Motivation

Linear Algebra for Data Science in R

Eric Eager

Data Scientist at Pro Football Focus

Data - The Atom of Data Science

  height weight forty vertical bench broad_jump three_cone shuttle
1     71    192  4.38     35.0    14        127       6.71    3.98
2     73    298  5.34     26.5    27         99       7.81    4.71
3     77    256  4.67     31.0    17        113       7.34    4.38
4     74    198  4.34     41.0    16        131       6.56    4.03
5     76    257  4.87     30.0    20        118       7.12    4.23
6     78    262  4.60     38.5    18        128       7.53    4.48

Vectors - Storing Univariate Data

x <- rep(1, 4)
print(x)

1 1 1 1

y <- seq(2, 8, by = 2)
print(y)

2 4 6 8

z <- c(1, 5, -2, 4)
print(z)

1 5 -2 4

z[3] <- 7
print(z)

1 5 7 4

Matrices - Storing Tables of Data

matrix(2, 3, 2)

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

matrix(c(1, -1, 2, 3, 2, -2), nrow = 2, ncol = 3,
    byrow = TRUE)

     [,1] [,2] [,3]
[1,]    1   -1    2
[2,]    3    2   -2

matrix(c(1, -1, 2, 3, 2, -2), nrow = 2, ncol = 3, 
    byrow = FALSE)

     [,1] [,2] [,3]
[1,]    1    2    2
[2,]   -1    3   -2

A[2, 1] <- 100
print(A)

     [,1] [,2] [,3]
[1,]    1    2    2
[2,]  100    3   -2

Let's practice!

Linear Algebra for Data Science in R