ggplot2: Data
Gói plyr
Làm việc với dữ liệu
library(plyr)
library(ggplot2)
options(digits = 2, width = 60, max.print = 500)
# Select the smallest diamond in each colour
ddply(diamonds, .(color), subset, carat == min(carat))## carat cut color clarity depth table price x y
## 1 0.20 Ideal D VS2 62 57 367 3.8 3.8
## 2 0.20 Premium D VS2 62 60 367 3.7 3.7
## 3 0.20 Premium D VS2 62 60 367 3.8 3.7
## 4 0.20 Premium E SI2 60 62 345 3.8 3.8
## 5 0.20 Premium E VS2 60 62 367 3.8 3.8
## 6 0.20 Premium E VS2 59 60 367 3.8 3.8
## 7 0.20 Premium E VS2 61 59 367 3.8 3.8
## 8 0.20 Premium E VS2 60 62 367 3.8 3.8
## 9 0.20 Ideal E VS2 60 55 367 3.9 3.8
## 10 0.20 Very Good E VS2 63 59 367 3.7 3.7
## 11 0.20 Ideal E VS2 62 57 367 3.8 3.7
## 12 0.20 Premium F VS2 63 59 367 3.7 3.7
## 13 0.23 Very Good G VVS2 60 58 354 4.0 4.0
## 14 0.23 Ideal G VS1 62 54 404 3.9 4.0
## 15 0.23 Very Good G VVS1 61 59 414 3.9 4.0
## 16 0.23 Very Good G VVS1 60 59 415 4.0 4.0
## 17 0.23 Good G VVS2 64 56 428 3.9 3.9
## 18 0.23 Very Good G VVS2 63 55 431 3.9 3.9
## 19 0.23 Very Good G VVS2 63 54 431 3.9 4.0
## 20 0.23 Very Good G VVS2 62 57 431 3.9 3.9
## 21 0.23 Very Good G VVS2 63 55 431 3.9 4.0
## 22 0.23 Ideal G SI1 62 54 364 4.0 4.0
## 23 0.23 Ideal G SI1 62 53 364 3.9 4.0
## 24 0.23 Very Good G VVS2 58 59 438 4.1 4.0
## 25 0.23 Very Good G VS2 63 53 369 3.9 3.9
## 26 0.23 Fair G VVS2 61 66 369 3.9 3.9
## 27 0.23 Ideal G SI1 61 56 375 4.0 4.0
## 28 0.23 Very Good G VVS1 62 56 484 3.9 4.0
## 29 0.23 Very Good G VVS1 62 56 484 4.0 4.0
## 30 0.23 Ideal G VS1 62 55 384 4.0 4.0
## 31 0.23 Very Good G VS1 62 55 391 3.9 4.0
## 32 0.23 Good G VVS1 56 60 395 4.1 4.2
## 33 0.23 Premium G VVS1 61 59 531 4.0 3.9
## 34 0.23 Ideal G VVS1 62 58 536 3.9 4.0
## 35 0.23 Ideal G VVS1 62 57 536 3.9 4.0
## 36 0.23 Ideal G VVS1 61 57 536 4.0 4.0
## 37 0.23 Ideal G IF 62 56 536 3.9 3.9
## 38 0.23 Ideal G IF 63 55 536 3.9 4.0
## 39 0.23 Ideal G IF 62 55 536 3.9 4.0
## 40 0.23 Good G VVS2 64 56 537 3.9 3.9
## 41 0.23 Premium G VVS2 58 59 550 4.1 4.0
## 42 0.23 Very Good H VS1 59 61 338 4.0 4.0
## 43 0.23 Very Good H VS1 61 57 353 3.9 4.0
## 44 0.23 Ideal H VS1 61 55 423 3.9 4.0
## 45 0.23 Very Good H VVS2 62 55 431 3.9 3.9
## 46 0.23 Very Good H VVS2 63 56 431 3.9 4.0
## 47 0.23 Very Good H VVS1 62 56 434 3.9 4.0
## 48 0.23 Premium H VS1 59 61 434 4.0 4.0
## 49 0.23 Very Good H IF 64 55 369 3.9 3.9
## 50 0.23 Very Good H VVS2 62 55 378 3.9 4.0
## z
## 1 2.3
## 2 2.3
## 3 2.3
## 4 2.3
## 5 2.3
## 6 2.2
## 7 2.3
## 8 2.3
## 9 2.3
## 10 2.4
## 11 2.3
## 12 2.3
## 13 2.4
## 14 2.4
## 15 2.4
## 16 2.4
## 17 2.5
## 18 2.5
## 19 2.5
## 20 2.4
## 21 2.5
## 22 2.5
## 23 2.5
## 24 2.4
## 25 2.5
## 26 2.4
## 27 2.4
## 28 2.4
## 29 2.5
## 30 2.5
## 31 2.5
## 32 2.3
## 33 2.4
## 34 2.4
## 35 2.4
## 36 2.4
## 37 2.4
## 38 2.5
## 39 2.4
## 40 2.5
## 41 2.4
## 42 2.4
## 43 2.4
## 44 2.4
## 45 2.5
## 46 2.5
## 47 2.4
## 48 2.4
## 49 2.5
## 50 2.4
## [ reached getOption("max.print") -- omitted 7 rows ]# Select the two smallest diamonds
ddply(diamonds, .(color), subset, order(carat) <= 2)## carat cut color clarity depth table price x y
## 1 0.30 Ideal D SI1 62 56 552 4.3 4.3
## 2 0.24 Very Good D VVS1 62 60 553 4.0 4.0
## 3 0.23 Very Good E VS1 60 58 402 4.0 4.1
## 4 0.26 Very Good E VVS1 63 59 554 4.0 4.0
## 5 0.23 Very Good F VS1 61 57 357 4.0 4.0
## 6 0.23 Very Good F VS1 60 57 402 4.0 4.1
## 7 0.23 Very Good G VVS2 60 58 354 4.0 4.0
## 8 0.23 Ideal G VS1 62 54 404 3.9 4.0
## 9 0.26 Very Good H SI1 62 55 337 4.1 4.1
## 10 0.34 Ideal H VS2 60 57 555 4.5 4.6
## 11 0.30 Ideal I SI2 62 54 348 4.3 4.3
## 12 0.36 Ideal I VS2 62 56 556 4.5 4.6
## 13 0.24 Very Good J VVS2 63 57 336 3.9 4.0
## 14 1.01 Very Good J SI2 63 53 3088 6.3 6.3
## z
## 1 2.7
## 2 2.5
## 3 2.4
## 4 2.5
## 5 2.4
## 6 2.4
## 7 2.4
## 8 2.4
## 9 2.5
## 10 2.8
## 11 2.7
## 12 2.8
## 13 2.5
## 14 4.0# Select the 1% largest diamonds in each group
ddply(diamonds, .(color), subset, carat >
quantile(carat, 0.99))## carat cut color clarity depth table price x
## 1 1.8 Ideal D SI2 63 54 7862 7.8
## 2 1.9 Very Good D SI2 63 60 9791 7.8
## 3 1.8 Ideal D SI2 61 53 10051 7.9
## 4 2.0 Good D SI2 64 57 10528 7.9
## 5 1.8 Very Good D SI2 63 60 10730 7.7
## 6 1.8 Premium D SI1 61 60 12063 7.8
## 7 2.0 Premium D SI2 61 57 12108 8.1
## 8 1.8 Premium D SI1 61 58 12338 7.9
## 9 2.0 Premium D SI2 59 62 12576 8.1
## 10 2.0 Very Good D SI2 58 63 12639 8.3
## 11 2.1 Ideal D SI2 63 55 12707 8.2
## 12 2.0 Very Good D SI2 63 59 12765 8.1
## 13 2.0 Premium D SI2 60 58 12891 8.3
## 14 1.8 Very Good D SI2 62 57 12981 7.8
## 15 2.1 Fair D SI2 64 60 13016 8.0
## 16 1.8 Ideal D SI2 62 57 13026 7.8
## 17 2.0 Premium D SI2 62 59 13098 8.0
## 18 2.0 Good D SI2 63 59 13109 8.1
## 19 2.8 Ideal D I1 61 57 13156 9.0
## 20 2.0 Premium D SI2 62 58 13229 8.1
## 21 2.0 Premium D SI2 59 60 13811 8.3
## 22 2.0 Very Good D SI2 63 58 13858 8.0
## 23 1.8 Premium D SI2 60 58 13867 7.9
## 24 2.0 Premium D SI2 61 58 14406 8.1
## 25 1.8 Very Good D SI1 59 61 14561 7.9
## 26 2.6 Very Good D SI2 59 63 14749 9.1
## 27 2.1 Premium D SI2 59 60 15198 8.4
## 28 2.0 Very Good D SI2 61 57 15334 8.2
## 29 2.0 Good D SI2 65 55 15394 7.8
## 30 2.0 Fair D SI1 65 58 15540 8.0
## 31 1.9 Very Good D SI2 63 58 15594 7.8
## 32 2.0 Fair D SI2 59 66 15627 8.2
## 33 3.4 Fair D I1 67 52 15964 9.4
## 34 2.0 Fair D SI2 67 57 16086 7.9
## 35 2.0 Fair D SI1 65 55 16386 7.9
## 36 2.1 Ideal D SI2 63 56 16404 8.2
## 37 2.0 Good D SI2 60 56 16425 8.1
## 38 2.2 Premium D SI2 62 59 16472 8.3
## 39 2.0 Premium D SI2 61 60 16484 8.2
## 40 2.2 Ideal D SI2 62 57 16558 8.4
## 41 2.0 Good D SI2 60 65 16624 8.0
## 42 2.0 Good D SI2 60 65 16776 8.1
## 43 2.0 Premium D