#设置图形属性group=1表示绘制通过所有点的单个线性模型ggplot(mtcars,aes(x=wt,y=mpg,col=cyl))+geom_point()+geom_smooth(method="lm",se=FALSE)+geom_smooth(aes(group=1),method="lm",se=FALSE,linetype=2)ggplot(mtcars,aes(x=wt,y=mpg,col=factor(cyl)))+geom_point()+stat_smooth(method="l...
Test Plan: Load Testing Aes Parameters Thread Group: Number of Threads: 100 Ramp-Up Period: 1 Loop Count: 10 HTTP Request: URL: "/path/to/aes/api" Method: "GET" 1. 2. 3. 4. 5. 6. 7. 8. 在排错中,有时需要修复方案来解决错误。使用代码diff,我们可以清楚地看到修复前后的不同之处。
library(ggplot2) # 创建一个包含变量的数据框 data <- data.frame( x = c(1, 2, 3, 4), y = c(10, 20, 30, 40), group = c("A", "A", "B", "B") ) # 定义一个变量,用于替换图形属性 color_var <- "group" # 使用变量替换创建散点图 ggplot(data, aes(x = x, y = y, ...
## 1 AAPL 0.0213 0.0725## 2 AMZN 0.0320 0.0800## 3 FB 0.0339 0.0900## 4 GOOG 0.0198 0.0568## 5 NFLX 0.0614 0.157 计算收益的年均值和标准差。 %>% group_by(symbol, year) %>% summarise(mean = mean(returns), sd = sd(returns)) ## # A tibble: 30 x 4## # Groups: symbol [?] ...
group_by(category) %>% summarise(across(where(is.numeric), mean, na.rm =TRUE, .names ="{col}_")) # 自定义一个汇总函数 summarizer <-function(data, numeric_cols =NULL,...) { data %>% group_by(...) %>% summarise(across({{numeric_cols}}, list( ...
R语言 解密AES算法 1. 概述在网络通信中,经常会用到加解密技术,其中AES加解密算法是比较广泛的应用于大块数据的对称加解密,本文主要介绍算法算法算法
# plot 1: Density of price for each type of cut of the diamond: ggplot(data=diamonds,aes(x=price, group=cut, fill=cut)) + geom_density(adjust=1.5) #adjust起到了调节曲线拟合程度的一个作用,默认参数为1 # plot 2: 归一化后的叠加图: ggplot(data=diamonds,aes(x=price, group=cut, fill...
geom_text(aes(service,share+1,label=paste0(share,"%"),group=year), position=position_dodge(width=0.9),size=3)+ scale_fill_manual(values=c(`2020` ="red3", `2021` ="black")) + scale_color_manual(values=c(`2020` ="red3", `2021` ="black")) + ...
group = class), size = 1, alpha = .5, position = "jitter", shape = 21, col = "white")+ geom_smooth(aes(x = extrav, y = popular, col = high_and_low, group = class, size = as.factor(high_and_low), alpha = as.factor(high_and_low)), ...
## 1 AAPL 0.00100 0.0153## 2 AMZN 0.00153 0.0183## 3 FB 0.00162 0.0202## 4 GOOG 0.000962 0.0141## 5 NFLX 0.00282 0.0300 group_by(symbol) %>% summarise(mean = mean(returns), sd = sd(returns)) ## # A tibble: 5 x 3## symbol mean sd ...