# R is a versatile program. At its simplest, it can be
# a simple calculator:
> 2 + 2
[1] 4
> (2 + 2)^2
[1] 16

# It is possible to create and save variables in R. Here,
# for example, we create a variable called "x" which contains
# the integers from 1 to 10:

> x <- 1:10
> x
 [1]  1  2  3  4  5  6  7  8  9 10

# If we perform an arithmetic operation on such a variable, R
# will apply the operation to every element of the variable:

> 2*x
 [1]  2  4  6  8 10 12 14 16 18 20
> x^2
 [1]   1   4   9  16  25  36  49  64  81 100

# Here's another variable:

> y <- c(1, pi, 32, 1634)
> y
[1]    1.000000    3.141593   32.000000 1634.000000
> sqrt(y)
[1]  1.000000  1.772454  5.656854 40.422766

# Most R commands are simple, short, and intuitive. For example,
# if we want the mean of a variable (i.e., the simple arithmetic
# average), the command is mean():

> mean(x)
[1] 5.5
> mean(y)
[1] 417.5354

# The command for standard deviation is sd():

> sd(x)
[1] 3.02765

# Note that R commands are case sensitive. So SD() won't work:

> SD(x)
Error in SD(x) : could not find function "SD"

# It might be reasonable to think that mode() would give us
# the most frequently occuring value of a variable. But R actually
# means something else by mode():

> mode(x)
[1] "numeric"

# R is telling us that the variable x consists of numbers. Here,
# we create another variable where the values consist of alphabetic
# characters:

> names <- c("Jack","Kyle")

# R knows that these are not numbers:

> mode(names)
[1] "character"

# Because they aren't numbers, it doesn't make sense to try to
# do operations such as mean():

> mean(names)
[1] NA
Warning message:
In mean.default(names) : argument is not numeric or logical: returning NA

# Another mode that R has is "logical." If I enter names=="Jack" I am asking
# R whether each value of names is "Jack". That's true only for the first one:

> names=="Jack"
[1]  TRUE FALSE

# And the mode of such a variable (i.e., the one that contains TRUE, FALSE here)
# is logical:

> mode(names=="Jack")
[1] "logical"

# We can see what variables we have in our workspace using the ls() command
# (short for "list"):

> ls()
[1] "names" "x"     "y"    

# If I specify a particular working directory, R will save my data space
# in that directory in a file called ".Rdata". If I start R next time by
# double clicking on that file, I will get the same R space I left, not
# including any variables that I have in other R spaces. This is useful for
# avoiding clutter and keeping a limited number of variables that are
# relevant to a particular project (such as this class).

> setwd("c:/users/jvevea/desktop/Classes/105/2021 Spring")
>