2/6/24

data(iris) head(iris)

years<-c(1980,1980,1985,1990) scores<-c(34,44,56,83) df<-data.frame(years,scores) df[,1] df$years df[df$scores<50,] df[df$years==1980,“scores”]

df$initial=c(‘d’,‘b’,‘s’,‘u’) dim(df) df

subject_name<-c(“John Doe”,“Jane Doe”,“Steve Graves”) temperature<-c(98.1,98.6,101.4) flu_status<-c(FALSE,FALSE,TRUE)

temperature[2] temperature[2:3] temperature[-2]

subject_name[2] subject_name[2:3] subject_name[-2]

gender<-factor(c(“MALE”,“FEMALE”,“MALE”)) gender blood<-factor(c(“O”,“AB”,“A”),levels=c(“A”,“B”,“AB”,“O”))

symptoms<-factor(c(“SEVERE”,“MILD”,“MODERATE”), levels=c(“MILD”,“MODERATE”,“SEVERE”), ordered=TRUE) symptoms

pt_data<-data.frame(subject_name,temperature,flu_status,gender,blood,symptoms, stringsAsFactors=FALSE) pt_data

pt_data$subject_name pt_data[1,2] pt_data[,1] pt_data[c(1,3),c(2,4)] pt_data[,] pt_data[c(1,3),c(“temperature”,“gender”)]

pt_data$temp_c<-(pt_data$temperature-32)*(5/9) pt_data pt_data[c(“temperature”,“temp_c”)]

?plot x<-rnorm(100) y<-rnorm(100) plot(x,y) plot(x,y,xlab=“This is the x-axis”,ylab=“This is the y-axis”,main=“Plot of x vs. y”)

library(tidyverse) library(ggplot2) library(dplyr)

mpg head(mpg) tail(mpg)

ggplot2::mpg

ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy))

ggplot(data=mpg)+ geom_point(mapping=aes(x=displ,y=cty)) ggplot(data=mpg)+ geom_point(mapping=aes(x=displ,y=cyl))

ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy,color=class)) ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy,size=class)) ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy,alpha=class)) ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy,shape=class)) ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy),color=“purple”)

ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy))+facet_wrap(~class,nrow=2) ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy))+facet_wrap(~cty,nrow=3) ggplot(data=mpg)+geom_point(mapping=aes(x=displ,y=hwy))+facet_grid(drv~cyl)