World Happiness Dataset
Download the dataset from the following link:
https://www.kaggle.com/unsdsn/world-happiness
You can see some basic characteristics of the dataset with the dim() and str() functions.
Before we merge these years together in one file, we might want to create a year variable in each one.
'data.frame': 158 obs. of 13 variables:
$ Country : chr "Switzerland" "Iceland" "Denmark" "Norway" ...
$ Region : chr "Western Europe" "Western Europe" "Western Europe" "Western Europe" ...
$ Happiness.Rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Happiness.Score : num 7.59 7.56 7.53 7.52 7.43 ...
$ Standard.Error : num 0.0341 0.0488 0.0333 0.0388 0.0355 ...
$ Economy..GDP.per.Capita. : num 1.4 1.3 1.33 1.46 1.33 ...
$ Family : num 1.35 1.4 1.36 1.33 1.32 ...
$ Health..Life.Expectancy. : num 0.941 0.948 0.875 0.885 0.906 ...
$ Freedom : num 0.666 0.629 0.649 0.67 0.633 ...
$ Trust..Government.Corruption.: num 0.42 0.141 0.484 0.365 0.33 ...
$ Generosity : num 0.297 0.436 0.341 0.347 0.458 ...
$ Dystopia.Residual : num 2.52 2.7 2.49 2.47 2.45 ...
$ year : num 2015 2015 2015 2015 2015 ...
'data.frame': 157 obs. of 14 variables:
$ Country : chr "Denmark" "Switzerland" "Iceland" "Norway" ...
$ Region : chr "Western Europe" "Western Europe" "Western Europe" "Western Europe" ...
$ Happiness.Rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Happiness.Score : num 7.53 7.51 7.5 7.5 7.41 ...
$ Lower.Confidence.Interval : num 7.46 7.43 7.33 7.42 7.35 ...
$ Upper.Confidence.Interval : num 7.59 7.59 7.67 7.58 7.47 ...
$ Economy..GDP.per.Capita. : num 1.44 1.53 1.43 1.58 1.41 ...
$ Family : num 1.16 1.15 1.18 1.13 1.13 ...
$ Health..Life.Expectancy. : num 0.795 0.863 0.867 0.796 0.811 ...
$ Freedom : num 0.579 0.586 0.566 0.596 0.571 ...
$ Trust..Government.Corruption.: num 0.445 0.412 0.15 0.358 0.41 ...
$ Generosity : num 0.362 0.281 0.477 0.379 0.255 ...
$ Dystopia.Residual : num 2.74 2.69 2.83 2.66 2.83 ...
$ year : num 2016 2016 2016 2016 2016 ...
'data.frame': 155 obs. of 13 variables:
$ Country : chr "Norway" "Denmark" "Iceland" "Switzerland" ...
$ Happiness.Rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Happiness.Score : num 7.54 7.52 7.5 7.49 7.47 ...
$ Whisker.high : num 7.59 7.58 7.62 7.56 7.53 ...
$ Whisker.low : num 7.48 7.46 7.39 7.43 7.41 ...
$ Economy..GDP.per.Capita. : num 1.62 1.48 1.48 1.56 1.44 ...
$ Family : num 1.53 1.55 1.61 1.52 1.54 ...
$ Health..Life.Expectancy. : num 0.797 0.793 0.834 0.858 0.809 ...
$ Freedom : num 0.635 0.626 0.627 0.62 0.618 ...
$ Generosity : num 0.362 0.355 0.476 0.291 0.245 ...
$ Trust..Government.Corruption.: num 0.316 0.401 0.154 0.367 0.383 ...
$ Dystopia.Residual : num 2.28 2.31 2.32 2.28 2.43 ...
$ year : num 2017 2017 2017 2017 2017 ...
'data.frame': 156 obs. of 10 variables:
$ Overall.rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Country.or.region : chr "Finland" "Norway" "Denmark" "Iceland" ...
$ Score : num 7.63 7.59 7.55 7.5 7.49 ...
$ GDP.per.capita : num 1.3 1.46 1.35 1.34 1.42 ...
