Learning Objectives
By the end of this class, we will cover as much of the following as we can:
- More R Basics:
- vectors and lists
- Make a reproducible example using the
reprexpackage. - Use of
{}. - attributes
- style guides
- Turning your GitHub repository into a Website
- Turning your Rmd into a presentation (2 min)
Parallel Resources
This topic parallels the following resources. Take about 30 minutes to get acquainted with the following before coming to class:
We suggest referring to this material when engaging with today’s topic.
Additional Resources
- The book Advanced R is an approachable, free, online book for learning more about R.
- Vectors chapter for vectors and lists.
- Subsetting chapter for subsetting vectors.
- Functions chapter
- Environments chapter
- S3 chapter
Quiz
No quiz today
Demonstration
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(gapminder)
library(palmerpenguins)
knitr::opts_chunk$set(error = TRUE)Start with reprex:
reprex::reprex(mean(rnorm(10)))## Rendering reprex...mean(rnorm(10))
#> [1] -0.343914Created on 2020-10-22 by the reprex package (v0.3.0)
Part I: R Basics beyond the worksheet
Curly brackets
Curly brackets can hold multiple lines of code, and outputs the result at the end.
There’s not a whole lot of difference if you just using them on their own:
y <- {
x <- 10
x ^ 2
x ^ 3
}
y## [1] 1000They start to matter when you need to add multiple lines of code as an argument.
Question 1
You’ve encountered an error in the following code, and want to ask for help. Write a reprex to send over to someone, and paste it below the code chunk.
library(tidyverse)
library(gapminder)
gapminder %>%
group_by(Country) %>%
summarise(mean_pop == mean(pop))reprex::reprex({
library(tidyverse)
library(gapminder)
gapminder %>%
group_by(Country) %>%
summarise(mean_pop == mean(pop))
})## Rendering reprex...library(tidyverse)
library(gapminder)
gapminder %>%
group_by(Country) %>%
summarise(mean_pop == mean(pop))
#> Error: Must group by variables found in `.data`.
#> * Column `Country` is not found.Created on 2020-10-22 by the reprex package (v0.3.0)
Question 2
Sample 10 rows randomly from the gapminder tibble using dplyr::sample_n(), so that you get the same results each time. Do this all in a single pipe.
gapminder %>%
{
my_lm <- lm(lifeExp ~ gdpPercap, data = .)
beta1 <- coef(my_lm)[2]
.
} %>%
sample_n(10)## # A tibble: 10 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Madagascar Africa 1997 55.0 14165114 986.
## 2 Morocco Africa 1992 65.4 25798239 2948.
## 3 Mauritius Africa 1977 64.9 913025 3711.
## 4 Chile Americas 1972 63.4 9717524 5494.
## 5 Zambia Africa 1957 44.1 3016000 1312.
## 6 Nicaragua Americas 1977 57.5 2554598 5486.
## 7 Ethiopia Africa 1952 34.1 20860941 362.
## 8 Iceland Europe 1952 72.5 147962 7268.
## 9 Congo, Rep. Africa 1962 48.4 1047924 2465.
## 10 Australia Oceania 1982 74.7 15184200 19477.Attributes
Did you know that a tibble is actually just a list?
is.list(penguins)## [1] TRUEWhy, then, doesn’t it look like a list? Because it carries metadata that tells R to treat it in a special way. The metadata is held in something called attributes.
The attributes of penguins:
attributes(penguins)## $names
## [1] "species" "island" "bill_length_mm"
## [4] "bill_depth_mm" "flipper_length_mm" "body_mass_g"
## [7] "sex" "year"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
## [271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## [289] 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306
## [307] 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324
## [325] 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342
## [343] 343 344
##
## $class
## [1] "tbl_df" "tbl" "data.frame"penguins[1, 3]## # A tibble: 1 x 1
## bill_length_mm
## <dbl>
## 1 39.1Remove attributes with unclass():
#unclass(penguins)Question 3
Make the following list stored in homemade_tibble a tibble by changing the attributes.
