ggpmisc和gginnards两个包包含了ggplot2很多扩展的功能。

介绍其中几个实用函数。

代码语言：javascript代码运行次数：0运行复制#install.packages("gginnards")

#install.packages("ggpmisc")

library(gginnards)

library(ggpmisc)

stat_poly_eq可以直接获得曲线拟合的方程，p值，决定系数(R^2)， AIC和BIC等指标，不需要自己计算和标注了。不过只能做多项式拟合lm()的结果。

代码语言：javascript代码运行次数：0运行复制##例子

# generate artificial data

set.seed(4321)

x <- 1:100

y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)

my.data <- data.frame(x = x, y = y,

group = c("A", "B"),

y2 = y * c(0.5,2),

w = sqrt(x))

# give a name to a formula

formula <- y ~ poly(x, 3, raw = TRUE)

# user specified label

ggplot(my.data, aes(x, y)) +

geom_point() +

geom_smooth(method = "lm", formula = formula) +

stat_poly_eq(aes(label = paste(stat(eq.label),

stat(adj.rr.label),

stat(p.value.label),

sep = "*\", \"*")),

formula = formula,

rr.digits = 3, coef.digits = 4,

parse = TRUE)

geom_linked_text花式加标签

代码语言：javascript代码运行次数：0运行复制##例子

my.cars <- mtcars[c(TRUE, FALSE, FALSE, FALSE), ]

my.cars$name <- rownames(my.cars)

p <- ggplot(my.cars, aes(wt, mpg, label = name))

p +

geom_point() +

geom_linked_text(aes(colour = factor(cyl)),

hjust = -0.04, nudge_x = 0.12,

vjust = -0.2, nudge_y = 0.5,

size = 4,

check_overlap = TRUE, # avoid overlap

arrow = arrow(length = grid::unit(1.5, "mm"))) +

expand_limits(x = 6.2)

geom_table插入表格

代码语言：javascript代码运行次数：0运行复制##例子

library(dplyr)

library(tibble)

mtcars %>%

group_by(cyl) %>%

summarize(wt = mean(wt), mpg = mean(mpg)) %>%

ungroup() %>%

mutate(wt = sprintf("%.2f", wt),

mpg = sprintf("%.1f", mpg)) -> tb

df <- tibble(x = 5.45, y = 34, tb = list(tb))

ggplot(mtcars, aes(wt, mpg, colour = factor(cyl))) +

geom_point() +

geom_table(data = df, aes(x = x, y = y, label = tb),

table.rownames = TRUE, table.theme = ttheme_gtstripes)

stat_fit_tb对多种模型进行拟合并返回拟合结果; 对拟合结果做ANOVA

代码语言：javascript代码运行次数：0运行复制library(broom)

# data for examples

x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)

covariate <- sqrt(x) + rnorm(9)

group <- factor(c(rep("A", 4), rep("B", 5)))

my.df <- data.frame(x, group, covariate)

# Linear regression fit summary, by default

ggplot(my.df, aes(covariate, x)) +

geom_point() +

stat_fit_tb(digits = 2, p.digits = 4) +

expand_limits(y = 70)

其他还有很多功能，自行探索吧~