`r Sys.Date()`" 4 | subtitle: "“All R is quite useless.”" 5 | output: 6 | xaringan::moon_reader: 7 | lib_dir: libs 8 | css: "css/remark.css" 9 | # chakra: libs/remark-js-latest-min.js 10 | nature: 11 | highlightStyle: github 12 | highlightLines: true 13 | countIncrementalSlides: false 14 | ratio: '16:9' 15 | editor_options: 16 | chunk_output_type: console 17 | --- 18 | 19 | class: middle, center 20 | 21 | ```{r setup, include=FALSE} 22 | knitr::opts_chunk$set(dev = "svglite") 23 | # source(file = here::here("slides/libs-reveal/xaringan_reveal_parentheses_balanced.R")) 24 | # options(tibble.width = 55, 25 | # tibble.max_extra_cols = 20) 26 | # library(tidyverse) 27 | ``` 28 | 29 |
30 |
31 | # My contacts...
32 |
33 | email: [otho.mantegazza@accurat.it](mailto:otho.mantegazza@accurat.it)34 | Github: [@othomantegazza](https://github.com/othomantegazza)
35 | Twitter: [@othomn](https://twitter.com/othomn)
36 | Website: [otho.netlify.com](otho.netlify.com)
37 | 38 | 39 | 40 | --- 41 | 42 | class: blueblue, middle 43 | 44 | .verybig[Why Data?] 45 | 46 | .big[What's your experience with Data?] 47 | 48 | --- 49 | 50 | class: blueblue, middle 51 | 52 | .right[ 53 | 54 | .verybig[Why R?] 55 | 56 | .big[Let's check your R setup!] 57 | 58 | ] 59 | 60 | --- 61 | 62 | class: center, middle 63 | 64 | # How is this course structured? 65 | 66 | [All the material + Resources are here on Github](https://github.com/othomantegazza/eda-class) 67 | -------------------------------------------------------------------------------- /slides/00-intro.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 |
`r Sys.Date()`" 5 | output: 6 | xaringan::moon_reader: 7 | lib_dir: libs 8 | css: "css/remark.css" 9 | # chakra: libs/remark-js-latest-min.js 10 | nature: 11 | highlightStyle: github 12 | highlightLines: true 13 | countIncrementalSlides: false 14 | ratio: '16:9' 15 | --- 16 | 17 | ```{r setup, include=FALSE} 18 | knitr::opts_chunk$set(dev = "svglite") 19 | source(file = here::here("slides/libs-reveal/xaringan_reveal_parentheses_balanced.R")) 20 | options(tibble.width = 55, 21 | tibble.max_extra_cols = 20) 22 | library(ggbeeswarm) 23 | ``` 24 | 25 | 26 | # The Tidyverse 27 | 28 | .center[ 29 | 30 | 31 | 32 |
33 | 34 | 35 | ] 36 | 37 | --- 38 | 39 | class: blueblue, middle 40 | 41 | .verybig[The Tidyverse is a collection of packages for Data Science] 42 | 43 | --- 44 | 45 | 46 | class: blueblue, middle 47 | 48 | .big[.right[Let's practice it!]] 49 | 50 | --- 51 | 52 | # NYC Squirrel Census 53 | 54 | 55 | .pull-left[ 56 | 57 | Because: 58 | 59 | .middle[ 60 | 61 | - It is large enough to provide a challenge. 62 | - It is tidy and detailed, 63 | - It stores quantitative and categorical variables, 64 | - it stores spatial variables, 65 | - it stores time variables. 66 | 67 | credits: [NYC Squirrel census](https://www.thesquirrelcensus.com/). 68 | 69 | ] 70 | ] 71 | 72 | 73 | .pull-right[ 74 | .center[ 75 | 76 |
77 | 78 |
79 | 80 | 81 | ] 82 | ] 83 | 84 | 85 | --- 86 | 87 | # TidyTuesday 88 | 89 |  90 | 91 | [TidyTuesday on Github](https://github.com/rfordatascience/tidytuesday), also check the [tidytuesday hashstag on twitter](https://twitter.com/hashtag/TidyTuesday). 92 | 93 | --- 94 | 95 | # NYC Squirrel Census 96 | 97 | `r # emo::ji("squirrel")` 98 | 99 | ```{r, message=FALSE} 100 | library(tidyverse) 101 | ``` 102 | 103 | 104 | ```{r, message=FALSE} 105 | squirrels_url <- paste0("https://raw.githubusercontent.com/rfordatascience/tidytuesday/", 106 | "master/data/2019/2019-10-29/nyc_squirrels.csv") 107 | 108 | squirrels <- read_csv(squirrels_url) 109 | ``` 110 | 111 | ```{r squirrel-pipe-1, echo=FALSE, eval=FALSE} 112 | squirrels %>% 113 | select(lat_long, 114 | primary_fur_color, 115 | location) %>% 116 | filter(primary_fur_color == 117 | "Cinnamon") %>% 118 | drop_na(location) 119 | ``` 120 | 121 | --- 122 | 123 | `r apply_reveal("squirrel-pipe-1")` 124 | 125 | --- 126 | 127 | class: blueblue, middle 128 | 129 | .big[.right[Run many operations in sequence 130 | 131 | With the .orange[PIPE]]] 132 | 133 | --- 134 | 135 | # The pipe operator 136 | 137 | .pull-left[ 138 | 139 | .middle[ 140 | 141 | Most of the functions in the Tidyverse take a tibble as first argument and produce a tibble as an output. 142 | 143 | ```{r, eval=FALSE} 144 | select(.data = squirrels, 145 | lat_long, primary_fur_color) 146 | ``` 147 | 148 | 149 | The pipe take whats on the left and passes it to the first argument of the function on the right. 150 | 151 | ```{r, eval=FALSE} 152 | squirrels %>% 153 | select(lat_lon, primary_fur_color) %>% 154 | do_something() %>% 155 | and_something_else() 156 | ``` 157 | 158 | 159 | ] 160 | ] 161 | 162 | 163 | .pull-right[ 164 | .center[ 165 | 166 |
167 | 168 |
169 | 170 | 171 | ] 172 | ] 173 | 174 | --- 175 | 176 | class: blueblue, middle 177 | 178 | .big[.right[Ok, but which operation?]] 179 | 180 | --- 181 | 182 | 183 | # DPLYR Verbs 184 | 185 | .pull-left[ 186 | 187 | .middle[ 188 | 189 | DPLYR contains funcions that are Verbs for data manipulations 190 | 191 | This verbs allow you to perform the operations that you want on data with a declarative synthax. You tell your computer what you want to do, not how to do it. 192 | 193 | For example you can: 194 | 195 | - select columns with `select()`, 196 | - sort rows with `arrange()`, 197 | - filter rows with `filter()`, 198 | 199 | ...and much more. 200 | 201 | 202 | ] 203 | ] 204 | 205 | 206 | .pull-right[ 207 | .center[ 208 | 209 |