(homemade_tibble <- list(
species = c("Chinstrap", "Adelie"),
body_mass_g = c(3405, 2304)
))## $species
## [1] "Chinstrap" "Adelie"
##
## $body_mass_g
## [1] 3405 2304attributes(homemade_tibble)## $names
## [1] "species" "body_mass_g"attr(homemade_tibble, which = "class") <- c("tbl_df" , "tbl" , "data.frame")
attr(homemade_tibble, which = "row.names") <- 1:2
homemade_tibble## # A tibble: 2 x 2
## species body_mass_g
## * <chr> <dbl>
## 1 Chinstrap 3405
## 2 Adelie 2304Question 4
Here’s a regression model:
(my_model <- lm(mpg ~ wt, data = mtcars))##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Coefficients:
## (Intercept) wt
## 37.285 -5.344What object is this? Use unclass() to find out. What are its attributes?
unclass(my_model)## $coefficients
## (Intercept) wt
## 37.285126 -5.344472
##
## $residuals
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## -2.2826106 -0.9197704 -2.0859521 1.2973499
## Hornet Sportabout Valiant Duster 360 Merc 240D
## -0.2001440 -0.6932545 -3.9053627 4.1637381
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 2.3499593 0.2998560 -1.1001440 0.8668731
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## -0.0502472 -1.8830236 1.1733496 2.1032876
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 5.9810744 6.8727113 1.7461954 6.4219792
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## -2.6110037 -2.9725862 -3.7268663 -3.4623553
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 2.4643670 0.3564263 0.1520430 1.2010593
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## -4.5431513 -2.7809399 -3.2053627 -1.0274952
##
## $effects
## (Intercept) wt
## -113.6497374 -29.1157217 -1.6613339 1.6313943 0.1111305 -0.3840041
##
## -3.6072442 4.5003125 2.6905817 0.6111305 -0.7888695 1.1143917
##
## 0.2316793 -1.6061571 1.3014525 2.2137818 6.0995633 7.3094734
##
## 2.2421594 6.8956792 -2.2010595 -2.6694078 -3.4150859 -3.1915608
##
## 2.7346556 0.8200064 0.5948771 1.7073457 -4.2045529 -2.4018616
##
## -2.9072442 -0.6494289
##
## $rank
## [1] 2
##
## $fitted.values
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
## 23.282611 21.919770 24.885952 20.102650
## Hornet Sportabout Valiant Duster 360 Merc 240D
## 18.900144 18.793255 18.205363 20.236262
## Merc 230 Merc 280 Merc 280C Merc 450SE
## 20.450041 18.900144 18.900144 15.533127
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
## 17.350247 17.083024 9.226650 8.296712
## Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
## 8.718926 25.527289 28.653805 27.478021
## Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## 24.111004 18.472586 18.926866 16.762355
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
## 16.735633 26.943574 25.847957 29.198941
## Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
## 20.343151 22.480940 18.205363 22.427495
##
## $assign
## [1] 0 1
##
## $qr
## $qr
## (Intercept) wt
## Mazda RX4 -5.6568542 -18.199514334
## Mazda RX4 Wag 0.1767767 5.447820482
## Datsun 710 0.1767767 0.148230003
## Hornet 4 Drive 0.1767767 -0.016055881
## Hornet Sportabout 0.1767767 -0.057356801
## Valiant 0.1767767 -0.061027994
## Duster 360 0.1767767 -0.081219555
## Merc 240D 0.1767767 -0.011466889
## Merc 230 0.1767767 -0.004124504
## Merc 280 0.1767767 -0.057356801
## Merc 280C 0.1767767 -0.057356801
## Merc 450SE 0.1767767 -0.172999378
## Merc 450SL 0.1767767 -0.110589098
## Merc 450SLC 0.1767767 -0.119767081
## Cadillac Fleetwood 0.1767767 -0.389599760
## Lincoln Continental 0.1767767 -0.421539139
## Chrysler Imperial 0.1767767 -0.407037927
## Fiat 128 0.1767767 0.170257160
## Honda Civic 0.1767767 0.277639553
## Toyota Corolla 0.1767767 0.237256431
## Toyota Corona 0.1767767 0.121613854
## Dodge Challenger 0.1767767 -0.072041573
## AMC Javelin 0.1767767 -0.056439003
## Camaro Z28 0.1767767 -0.130780659
## Pontiac Firebird 0.1767767 -0.131698458
## Fiat X1-9 0.1767767 0.218900467
## Porsche 914-2 0.1767767 0.181270739
## Lotus Europa 0.1767767 0.296362637
## Ford Pantera L 0.1767767 -0.007795696
## Ferrari Dino 0.1767767 0.065628162
## Maserati Bora 0.1767767 -0.081219555
## Volvo 142E 0.1767767 0.063792566
## attr(,"assign")
## [1] 0 1
##
## $qraux
## [1] 1.176777 1.046354
##
## $pivot
## [1] 1 2
##
## $tol
## [1] 1e-07
##
## $rank
## [1] 2
##
## attr(,"class")
## [1] "qr"
##
## $df.residual
## [1] 30
##
## $xlevels
## named list()
##
## $call
## lm(formula = mpg ~ wt, data = mtcars)
##
## $terms
## mpg ~ wt
