R Homework1
첫 번째 과제(202AIE10 김현정)
2020년 9월 27일
교수님께서 말씀하신 대로 구글 검색과 강의 자료의 도움을 받아 과제를 완성했습니다. 재미있기도 하고 하나씩 해내는 기분이 좋았어요. 그런데 왜 이 코드를 넣는지 잘 모르고 따라 한 것들도 있어서, 조금은 개운하지 못한 기분도 드는 것이 사실입니다. 앞으로 더 배우면 알게 되겠죠? 어찌 됐건 이렇게까지 할 수 있도록 하나씩 차근차근 알려주셔서 감사합니다.
<1번>
library(ggplot2)mpg## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto(l… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manual… f 21 29 p comp…
## 3 audi a4 2 2008 4 manual… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto(a… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto(l… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manual… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto(a… f 18 27 p comp…
## 8 audi a4 quat… 1.8 1999 4 manual… 4 18 26 p comp…
## 9 audi a4 quat… 1.8 1999 4 auto(l… 4 16 25 p comp…
## 10 audi a4 quat… 2 2008 4 manual… 4 20 28 p comp…
## # … with 224 more rowsggplot(data=mpg, aes(x=hwy)) +geom_histogram(aes(fill=drv), alpha=0.5) +
labs(title="Histogram", subtitle="Histogram of Highway Mile Per Gallon",
caption="Source:mpg") +theme_minimal()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
<2번>
ggplot(data=mpg, aes(x=hwy)) +geom_histogram(aes(fill=drv), alpha=0.5)+
labs(title="Histogram using facet_grid()", subtitle="Histogram of Highway Mile Per Gallon",
caption="Source:mpg") + facet_grid(rows=vars(drv)) +theme_minimal()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
<3번>
library(ggplot2)midwest## # A tibble: 437 x 28
## PID county state area poptotal popdensity popwhite popblack popamerindian
## <int> <chr> <chr> <dbl> <int> <dbl> <int> <int> <int>
## 1 561 ADAMS IL 0.052 66090 1271. 63917 1702 98
## 2 562 ALEXA… IL 0.014 10626 759 7054 3496 19
## 3 563 BOND IL 0.022 14991 681. 14477 429 35
## 4 564 BOONE IL 0.017 30806 1812. 29344 127 46
## 5 565 BROWN IL 0.018 5836 324. 5264 547 14
## 6 566 BUREAU IL 0.05 35688 714. 35157 50 65
## 7 567 CALHO… IL 0.017 5322 313. 5298 1 8
## 8 568 CARRO… IL 0.027 16805 622. 16519 111 30
## 9 569 CASS IL 0.024 13437 560. 13384 16 8
## 10 570 CHAMP… IL 0.058 173025 2983. 146506 16559 331
## # … with 427 more rows, and 19 more variables: popasian <int>, popother <int>,
## # percwhite <dbl>, percblack <dbl>, percamerindan <dbl>, percasian <dbl>,
## # percother <dbl>, popadults <int>, perchsd <dbl>, percollege <dbl>,
## # percprof <dbl>, poppovertyknown <int>, percpovertyknown <dbl>,
## # percbelowpoverty <dbl>, percchildbelowpovert <dbl>, percadultpoverty <dbl>,
## # percelderlypoverty <dbl>, inmetro <int>, category <chr>options(scipen=999)ggplot(data=midwest, aes(x=area, y=poptotal))+geom_point(aes(color=state, size=popdensity), alpha=0.4) +xlim(0, 0.1) +ylim(0, 500000)+ theme_classic()+
labs(title="Scatterplot", subtitle="Area Vs Population",