$ Social.support : num 1.59 1.58 1.59 1.64 1.55 ...
$ Healthy.life.expectancy : num 0.874 0.861 0.868 0.914 0.927 0.878 0.896 0.876 0.913 0.91 ...
$ Freedom.to.make.life.choices: num 0.681 0.686 0.683 0.677 0.66 0.638 0.653 0.669 0.659 0.647 ...
$ Generosity : num 0.202 0.286 0.284 0.353 0.256 0.333 0.321 0.365 0.285 0.361 ...
$ Perceptions.of.corruption : chr "0.393" "0.340" "0.408" "0.138" ...
$ year : num 2018 2018 2018 2018 2018 ...
'data.frame': 156 obs. of 10 variables:
$ Overall.rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Country.or.region : chr "Finland" "Denmark" "Norway" "Iceland" ...
$ Score : num 7.77 7.6 7.55 7.49 7.49 ...
$ GDP.per.capita : num 1.34 1.38 1.49 1.38 1.4 ...
$ Social.support : num 1.59 1.57 1.58 1.62 1.52 ...
$ Healthy.life.expectancy : num 0.986 0.996 1.028 1.026 0.999 ...
$ Freedom.to.make.life.choices: num 0.596 0.592 0.603 0.591 0.557 0.572 0.574 0.585 0.584 0.532 ...
$ Generosity : num 0.153 0.252 0.271 0.354 0.322 0.263 0.267 0.33 0.285 0.244 ...
$ Perceptions.of.corruption : num 0.393 0.41 0.341 0.118 0.298 0.343 0.373 0.38 0.308 0.226 ...
$ year : num 2019 2019 2019 2019 2019 ...
Join functionsWe have seen these functions before.
Bind functionsRow bindbind_rows() function is similar to rbind, however this one matches the variable names and if there is no such similar variable in one of the files, it creates a variable with NAs.
Here in our example, simply
<- bind_rows (happiness2015, happiness2016, happiness2017, happiness2018, happiness2019)
We get an error! What are the problems here?
The names of the variables are not consistent such as:
Country
Economy..GDP.per.Capita. into GDP.per.capita
Freedom.to.make.life.choices into Freedom
Happiness.Score into
[1] "Country" "Region"
[3] "Happiness.Rank" "Happiness.Score"
[5] "Standard.Error" "Economy..GDP.per.Capita."
[7] "Family" "Health..Life.Expectancy."
[9] "Freedom" "Trust..Government.Corruption."
[11] "Generosity" "Dystopia.Residual"
[13] "year"
[1] "Country" "Region" "Rank" "Score"
[5] "GDP.per.capita" "Family" "Life.expectancy" "Freedom"
[9] "Trust" "Generosity" "year"
[1] "Country" "Region"
[3] "Happiness.Rank" "Happiness.Score"
[5] "Lower.Confidence.Interval" "Upper.Confidence.Interval"
[7] "Economy..GDP.per.Capita." "Family"
[9] "Health..Life.Expectancy." "Freedom"
[11] "Trust..Government.Corruption." "Generosity"
[13] "Dystopia.Residual" "year"
[1] "Country" "Region" "Rank" "Score"
[5] "GDP.per.capita" "Family" "Life.expectancy" "Freedom"
[9] "Trust" "Generosity" "year"
[1] "Country" "Happiness.Rank"
[3] "Happiness.Score" "Whisker.high"
[5] "Whisker.low" "Economy..GDP.per.Capita."
[7] "Family" "Health..Life.Expectancy."
