We have our own data

survey <- read.table("survey1-tidy.txt")

We want to tell something about them

• Counting them
• Locating them
• Describe them
• Tell a story

There are many. You have to explore and learn

So far we have seen:

• length()
• min(), max(), range()
• head(), tail()
• summary()
• table()

• Each value has a different position in the horizontal axis
• The vector’s index is a number from 1 to length(vector)
• The vertical axis represent the value of the element
• So if vector[3] contains the value 170, we will have a point at the coordinates (3,170)

plot(survey$height_cm)

There are several ways to specify the color

The easiest one is to use a number

Each point can have a different color. You use a vector of the same lenght as the data

Something like this

plot(1:8, col=1:8)

The parameter cex means character expansion

Each point can have a different size

You use a vector of the same lenght as the data

plot(1:8, cex=1:8)

The parameter pch means plot character

Each point can have a different symbol. You use a vector of the same lenght as the data

Plot char can be chosen by a number

plot(1:25, pch=1:25)

The parameter pch means plot character

Each point can have a different symbol. You use a vector of the same lenght as the data

Plot char can be also chosen by a letter

plot(1:7, pch=c("A", "T", "a", "t", ".", "0", "1"))

Notice that:

• If the number of points is big, using pch="." is faster and it is understood better
• pch=1 is different from pch="1"
• We can use the vector LETTERS and letters to transform numbers into letters
• A plot with too many symbols is hard to understand
  • It is better to simplify the message

The type depends on the story you want to tell

• Lines are mostly used to tell a story of change through time
• Using both or over is better to see the indivudual points in the line
• If you do not specify, the default is type="p"
• When there are many values, it is better to use points
  • The screen has approx 2000 points horizontally
  • The projector has 1000 points
  • If length(vector)>300, better use type="p"

Plotting a vector of type factor produces a barlplot

Each bar size corresponds to

• the frequency of each factor level
• that is, counting how many times for each level

• Numeric vectors are shown element by element
  • bars starts at 0
  • hard to see when the vector length is large
• Factor vectors are shown as a “table”
  • i.e. the frequency of each value
• Can we do the same for a numeric vector?
  • all values are different
  • we have to group them in “similar” sets

Remember that we can use cut() to make a factor vector from numeric values. We need to say how many groups we want

cut(survey$weight_kg, 10)

 [1] (61.5,67.9] (55.2,61.5] (55.2,61.5] (93.3,99.7]
 [5] (55.2,61.5] (74.2,80.6] (55.2,61.5] (74.2,80.6]
 [9] (74.2,80.6] (99.7,106]  (55.2,61.5] (67.9,74.2]
[13] (55.2,61.5] (48.9,55.2] (74.2,80.6] (48.9,55.2]
[17] (99.7,106]  (67.9,74.2] (67.9,74.2] (61.5,67.9]
[21] (74.2,80.6] (42.4,48.9] (48.9,55.2] (67.9,74.2]
[25] (55.2,61.5] (55.2,61.5] (48.9,55.2] (42.4,48.9]
[29] (61.5,67.9] (61.5,67.9] (67.9,74.2] (67.9,74.2]
[33] (48.9,55.2] (48.9,55.2] (55.2,61.5] (48.9,55.2]
[37] (48.9,55.2] (55.2,61.5] (74.2,80.6] (48.9,55.2]
[41] (80.6,86.9] (48.9,55.2] (48.9,55.2] (67.9,74.2]
[45] (61.5,67.9] (61.5,67.9] (48.9,55.2] (80.6,86.9]
[49] (61.5,67.9] (74.2,80.6] (74.2,80.6]
10 Levels: (42.4,48.9] (48.9,55.2] ... (99.7,106]

Now we have a factor that we can plot

plot(cut(survey$weight_kg, 10))

Numeric data can be grouped into classes

• The default number of classes is automatic, but you can change it

• Frequency means “how many times”

Histogram bars are not separated

• This is because numerical values are continuous, and there is no “space” between them