caption="Source:midwest", x="Area", y="Population") +geom_smooth(se=FALSE) ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
<4번>
iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicaggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width))+geom_point(aes(color=Species, shape=Species), size=6, alpha=0.5) +labs(title="Scatterplot", subtitle="Sepal.Length Vs Sepal.Width",
caption="Source:iris") +theme_minimal()
<5번>
library(gcookbook)heightweight## sex ageYear ageMonth heightIn weightLb
## 1 f 11.92 143 56.3 85.0
## 2 f 12.92 155 62.3 105.0
## 3 f 12.75 153 63.3 108.0
## 4 f 13.42 161 59.0 92.0
## 5 f 15.92 191 62.5 112.5
## 6 f 14.25 171 62.5 112.0
## 7 f 15.42 185 59.0 104.0
## 8 f 11.83 142 56.5 69.0
## 9 f 13.33 160 62.0 94.5
## 10 f 11.67 140 53.8 68.5
## 11 f 11.58 139 61.5 104.0
## 12 f 14.83 178 61.5 103.5
## 13 f 13.08 157 64.5 123.5
## 14 f 12.42 149 58.3 93.0
## 15 f 11.92 143 51.3 50.5
## 16 f 12.08 145 58.8 89.0
## 17 f 15.92 191 65.3 107.0
## 18 f 12.50 150 59.5 78.5
## 19 f 12.25 147 61.3 115.0
## 20 f 15.00 180 63.3 114.0
## 21 f 11.75 141 61.8 85.0
## 22 f 11.67 140 53.5 81.0
## 23 f 13.67 164 58.0 83.5
## 24 f 14.67 176 61.3 112.0
## 25 f 15.42 185 63.3 101.0
## 26 f 13.83 166 61.5 103.5
## 27 f 14.58 175 60.8 93.5
## 28 f 15.00 180 59.0 112.0
## 29 f 17.50 210 65.5 140.0
## 30 f 12.17 146 56.3 83.5
## 31 f 14.17 170 64.3 90.0
## 32 f 13.50 162 58.0 84.0
## 33 f 12.42 149 64.3 110.5
## 34 f 11.58 139 57.5 96.0
## 35 f 15.50 186 57.8 95.0
## 36 f 16.42 197 61.5 121.0
## 37 f 14.08 169 62.3 99.5
## 38 f 14.75 177 61.8 142.5
## 39 f 15.42 185 65.3 118.0
## 40 f 15.17 182 58.3 104.5
## 41 f 14.42 173 62.8 102.5
## 42 f 13.83 166 59.3 89.5
## 43 f 14.00 168 61.5 95.0
## 44 f 14.08 169 62.0 98.5
## 45 f 12.50 150 61.3 94.0
## 46 f 15.33 184 62.3 108.0
## 47 f 11.58 139 52.8 63.5
## 48 f 12.25 147 59.8 84.5
## 49 f 12.00 144 59.5 93.5
## 50 f 14.75 177 61.3 112.0
## 51 f 14.83 178 63.5 148.5
## 52 f 16.42 197 64.8 112.0
## 53 f 12.17 146 60.0 109.0
## 54 f 12.08 145 59.0 91.5
## 55 f 12.25 147 55.8 75.0
## 56 f 12.08 145 57.8 84.0
## 57 f 12.92 155 61.3 107.0
## 58 f 13.92 167 62.3 92.5
## 59 f 15.25 183 64.3 109.5
## 60 f 11.92 143 55.5 84.0
## 61 f 15.25 183 64.5 102.5
## 62 f 15.42 185 60.0 106.0
## 63 f 12.33 148 56.3 77.0
## 64 f 12.25 147 58.3 111.5
## 65 f 12.83 154 60.0 114.0
## 66 f 13.00 156 54.5 75.0
## 67 f 12.00 144 55.8 73.5
## 68 f 12.83 154 62.8 93.5
## 69 f 12.67 152 60.5 105.0
## 70 f 15.92 191 63.3 113.5
## 71 f 15.83 190 66.8 140.0
## 72 f 11.67 140 60.0 77.0
## 73 f 12.33 148 60.5 84.5
## 74 f 15.75 189 64.3 113.5
## 75 f 