legend.position=c(1, 0.55), # Put legend inside plot area
+ legend.justification=c(1, 0.5))
> ggplot(tophit, aes(x=avg, y=name)) +
+ geom_segment(aes(yend=name), xend=0, colour="grey50") +
+ geom_point(size=3, aes(colour=lg)) +
+ scale_colour_brewer(palette="Set1", limits=c("NL","AL"), guide=FALSE) +
+ theme_bw() +
+ theme(panel.grid.major.y = element_blank()) +
+ facet_grid(lg ~ ., scales="free_y", space="free_y")
>
> ggplot(worldpop, aes(x=Year, y=Population)) + geom_line() + geom_point()
>
> ggplot(worldpop, aes(x=Year, y=Population)) + geom_line() + geom_point() +
+ scale_y_log10()
>
> tg <- ddply(ToothGrowth, c("supp", "dose"), summarise, length=mean(len))
> ggplot(tg, aes(x=dose, y=length, colour=supp)) + geom_line()
> ggplot(tg, aes(x=dose, y=length, linetype=supp)) + geom_line()
> sunspotyear <- data.frame(
+ Year = as.numeric(time(sunspot.year)),
+ Sunspots = as.numeric(sunspot.year)
+ )
>
> ggplot(sunspotyear, aes(x=Year, y=Sunspots)) + geom_area()
> ggplot(sunspotyear, aes(x=Year, y=Sunspots)) +
+ geom_area(colour="black", fill="blue", alpha=.2)
> ggplot(sunspotyear, aes(x=Year, y=Sunspots)) +
+ geom_area(fill="blue", alpha=.2) +
+ geom_line()
>
> ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) + geom_area()
> ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) +
+ geom_area(colour="black", size=.2, alpha=.4) +
+ scale_fill_brewer(palette="Blues", breaks=rev(levels(uspopage$AgeGroup)))
>
> ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup, order=desc(AgeGroup))) +
+ geom_area(colour=NA, alpha=.4) +
+ scale_fill_brewer(palette="Blues") +
+ geom_line(position="stack", size=.2)
>
> uspopage_prop <- ddply(uspopage, "Year", transform,
+ Percent = Thousands / sum(Thousands) * 100)
>
> ggplot(uspopage_prop, aes(x=Year, y=Percent, fill=AgeGroup)) +
+ geom_area(colour="black", size=.2, alpha=.4) +
+ scale_fill_brewer(palette="Blues", breaks=rev(levels(uspopage$AgeGroup)))
>
> clim <- subset(climate, Source == "Berkeley",
+ select=c("Year", "Anomaly10y", "Unc10y"))
>
> ggplot(clim, aes(x=Year, y=Anomaly10y)) +
+ geom_ribbon(aes(ymin=Anomaly10y-Unc10y, ymax=Anomaly10y+Unc10y),
+ alpha=0.2) +
+ geom_line()
>
> ggplot(clim, aes(x=Year, y=Anomaly10y)) +
+ geom_line(aes(y=Anomaly10y-Unc10y), colour="grey50", linetype="dotted") +
+ geom_line(aes(y=Anomaly10y+Unc10y), colour="grey50", linetype="dotted") +
+ geom_line()
>
> heightweight[, c("ageYear", "heightIn")]
ageYear heightIn
1 11.92 56.3
2 12.92 62.3
3 12.75 63.3
4 13.42 59.0
5 15.92 62.5
6 14.25 62.5
7 15.42 59.0
8 11.83 56.5
9 13.33 62.0
10 11.67 53.8
11 11.58 61.5
12 14.83 61.5
13 13.08 64.5
14 12.42 58.3
15 11.92 51.3
16 12.08 58.8
17 15.92 65.3
18 12.50 59.5
19 12.25 61.3
20 15.00 63.3
21 11.75 61.8
22 11.67 53.5
23 13.67 58.0
24 14.67 61.3
25 15.42 63.3
26 13.83 61.5
27 14.58 60.8
28 15.00 59.0
29 17.50 65.5
30 12.17 56.3
31 14.17 64.3
32 13.50 58.0
33 12.42 64.3
34 11.58 57.5
35 15.50 57.8
36 16.42 61.5
37 14.08 62.3
38 14.75 61.8
39 15.42 65.3
40 15.17 58.3
41 14.42 62.8
42 13.83 59.3
43 14.00 61.5
44 14.08 62.0
45 12.50 61.3
46 15.33 62.3
47 11.58 52.8
48 12.25 59.8
49 12.00 59.5
50 14.75 61.3
51 14.83 63.5
52 16.42 64.8
53 12.17 60.0
54 12.08 59.0
55 12.25 55.8
56 12.08 57.8
57 12.92 61.3
58 13.92 62.3
59 15.25 64.3
60 11.92 55.5
61 15.25 64.5
62 15.42 60.0
63 12.33 56.3
64 12.25 58.3
65 12.83 60.0
66 13.00 54.5
67 12.00 55.8
68 12.83 62.8
69 12.67 60.5
70 15.92 63.3
71 15.83 66.8
72 11.67 60.0
73 12.33 60.5
74 15.75 64.3
75 11.92 58.3
76 14.83 66.5
77 13.67 65.3
78 13.08 60.5
79 12.25 59.5
80 12.33 59.0
81 14.75 61.3
82 14.25 61.5
83 14.33 64.8
84 15.83 56.8
85 15.25 66.5
86 11.92 61.5
87 14.92 63.0
88 15.50 57.0
89 15.17 65.5
90 15.17 62.0
91 11.83 56.0
92 13.75 61.3
93 13.75 55.5
94 12.83 61.0
95 12.50 54.5
96 12.92 66.0
97 13.58 56.5
98 11.75 56.0
99 12.25 51.5
100 17.50 62.0
101 14.25 63.0
102 13.92 61.0
103 15.17 64.0
104 12.00 61.0
105 16.08 59.8
106 11.75 61.3
107 13.67 63.3
108 15.50 63.5
109 14.08 61.5
110 14.58 60.3
111 15.00 61.3
112 13.75 64.8
113 13.08 60.5
114 12.00 57.3
115 12.50 59.5
116 12.50 60.8
117 11.58 60.5
118 15.75 67.0
119 15.25 64.8
120 12.25 50.5
121 12.17 57.5
122 13.33 60.5
123 13.00 61.8
124 14.42 61.3
125 12.58 66.3
126 11.75 53.3
127 12.50 59.0
128 13.67 57.8
129 12.75 60.0
130 17.17 68.3
132 14.67 63.8
133 14.67 65.0
134 11.67 59.5
135 15.42 66.0
136 15.00 61.8
137 12.17 57.3
138 15.25 66.0
139 11.67 56.5
140 12.58 58.3
141 12.58 61.0
142 12.00 62.8
143 13.33 59.3
144 14.83 67.3
145 16.08 66.3
146 13.50 64.5
147 13.67 60.5
148 15.50 66.0
149 11.92 57.5
150 14.58 64.0
151 14.58 68.0
152 14.58 63.5
153 14.42 69.0
154 14.17 63.8
155 14.50 66.0
156 13.67 63.5
157 12.00 59.5
158 13.00 66.3
159 12.42 57.0
160 12.00 60.0
161 12.25 57.0
162 15.67 67.3
163 14.08 62.0
164 14.33 65.0
165 12.50 59.5
166 16.08 67.8
167 13.08 58.0
168 14.00 60.0
169 11.67 58.5
170 13.00 58.3
171 13.00 61.5
172 13.17 65.0
173 15.33 66.5
174 13.00 68.5
175 12.00 57.0
176 14.67 61.5
177 14.00 66.5
178 12.42 52.5
179 11.83 55.0
180 15.67 71.0
181 16.92 66.5
182 11.83 58.8
183 15.75 66.3
184 15.67 65.8
185 16.67 71.0
186 12.67 59.5
187 14.50 69.8
188 13.83 62.5
189 12.08 56.5
190 11.92 57.5
191 13.58 65.3
192 13.83 67.3
193 15.17 67.0
194 14.42 66.0
195 12.92 61.8
196 13.50 60.0
197 14.75 63.0
198 14.75 60.5
199 14.58 65.5
