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> library(tidyr)
> library(ggplot2)
> mtcars %>% gather(-mpg, -hp, -cyl, key = "var", value = "value") %>% ggpl
ot(aes(x = value, y = mpg, color = hp, shape = factor(cyl))) + geom_point()
+ facet_wrap(~ var, scales = "free") + theme_bw()
>
am carb disp
drat gear qsec
vs wt
10
15
20
25
30
35
10
15
20
25
30
35
10
15
20
25
30
35
10
15
20
25
30
35
10
15
20
25
30
35
10
15
20
25
30
35
10
15
20
25
30
35
10
15
20
25
30
35
0.00 0.25 0.50 0.75 1.00 2 4 6 8 100 200 300 400
3.0 3.5 4.0 4.5 5.0 3.0 3.5 4.0 4.5 5.0 16 18 20 22
0.00 0.25 0.50 0.75 1.00 2 3 4 5
value
mpg
factor(cyl)
4
6
8
100
150
200
250
300
hp
> mtcars %>% gather(-mpg, key = "var", value = "value") %>% ggplot(aes(x =
value, y = mpg)) + geom_point() + stat_smooth() + facet_wrap(~ var, scales
= "free") + theme_bw()
am carb cyl disp
drat gear hp qsec
vs wt
10
15
20
25
30
35
10
20
30
10
15
20
25
30
35
10
15
20
25
30
35
10
20
30
40
10
15
20
25
30
35
10
20
30
10
15
20
25
30
35
10
15
20
25
30
35
10
20
30
0.000.250.500.751.00 2 4 6 8 4 5 6 7 8 100 200 300 400
3.0 3.5 4.0 4.5 5.0 3.0 3.5 4.0 4.5 5.0 100 200 300 16 18 20 22
0.000.250.500.751.00 2 3 4 5
value
mpg

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library(tidyr) and library(ggplot2)

  • 1. > library(tidyr) > library(ggplot2) > mtcars %>% gather(-mpg, -hp, -cyl, key = "var", value = "value") %>% ggpl ot(aes(x = value, y = mpg, color = hp, shape = factor(cyl))) + geom_point() + facet_wrap(~ var, scales = "free") + theme_bw() > am carb disp drat gear qsec vs wt 10 15 20 25 30 35 10 15 20 25 30 35 10 15 20 25 30 35 10 15 20 25 30 35 10 15 20 25 30 35 10 15 20 25 30 35 10 15 20 25 30 35 10 15 20 25 30 35 0.00 0.25 0.50 0.75 1.00 2 4 6 8 100 200 300 400 3.0 3.5 4.0 4.5 5.0 3.0 3.5 4.0 4.5 5.0 16 18 20 22 0.00 0.25 0.50 0.75 1.00 2 3 4 5 value mpg factor(cyl) 4 6 8 100 150 200 250 300 hp
  • 2. > mtcars %>% gather(-mpg, key = "var", value = "value") %>% ggplot(aes(x = value, y = mpg)) + geom_point() + stat_smooth() + facet_wrap(~ var, scales = "free") + theme_bw() am carb cyl disp drat gear hp qsec vs wt 10 15 20 25 30 35 10 20 30 10 15 20 25 30 35 10 15 20 25 30 35 10 20 30 40 10 15 20 25 30 35 10 20 30 10 15 20 25 30 35 10 15 20 25 30 35 10 20 30 0.000.250.500.751.00 2 4 6 8 4 5 6 7 8 100 200 300 400 3.0 3.5 4.0 4.5 5.0 3.0 3.5 4.0 4.5 5.0 100 200 300 16 18 20 22 0.000.250.500.751.00 2 3 4 5 value mpg