 16\\\\\n", "\tpt-3 & 9 & 10 & 7 & 12 & A & 10 & 11\\\\\n", "\tpt-4 & 13 & 14 & 15 & 1 & B & 8 & 5\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | gene.1 | gene.2 | gene.3 | gene.4 | group | gene.5 | gene.6 | \n", "|---|---|---|---|\n", "| pt-1 | 1 | 2 | 3 | 4 | A | 3 | 5 | \n", "| pt-2 | 5 | 1 | 7 | 8 | B | 7 | 16 | \n", "| pt-3 | 9 | 10 | 7 | 12 | A | 10 | 11 | \n", "| pt-4 | 13 | 14 | 15 | 1 | B | 8 | 5 | \n", "\n", "\n" ], "text/plain": [ " gene.1 gene.2 gene.3 gene.4 group gene.5 gene.6\n", "pt-1 1 2 3 4 A 3 5 \n", "pt-2 5 1 7 8 B 7 16 \n", "pt-3 9 10 7 12 A 10 11 \n", "pt-4 13 14 15 1 B 8 5 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(df %>% rownames_to_column('key')) %>% \n", "full_join((df3 %>% rownames_to_column('key')), by='key') %>%\n", "column_to_rownames('key')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**17**. Generate data using the code shown below. Then fit a linear model to the data and print the coefficients and associated p-values for `x1, x2, x3`.\n", "\n", "```R\n", "set.seed(123)\n", "n <- 10\n", "x1 <- runif(n, 0, 10)\n", "x2 <- runif(n, 0, 10)\n", "x3 <- runif(n, 0, 10)\n", "y <- 2 + 0.5*x1 + 0.05*x3 + rnorm(n)\n", "df <- data.frame(y=y, x1=x1, x2=x2, x3=x3)\n", "```" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "set.seed(123)\n", "n <- 10\n", "x1 <- runif(n, 0, 10)\n", "x2 <- runif(n, 0, 10)\n", "x3 <- runif(n, 0, 10)\n", "y <- 2 + 0.5*x1 + 0.05*x3 + rnorm(n)\n", "df <- data.frame(y=y, x1=x1, x2=x2, x3=x3)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fit <- lm(y ~ x1 + x2 + x3, data=df)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = y ~ x1 + x2 + x3, data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-1.66716 -0.36824 -0.07808 0.50262 1.39160 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) -1.2514 1.7969 -0.696 0.5122 \n", "x1 0.6710 0.1834 3.659 0.0106 *\n", "x2 0.1676 0.1560 1.074 0.3241 \n", "x3 0.2244 0.1419 1.582 0.1648 \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Residual standard error: 1.035 on 6 degrees of freedom\n", "Multiple R-squared: 0.8142,\tAdjusted R-squared: 0.7214 \n", "F-statistic: 8.767 on 3 and 6 DF, p-value: 0.01301\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(fit)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
EstimateStd. Errort valuePr(>|t|)
(Intercept)-1.25139621.7968727 -0.69643010.51222230
x1 0.67098670.1834009 3.65857860.01059738
x2 0.16757400.1560274 1.07400360.32410345
x3 0.22442580.1418759 1.58184530.16477061
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & Estimate & Std. Error & t value & Pr(>\\textbar{}t\\textbar{})\\\\\n", "\\hline\n", "\t(Intercept) & -1.2513962 & 1.7968727 & -0.6964301 & 0.51222230\\\\\n", "\tx1 & 0.6709867 & 0.1834009 & 3.6585786 & 0.01059738\\\\\n", "\tx2 & 0.1675740 & 0.1560274 & 1.0740036 & 0.32410345\\\\\n", "\tx3 & 0.2244258 & 0.1418759 & 1.5818453 & 0.16477061\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Estimate | Std. Error | t value | Pr(>|t|) | \n", "|---|---|---|---|\n", "| (Intercept) | -1.2513962 | 1.7968727 | -0.6964301 | 0.51222230 | \n", "| x1 | 0.6709867 | 0.1834009 | 3.6585786 | 0.01059738 | \n", "| x2 | 0.1675740 | 0.1560274 | 1.0740036 | 0.32410345 | \n", "| x3 | 0.2244258 | 0.1418759 | 1.5818453 | 0.16477061 | \n", "\n", "\n" ], "text/plain": [ " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) -1.2513962 1.7968727 -0.6964301 0.51222230\n", "x1 0.6709867 0.1834009 3.6585786 0.01059738\n", "x2 0.1675740 0.1560274 1.0740036 0.32410345\n", "x3 0.2244258 0.1418759 1.5818453 0.16477061" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coef(summary(fit))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\t