210 | 211 |
212 | 213 | 214 | ] 215 | ] 216 | 217 | --- 218 | 219 | class: blueblue, middle 220 | 221 | .big[.right[ 222 | 223 | .orange[Let's try it:] 224 | 225 | How many .orange[gray] squirrels... 226 | 227 | ...where seen .orange[above ground]... 228 | 229 | ....orange[eating]... 230 | 231 | ...devided by .orange[age] 232 | 233 | ]] 234 | 235 | ```{r squirrel-pipe-2, eval = FALSE, echo = FALSE} 236 | squirrels %>% 237 | select(primary_fur_color, 238 | location, 239 | eating, age) %>% 240 | filter(primary_fur_color == 241 | "Gray") %>% 242 | filter(location == 243 | "Above Ground") %>% 244 | filter(eating) %>% 245 | count(age) 246 | ``` 247 | 248 | --- 249 | 250 | `r apply_reveal("squirrel-pipe-2")` 251 | 252 | --- 253 | 254 | class: exercise, middle 255 | 256 | .exercise-title[Exercise:] 257 | 258 | .exercise-body[ 259 | 260 | Count how many **Juvenile** squirrels... 261 | 262 | ...where seen **foraging**... 263 | 264 | ...aggregated by **primary fur color** 265 | 266 | ] 267 | 268 | --- 269 | 270 | class: exercise, middle 271 | 272 | .exercise-title[Exercise:] 273 | 274 | .exercise-body[ 275 | 276 | Which values does the column **other_activities** take? 277 | 278 | Which value, besides NA, does it take most often? 279 | 280 | ] 281 | 282 | --- 283 | 284 | class: exercise, middle 285 | 286 | .exercise-title[Exercise:] 287 | 288 | .exercise-body[ 289 | 290 | When squirrels are observed **above ground**... 291 | 292 | ...at what height are they on average? 293 | 294 | ] 295 | 296 | ```{r, eval = FALSE, echo = FALSE} 297 | squirrels %>% 298 | filter(!is.na(above_ground_sighter_measurement)) %>% 299 | filter(above_ground_sighter_measurement != FALSE) %>% 300 | summarise(mean_height = above_ground_sighter_measurement %>% 301 | as.numeric() %>% 302 | mean(na.rm = TRUE)) 303 | ``` 304 | 305 | 306 | --- 307 | 308 | class: blueblue, middle 309 | 310 | .verybig[We have identified one column that must be cleaned] 311 | 312 | --- 313 | 314 | # Data Cleaning 1 315 | 316 | Problem: 317 | 318 | ```{r, R.options=list(max.print=30)} 319 | squirrels %>% pull(above_ground_sighter_measurement) 320 | ``` 321 | 322 | ```{r} 323 | squirrels %>% pull(above_ground_sighter_measurement) %>% class() 324 | ``` 325 | 326 | --- 327 | 328 | # Data Cleaning 1 329 | 330 | solution: 331 | 332 | ```{r, R.options=list(max.print=50)} 333 | squirrels %>% 334 | rename(height = above_ground_sighter_measurement) %>% 335 | mutate(height = height %>% { 336 | if_else(. == "FALSE", "0", ., NA_character_) 337 | }) %>% 338 | mutate(height = height %>% 339 | as.numeric()) %>% 340 | pull(height) 341 | ``` 342 | 343 | --- 344 | 345 | # Data Cleaning 2 346 | 347 | Problem: 348 | 349 | ```{r, R.options=list(max.print=30)} 350 | squirrels %>% pull(date) 351 | ``` 352 | 353 | ```{r} 354 | squirrels %>% pull(date) %>% class() 355 | ``` 356 | 357 | --- 358 | 359 | # Lubridate, because dates and times are special 360 | 361 | .pull-left[ 362 | 363 | .middle[ 364 | 365 | Instead of trying to remember how many seconds are in an hour, how many days are in which month, and which year is a leap year, R has special objects and classes to store time, and to perform operations on it. 366 | 367 | Lubridate makes it easier to deal with those objects. 368 | 369 | ```{r, eval=FALSE} 370 | ymd(20101215) 371 | #> [1] "2010-12-15" 372 | 373 | mdy("4/1/17") 374 | #> [1] "2017-04-01" 375 | ``` 376 | 377 | 378 | (from the package examples) 379 | 380 | ] 381 | ] 382 | 383 | 384 | .pull-right[ 385 | .center[ 386 | 387 |
388 | 389 |
390 |
391 |
392 | ]
393 | ]
394 |
395 | ---
396 |
397 | # Data Cleaning 2
398 |
399 | Solution:
400 |
401 | ```{r, message=FALSE}
402 | library(lubridate)
403 | ```
404 |
405 |
406 | ```{r, R.options=list(max.print=15)}
407 | squirrels %>%
408 | mutate(date = mdy(date)) %>%
409 | pull(date)
410 | ```
411 |
412 | ```{r}
413 | squirrels %>%
414 | mutate(date = mdy(date)) %>%
415 | pull(date) %>%
416 | class()
417 | ```
418 |
419 | ---
420 |
421 | # Put it all together and assign it to a new variable
422 |
423 | ```{r squirrel-data-cleaning}
424 | squirrels_tidy <-
425 | # assign to a new object
426 | squirrels %>%
427 | # first part
428 | rename(height = above_ground_sighter_measurement) %>%
429 | mutate(height = height %>% {
430 | if_else(. == "FALSE", "0", ., NA_character_)
431 | }) %>%
432 | mutate(height = height %>%
433 | as.numeric()) %>%
434 | # Second part
435 | mutate(date = mdy(date))
436 | ```
437 |
438 | From now on we are going to work with `squirrels_tidy`!
439 |
440 | ```{r, include=FALSE}
441 | save(squirrels_tidy, file = here::here("data/squirrels_tidy.Rdata"))
442 | ```
443 |
444 | ---
445 |
446 | class: exercise, middle
447 |
448 | .exercise-title[Exercise:]
449 |
450 | .exercise-body[
451 |
452 | When do the observations **start**?
453 |
454 | And when do they **end**?