[9] "Freedom" "Generosity"
[11] "Trust..Government.Corruption." "Dystopia.Residual"
[13] "year"
[1] "Country" "Rank" "Score" "GDP.per.capita"
[5] "Family" "Life.expectancy" "Freedom" "Generosity"
[9] "Trust" "year"
[1] "Overall.rank" "Country.or.region"
[3] "Score" "GDP.per.capita"
[5] "Social.support" "Healthy.life.expectancy"
[7] "Freedom.to.make.life.choices" "Generosity"
[9] "Perceptions.of.corruption" "year"
[1] "Rank" "Country" "Score" "GDP.per.capita"
[5] "Social.support" "Life.expectancy" "Freedom" "Generosity"
[9] "Trust" "year"
[1] "Overall.rank" "Country.or.region"
[3] "Score" "GDP.per.capita"
[5] "Social.support" "Healthy.life.expectancy"
[7] "Freedom.to.make.life.choices" "Generosity"
[9] "Perceptions.of.corruption" "year"
[1] "Rank" "Country" "Score" "GDP.per.capita"
[5] "Social.support" "Life.expectancy" "Freedom" "Generosity"
[9] "Trust" "year"
Perceptions.of.corruption (or Trust in the new name) in 2018 appears as a factor variable. We need to convert this into numeric.
'data.frame': 156 obs. of 10 variables:
$ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Country : chr "Finland" "Norway" "Denmark" "Iceland" ...
$ Score : num 7.63 7.59 7.55 7.5 7.49 ...
$ GDP.per.capita : num 1.3 1.46 1.35 1.34 1.42 ...
$ Social.support : num 1.59 1.58 1.59 1.64 1.55 ...
$ Life.expectancy: num 0.874 0.861 0.868 0.914 0.927 0.878 0.896 0.876 0.913 0.91 ...
$ Freedom : num 0.681 0.686 0.683 0.677 0.66 0.638 0.653 0.669 0.659 0.647 ...
$ Generosity : num 0.202 0.286 0.284 0.353 0.256 0.333 0.321 0.365 0.285 0.361 ...
$ Trust : chr "0.393" "0.340" "0.408" "0.138" ...
$ year : num 2018 2018 2018 2018 2018 ...
Rank Country Score GDP.per.capita Social.support
1 20 United Arab Emirates 6.774 2.096 0.776
2 34 Singapore 6.343 1.529 1.451
3 151 Rwanda 3.408 0.332 0.896
4 3 Denmark 7.555 1.351 1.590
5 1 Finland 7.632 1.305 1.592
6 8 New Zealand 7.324 1.268 1.601
7 9 Sweden 7.314 1.355 1.501
8 5 Switzerland 7.487 1.420 1.549
9 2 Norway 7.594 1.456 1.582
10 17 Luxembourg 6.910 1.576 1.520
11 14 Ireland 6.977 1.448 1.583
12 10 Australia 7.272 1.340 1.573
13 6 Netherlands 7.441 1.361 1.488
14 7 Canada 7.328 1.330 1.532
15 76 Hong Kong 5.430 1.405 1.290
16 98 Somalia 4.982 0.000 0.712
17 15 Germany 6.965 1.340 1.474
18 19 Israel 6.814 1.301 1.559
19 44 Uzbekistan 6.096 0.719 1.584
20 16 Belgium 6.927 1.324 1.483
21 12 Austria 7.139 1.341 1.504
22 128 Georgia 4.340 0.853 0.592
23 130 Myanmar 4.308 0.682 1.174
24 23 France 6.489 1.293 1.466
25 87 Azerbaijan 5.201 1.024 1.161
26 63 Estonia 5.739 1.200 1.532
27 110 Laos 4.623 0.720 1.034
28 32 Qatar 6.374 1.649 1.303
29 97 Bhutan 5.082 0.796 1.335
