rm(list = ls()) #remove ~ everything in the working environment
myX<-1:10
myY<-jitter(1:10)
adjust<-lm(myY ~ myX)  # Use adjust<-lm(myY ~ 0 + myX) to force the intercept at 0
plot(myX,myY,abline(adjust))

The first line is to clear the working environment (e.g. delete variables) and I recommend to use it everytime you start a new script. The next two lines are used to generate 2 vectors (myX and myY). Note that I use <- and not = to assign a value to a variable (= works nicely here but it is best not to use it. See here for some explanations). myX is a vector containing 10 integer values (from 1 to 10), myY is myX with some noise (using the function jitter). We can have a look at these two vectors just by typing their names.

myX
##  [1]  1  2  3  4  5  6  7  8  9 10
myY
##   [1] 0.9119364 1.8619898 2.8056814 4.1016091 4.8022902 5.8452231 7.1772783 7.9499693
##   [9] 9.1782965 9.9774281
Because jitter add random values, you will produce different numbers everytime you run this function. The function lm (linear model) is used for fitting. The result is passed into a new variable (here adjust but I could use any other names) and then (line 5), we make a simple plot (myY versus myX plus the result of the fit). Note that I use the function abline, which adds a simple line to any existing plot.

It is easy to get the estimated parameters for the Slope (0.9843) and the Intercept (0.1337). Just type:

adjust
##
## Call:
## lm(formula = myY ~ myX)
##
## Coefficients:
## (Intercept)          myX
##     -0.1653        1.0230

The Coefficient of determination (which can be used to asses the goodness of fit for a linear model) is:

summary(adjust)[8]
## \$r.squared
## [1] 0.9983183