455 |
456 | ]
457 |
458 | ---
459 |
460 | class: exercise, middle
461 |
462 | .exercise-title[Exercise:]
463 |
464 | .exercise-body[
465 |
466 | How many squirrels where observed in each **weekday**?
467 |
468 | ]
469 |
470 | ```{r, eval = FALSE, echo = FALSE}
471 | squirrels_tidy %>%
472 | count(date) %>%
473 | mutate(date = wday(date, label = T)) %>%
474 | group_by(date) %>%
475 | summarise(mean_n = mean(n))
476 | ```
477 |
478 | ---
479 |
480 | class: exercise, middle
481 |
482 | .exercise-title[Exercise:]
483 |
484 | .exercise-body[
485 |
486 | At what **average height**...
487 |
488 | ...were squirrles from the "three" **age** groups observed?
489 |
490 | ]
491 |
492 | ```{r, eval = FALSE, echo = FALSE}
493 | squirrels_tidy %>%
494 | group_by(age) %>%
495 | summarise(mean_height = mean(height, na.rm = T))
496 | ```
497 |
--------------------------------------------------------------------------------
/slides/03-intro-to-the-tidyverse.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | title: "The Tidyverse, Part 2"
3 | subtitle: "“R is not in some far-off place.”"
4 | author: "Otho Mantegazza
`r Sys.Date()`"
5 | output:
6 | xaringan::moon_reader:
7 | lib_dir: libs
8 | css: "css/remark.css"
9 | # chakra: libs/remark-js-latest-min.js
10 | nature:
11 | highlightStyle: github
12 | highlightLines: true
13 | countIncrementalSlides: false
14 | ratio: '16:9'
15 | ---
16 |
17 | ```{r setup, include=FALSE}
18 | knitr::opts_chunk$set(dev = "svglite")
19 | source(file = here::here("slides/libs-reveal/xaringan_reveal_parentheses_balanced.R"))
20 | options(tibble.width = 55,
21 | tibble.max_extra_cols = 20)
22 | library(tidyverse)
23 | library(ggbeeswarm)
24 | load(here::here("data/squirrels_tidy.Rdata"))
25 | ```
26 |
27 | class: blueblue, middle
28 |
29 | .big[You are already .orange[exploring data] using summary statistics.]
30 |
31 | .big[Could we learn more turning them into visual representation?]
32 |
33 | ---
34 |
35 | `r apply_reveal("squirrel-height-boxplot")`
36 |
37 | ```{r squirrel-height-boxplot, eval = FALSE, echo = FALSE}
38 | squirrels_tidy %>%
39 | drop_na(height) %>%
40 | ggplot(aes(x = age,
41 | y = height)) +
42 | geom_point() +
43 | geom_boxplot() +
44 | labs(title = "Height Above Ground of Squirrels by Age",
45 | y = "Feet from Ground",
46 | caption = "Data from data.cityofnewyork.us") +
47 | theme_gray(base_size = 18) +
48 | coord_flip() +
49 | scale_y_sqrt()
50 | ```
51 |
52 | ---
53 |
54 | `r apply_reveal("squirrel-beeswarm")`
55 |
56 | ```{r squirrel-beeswarm, eval = FALSE, echo = FALSE}
57 | squirrels_tidy %>%
58 | drop_na(height) %>%
59 | filter(height > 0) %>%
60 | ggplot(aes(x = age,
61 | y = height)) +
62 | geom_quasirandom() +
63 | theme_gray(base_size = 18) +
64 | labs(title = "Height Above Ground of Squirrels by Age",
65 | y = "Feet from Ground",
66 | caption = "Data from data.cityofnewyork.us") +
67 | coord_flip() +
68 | scale_y_sqrt() +
69 | stat_summary(fun.y = "median",
70 | geom = "point",
71 | size = 10,
72 | alpha = .9,
73 | colour = "#FF6C0D")
74 | ```
75 |
76 | ---
77 |
78 | `r apply_reveal("squirrel-histogram")`
79 |
80 | ```{r squirrel-histogram, eval = FALSE, echo = FALSE}
81 | squirrels_tidy %>%
82 | drop_na(height) %>%
83 | filter(height > 0) %>%
84 | ggplot(aes(x = height)) +
85 | geom_density(fill = "grey40",
86 | alpha = .6,
87 | adjust = 1/2) +
88 | labs(title = "Height Above Ground of Squirrels by Age",
89 | x = "Feet from Ground",
90 | caption = "Data from data.cityofnewyork.us") +
91 | theme_bw(base_size = 18) +
92 | facet_grid(age ~ .) +
93 | stat_sum(
94 | aes(y = -.005,
95 | size = 1,
96 | colour = ..n..,
97 | label = "|"),
98 | geom = "text",
99 | fontface = "bold",
100 | show.legend = F) +
101 | scale_colour_viridis_c()
102 | ```
103 |
104 | ---
105 |
106 | # GGPLOT2 for Data Visualization
107 |
108 | .pull-left[
109 |
110 | .middle[
111 |
112 | GGPLOT2 is a library for graphical representation of Data.
113 |
114 |
115 | - It is widely developed, you can use it to plot almost anything,
116 | - It is based on the Layered Grammar of Graphics,
117 | - It's designed both for exploratory viz and for communiacation
118 |
119 | To get used to ggplot you must think in a layered way.
120 |
121 | ]
122 | ]
123 |
124 |
125 | .pull-right[
126 | .center[
127 |
128 |
131 |
132 |
133 | ]
134 | ]
135 |
136 | ---
137 |
138 | class: middle
139 |
140 | 
141 |
142 | Image credits: [@dgkeyes](https://twitter.com/dgkeyes), https://rfortherestofus.com/ . Found through [@W_R_Chase](https://twitter.com/W_R_Chase)
143 |
144 | ---
145 |
146 | # The Grammar of Graphics
147 |
148 | You map **data**...
149 |
150 | 1. to **Aesthetic** properties of objects,
151 | 2. according **Scale**, placed on **Coordinates**
152 | 3. Those objects are represented by **Geometric Shapes**.
153 | 4. You might need to use **statistical** transformations, to highlight those properties,
154 | 5. You might want to use **Facets** to represent multiple dimentions
155 |
156 |
157 |
158 | ---
159 |
160 | class: exercise, middle
161 |
162 | .exercise-title[Exercise:]
163 |
164 | .exercise-body-small[
165 |
166 | Show the number of squirrels by their primary and highlight fur colour in a barchart.
167 |
168 | - Map the **highlight fur colour** to the **x axis**. Can you make the x axis vertical?
169 |
170 | - Map the **primary fur colour** to the **fill aesthetic** of the bars. Can you map them to to their real colours?