x1
\n", "\t\t
0.0105973845862592
\n", "\t
x2
\n", "\t\t
0.324103450370588
\n", "\t
x3
\n", "\t\t
0.164770608907771
\n", "
\n" ], "text/latex": [ "\\begin{description*}\n", "\\item[x1] 0.0105973845862592\n", "\\item[x2] 0.324103450370588\n", "\\item[x3] 0.164770608907771\n", "\\end{description*}\n" ], "text/markdown": [ "x1\n", ": 0.0105973845862592x2\n", ": 0.324103450370588x3\n", ": 0.164770608907771\n", "\n" ], "text/plain": [ " x1 x2 x3 \n", "0.01059738 0.32410345 0.16477061 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coef(summary(fit))[2:4,4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Brief note on using R formula\n", "\n", "Meaning of symbols in R formula:\n", "\n", "| Formula | Definition | Example | Interpretation |\n", "|:--------|:---------------------------------------------|:--------------|:----------------------------------------------------------|\n", "| ~ | Is modeled as | Y ~ X | Y is modeled as a function of X |\n", "| 1 | Intercept | Y ~ X - 1 | Exclude intercept |\n", "| + | Include | +X | Include X |\n", "| - | Exclude | -X | Exclude X |\n", "| : | Interaction | U:V | Interaction between U an V |\n", "| * | Include with interactions | U*V | U + V + U:V |\n", "| ^ | Include with interactions up to given degree | (U + V + W)^2 | U + V + W + U:W + U:V + W:V |\n", "| I | As is | I(U * V) | X $\\times$ Y |\n", "| . | Everything else | Y ~ . | Y is modeled as a function of all other variables in data |\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = y ~ ., data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-1.66716 -0.36824 -0.07808 0.50262 1.39160 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) -1.2514 1.7969 -0.696 0.5122 \n", "x1 0.6710 0.1834 3.659 0.0106 *\n", "x2 0.1676 0.1560 1.074 0.3241 \n", "x3 0.2244 0.1419 1.582 0.1648 \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Residual standard error: 1.035 on 6 degrees of freedom\n", "Multiple R-squared: 0.8142,\tAdjusted R-squared: 0.7214 \n", "F-statistic: 8.767 on 3 and 6 DF, p-value: 0.01301\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(lm(y ~ ., data=df))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**18**. Fit a linear model to the data below. Explain the results.\n", "\n", "```R\n", "set.seed(123)\n", "n <- 10\n", "x1 <- 1:10\n", "x2 <- seq(2,20,by=2)\n", "x3 <- seq(3,30,by=3)\n", "y <- 2 + 0.5*x1 + 0.05*x3 + rnorm(n)\n", "df <- data.frame(y=y, x1=x1, x2=x2, x3=x3)\n", "```" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "set.seed(123)\n", "n <- 10\n", "x1 <- 1:10\n", "x2 <- seq(2,20,by=2)\n", "x3 <- seq(3,30,by=3)\n", "y <- 2 + 0.5*x1 + 0.05*x3 + rnorm(n)\n", "df <- data.frame(y=y, x1=x1, x2=x2, x3=x3)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = y ~ ., data = df)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-1.1348 -0.5624 -0.1393 0.3854 1.6814 \n", "\n", "Coefficients: (2 not defined because of singularities)\n", " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) 2.5255 0.6673 3.785 0.005352 ** \n", "x1 0.5680 0.1075 5.282 0.000744 ***\n", "x2 NA NA NA NA \n", "x3 NA NA NA NA \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Residual standard error: 0.9768 on 8 degrees of freedom\n", "Multiple R-squared: 0.7772,\tAdjusted R-squared: 0.7493 \n", "F-statistic: 27.9 on 1 and 8 DF, p-value: 0.0007444\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(lm(y ~ ., data=df))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpretation\n", "\n", "`x1`, `,x2` and `x3` are linear combination of each other. For example\n", "\n", "`y = a + b1*x1 + b2*x2 + b3*x3`\n", "\n", "will