The Art of R Programming - Index of

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142 CHAPTER 15. WRITING FAST R CODE z z[3] tracemem(z) [1] "" Thus though one should be vigilant about location change, on the other hand we can’t assume it. 15.5 Extended Example: Avoiding Memory Copy This example, though artificial, will illustrate the memory-copy issues discussed in Section 15.4. Suppose we have a large number of unrelated vectors, and among other things, we wish to set the third element of each to 8. We could store the vectors in a matrix, one vector per row. But since they are unrelated, we may consider storing them in a list. This would have the potential advantage of being apply to use lapply(), which as was pointed out earlier, is a true loop-avoider, in contrast to apply(). Let’s try it out: > m n > z for (i in 1:m) z[[i]] print(system.time({
15.5. EXTENDED EXAMPLE: AVOIDING MEMORY COPY 143 + for (i in 1:m) z[[i]][3] > z print(system.time({ + for (i in 1:m) z[i,3] z print(system.time({ + z[,3] > set3
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The Art of R Programming Norman Mat
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iv CONTENTS 3.2 “Declarations”
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vi CONTENTS 8.2 Arithmetic and Bool
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viii CONTENTS 13 Graphics 111 13.1
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x CONTENTS 17.3 The snow Package .
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xii CONTENTS
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xiv CONTENTS • The material is wr
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2 CHAPTER 1. WHY R? • your data s
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4 CHAPTER 1. WHY R?
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6 CHAPTER 2. GETTING STARTED 2.1.2
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8 CHAPTER 2. GETTING STARTED > y #
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10 CHAPTER 2. GETTING STARTED Here
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12 CHAPTER 2. GETTING STARTED Inste
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14 CHAPTER 2. GETTING STARTED > lma
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16 CHAPTER 2. GETTING STARTED
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18 CHAPTER 3. VECTORS 3.2 “Declar
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20 CHAPTER 3. VECTORS 3.5 Recycling
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22 CHAPTER 3. VECTORS We can remove
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24 CHAPTER 3. VECTORS 3.8.2 The Cas
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26 CHAPTER 3. VECTORS 3.10 Combinin
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28 CHAPTER 3. VECTORS
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30 CHAPTER 4. MATRICES [,1] [,2] [1
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32 CHAPTER 4. MATRICES You can copy
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34 CHAPTER 4. MATRICES > head(mtc)
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36 CHAPTER 4. MATRICES > u [1] 1 2
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38 CHAPTER 4. MATRICES As will be d
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40 CHAPTER 4. MATRICES 113 # more s
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42 CHAPTER 4. MATRICES the statisti
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44 CHAPTER 4. MATRICES [3,] 1 0 0 1
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46 CHAPTER 4. MATRICES The numbers
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48 CHAPTER 5. LISTS > jalt jalt [[
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50 CHAPTER 5. LISTS $union [1] TRUE
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52 CHAPTER 5. LISTS trace("f",brows
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54 CHAPTER 5. LISTS > lapply(list(1
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56 CHAPTER 6. DATA FRAMES Suppose t
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58 CHAPTER 6. DATA FRAMES 6.3 Exten
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60 CHAPTER 6. DATA FRAMES > z z X1
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62 CHAPTER 6. DATA FRAMES 3 12 yabc
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64 CHAPTER 7. FACTORS AND TABLES Th
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66 CHAPTER 7. FACTORS AND TABLES
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68 CHAPTER 8. R PROGRAMMING STRUCTU
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74 CHAPTER 9. R FUNCTIONS you may u
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76 CHAPTER 9. R FUNCTIONS + x u [1
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78 CHAPTER 9. R FUNCTIONS 9.5 Funct
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80 CHAPTER 9. R FUNCTIONS
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82 CHAPTER 10. DOING MATH IN R > in
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84 CHAPTER 10. DOING MATH IN R The
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86 CHAPTER 10. DOING MATH IN R Sinc
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88 CHAPTER 10. DOING MATH IN R 10.7
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90 CHAPTER 10. DOING MATH IN R 1 #
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Page 126 and 127: 112 CHAPTER 13. GRAPHICS Figure 13.
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Page 134 and 135: 120 CHAPTER 13. GRAPHICS As with mo
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Page 138 and 139: 124 CHAPTER 13. GRAPHICS > curve((x
Page 140 and 141: 126 CHAPTER 13. GRAPHICS } } newxy
Page 142 and 143: 128 CHAPTER 13. GRAPHICS 13.19 3-Di
Page 144 and 145: 130 CHAPTER 14. DEBUGGING browser()
Page 146 and 147: 132 CHAPTER 14. DEBUGGING 14.4 The
Page 148 and 149: 134 CHAPTER 14. DEBUGGING 14.6 Synt
Page 150 and 151: 136 CHAPTER 15. WRITING FAST R CODE
Page 160 and 161: 146 CHAPTER 16. INTERFACING R TO OT
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Page 164 and 165: 150 CHAPTER 17. PARALLEL R The firs
Page 166 and 167: 152 CHAPTER 17. PARALLEL R increase
Page 168 and 169: 154 CHAPTER 17. PARALLEL R which sh
Page 170 and 171: 156 CHAPTER 17. PARALLEL R } xlarge
Page 172 and 173: 158 CHAPTER 17. PARALLEL R indicati
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Page 176 and 177: 162 CHAPTER 17. PARALLEL R } return
Page 178 and 179: 164 CHAPTER 17. PARALLEL R
Page 180 and 181: 166 CHAPTER 18. STRING MANIPULATION
Page 182 and 183: 168 CHAPTER 18. STRING MANIPULATION
Page 184 and 185: 170 CHAPTER 19. INSTALLATION: R BAS
Page 186 and 187: 172 CHAPTER 19. INSTALLATION: R BAS
Page 188 and 189: 174 CHAPTER 20. USER INTERFACES (ht
Page 190 and 191: 176 CHAPTER 21. TO LEARN MORE seq>
Page 192 and 193: 178 CHAPTER 21. TO LEARN MORE • h
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