200 13.83 62.0
201 12.50 59.0
202 12.50 61.8
203 15.67 63.3
204 13.58 66.0
205 14.25 61.8
206 13.50 63.0
207 11.75 57.5
208 14.50 63.0
209 11.83 56.0
210 12.33 60.5
211 11.67 56.8
212 13.33 64.0
213 12.00 60.0
214 17.17 69.5
215 13.25 63.3
216 12.42 56.3
217 16.08 72.0
218 16.17 65.3
219 12.67 60.8
220 12.17 55.0
221 11.58 55.0
222 15.50 66.5
223 13.42 56.8
224 12.75 64.8
225 16.33 64.5
226 13.67 58.0
227 13.25 62.8
228 14.83 63.8
229 12.75 57.8
230 12.92 57.3
231 14.83 63.5
232 11.83 55.0
233 13.67 66.5
234 15.75 65.0
235 13.67 61.5
236 13.92 62.0
237 12.58 59.3
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point()
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(shape=21)
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(size=1.5)
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(size=1.5)
> heightweight[, c("sex", "ageYear", "heightIn")]
sex ageYear heightIn
1 f 11.92 56.3
2 f 12.92 62.3
3 f 12.75 63.3
4 f 13.42 59.0
5 f 15.92 62.5
6 f 14.25 62.5
7 f 15.42 59.0
8 f 11.83 56.5
9 f 13.33 62.0
10 f 11.67 53.8
11 f 11.58 61.5
12 f 14.83 61.5
13 f 13.08 64.5
14 f 12.42 58.3
15 f 11.92 51.3
16 f 12.08 58.8
17 f 15.92 65.3
18 f 12.50 59.5
19 f 12.25 61.3
20 f 15.00 63.3
21 f 11.75 61.8
22 f 11.67 53.5
23 f 13.67 58.0
24 f 14.67 61.3
25 f 15.42 63.3
26 f 13.83 61.5
27 f 14.58 60.8
28 f 15.00 59.0
29 f 17.50 65.5
30 f 12.17 56.3
31 f 14.17 64.3
32 f 13.50 58.0
33 f 12.42 64.3
34 f 11.58 57.5
35 f 15.50 57.8
36 f 16.42 61.5
37 f 14.08 62.3
38 f 14.75 61.8
39 f 15.42 65.3
40 f 15.17 58.3
41 f 14.42 62.8
42 f 13.83 59.3
43 f 14.00 61.5
44 f 14.08 62.0
45 f 12.50 61.3
46 f 15.33 62.3
47 f 11.58 52.8
48 f 12.25 59.8
49 f 12.00 59.5
50 f 14.75 61.3
51 f 14.83 63.5
52 f 16.42 64.8
53 f 12.17 60.0
54 f 12.08 59.0
55 f 12.25 55.8
56 f 12.08 57.8
57 f 12.92 61.3
58 f 13.92 62.3
59 f 15.25 64.3
60 f 11.92 55.5
61 f 15.25 64.5
62 f 15.42 60.0
63 f 12.33 56.3
64 f 12.25 58.3
65 f 12.83 60.0
66 f 13.00 54.5
67 f 12.00 55.8
68 f 12.83 62.8
69 f 12.67 60.5
70 f 15.92 63.3
71 f 15.83 66.8
72 f 11.67 60.0
73 f 12.33 60.5
74 f 15.75 64.3
75 f 11.92 58.3
76 f 14.83 66.5
77 f 13.67 65.3
78 f 13.08 60.5
79 f 12.25 59.5
80 f 12.33 59.0
81 f 14.75 61.3
82 f 14.25 61.5
83 f 14.33 64.8
84 f 15.83 56.8
85 f 15.25 66.5
86 f 11.92 61.5
87 f 14.92 63.0
88 f 15.50 57.0
89 f 15.17 65.5
90 f 15.17 62.0
91 f 11.83 56.0
92 f 13.75 61.3
93 f 13.75 55.5
94 f 12.83 61.0
95 f 12.50 54.5
96 f 12.92 66.0
97 f 13.58 56.5
98 f 11.75 56.0
99 f 12.25 51.5
100 f 17.50 62.0
101 f 14.25 63.0
102 f 13.92 61.0
103 f 15.17 64.0
104 f 12.00 61.0
105 f 16.08 59.8
106 f 11.75 61.3
107 f 13.67 63.3
108 f 15.50 63.5
109 f 14.08 61.5
110 f 14.58 60.3
111 f 15.00 61.3
112 m 13.75 64.8
113 m 13.08 60.5
114 m 12.00 57.3
115 m 12.50 59.5
116 m 12.50 60.8
117 m 11.58 60.5
118 m 15.75 67.0
119 m 15.25 64.8
120 m 12.25 50.5
121 m 12.17 57.5
122 m 13.33 60.5
123 m 13.00 61.8
124 m 14.42 61.3
125 m 12.58 66.3
126 m 11.75 53.3
127 m 12.50 59.0
128 m 13.67 57.8
129 m 12.75 60.0
130 m 17.17 68.3
132 m 14.67 63.8
133 m 14.67 65.0
134 m 11.67 59.5
135 m 15.42 66.0
136 m 15.00 61.8
137 m 12.17 57.3
138 m 15.25 66.0
139 m 11.67 56.5
140 m 12.58 58.3
141 m 12.58 61.0
142 m 12.00 62.8
143 m 13.33 59.3
144 m 14.83 67.3
145 m 16.08 66.3
146 m 13.50 64.5
147 m 13.67 60.5
148 m 15.50 66.0
149 m 11.92 57.5
150 m 14.58 64.0
151 m 14.58 68.0
152 m 14.58 63.5
153 m 14.42 69.0
154 m 14.17 63.8
155 m 14.50 66.0
156 m 13.67 63.5
157 m 12.00 59.5
158 m 13.00 66.3
159 m 12.42 57.0
160 m 12.00 60.0
161 m 12.25 57.0
162 m 15.67 67.3
163 m 14.08 62.0
164 m 14.33 65.0
165 m 12.50 59.5
166 m 16.08 67.8
167 m 13.08 58.0
168 m 14.00 60.0
169 m 11.67 58.5
170 m 13.00 58.3
171 m 13.00 61.5
172 m 13.17 65.0
173 m 15.33 66.5
174 m 13.00 68.5
175 m 12.00 57.0
176 m 14.67 61.5
177 m 14.00 66.5
178 m 12.42 52.5
179 m 11.83 55.0
180 m 15.67 71.0
181 m 16.92 66.5
182 m 11.83 58.8
183 m 15.75 66.3
184 m 15.67 65.8
185 m 16.67 71.0
186 m 12.67 59.5
187 m 14.50 69.8
188 m 13.83 62.5
189 m 12.08 56.5
190 m 11.92 57.5
191 m 13.58 65.3
192 m 13.83 67.3
193 m 15.17 67.0
194 m 14.42 66.0
195 m 12.92 61.8
196 m 13.50 60.0
197 m 14.75 63.0
198 m 14.75 60.5
199 m 14.58 65.5
200 m 13.83 62.0
201 m 12.50 59.0
202 m 12.50 61.8
203 m 15.67 63.3
204 m 13.58 66.0
205 m 14.25 61.8
206 m 13.50 63.0
207 m 11.75 57.5
208 m 14.50 63.0
209 m 11.83 56.0
210 m 12.33 60.5
211 m 11.67 56.8
212 m 13.33 64.0
213 m 12.00 60.0
214 m 17.17 69.5
215 m 13.25 63.3
216 m 12.42 56.3
217 m 16.08 72.0
218 m 16.17 65.3
219 m 12.67 60.8
220 m 12.17 55.0
221 m 11.58 55.0
222 m 15.50 66.5
223 m 13.42 56.8
224 m 12.75 64.8
225 m 16.33 64.5
226 m 13.67 58.0
227 m 13.25 62.8
228 m 14.83 63.8
229 m 12.75 57.8
230 m 12.92 57.3
231 m 14.83 63.5
232 m 11.83 55.0
233 m 13.67 66.5
234 m 15.75 65.0
235 m 13.67 61.5
236 m 13.92 62.0
237 m 12.58 59.3
> ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex)) + geom_point()
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex)) + geom_point()
> ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex, colour=sex)) +
+ geom_point()
> ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex, colour=sex)) +
+ geom_point() +
+ scale_shape_manual(values=c(1,2)) +
+ scale_colour_brewer(palette="Set1")
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(shape=3)
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex)) +
+ geom_point(size=3) + scale_shape_manual(values=c(1, 4))
> hw$weightGroup <- cut(hw$weightLb, breaks=c(-Inf, 100, Inf),
+ labels=c("< 100", ">= 100"))
Error in cut(hw$weightLb, breaks = c(-Inf, 100, Inf), labels = c("< 100", :
object 'hw' not found
>
> hw <- heightweight
> hw$weightGroup <- cut(hw$weightLb, breaks=c(-Inf, 100, Inf),
+ labels=c("< 100", ">= 100"))
>
> ggplot(hw, aes(x=ageYear, y=heightIn, shape=sex, fill=weightGroup)) +
+ geom_point(size=2.5) +
+ scale_shape_manual(values=c(21, 24)) +
+ scale_fill_manual(values=c(NA, "black"),
+ guide=guide_legend(override.aes=list(shape=21)))
> heightweight[, c("sex", "ageYear", "heightIn", "weightLb")]
sex ageYear heightIn weightLb
1 f 11.92 56.3 85.0
2 f 12.92 62.3 105.0
3 f 12.75 63.3 108.0
4 f 13.42 59.0 92.0
5 f 15.92 62.5 112.5
6 f 14.25 62.5 112.0
7 f 15.42 59.0 104.0
8 f 11.83 56.5 69.0
9 f 13.33 62.0 94.5
10 f 11.67 53.8 68.5
11 f 11.58 61.5 104.0
12 f 14.83 61.5 103.5
13 f 13.08 64.5 123.5
14 f 12.42 58.3 93.0
15 f 11.92 51.3 50.5
16 f 12.08 58.8 89.0
17 f 15.92 65.3 107.0
18 f 12.50 59.5 78.5
19 f 12.25 61.3 115.0
20 f 15.00 63.3 114.0
21 f 11.75 61.8 85.0
22 f 11.67 53.5 81.0
23 f 13.67 58.0 83.5
24 f 14.67 61.3 112.0
25 f 15.42 63.3 101.0
26 f 13.83 61.5 103.5
27 f 14.58 60.8 93.5
28 f 15.00 59.0 112.0
29 f 17.50 65.5 140.0
30 f 12.17 56.3 83.5
31 f 14.17 64.3 90.0
32 f 13.50 58.0 84.0
33 f 12.42 64.3 110.5
34 f 11.58 57.5 96.0
35 f 15.50 57.8 95.0
36 f 16.42 61.5 121.0
37 f 14.08 62.3 99.5
38 f 14.75 61.8 142.5
39 f 15.42 65.3 118.0
40 f 15.17 58.3 104.5
41 f 14.42 62.8 102.5
42 f 13.83 59.3 89.5
43 f 14.00 61.5 95.0
44 f 14.08 62.0 98.5
45 f 12.50 61.3 94.0
46 f 15.33 62.3 108.0
47 f 11.58 52.8 63.5
48 f 12.25 59.8 84.5
49 f 12.00 59.5 93.5
50 f 14.75 61.3 112.0
51 f 14.83 63.5 148.5
52 f 16.42 64.8 112.0
53 f 12.17 60.0 109.0
54 f 12.08 59.0 91.5
55 f 12.25 55.8 75.0
56 f 12.08 57.8 84.0
57 f 12.92 61.3 107.0
58 f 13.92 62.3 92.5
59 f 15.25 64.3 109.5
60 f 11.92 55.5 84.0
61 f 15.25 64.5 102.5
62 f 15.42 60.0 106.0
63 f 12.33 56.3 77.0
64 f 12.25 58.3 111.5
65 f 12.83 60.0 114.0
66 f 13.00 54.5 75.0
67 f 12.00 55.8 73.5
68 f 12.83 62.8 93.5
69 f 12.67 60.5 105.0
70 f 15.92 63.3 113.5
71 f 15.83 66.8 140.0
72 f 11.67 60.0 77.0
73 f 12.33 60.5 84.5
74 f 15.75 64.3 113.5
75 f 11.92 58.3 77.5
76 f 14.83 66.5 117.5
77 f 13.67 65.3 98.0
78 f 13.08 60.5 112.0
79 f 12.25 59.5 101.0
80 f 12.33 59.0 95.0
81 f 14.75 61.3 81.0
82 f 14.25 61.5 91.0
83 f 14.33 64.8 142.0
84 f 15.83 56.8 98.5
85 f 15.25 66.5 112.0
86 f 11.92 61.5 116.5
87 f 14.92 63.0 98.5
88 f 15.50 57.0 83.5
89 f 15.17 65.5 133.0
90 f 15.17 62.0 91.5
91 f 11.83 56.0 72.5
92 f 13.75 61.3 106.5
93 f 13.75 55.5 67.0
94 f 12.83 61.0 122.5
95 f 12.50 54.5 74.0
96 f 12.92 66.0 144.5
97 f 13.58 56.5 84.0
98 f 11.75 56.0 72.5
99 f 12.25 51.5 64.0
100 f 17.50 62.0 116.0
101 f 14.25 63.0 84.0
102 f 13.92 61.0 93.5
103 f 15.17 64.0 111.5
104 f 12.00 61.0 92.0
105 f 16.08 59.8 115.0
106 f 11.75 61.3 85.0
107 f 13.67 63.3 108.0
108 f 15.50 63.5 108.0
109 f 14.08 61.5 85.0
110 f 14.58 60.3 86.0
111 f 15.00 61.3 110.5
112 m 13.75 64.8 98.0
113 m 13.08 60.5 105.0
114 m 12.00 57.3 76.5
115 m 12.50 59.5 84.0
116 m 12.50 60.8 128.0
117 m 11.58 60.5 87.0
118 m 15.75 67.0 128.0
119 m 15.25 64.8 111.0
120 m 12.25 50.5 79.0
121 m 12.17 57.5 90.0
122 m 13.33 60.5 84.0
123 m 13.00 61.8 112.0
124 m 14.42 61.3 93.0
125 m 12.58 66.3 117.0
126 m 11.75 53.3 84.0
127 m 12.50 59.0 99.5
128 m 13.67 57.8 95.0
129 m 12.75 60.0 84.0
130 m 17.17 68.3 134.0
132 m 14.67 63.8 98.5
133 m 14.67 65.0 118.5
134 m 11.67 59.5 94.5
135 m 15.42 66.0 105.0
136 m 15.00 61.8 104.0
137 m 12.17 57.3 83.0
138 m 15.25 66.0 105.5
139 m 11.67 56.5 84.0
140 m 12.58 58.3 86.0
141 m 12.58 61.0 81.0
142 m 12.00 62.8 94.0
143 m 13.33 59.3 78.5
144 m 14.83 67.3 119.5
145 m 16.08 66.3 133.0
146 m 13.50 64.5 119.0
147 m 13.67 60.5 95.0
148 m 15.50 66.0 112.0
149 m 11.92 57.5 75.0
150 m 14.58 64.0 92.0
151 m 14.58 68.0 112.0
152 m 14.58 63.5 98.5
153 m 14.42 69.0 112.5
154 m 14.17 63.8 112.5
155 m 14.50 66.0 108.0
156 m 13.67 63.5 108.0
157 m 12.00 59.5 88.0
158 m 13.00 66.3 106.0
159 m 12.42 57.0 92.0
160 m 12.00 60.0 117.5
161 m 12.25 57.0 84.0
162 m 15.67 67.3 112.0
163 m 14.08 62.0 100.0
164 m 14.33 65.0 112.0
165 m 12.50 59.5 84.0
166 m 16.08 67.8 127.5
167 m 13.08 58.0 80.5
168 m 14.00 60.0 93.5
169 m 11.67 58.5 86.5
170 m 13.00 58.3 92.5
171 m 13.00 61.5 108.5
172 m 13.17 65.0 121.0
173 m 15.33 66.5 112.0
174 m 13.00 68.5 114.0
175 m 12.00 57.0 84.0
176 m 14.67 61.5 81.0
177 m 14.00 66.5 111.5
178 m 12.42 52.5 81.0
179 m 11.83 55.0 70.0
180 m 15.67 71.0 140.0
181 m 16.92 66.5 117.0
182 m 11.83 58.8 84.0
183 m 15.75 66.3 112.0
184 m 15.67 65.8 150.5
185 m 16.67 71.0 147.0
186 m 12.67 59.5 105.0
187 m 14.50 69.8 119.5
188 m 13.83 62.5 84.0
189 m 12.08 56.5 91.0
190 m 11.92 57.5 101.0
191 m 13.58 65.3 117.5
192 m 13.83 67.3 121.0
193 m 15.17 67.0 133.0
194 m 14.42 66.0 112.0
195 m 12.92 61.8 91.5
196 m 13.50 60.0 105.0
197 m 14.75 63.0 111.0
198 m 14.75 60.5 112.0
199 m 14.58 65.5 114.0
200 m 13.83 62.0 91.0
201 m 12.50 59.0 98.0
202 m 12.50 61.8 118.0
203 m 15.67 63.3 115.5
204 m 13.58 66.0 112.0
205 m 14.25 61.8 112.0
206 m 13.50 63.0 91.0
207 m 11.75 57.5 85.0
208 m 14.50 63.0 112.0
209 m 11.83 56.0 87.5
210 m 12.33 60.5 118.0
211 m 11.67 56.8 83.5
212 m 13.33 64.0 116.0
213 m 12.00 60.0 89.0
214 m 17.17 69.5 171.5
215 m 13.25 63.3 112.0
216 m 12.42 56.3 72.0
217 m 16.08 72.0 150.0
218 m 16.17 65.3 134.5
219 m 12.67 60.8 97.0
220 m 12.17 55.0 71.5
221 m 11.58 55.0 73.5
222 m 15.50 66.5 112.0
223 m 13.42 56.8 75.0
224 m 12.75 64.8 128.0
225 m 16.33 64.5 98.0
226 m 13.67 58.0 84.0
227 m 13.25 62.8 99.0
228 m 14.83 63.8 112.0
229 m 12.75 57.8 79.5
230 m 12.92 57.3 80.5
231 m 14.83 63.5 102.5
232 m 11.83 55.0 76.0
233 m 13.67 66.5 112.0
234 m 15.75 65.0 114.0
235 m 13.67 61.5 140.0
236 m 13.92 62.0 107.5
237 m 12.58 59.3 87.0
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=weightLb)) + geom_point()
> ggplot(heightweight, aes(x=ageYear, y=heightIn, size=weightLb)) + geom_point()
> ggplot(heightweight, aes(x=weightLb, y=heightIn, fill=ageYear)) +
+ geom_point(shape=21, size=2.5) +
+ scale_fill_gradient(low="black", high="white")
>
> ggplot(heightweight, aes(x=weightLb, y=heightIn, fill=ageYear)) +
+ geom_point(shape=21, size=2.5) +
+ scale_fill_gradient(low="black", high="white", breaks=12:17,
+ guide=guide_legend())
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn, size=weightLb, colour=sex)) +
+ geom_point(alpha=.5) +
+ scale_size_area() + # Make area proportional to numeric value
+ scale_colour_brewer(palette="Set1")
>
> sp <- ggplot(diamonds, aes(x=carat, y=price))
>
> sp + geom_point()
>
> sp + geom_point(alpha=.1)
> sp + geom_point(alpha=.01)
> sp + stat_bin2d()
>
> sp + stat_bin2d(bins=50) +
+ scale_fill_gradient(low="lightblue", high="red", limits=c(0, 6000))
>
> library(hexbin)
Warning message:
package ‘hexbin’ was built under R version 3.5.2
> library(hexbin)
> sp + stat_binhex() +
+ scale_fill_gradient(low="lightblue", high="red",
+ limits=c(0, 8000))
>
> sp + stat_binhex() +
+ scale_fill_gradient(low="lightblue", high="red",
+ breaks=c(0, 250, 500, 1000, 2000, 4000, 6000),
+ limits=c(0, 6000))
>
> sp1 <- ggplot(ChickWeight, aes(x=Time, y=weight))
>
> sp1 + geom_point()
>
> sp1 + geom_point(position="jitter")
> sp1 + geom_point(position=position_jitter(width=.5, height=0))
>
> sp1 + geom_boxplot(aes(group=Time))
> sp <- ggplot(heightweight, aes(x=ageYear, y=heightIn))
>
> sp + geom_point() + stat_smooth(method=lm)
> sp + geom_point() + stat_smooth(method=lm, level=0.99)
> sp + geom_point() + stat_smooth(method=lm, se=FALSE)
>
> sp + geom_point(colour="grey60") +
+ stat_smooth(method=lm, se=FALSE, colour="black")
>
> sp + geom_point(colour="grey60") + stat_smooth()
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
> sp + geom_point(colour="grey60") + stat_smooth(method=loess)
>
> library(MASS)
> b <- biopsy
> b$classn[b$class=="benign"] <- 0
> b$classn[b$class=="malignant"] <- 1
>
> ggplot(b, aes(x=V1, y=classn)) +
+ geom_point(position=position_jitter(width=0.3, height=0.06), alpha=0.4,
+ shape=21, size=1.5) +
+ stat_smooth(method=glm, family=binomial)
Warning: Ignoring unknown parameters: family
>
> sps <- ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex)) +
+ geom_point() +
+ scale_colour_brewer(palette="Set1")
>
> sps + geom_smooth()
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
>
> sps + geom_smooth(method=lm, se=FALSE, fullrange=TRUE)
> model <- lm(heightIn ~ ageYear + I(ageYear^2), heightweight)
>
> xmin <- min(heightweight$ageYear)
>
> xmax <- max(heightweight$ageYear)
> predicted <- data.frame(ageYear=seq(xmin, xmax, length.out=100))
>
> predicted$heightIn <- predict(model, predicted)
> sp <- ggplot(heightweight, aes(x=ageYear, y=heightIn)) +
+ geom_point(colour="grey40")
>
> sp + geom_line(data=predicted, size=1)
>
> predictvals <- function(model, xvar, yvar, xrange=NULL, samples=100, ...) {
+ if (is.null(xrange)) {
+ if (any(class(model) %in% c("lm", "glm")))
+ xrange <- range(model$model[[xvar]])
+ else if (any(class(model) %in% "loess"))
+ xrange <- range(model$x)
+ }
+
+ newdata <- data.frame(x = seq(xrange[1], xrange[2], length.out = samples))
+ names(newdata) <- xvar
+ newdata[[yvar]] <- predict(model, newdata = newdata, ...)
+ newdata
+ }
> modlinear <- lm(heightIn ~ ageYear, heightweight)
> modloess <- loess(heightIn ~ ageYear, heightweight)
>
> lm_predicted <- predictvals(modlinear, "ageYear", "heightIn")
> loess_predicted <- predictvals(modloess, "ageYear", "heightIn")
> sp + geom_line(data=lm_predicted, colour="red", size=.8) +
+ geom_line(data=loess_predicted, colour="blue", size=.8)
> md <- data.frame(deaths = as.numeric(mdeaths),
+ month = as.numeric(cycle(mdeaths)))
> md <- ddply(md, "month", summarise, deaths = mean(deaths))
> p <- ggplot(md, aes(x=month, y=deaths)) + geom_line() +
+ scale_x_continuous(breaks=1:12)
> p + coord_polar()
>
> p + coord_polar() + ylim(0, max(md$deaths))
> mdx <- md[md$month==12, ]
>
> mdx$month <- 0
> mdnew <- rbind(mdx, md)
>
> p %+% mdnew + coord_polar() + ylim(0, max(md$deaths))
>
> str(economics)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 574 obs. of 6 variables:
$ date : Date, format: ...
$ pce : num 507 510 516 513 518 ...
$ pop : int 198712 198911 199113 199311 199498 199657 199808 199920 200056 200208 ...
$ psavert : num 12.5 12.5 11.7 12.5 12.5 12.1 11.7 12.2 11.6 12.2 ...
$ uempmed : num 4.5 4.7 4.6 4.9 4.7 4.8 5.1 4.5 4.1 4.6 ...
$ unemploy: int 2944 2945 2958 3143 3066 3018 2878 3001 2877 2709 ...
> ggplot(economics, aes(x=date, y=psavert)) + geom_line()
>
> econ <- subset(economics, date >= as.Date("1992-05-01") &
+ date < as.Date("1993-06-01"))
> p <- ggplot(econ, aes(x=date, y=psavert)) + geom_line()
> datebreaks <- seq(as.Date("1992-06-01"), as.Date("1993-06-01"), by="2 month")
>
> p + scale_x_date(breaks=datebreaks) +
+ theme(axis.text.x = element_text(angle=30, hjust=1))
> library(scales)
Warning message:
package ‘scales’ was built under R version 3.5.2
> library(scales)
> p + scale_x_date(breaks=datebreaks, labels=date_format("%Y %b")) +
+ theme(axis.text.x = element_text(angle=30, hjust=1))
>
> www <- data.frame(minute = as.numeric(time(WWWusage)),
+ users = as.numeric(WWWusage))
> timeHM_formatter <- function(x) {
+ h <- floor(x/60)
+ m <- floor(x %% 60)
+ lab <- sprintf("%d:%02d", h, m) # Format the strings as HH:MM
+ return(lab)
+ }
>
> ggplot(www, aes(x=minute, y=users)) + geom_line()
> ggplot(www, aes(x=minute, y=users)) + geom_line() +
+ scale_x_continuous(name="time", breaks=seq(0, 100, by=10),
+ labels=timeHM_formatter)
> scale_x_continuous(breaks=c(0, 20, 40, 60, 80, 100),
+ labels=c("0:00", "0:20", "0:40", "1:00", "1:20", "1:40"))
<ScaleContinuousPosition>
Range:
Limits: 0 -- 1
> timeHM_formatter(c(0, 50, 51, 59, 60, 130, 604))
[1] "0:00" "0:50" "0:51" "0:59" "1:00" "2:10"
[7] "10:04"
>
> timeHMS_formatter <- function(x) {
+ h <- floor(x/3600)
+ m <- floor((x/60) %% 60)
+ s <- round(x %% 60) # Round to nearest second
+ lab <- sprintf("%02d:%02d:%02d", h, m, s)
+ lab <- sub("^00:", "", lab) # Remove leading 00: if present
+ lab <- sub("^0", "", lab) # Remove leading 0 if present
+ return(lab)
+ }
> islice <- subset(isabel, z == min(z))
>
> ggplot(islice, aes(x=x, y=y)) +
+ geom_segment(aes(xend = x + vx/50, yend = y + vy/50),
+ size = 0.25)
+ legend.justification=c(1, 0.5))
> ggplot(tophit, aes(x=avg, y=name)) +
+ geom_segment(aes(yend=name), xend=0, colour="grey50") +
+ geom_point(size=3, aes(colour=lg)) +
+ scale_colour_brewer(palette="Set1", limits=c("NL","AL"), guide=FALSE) +
+ theme_bw() +
+ theme(panel.grid.major.y = element_blank()) +
+ facet_grid(lg ~ ., scales="free_y", space="free_y")
>
>
> ggplot(worldpop, aes(x=Year, y=Population)) + geom_line() + geom_point() +
+ scale_y_log10()
>
> tg <- ddply(ToothGrowth, c("supp", "dose"), summarise, length=mean(len))
> ggplot(tg, aes(x=dose, y=length, colour=supp)) + geom_line()
> ggplot(tg, aes(x=dose, y=length, linetype=supp)) + geom_line()
> sunspotyear <- data.frame(
+ Year = as.numeric(time(sunspot.year)),
+ Sunspots = as.numeric(sunspot.year)
+ )
>
> ggplot(sunspotyear, aes(x=Year, y=Sunspots)) + geom_area()
> ggplot(sunspotyear, aes(x=Year, y=Sunspots)) +
+ geom_area(colour="black", fill="blue", alpha=.2)
> ggplot(sunspotyear, aes(x=Year, y=Sunspots)) +
+ geom_area(fill="blue", alpha=.2) +
+ geom_line()
>
> ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) + geom_area()
> ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup)) +
+ geom_area(colour="black", size=.2, alpha=.4) +
+ scale_fill_brewer(palette="Blues", breaks=rev(levels(uspopage$AgeGroup)))
>
> ggplot(uspopage, aes(x=Year, y=Thousands, fill=AgeGroup, order=desc(AgeGroup))) +
+ geom_area(colour=NA, alpha=.4) +
+ scale_fill_brewer(palette="Blues") +
+ geom_line(position="stack", size=.2)
>
> uspopage_prop <- ddply(uspopage, "Year", transform,
+ Percent = Thousands / sum(Thousands) * 100)
>
> ggplot(uspopage_prop, aes(x=Year, y=Percent, fill=AgeGroup)) +
+ geom_area(colour="black", size=.2, alpha=.4) +
+ scale_fill_brewer(palette="Blues", breaks=rev(levels(uspopage$AgeGroup)))
>
> clim <- subset(climate, Source == "Berkeley",
+ select=c("Year", "Anomaly10y", "Unc10y"))
>
> ggplot(clim, aes(x=Year, y=Anomaly10y)) +
+ geom_ribbon(aes(ymin=Anomaly10y-Unc10y, ymax=Anomaly10y+Unc10y),
+ alpha=0.2) +
+ geom_line()
>
> ggplot(clim, aes(x=Year, y=Anomaly10y)) +
+ geom_line(aes(y=Anomaly10y-Unc10y), colour="grey50", linetype="dotted") +
+ geom_line(aes(y=Anomaly10y+Unc10y), colour="grey50", linetype="dotted") +
+ geom_line()
>
> heightweight[, c("ageYear", "heightIn")]
ageYear heightIn
1 11.92 56.3
2 12.92 62.3
3 12.75 63.3
4 13.42 59.0
5 15.92 62.5
6 14.25 62.5
7 15.42 59.0
8 11.83 56.5
9 13.33 62.0
10 11.67 53.8
11 11.58 61.5
12 14.83 61.5
13 13.08 64.5
14 12.42 58.3
15 11.92 51.3
16 12.08 58.8
17 15.92 65.3
18 12.50 59.5
19 12.25 61.3
20 15.00 63.3
21 11.75 61.8
22 11.67 53.5
23 13.67 58.0
24 14.67 61.3
25 15.42 63.3
26 13.83 61.5
27 14.58 60.8
28 15.00 59.0
29 17.50 65.5
30 12.17 56.3
31 14.17 64.3
32 13.50 58.0
33 12.42 64.3
34 11.58 57.5
35 15.50 57.8
36 16.42 61.5
37 14.08 62.3
38 14.75 61.8
39 15.42 65.3
40 15.17 58.3
41 14.42 62.8
42 13.83 59.3
43 14.00 61.5
44 14.08 62.0
45 12.50 61.3
46 15.33 62.3
47 11.58 52.8
48 12.25 59.8
49 12.00 59.5
50 14.75 61.3
51 14.83 63.5
52 16.42 64.8
53 12.17 60.0
54 12.08 59.0
55 12.25 55.8
56 12.08 57.8
57 12.92 61.3
58 13.92 62.3
59 15.25 64.3
60 11.92 55.5
61 15.25 64.5
62 15.42 60.0
63 12.33 56.3
64 12.25 58.3
65 12.83 60.0
66 13.00 54.5
67 12.00 55.8
68 12.83 62.8
69 12.67 60.5
70 15.92 63.3
71 15.83 66.8
72 11.67 60.0
73 12.33 60.5
74 15.75 64.3
75 11.92 58.3
76 14.83 66.5
77 13.67 65.3
78 13.08 60.5
79 12.25 59.5
80 12.33 59.0
81 14.75 61.3
82 14.25 61.5
83 14.33 64.8
84 15.83 56.8
85 15.25 66.5
86 11.92 61.5
87 14.92 63.0
88 15.50 57.0
89 15.17 65.5
90 15.17 62.0
91 11.83 56.0
92 13.75 61.3
93 13.75 55.5
94 12.83 61.0
95 12.50 54.5
96 12.92 66.0
97 13.58 56.5
98 11.75 56.0
99 12.25 51.5
100 17.50 62.0
101 14.25 63.0
102 13.92 61.0
103 15.17 64.0
104 12.00 61.0
105 16.08 59.8
106 11.75 61.3
107 13.67 63.3
108 15.50 63.5
109 14.08 61.5
110 14.58 60.3
111 15.00 61.3
112 13.75 64.8
113 13.08 60.5
114 12.00 57.3
115 12.50 59.5
116 12.50 60.8
117 11.58 60.5
118 15.75 67.0
119 15.25 64.8
120 12.25 50.5
121 12.17 57.5
122 13.33 60.5
123 13.00 61.8
124 14.42 61.3
125 12.58 66.3
126 11.75 53.3
127 12.50 59.0
128 13.67 57.8
129 12.75 60.0
130 17.17 68.3
132 14.67 63.8
133 14.67 65.0
134 11.67 59.5
135 15.42 66.0
136 15.00 61.8
137 12.17 57.3
138 15.25 66.0
139 11.67 56.5
140 12.58 58.3
141 12.58 61.0
142 12.00 62.8
143 13.33 59.3
144 14.83 67.3
145 16.08 66.3
146 13.50 64.5
147 13.67 60.5