always have the same values as\n", "\n", "`y = a + b1*x1 + b2*(2*x1) + b3*(3*x1)`\n", "\n", "which is, for some coefficient `k`, the same as\n", "\n", "`y = a + k*x1`\n", "\n", "and so the 3 `x` variables are linearly dependent and effectively only a single variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**19**. Load the data set at https://www.openintro.org/stat/data/bdims.csv into a `data.frame` using `read.csv`. Use `knn` with 5 neighbors on the `wgt` and `hgt` columns to predict the `sex` and generate a classification table of true and predicted values from LOOCV." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bdims <- read.csv('https://www.openintro.org/stat/data/bdims.csv')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
bia.dibii.dibit.diche.deche.dielb.diwri.dikne.diank.disho.gi⋯bic.gifor.gikne.gical.giank.giwri.giagewgthgtsex
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42.5 29.9 34.0 21.5 29.4 15.2 11.6 20.7 14.9 107.5⋯ 32.0 28.4 37.7 38.6 24.4 18.0 22 78.8 187.21
43.3 27.0 31.5 19.6 31.3 14.0 11.5 18.8 13.9 119.8⋯ 33.0 28.0 36.6 36.1 23.5 16.9 21 74.8 181.51
\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllllllllllllllllllll}\n", " bia.di & bii.di & bit.di & che.de & che.di & elb.di & wri.di & kne.di & ank.di & sho.gi & ⋯ & bic.gi & for.gi & kne.gi & cal.gi & ank.gi & wri.gi & age & wgt & hgt & sex\\\\\n", "\\hline\n", "\t 42.9 & 26.0 & 31.5 & 17.7 & 28.0 & 13.1 & 10.4 & 18.8 & 14.1 & 106.2 & ⋯ & 32.5 & 26.0 & 34.5 & 36.5 & 23.5 & 16.5 & 21 & 65.6 & 174.0 & 1 \\\\\n", "\t 43.7 & 28.5 & 33.5 & 16.9 & 30.8 & 14.0 & 11.8 & 20.6 & 15.1 & 110.5 & ⋯ & 34.4 & 28.0 & 36.5 & 37.5 & 24.5 & 17.0 & 23 & 71.8 & 175.3 & 1 \\\\\n", "\t 40.1 & 28.2 & 33.3 & 20.9 & 31.7 & 13.9 & 10.9 & 19.7 & 14.1 & 115.1 & ⋯ & 33.4 & 28.8 & 37.0 & 37.3 & 21.9 & 16.9 & 28 & 80.7 & 193.5 & 1 \\\\\n", "\t 44.3 & 29.9 & 34.0 & 18.4 & 28.2 & 13.9 & 11.2 & 20.9 & 15.0 & 104.5 & ⋯ & 31.0 & 26.2 & 37.0 & 34.8 & 23.0 & 16.6 & 23 & 72.6 & 186.5 & 1 \\\\\n", "\t 42.5 & 29.9 & 34.0 & 21.5 & 29.4 & 15.2 & 11.6 & 20.7 & 14.9 & 107.5 & ⋯ & 32.0 & 28.4 & 37.7 & 38.6 & 24.4 & 18.0 & 22 & 78.8 & 187.2 & 1 \\\\\n", "\t 43.3 & 27.0 & 31.5 & 19.6 & 31.3 & 14.0 & 11.5 & 18.8 & 13.9 & 119.8 & ⋯ & 33.0 & 28.0 & 36.6 & 36.1 & 23.5 & 16.9 & 21 & 74.8 & 181.5 & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "bia.di | bii.di | bit.di | che.de | che.di | elb.di | wri.di | kne.di | ank.di | sho.gi | ⋯ | bic.gi | for.gi | kne.gi | cal.gi | ank.gi | wri.gi | age | wgt | hgt | sex | \n", "|---|---|---|---|---|---|\n", "| 42.9 | 26.0 | 31.5 | 17.7 | 28.0 | 13.1 | 10.4 | 18.8 | 14.1 | 106.2 | ⋯ | 32.5 | 26.0 | 34.5 | 36.5 | 23.5 | 16.5 | 21 | 65.6 | 174.0 | 1 | \n", "| 43.7 | 28.5 | 33.5 | 16.9 | 30.8 | 14.0 | 11.8 | 20.6 | 15.1 | 110.5 | ⋯ | 34.4 | 28.0 | 36.5 | 37.5 | 24.5 | 17.0 | 23 | 71.8 | 175.3 | 1 | \n", "| 40.1 | 28.2 | 33.3 | 20.9 | 31.7 | 13.9 | 10.9 | 19.7 | 14.1 | 115.1 | ⋯ | 33.4 | 28.8 | 37.0 | 37.3 | 21.9 | 16.9 | 28 | 80.7 | 193.5 | 1 | \n", "| 44.3 | 29.9 | 34.0 | 18.4 | 28.2 | 13.9 | 11.2 | 20.9 | 15.0 | 104.5 | ⋯ | 31.0 | 26.2 | 37.0 | 34.8 | 23.0 | 16.6 | 23 | 72.6 | 186.5 | 1 | \n", "| 42.5 | 29.9 | 34.0 | 21.5 | 29.4 | 15.2 | 11.6 | 20.7 | 14.9 | 107.5 | ⋯ | 32.0 | 28.4 | 37.7 | 38.6 | 24.4 | 18.0 | 22 | 78.8 | 187.2 | 1 | \n", "| 43.3 | 27.0 | 31.5 | 19.6 | 31.3 | 14.0 | 11.5 | 18.8 | 13.9 | 119.8 | ⋯ | 33.0 | 28.0 | 36.6 | 36.1 | 23.5 | 16.9 | 21 | 74.8 | 181.5 | 1 | \n", "\n", "\n" ], "text/plain": [ " bia.di bii.di bit.di che.de che.di elb.di wri.di kne.di ank.di sho.gi ⋯\n", "1 42.9 26.0 31.5 17.7 28.0 13.1 10.4 18.8 14.1 106.2 ⋯\n", "2 43.7 