SI2 63 58 16776 8.1
## 44 2.0 Premium D SI2 62 60 16872 8.1
## 45 2.0 Very Good D SI2 62 59 17079 8.1
## 46 2.0 Good D SI2 64 57 17094 7.9
## 47 2.1 Premium D SI2 60 59 17127 8.4
## 48 2.0 Premium D SI2 62 59 17235 8.2
## 49 2.0 Very Good D SI2 62 56 17263 8.0
## 50 2.0 Premium D SI2 62 59 17674 8.1
## y z
## 1 7.7 4.9
## 2 7.8 4.9
## 3 7.8 4.8
## 4 7.9 5.0
## 5 7.6 4.8
## 6 7.7 4.8
## 7 8.1 4.9
## 8 7.7 4.8
## 9 8.1 4.8
## 10 8.2 4.8
## 11 8.1 5.1
## 12 8.0 5.1
## 13 8.2 5.0
## 14 7.8 4.9
## 15 7.9 5.1
## 16 7.7 4.8
## 17 8.0 5.0
## 18 8.0 5.1
## 19 9.0 5.5
## 20 8.1 5.0
## 21 8.2 4.9
## 22 8.0 5.0
## 23 7.9 4.7
## 24 8.0 4.9
## 25 8.0 4.7
## 26 9.0 5.3
## 27 8.4 5.0
## 28 8.2 5.0
## 29 8.0 5.1
## 30 7.9 5.1
## 31 7.8 4.9
## 32 8.2 4.9
## 33 9.3 6.3
## 34 7.8 5.2
## 35 7.8 5.1
## 36 8.2 5.1
## 37 8.1 4.9
## 38 8.3 5.2
## 39 8.1 4.9
## 40 8.3 5.2
## 41 8.1 4.8
## 42 8.0 4.8
## 43 8.0 5.0
## 44 8.1 5.0
## 45 8.2 5.0
## 46 7.8 5.0
## 47 8.4 5.0
## 48 8.1 5.0
## 49 8.1 5.0
## 50 8.2 5.0
## [ reached getOption("max.print") -- omitted 450 rows ]# Select all diamonds bigger than the group average
ddply(diamonds, .(color), subset, price > mean(price))## carat cut color clarity depth table price x
## 1 0.80 Ideal D SI2 62 58 3173 6.0
## 2 0.72 Ideal D SI2 60 57 3176 5.8
## 3 0.74 Ideal D VS2 61 54 3177 5.9
## 4 0.72 Ideal D VS2 61 56 3179 5.8
## 5 0.72 Ideal D VS2 62 57 3179 5.7
## 6 0.72 Ideal D VS2 63 57 3179 5.7
## 7 0.81 Good D SI1 64 58 3179 5.9
## 8 0.73 Very Good D VS2 64 58 3182 5.7
## 9 0.73 Ideal D VS2 62 56 3182 5.8
## 10 0.70 Premium D VS1 61 60 3183 5.8
## 11 0.79 Ideal D SI1 61 56 3185 6.0
## 12 0.73 Ideal D SI1 62 56 3189 5.8
## 13 0.73 Ideal D SI1 62 56 3189 5.8
## 14 0.71 Ideal D VS1 62 55 3192 5.7
## 15 0.70 Good D VS1 60 63 3193 5.7
## 16 0.54 Premium D VVS1 61 58 3194 5.2
## 17 0.73 Ideal D SI1 62 53 3195 5.8
## 18 0.80 Ideal D SI1 62 56 3195 6.0
## 19 0.70 Ideal D SI1 62 57 3199 5.8
## 20 0.71 Very Good D VS2 60 55 3203 5.8
## 21 0.90 Premium D SI2 63 59 3205 6.2
## 22 0.90 Fair D SI2 66 59 3205 6.0
## 23 0.90 Fair D SI2 66 58 3205 6.0
## 24 0.90 Fair D SI2 65 54 3205 6.1
## 25 0.90 Fair D SI2 66 60 3205 6.0
## 26 0.90 Fair D SI2 65 59 3205 6.1
## 27 0.90 Premium D SI2 59 61 3205 6.3
## 28 0.72 Premium D VS2 61 57 3205 5.8
## 29 0.58 Ideal D VVS1 61 57 3206 5.4
## 30 0.83 Ideal D SI2 62 56 3207 6.0
## 31 0.70 Very Good D VS1 60 60 3208 5.7
## 32 0.79 Ideal D SI1 61 57 3209 5.9
## 33 0.80 Ideal D SI2 62 54 3210 6.0
## 34 0.70 Premium D VVS2 62 60 3210 5.7
## 35 0.71 Ideal D VS2 63 57 3212 5.7
## 36 0.78 Ideal D SI1 61 57 3214 6.0
## 37 0.70 Premium D VS1 63 59 3214 5.7
## 38 0.95 Good D SI2 56 61 3214 6.5
## 39 0.71 Premium D VS2 59 59 3217 5.8
## 40 0.71 Ideal D VS2 62 55 3217 5.7
## 41 0.71 Very Good D VS2 62 59 3217 5.7
## 42 0.52 Ideal D VVS1 62 57 3218 5.2
## 43 0.72 