#This is a command that clears everything out of the "environment" giving us a blank slate: rm(list=ls()) 4+3 sqrt(4) sqrt(4) + 2 #This Line is commented out #sqrt(4 #) # # sqrt(4) +2 # sqrt(4) +2 #Division 5/3 #Multiplication 5*3 #Exponents 5^3 #Using parentheses: 5*(3+3) #Incorrect (Need the multiplication sign) #5(3*3) result <- 5+3 result result + 2 batman <- 5+3 batman batman <- "Bruce Wayne" batman #Batman #(Uncomment the below) #batman/8 Result <- "8" #Uncomment the below line #Result/8 8^.5 sqrt(8) #Yes sqrt(result) #No #sqrt(Result) nchar(batman) class(Result) class(result) class(sqrt) #(These are not all the classes) world.pop <- c(2525,3026,3691,4449,5320,6127,6916) world.pop pop1 <- c(2525,3026,3691) pop2 <- c(4449,5320,6127,6916) world.pop <- c(pop1, pop2) #The second item in world.pop world.pop[2] #The third entry in world.pop world.pop[3] world.pop[c(2,5)] world.pop[c(2,3,4)] 2:4 1:25 world.pop[2:4] world.pop world.pop[-3] world.pop*1000000 world.pop/world.pop[1] pop.rate <- world.pop/world.pop[1] pop.rate year <- c(1950,1960,1970,1980,1990,2000,2010) c(year, world.pop, pop.rate) cbind(year,world.pop,pop.rate) #We can, of course, save this: population.table <- cbind(year,world.pop,pop.rate) population.table emissions <- c(6,9.33,14.83,19.37,22.7,25.12,33.13) cbind(year,world.pop,pop.rate, emissions) population.table <- cbind(population.table, emissions) population.table[3,1] population.table[1,3] population.table[1,1:3] population.table[1, ] population.table[ ,2] mean(population.table[,2]) min(population.table[,2]) max(population.table[,2]) range(population.table[,2]) sum(population.table[,2]) world.pop2 <- c(2525,3026,3691,4449,6127,6916) world.pop2 <- c(2525,3026,3691,4449,0,6127,6916) population.table[4,2:3] population.table[4,2:3] <- NA population.table mean(population.table[,2]) mean(population.table[,2],na.rm=TRUE) #?mean population.table[4,2:3] <- c(4449,1.76198) population.table #install.packages("rio") #Note that rio is *not* in quotation marks library(rio) #If it prompts you to you should also un-comment and run: #install_formats() college <- import("https://github.com/marctrussler/IDS-Data/raw/main/college.Rds") education <- import("https://github.com/marctrussler/IDS-Data/raw/main/education.Rds") income <- import("https://github.com/marctrussler/IDS-Data/raw/main/income.Rds") race <- import("https://github.com/marctrussler/IDS-Data/raw/main/race.Rds") # #You will have to paste your own filepath into the import command # pa.counties <- import("C:/Users/trussler-adm/PORES Dropbox/PORES/PSCI1800/Data/PACounties.Rds") getwd() # setwd("C:/Users/trussler-adm/PORES Dropbox/PORES/PSCI1800/Data") # pa.counties <- import("PACounties.Rds") # #Load some other data from this same folder: # acs <- import("ACSCountyData.csv") # if(Sys.info()["user"]=="206652XXX"){ # setwd("C:/Users/206652XXX/PORES Dropbox/Marc Trussler/election-day-turnout/modeling-turnout/GeneralElectionTurnoutModel24") # } else { # setwd("~/PORES Dropbox/Marc Trussler/election-day-turnout/modeling-turnout/GeneralElectionTurnoutModel24") # } # # big.data <- rio::import("https://github.com/marctrussler/IIS-Data/raw/refs/heads/main/FullSamples.rds") # rm(big.data) mean(population.table[,2]) median(population.table[,2]) sd(population.table[,2]) summary(population.table[,2]) head(college) mean(college) median(college) table(college) head(table(income)) hist(college) head(race) mean(race) table(race) barplot(table(race)) table(race)/1000 prop.table(table(race)) head(education) table(education) #And use a barplot to visualize the relative frequency barplot(table(education)) head(income) mean(income) median(income) sd(income) summary(income) #table(income) boxplot(income) density(income) plot(density(income)) population.table boxplot(population.table[,4]) plot(density(population.table[,4])) #?plot plot(population.table[,1], population.table[,2]) #plot(population.table[,1], population.table[-1,2]) plot(population.table[,1], population.table[,2], main="Global Population Growth", xlab="Year", ylab="Population (Millions)") plot(population.table[,1], population.table[,2], main="Global Population Growth", xlab="Year", ylab="Population (Millions)", pch=16) plot(seq(1,25), seq(1,25), pch=seq(1,25)) plot(population.table[,1], population.table[,2], main="Global Population Growth", xlab="Year", ylab="Population (Millions)", pch=16, col="darkorange") plot(population.table[,1], population.table[,2], main="Global Population Growth", xlab="Year", ylab="Population (Millions)", pch=16, col="darkorange", type="b") plot(population.table[,3], population.table[,4], xlab="Population Growth vs. 1950", ylab="Co2 Emissions (billions of tons)", main="Population Growth vs. Co2 Emissions") plot(population.table[,3], population.table[,4], xlab="Population Growth vs. 1950", ylab="Co2 Emissions (billions of tons)", main="Population Growth vs. Co2 Emissions") text(population.table[,3], population.table[,4], labels=population.table[,1]) plot(population.table[,3], population.table[,4], xlab="Population Growth vs. 1950", ylab="Co2 Emissions (billions of tons)", main="Population Growth vs. Co2 Emissions", type="n") text(population.table[,3], population.table[,4], labels=population.table[,1]) pa.counties <- import("https://github.com/marctrussler/IDS-Data/raw/main/PACounties.Rds") head(pa.counties) #Basic plot(pa.counties[,1], pa.counties[,3]) #Full Graph plot(pa.counties[,1], pa.counties[,3], main="Relationship between Population and Median Income in PA", xlab="Population", ylab="Median Income", pch=16, col="Darkblue") 2+2 x <- 2 x+2 plot(c(0,1),c(0,1)) abline(0,1) plot(c(0,1),c(0,1)) # #None of these variables exist so usually this would error out # #But eval=F is on so it won't actually be run # c+2*v^2 # pa.counties <- import("Data/PACounties.Rds"