148 15.50 66.0
149 11.92 57.5
150 14.58 64.0
151 14.58 68.0
152 14.58 63.5
153 14.42 69.0
154 14.17 63.8
155 14.50 66.0
156 13.67 63.5
157 12.00 59.5
158 13.00 66.3
159 12.42 57.0
160 12.00 60.0
161 12.25 57.0
162 15.67 67.3
163 14.08 62.0
164 14.33 65.0
165 12.50 59.5
166 16.08 67.8
167 13.08 58.0
168 14.00 60.0
169 11.67 58.5
170 13.00 58.3
171 13.00 61.5
172 13.17 65.0
173 15.33 66.5
174 13.00 68.5
175 12.00 57.0
176 14.67 61.5
177 14.00 66.5
178 12.42 52.5
179 11.83 55.0
180 15.67 71.0
181 16.92 66.5
182 11.83 58.8
183 15.75 66.3
184 15.67 65.8
185 16.67 71.0
186 12.67 59.5
187 14.50 69.8
188 13.83 62.5
189 12.08 56.5
190 11.92 57.5
191 13.58 65.3
192 13.83 67.3
193 15.17 67.0
194 14.42 66.0
195 12.92 61.8
196 13.50 60.0
197 14.75 63.0
198 14.75 60.5
199 14.58 65.5
200 13.83 62.0
201 12.50 59.0
202 12.50 61.8
203 15.67 63.3
204 13.58 66.0
205 14.25 61.8
206 13.50 63.0
207 11.75 57.5
208 14.50 63.0
209 11.83 56.0
210 12.33 60.5
211 11.67 56.8
212 13.33 64.0
213 12.00 60.0
214 17.17 69.5
215 13.25 63.3
216 12.42 56.3
217 16.08 72.0
218 16.17 65.3
219 12.67 60.8
220 12.17 55.0
221 11.58 55.0
222 15.50 66.5
223 13.42 56.8
224 12.75 64.8
225 16.33 64.5
226 13.67 58.0
227 13.25 62.8
228 14.83 63.8
229 12.75 57.8
230 12.92 57.3
231 14.83 63.5
232 11.83 55.0
233 13.67 66.5
234 15.75 65.0
235 13.67 61.5
236 13.92 62.0
237 12.58 59.3
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point()
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(shape=21)
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(size=1.5)
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(size=1.5)
> heightweight[, c("sex", "ageYear", "heightIn")]
sex ageYear heightIn
1 f 11.92 56.3
2 f 12.92 62.3
3 f 12.75 63.3
4 f 13.42 59.0
5 f 15.92 62.5
6 f 14.25 62.5
7 f 15.42 59.0
8 f 11.83 56.5
9 f 13.33 62.0
10 f 11.67 53.8
11 f 11.58 61.5
12 f 14.83 61.5
13 f 13.08 64.5
14 f 12.42 58.3
15 f 11.92 51.3
16 f 12.08 58.8
17 f 15.92 65.3
18 f 12.50 59.5
19 f 12.25 61.3
20 f 15.00 63.3
21 f 11.75 61.8
22 f 11.67 53.5
23 f 13.67 58.0
24 f 14.67 61.3
25 f 15.42 63.3
26 f 13.83 61.5
27 f 14.58 60.8
28 f 15.00 59.0
29 f 17.50 65.5
30 f 12.17 56.3
31 f 14.17 64.3
32 f 13.50 58.0
33 f 12.42 64.3
34 f 11.58 57.5
35 f 15.50 57.8
36 f 16.42 61.5
37 f 14.08 62.3
38 f 14.75 61.8
39 f 15.42 65.3
40 f 15.17 58.3
41 f 14.42 62.8
42 f 13.83 59.3
43 f 14.00 61.5
44 f 14.08 62.0
45 f 12.50 61.3
46 f 15.33 62.3
47 f 11.58 52.8
48 f 12.25 59.8
49 f 12.00 59.5
50 f 14.75 61.3
51 f 14.83 63.5
52 f 16.42 64.8
53 f 12.17 60.0
54 f 12.08 59.0
55 f 12.25 55.8
56 f 12.08 57.8
57 f 12.92 61.3
58 f 13.92 62.3
59 f 15.25 64.3
60 f 11.92 55.5
61 f 15.25 64.5
62 f 15.42 60.0
63 f 12.33 56.3
64 f 12.25 58.3
65 f 12.83 60.0
66 f 13.00 54.5
67 f 12.00 55.8
68 f 12.83 62.8
69 f 12.67 60.5
70 f 15.92 63.3
71 f 15.83 66.8
72 f 11.67 60.0
73 f 12.33 60.5
74 f 15.75 64.3
75 f 11.92 58.3
76 f 14.83 66.5
77 f 13.67 65.3
78 f 13.08 60.5
79 f 12.25 59.5
80 f 12.33 59.0
81 f 14.75 61.3
82 f 14.25 61.5
83 f 14.33 64.8
84 f 15.83 56.8
85 f 15.25 66.5
86 f 11.92 61.5
87 f 14.92 63.0
88 f 15.50 57.0
89 f 15.17 65.5
90 f 15.17 62.0
91 f 11.83 56.0
92 f 13.75 61.3
93 f 13.75 55.5
94 f 12.83 61.0
95 f 12.50 54.5
96 f 12.92 66.0
97 f 13.58 56.5
98 f 11.75 56.0
99 f 12.25 51.5
100 f 17.50 62.0
101 f 14.25 63.0
102 f 13.92 61.0
103 f 15.17 64.0
104 f 12.00 61.0
105 f 16.08 59.8
106 f 11.75 61.3
107 f 13.67 63.3
108 f 15.50 63.5
109 f 14.08 61.5
110 f 14.58 60.3
111 f 15.00 61.3
112 m 13.75 64.8
113 m 13.08 60.5
114 m 12.00 57.3
115 m 12.50 59.5
116 m 12.50 60.8
117 m 11.58 60.5
118 m 15.75 67.0
119 m 15.25 64.8
120 m 12.25 50.5
121 m 12.17 57.5
122 m 13.33 60.5
123 m 13.00 61.8
124 m 14.42 61.3
125 m 12.58 66.3
126 m 11.75 53.3
127 m 12.50 59.0
128 m 13.67 57.8
129 m 12.75 60.0
130 m 17.17 68.3
132 m 14.67 63.8
133 m 14.67 65.0
134 m 11.67 59.5
135 m 15.42 66.0
136 m 15.00 61.8
137 m 12.17 57.3
138 m 15.25 66.0
139 m 11.67 56.5
140 m 12.58 58.3
141 m 12.58 61.0
142 m 12.00 62.8
143 m 13.33 59.3
144 m 14.83 67.3
145 m 16.08 66.3
146 m 13.50 64.5
147 m 13.67 60.5
148 m 15.50 66.0
149 m 11.92 57.5
150 m 14.58 64.0
151 m 14.58 68.0
152 m 14.58 63.5
153 m 14.42 69.0
154 m 14.17 63.8
155 m 14.50 66.0
156 m 13.67 63.5
157 m 12.00 59.5
158 m 13.00 66.3
159 m 12.42 57.0
160 m 12.00 60.0
161 m 12.25 57.0
162 m 15.67 67.3
163 m 14.08 62.0
164 m 14.33 65.0
165 m 12.50 59.5
166 m 16.08 67.8
167 m 13.08 58.0
168 m 14.00 60.0
169 m 11.67 58.5
170 m 13.00 58.3
171 m 13.00 61.5
172 m 13.17 65.0
173 m 15.33 66.5
174 m 13.00 68.5
175 m 12.00 57.0
176 m 14.67 61.5
177 m 14.00 66.5
178 m 12.42 52.5
179 m 11.83 55.0
180 m 15.67 71.0
181 m 16.92 66.5
182 m 11.83 58.8
183 m 15.75 66.3
184 m 15.67 65.8
185 m 16.67 71.0
186 m 12.67 59.5
187 m 14.50 69.8
188 m 13.83 62.5
189 m 12.08 56.5
190 m 11.92 57.5
191 m 13.58 65.3
192 m 13.83 67.3
193 m 15.17 67.0
194 m 14.42 66.0
195 m 12.92 61.8
196 m 13.50 60.0
197 m 14.75 63.0
198 m 14.75 60.5
199 m 14.58 65.5
200 m 13.83 62.0
201 m 12.50 59.0
202 m 12.50 61.8
203 m 15.67 63.3
204 m 13.58 66.0
205 m 14.25 61.8
206 m 13.50 63.0
207 m 11.75 57.5
208 m 14.50 63.0
209 m 11.83 56.0
210 m 12.33 60.5
211 m 11.67 56.8
212 m 13.33 64.0
213 m 12.00 60.0
214 m 17.17 69.5
215 m 13.25 63.3
216 m 12.42 56.3
217 m 16.08 72.0
218 m 16.17 65.3
219 m 12.67 60.8
220 m 12.17 55.0
221 m 11.58 55.0
222 m 15.50 66.5
223 m 13.42 56.8
224 m 12.75 64.8
225 m 16.33 64.5
226 m 13.67 58.0
227 m 13.25 62.8
228 m 14.83 63.8
229 m 12.75 57.8
230 m 12.92 57.3
231 m 14.83 63.5
232 m 11.83 55.0
233 m 13.67 66.5
234 m 15.75 65.0
235 m 13.67 61.5
236 m 13.92 62.0
237 m 12.58 59.3
> ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex)) + geom_point()
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex)) + geom_point()
> ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex, colour=sex)) +
+ geom_point()
> ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex, colour=sex)) +
+ geom_point() +
+ scale_shape_manual(values=c(1,2)) +
+ scale_colour_brewer(palette="Set1")
> ggplot(heightweight, aes(x=ageYear, y=heightIn)) + geom_point(shape=3)
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn, shape=sex)) +
+ geom_point(size=3) + scale_shape_manual(values=c(1, 4))