Ideal D VS2 63 56 3219 5.7
## 44 0.72 Ideal D VS2 63 56 3219 5.7
## 45 0.71 Very Good D VS2 63 58 3222 5.7
## 46 0.71 Ideal D VS2 61 57 3222 5.7
## 47 0.51 Ideal D VVS2 61 56 3223 5.1
## 48 0.80 Premium D SI1 59 55 3226 6.1
## 49 0.65 Ideal D VVS2 61 60 3228 5.5
## 50 0.70 Premium D VS1 62 60 3229 5.6
## y z
## 1 6.0 3.7
## 2 5.9 3.5
## 3 5.9 3.6
## 4 5.8 3.5
## 5 5.8 3.5
## 6 5.7 3.6
## 7 5.8 3.8
## 8 5.7 3.6
## 9 5.8 3.6
## 10 5.7 3.5
## 11 6.0 3.7
## 12 5.8 3.6
## 13 5.8 3.6
## 14 5.7 3.6
## 15 5.7 3.5
## 16 5.3 3.2
## 17 5.8 3.6
## 18 6.0 3.7
## 19 5.7 3.5
## 20 5.9 3.5
## 21 6.1 3.9
## 22 6.0 3.9
## 23 6.0 4.0
## 24 6.0 3.9
## 25 5.9 3.9
## 26 6.0 3.9
## 27 6.3 3.7
## 28 5.8 3.5
## 29 5.4 3.3
## 30 6.0 3.7
## 31 5.8 3.5
## 32 6.0 3.6
## 33 6.0 3.7
## 34 5.6 3.5
## 35 5.8 3.6
## 36 6.0 3.6
## 37 5.7 3.6
## 38 6.5 3.7
## 39 5.9 3.5
## 40 5.7 3.6
## 41 5.7 3.6
## 42 5.2 3.2
## 43 5.8 3.6
## 44 5.7 3.6
## 45 5.7 3.6
## 46 5.8 3.5
## 47 5.2 3.2
## 48 6.0 3.6
## 49 5.6 3.4
## 50 5.6 3.5
## [ reached getOption("max.print") -- omitted 19353 rows ]# Within each colour, scale price to mean 0 and variance 1
ddply(diamonds, .(color), transform, price = scale(price))## carat cut color clarity depth table price
## 1 0.23 Very Good D VS2 60 61 -8.4e-01
## 2 0.23 Very Good D VS1 62 58 -8.2e-01
## 3 0.26 Very Good D VS2 61 59 -8.2e-01
## 4 0.26 Good D VS2 65 56 -8.2e-01
## 5 0.26 Good D VS1 58 63 -8.2e-01
## 6 0.22 Premium D VS2 59 62 -8.2e-01
## 7 0.30 Premium D SI1 63 59 -7.8e-01
## 8 0.30 Ideal D SI1 62 57 -7.8e-01
## 9 0.30 Ideal D SI1 62 56 -7.8e-01
## 10 0.24 Very Good D VVS1 62 60 -7.8e-01
## 11 0.26 Very Good D VVS2 62 54 -7.8e-01
## 12 0.26 Very Good D VVS2 63 60 -7.8e-01
## 13 0.26 Very Good D VVS1 62 60 -7.8e-01
## 14 0.75 Very Good D SI1 63 56 -1.2e-01
## 15 0.71 Ideal D SI2 62 56 -1.2e-01
## 16 0.61 Very Good D VVS2 60 57 -1.2e-01
## 17 0.71 Very Good D SI1 64 58 -1.2e-01
## 18 0.71 Ideal D SI1 62 59 -1.2e-01
## 19 0.70 Very Good D VS2 62 55 -1.2e-01
## 20 0.71 Ideal D SI2 62 55 -1.2e-01
## 21 0.73 Very Good D SI1 60 56 -1.2e-01
## 22 0.70 Very Good D SI1 61 58 -1.2e-01
## 23 0.71 Premium D SI2 62 59 -1.2e-01
## 24 0.71 Premium D VS2 62 60 -1.2e-01
## 25 0.76 Ideal D SI2 62 57 -1.2e-01
## 26 0.73 Ideal D SI2 60 57 -1.2e-01
## 27 0.75 Ideal D SI2 61 56 -1.2e-01
## 28 0.70 Premium D VS2 58 62 -1.2e-01
## 29 0.70 Very Good D VS1 63 58 -1.2e-01
## 30 0.53 Very Good D VVS2 58 64 -1.2e-01
## 31 0.75 Very Good D SI2 63 58 -1.2e-01
## 32 0.72 Ideal D SI1 61 57 -1.2e-01
## 33 0.72 Premium D SI1 63 59 -1.2e-01
## 34 0.70 Premium D SI1 63 60 -1.2e-01
## 35 0.64 Ideal D VS1 62 56 -1.1e-01
## 36 0.71 Good D VS2 63 56 -1.1e-01
## 37 0.72 Premium D SI2 62 60 -1.1e-01
## 38 0.71 Premium D SI1 63 60 -1.1e-01
## 39 0.71 Premium D SI1 61 58 -1.1e-01
## 40 0.71 Premium D SI1 62 60 -1.1e-01
## 41 0.51 Ideal