这是我制作的。不确定它是否接近您正在寻找的东西,但我正在使用 ggpubr 包来解决这些问题:
# Load ggpubr library for scatterplot:
library(ggpubr)
# Create temperature by population data frame:
cold_temp <- c(3,5,2,1,0,20)
hot_temp <- c(70,60,80,50,99,80)
pop <- c(500, 600, 200, 400, 300, 100)
temps <- data.frame(cold_temp,hot_temp, pop)
# Create scatterplot colored by temps:
ggscatter(temps,
x="pop",
y=c("hot_temp", "cold_temp"),
merge = T)
创建此图:
Can decorate it more with this code:
# Create scatterplot colored by temps:
ggscatter(temps,
x="pop",
y=c("hot_temp", "cold_temp"),
merge = T)+
labs(title = "Average Temperature High/Cold by Population",
subtitle = "Scatterplot Using GGPUBR Package",
caption = "Data obtained from (insert place).",
x="Population",
y="Temperature (F)")+
theme_bw()+
theme(plot.title = element_text(face = "bold"),
plot.caption = element_text(face = "italic"))
这是什么原因:
正如 Vishal 已经指出的那样,由于没有数据存在,这会更容易一些,因为您可能会考虑那里的数据。例如,您可以像这样使用 pivot_longer:
# Load tidyverse for "pivot_longer" function:
library(tidyverse)
# Pivot data:
pivot_temp <- temps %>%
pivot_longer(cols = c(hot_temp,cold_temp),
names_to = "Temperature_Type",
values_to = "Fahrenheit")
# Make faceted plot:
ggscatter(pivot_temp,
x="pop",
y="Fahrenheit",
color = "Temperature_Type",
palette = "jco")+
facet_wrap(~Temperature_Type)+
labs(title = "Average Temperature High/Cold by Population",
subtitle = "Scatterplot Using GGPUBR Package",
caption = "Data obtained from (insert place).",
x="Population",
y="Temperature (F)")+
theme_bw()+
theme(plot.title = element_text(face = "bold"),
plot.caption = element_text(face = "italic"))
这是什么原因:
也可以添加行:
# Make faceted plot:
ggscatter(pivot_temp,
x="pop",
y="Fahrenheit",
color = "Temperature_Type",
palette = "jco",
merge = T)+
geom_line(aes(color=Temperature_Type))+
labs(title = "Average Temperature High/Cold by Population",
subtitle = "Scatterplot Using GGPUBR Package",
caption = "Data obtained from (insert place).",
x="Population",
y="Temperature (F)")+
theme_bw()+
theme(plot.title = element_text(face = "bold"),
plot.caption = element_text(face = "italic"))
这使得这条线和散点图看起来更好看: