## ----风格，echo = false，结果='Asis'-----------------------------------------------------生物焦质:: markdown（）选项（width = 100，max.print = 1000）选项（usefancyquotes = false）knitr ::opts_chunk $ set（eval = as.logical（sys.getenv（sys.getenv（“true”）），cache = as.logical（sys.getenv（“knitr_cache”，“true”）），错误= false）##----------------------------------------------------------------------------------------------------------------------------------------------：10）Rnorm（1:10）摘要（Rnorm（1:10））## -----------------------------------------------------------------------------------------------------------------数据（虹膜）＃查找花瓣.Width正好0.2虹膜[虹膜$ petal.width == 0.2，]＃查找萼片的那些行.Length小于4.5虹膜[虹膜$ sepal.length <4.5，]＃查找属于setosa setosa_iris的所有行=虹膜[虹膜$ speias ==“setosa”，] dim（setosa_iris）## -------------------------------------------------------------------------------------------------------------------------删除包含的列夏洛cters i.e., Species iris <- iris[,!( names(iris) %in% "Species")] dim(iris) # find the mean of the first 4 numerical columns lapply(iris, mean) # simpler: colMeans(iris) # simplify the result sapply(iris, mean) # find the mean for each row. apply(iris, 1 , mean) #simpler : rowMeans(iris) ## ------------------------------------------------------------------------------------------------- # define a vector x <- rnorm(1000) # vectorized calculation y <- x + rnorm(1000, sd=.8) # object construction df <- data.frame(x=x, y=y) # linear model fit <- lm(y ~ x, df) ## ------------------------------------------------------------------------------------------------- par(mfrow=c(1,2)) plot(y ~ x, df, cex.lab=2) abline(fit, col="red", lwd=2) library(ggplot2) ggplot(df, aes(x, y)) + geom_point() + stat_smooth(method="lm") ## ----sessionInfo---------------------------------------------------------------------------------- sessionInfo()