30 123 Mozambique 4.417 0.198 0.902
31 31 Uruguay 6.379 1.093 1.459
32 58 Northern Cyprus 5.835 1.229 1.211
33 73 Belarus 5.483 1.039 1.498
34 54 Japan 5.915 1.294 1.462
35 70 Libya 5.566 0.985 1.350
36 127 Ethiopia 4.350 0.308 0.950
37 115 Bangladesh 4.500 0.532 0.850
38 150 Syria 3.462 0.689 0.382
39 22 Malta 6.627 1.270 1.525
40 4 Iceland 7.495 1.343 1.644
41 80 Lebanon 5.358 0.965 1.179
42 84 Algeria 5.295 0.979 1.154
43 90 Jordan 5.161 0.822 1.265
44 18 United States 6.886 1.398 1.471
45 106 Iran 4.707 1.059 0.771
46 41 Nicaragua 6.141 0.668 1.319
47 121 Burkina Faso 4.424 0.314 1.097
48 33 Saudi Arabia 6.371 1.379 1.331
49 43 Bahrain 6.105 1.338 1.366
50 60 Kazakhstan 5.790 1.143 1.516
51 48 Ecuador 5.973 0.889 1.330
52 75 Pakistan 5.472 0.652 0.810
53 141 Lesotho 3.808 0.472 1.215
54 74 Turkey 5.483 1.148 1.380
55 122 Egypt 4.419 0.885 1.025
56 45 Kuwait 6.083 1.474 1.301
57 83 Dominican Republic 5.302 0.982 1.441
58 154 South Sudan 3.254 0.337 0.608
59 71 Philippines 5.524 0.775 1.312
60 148 Haiti 3.582 0.315 0.714
61 86 China 5.246 0.989 1.142
62 107 Ivory Coast 4.671 0.541 0.872
63 13 Costa Rica 7.072 1.010 1.459
64 139 Togo 3.999 0.259 0.474
65 134 Niger 4.166 0.131 0.867
66 144 Zimbabwe 3.692 0.357 1.094
67 153 Tanzania 3.303 0.455 0.991
68 24 Mexico 6.488 1.038 1.252
69 117 Iraq 4.456 1.010 0.971
70 140 Guinea 3.964 0.344 0.792
71 126 Mauritania 4.356 0.557 1.245
72 133 India 4.190 0.721 0.747
73 146 Botswana 3.590 1.017 1.174
74 49 Belize 5.956 0.807 1.101
75 28 Brazil 6.419 0.986 1.474
76 11 United Kingdom 7.190 1.244 1.433
77 40 El Salvador 6.167 0.806 1.231
78 109 Senegal 4.631 0.429 1.117
79 125 Zambia 4.377 0.562 1.047
80 137 Sudan 4.139 0.605 1.240
81 81 Montenegro 5.347 1.017 1.279
82 147 Malawi 3.587 0.186 0.541
83 95 Vietnam 5.103 0.715 1.365
84 26 Taiwan 6.441 1.365 1.436
85 101 Nepal 4.880 0.425 1.228
86 114 Congo (Brazzaville) 4.559 0.682 0.811
87 104 Palestinian Territories 4.743 0.642 1.217
88 156 Burundi 2.905 0.091 0.627
89 36 Spain 6.310 1.251 1.538
90 64 Paraguay 5.681 0.835 1.522
91 72 Honduras 5.504 0.620 1.205
92 85 Morocco 5.254 0.779 0.797
93 30 Guatemala 6.382 0.781 1.268
94 136 Benin 4.141 0.378 0.372
95 53 Latvia 5.933 1.148 1.454
96 120 Cambodia 4.433 0.549 1.088
97 42 Poland 6.123 1.176 1.448
98 119 Namibia 4.441 0.874 1.281
99 152 Yemen 3.355 0.442 1.073
100 27 Panama 6.430 1.112 1.438
101 111 Tunisia 4.592 0.900 0.906
102 25 Chile 6.476 1.131 1.331
103 135 Uganda 4.161 0.322 1.090
104 142 Angola 3.795 0.730 1.125
105 131 Chad 4.301 0.358 0.907
106 35 Malaysia 6.322 1.161 1.258
107 105 South Africa 4.724 0.940 1.410
108 118 Mali 4.447 0.370 1.233
109 124 Kenya 4.410 0.493 1.048
110 29 Argentina 6.388 1.073 1.468
111 62 Bolivia 5.752 0.751 1.223
112 113 Sierra Leone 4.571 0.256 0.813
113 89 Macedonia 5.185 0.959 1.239
114 102 Venezuela 4.806 0.996 1.469
115 103 Gabon 4.758 1.036 1.164
116 132 Congo (Kinshasa) 4.245 0.069 1.136
117 51 Slovenia 5.948 1.219 1.506
118 57 South Korea 5.875 1.266 1.204
119 55 Mauritius 5.891 1.090 1.387
120 116 Sri Lanka 4.471 0.918 1.314
121 143 Madagascar 3.774 0.262 0.908
122 79 Greece 5.358 1.154 1.202
123 78 Serbia 5.398 0.975 1.369
124 99 Cameroon 4.975 0.535 0.891
125 37 Colombia 6.260 0.960 1.439
126 82 Croatia 5.321 1.115 1.161
127 155 Central African Republic 3.083 0.024 0.000
128 68 Turkmenistan 5.636 1.016 1.533
129 145 Afghanistan 3.632 0.332 0.537
130 61 Cyprus 5.762 1.229 1.191
131 92 Kyrgyzstan 5.131 0.530 1.416
132 21 Czech Republic 6.711 1.233 1.489
133 65 Peru 5.663 0.934 1.249
134 88 Tajikistan 5.199 0.474 1.166
135 91 Nigeria 5.155 0.689 1.172
136 94 Mongolia 5.125 0.914 1.517
137 112 Albania 4.586 0.916 0.817
138 56 Jamaica 5.890 0.819 1.493
139 149 Liberia 3.495 0.076 0.858
140 46 Thailand 6.072 1.016 1.417
141 108 Ghana 4.657 0.592 0.896
142 47 Italy 6.000 1.264 1.501
143 129 Armenia 4.321 0.816 0.990
144 59 Russia 5.810 1.151 1.479
145 66 Kosovo 5.662 0.855 1.230
146 69 Hungary 5.620 1.171 1.401
147 38 Trinidad & Tobago 6.192 1.223 1.492
148 96 Indonesia 5.093 0.899 1.215
149 77 Portugal 5.410 1.188 1.429
150 39 Slovakia 6.173 1.210 1.537
151 138 Ukraine 4.103 0.793 1.413
152 100 Bulgaria 4.933 1.054 1.515
153 50 Lithuania 5.952 1.197 1.527
154 52 Romania 5.945 1.116 1.219
155 67 Moldova 5.640 0.657 1.301
156 93 Bosnia and Herzegovina 5.129 0.915 1.078
Life.expectancy Freedom Generosity Trust year
1 0.670 0.284 0.186 N/A 2018
2 1.008 0.631 0.261 0.457 2018
3 0.400 0.636 0.200 0.444 2018
4 0.868 0.683 0.284 0.408 2018
5 0.874 0.681 0.202 0.393 2018
6 0.876 0.669 0.365 0.389 2018
7 0.913 0.659 0.285 0.383 2018
8 0.927 0.660 0.256 0.357 2018
9 0.861 0.686 0.286 0.340 2018
10 0.896 0.632 0.196 0.321 2018
11 0.876 0.614 0.307 0.306 2018
12 0.910 0.647 0.361 0.302 2018
13 0.878 0.638 0.333 0.295 2018
14 0.896 0.653 0.321 0.291 2018
15 1.030 0.524 0.246 0.291 2018
16 0.115 0.674 0.238 0.282 2018
17 0.861 0.586 0.273 0.280 2018
18 0.883 0.533 0.354 0.272 2018
19 0.605 0.724 0.328 0.259 2018
20 0.894 0.583 0.188 0.240 2018
21 0.891 0.617 0.242 0.224 2018
22 0.643 0.375 0.038 0.215 2018
23 0.429 0.580 0.598 0.178 2018
24 0.908 0.520 0.098 0.176 2018
25 0.603 0.430 0.031 0.176 2018
26 0.737 0.553 0.086 0.174 2018
27 0.441 0.626 0.230 0.174 2018
28 0.748 0.654 0.256 0.171 2018
29 0.527 0.541 0.364 0.171 2018
30 0.173 0.531 0.206 0.158 2018
31 0.771 0.625 0.130 0.155 2018
32 0.909 0.495 0.179 0.154 2018
33 0.700 0.307 0.101 0.154 2018
34 0.988 0.553 0.079 0.150 2018
35 0.553 0.496 0.116 0.148 2018
36 0.391 0.452 0.220 0.146 2018
37 0.579 0.580 0.153 0.144 2018
38 0.539 0.088 0.376 0.144 2018
39 0.884 0.645 0.376 0.142 2018
40 0.914 0.677 0.353 0.138 2018
41 0.785 0.503 0.214 0.136 2018
42 0.687 0.077 0.055 0.135 2018
43 0.645 0.468 0.130 0.134 2018
44 0.819 0.547 0.291 0.133 2018
45 0.691 0.459 0.282 0.129 2018
46 0.700 0.527 0.208 0.128 2018
47 0.254 0.312 0.175 0.128 2018
48 0.633 0.509 0.098 0.127 2018
49 0.698 0.594 0.243 0.123 2018
50 0.631 0.454 0.148 0.121 2018
51 0.736 0.556 0.114 0.120 2018
52 0.424 0.334 0.216 0.113 2018
53 0.079 0.423 0.116 0.112 2018
54 0.686 0.324 0.106 0.109 2018
55 0.553 0.312 0.092 0.107 2018
56 0.675 0.554 0.167 0.106 2018
57 0.614 0.578 0.120 0.106 2018
58 0.177 0.112 0.224 0.106 2018
59 0.513 0.643 0.120 0.105 2018
60 0.289 0.025 0.392 0.104 2018
61 0.799 0.597 0.029 0.103 2018
62 0.080 0.467 0.146 0.103 2018
63 0.817 0.632 0.143 0.101 2018
64 0.253 0.434 0.158 0.101 2018
65 0.221 0.390 0.175 0.099 2018
66 0.248 0.406 0.132 0.099 2018
67 0.381 0.481 0.270 0.097 2018
68 0.761 0.479 0.069 0.095 2018
69 0.536 0.304 0.148 0.095 2018
70 0.211 0.394 0.185 0.094 2018
71 0.292 0.129 0.134 0.093 2018
72 0.485 0.539 0.172 0.093 2018
73 0.417 0.557 0.042 0.092 2018
74 0.474 0.593 0.183 0.089 2018
75 0.675 0.493 0.110 0.088 2018
76 0.888 0.464 0.262 0.082 2018
77 0.639 0.461 0.065 0.082 2018
78 0.433 0.406 0.138 0.082 2018
79 0.295 0.503 0.221 0.082 2018
80 0.312 0.016 0.134 0.082 2018
81 0.729 0.259 0.111 0.081 2018
82 0.306 0.531 0.210 0.080 2018
83 0.702 0.618 0.177 0.079 2018
84 0.857 0.418 0.151 0.078 2018
85 0.539 0.526 0.302 0.078 2018
86 0.343 0.514 0.091 0.077 2018
87 0.602 0.266 0.086 0.076 2018
88 0.145 0.065 0.149 0.076 2018
89 0.965 0.449 0.142 0.074 2018
90 0.615 0.541 0.162 0.074 2018
91 0.622 0.459 0.197 0.074 2018
92 0.669 0.460 0.026 0.074 2018
93 0.608 0.604 0.179 0.071 2018
94 0.240 0.440 0.163 0.067 2018
95 0.671 0.363 0.092 0.066 2018
96 0.457 0.696 0.256 0.065 2018
97 0.781 0.546 0.108 0.064 2018
98 0.365 0.519 0.051 0.064 2018
99 0.343 0.244 0.083 0.064 2018
100 0.759 0.597 0.125 0.063 2018
101 0.690 0.271 0.040 0.063 2018
102 0.808 0.431 0.197 0.061 2018
103 0.237 0.450 0.259 0.061 2018
104 0.269 0.000 0.079 0.061 2018
105 0.053 0.189 0.181 0.060 2018
106 0.669 0.356 0.311 0.059 2018
107 0.330 0.516 0.103 0.056 2018
108 0.152 0.367 0.139 0.056 2018
109 0.454 0.504 0.352 0.055 2018