> hw$weightGroup <- cut(hw$weightLb, breaks=c(-Inf, 100, Inf),
+ labels=c("< 100", ">= 100"))
Error in cut(hw$weightLb, breaks = c(-Inf, 100, Inf), labels = c("< 100", :
object 'hw' not found
>
> hw <- heightweight
> hw$weightGroup <- cut(hw$weightLb, breaks=c(-Inf, 100, Inf),
+ labels=c("< 100", ">= 100"))
>
> ggplot(hw, aes(x=ageYear, y=heightIn, shape=sex, fill=weightGroup)) +
+ geom_point(size=2.5) +
+ scale_shape_manual(values=c(21, 24)) +
+ scale_fill_manual(values=c(NA, "black"),
+ guide=guide_legend(override.aes=list(shape=21)))
> heightweight[, c("sex", "ageYear", "heightIn", "weightLb")]
sex ageYear heightIn weightLb
1 f 11.92 56.3 85.0
2 f 12.92 62.3 105.0
3 f 12.75 63.3 108.0
4 f 13.42 59.0 92.0
5 f 15.92 62.5 112.5
6 f 14.25 62.5 112.0
7 f 15.42 59.0 104.0
8 f 11.83 56.5 69.0
9 f 13.33 62.0 94.5
10 f 11.67 53.8 68.5
11 f 11.58 61.5 104.0
12 f 14.83 61.5 103.5
13 f 13.08 64.5 123.5
14 f 12.42 58.3 93.0
15 f 11.92 51.3 50.5
16 f 12.08 58.8 89.0
17 f 15.92 65.3 107.0
18 f 12.50 59.5 78.5
19 f 12.25 61.3 115.0
20 f 15.00 63.3 114.0
21 f 11.75 61.8 85.0
22 f 11.67 53.5 81.0
23 f 13.67 58.0 83.5
24 f 14.67 61.3 112.0
25 f 15.42 63.3 101.0
26 f 13.83 61.5 103.5
27 f 14.58 60.8 93.5
28 f 15.00 59.0 112.0
29 f 17.50 65.5 140.0
30 f 12.17 56.3 83.5
31 f 14.17 64.3 90.0
32 f 13.50 58.0 84.0
33 f 12.42 64.3 110.5
34 f 11.58 57.5 96.0
35 f 15.50 57.8 95.0
36 f 16.42 61.5 121.0
37 f 14.08 62.3 99.5
38 f 14.75 61.8 142.5
39 f 15.42 65.3 118.0
40 f 15.17 58.3 104.5
41 f 14.42 62.8 102.5
42 f 13.83 59.3 89.5
43 f 14.00 61.5 95.0
44 f 14.08 62.0 98.5
45 f 12.50 61.3 94.0
46 f 15.33 62.3 108.0
47 f 11.58 52.8 63.5
48 f 12.25 59.8 84.5
49 f 12.00 59.5 93.5
50 f 14.75 61.3 112.0
51 f 14.83 63.5 148.5
52 f 16.42 64.8 112.0
53 f 12.17 60.0 109.0
54 f 12.08 59.0 91.5
55 f 12.25 55.8 75.0
56 f 12.08 57.8 84.0
57 f 12.92 61.3 107.0
58 f 13.92 62.3 92.5
59 f 15.25 64.3 109.5
60 f 11.92 55.5 84.0
61 f 15.25 64.5 102.5
62 f 15.42 60.0 106.0
63 f 12.33 56.3 77.0
64 f 12.25 58.3 111.5
65 f 12.83 60.0 114.0
66 f 13.00 54.5 75.0
67 f 12.00 55.8 73.5
68 f 12.83 62.8 93.5
69 f 12.67 60.5 105.0
70 f 15.92 63.3 113.5
71 f 15.83 66.8 140.0
72 f 11.67 60.0 77.0
73 f 12.33 60.5 84.5
74 f 15.75 64.3 113.5
75 f 11.92 58.3 77.5
76 f 14.83 66.5 117.5
77 f 13.67 65.3 98.0
78 f 13.08 60.5 112.0
79 f 12.25 59.5 101.0
80 f 12.33 59.0 95.0
81 f 14.75 61.3 81.0
82 f 14.25 61.5 91.0
83 f 14.33 64.8 142.0
84 f 15.83 56.8 98.5
85 f 15.25 66.5 112.0
86 f 11.92 61.5 116.5
87 f 14.92 63.0 98.5
88 f 15.50 57.0 83.5
89 f 15.17 65.5 133.0
90 f 15.17 62.0 91.5
91 f 11.83 56.0 72.5
92 f 13.75 61.3 106.5
93 f 13.75 55.5 67.0
94 f 12.83 61.0 122.5
95 f 12.50 54.5 74.0
96 f 12.92 66.0 144.5
97 f 13.58 56.5 84.0
98 f 11.75 56.0 72.5
99 f 12.25 51.5 64.0
100 f 17.50 62.0 116.0
101 f 14.25 63.0 84.0
102 f 13.92 61.0 93.5
103 f 15.17 64.0 111.5
104 f 12.00 61.0 92.0
105 f 16.08 59.8 115.0
106 f 11.75 61.3 85.0
107 f 13.67 63.3 108.0
108 f 15.50 63.5 108.0
109 f 14.08 61.5 85.0
110 f 14.58 60.3 86.0
111 f 15.00 61.3 110.5
112 m 13.75 64.8 98.0
113 m 13.08 60.5 105.0
114 m 12.00 57.3 76.5
115 m 12.50 59.5 84.0
116 m 12.50 60.8 128.0
117 m 11.58 60.5 87.0
118 m 15.75 67.0 128.0
119 m 15.25 64.8 111.0
120 m 12.25 50.5 79.0
121 m 12.17 57.5 90.0
122 m 13.33 60.5 84.0
123 m 13.00 61.8 112.0
124 m 14.42 61.3 93.0
125 m 12.58 66.3 117.0
126 m 11.75 53.3 84.0
127 m 12.50 59.0 99.5
128 m 13.67 57.8 95.0
129 m 12.75 60.0 84.0
130 m 17.17 68.3 134.0
132 m 14.67 63.8 98.5
133 m 14.67 65.0 118.5
134 m 11.67 59.5 94.5
135 m 15.42 66.0 105.0
136 m 15.00 61.8 104.0
137 m 12.17 57.3 83.0
138 m 15.25 66.0 105.5
139 m 11.67 56.5 84.0
140 m 12.58 58.3 86.0
141 m 12.58 61.0 81.0
142 m 12.00 62.8 94.0
143 m 13.33 59.3 78.5
144 m 14.83 67.3 119.5
145 m 16.08 66.3 133.0
146 m 13.50 64.5 119.0
147 m 13.67 60.5 95.0
148 m 15.50 66.0 112.0
149 m 11.92 57.5 75.0
150 m 14.58 64.0 92.0
151 m 14.58 68.0 112.0
152 m 14.58 63.5 98.5
153 m 14.42 69.0 112.5
154 m 14.17 63.8 112.5
155 m 14.50 66.0 108.0
156 m 13.67 63.5 108.0
157 m 12.00 59.5 88.0
158 m 13.00 66.3 106.0
159 m 12.42 57.0 92.0
160 m 12.00 60.0 117.5
161 m 12.25 57.0 84.0
162 m 15.67 67.3 112.0
163 m 14.08 62.0 100.0
164 m 14.33 65.0 112.0
165 m 12.50 59.5 84.0
166 m 16.08 67.8 127.5
167 m 13.08 58.0 80.5
168 m 14.00 60.0 93.5
169 m 11.67 58.5 86.5
170 m 13.00 58.3 92.5
171 m 13.00 61.5 108.5
172 m 13.17 65.0 121.0
173 m 15.33 66.5 112.0
174 m 13.00 68.5 114.0
175 m 12.00 57.0 84.0
176 m 14.67 61.5 81.0
177 m 14.00 66.5 111.5
178 m 12.42 52.5 81.0
179 m 11.83 55.0 70.0
180 m 15.67 71.0 140.0
181 m 16.92 66.5 117.0
182 m 11.83 58.8 84.0
183 m 15.75 66.3 112.0
184 m 15.67 65.8 150.5
185 m 16.67 71.0 147.0
186 m 12.67 59.5 105.0
187 m 14.50 69.8 119.5
188 m 13.83 62.5 84.0
189 m 12.08 56.5 91.0
190 m 11.92 57.5 101.0
191 m 13.58 65.3 117.5
192 m 13.83 67.3 121.0
193 m 15.17 67.0 133.0
194 m 14.42 66.0 112.0
195 m 12.92 61.8 91.5
196 m 13.50 60.0 105.0
197 m 14.75 63.0 111.0
198 m 14.75 60.5 112.0
199 m 14.58 65.5 114.0
200 m 13.83 62.0 91.0
201 m 12.50 59.0 98.0
202 m 12.50 61.8 118.0
203 m 15.67 63.3 115.5
204 m 13.58 66.0 112.0
205 m 14.25 61.8 112.0
206 m 13.50 63.0 91.0
207 m 11.75 57.5 85.0
208 m 14.50 63.0 112.0
209 m 11.83 56.0 87.5
210 m 12.33 60.5 118.0
211 m 11.67 56.8 83.5
212 m 13.33 64.0 116.0
213 m 12.00 60.0 89.0
214 m 17.17 69.5 171.5
215 m 13.25 63.3 112.0
216 m 12.42 56.3 72.0
217 m 16.08 72.0 150.0
218 m 16.17 65.3 134.5
219 m 12.67 60.8 97.0
220 m 12.17 55.0 71.5
221 m 11.58 55.0 73.5
222 m 15.50 66.5 112.0
223 m 13.42 56.8 75.0
224 m 12.75 64.8 128.0
225 m 16.33 64.5 98.0
226 m 13.67 58.0 84.0
227 m 13.25 62.8 99.0
228 m 14.83 63.8 112.0
229 m 12.75 57.8 79.5
230 m 12.92 57.3 80.5
231 m 14.83 63.5 102.5
232 m 11.83 55.0 76.0
233 m 13.67 66.5 112.0
234 m 15.75 65.0 114.0
235 m 13.67 61.5 140.0
236 m 13.92 62.0 107.5
237 m 12.58 59.3 87.0
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=weightLb)) + geom_point()
> ggplot(heightweight, aes(x=ageYear, y=heightIn, size=weightLb)) + geom_point()
> ggplot(heightweight, aes(x=weightLb, y=heightIn, fill=ageYear)) +
+ geom_point(shape=21, size=2.5) +
+ scale_fill_gradient(low="black", high="white")
>
> ggplot(heightweight, aes(x=weightLb, y=heightIn, fill=ageYear)) +