D VVS1 62 56 -1.1e-01
## 42 0.78 Ideal D SI1 62 57 -1.1e-01
## 43 0.91 Ideal D SI2 62 57 -1.1e-01
## 44 0.70 Very Good D SI1 62 58 -1.1e-01
## 45 0.70 Ideal D SI1 61 59 -1.1e-01
## 46 0.72 Ideal D SI1 62 56 -1.1e-01
## 47 0.72 Ideal D SI1 62 54 -1.1e-01
## 48 0.73 Very Good D SI1 63 57 -1.1e-01
## 49 0.81 Premium D SI2 59 57 -1.1e-01
## 50 0.74 Ideal D SI2 62 55 -1.1e-01
## x y z
## 1 4.0 4.0 2.4
## 2 3.9 4.0 2.4
## 3 4.1 4.2 2.5
## 4 4.0 4.0 2.6
## 5 4.2 4.2 2.5
## 6 3.9 3.9 2.3
## 7 4.2 4.3 2.7
## 8 4.3 4.3 2.7
## 9 4.3 4.3 2.7
## 10 4.0 4.0 2.5
## 11 4.1 4.1 2.6
## 12 4.0 4.0 2.5
## 13 4.0 4.1 2.5
## 14 5.8 5.8 3.6
## 15 5.7 5.7 3.6
## 16 5.6 5.6 3.3
## 17 5.6 5.7 3.6
## 18 5.7 5.7 3.5
## 19 5.7 5.7 3.5
## 20 5.7 5.8 3.5
## 21 5.8 5.9 3.5
## 22 5.7 5.7 3.5
## 23 5.7 5.7 3.5
## 24 5.7 5.6 3.5
## 25 5.8 5.8 3.6
## 26 5.9 5.9 3.5
## 27 5.8 5.9 3.6
## 28 5.9 5.8 3.4
## 29 5.7 5.7 3.6
## 30 5.3 5.4 3.1
## 31 5.8 5.7 3.6
## 32 5.8 5.8 3.5
## 33 5.7 5.7 3.6
## 34 5.7 5.7 3.6
## 35 5.5 5.5 3.4
## 36 5.6 5.7 3.6
## 37 5.7 5.7 3.5
## 38 5.7 5.7 3.6
## 39 5.7 5.8 3.5
## 40 5.7 5.7 3.5
## 41 5.1 5.2 3.2
## 42 5.9 5.9 3.6
## 43 6.2 6.2 3.9
## 44 5.7 5.7 3.6
## 45 5.7 5.7 3.5
## 46 5.7 5.8 3.6
## 47 5.8 5.8 3.6
## 48 5.7 5.7 3.6
## 49 6.2 6.0 3.6
## 50 5.8 5.8 3.6
## [ reached getOption("max.print") -- omitted 53890 rows ]# Subtract off group mean
ddply(diamonds, .(color), transform,
price = price - mean(price))## carat cut color clarity depth table price
## 1 0.23 Very Good D VS2 60 61 -2812.95
## 2 0.23 Very Good D VS1 62 58 -2767.95
## 3 0.26 Very Good D VS2 61 59 -2766.95
## 4 0.26 Good D VS2 65 56 -2766.95
## 5 0.26 Good D VS1 58 63 -2766.95
## 6 0.22 Premium D VS2 59 62 -2765.95
## 7 0.30 Premium D SI1 63 59 -2617.95
## 8 0.30 Ideal D SI1 62 57 -2617.95
## 9 0.30 Ideal D SI1 62 56 -2617.95
## 10 0.24 Very Good D VVS1 62 60 -2616.95
## 11 0.26 Very Good D VVS2 62 54 -2615.95
## 12 0.26 Very Good D VVS2 63 60 -2615.95
## 13 0.26 Very Good D VVS1 62 60 -2615.95
## 14 0.75 Very Good D SI1 63 56 -409.95
## 15 0.71 Ideal D SI2 62 56 -407.95
## 16 0.61 Very Good D VVS2 60 57 -406.95
## 17 0.71 Very Good D SI1 64 58 -405.95
## 18 0.71 Ideal D SI1 62 59 -405.95
## 19 0.70 Very Good D VS2 62 55 -402.95
## 20 0.71 Ideal D SI2 62 55 -402.95
## 21 0.73 Very Good D SI1 60 56 -401.95
## 22 0.70 Very Good D SI1 61 58 -401.95
## 23 0.71 Premium D SI2 62 59 -401.95
## 24 0.71 Premium D VS2 62 60 -399.95
## 25 0.76 Ideal D SI2 62 57 -399.95
## 26 0.73 Ideal D SI2 60 57 -399.95
## 27 0.75 Ideal D SI2 61 56 -396.95
## 28 0.70 Premium D VS2 58 62 -396.95
## 29 0.70 Very Good D VS1 63 58 -392.95
## 30 0.53 Very Good D VVS2 58 64 -387.95
## 31 0.75 Very Good D SI2 63 58 -387.95
## 32 0.72 Ideal D SI1 61 57 -387.95
## 33 0.72 Premium D SI1 63 59 -387.95
## 34 0.70 Premium D SI1 63 60 -387.95
## 35 0.64 Ideal D VS1 