-
Notifications
You must be signed in to change notification settings - Fork 1
/
module_1_slides.html
583 lines (384 loc) · 12 KB
/
module_1_slides.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<title>Module 1 Dealing with Data</title>
<meta charset="utf-8" />
<meta name="author" content="" />
<link rel="stylesheet" href="xaringan-themer.css" type="text/css" />
<link rel="stylesheet" href="other.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Module 1 <br><br> Dealing with Data
---
<!-- Make default font bigger -->
<style type="text/css">
.remark-slide-content {
font-size: 30px;
}
</style>
## Introduction
Dealing with data is challenging
Most of your time may be spent here
- More time = Better analysis
Want data you can feel confident about
---
## Objects and Classes
Everything you use in R is an <span class="emph">object</span>
- of a certain <span class="emph">class</span>
Objects can be anything:
- a single value
- a function
- a vector of values
- a data frame/table
- a list of 1000 models
*Anything!*
```r
x = 1:3
y = 'a'
z = list(one = x, two = y)
```
---
## Packages
Base R is a fully functioning data science environment
Packages add a lot of additional functionality
- easier programming
- fancier analysis
- better visualization
You'll always be using packages
---
## Functions
<span class="func">Functions</span> are special objects
- take an input
- return a value
We use them to manipulate the objects we create
- including the output of other functions.
The value can be anything, and is often many things.
```r
mydata = read_csv('myfile', na = c('', 'NA', '999'), skip = 10)
summary(myobject)
```
---
## Data Structures
<span class="objclass">Vectors</span> form the basis of R data structures
Two main types are <span class="objclass">atomic</span> and <span class="objclass">lists</span>
```r
my_vector <- c(1, 2, 3) # standard vector
```
```r
my_list <- list(a = 1, b = 2) # a named list
my_list
```
```
$a
[1] 1
$b
[1] 2
```
---
## Data Frames
Data frames are a special kind of list
- The most commonly used for most data science
```r
my_data = data.frame(
id = 1:3,
name = c('Vernon', 'Ace', 'Cora')
)
```
---
## Data Frames
```r
my_data
```
```
id name
1 1 Vernon
2 2 Ace
3 3 Cora
```
```r
class(my_data)
```
```
[1] "data.frame"
```
---
## Importing Data
Typically data is already available...
So importing data is the first step
Data may come in various types
- **text**: csv, tsv, json
- **database**: SQL, MongoDB
- **proprietary**: Excel, SAS
```r
demographics = read.csv('data/demos_anonymized.csv')
ids = read.csv('data/ids_anonymized.csv')
```
---
## Working with Databases
Databases must be connected to
But otherwise are used just like data frames
Not all <span class="pack">dplyr</span> operations will translate
- The most common ones will though
- And it will work across SQL flavors
- And it allows one to stay within the R world
```r
library(DBI)
con <- dbConnect(RSQLite::SQLite(), ":memory:")
# con
copy_to(con, demographics, 'demos')
```
---
## Working with Databases
```r
demos_db <- tbl(con, "demos")
demos_db %>%
filter(award_total_amount > 100000) %>%
show_query()
```
```
<SQL>
SELECT *
FROM `demos`
WHERE (`award_total_amount` > 100000.0)
```
---
## Using SQL directly in an R Notebook
If you already have an SQL database and want to use SQL directly, this can be done with R Markdown
```
SELECT "year", "month", "libuser"
SUM(CASE WHEN ("libuser" = 'yes')
THEN (1.0) ELSE (0.0) END) AS "subscribe",
COUNT(*) AS "total"
FROM ("demos")
GROUP BY "year", "month"
```
---
## Data Processing
What is the tidyverse?
The tidyverse consists of a few key packages:
- <span class="pack">ggplot2</span>: data visualization
- <span class="pack">dplyr</span>: data manipulation
- <span class="pack">tidyr</span>: data tidying
- <span class="pack">readr</span>: data import
- <span class="pack">purrr</span>: functional programming
- <span class="pack">tibble</span>: tibbles, a modern re-imagining of data frames
And of course the <span class="pack">tidyverse</span> package itself
- loads all of the above in a way that will avoid naming conflicts
---
## Selecting Columns
A common step is to subset the data by column
```r
demographics %>%
select(gender, age, libuser)
demographics %>%
select(-libuser)
```
### Select helpers
- <span class="func">starts_with</span>: starts with a prefix
- <span class="func">ends_with</span>: ends with a suffix
- <span class="func">contains</span>: contains a literal string
- <span class="func">matches</span>: matches a regular expression
- <span class="func">num_range</span>: a numerical range like x01, x02, x03.
- <span class="func">one_of</span>: variables in character vector.
- <span class="func">everything</span>: all variables.
---
## Filtering Rows
To filtering data, think of a logical statement
- Can be `TRUE` or `FALSE`
```r
my_filtered_data = demographics %>%
filter(age < 40)
my_filtered_data = demographics %>%
filter(libuser == 1)
```
---
## Generating new data
Use mutate to create a new column
```r
mydata = demographics %>%
mutate(new_age = (age - mean(age, na.rm = T))/sd(age, na.rm = T))
```
### For specific scenarios:
- <span class="func">mutate_at</span>
- <span class="func">mutate_if</span>
- <span class="func">mutate_all</span>
---
## Renaming columns
```r
demographics = demographics %>%
rename(age_std = new_age)
```
Similar variants as <span class="func">mutate</span>
- <span class="func">rename_at</span>
- <span class="func">rename_if</span>
- <span class="func">rename_all</span>
---
## Merging data from different sources
Merging data can take on a variety of forms
Mutating Joins:
- <span class="func">left_join</span>
- <span class="func">right_join</span>
- <span class="func">full_join</span>
- <span class="func">inner_join</span>
Filtering Joins:
- <span class="func">semi_join</span>
- <span class="func">anti_join</span>
---
## Original Data Frame
<img src="img/original-dfs.png" width="100%" style="display: block; margin: auto;" />
---
## Left Join
> All rows from x, and all columns from x and y. Rows in x with no match in y will have NA values in the new columns.
<img src="img/left-join.gif" width="66%" style="display: block; margin: auto;" />
---
## Left Join (extra Rows in y)
> If there are multiple matches between x and y, all combinations of the matches are returned.
<img src="img/left-join-extra.gif" width="66%" style="display: block; margin: auto;" />
---
## Right Join
> All rows from y, and all columns from x and y. Rows in y with no match in x will have NA values in the new columns.
<img src="img/right-join.gif" width="66%" style="display: block; margin: auto;" />
---
## Full Join
> All rows and all columns from both x and y. Where there are not matching values, returns NA for the one missing.
<img src="img/full-join.gif" width="66%" style="display: block; margin: auto;" />
---
## Inner Join
> All rows from x where there are matching values in y, and all columns from x and y.
<img src="img/inner-join.gif" width="66%" style="display: block; margin: auto;" />
---
## Semi Join
> All rows from x where there are matching values in y, keeping just columns from x.
<img src="img/semi-join.gif" width="66%" style="display: block; margin: auto;" />
---
## Anti Join
> All rows from x where there are not matching values in y, keeping just columns from x.
<img src="img/anti-join.gif" width="66%" style="display: block; margin: auto;" />
---
## Example Joins
```r
# same N rows as demos
left_join(demographics, ids)
# only ~ 50k rows
inner_join(demographics, ids)
```
---
## Reshaping Data
Wide to long
Long to Wide
---
## Reshaping Data
<img src="img/original-dfs-tidy.png" width="66%" style="display: block; margin: auto;" />
---
## Reshaping Data
<img src="img/tidyr-spread-gather.gif" width="66%" style="display: block; margin: auto;" />
---
## Benefits of long data
More 'tidy'
Easier visualizations
Assumed for many common models
---
## Caveat
The next major release of tidyr will change approach
More flexible, consistent
- <span class="func">pivot_longer</span>
- <span class="func">pivot_wider</span>
---
## Summary
All data requires processing, cleaning, etc.
Data processing takes care and consideration
Getting comfortable with your tool makes it easier
#### Better processing means great analysis and visualization!
---
## Tidyverse
Figure credits: https://github.com/gadenbuie/tidyexplain
</textarea>
<style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
<script src="https://remarkjs.com/downloads/remark-latest.min.js"></script>
<script>var slideshow = remark.create({
"highlightStyle": "pygments",
"highlightLines": true,
"countIncrementalSlides": false
});
if (window.HTMLWidgets) slideshow.on('afterShowSlide', function (slide) {
window.dispatchEvent(new Event('resize'));
});
(function(d) {
var s = d.createElement("style"), r = d.querySelector(".remark-slide-scaler");
if (!r) return;
s.type = "text/css"; s.innerHTML = "@page {size: " + r.style.width + " " + r.style.height +"; }";
d.head.appendChild(s);
})(document);
(function(d) {
var el = d.getElementsByClassName("remark-slides-area");
if (!el) return;
var slide, slides = slideshow.getSlides(), els = el[0].children;
for (var i = 1; i < slides.length; i++) {
slide = slides[i];
if (slide.properties.continued === "true" || slide.properties.count === "false") {
els[i - 1].className += ' has-continuation';
}
}
var s = d.createElement("style");
s.type = "text/css"; s.innerHTML = "@media print { .has-continuation { display: none; } }";
d.head.appendChild(s);
})(document);
// delete the temporary CSS (for displaying all slides initially) when the user
// starts to view slides
(function() {
var deleted = false;
slideshow.on('beforeShowSlide', function(slide) {
if (deleted) return;
var sheets = document.styleSheets, node;
for (var i = 0; i < sheets.length; i++) {
node = sheets[i].ownerNode;
if (node.dataset["target"] !== "print-only") continue;
node.parentNode.removeChild(node);
}
deleted = true;
});
})();</script>
<script>
(function() {
var links = document.getElementsByTagName('a');
for (var i = 0; i < links.length; i++) {
if (/^(https?:)?\/\//.test(links[i].getAttribute('href'))) {
links[i].target = '_blank';
}
}
})();
</script>
<script>
slideshow._releaseMath = function(el) {
var i, text, code, codes = el.getElementsByTagName('code');
for (i = 0; i < codes.length;) {
code = codes[i];
if (code.parentNode.tagName !== 'PRE' && code.childElementCount === 0) {
text = code.textContent;
if (/^\\\((.|\s)+\\\)$/.test(text) || /^\\\[(.|\s)+\\\]$/.test(text) ||
/^\$\$(.|\s)+\$\$$/.test(text) ||
/^\\begin\{([^}]+)\}(.|\s)+\\end\{[^}]+\}$/.test(text)) {
code.outerHTML = code.innerHTML; // remove <code></code>
continue;
}
}
i++;
}
};
slideshow._releaseMath(document);
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement('script');
script.type = 'text/javascript';
script.src = 'https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-MML-AM_CHTML';
if (location.protocol !== 'file:' && /^https?:/.test(script.src))
script.src = script.src.replace(/^https?:/, '');
document.getElementsByTagName('head')[0].appendChild(script);
})();
</script>
</body>
</html>