Again I've stumbled across a different problem. I want to assign different stat_smooth to different facets. In this case, i want to keep Walking and Transit fabrics as they are but make Automobile quadratic.
Below is my data:
commutedistance carcommute Fabric
3 11.3 35.9 Walking
4 8.0 84.0 Walking
7 7.9 28.3 Transit
8 6.0 82.6 Transit
11 7.1 23.3 Walking
12 4.8 63.0 Walking
15 9.3 34.2 Walking
16 6.1 80.7 Walking
19 8.7 31.1 Walking
20 7.2 76.1 Walking
23 10.1 29.9 Walking
24 8.1 78.4 Walking
27 9.5 32.2 Walking
28 7.0 79.8 Walking
31 11.3 31.7 Walking
32 9.3 82.9 Walking
35 10.4 34.1 Walking
36 8.2 79.7 Walking
39 11.2 28.5 Walking
40 8.9 76.3 Walking
43 13.3 32.2 Walking
44 10.4 81.9 Walking
47 14.7 36.6 Walking
48 11.3 85.5 Walking
51 11.3 34.4 Walking
52 8.9 83.3 Walking
55 9.7 36.7 Walking
56 6.5 91.0 Walking
59 9.7 33.8 Walking
60 6.5 82.5 Walking
63 12.8 47.0 Walking
64 8.8 89.9 Walking
67 13.2 41.1 Walking
68 9.6 87.8 Walking
71 12.5 33.6 Walking
72 8.4 90.4 Walking
75 11.4 38.9 Walking
76 8.4 86.7 Walking
79 10.5 32.1 Walking
80 7.3 84.4 Walking
83 10.8 32.2 Walking
84 7.5 88.7 Walking
87 11.0 32.9 Walking
88 7.5 87.5 Walking
91 11.0 41.2 Transit
92 7.5 89.1 Transit
95 11.5 42.6 Walking
96 6.8 87.6 Walking
99 11.8 42.2 Walking
100 8.8 84.2 Walking
103 12.7 40.2 Walking
104 7.7 88.5 Walking
107 12.0 59.9 Automobile
108 11.8 76.9 Automobile
111 9.9 59.4 Automobile
112 12.2 75.8 Automobile
115 10.5 57.0 Walking
116 11.4 67.1 Transit
119 10.9 56.9 Walking
120 9.6 76.2 Walking
123 12.7 67.9 Transit
124 12.2 78.7 Transit
127 11.7 60.7 Transit
128 11.8 75.2 Transit
131 12.1 50.6 Walking
132 11.9 81.5 Walking
135 13.8 61.0 Transit
136 12.9 76.5 Transit
139 12.6 67.2 Transit
140 14.2 83.5 Transit
143 6.9 60.7 Automobile
144 4.6 49.7 Automobile
147 13.0 70.4 Automobile
148 6.4 87.4 Automobile
151 13.2 61.6 Automobile
152 9.3 80.1 Automobile
155 12.7 62.7 Transit
156 10.6 79.6 Transit
175 14.1 50.4 Transit
176 9.6 67.0 Transit
191 14.4 50.1 Transit
192 8.2 83.1 Transit
195 13.0 51.0 Walking
196 8.7 75.1 Walking
207 12.0 52.1 Walking
208 8.8 78.3 Walking
223 13.0 46.0 Transit
224 7.0 77.7 Transit
227 10.1 74.0 Automobile
228 5.9 80.7 Automobile
231 12.1 62.4 Automobile
232 15.3 88.1 Automobile
235 12.2 66.4 Transit
236 9.8 81.6 Transit
247 15.6 59.8 Transit
248 10.9 81.5 Transit
267 14.4 52.4 Automobile
268 9.5 83.6 Automobile
271 13.1 55.0 Transit
272 8.4 80.5 Transit
275 15.2 61.5 Automobile
276 10.4 82.3 Automobile
279 12.3 73.5 Transit
280 7.1 89.5 Transit
283 10.5 74.0 Automobile
284 8.5 81.8 Automobile
287 16.1 50.6 Transit
288 9.4 79.0 Transit
291 13.4 60.5 Automobile
292 11.3 82.8 Automobile
295 13.6 55.3 Automobile
296 9.3 77.8 Automobile
299 17.5 68.7 Automobile
300 13.1 82.1 Automobile
315 14.9 69.9 Automobile
316 9.1 86.6 Automobile
319 8.7 78.9 Automobile
320 5.8 73.9 Automobile
323 8.9 76.8 Automobile
324 8.7 76.3 Automobile
327 13.4 72.0 Automobile
328 12.6 86.4 Automobile
331 13.7 74.1 Automobile
332 10.8 86.3 Automobile
335 10.9 74.6 Automobile
336 7.8 78.6 Automobile
339 13.6 65.0 Automobile
340 8.7 90.5 Automobile
343 7.5 73.2 Automobile
344 7.6 79.0 Automobile
351 11.5 73.6 Automobile
352 4.9 63.0 Automobile
355 5.6 74.7 Automobile
356 4.3 72.7 Automobile
359 7.7 77.4 Automobile
360 7.5 79.2 Automobile
My code also looks like this:
library(tidyverse)
library(ggpubr)
df %>%
ggplot(aes(commutedistance, carcommute, color = Fabric, shape = Fabric)) +
geom_point(size = 3.5)+
stat_smooth(method = "lm", se = FALSE) +
facet_wrap(~Fabric, ncol=2, scales = "free")+
theme(strip.text.x = element_text(size = 12, colour = "black", angle = 0, face = "bold"))+
theme(axis.title.x = element_text(color="black", size=12, face="bold"),
axis.title.y = element_text(color="black", size=12, face="bold"))+
theme(axis.text.x = element_text(colour = "black", size = 12),
axis.text.y = element_text(colour = "black", size = 12))+
scale_y_continuous(labels = function(x) paste0(x, "%"))+
labs(x = "Commute Distance (km)",
y = "Share of commuters getting to work by car")+
theme(legend.position = c(1, 0),
legend.justification = c(1, 0))+
stat_cor(
aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")),
label.x = 5
)+
stat_regline_equation(label.x = 10, label.y = 90)+
scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
After running the code above,I've got the following graph. How can keep transit and walking as linear regression models and convert automobile regression into quadratic. I would greatly appreciate your input!!!
