-
-
Notifications
You must be signed in to change notification settings - Fork 60
/
hw08_data-cleaning-regex.html
576 lines (444 loc) · 28.1 KB
/
hw08_data-cleaning-regex.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
<!DOCTYPE html>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>Homework 8 - Data cleaning</title>
<script type="text/javascript">
window.onload = function() {
var imgs = document.getElementsByTagName('img'), i, img;
for (i = 0; i < imgs.length; i++) {
img = imgs[i];
// center an image if it is the only element of its parent
if (img.parentElement.childElementCount === 1)
img.parentElement.style.textAlign = 'center';
}
};
</script>
<!-- Styles for R syntax highlighter -->
<style type="text/css">
pre .operator,
pre .paren {
color: rgb(104, 118, 135)
}
pre .literal {
color: #990073
}
pre .number {
color: #099;
}
pre .comment {
color: #998;
font-style: italic
}
pre .keyword {
color: #900;
font-weight: bold
}
pre .identifier {
color: rgb(0, 0, 0);
}
pre .string {
color: #d14;
}
</style>
<!-- R syntax highlighter -->
<script type="text/javascript">
var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/</gm,"<")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};
hljs.initHighlightingOnLoad();
</script>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 13px;
}
body {
max-width: 800px;
margin: auto;
padding: 1em;
line-height: 20px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre, img {
max-width: 100%;
}
pre {
overflow-x: auto;
}
pre code {
display: block; padding: 0.5em;
}
code {
font-size: 92%;
border: 1px solid #ccc;
}
code[class] {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
</head>
<body>
<h1>Homework 8 - Data cleaning</h1>
<p>Dean Attali<br/>
Nov 1 2014 </p>
<p>Last updated: 2014-11-07 23:56:26</p>
<h2>Overview</h2>
<p>In this assignment I will clean up a dirty dataset to get it ready for analysis
using various string operations and regexes. </p>
<h3>Loading dirty Gapminder</h3>
<p>I will load the data with <code>strip.white</code> = TRUE and FALSE and compare the difference</p>
<h4>strip.white = FALSE</h4>
<pre><code class="r">gDatRaw <- read.table(file.path(DATA_DIR, "gapminderDataFiveYear_dirty.txt"),
header = TRUE, sep = "\t", row.names = NULL, as.is = TRUE,
strip.white = FALSE)
</code></pre>
<h4>strip.white = TRUE</h4>
<pre><code class="r">gDatRawStrip <- read.table(file.path(DATA_DIR, "gapminderDataFiveYear_dirty.txt"),
header = TRUE, sep = "\t", row.names = NULL, as.is = TRUE,
strip.white = TRUE)
</code></pre>
<h4>Comparing strip.white versions</h4>
<p>Let's do a very high-level inspection of the two</p>
<pre><code class="r">str(gDatRaw)
</code></pre>
<pre><code>## 'data.frame': 1698 obs. of 5 variables:
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ gdpPercap: num 779 821 853 836 740 ...
## $ region : chr "Asia_Afghanistan" "Asia_Afghanistan" "Asia_Afghanistan" "Asia_Afghanistan" ...
</code></pre>
<pre><code class="r">str(gDatRawStrip)
</code></pre>
<pre><code>## 'data.frame': 1698 obs. of 5 variables:
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ gdpPercap: num 779 821 853 836 740 ...
## $ region : chr "Asia_Afghanistan" "Asia_Afghanistan" "Asia_Afghanistan" "Asia_Afghanistan" ...
</code></pre>
<p>Based on this, we can't really see any difference. Let's dig deeper.</p>
<pre><code class="r">all.equal(gDatRaw, gDatRawStrip)
</code></pre>
<pre><code>## [1] "Component \"region\": 5 string mismatches"
</code></pre>
<p>Ah, it looks like some regions are not identical.<br/>
To try to find the mismatching regions, I will find the difference between
the union of the regions in both datasets and the intersection of the regions </p>
<pre><code class="r">setdiff(union(gDatRaw$region, gDatRawStrip$region),
intersect(gDatRaw$region, gDatRawStrip$region))
</code></pre>
<pre><code>## [1] "Americas_Colombia " " Asia_Jordan"
## [3] " Asia_Korea, Dem. Rep."
</code></pre>
<p>Now we see the difference. There are some regions that have extra whitespace
in the beginning/end of the region string.
Conclusion: according to the <code>read.table</code> documentation, <code>strip.white</code> strips
leading and trailing whitespace from unquoted character fields. That's pretty
self-explanatory, but it's nice to see it in action: when <code>strip.white = FALSE</code>,
some regions had extra unwanted whitespace. In our case, we don't care about
leading/trailing whitespace, so we want to work with the dataset that was
read using <code>whitespace = TRUE</code> - the version that removed whitespace. </p>
<pre><code class="r">gDat <- gDatRawStrip
</code></pre>
<h3>Splitting or merging the columns</h3>
<p>Let's look at a bit of the data to see if any columns need to be split/merged</p>
<pre><code class="r">head(gDat)
</code></pre>
<pre><code>## year pop lifeExp gdpPercap region
## 1 1952 8425333 28.801 779.4453 Asia_Afghanistan
## 2 1957 9240934 30.332 820.8530 Asia_Afghanistan
## 3 1962 10267083 31.997 853.1007 Asia_Afghanistan
## 4 1967 11537966 34.020 836.1971 Asia_Afghanistan
## 5 1972 13079460 36.088 739.9811 Asia_Afghanistan
## 6 1977 14880372 38.438 786.1134 Asia_Afghanistan
</code></pre>
<p>The <em>region</em> column seems to be holding two pieces of information that could
be split (continent and country). Let's try using <code>tidyr::separate</code> to do that </p>
<blockquote>
<p><code>tidyr</code> is awesome! Two weeks ago I wouldn't have known to do this :)</p>
</blockquote>
<pre><code class="r">gDat <-
gDat %>%
separate("region", c("continent", "country"), sep = "_")
</code></pre>
<pre><code>## Error: Values not split into 2 pieces at 361, 362, 363, 364, 365, 366
</code></pre>
<p>Hmm… there's some problem with a few of the rows. Let's have a look at one
of these rows to see if it can give us any insight into why the <code>separate</code>
didnt work.</p>
<pre><code class="r">gDat[361, ]
</code></pre>
<pre><code>## year pop lifeExp gdpPercap
## 361 1952 2977019 40.477 1388.595
## region
## 361 Africa_Cote dIvoire\n1957\t3300000\t42.469\t1500.895925\tAfrica_Cote dIvoire
</code></pre>
<p>Well, there's clearly a problem with the <em>region</em> value of this observation…
It looks like the import was messed up on Cote d'Ivoire. We can see that the
apostrophe is missing from the country's name, so an educated guess here would
be that the import step choked on the apostrophe/single quote.<br/>
Upon looking at the <code>read.table</code> documentation, I learned that by default
it uses both souble and single quotes as quoting characters. In our dataset
we don't have any quotes so I should disable them. Let's try doing data import
again (with strip.white = TRUE) and also setting the quote character to nothing.</p>
<pre><code class="r">gDat <- read.table(file.path(DATA_DIR, "gapminderDataFiveYear_dirty.txt"),
header = TRUE, sep = "\t", row.names = NULL, as.is = TRUE,
strip.white = TRUE, quote = "")
</code></pre>
<p>Now let's try splitting up the <em>region</em> column again</p>
<pre><code class="r">gDat <-
gDat %>%
separate("region", c("continent", "country"), sep = "_")
head(gDat)
</code></pre>
<pre><code>## year pop lifeExp gdpPercap continent country
## 1 1952 8425333 28.801 779.4453 Asia Afghanistan
## 2 1957 9240934 30.332 820.8530 Asia Afghanistan
## 3 1962 10267083 31.997 853.1007 Asia Afghanistan
## 4 1967 11537966 34.020 836.1971 Asia Afghanistan
## 5 1972 13079460 36.088 739.9811 Asia Afghanistan
## 6 1977 14880372 38.438 786.1134 Asia Afghanistan
</code></pre>
<p>Success!</p>
<h3>Missing values</h3>
<p>Now we will see if there are any missing (NA or empty) values in the data
and attempt to fill them properly. </p>
<p>To find out which rows (if any) have missing values, I use the <code>apply</code> function
to iterate row-by-row and see if there are any NA or empty strings.
Then based on the result we can see which rows contain missing values.</p>
<pre><code class="r">rowsMissing <-
apply(gDat, 1, function(row) {
sum(is.na(row)) > 0 | sum(row == "") > 0
})
gDat[which(rowsMissing), ]
</code></pre>
<pre><code>## year pop lifeExp gdpPercap continent country
## 241 1952 14785584 68.75 11367.16 Canada
## 242 1957 17010154 69.96 12489.95 Canada
## 243 1962 18985849 71.30 13462.49 Canada
</code></pre>
<p>Alright, so it looks like a few of Canada's rows are missing the continent variable.<br/>
For the sake of programming, let's play dumb and pretend we don't know where
Canada is. We'll first find out what continent the other Canada rows use,
and then use that value to fill in the blanks</p>
<pre><code class="r">gDat %>%
filter(country == "Canada") %>%
dplyr::select(continent) %>%
first()
</code></pre>
<pre><code>## [1] "" "" "" "Americas" "Americas" "Americas"
## [7] "Americas" "Americas" "Americas" "Americas" "Americas" "Americas"
</code></pre>
<pre><code class="r">gDat[which(rowsMissing), "continent"] <- "Americas"
</code></pre>
<p>Now just a final check to make sure there are no more missing values</p>
<pre><code class="r">rowsMissing <-
apply(gDat, 1, function(row) {
sum(is.na(row)) > 0 | sum(row == "") > 0
})
gDat[which(rowsMissing), ]
</code></pre>
<pre><code>## [1] year pop lifeExp gdpPercap continent country
## <0 rows> (or 0-length row.names)
</code></pre>
<p>Great!</p>
<h3>Inconsistent spelling/capitalization</h3>
<p>Ah, the worst part of dealing with human-entered data…<br/>
There are two character variables, <em>continent</em> and <em>country</em>, so that's
where we will look for potential problems. First let's examine the continents</p>
<pre><code class="r">unique(gDat$continent)
</code></pre>
<pre><code>## [1] "Asia" "Europe" "Africa" "Americas" "Oceania"
</code></pre>
<p>Well, the continents looks pretty good, there are only a few of them and it's
easy enough to visually see that they are all legitimately unique continents. </p>
<p>With countries we will need to follow a more complicated approach since there
are many countries and it's hard to visually look at all the names and find duplicates.
We know that every country should have one row per year. So if we know how many
years we have, we know how many observations per country we expect. So how many
years are there in the data?</p>
<pre><code class="r">length(unique(gDat$year))
</code></pre>
<pre><code>## [1] 12
</code></pre>
<p>Ok, this means we expect 12 rows for every country.<br/>
So now to know which countries have more than one spelling, we simply count how
many observations each country has, and note the ones that don't match the expected.</p>
<pre><code class="r">which(table(gDat$country) != length(unique(gDat$year)))
</code></pre>
<pre><code>## Central african republic Central African Republic
## 22 23
## china China
## 26 27
## Congo, Dem. Rep. Congo, Democratic Republic
## 30 31
## Cote d'Ivoire Cote d'Ivore
## 34 35
## Democratic Republic of the Congo
## 39
</code></pre>
<p>This looks pretty good, we can see that <em>Central African Republic</em> and “<em>China</em>
have multiple names that are simply different cases, the <em>Democratic Republic of
Congo</em> has 3 conflicting names, and <em>Cote d'Ivoire</em> has two names. The integers
that we see here are the index of the country in the data, which isn't very useful.
If we want to know which spelling is the correct one for each country, we can
see how many rows correspond to each of the spellings, and combined with common sense
we can determine how to correct the other values.</p>
<pre><code class="r">table(gDat$country)[table(gDat$country) != length(unique(gDat$year))]
</code></pre>
<pre><code>##
## Central african republic Central African Republic
## 4 8
## china China
## 4 8
## Congo, Dem. Rep. Congo, Democratic Republic
## 10 1
## Cote d'Ivoire Cote d'Ivore
## 11 1
## Democratic Republic of the Congo
## 1
</code></pre>
<p>Now we can see that <em>Congo, Dem. Rep.</em> seems to be the consensus name for that
country, and that <em>Cote d'Ivore</em> is probably a typo since it only appeared once.
For China and Central African Republic, the uncapitalized versions appear fewer
times, and we will simply capitalize them.<br/>
We will use regular expressions to do these changes.</p>
<p>China, Central African Republic and Cote d'Ivoire are very trivial to fix because
there is only one alternative spelling.</p>
<pre><code class="r">gDat <-
gDat %>%
mutate(country = gsub("^Central african republic$",
"Central African Republic",
country)) %>%
mutate(country = gsub("^china$",
"China",
country)) %>%
mutate(country = gsub("^Cote d'Ivore$",
"Cote d'Ivoire",
country))
</code></pre>
<p>The situation is a little more complex for Congo since there are two different
wrong spellings that are not super similar. We want to capture both
<em>Congo, Democratic Republic</em> and <em>Democratic Republic of the Congo</em>, so
the regex will capture strings that either begin with "congo” and end with
“democratic republic”, or the other way around (and ignore the case)</p>
<pre><code class="r">gDat <-
gDat %>%
mutate(country = gsub("^((congo)|(democratic republic)).*((democratic republic)|(congo))$",
"Congo, Dem. Rep.",
country,
ignore.case = TRUE))
</code></pre>
<p>Let's make sure we don't have any more countries with multiple spellings</p>
<pre><code class="r">table(gDat$country)[table(gDat$country) != length(unique(gDat$year))]
</code></pre>
<pre><code>## named integer(0)
</code></pre>
<p>Sweet!</p>
<h3>Final check</h3>
<p>Load the clean Gadpminder data and compare it to our version to see
if we cleaned it properly</p>
<pre><code class="r">gDatClean <- read.table(file.path(DATA_DIR, "gapminderDataFiveYear.txt"),
header = TRUE, sep = "\t", row.names = NULL, as.is = TRUE,
quote = "")
identical(gDat, gDatClean)
</code></pre>
<pre><code>## [1] FALSE
</code></pre>
<p>:(<br/>
I know from past experience that if the order of the columns is not the same
in both dataframes, this will return <code>FALSE</code>. Let's just change the order of
the columns in my dataframe to match the order in the clean version </p>
<pre><code class="r">gDat <- gDat %>%
dplyr::select(match(names(gDatClean), names(gDat)))
</code></pre>
<p>And now check again if they're the same</p>
<pre><code class="r">identical(gDat, gDatClean)
</code></pre>
<pre><code>## [1] TRUE
</code></pre>
<p>Hooray, the dirty data I read from the file is now clean and matches exactly
the good version! This means data cleaning is done :)</p>
</body>
</html>