-
Notifications
You must be signed in to change notification settings - Fork 1
/
index.Rmd
555 lines (346 loc) · 19 KB
/
index.Rmd
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
---
title: "Analysis Reproducibility"
subtitle: "Why it matters and how to do it?"
author: ""
institute: ""
date: "8 July 2021"
output:
xaringan::moon_reader:
lib_dir: libs
nature:
highlightStyle: github
highlightLines: true
countIncrementalSlides: false
ratio: 16:9
css: ["unhcr-slides.css", "unhcr-slides-fonts.css"]
---
layout: true
<div class="my-footer"><span></span></div>
---
# Learning objectives
**About today:**
- Understand what you can gain from analysis reproducibility.
- Know what the main technical requirements are to set up for their analysis to be reproducible.
- Have a demonstration of a practical way to make a cake using household survey data: crunching, analysis & interpretation & data stories!
**Not today:**
- Induction Training on R language! For this head to [UNHCR Learn & Connect- R training](https://unhcr.csod.com/ui/lms-learner-playlist/PlaylistDetails?playlistId=e90e2279-e3a4-4ef2-8b74-757f91d224b2)
???
Have you ever completed the development of a data product with the feeling that you may have done a mistake or not have used the optimal way to clean or process your data?
As an analyst, if using “point & click” interface, “rewinding” all the steps at an advanced stage of the development of your product can be extremely painful and lengthy. Errare humanum est sed perseverare... If you want to learn from your mistake rather than to suffer from them, then analysis reproducibility is what you need...
In this session, we will introduce you to the basics of analysis reproducibility and explain you what elements you need to watch for when you kick start your analysis so that you can always rewind and improve any products you have already spent time on. We will also show you how you can learn from analysis done in a reproducible way done by other colleagues.
We will also show you through practical examples how to implement a fully reproducible data analysis workflow applied to a Household Survey dataset using R statistical Language: from initial data exploration to joint interpretation till the creation of data stories.
Last, we hope that this session will motivate you to join the vibrant R users community in UNHCR and soon become an R champion. In order to make the most of the session, we would advise you to install the following open source environment:
R - https://cran.r-project.org/bin/windows/base/
Rstudio Free version: https://www.rstudio.com/products/rstudio/download/
Create an account on Github - https://github.com/join? and install Github desktop https://desktop.github.com/
You may also start installing UNHCR Packages – following the instruction in their respective documentation published on Github:
Use UNHCR Open data - https://unhcr.github.io/unhcrdatapackage/docs/
API to connect to internal data source - https://unhcr-web.github.io/hcrdata/docs/
Perform High Frequency Check https://unhcr.github.io/HighFrequencyChecks/docs/
Process data crunching for survey dataset - https://unhcr.github.io/koboloadeR/docs/
Use UNHCR graphical template- https://unhcr-web.github.io/unhcRstyle/docs/
Last, you may also take advantage of going through one or more of the R learning content on Learn & Connect: Achieve your potential: UNHCR (csod.com) and see some practical tutorial on https://humanitarian-user-group.github.io/
The best way to start and learn is to have a concrete project! If you have one and need mentoring, we can liaise after the session.
---
## A Vision for data analysis
<span style='font-size:50px; color:grey;'>"Multi-functional teams, with strengthened data literacy, regularly conduct meaningful and documented joint data interpretation sessions to define their strategic directions based on statistical evidences"</span>
---
## A Theory of Change for Data analysis
<span style='font-size:30px;'>Proper user of data for advocacy & programmatic decision making </span>
<span style='font-size:40px;'>↪</span> Corporate __Standards__ exist to define how to encode & process household surveys dataset
<span style='font-size:40px;'>↪</span> Field data experts are trained based on precise recipes and predefined tools at each step of the __data life cycle__
<span style='font-size:40px;'>↪</span> Data are presented, discussed and linked to expert knowledge during data __interpretation__ sessions with a multi-functional team
<span style='font-size:40px;'>↪</span> All potential valid interpretations, including diverging views, are systematically __recorded__
<span style='font-size:40px;'>↪</span> __Persuasive__ "Data Stories“ and Policy papers are generated
---
## Data Science is like cooking
.pull-left[
When a chef is starting out with a new dish...