## attr(,"variables")
## list(mpg, wt)
## attr(,"factors")
## wt
## mpg 0
## wt 1
## attr(,"term.labels")
## [1] "wt"
## attr(,"order")
## [1] 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(mpg, wt)
## attr(,"dataClasses")
## mpg wt
## "numeric" "numeric"
##
## $model
## mpg wt
## Mazda RX4 21.0 2.620
## Mazda RX4 Wag 21.0 2.875
## Datsun 710 22.8 2.320
## Hornet 4 Drive 21.4 3.215
## Hornet Sportabout 18.7 3.440
## Valiant 18.1 3.460
## Duster 360 14.3 3.570
## Merc 240D 24.4 3.190
## Merc 230 22.8 3.150
## Merc 280 19.2 3.440
## Merc 280C 17.8 3.440
## Merc 450SE 16.4 4.070
## Merc 450SL 17.3 3.730
## Merc 450SLC 15.2 3.780
## Cadillac Fleetwood 10.4 5.250
## Lincoln Continental 10.4 5.424
## Chrysler Imperial 14.7 5.345
## Fiat 128 32.4 2.200
## Honda Civic 30.4 1.615
## Toyota Corolla 33.9 1.835
## Toyota Corona 21.5 2.465
## Dodge Challenger 15.5 3.520
## AMC Javelin 15.2 3.435
## Camaro Z28 13.3 3.840
## Pontiac Firebird 19.2 3.845
## Fiat X1-9 27.3 1.935
## Porsche 914-2 26.0 2.140
## Lotus Europa 30.4 1.513
## Ford Pantera L 15.8 3.170
## Ferrari Dino 19.7 2.770
## Maserati Bora 15.0 3.570
## Volvo 142E 21.4 2.780attributes(my_model)## $names
## [1] "coefficients" "residuals" "effects" "rank"
## [5] "fitted.values" "assign" "qr" "df.residual"
## [9] "xlevels" "call" "terms" "model"
##
## $class
## [1] "lm"Style Guide
Write code that’s easier to read by referring to the tidyverse style guide
Part 2: Turning Your GitHub Repo into a Website
It’s common to turn your GitHub repository into a website. Case in point: stat545.stat.ubc.ca; vincenzocoia.com. At the very least, this is useful for making your html files visible as a webpage.
GitHub Pages
Activate GitHub pages in your repo’s settings, and your repository’s files will be served on a website.
By default, your main README is shown on the website homepage. So, if you put links to other files in your README, you now have a navigable (albeit rudimentary) website.
If you want a beautiful website, you have a couple options:
- Code up your own css files etc. (Hard)
- Use a template (easier)
- GitHub Pages is powered by Jekyll behind the scenes. Dean Attali made a template called Beautiful Jekyll. I used this for some time.
- Use blogdown (see the section after next). stat545.stat.ubc.ca and vincenzocoia.com both use this.
Viewing an HTML file from your GitHub repo
Viewing an HTML file on GitHub only shows you the HTML code, not the rendered script. You’ll need to provide a link to a rendered, viewable version of each HTML file you produce. Here’s how you do that using GitHub Pages:
- Enable “GitHub pages” on your repo:
- Go to “settings” on your repo, and stay on the default “Options” tab.
- Scroll down to the “GitHub Pages” section.
- Under “Source”, click the “None” drop-down button, and select the branch you want to turn into a website (probably “Master”).
- Also under the “GitHub Pages” section, you’ll find your website URL. Make note of this URL.
- This URL will show a rendered version of your repo’s README, but this is not important.
Just by enabling GitHub Pages, your HTML files are now viewable rendered by your browser. Technically, you just turned your GitHub repo into a website. The only trick is navigating to that HTML page, since there’s no default interface to your website.
- Obtain the URL to HTML file on your repo:
- Start with your GitHub Pages URL that you made note of in Step 2. This points to the root of your repository.
- Get the path to an HTML file you want to view. It should be something like
/path/to/file.html(in this case,file.htmllives in thetofolder, which lives in thepathfolder in the root of your repo). - Append the path to the HTML file to your GitHub Pages URL. Try the URL to see that it works.
- Make it easy for a visitor to your repo to find the rendered HTML file! Add a link to the rendered HTML file somewhere in your repo, probably in a README file in your homework folder.
Blogdown
Blogdown (an R package) is actually just a wrapper around a non-R software called hugo.
Let’s explore:
- Check out the blogdown website. This is really the only step you need.
- Check out the recommended workflow
- Add this updated html page to the STAT 545 website?
Part 3: Making Presentation Slides from Rmd
- Try knitting this to
ioslides_presentation. - Take a look at other options beside ioslides.
- Feeling fancy? Try
output: revealjs::revealjs_presentation.