install.packages('csucistats',
repos = c('https://inqs909.r-universe.dev',
'https://cloud.r-project.org'))R Basics
Introductions
Introductions
San Bernardino, CA
CSU Monterey Bay
- BS Biology
San Diego State University
- Master’s in Public Health
UC Riverside
- PhD in Applied Statistics
Introductions
Name
Year
Major
Fun Fact
Career Goal
Syllabus
R
R Programming
R is a statistical programming package that allows you to conduct different types of analysis.
RStudio
A piece of software that organizes how you conduct statistical analysis in R.
Posit Cloud
A web version of RStudio.
R Packages
Tidyverse
csucistats
ISLR2
leaps
gam
tree
gbm
e1071
glmnet
torch
RMarkdown
Quarto
extraInserts (GitHub Package)
Load R Package
library(tidyverse)
library(torch)- You must load packages every new R Session
R Basics
R as a calculator
R can evaluate different expressions in the console tab.
Try the following:
- \(4(4+2)/34\)
- \(6^3\)
- \(3-1\)
- \(4+4/3+45(32*34-54)\)
R Calculator
R Functions
R functions performs tasks to specific data values.
Evaluate the following values in R:
- \(\sqrt{3}\)
- \(e^3\)
- \(\ln(53)\)
- \(\log(324)\)
- \(\sin(3)\)
- \(\sin(3\pi)\)
R Functions
Types of Data
Numeric
Character
Logical
Missing
Evaluate the following code:
is.numeric(1)
is.numeric("1")
is.numeric(T)
is.numeric(NA)Types of Data
Types of Objects
In R, an object contains a set of data. The most common types are vectors and matrix.
Run this code and print out the objects in the console:
x <- 3:34
y <- matrix(1:20, nrow = 4)Types of objects
Data Frames
Data frames can be thought of as R’s version of a data set.
Play around with mtcars:
mtcars #> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Lists
List can be thought as an extended vector, but each element is a different R object.
Try playing with this R object:
list_one <- list(mtcars, rep(0, 4),
diag(rep(1, 3)))