11.92 143 58.3 77.5
## 76 f 14.83 178 66.5 117.5
## 77 f 13.67 164 65.3 98.0
## 78 f 13.08 157 60.5 112.0
## 79 f 12.25 147 59.5 101.0
## 80 f 12.33 148 59.0 95.0
## 81 f 14.75 177 61.3 81.0
## 82 f 14.25 171 61.5 91.0
## 83 f 14.33 172 64.8 142.0
## 84 f 15.83 190 56.8 98.5
## 85 f 15.25 183 66.5 112.0
## 86 f 11.92 143 61.5 116.5
## 87 f 14.92 179 63.0 98.5
## 88 f 15.50 186 57.0 83.5
## 89 f 15.17 182 65.5 133.0
## 90 f 15.17 182 62.0 91.5
## 91 f 11.83 142 56.0 72.5
## 92 f 13.75 165 61.3 106.5
## 93 f 13.75 165 55.5 67.0
## 94 f 12.83 154 61.0 122.5
## 95 f 12.50 150 54.5 74.0
## 96 f 12.92 155 66.0 144.5
## 97 f 13.58 163 56.5 84.0
## 98 f 11.75 141 56.0 72.5
## 99 f 12.25 147 51.5 64.0
## 100 f 17.50 210 62.0 116.0
## 101 f 14.25 171 63.0 84.0
## 102 f 13.92 167 61.0 93.5
## 103 f 15.17 182 64.0 111.5
## 104 f 12.00 144 61.0 92.0
## 105 f 16.08 193 59.8 115.0
## 106 f 11.75 141 61.3 85.0
## 107 f 13.67 164 63.3 108.0
## 108 f 15.50 186 63.5 108.0
## 109 f 14.08 169 61.5 85.0
## 110 f 14.58 175 60.3 86.0
## 111 f 15.00 180 61.3 110.5
## 112 m 13.75 165 64.8 98.0
## 113 m 13.08 157 60.5 105.0
## 114 m 12.00 144 57.3 76.5
## 115 m 12.50 150 59.5 84.0
## 116 m 12.50 150 60.8 128.0
## 117 m 11.58 139 60.5 87.0
## 118 m 15.75 189 67.0 128.0
## 119 m 15.25 183 64.8 111.0
## 120 m 12.25 147 50.5 79.0
## 121 m 12.17 146 57.5 90.0
## 122 m 13.33 160 60.5 84.0
## 123 m 13.00 156 61.8 112.0
## 124 m 14.42 173 61.3 93.0
## 125 m 12.58 151 66.3 117.0
## 126 m 11.75 141 53.3 84.0
## 127 m 12.50 150 59.0 99.5
## 128 m 13.67 164 57.8 95.0
## 129 m 12.75 153 60.0 84.0
## 130 m 17.17 206 68.3 134.0
## 132 m 14.67 176 63.8 98.5
## 133 m 14.67 176 65.0 118.5
## 134 m 11.67 140 59.5 94.5
## 135 m 15.42 185 66.0 105.0
## 136 m 15.00 180 61.8 104.0
## 137 m 12.17 146 57.3 83.0
## 138 m 15.25 183 66.0 105.5
## 139 m 11.67 140 56.5 84.0
## 140 m 12.58 151 58.3 86.0
## 141 m 12.58 151 61.0 81.0
## 142 m 12.00 144 62.8 94.0
## 143 m 13.33 160 59.3 78.5
## 144 m 14.83 178 67.3 119.5
## 145 m 16.08 193 66.3 133.0
## 146 m 13.50 162 64.5 119.0
## 147 m 13.67 164 60.5 95.0
## 148 m 15.50 186 66.0 112.0
## 149 m 11.92 143 57.5 75.0
## 150 m 14.58 175 64.0 92.0
## 151 m 14.58 175 68.0 112.0
## 152 m 14.58 175 63.5 98.5
## 153 m 14.42 173 69.0 112.5
## 154 m 14.17 170 63.8 112.5
## 155 m 14.50 174 66.0 108.0
## 156 m 13.67 164 63.5 108.0
## 157 m 12.00 144 59.5 88.0
## 158 m 13.00 156 66.3 106.0
## 159 m 12.42 149 57.0 92.0
## 160 m 12.00 144 60.0 117.5
## 161 m 12.25 147 57.0 84.0
## 162 m 15.67 188 67.3 112.0
## 163 m 14.08 169 62.0 100.0
## 164 m 14.33 172 65.0 112.0
## 165 m 12.50 150 59.5 84.0
## 166 m 16.08 193 67.8 127.5
## 167 m 13.08 157 58.0 80.5
## 168 m 14.00 168 60.0 93.5
## 169 m 11.67 140 58.5 86.5
## 170 m 13.00 156 58.3 92.5
## 171 m 13.00 156 61.5 108.5
## 172 m 13.17 158 65.0 121.0
## 173 m 15.33 184 66.5 112.0
## 174 m 13.00 156 68.5 114.0
## 175 m 12.00 144 57.0 84.0
## 176 m 14.67 176 61.5 81.0
## 177 m 14.00 168 66.5 111.5
## 178 m 12.42 149 52.5 81.0
## 179 m 11.83 142 55.0 70.0
## 180 m 15.67 188 71.0 140.0
## 181 m 16.92 203 66.5 117.0
## 182 m 11.83 142 58.8 84.0
## 183 m 15.75 189 66.3 112.0
## 184 m 15.67 188 65.8 150.5
## 185 m 16.67 200 71.0 147.0
## 186 m 12.67 152 59.5 105.0
## 187 m 14.50 174 69.8 119.5
## 188 m 13.83 166 62.5 84.0
## 189 m 12.08 145 56.5 91.0
## 190 m 11.92 143 57.5 101.0
## 191 m 13.58 163 65.3 117.5
## 192 m 13.83 166 67.3 121.0
## 193 m 15.17 182 67.0 133.0
## 194 m 14.42 173 66.0 112.0
## 195 m 12.92 155 61.8 91.5
## 196 m 13.50 162 60.0 105.0
## 197 m 14.75 177 63.0 111.0
## 198 m 14.75 177 60.5 112.0
## 199 m 14.58 175 65.5 114.0
## 200 m 13.83 166 62.0 91.0
## 201 m 12.50 150 59.0 98.0
## 202 m 12.50 150 61.8 118.0
## 203 m 15.67 188 63.3 115.5
## 204 m 13.58 163 66.0 112.0
## 205 m 14.25 171 61.8 112.0
## 206 m 13.50 162 63.0 91.0
## 207 m 11.75 141 57.5 85.0
## 208 m 14.50 174 63.0 112.0
## 209 m 11.83 142 56.0 87.5
## 210 m 12.33 148 60.5 118.0
## 211 m 11.67 140 56.8 83.5
## 212 m 13.33 160 64.0 116.0
## 213 m 12.00 144 60.0 89.0
## 214 m 17.17 206 69.5 171.5
## 215 m 13.25 159 63.3 112.0
## 216 m 12.42 149 56.3 72.0
## 217 m 16.08 193 72.0 150.0
## 218 m 16.17 194 65.3 134.5
## 219 m 12.67 152 60.8 97.0
## 220 m 12.17 146 55.0 71.5
## 221 m 11.58 139 55.0 73.5
## 222 m 15.50 186 66.5 112.0
## 223 m 13.42 161 56.8 75.0
## 224 m 12.75 153 64.8 128.0
## 225 m 16.33 196 64.5 98.0
## 226 m 13.67 164 58.0 84.0
## 227 m 13.25 159 62.8 99.0
## 228 m 14.83 178 63.8 112.0
## 229 m 12.75 153 57.8 79.5
## 230 m 12.92 155 57.3 80.5
## 231 m 14.83 178 63.5 102.5
## 232 m 11.83 142 55.0 76.0
## 233 m 13.67 164 66.5 112.0
## 234 m 15.75 189 65.0 114.0
## 235 m 13.67 164 61.5 140.0
## 236 m 13.92 167 62.0 107.5
## 237 m 12.58 151 59.3 87.0ggplot(data=heightweight, aes(x=heightIn, y=weightLb, color=sex))+geom_point(aes(color=sex), size=3, alpha=0.5) +
geom_smooth(method = "lm", se=FALSE) +
labs(title = "Scatterplot", subtitle = "Weight Vs Height", caption = "Source:heightweight") + theme_classic()## `geom_smooth()` using formula 'y ~ x'
<6번>
library(RColorBrewer)ggplot(data=mpg, aes(x=manufacturer, fill=class)) + geom_bar(width = 0.5) +
scale_fill_brewer(palette = "Spectral") + theme_minimal() +
theme(axis.text.x=element_text(angle = 65, hjust=0.5, vjust=0.5)) +
labs(title = "Barplot", subtitle = "Manufacturer across Vehicle Classes")