Working with Data

Scalars

In [1]:

n <- 3.14
s <- 'c'
b <- TRUE

In [2]:

typeof(n)

Out[2]:

'double'

In [3]:

typeof(s)

Out[3]:

'character'

In [4]:

typeof(b)

Out[4]:

'logical'

Vectors

Vectors are 1D collections of the same scalar type.

In [5]:

xs <- c(1, 0.5, 0.25)
ss <- c('G', 'A', 'T', 'T', 'A', 'C', 'A')
bs <- c(T, T, F, F, T, T, F, F)

In [6]:

xs

Out[6]:

1. 1
2. 0.5
3. 0.25

In [7]:

ss

Out[7]:

1. 'G'
2. 'A'
3. 'T'
4. 'T'
5. 'A'
6. 'C'
7. 'A'

In [8]:

bs

Out[8]:

1. TRUE
2. TRUE
3. FALSE
4. FALSE
5. TRUE
6. TRUE
7. FALSE
8. FALSE

Extracting a single element

In [9]:

xs[1]

Out[9]:

1

Extracting elments with a position vector

In [10]:

ss[2:5]

Out[10]:

1. 'A'
2. 'T'
3. 'T'
4. 'A'

Extracting elemnents wiht a logical vector

In [11]:

ss[bs]

Out[11]:

1. 'G'
2. 'A'
3. 'A'
4. 'C'

Extracting elements with a logical condition

In [12]:

ss[ss %in% c('A', 'T')]

Out[12]:

1. 'A'
2. 'T'
3. 'T'
4. 'A'
5. 'A'

Matrices and Arrays

Like vecorrs, only in 2D (matrices) or more (arrays).

In [13]:

m <- matrix(1:12, ncol=4)

In [14]:

m

Out[14]:

1	4	7	10
2	5	8	11
3	6	9	12

In [15]:

m[6:10]

Out[15]:

1. 6
2. 7
3. 8
4. 9
5. 10

In [16]:

m[m < 10]

Out[16]:

1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9

In [17]:

m[2,]

Out[17]:

1. 2
2. 5
3. 8
4. 11

In [18]:

m[,2]

Out[18]:

1. 4
2. 5
3. 6

Work!

Try to solve the following problems without searching the web. You can use the built-in help() function.

Create the following \(3 \times 3\) matrix and save in a variable called A.

• Row 1 = 4, 5, 6
• Row 2 = 1, 2, 3
• Row 3 = 7, 8, 9

In [ ]:

What is the sum of all the numbers in A?

In [ ]:

Create a vector of the column sums in A using the colSums function.

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Create a vector of the row sums in A using the apply function.

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What is the sum of the numbers in bottom right \(2 \times2\) block (i.e the numbers 2, 3, 8, 9)

In [ ]:

Lists

In [19]:

ls <- list(dna=ss, ispurine=ss %in% c('A', 'G'))

In [20]:

ls

Out[20]:

$dna

1. 'G'
2. 'A'
3. 'T'
4. 'T'
5. 'A'
6. 'C'
7. 'A'

$ispurine

1. TRUE
2. TRUE
3. FALSE
4. FALSE
5. TRUE
6. FALSE
7. TRUE

Extracting a sublist from a list

In [21]:

ls[1]

Out[21]:

$dna =

1. 'G'
2. 'A'
3. 'T'
4. 'T'
5. 'A'
6. 'C'
7. 'A'

In [22]:

class(ls[1])

Out[22]:

'list'

Extracting an element from a list

In [23]:

ls$dna

Out[23]:

1. 'G'
2. 'A'
3. 'T'
4. 'T'
5. 'A'
6. 'C'
7. 'A'

In [24]:

class(ls$dna)

Out[24]:

'character'

In [25]:

ls[[1]]

Out[25]:

1. 'G'
2. 'A'
3. 'T'
4. 'T'
5. 'A'
6. 'C'
7. 'A'

In [26]:

class(ls[[1]])

Out[26]:

'character'

Data frames

A data frame is a special list of vectors where all the vectors have the same length. Because all the vectors have the same length, it can also be thought of as a 2D table or matrix and manipulated in the same way.

In [27]:

df <- as.data.frame(ls)

In [28]:

class(ls)

Out[28]:

'list'

In [29]:

class(df)

Out[29]:

'data.frame'

In [30]:

df

Out[30]:

	dna	ispurine
1	G	TRUE
2	A	TRUE
3	T	FALSE
4	T	FALSE
5	A	TRUE
6	C	FALSE
7	A	TRUE

In [31]:

df[4:6, ]

Out[31]:

	dna	ispurine
4	T	FALSE
5	A	TRUE
6	C	FALSE

In [32]:

df$ispurine

Out[32]:

1. TRUE
2. TRUE
3. FALSE
4. FALSE
5. TRUE
6. FALSE
7. TRUE

In [33]:

df[df$ispurine, ]

Out[33]:

	dna	ispurine
1	G	TRUE
2	A	TRUE
5	A	TRUE
7	A	TRUE

Creating a data frame from scrach

In [34]:

gender <- c('M', 'M', 'F', 'F', 'M', 'F', 'M')
height <- c(1.65, 1.82, 1.56, 1.66, 1.72, 1.6, 1.8)
weight <- c(65, 102, 55, 46, 78, 60, 72)

bods <- data.frame(gender, height, weight)

In [35]:

bods

Out[35]:

	gender	height	weight
1	M	1.65	65
2	M	1.82	102
3	F	1.56	55
4	F	1.66	46
5	M	1.72	78
6	F	1.6	60
7	M	1.8	72

We can add a new calculated column easily. Let’s include the body mass index (bmi).

In [36]:

bods$bmi <- bods$weight/bods$height^2

In [37]:

bods

Out[37]:

	gender	height	weight	bmi
1	M	1.65	65	23.87511
2	M	1.82	102	30.79338
3	F	1.56	55	22.60026
4	F	1.66	46	16.69328
5	M	1.72	78	26.3656
6	F	1.6	60	23.4375
7	M	1.8	72	22.22222

Let’s get rid of the bmi column.

In [38]:

bods$bmi <- NULL

In [39]:

bods

Out[39]:

	gender	height	weight
1	M	1.65	65
2	M	1.82	102
3	F	1.56	55
4	F	1.66	46
5	M	1.72	78
6	F	1.6	60
7	M	1.8	72

Work!

How many males are there?

In [ ]:

What is the mean height?

What is the mean weight for femalse?

In [ ]:

A person is classified as obese if their BMI exceeds 30. Add the BMI column back into the data frame, as well as a new logical column is.obese indicating if a person is obese or not.

In [ ]:

Reading data from files or URLs to dataframes

See Examples from the Quick-R website

In [ ]: