R中带分格中值曲线/线的散点图
我正在尝试制作一个散点图，将V2和V3绘制成两种不同的颜色，我可以使用下面的代码来完成R中带分格中值曲线/线的散点图,r,R,我正在尝试制作一个散点图，将V2和V3绘制成两种不同的颜色，我可以使用下面的代码来完成 Dataset %>% filter(V2 < 1700) %>% ggplot(aes(x=V3, y=V2, col=V5))+ geom_point(alpha=0.3) 不知道如何让它看起来不那么糟糕 Turbine_Power_002 Wind_Speed_002 Status 1670.233 10.614 good 1662.4911 10.72
Dataset %>%
filter(V2 < 1700) %>%
ggplot(aes(x=V3, y=V2, col=V5))+
geom_point(alpha=0.3)
Turbine_Power_002 Wind_Speed_002 Status
1670.233 10.614 good
1662.4911 10.7256 good
1669.3992 10.7985 good
1676.4406 10.7571 good
1670.4106 10.7594 good
1667.5694 10.8875 good
1676.624 11.0847 good
1678.1306 10.9093 good
1672.9572 10.7633 good
1679.0921 10.553 good
1673.1359 9.6269 good
1676.1989 9.6936 good
1671.6978 9.4339 good
1672.3488 9.1243 good
1670.723 9.3979 good
1667.0172 9.3083 good
1660.8016 8.6243 good
1666.5119 8.55 good
1664.0723 8.653 good
1662.4677 8.7922 good
1660.4893 8.7225 good
1660.5033 8.6905 good
1658.2013 8.553 good
1662.0981 8.5556 good
1655.6329 8.5773 good
1652.6624 8.6682 good
1651.0581 8.6355 good
1648.85 8.6509 good
1652.0266 8.6767 good
1651.4511 8.4967 good
1649.2938 8.4006 good
1650.1171 8.5276 good
1660.2227 8.694 good
1661.316 9.2125 good
1666.9459 8.5619 good
1636.5026 8.4786 good
1513.7669 7.9269 good
1328.3449 7.461 good
1177.9153 7.2388 good
1388.2277 7.1903 good
1361.618 7.2447 good
1528.8687 7.3363 good
1578.7937 7.3915 good
1617.7642 7.1909 good
1610.1726 6.7161 bad
1525.3414 6.3915 bad
1357.1954 6.4324 bad
1378.4442 6.1211 bad
1154.6986 6.2195 bad
1164.8143 6.3502 good
878.4577 6.2055 good
845.1663 6.1375 good
839.8225 5.3411 bad
740.9848 5.3231 bad
717.6297 5.9587 good
629.4423 6.3726 bad
681.7375 6.2558 bad
501.3443 6.1907 bad
542.5272 6.1772 bad
618.3111 6.207 bad
599.9095 6.4053 bad
680.3778 6.5109 bad
724.3055 6.3652 bad
566.6395 6.7921 bad
634.0534 6.5656 bad
759.797 6.3933 bad
728.6984 6.3548 bad
752.8808 6.4828 bad
841.8173 6.5872 bad
1184.8236 6.5717 good
1597.3052 7.4201 good
1582.1896 7.3904 good
1507.2633 7.3151 good
1669.0799 7.5944 good
1675.5728 7.4698 good
1619.1406 8.1251 good
1631.349 7.4671 good
1473.9408 7.7898 good
1493.4729 7.4669 good
1301.4209 7.4444 good
1312.0625 7.5537 good
1550.2915 7.7127 good
1663.0816 7.8666 good
1689.5697 7.7562 good
1690.6906 8.1654 good
1689.2514 8.3819 good
1691.266 8.4663 good
1685.1839 8.3663 good
1687.3187 8.8485 good
1684.73 9.5838 good
1683.7781 9.396 good
1688.4965 9.5373 good
1687.2317 9.3826 good
1692.5478 9.3901 good
1689.7046 9.4029 good
1688.2174 9.4286 good
您可以独立于
状态library(tidyverse)
Breaks=seq(0, ceiling(max(d$Wind_Speed_002)), .5)
d %>%
mutate(bins=.bincode(Wind_Speed_002,
breaks = Breaks)) %>%
mutate(bins=factor(bins,levels = seq_along(Breaks), labels = Breaks)) %>%
group_by(bins) %>%
mutate(Med=median(Turbine_Power_002)) %>%
ungroup() %>%
mutate(bins2=as.numeric(as.character(bins))) %>%
ggplot()+
geom_point(aes(x=Wind_Speed_002, y=Turbine_Power_002, col=Status), alpha=0.3) +
geom_segment(aes(x=bins2, xend=bins2+0.5, y=Med, yend=Med), size=1.5)
请显示一些数据，粘贴
dput（数据集）dput（Dataset）V3、V2和V5library(tidyverse)
Breaks=seq(0, ceiling(max(d$Wind_Speed_002)), .5)
d %>%
mutate(bins=.bincode(Wind_Speed_002,
breaks = Breaks)) %>%
mutate(bins=factor(bins,levels = seq_along(Breaks), labels = Breaks)) %>%
group_by(bins) %>%
mutate(Med=median(Turbine_Power_002)) %>%
ungroup() %>%
mutate(bins2=as.numeric(as.character(bins))) %>%
ggplot()+
geom_point(aes(x=Wind_Speed_002, y=Turbine_Power_002, col=Status), alpha=0.3) +
geom_segment(aes(x=bins2, xend=bins2+0.5, y=Med, yend=Med), size=1.5)