171 |
172 | - What are you mapping to the y axis, can you order the bars by their height?
173 |
174 | ]
175 |
176 | ```{r, eval=FALSE, echo=FALSE}
177 | # exercise
178 | squirrels_tidy %>%
179 | ggplot(aes(x = forcats::fct_infreq(highlight_fur_color),
180 | fill = primary_fur_color)) +
181 | geom_bar() +
182 | coord_flip() +
183 | labs(x = "Highlight Fur Color") +
184 | scale_fill_manual(values = c(Black = "black",
185 | Cinnamon = "#ff9100",
186 | Gray = "gray60"),
187 | na.value = "purple")
188 | ```
189 |
190 |
191 | ---
192 |
193 | class: exercise, middle
194 |
195 | .exercise-title[Exercise:]
196 |
197 | .exercise-body[
198 |
199 | Use an histogram to visualize how are the black squirrels distributed along the longitude variable.
200 |
201 | - If you map the longitude on the x axis, which variable are you mapping on the y axis?
202 |
203 | - Are you using a statistical transformation?
204 |
205 | ]
206 |
207 | ---
208 |
209 | class: exercise, middle
210 |
211 | .exercise-title[Exercise:]
212 |
213 | .exercise-body-small[
214 |
215 | Can you show where are the squirrels with a scatterplot?
216 |
217 | - Map the **longitude** to the **x axis** and the **latitude** to the **y axis**.
218 |
219 | - Map the colour of the points to the real fur colour of the squirrels.
220 |
221 | - Can you also split the fur colours in facets?
222 |
223 | - How can you avoid overplotting? Can you bin the squirrel position on the x and y axis?
224 |
225 | ]
226 |
227 | ```{r, eval = FALSE, echo = FALSE}
228 | p <-
229 | squirrels_tidy %>%
230 | ggplot(aes(x = long,
231 | y = lat)) +
232 | coord_quickmap() +
233 | theme_bw()
234 |
235 | p +
236 | geom_point(aes(colour = primary_fur_color),
237 | alpha = .7) +
238 | scale_colour_manual(values = c(Black = "black",
239 | Cinnamon = "#ff9100",
240 | Gray = "gray60"),
241 | na.value = "purple") +
242 | facet_grid(. ~ primary_fur_color)
243 |
244 | p +
245 | geom_hex(colour = "#FFFFFF",
246 | size = .2,
247 | binwidth = c(.0015,
248 | .001))
249 | ```
250 |
251 |
252 | ---
253 |
254 | `r apply_reveal("squirrels-geo")`
255 |
256 | ```{r, squirrels-geo, eval = FALSE, echo = FALSE}
257 | squirrels_tidy %>%
258 | ggplot(aes(x = long,
259 | y = lat)) +
260 | geom_point(alpha = .1) +
261 | geom_hex(colour = "#FFFFFF",
262 | size = .2,
263 | binwidth = c(.0015,
264 | .001)) +
265 | coord_quickmap() +
266 | theme_bw()
267 | ```
268 |
269 |
270 | ```{r, eval = FALSE, echo = FALSE}
271 |
272 | # map background with ggmap, but it does not work with hexagons
273 |
274 | tst <-
275 | ggmap::get_stamenmap(c(left = min(squirrels_tidy$long),
276 | top = max(squirrels_tidy$lat),
277 | right = max(squirrels_tidy$long),
278 | bottom = min(squirrels_tidy$lat)),
279 | zoom = 15, maptype = "toner-lite")
280 |
281 | ggmap::ggmap(tst) +
282 | geom_point(data = squirrels_tidy,
283 | aes(x = long,
284 | y = lat),
285 | alpha = .3,
286 | colour = "#0693c7")
287 | ```
288 |
289 |
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/slides/04-intro-to-tidyverse.Rmd:
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1 | ---
2 | title: "The Tidyverse, Part 3"
3 | subtitle: "“Without R, the crudeness of reality would make the world unbearable”"
4 | author: "Otho Mantegazza
`r Sys.Date()`"
5 | output:
6 | xaringan::moon_reader:
7 | lib_dir: libs
8 | css: "css/remark.css"
9 | # chakra: libs/remark-js-latest-min.js
10 | nature:
11 | highlightStyle: github
12 | highlightLines: true
13 | countIncrementalSlides: false
14 | ratio: '16:9'
15 | ---
16 |
17 | ```{r setup, include=FALSE}
18 | knitr::opts_chunk$set(dev = "svglite")
19 | # source(file = here::here("slides/libs-reveal/xaringan_reveal_parentheses_balanced.R"))
20 | options(tibble.width = 55,
21 | tibble.max_extra_cols = 20)
22 | library(tidyverse)
23 | load(here::here("data/squirrels_tidy.Rdata"))
24 | ```
25 |
26 | class: blueblue, middle
27 |
28 | .big[A couple of .orange[Data Cleaning] steps.]
29 |
30 | ---
31 |
32 | # NA encoded as question marks
33 |
34 | ```{r}
35 | squirrels_tidy %>%
36 | pull(age) %>%
37 | unique()
38 | ```
39 |
40 | --
41 |
42 | # Two columns in one
43 |
44 | ```{r, R.options=list(max.print=40)}
45 | squirrels_tidy %>%
46 | pull(hectare) %>%
47 | unique()
48 | ```
49 |
50 | ---
51 |
52 | # TIDYR
53 |
54 | .pull-left[
55 |
56 | .middle[
57 |
58 | TIDYR is a package dedicated to tidying data. It stores functions for
59 |
60 |
61 | - Pivoting,
62 | - Rectangling,
63 | - Nesting,
64 | - Separating and combining columns,
65 | - Deal with missing data.
66 |
67 | At this moment we need the last two.