28.5 33.5 16.9 30.8 14.0 11.8 20.6 15.1 110.5 ⋯\n", "3 40.1 28.2 33.3 20.9 31.7 13.9 10.9 19.7 14.1 115.1 ⋯\n", "4 44.3 29.9 34.0 18.4 28.2 13.9 11.2 20.9 15.0 104.5 ⋯\n", "5 42.5 29.9 34.0 21.5 29.4 15.2 11.6 20.7 14.9 107.5 ⋯\n", "6 43.3 27.0 31.5 19.6 31.3 14.0 11.5 18.8 13.9 119.8 ⋯\n", " bic.gi for.gi kne.gi cal.gi ank.gi wri.gi age wgt hgt sex\n", "1 32.5 26.0 34.5 36.5 23.5 16.5 21 65.6 174.0 1 \n", "2 34.4 28.0 36.5 37.5 24.5 17.0 23 71.8 175.3 1 \n", "3 33.4 28.8 37.0 37.3 21.9 16.9 28 80.7 193.5 1 \n", "4 31.0 26.2 37.0 34.8 23.0 16.6 23 72.6 186.5 1 \n", "5 32.0 28.4 37.7 38.6 24.4 18.0 22 78.8 187.2 1 \n", "6 33.0 28.0 36.6 36.1 23.5 16.9 21 74.8 181.5 1 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(bdims)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "library(class)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "X <- bdims %>% select(wgt, hgt)\n", "y <- bdims$sex\n", "n <- nrow(bdims) \n", "k <- 5\n", "\n", "pred <- numeric(n)\n", "for (i in (1:n)) {\n", " train <- X[-i,]\n", " test <- X[i,]\n", " cls <- y[-i]\n", " pred[i] <- knn(train, test, cls, k)\n", "}" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " y\n", "pred 0 1\n", " 1 218 41\n", " 2 42 206" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table(pred, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**20**. Using the same data set from Q19, perform a linear regression to predict the age from the 5 variables most correlated with the age. Use LOOCV correctly to get predicted age for each subject. Calculate the root mean square error (square root of average of the squared residuals) from the LOOCV predictions. Plot the predicted against the observed ages." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "n <- nrow(bdims)\n", "pred <- numeric(n)\n", "\n", "yy <- bdims$age\n", "XX <- bdims %>% select(-age)\n", "\n", "for (i in 1:n) {\n", "\n", " stats <- cor(yy[-i], XX[-i,])\n", " ii <- order(desc(abs(stats)))\n", " X <- XX[-i, ii[1:5]]\n", " y <- yy[-i]\n", " df <- data.frame(y, X)\n", " model <- lm(y ~ ., data=df)\n", " pred[i] <- predict(model, XX[i,])\n", "}" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "8.62" ], "text/latex": [ "8.62" ], "text/markdown": [ "8.62" ], "text/plain": [ "[1] 8.62" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rms <- sqrt(mean((yy - pred)^2))\n", "round(rms, 2)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width=6, repr.plot.height=4)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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hc7fk/XgJ4yZYrhYNfDMdOEhejTYIY/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LuEJd5NG6Th3jvURDwTPAyT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\nGsMpijm4Zsydckjqg9m6yqrA5odn184UeOZx9BNJi/+9ZdLd2CBJJNDxQS8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Mn3K6BF98JuWXrBBKTGLTZkr36ecU7cJBloLOj98oDJYC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swUXI1QyUcyben5Enj5Wbgrbc+4Q/S0OT0m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\n+/qvf/2r2Xbbb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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ggplot2 only works on `data.frames`\n", "results <- data.frame(observed=yy, predicted=pred)\n", "\n", "ggplot(results, aes(x=pred, y=observed)) + # map properties to visual elemeents with `aes`\n", "geom_point(color='salmon') + # plot tye ith `geom` (scatter plot)\n", "geom_smooth(method='lm') + # add another plot type (linear regresion fit)\n", "labs(x=\"Predicted ages\", y=\"Observed ages\", title=\"Prediction of age by LOOCV\") # set custom lables" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 2 }