You can think of a data frame as something akin to a database table or an Excel spreadsheet. It has a specific number of columns, each of which is expected to contain values of a particular type. It also has an indeterminate number of rows - sets of related values for each column.
#bind variables to a dataframe:
d <- data.frame(weights, prices, types)
#access one column of the dataframe:
d$prices
[1] 9000 5000 12000 7500 18000# Importing data
piracy <- read.csv("piracy.csv")
gdp <- read.table("gdp.txt", sep=" ", header=TRUE)
#Merging
d <- merge(x = gdp, y = piracy)
# merge two data frames by ID
total <- merge(gdp,piracy,by="ID")
# http://www.statmethods.net/management/merging.html
#change data format
as.integer(d$gdp)#show how often an element occurs
> table(d$cat_short)
bks blg isch nws shop vid web
8170 35 40 6214 111 8 25 33832
# Barplot this table
> barplot(table(mtcars$cyl))
#Plotting GDP against Piracy rate:
> plot(d$GDP, d$Piracy)#Finding correlation coefficient:
cor.test(d$GDP, d$Piracy)
-0.8203183
#Calculating the linear model for piracy rate by GDP
#Assign it to the line variable & draw it:
line <- lm(d$Piracy ~ d$GDP)
abline(line)