+ geom_point(shape=21, size=2.5) +
+ scale_fill_gradient(low="black", high="white", breaks=12:17,
+ guide=guide_legend())
>
> ggplot(heightweight, aes(x=ageYear, y=heightIn, size=weightLb, colour=sex)) +
+ geom_point(alpha=.5) +
+ scale_size_area() + # Make area proportional to numeric value
+ scale_colour_brewer(palette="Set1")
>
> sp <- ggplot(diamonds, aes(x=carat, y=price))
>
> sp + geom_point()
>
> sp + geom_point(alpha=.1)
> sp + geom_point(alpha=.01)
> sp + stat_bin2d()
>
> sp + stat_bin2d(bins=50) +
+ scale_fill_gradient(low="lightblue", high="red", limits=c(0, 6000))
>
> library(hexbin)
Warning message:
package ‘hexbin’ was built under R version 3.5.2
> library(hexbin)
> sp + stat_binhex() +
+ scale_fill_gradient(low="lightblue", high="red",
+ limits=c(0, 8000))
>
> sp + stat_binhex() +
+ scale_fill_gradient(low="lightblue", high="red",
+ breaks=c(0, 250, 500, 1000, 2000, 4000, 6000),
+ limits=c(0, 6000))
>
> sp1 <- ggplot(ChickWeight, aes(x=Time, y=weight))
>
> sp1 + geom_point()
>
> sp1 + geom_point(position="jitter")
> sp1 + geom_point(position=position_jitter(width=.5, height=0))
>
> sp1 + geom_boxplot(aes(group=Time))
> sp <- ggplot(heightweight, aes(x=ageYear, y=heightIn))
>
> sp + geom_point() + stat_smooth(method=lm)
> sp + geom_point() + stat_smooth(method=lm, level=0.99)
> sp + geom_point() + stat_smooth(method=lm, se=FALSE)
>
> sp + geom_point(colour="grey60") +
+ stat_smooth(method=lm, se=FALSE, colour="black")
>
> sp + geom_point(colour="grey60") + stat_smooth()
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
> sp + geom_point(colour="grey60") + stat_smooth(method=loess)
>
> library(MASS)
> b <- biopsy
> b$classn[b$class=="benign"] <- 0
> b$classn[b$class=="malignant"] <- 1
>
> ggplot(b, aes(x=V1, y=classn)) +
+ geom_point(position=position_jitter(width=0.3, height=0.06), alpha=0.4,
+ shape=21, size=1.5) +
+ stat_smooth(method=glm, family=binomial)
Warning: Ignoring unknown parameters: family
>
> sps <- ggplot(heightweight, aes(x=ageYear, y=heightIn, colour=sex)) +
+ geom_point() +
+ scale_colour_brewer(palette="Set1")
>
> sps + geom_smooth()
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
>
> sps + geom_smooth(method=lm, se=FALSE, fullrange=TRUE)
> model <- lm(heightIn ~ ageYear + I(ageYear^2), heightweight)
>
> xmin <- min(heightweight$ageYear)
>
> xmax <- max(heightweight$ageYear)
> predicted <- data.frame(ageYear=seq(xmin, xmax, length.out=100))
>
> predicted$heightIn <- predict(model, predicted)
> sp <- ggplot(heightweight, aes(x=ageYear, y=heightIn)) +
+ geom_point(colour="grey40")
>
> sp + geom_line(data=predicted, size=1)
>
> predictvals <- function(model, xvar, yvar, xrange=NULL, samples=100, ...) {
+ if (is.null(xrange)) {
+ if (any(class(model) %in% c("lm", "glm")))
+ xrange <- range(model$model[[xvar]])
+ else if (any(class(model) %in% "loess"))
+ xrange <- range(model$x)
+ }
+
+ newdata <- data.frame(x = seq(xrange[1], xrange[2], length.out = samples))
+ names(newdata) <- xvar
+ newdata[[yvar]] <- predict(model, newdata = newdata, ...)
+ newdata
+ }
> modlinear <- lm(heightIn ~ ageYear, heightweight)
> modloess <- loess(heightIn ~ ageYear, heightweight)
>
> lm_predicted <- predictvals(modlinear, "ageYear", "heightIn")
> loess_predicted <- predictvals(modloess, "ageYear", "heightIn")
> sp + geom_line(data=lm_predicted, colour="red", size=.8) +
+ geom_line(data=loess_predicted, colour="blue", size=.8)
> md <- data.frame(deaths = as.numeric(mdeaths),
+ month = as.numeric(cycle(mdeaths)))
> md <- ddply(md, "month", summarise, deaths = mean(deaths))
> p <- ggplot(md, aes(x=month, y=deaths)) + geom_line() +
+ scale_x_continuous(breaks=1:12)
> p + coord_polar()
>
> p + coord_polar() + ylim(0, max(md$deaths))
> mdx <- md[md$month==12, ]
>
> mdx$month <- 0
> mdnew <- rbind(mdx, md)
>
> p %+% mdnew + coord_polar() + ylim(0, max(md$deaths))
>
> str(economics)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 574 obs. of 6 variables:
$ date : Date, format: ...
$ pce : num 507 510 516 513 518 ...
$ pop : int 198712 198911 199113 199311 199498 199657 199808 199920 200056 200208 ...
$ psavert : num 12.5 12.5 11.7 12.5 12.5 12.1 11.7 12.2 11.6 12.2 ...
$ uempmed : num 4.5 4.7 4.6 4.9 4.7 4.8 5.1 4.5 4.1 4.6 ...
$ unemploy: int 2944 2945 2958 3143 3066 3018 2878 3001 2877 2709 ...
> ggplot(economics, aes(x=date, y=psavert)) + geom_line()
>
> econ <- subset(economics, date >= as.Date("1992-05-01") &
+ date < as.Date("1993-06-01"))
> p <- ggplot(econ, aes(x=date, y=psavert)) + geom_line()
> datebreaks <- seq(as.Date("1992-06-01"), as.Date("1993-06-01"), by="2 month")
>
> p + scale_x_date(breaks=datebreaks) +
+ theme(axis.text.x = element_text(angle=30, hjust=1))
> library(scales)
Warning message:
package ‘scales’ was built under R version 3.5.2
> library(scales)
> p + scale_x_date(breaks=datebreaks, labels=date_format("%Y %b")) +
+ theme(axis.text.x = element_text(angle=30, hjust=1))
>
> www <- data.frame(minute = as.numeric(time(WWWusage)),
+ users = as.numeric(WWWusage))
> timeHM_formatter <- function(x) {
+ h <- floor(x/60)
+ m <- floor(x %% 60)
+ lab <- sprintf("%d:%02d", h, m) # Format the strings as HH:MM
+ return(lab)
+ }
>
> ggplot(www, aes(x=minute, y=users)) + geom_line()
> ggplot(www, aes(x=minute, y=users)) + geom_line() +
+ scale_x_continuous(name="time", breaks=seq(0, 100, by=10),
+ labels=timeHM_formatter)
> scale_x_continuous(breaks=c(0, 20, 40, 60, 80, 100),
+ labels=c("0:00", "0:20", "0:40", "1:00", "1:20", "1:40"))
<ScaleContinuousPosition>
Range:
Limits: 0 -- 1
> timeHM_formatter(c(0, 50, 51, 59, 60, 130, 604))
[1] "0:00" "0:50" "0:51" "0:59" "1:00" "2:10"
[7] "10:04"
>
> timeHMS_formatter <- function(x) {
+ h <- floor(x/3600)
+ m <- floor((x/60) %% 60)
+ s <- round(x %% 60) # Round to nearest second
+ lab <- sprintf("%02d:%02d:%02d", h, m, s)
+ lab <- sub("^00:", "", lab) # Remove leading 00: if present
+ lab <- sub("^0", "", lab) # Remove leading 0 if present
+ return(lab)
+ }
> islice <- subset(isabel, z == min(z))
>
> ggplot(islice, aes(x=x, y=y)) +
+ geom_segment(aes(xend = x + vx/50, yend = y + vy/50),
+ size = 0.25)
%20(1).webp)
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