Examples from lesson number 1 - Intro to R - presented by Rich FitzJohn

Vector types

Rich just told us that in R we have 5 different types of vector.

x <- TRUE       # x becomes TRUE
x <- 1          # x now has a value of 1 (overwritten the line above)
x <- c(1, TRUE) # x now contains two values of 1, because a vector always stores the same mode of elements
x

## [1] 1 1

x <- c(1, TRUE, '1') #now all values are characters, because there is a underlying hierarchy. For instance, if there is one element of mode 'character' in your vector, then all elements will be treated as so
x

## [1] "1"    "TRUE" "1"

Lists

If you want to store the different modes of elements in one single object, you use lists.

x <- list(1, TRUE, "1")
x

## [[1]]
## [1] 1
## 
## [[2]]
## [1] TRUE
## 
## [[3]]
## [1] "1"

Querrying

You can ask yourself about the nature of an unknown vector - suppose you did not know what x is:

storage.mode(x)

## [1] "list"

typeof(x)

## [1] "list"

class(x)

## [1] "list"

Examples from lesson number 2 - Writing functions - presented by Rich FitzJohn

Playing with the basics

When you write your function, you define it based on arguments

f <- function(something) {
    something
}
f(100)

## [1] 100

The function f() is what we call the identity function, i.e., the function returns the exact input. In this particular case, the argument something took the value 100. You could instead define an object before, and then calling the function on that object, which should do exactly the same thing.

x  <-  100 #x now is a numeric vector of one element of value 100
f(x)

## [1] 100

It returned exactly the same thing. In this case, something took the value of x, which was 100.

Downloading the gapminder data

We have to download a .csv file that we will use for the rest of our course. Right-click this link and save it to some new directory. Let's play with it now.

data  <-  read.csv('gapminder-FiveYearData.csv', header=TRUE, stringsAsFactors=FALSE)
head(data) #try the function tail() instead, what happened?

##       country year      pop continent lifeExp gdpPercap
## 1 Afghanistan 1952  8425333      Asia   28.80     779.4
## 2 Afghanistan 1957  9240934      Asia   30.33     820.9
## 3 Afghanistan 1962 10267083      Asia   32.00     853.1
## 4 Afghanistan 1967 11537966      Asia   34.02     836.2
## 5 Afghanistan 1972 13079460      Asia   36.09     740.0
## 6 Afghanistan 1977 14880372      Asia   38.44     786.1

#subset the data for year 1982 only
data.1982  <-  data[data$year == 1982, ]

#let's create a function to compute the arithmetic average of a vector
average  <-  function(x) {
    sum(x) / length(x)
}

average(data.1982$gdpPercap)

## [1] 7519

#same as 
sum(data.1982$gdpPercap) / length(data.1982$gdpPercap)

## [1] 7519

Notice that repeating things may be problematic; first because it increases your chances of typos; second, if for some reason you change the name of your columns in dat, you will have to manually change all these names, which will be quite annoying.

Making a plot

col.table  <-  c(Asia='tomato',
                 Europe='chocolate4',
                 Africa='dodgerblue2',
                 Americas='darkgoldenrod1',
                 Oceania='green4')

for (year in unique(data$year))
    my.plot(year, data, col.table)