- Hypothesis Tasting -- Setting the right questions
- Ingredients = source the Data
- Wash your food = clean your data
- Flavor engineering = create calculated & derived variables
- Taste and explore = reshape & visualize the data
- Tune your oven = statistical modeling
- Art of plating = use styled brand
- Document your recipe = add technical comments
]
.pull-right[
.img85[]
]
???
https://towardsdatascience.com/data-science-explained-with-cooking-1a801731d749
https://towardsdatascience.com/5-reasons-why-data-science-is-like-cooking-daa506b4166a
Without good ingredients, you can’t cook a good dish.
Most time and effort are spent on cleaning and preparing the ingredients.
Different tools and techniques are needed for different recipes.
Cooking is both a science and an art.
You can’t become a great cook overnight.
---
## Information Anxiety & Analysis paralysis
<span style='font-size:30px;'> _When people do not want to eat the cake..._ </span>
.pull-left[
Potential source of reluctance...
* I do not know how to eat it: I see all those elements on it without being able to understand why they were added there and how this works...
* I do not trust this cake: How was it created? Did you follow correctly the recipe? Were the ingredient fresh? Can I trust how you sourced the ingredient?
* This is not the cake I need! It looks too heavy & too big: I will not be able to digest it...
* I am not hungry and do not even know what cake I want...
]
.pull-right[
.img[]
]
???
simple data visualization for decision making or complex patterns interpretation for knowledge building
---
.pull-left[
## Data Products: When What?
<span style='font-size:30px;'> Dashboard are relevant for displaying KPIs! (_like when you drive your car..._)</span>
> Key Performance Indicators (KPIs) are indicators specificlly designed to show progress toward an intended result, i.e a predefined __target__
> Create an analytical basis for __decision making__, aka Business Intelligence
> Help focus attention of Snr Management on what matters most - a good dashbaord needs to be __concise__
]
.pull-right[
```{r echo=FALSE, out.width='100%', message=FALSE, warning=FALSE}
#install.packages('DiagrammeR')
# /* https://mermaid-js.github.io/mermaid/#/flowchart */
# https://mermaid-js.github.io/mermaid-live-editor/edit
# https://mermaid-js.github.io/mermaid/#/flowchart?id=styling-and-classes
# https://bookdown.org/yihui/rmarkdown-cookbook/diagrams.html
# library(DiagrammeR)
# mermaid("dataproduct.mmd")
library("DiagrammeR")
grViz("
digraph CFA {
# direction of flowchart left to right
graph[layout = dot, # neato|dot|twopi|circo|twopi
#rankdir = LR,
label= '',
fontsize= 135]
# Decision
node [shape = oval, fontname = Lato, fontsize= 150, margin=1]
X [label = 'Building a data product'];
# Decision
node [shape = diamond,
style = filled,
color = Gold,
fillcolor = Gold,
fontname = Lato,
fontsize= 135]
a [label = 'Need to display\\n Key Performance\\n Indicators (KPI)?'];
b [label = 'Basic\\n Statistics?'];
c [label = 'Polished visuals with\\n brand style?'];
d [label = 'Satistical Modeling \\n Machine Learning?'];
e [label = 'Quick processing \\n is critical ?'];
f [label = 'Need for \\n Interactivity ?'];
# Choices
node [shape = box,
style = filled,
fillcolor = Lavender,
fontname = Lato,
fontsize= 160]
1 [label = 'Dashboard \\n with PowerBI',
constraint = false , margin=1.5 ];
2 [label = 'Notebook \\n with Rmd ', margin=1.5];
3 [label = 'Application\\n with Python', margin=1.5];
4 [label = 'Interface\\n with R-Shiny', margin=1.5];
# Connect nodes with edges and labels
X -> a [label = 'Start', fontsize= 130, penwidth = 15]
a -> 1 [label = 'Yes', fontsize= 130, penwidth = 15] ## PowerBI
a -> b [label = 'No', fontsize= 130, penwidth = 15] ## Continue
## Basic stat
b -> c [label = 'Yes', fontsize= 130, penwidth = 15]
b -> d [label = 'No', fontsize= 130, penwidth = 15]
## Polished visuals
c -> 2 [label = 'Yes', fontsize= 130, penwidth = 15]
c -> e [label = 'No', fontsize= 130, penwidth = 15]
## Machine learning
d -> e [label = 'Yes', fontsize= 130, penwidth = 15]
d -> 2 [label = 'No', fontsize= 130, penwidth = 15]
## Quick processing
e -> 3 [label = 'Yes', fontsize= 130, penwidth = 15]
e -> f [label = 'No', fontsize= 130, penwidth = 15]
## Interactivity
f -> 4 [label = 'Yes', fontsize= 130, penwidth = 15]
f -> 2 [label = 'No', fontsize= 130, penwidth = 15]
}
")
```
]
???
