Code from demo 1

## 1.1 How to use R

## operators
# simple mathematical operations work as you would expect
1+1
5*10
2^3+2
2^(3+2)

## variables
x <- 1
x
# R is a bit dumb, so make sure you type the variable name exactly as it is 
x1

# a variable only remembers the latest value you assigned to it
x <- 2 
x
# variable does not change its value unless you expressly tell it to
2*x
x 
x <- x*2
x
# you can also capture the result in another variable
y <- x^2
y 

## functions
# function call comprises of function itself ('nchar' below), arguments('abcd'), which can be given directly or from a variable,
# and output which in the first example gets printed on the screen and on the second gets assigned to variable n
nchar('abcd')
letters <- 'abcd'
n <- nchar(letters)
n

# a function does not need to take arguments 
ls()

# You can find the documentation of the function by running ? + function name, from the 'Help' tab in the bottom right panel or
# by googling (you can use 'cran' instead of R for better google hits)
?ls()

# some arguments are location based and some (usually optional arguments) need to be specified 
log(10)           # from help we see that the default base is e
log(10, base=2)   # we can use base-argument to define another base if we'd like

# functions can be picky with respect to what kinds of arguments they accept
log('a')

## 1.2. Basic data types (+ one data structure)

# data types

typeof(x)
typeof('x')
typeof(TRUE)
typeof(5+2i)

# more about logical data type
5 > 6
4 < 1000
5 >= 5
7 <= -2
x == 4
x != y
# logical operators can also be chained using & (logical AND) and | (logical inclusive OR)
# you can make the pipe | sign with option + 7 on Mac and ctrl + alt + >< key on Windows
x >= 2 & x < 5

# vector, a very simple data structure 
vect <- rnorm(10)   # if you run this at home, you will see a slightly different set of numbers since they are random
vect
mean(vect)
sd(vect)
vect <- rnorm(100)
mean(vect)
sd(vect)
vect+1
mean(vect + 1) 

#vector indexing

vect >=0
vect[4]
vect[1:3]
vect[101]            # we will discuss NA more in future lessons!
vect[vect >= 0]

## 1.3 loops
# a very simple loop may look like this
for(j in 1:5){
  print(j)
}

# loops can (and often do) have a lot more in the middle

z <- c(2,5,3,9,8,11,6)
currentsum <- 0

# we loop over all the values in z
for(value in z){
  # if the value is even (modulo 2 is 0) 
  if(value %% 2 == 0){ # the %% in the middle is the operator for modulo
    # add the value to the currentsum variable
    currentsum <- currentsum + value
  }
  # print currentsum each time, regardless of whether we changed it
  print(currentsum)
}

# 1.4 Packages 
# you can install packages from the graphical user interface (tools -> install packages) or from the command line with
install.packages('psych')
install.packages('ggplot2')

# you only need to download packages once per computer you use
# however, at the beginning of each R session you need to tell R which packages you want to use
library(ggplot2)

 

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Created by Juulia Suvilehto
juulia.suvilehto@iki.fi

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License