68 |
69 | ]
70 | ]
71 |
72 |
73 | .pull-right[
74 | .center[
75 |
76 |
79 | 80 | 81 | ] 82 | ] 83 | 84 | --- 85 | 86 | 87 | # How many NAs 88 | 89 | 90 | ```{r, R.options=list(max.print=10)} 91 | squirrels_tidy %>% 92 | map(~is.na(.) %>% sum()) %>% unlist() 93 | ``` 94 | 95 | --- 96 | 97 | # How many question marks 98 | 99 | ```{r, R.options=list(max.print=10)} 100 | squirrels_tidy %>% 101 | map(~str_detect(., "\\?") %>% sum(na.rm = T)) %>% unlist() 102 | ``` 103 | 104 | --- 105 | 106 | # Transform question mark to NA 107 | 108 | ```{r} 109 | squirrels_tidy <- 110 | squirrels_tidy %>% 111 | mutate_if(is.character, ~na_if(., "?")) 112 | ``` 113 | 114 | No more NAs coded as question marks. 115 | 116 | ```{r, R.options=list(max.print=10)} 117 | squirrels_tidy %>% 118 | map(~str_detect(., "\\?") %>% sum(na.rm = T)) %>% unlist() 119 | ``` 120 | 121 | --- 122 | 123 | # Separate columns 124 | 125 | 126 | ```{r} 127 | squirrels_tidy <- 128 | squirrels_tidy %>% 129 | separate(hectare, c("hectare_lat", "hectare_lon"), sep = 2) 130 | 131 | squirrels_tidy %>% select(hectare_lat, hectare_lon) 132 | ``` 133 | 134 | --- 135 | 136 | # Janitor 137 | 138 | .pull-left[ 139 | 140 | .middle[ 141 | 142 | Janitor encodes many useful routines to clean data that have been collected in spreadheets. 143 | 144 | According to the package readme, dirtiness includes: 145 | 146 | - Dreadful column names. 147 | - Rows and columns containing Excel formatting but no data. 148 | - Dates stored as numbers. 149 | - Values spread inconsistently over [...] columns. 150 | 151 | 152 | ] 153 | ] 154 | 155 | 156 | .pull-right[ 157 | .center[ 158 | 159 |
160 | 161 |
162 |
163 |
164 | ]
165 | ]
166 |
167 | ---
168 |
169 | .center[
170 | .middle[
171 |
172 | 
173 |
174 | ]
175 | ]
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/slides/04-intro-to-tidyverse.html:
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1 |
2 |
3 |
`r Sys.Date()`" 4 | subtitle: "“Science is what we understand well enough to explain to a computer; R is everything else.”" 5 | output: 6 | xaringan::moon_reader: 7 | lib_dir: libs 8 | css: "css/remark.css" 9 | # chakra: libs/remark-js-latest-min.js 10 | nature: 11 | highlightStyle: github 12 | highlightLines: true 13 | countIncrementalSlides: false 14 | ratio: '16:9' 15 | editor_options: 16 | chunk_output_type: console 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | knitr::opts_chunk$set(dev = "svglite") 21 | source(file = here::here("slides/libs-reveal/xaringan_reveal_parentheses_balanced.R")) 22 | options(tibble.width = 55, 23 | tibble.max_extra_cols = 20) 24 | library(tidyverse) 25 | ``` 26 | 27 | class: blueblue, middle 28 | 29 | .big[More of the same...] 30 | 31 | --- 32 | 33 | # Milano Open Data 34 | 35 | .pull-left[ 36 | 37 | .middle[ 38 | 39 | The city of Milan releases carefully curated and well organized open data. 40 | 41 | Check the website: https://dati.comune.milano.it/ 42 | 43 | Also Regione Lombardia has a great open data portal: https://www.dati.lombardia.it/ 44 | 45 | Did you know that, in **Europe**, you can ask for **Open Data** to the of public administration? 46 | 47 | ] 48 | ] 49 | 50 | 51 | .pull-right[ 52 | .middle[ 53 | 54 | 55 | 56 |
57 |
58 |
59 | ]
60 | ]
61 |
62 | ---
63 |
64 | # Let's download some...
65 |
66 |
67 | Air pollution in Milan: https://dati.comune.milano.it/dataset/ds417-rilevazione-qualita-aria-2019
68 |
69 | ```{r}
70 | mil_url <-
71 | paste0("http://dati.comune.milano.it/dataset/",
72 | "3e752fec-06fd-421b-ae9b-4d5d7a177640/",
73 | "resource/698a58e6-f276-44e1-92b1-3d2b81a4ad47/download/",
74 | "qaria_datoariagiornostazione_2019-11-12.csv")
75 |
76 | mil_path <- here::here("data/qaria_datoariagiornostazione_2019-11-12.csv")
77 | ```
78 |
79 |
80 | ```{r, eval=FALSE}
81 | download.file(url = mil_url,
82 | destfile = mil_path)
83 | ```
84 |
85 |
86 | ---
87 |
88 | # Let's read the data...
89 |
90 | ```{r}
91 | dat_mil <- read_csv(mil_path)
92 | ```
93 |
94 | ```{r}
95 | dat_mil %>% print(n = 6)
96 | ```
97 |
98 | ---
99 |
100 |
101 | class: exercise, middle
102 |
103 | .exercise-title[Exercise:]
104 |
105 | .exercise-body[
106 |
107 | Describe the data:
108 |
109 | - How are they structured?
110 | - What's stored in the columns? What's their type?
111 | - Are there any NA?
112 |
113 | ]
114 |
115 | ---
116 |
117 | # Six Pollutants over Eight Stations
118 |
119 | .pull-left[
120 |
121 | ```{r}
122 | dat_mil %>%
123 | count(stazione_id)
124 | ```
125 |
126 | ]
127 |
128 | .pull-right[
129 |
130 | ```{r}
131 | dat_mil %>%
132 | count(inquinante)
133 | ```
134 |
135 | ]
136 |
137 | ---
138 |
139 |
140 | class: exercise, middle
141 |
142 | .exercise-title[Exercise:]
143 |
144 | .exercise-body[
145 |
146 | Use a visualization to learn when were the records taken?
147 |
148 | ]
149 |
150 | ```{r, eval = FALSE, echo = FALSE}
151 | dat_mil %>%
152 | ggplot(aes(x = data,
153 | y = inquinante)) +
154 | geom_text(label = "|")
155 | ```
156 |
157 | ```{r, eval = FALSE, echo = FALSE}
158 | dat_mil %>%
159 | ggplot(aes(x = data,
160 | y = stazione_id)) +
161 | geom_text(label = "|")
162 | ```
163 |
164 | ---
165 |
166 | ```{r}
167 | dat_mil %>%
168 | group_by(stazione_id, inquinante) %>%
169 | count()
170 | ```
171 |
172 | ---
173 |
174 |
175 | class: exercise, middle
176 |
177 | .exercise-title[Exercise:]
178 |
179 | .exercise-body[
180 |
181 | Count the NAs per column of the dataset.