---
class: center, middle, inverse
# Why we need to work in a reproducible way?
### Ethics, Productivity, Learning
---
## Ethics: Science is '_show me_' - not '_trust me_'
.pull-left[
<span style='font-size:30px;'>Reproducibility allows for __peer review__</span>
Peer Review allows for __transparency__
Transparency allows for __scrutinity__
Scrutinity allows for __accountability__
> _It's okay to make mistakes, as long as one can detect them and that we can learn from them..._
]
.pull-right[
.img60[]
]
???
Ethical principles into algorithmic design - would apply as well when designing household vulnerability scoring formula to inform humanitarian targeting - https://www.hum-dseg.org/sites/default/files/2020-10/Framework%20for%20the%20ethical%20use.pdf
---
## Productivity: getting things done quickly and safely!
.pull-left[
<span style='font-size:30px;'>Automation through functions & scripts can help skipping __repetitive tasks__</span>
> Tasks that involve recurrent __data manipulation__ are undertaken by teams...
> .. but not everyone in the team needs to be a __geek/coder__!
When enough investment can be made, __Graphical User Interface__ (GUI) can be developed for specific functions to ease the learning curve of new users while they are still in the process of building up their personal R skills.
]
.pull-right[
.img130[]
]
???
https://blog.jonudell.net/2012/01/09/another-way-to-think-about-geeks-and-repetitive-tasks/
---
## An R-Community geared towards learning
.pull-left[
Which approach is the most appealing exercise among the 2 proposed aside?!!
<span style='font-size:40px;'>Start from an end-product and __reverse engineer it__!</span>
Eat the cake first! (then play with and change ingredients...)
]
.pull-right[
.img[]
]
???
https://speakerdeck.com/player/078592d490ae459790af7e77695fafcf?title=false&skipResize=true
---
class: center, middle, inverse
# Conditions for reproducibility.
### Sourcing data, documenting analysis, & packaging output
---
## Preparing data
.pull-left[
Data Wrangling takes usually more than 80% of any data project time...
Imagine if you need to rewind your analysis...
Correct at any steps in the process and re-run all..
]
.pull-right[
.img[]
]
---
## Documenting analysis
.img[]
---
## Packaging functions
.pull-left[
Gradual automation
* level 1: write a command
* level 2: organize multiple command together in re-usable function
* Level 3: organize multiple functions together in a package
* Level 4: includes test data & Documentation
* Level 5: <span style='font-size:30px;'>Unit testing, aka code review</span>
* Level 6: <span style='font-size:30px;'>Graphical User Interface (GUI)</span>
]
.pull-right[
.img75[]
]
---
class: center, middle, inverse
# Hands-on practice: a practical run-through based on Household survey dataset
### Crunching, Interpretation & Dissemination
---
## Step 1- Notebook for Automatic Data exploration, aka "crunching"
.img75[]
???
A comparison of packages for Automated Exploratory Data Analysis
https://arxiv.org/pdf/1904.02101.pdf
https://github.com/mstaniak/autoEDA-resources
One of the most significant disadvantages of PowerBI is that it is read-only. As a user, you cannot use charts/tables to make decisions and save them in a database directly. Also, PowerBI doesn’t have an accessible source code. You can only edit fields in WYSIWYG mode, which makes PowerBI easy to start but difficult to maintain. Having no source code makes it nearly impossible to have proper version control, automatically test logic, or collaborate on large projects. https://appsilon.com/powerbi-vs-r-shiny/
Pros of Power BI - Cross-filtering
Pros of RStudio -
- Visual editor for R Markdown documents
- In-line code execution using blocks
- Sophisticated statistical packages
- Supports Rcpp, python and SQL
- Can be themed
- In-line graphing support
- Latex support
What is RStudio?
An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution. Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages.
