library(tidyverse)
基礎(chǔ)作圖
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
分別設(shè)好 x y軸及數(shù)據(jù)集即可
image.png
加顏色
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class))
image.png
大小
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, size = class))
image.png
透明度和形狀
# 將車型class映射給透明度
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, alpha = class))
image.png
# 將車型class映射給形狀
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, shape = class))
image.png
（4）手動(dòng)設(shè)置圖形屬性
# 例：所有點(diǎn)設(shè)為藍(lán)色
# （注意：color="blue"在aes（）外）
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
image.png
（5）stroke-輪廓
# 適用于散點(diǎn)圖约啊，21-24號(hào)形狀
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, stroke = 3),shape=21)
image.png
5.分面
# （1）依據(jù)單個(gè)變量分面 facet_wrap() #分子圖
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ class, nrow = 2) #分兩行展示
# nrow指定分面后顯示幾行
# ncol指定分面后顯示幾列
# 注意~分面依據(jù)必須是離散型變量特碳。
image.png
（2）依據(jù)兩個(gè)變量分面 facet_grid()
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ cyl)
# 不需要指定nrow和ncol扣猫。
image.png
(3)不想在行或列維度中分面晋南，用.代替變量名
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(. ~ cyl)
image.png
6.幾何對(duì)象
# 也就是圖的不同類型寄摆，如點(diǎn)圖、折線圖愉粤、直方圖等杀迹。
# （1）理解分組
#
# 將一個(gè)圖形屬性映射為一個(gè)離散型變量，ggplot2就會(huì)自動(dòng)對(duì)數(shù)據(jù)進(jìn)行分組來(lái)繪制多個(gè)幾何對(duì)象校镐。這種形式是隱式分組亿扁，不需要添加圖例和區(qū)分特征。
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy))
image.png
# 將線性映射為drv（驅(qū)動(dòng)方式鸟廓，d,f,4）就會(huì)自動(dòng)變成三條線型不同的線从祝。
# 將顏色映射為drv，就會(huì)自動(dòng)變成三條顏色不用的線引谜。
#分組
ggplot(data = mpg) +
geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))
image.png
隱式分組-線型
ggplot(data = mpg) +
geom_smooth(
mapping = aes(x = displ, y = hwy, linetype = drv),
)
image.png
隱式分組-顏色
ggplot(data = mpg) +
geom_smooth(
mapping = aes(x = displ, y = hwy, color = drv),
show.legend = FALSE #不顯示圖例
)
image.png
（2）同一張圖顯示多個(gè)幾何對(duì)象--局部映射和全局映射
# --這里涉及到圖層啦牍陌。
# 局部映射-映射只對(duì)改圖層有效
#
# 有多個(gè)幾何對(duì)象時(shí)，映射語(yǔ)句要重復(fù)多次员咽，又丑又麻煩毒涧。
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
geom_smooth(mapping = aes(x = displ, y = hwy))
#全局映射--對(duì)所有圖層生效
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth()
# 局部映射與全局映射沖突時(shí)，服從局部映射骏融。
例如:
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(mapping = aes(color = class)) +
geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)
# 如果出現(xiàn)報(bào)錯(cuò)链嘀，請(qǐng)library(dplyr) 或library(tidyverse)
# Error in class == "subcompact" : comparison (1) is possible only for atomic and list types
# 這個(gè)報(bào)錯(cuò)是因?yàn)閒ilter函數(shù)出自dplyr包，不加載就不能用~
# ps：關(guān)于se=FALSE
#
# se是standard error的縮寫(xiě)档玻，se參數(shù)為擬合曲線添加標(biāo)準(zhǔn)誤差帶怀泊，也就是那個(gè)灰不啦嘰的灰色背景帶，默認(rèn)是TRUE误趴。
參考
[R數(shù)據(jù)科學(xué)--詳解ggplot2](https://mp.weixin.qq.com/s?__biz=MzU4NjU4ODQ2MQ==&mid=2247484642&idx=1&sn=182a3a95a2dca006bdcb977bb8f53763&chksm=fdf84ca0ca8fc5b6a1982341ba0c659554c669146f75438f7a3c39eaac9171679d66059640ab&scene=21#wechat_redirect)