Youssef-Module-2

the first data struct we learn is vectors

create vectors of data for three med patient

subject_name <- c("Youssef Hemimy", "Jane Doe", "Steve Graves")
temperature <- c(98.1, 98.6, 101.4)
flu_status <- c(FALSE, FALSE, TRUE)

now we can access any element in the body temp vector

note that indexing in r starts at 0

temperature[1]

[1] 98.1

temperature[2]

[1] 98.6

temperature[3]

[1] 101.4

other ways to access (range, exclude)

include items in the range 2 to 3

temperature[2:3]

[1]  98.6 101.4

exclude item 2 using the minus sign

temperature[-2]

[1]  98.1 101.4

Factors

it is a data type used to represent categorical variables.

Think: labels, not numbers.

add gender factor

gender <- factor(c("Male","Female", "Male"))
gender

[1] Male   Female Male  
Levels: Female Male

Levels are the distinct categories a factor can take.

They are the allowed labels that factor can take.

add blood type factor

blood <- factor(c("O", "AB", "A"),
                levels = c("A", "B", "AB", "O"))
blood

[1] O  AB A 
Levels: A B AB O

to add an ordered factor, we add arg ordered=true

symptoms <- factor(c("SEVERE", "MILD", "MODERATE"),
                   levels = c("MILD", "MODERATE", "SEVERE"),
                   ordered = TRUE)
symptoms

[1] SEVERE   MILD     MODERATE
Levels: MILD < MODERATE < SEVERE

I am confused on how the ordering works

adding a numbered factor to understand exactly how it works

sy <- factor(c("1", "2", "3"),
                   levels = c("1", "2", "3"),
                   ordered = TRUE)
sy

[1] 1 2 3
Levels: 1 < 2 < 3

sy2 <- factor(c("1a", "2b", "2a", "3a"),
                   levels = c("1a","2a", "2b", "3a"),
                   ordered = TRUE)
sy2

[1] 1a 2b 2a 3a
Levels: 1a < 2a < 2b < 3a

this output shows that levels defines the order, ascending

Lists

display info for a patient

subject_name[1]

[1] "Youssef Hemimy"

temperature[1]

[1] 98.1

flu_status[1]

[1] FALSE

gender[1]

[1] Male
Levels: Female Male

blood[1]

[1] O
Levels: A B AB O

symptoms[1]

[1] SEVERE
Levels: MILD < MODERATE < SEVERE

a vector and a list are both containers, but they obey very different rules.

A vector is homogeneous, all elements must be the same type.

Types: numeric, character, logical, factor

lists can take any type

there are a few methods for accessing a list

some return sublists other returns numeric vector

# create list for a patient
subject1 <- list(fullname = subject_name[1], 
                 temperature = temperature[1],
                 flu_status = flu_status[1],
                 gender = gender[1],
                 blood = blood[1],
                 symptoms = symptoms[1])
subject1

$fullname
[1] "Youssef Hemimy"

$temperature
[1] 98.1

$flu_status
[1] FALSE

$gender
[1] Male
Levels: Female Male

$blood
[1] O
Levels: A B AB O

$symptoms
[1] SEVERE
Levels: MILD < MODERATE < SEVERE

get a single list value by position

returns a sub-list

subject1[2]

get a single list value by position

returns a numeric vector

subject1[[2]]

[1] 98.1

get a single list value by name

subject1$temperature

[1] 98.1

get several lists items by specifiying a vector of names

subject1[c("temperature", "flu_status")]

$temperature
[1] 98.1

$flu_status
[1] FALSE

we can also access a list like a vector

get values 1 to 3 (range)

subject1[1:3]

$fullname
[1] "Youssef Hemimy"

$temperature
[1] 98.1

$flu_status
[1] FALSE

Data frames

create a data frame from medical patient data

note that in R version 4+ we don’t need the arg

stringsAsFactors = False

it is already false by default

in prev versions of R, this caused silent bugs

because it would convert it internally to integers

pt_data <- data.frame(subject_name, temperature, flu_status, gender,
                      blood, symptoms, stringsAsFactors = FALSE)

display the data frame

pt_data

accessing a data frame

get a single column

pt_data$subject_name

[1] "Youssef Hemimy" "Jane Doe"       "Steve Graves"  

get several columns by specifying a vector of names

pt_data[c("temperature", "flu_status")]

this is the same as above, extracting temperature and #flu_status

pt_data[2:3]

NA

accessing by row and column

pt_data[1, 2]

[1] 98.1

accessing several rows and several columns using vectors

pt_data[c(1, 3), c(2, 4)]

NA

Leave a row or column blank to extract all rows or columns

column 1, all rows

pt_data[, 1]

[1] "Youssef Hemimy" "Jane Doe"       "Steve Graves"  

row 1, all columns

pt_data[1, ]

NA

all rows and all columns

pt_data[ , ]

NA

the following are equivalent

pt_data[c(1, 3), c("temperature", "gender")]

NA

pt_data[-2, c(-1, -3, -5, -6)]

NA

creating a Celsius temperature column

pt_data$temp_c <- (pt_data$temperature - 32) * (5 / 9)

comparing before and after

pt_data[c("temperature", "temp_c")]

NA

Matrixes

create a 2x2 matrix

m <- matrix(c(1, 2, 3, 4), nrow = 2)
m

     [,1] [,2]
[1,]    1    3
[2,]    2    4

equivalent to the above

m <- matrix(c(1, 2, 3, 4), ncol = 2)
m

     [,1] [,2]
[1,]    1    3
[2,]    2    4

create a 2x3 matrix

m <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2)
m

     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6

create a 3x2 matrix

m <- matrix(c(1, 2, 3, 4, 5, 6), ncol = 2)
m

     [,1] [,2]
[1,]    1    4
[2,]    2    5
[3,]    3    6

extract values from matrixes

m[1,1]

[1] 1

m[3, 2]

[1] 6

# extract rows
m[1, ]

[1] 1 4

# extract columns
m[, 1]

[1] 1 2 3