You can use R script with Power BI in several ways. The key ones include:
- Data source – expand the range of available data sources, provide data processing logic at the import stage
- Processing script – enrich data processing capabilities and engineering features
- Visualization – extend the range of visualizations available in R and implement charts
- As a part of Power BI visualization – gain the ability to build R-based visualizations and package them into ready-to-put boxes directly into Power BI.
https://www.predicagroup.com/blog/visualizing-data-r-script-power-bi/
---
## Step 2- Notebook for Data Insights documentation: Analysis Repo
.pull-left[
> Insight: The capacity to gain an accurate and deep understanding of someone or something
Not all charts will emulate need for interpretation - the data analyst need to gemerate the one that can create __debates__.
Charts need to be __crafted__ - for instance use chart title framed as "opening question"...
Insights arive when a multifunctional team is able to explain __unexpected patterns__, to challenge or revise __existing assumptions__, or to identify evidence to support __Call to action__.
]
.pull-right[
.img[]
]
???
https://www.ictworks.org/wp-content/uploads/2021/02/usaid-guide-artificial-intelligence.pdf
https://bluemoondigital.co/our-blog/5-elements-storytelling-data/
Check the analysis repo: http://analysis.unhcr.org
---
## Step 3- Notebook to communicate with data: Microsite
.pull-left[
<span style='font-size:30px;'>From __assumptions to evidence__ based statement</span>
Data is to support Narrative - not the other way around!
Leverage Art Data Storytelling to:
* __Explain__,
* __Enlighten__,
* __Engage__
]
.pull-right[
.img75[]
]
???
See https://github.com/unhcr-americas/ageingonthemove/blob/main/README.md
https://distill.pub/2020/communicating-with-interactive-articles/#applications-tab
Research Dissemination
Conducting novel research requires deep understanding and expertise in a specific area. Once achieved, researchers continue contributing new knowledge for future researchers to use and build upon. Over time, this consistent addition of new knowledge can build up, contributing to what some have called research debt. Not everyone is an expert in every field, and it can be easy to lose perspective and forget the bigger picture. Yet research should be understood by many. Interactive articles can be used to distill the latest progress in various research fields and make their methods and results accessible and understandable to a broader audience.
Opportunities
Engage and excite broader audience with latest research progress
Remove research debt, onboard new researchers
Make faster and clearer research progress
Challenges
No clear incentive structure for researchers
Little funding for bespoke research dissemination and communication
Not seen as a legitimate research contribution (e.g., to the field, or one's career)
---
class: center, middle, inverse
# Conclusion
---
## R in Humanitarian Context
.pull-left[
<span style='font-size:50px;'>You are not alone</span>
> More than 450 users from multiple organisation in the [humanitarian-useR-group](https://humanitarian-user-group.github.io/)
> Around already ≈20 R champions within UNHCR vs more than 420 PowerBI Pro users
> Try to start by using existing UNHCR packages and start from a project you can reproduce
]
.pull-right[
.img[]
]
---
## A call for Institutionalisation
.pull-left[
Using Standard Multi Tier IT Standard Support model to enhance reproducible analysis...
* Tier 4: Code Review & Quality Insurance / Contracted Company with global frame agreement
* Tier 3: Internal package development / Internal R champions team (cost: one yearly Rdev meeting to incentivize contributing staff)
* Tier 2: User induction & Advanced User Support / Global Data Service/DIMA (Data Science Team)
* Tier 1: Basic User Troubleshooting / Global Service Desk (WIPRO according to Documented Scenario)
* Tier 0: Self-support / Package documentation (maintained and improved on continuous basis)
]
.pull-right[
.img75[]
]
---
## Your Opinion Count
<span style='font-size:80px;'>
Please [fill this survey](https://enketo.unhcr.org/single/bac3b19c91c4e5f22f4fa409bc618119) to share your opinion and thoughts on the topic presented here
</span>
Slides are available in [English](https://unhcr-americas.github.io/reproducibility/index.html) & [French](https://unhcr-americas.github.io/reproducibility/index_fr.html) - slides note can be displayed by pressing keyboard shortcut `p` - and in PDF ([English](https://unhcr-americas.github.io/reproducibility/index.pdf) & [French](https://unhcr-americas.github.io/reproducibility/index_fr.pdf)) within this [github repo](https://github.com/unhcr-americas/reproducibility)