62 56 -382.95
## 36 0.71 Good D VS2 63 56 -381.95
## 37 0.72 Premium D SI2 62 60 -374.95
## 38 0.71 Premium D SI1 63 60 -372.95
## 39 0.71 Premium D SI1 61 58 -372.95
## 40 0.71 Premium D SI1 62 60 -372.95
## 41 0.51 Ideal D VVS1 62 56 -372.95
## 42 0.78 Ideal D SI1 62 57 -370.95
## 43 0.91 Ideal D SI2 62 57 -366.95
## 44 0.70 Very Good D SI1 62 58 -365.95
## 45 0.70 Ideal D SI1 61 59 -365.95
## 46 0.72 Ideal D SI1 62 56 -365.95
## 47 0.72 Ideal D SI1 62 54 -365.95
## 48 0.73 Very Good D SI1 63 57 -361.95
## 49 0.81 Premium D SI2 59 57 -360.95
## 50 0.74 Ideal D SI2 62 55 -359.95
## x y z
## 1 4.0 4.0 2.4
## 2 3.9 4.0 2.4
## 3 4.1 4.2 2.5
## 4 4.0 4.0 2.6
## 5 4.2 4.2 2.5
## 6 3.9 3.9 2.3
## 7 4.2 4.3 2.7
## 8 4.3 4.3 2.7
## 9 4.3 4.3 2.7
## 10 4.0 4.0 2.5
## 11 4.1 4.1 2.6
## 12 4.0 4.0 2.5
## 13 4.0 4.1 2.5
## 14 5.8 5.8 3.6
## 15 5.7 5.7 3.6
## 16 5.6 5.6 3.3
## 17 5.6 5.7 3.6
## 18 5.7 5.7 3.5
## 19 5.7 5.7 3.5
## 20 5.7 5.8 3.5
## 21 5.8 5.9 3.5
## 22 5.7 5.7 3.5
## 23 5.7 5.7 3.5
## 24 5.7 5.6 3.5
## 25 5.8 5.8 3.6
## 26 5.9 5.9 3.5
## 27 5.8 5.9 3.6
## 28 5.9 5.8 3.4
## 29 5.7 5.7 3.6
## 30 5.3 5.4 3.1
## 31 5.8 5.7 3.6
## 32 5.8 5.8 3.5
## 33 5.7 5.7 3.6
## 34 5.7 5.7 3.6
## 35 5.5 5.5 3.4
## 36 5.6 5.7 3.6
## 37 5.7 5.7 3.5
## 38 5.7 5.7 3.6
## 39 5.7 5.8 3.5
## 40 5.7 5.7 3.5
## 41 5.1 5.2 3.2
## 42 5.9 5.9 3.6
## 43 6.2 6.2 3.9
## 44 5.7 5.7 3.6
## 45 5.7 5.7 3.5
## 46 5.7 5.8 3.6
## 47 5.8 5.8 3.6
## 48 5.7 5.7 3.6
## 49 6.2 6.0 3.6
## 50 5.8 5.8 3.6
## [ reached getOption("max.print") -- omitted 53890 rows ]nmissing <- function(x) sum(is.na(x))
nmissing(msleep$name)## [1] 0nmissing(msleep$brainwt)## [1] 27nmissing_df <- colwise(nmissing)
nmissing_df(msleep)## name genus vore order conservation sleep_total sleep_rem
## 1 0 0 7 0 29 0 22
## sleep_cycle awake brainwt bodywt
## 1 51 0 27 0# This is shorthand for the previous two steps
colwise(nmissing)(msleep)## name genus vore order conservation sleep_total sleep_rem
## 1 0 0 7 0 29 0 22
## sleep_cycle awake brainwt bodywt
## 1 51 0 27 0msleep2 <- msleep[, -6] # Remove a column to save space
numcolwise(median)(msleep2, na.rm = T)## sleep_rem sleep_cycle awake brainwt bodywt
## 1 1.5 0.33 14 0.012 1.7numcolwise(quantile)(msleep2, na.rm = T)## sleep_rem sleep_cycle awake brainwt bodywt
## 1 0.1 0.12 4.1 0.00014 5.0e-03
## 2 0.9 0.18 10.2 0.00290 1.7e-01
## 3 1.5 0.33 13.9 0.01240 1.7e+00
## 4 2.4 0.58 16.1 0.12550 4.2e+01
## 5 6.6 1.50 22.1 5.71200 6.7e+03numcolwise(quantile)(msleep2, probs = c(0.25, 0.75),
na.rm = T)## sleep_rem sleep_cycle awake brainwt bodywt
## 1 0.9 0.18 10 0.0029 0.17
## 2 2.4 0.58 16 0.1255 41.75ddply(msleep2, .(vore), numcolwise(median), na.rm = T)## vore sleep_rem sleep_cycle awake brainwt bodywt
## 1 carni 1.95 0.38 13.6 0.0445 20.490
## 2 herbi 0.95 0.22 13.7 0.0123 1.225
## 3 insecti 3.00 0.17 5.9 0.0012 0.075
## 4 omni 1.85 0.50 14.1 0.0066 0.950
## 5 <NA> 2.00 0.18 13.4 0.0030 0.122ddply(msleep2, .(vore), numcolwise(mean), na.rm = T)## vore sleep_rem sleep_cycle awake brainwt bodywt
## 1 carni 2.3 0.37 13.6 0.0793 90.75
## 2 herbi 1.4 0.42 14.5 0.6216 366.88
## 3 insecti 3.5 0.16 9.1 0.0215 12.92
## 4 omni 2.0 0.59 13.1 0.1457 12.72
## 5 <NA> 1.9 0.18 13.8 0.0076 0.86my_summary <- function(df) {
with(df, data.frame(
pc_cor = cor(price, carat, method = "spearman"),
lpc_cor = cor(log(price), log(carat))
))
}
ddply(diamonds, .(cut), my_summary)## cut pc_cor lpc_cor
## 1 Fair 0.91 0.91
## 2 Good 0.96 0.97
## 3 Very Good 0.97 0.97
## 4 Premium 0.96 0.97
## 5 Ideal 0.95 0.97ddply(diamonds, .(color), my_summary)## color pc_cor lpc_cor
## 1 D 0.96 0.96
## 2 E 0.96 0.96
## 3 F 0.96 0.96
## 4 G 0.96 0.97
## 5 H 0.97 0.98
## 6 I 0.98 0.99
## 7 J 0.98 0.99Vẽ biểu đồ
library(ggplot2)
# Vẽ đường smooth tự động bằng ggplot2
qplot(carat, price, data = diamonds, geom = "smooth",
color = color)## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
dense <- subset(diamonds, carat < 2)
qplot(carat, price, data = dense, geom = "smooth",
colour = color, fullrange = TRUE)## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
# Vẽ đường smooth bằng tay
library(mgcv)
smooth <- function(df) {
mod <- gam(price ~ s(carat, bs = "cs"), data = df)
grid <- data.frame(carat = seq(0.2, 2, length = 50))
pred <- predict(mod, grid, se = T)
grid$price <- as.vector(pred$fit)
grid$se <- as.vector(pred$se.fit)
grid
}
smoothes <- ddply(dense, .(color), smooth)
qplot(carat, price, data = smoothes, colour = color,
geom = "line")
qplot(carat, price, data = smoothes, colour = color,
geom = "smooth", ymax = price + 2 * se, ymin = price - 2 * se)## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
mod <- gam(price ~ s(carat, bs = "cs") + color, data = dense)
grid <- with(diamonds, expand.grid(
carat = seq(0.2, 2, length = 50),
color = levels(color)
))
grid$pred <- predict(mod, grid)
qplot(carat, pred, data = grid, colour = color, geom = "line")
Gói reshape
Định dạng rộng
qplot(date, uempmed, data = economics, geom = "line")
qplot(date, unemploy, data = economics, geom = "line")
qplot(unemploy, uempmed, data = economics) + geom_smooth()## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
Biểu diễn 2 số liệu khác độ đo
ggplot(economics, aes(date)) +
geom_line(aes(y = unemploy, colour = "unemploy")) +
geom_line(aes(y = uempmed, colour = "uempmed")) +
scale_colour_hue("variable")
library(reshape2)
emp <- melt(economics, id = "date",
measure = c("unemploy", "uempmed"))
qplot(date, value, data = emp, geom = "line", colour = variable)
Có 2 lựa chọn để xử lý những số liệu không cùng độ đo:
# Rescaling
range01 <- function(x) {
rng <- range(x, na.rm = TRUE)
(x - rng[1]) / diff(rng)
}