182 |
183 | ]
184 |
185 | ```{r, echo = FALSE, eval = FALSE}
186 | dat_mil %>%
187 | map( ~ is.na(.) %>% sum())
188 | ```
189 |
190 |
191 | ---
192 |
193 |
194 | `r apply_reveal("count_na")`
195 |
196 | ```{r count_na, echo=FALSE, eval=FALSE}
197 | dat_mil %>%
198 | ggplot(aes(x = stazione_id,
199 | y = inquinante,
200 | colour = is.na(valore))) +
201 | geom_count(alpha = .8) +
202 | theme_bw() +
203 | scale_size_continuous(
204 | limits = c(0, NA)
205 | )
206 | ```
207 |
208 | ---
209 |
210 | # Some missing data are implicit
211 |
212 | https://tidyr.tidyverse.org/reference/complete.html
213 |
214 | ```{r}
215 | # complete
216 | dat_mil_all <-
217 | dat_mil %>%
218 | complete(stazione_id, nesting(data, inquinante))
219 | ```
220 |
221 | ```{r}
222 | dat_mil_all %>% print(n = 6)
223 | ```
224 |
225 | ---
226 |
227 | `r apply_reveal("count_na_exp")`
228 |
229 | ```{r count_na_exp, echo=FALSE, eval=FALSE}
230 | dat_mil_all %>%
231 | ggplot(aes(x = stazione_id,
232 | y = inquinante)) +
233 | geom_count() +
234 | theme_bw() +
235 | scale_size_continuous(
236 | limits = c(0, NA)
237 | )
238 | ```
239 |
240 | ---
241 |
242 |
243 | class: exercise, middle
244 |
245 | .exercise-title[Exercise:]
246 |
247 | .exercise-body[
248 |
249 | Some rows might be duplicated. How would you remove them?
250 |
251 | ]
252 |
253 | ```{r, include=FALSE}
254 | # dat_mil_all[dat_mil_all %>% duplicated(),] %>% view()
255 |
256 | dat_mil_all <-
257 | dat_mil_all %>%
258 | distinct()
259 |
260 | library(lubridate)
261 |
262 | ```
263 |
264 |
265 | ---
266 |
267 | class: exercise, middle
268 |
269 | .exercise-title[Exercise:]
270 |
271 | .exercise-body[
272 |
273 | How would you visualize the amount of pollutants in the air by month?
274 |
275 | Draw some sketches by hand.
276 |
277 | What value would you map to what object of your sketch?
278 |
279 | ]
280 |
281 | ---
282 |
283 |
284 | ```{r, message=FALSE, warning=FALSE, fig.height=3}
285 | dat_mil_all %>%
286 | mutate(month = month(data, label = T)) %>%
287 | ggplot(aes(x = month,
288 | y = valore)) +
289 | geom_boxplot() +
290 | aes(fill = inquinante)
291 | ```
292 |
293 | ---
294 |
295 | ```{r, message=FALSE, warning=FALSE, fig.height=6, dev="png", fig.width=14}
296 | dat_mil_all %>%
297 | ggplot(aes(x = data,
298 | y = valore)) +
299 | geom_point(alpha = .2) +
300 | aes(colour = inquinante) +
301 | geom_smooth()
302 | ```
303 |
304 | ---
305 |
306 | # Pivot
307 |
308 | Pivoting: https://tidyr.tidyverse.org/articles/pivot.html
309 |
310 | 
311 |
312 | Image Credits: https://r4ds.had.co.nz/tidy-data.html
313 |
314 | ```{r, echo=FALSE}
315 | options(tibble.print_min = 20)
316 | ```
317 |
318 |
319 | ---
320 |
321 | `r apply_reveal("pivot-wide")`
322 |
323 | ```{r pivot-wide, echo=FALSE, eval=FALSE}
324 | dat_mil_all %>%
325 | pivot_wider(
326 | names_from = inquinante,
327 | values_from = valore
328 | )
329 | ```
330 |
331 | ```{r, echo=FALSE}
332 | dat_mil_wide <-
333 | dat_mil_all %>%
334 | pivot_wider(
335 | names_from = inquinante,
336 | values_from = valore
337 | )
338 | ```
339 |
340 |
341 | ---
342 |
343 | `r apply_reveal("pivot-long")`
344 |
345 | ```{r pivot-long, eval=FALSE, echo=FALSE}
346 | # Pivot back
347 | dat_mil_wide %>%
348 | pivot_longer(
349 | cols = C6H6:SO2,
350 | names_to = "inquinante",
351 | values_to = "valore"
352 | )
353 | ```
354 |
355 | ---
356 |
357 | # Extend GGPLOT2
358 |
359 | https://www.ggplot2-exts.org/gallery/
360 |
361 | ```{r, eval = FALSE}
362 | # install.packages("GGally")
363 |
364 | library(GGally)
365 |
366 | ggpairs(dat_mil_wide)
367 | ```
368 |
369 | ---
370 |
371 | .center[
372 |
373 | ```{r, echo=FALSE, dev="png", message=FALSE, warning=FALSE, fig.height=8, fig.width=12}
374 | library(GGally)
375 |
376 | ggpairs(dat_mil_wide)
377 | ```
378 |
379 | ]
380 |
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1 | ---
2 | title: "Your Turn"
3 | author: "Otho Mantegazza
`r Sys.Date()`"
4 | subtitle: "I am my own experiment. I am my own work of R."
5 | output:
6 | xaringan::moon_reader:
7 | lib_dir: libs
8 | css: "css/remark.css"
9 | # chakra: libs/remark-js-latest-min.js
10 | nature:
11 | highlightStyle: github
12 | highlightLines: true
13 | countIncrementalSlides: false
14 | ratio: '16:9'
15 | editor_options:
16 | chunk_output_type: console
17 | ---
18 |
19 | ```{r setup, include=FALSE}
20 | knitr::opts_chunk$set(dev = "svglite")
21 | source(file = here::here("slides/libs-reveal/xaringan_reveal_parentheses_balanced.R"))
22 | options(tibble.width = 55,
23 | tibble.max_extra_cols = 20)
24 | library(tidyverse)
25 | ```
26 |
27 | class: blueblue, middle
28 |
29 | .big[Pick your dataset and go...]
30 |
31 | ---
32 |
33 | class: blueblue, middle
34 |
35 | .verybig[Structured]
36 |
37 | ---
38 |
39 | class: middle
40 |
41 | # 1 - Nobel Prizes
42 |
43 | https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-05-14
44 |
45 | --
46 |
47 | # 2 - US Births
48 |
49 | https://github.com/rfordatascience/tidytuesday/tree/master/data/2018/2018-10-02
50 |
51 | --
52 |
53 | # 3 - Ecological Footprint
54 |
55 | https://www.kaggle.com/footprintnetwork/ecological-footprint
56 |
57 | ---
58 |
59 | class: blueblue, middle
60 |
61 | .verybig[Challenging]
62 |
63 | ---
64 |
65 | class: middle
66 |
67 | # 4 - Forest Fires in Brazil
68 |
69 | https://www.kaggle.com/gustavomodelli/forest-fires-in-brazil
70 |
71 | --
72 |
73 | # 5 - Genetic Variations
74 |
75 | https://www.kaggle.com/kevinarvai/clinvar-conflicting
76 |
77 | ---
78 |
79 | class: blueblue, middle
80 |
81 | .verybig[When you approach new Data...]
82 |
83 | ---
84 |
85 | ## Get Familiar with the Topic
86 |
87 | Gather some background. Know at least just a bit of what the dataset is about. What is the issues that this dataset can solve?
88 |
89 | --
90 |
91 | ## Do you have a data dictionary?
92 |
93 | Which **variable are stored in the columns** and how?
94 |
95 | - If you don't have this information, can you gather it?
96 | - Do those description match what you observe in R? If not, what does not match?
97 | - Did you get warnings when you loaded the data in R? Can you fix them?
98 |
99 |
100 | ---
101 |
102 | # Are the data Tidy?
103 |
104 | Or can you make them so?
105 |
106 |
107 | - Are there missing values? How are they encoded? How are they distributed in the dataset?
108 | - Do the column have a practical names, do they need renaming?
109 | - Do the data are structured according to the tidy principles, do they need spreadong/gathering?
110 |
111 | Remember:
112 |
113 | - Each variable must have its own column.
114 | - Each observation must have its own row.
115 | - Each value must have its own cell.
116 |
117 | ---
118 |
119 | # Can you find any pattern in your data?
120 |
121 | Show it with a viz!
122 |
123 |
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1 |
2 |
3 |
`r Sys.Date()`" 5 | output: 6 | xaringan::moon_reader: 7 | lib_dir: libs 8 | css: "css/remark.css" 9 | # chakra: libs/remark-js-latest-min.js 10 | nature: 11 | highlightStyle: github 12 | highlightLines: true 13 | countIncrementalSlides: false 14 | ratio: '16:9' 15 | --- 16 | 17 | ```{r setup, include=FALSE} 18 | knitr::opts_chunk$set(dev = "svglite") 19 | # source(file = here::here("slides/libs-reveal/xaringan_reveal_parentheses_balanced.R")) 20 | options(tibble.width = 55, 21 | tibble.max_extra_cols = 20) 22 | library(tidyverse) 23 | # library(ggbeeswarm) 24 | # load(here::here("data/squirrels_tidy.Rdata")) 25 | ``` 26 | 27 | --- 28 | 29 | -------------------------------------------------------------------------------- /slides/css/remark.css: -------------------------------------------------------------------------------- 1 | /* Modified from the default css that comes from remark.js - xaringan */ 2 | /* https://github.com/yihui/xaringan */ 3 | 4 | /*from default fonts*/ 5 | 6 | @import url(https://fonts.googleapis.com/css?family=Yanone+Kaffeesatz); 7 | @import url(https://fonts.googleapis.com/css?family=Droid+Serif:400,700,400italic); 8 | @import url('https://fonts.googleapis.com/css?family=Inconsolata&display=swap'); 9 | @import url('https://fonts.googleapis.com/css?family=Indie+Flower&display=swap'); 10 | @import url('https://fonts.googleapis.com/css?family=Leckerli+One&display=swap'); 11 | @import url('https://fonts.googleapis.com/css?family=IBM+Plex+Sans&display=swap'); 12 | @import url('https://fonts.googleapis.com/css?family=News+Cycle&display=swap'); 13 | 14 | body { 15 | font-family: 'News Cycle', sans-serif; 16 | } 17 | 18 | /*From default.css*/ 19 | 20 | a, a > code { 21 | color: rgb(249, 38, 114); 22 | text-decoration: none; 23 | } 24 | .footnote { 25 | position: absolute; 26 | bottom: 3em; 27 | padding-right: 4em; 28 | font-size: 90%; 29 | } 30 | .remark-code-line-highlighted { background-color: #ffff88; } 31 | 32 | .inverse { 33 | background-color: #272822; 34 | color: #d6d6d6; 35 | text-shadow: 0 0 20px #333; 36 | } 37 | .inverse h1, .inverse h2, .inverse h3 { 38 | color: #f3f3f3; 39 | } 40 | /* Two-column layout */ 41 | .left-column { 42 | color: #777; 43 | width: 20%; 44 | height: 92%; 45 | float: left; 46 | } 47 | .left-column h2:last-of-type, .left-column h3:last-child { 48 | color: #000; 49 | } 50 | .right-column { 51 | width: 75%; 52 | float: right; 53 | padding-top: 1em; 54 | } 55 | .pull-left { 56 | float: left; 57 | width: 47%; 58 | } 59 | .pull-right { 60 | float: right; 61 | width: 47%; 62 | } 63 | .pull-right ~ * { 64 | clear: both; 65 | } 66 | img, video, iframe { 67 | max-width: 100%; 68 | } 69 | blockquote { 70 | border-left: solid 5px lightgray; 71 | padding-left: 1em; 72 | } 73 | .remark-slide table { 74 | margin: auto; 75 | border-top: 1px solid #666; 76 | border-bottom: 1px solid #666; 77 | } 78 | .remark-slide table thead th { border-bottom: 1px solid #ddd; } 79 | th, td { padding: 5px; } 80 | .remark-slide thead, .remark-slide tfoot, .remark-slide tr:nth-child(even) { background: #eee } 81 | 82 | @page { margin: 0; } 83 | @media print { 84 | .remark-slide-scaler { 85 | width: 100% !important; 86 | height: 100% !important; 87 | transform: scale(1) !important; 88 | top: 0 !important; 89 | left: 0 !important; 90 | } 91 | } 92 | 93 | /* Extra ------------------------------------------------------- */ 94 | 95 | /* Normal slide */ 96 | 97 | .remark-slide-content h1 { 98 | font-size: 40px; 99 | } 100 | 101 | /* Title slide ----------------------- */ 102 | .title-slide { 103 | background-image: linear-gradient(to right, #F1C232, #37C6C3); 104 | text-shadow: none; 105 | } 106 | 107 | .title-slide h1 { 108 | font-family: 'Leckerli One', cursive; 109 | color: #2087AC; 110 | text-align: left; 111 | font-size: 70px; 112 | margin-bottom: 20px; 113 | } 114 | 115 | .title-slide h2 { 116 | position: absolute; 117 | bottom: 20px; 118 | right: 80px; 119 | text-align: right; 120 | font-family: 'IBM Plex Sans', sans-serif; 121 | font-size: 30px; 122 | color: #707070; 123 | width: 500px; 124 | } 125 | 126 | .title-slide h3 { 127 | font-family: 'IBM Plex Sans', sans-serif; 128 | color: #2E3036; 129 | text-align: left; 130 | margin-top: 5px; 131 | margin-bottom: 5px; 132 | font-size: 24px; 133 | border-top-color: #2E3036; 134 | border-top-style: solid; 135 | border-top-width: 2.5px; 136 | box-sizing: content-box; 137 | } 138 | 139 | /* Blue transition slide -------------------- */ 140 | /* For tips and transiton */ 141 | .blueblue { 142 | background-image: linear-gradient(to right, #3A70DE, #9900FF) ; 143 | color: #E97E00; 144 | font-family: 'Indie Flower', cursive; 145 | } 146 | 147 | /* Big font */ 148 | .big { 149 | font-size: 45px; 150 | color: #C0C8E7; 151 | } 152 | 153 | /* very big font */ 154 | .verybig { 155 | font-size: 82px; 156 | } 157 | 158 | .orange { 159 | color: #E97E00; 160 | } 161 | 162 | /* Scroll text vertically --------------------- */ 163 | .scroller .remark-code { 164 | overflow-y: scroll; 165 | } 166 | 167 | /* Exercise ------------------------------------*/ 168 | .exercise { 169 | background-image: linear-gradient(to right, #ffe598, #ffcd43) ; 170 | font-family: 'Indie Flower', cursive; 171 | } 172 | 173 | .exercise-title { 174 | font-size: 82px; 175 | color: #00aaff; 176 | } 177 | 178 | .exercise-body { 179 | font-size: 45px; 180 | color: rgb(87, 156, 182); 181 | } 182 | 183 | .exercise-body-small { 184 | font-size: 32px; 185 | color: rgb(87, 156, 182); 186 | } 187 | 188 | /* Code output -------------------------------------*/ 189 | 190 | 191 | code.hljs { 192 | border-radius: 10px; 193 | background-image: linear-gradient(to right, #ffffff, #E7C2FF) !important; 194 | } 195 | 196 | /* hex stickers-------------------------------------*/ 197 | 198 | .imagelink { 199 | display: inline-block; 200 | margin: auto; 201 | } 202 | 203 | 204 | .imagelink:hover { 205 | filter: hue-rotate(60deg); 206 | } 207 | 208 | /* Splits --------------------------------------------*/ 209 | 210 | .split-40>.column:first-of-type { 211 | width: 40%; 212 | height:100%; 213 | position: absolute; 214 | top: 0; 215 | left: 0; } 216 | 217 | .split-40>.column:nth-of-type(2) { 218 | width: 60%; 219 | height:100%; 220 | position: absolute; 221 | top: 0; 222 | right: 0;} 223 | 224 | .pull-left-reveal { 225 | float: left; 226 | width: 35%; 227 | } 228 | .pull-right-reveal { 229 | float: right; 230 | width: 60%; 231 | font-size: 15px; 232 | } 233 | 234 | .remark-code-line-highlighted { 235 | border-radius: 7px; 236 | background-image: linear-gradient(to right, rgb(251, 241, 193), #ffffff) !important; 237 | } 238 | 239 | /* Portrait -------------------------------------- */ 240 | 241 | .portrait { 242 | max-width: 20%; 243 | border-radius: 50%; 244 | } 245 | -------------------------------------------------------------------------------- /slides/img/SVG/ggplot2.svg: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /slides/img/SVG/readr.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 148 | -------------------------------------------------------------------------------- /slides/img/SVG/tidyr.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 173 | -------------------------------------------------------------------------------- /slides/img/grammar-of-graphics.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/othomantegazza/eda-class/567295f0b1cec7b09469d03aa6f0b544845bb9c1/slides/img/grammar-of-graphics.png -------------------------------------------------------------------------------- /slides/img/janitor.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/othomantegazza/eda-class/567295f0b1cec7b09469d03aa6f0b544845bb9c1/slides/img/janitor.png -------------------------------------------------------------------------------- /slides/img/portrait.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/othomantegazza/eda-class/567295f0b1cec7b09469d03aa6f0b544845bb9c1/slides/img/portrait.jpg -------------------------------------------------------------------------------- /slides/img/readr.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 147 | -------------------------------------------------------------------------------- /slides/img/squirrel-svgrepo-com.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 82 | -------------------------------------------------------------------------------- /slides/quotes.md: -------------------------------------------------------------------------------- 1 | "Without art, the crudeness of reality would make the world unbearable" ― George Bernard Shaw 2 | 3 | “art is the most intense mode of individualism that the world has known.” 4 | ― Oscar Wilde 5 | 6 | “The artist always has the masters in his eyes.” 7 | ― Ralph Waldo Emerson 8 | 9 | “Science is what we understand well enough to explain to a computer; art is everything else.” 10 | ― Donald E. Knuth, Things a Computer Scientist Rarely Talks About 11 | 12 | “art, in itself, is an attempt to bring order out of chaos.” 13 | ― Stephen Sondheim 14 | 15 | “art never comes from happiness.” 16 | ― Chuck Palahniuk 17 | 18 | “You don’t make art out of good intentions.” 19 | ― Gustave Flaubert 20 | 21 | “Fuck art. I’ve gotta get out of the basement. I’ve gotta see the world. I’ve gotta make a difference.” 22 | ― Gerard Way 23 | 24 | “What is fair in men, passes away, but not so in art” 25 | ― Leonardo da Vinci 26 | 27 | “The point of art is to inspire you to create your own.” 28 | ― Misha Collins 29 | 30 | “art does not reproduce what we see. It makes us see.” 31 | ― Paul Klee 32 | 33 | “We need our art to teach us how to breathe” 34 | ― Ray Bradbury 35 | 36 | “To make us feel small in the right way is a function of ar. ” 37 | ― E.M. Forster 38 | 39 | “art is not in some far-off place.” 40 | ― Lydia Davis 41 | 42 | “I am my own experiment. I am my own work of art.” 43 | ― Madonna 44 | 45 | “Art and life are subjective. Not everybody's gonna dig what I dig, but I reserve the right to dig it.” 46 | ― Whoopi Goldberg 47 | 48 | “Art is science made clear.” 49 | ― Jean Cocteau 50 | 51 | “All art is quite useless.” 52 | ― Oscar Wilde, The Picture of Dorian Gray 53 | 54 | “Art is the proper task of life.” 55 | ― Friedrich Nietzsche --------------------------------------------------------------------------------