emp2 <- ddply(emp, .(variable), transform, value = range01(value))## Error in eval(expr, envir, enclos): could not find function "range01"qplot(date, value, data = emp2, geom = "line",
colour = variable, linetype = variable)## Error in ggplot(data, aesthetics, environment = env): object 'emp2' not found# Faceting
qplot(date, value, data = emp, geom = "line") +
facet_grid(variable ~ ., scales = "free_y")
Tọa độ song song
popular <- subset(movies, votes > 1e4)
ratings <- popular[, 7:16]
ratings$.row <- rownames(ratings)
molten <- melt(ratings, id = ".row")# mặc định
pcp <- ggplot(molten, aes(variable, value, group = .row))
pcp + geom_line()
# thêm `alpha`
pcp + geom_line(colour = alpha("black", 1 / 20))
# thêm `jitter`
jit <- position_jitter(width = 0.25, height = 2.5)
pcp + geom_line(position = jit)
# thêm alpha cho jitter
pcp + geom_line(colour = alpha("black", 1 / 20), position = jit)
cl <- kmeans(ratings[1:10], 6)
ratings$cluster <- reorder(factor(cl$cluster), popular$rating)
levels(ratings$cluster) <- seq_along(levels(ratings$cluster))
molten <- melt(ratings, id = c(".row", "cluster"))pcp_cl <- ggplot(molten,
aes(variable, value, group = .row, colour = cluster))
pcp_cl + geom_line(position = jit, alpha = 1/5)
pcp_cl + stat_summary(aes(group = cluster), fun.y = mean,
geom = "line")
library(scales)
pcp_cl + geom_line(position = jit, colour = alpha("black", 1/5)) +
facet_wrap(~ cluster)
Phương pháp ggplot()
Mô hình đơn giản
qplot(displ, cty, data = mpg) + geom_smooth(method = "lm")## Error: ggplot2 doesn't know how to deal with data of class numericmpgmod <- lm(cty ~ displ, data = mpg)## Error in eval(predvars, data, env): numeric 'envir' arg not of length onehead(fortify(mpgmod))## Error in head(fortify(mpgmod)): error in evaluating the argument 'x' in selecting a method for function 'head': Error in fortify(mpgmod) : object 'mpgmod' not foundĐiều chỉnh mô hình
mod <- lm(cty ~ displ, data = mpg)## Error in eval(predvars, data, env): numeric 'envir' arg not of length onebasic <- ggplot(mod, aes(.fitted, .resid)) +
geom_hline(yintercept = 0, colour = "grey50", size = 0.5) +
geom_point() +
geom_smooth(size = 0.5, se = F)## Error in infl$hat: $ operator is invalid for atomic vectorsbasic## Error in eval(expr, envir, enclos): object 'basic' not found# sử dụng residual chuẩn hóa thay vì residual
basic + aes(y = .stdresid)## Error in eval(expr, envir, enclos): object 'basic' not found# Thêm khoảng cách Cook
basic + aes(size = .cooksd) + scale_size_area("Cook's distance")## Error in eval(expr, envir, enclos): object 'basic' not foundThêm biến vào mô hình
full <- basic %+% fortify(mod, mpg)## Error in inherits(x, "theme"): object 'basic' not foundfull + aes(colour = factor(cyl))## Error in eval(expr, envir, enclos): object 'full' not foundfull + aes(displ, colour = factor(cyl))## Error in eval(expr, envir, enclos): object 'full' not found