110 0.744 0.570 0.062 0.054 2018
111 0.508 0.606 0.141 0.054 2018
112 0.000 0.355 0.238 0.053 2018
113 0.691 0.394 0.173 0.052 2018
114 0.657 0.133 0.056 0.052 2018
115 0.404 0.356 0.032 0.052 2018
116 0.204 0.312 0.197 0.052 2018
117 0.856 0.633 0.160 0.051 2018
118 0.955 0.244 0.175 0.051 2018
119 0.684 0.584 0.245 0.050 2018
120 0.672 0.585 0.307 0.050 2018
121 0.402 0.221 0.155 0.049 2018
122 0.879 0.131 0.000 0.044 2018
123 0.685 0.288 0.134 0.043 2018
124 0.182 0.454 0.183 0.043 2018
125 0.635 0.531 0.099 0.039 2018
126 0.737 0.380 0.120 0.039 2018
127 0.010 0.305 0.218 0.038 2018
128 0.517 0.417 0.199 0.037 2018
129 0.255 0.085 0.191 0.036 2018
130 0.909 0.423 0.202 0.035 2018
131 0.594 0.540 0.281 0.035 2018
132 0.854 0.543 0.064 0.034 2018
133 0.674 0.530 0.092 0.034 2018
134 0.598 0.292 0.187 0.034 2018
135 0.048 0.462 0.201 0.032 2018
136 0.575 0.395 0.253 0.032 2018
137 0.790 0.419 0.149 0.032 2018
138 0.693 0.575 0.096 0.031 2018
139 0.267 0.419 0.206 0.030 2018
140 0.707 0.637 0.364 0.029 2018
141 0.337 0.499 0.212 0.029 2018
142 0.946 0.281 0.137 0.028 2018
143 0.666 0.260 0.077 0.028 2018
144 0.599 0.399 0.065 0.025 2018
145 0.578 0.448 0.274 0.023 2018
146 0.732 0.259 0.061 0.022 2018
147 0.564 0.575 0.171 0.019 2018
148 0.522 0.538 0.484 0.018 2018
149 0.884 0.562 0.055 0.017 2018
150 0.776 0.354 0.118 0.014 2018
151 0.609 0.163 0.187 0.011 2018
152 0.712 0.359 0.064 0.009 2018
153 0.716 0.350 0.026 0.006 2018
154 0.726 0.528 0.088 0.001 2018
155 0.620 0.232 0.171 0.000 2018
156 0.758 0.280 0.216 0.000 2018
Rank Country Score GDP.per.capita Social.support
1 67 Moldova 5.640 0.657 1.301
2 93 Bosnia and Herzegovina 5.129 0.915 1.078
3 52 Romania 5.945 1.116 1.219
4 50 Lithuania 5.952 1.197 1.527
5 100 Bulgaria 4.933 1.054 1.515
6 138 Ukraine 4.103 0.793 1.413
7 39 Slovakia 6.173 1.210 1.537
8 77 Portugal 5.410 1.188 1.429
9 96 Indonesia 5.093 0.899 1.215
10 38 Trinidad & Tobago 6.192 1.223 1.492
11 69 Hungary 5.620 1.171 1.401
12 66 Kosovo 5.662 0.855 1.230
13 59 Russia 5.810 1.151 1.479
14 47 Italy 6.000 1.264 1.501
15 129 Armenia 4.321 0.816 0.990
16 46 Thailand 6.072 1.016 1.417
17 108 Ghana 4.657 0.592 0.896
18 149 Liberia 3.495 0.076 0.858
19 56 Jamaica 5.890 0.819 1.493
20 91 Nigeria 5.155 0.689 1.172
21 94 Mongolia 5.125 0.914 1.517
22 112 Albania 4.586 0.916 0.817
23 21 Czech Republic 6.711 1.233 1.489
24 65 Peru 5.663 0.934 1.249
25 88 Tajikistan 5.199 0.474 1.166
26 61 Cyprus 5.762 1.229 1.191
27 92 Kyrgyzstan 5.131 0.530 1.416
28 145 Afghanistan 3.632 0.332 0.537
29 68 Turkmenistan 5.636 1.016 1.533
30 155 Central African Republic 3.083 0.024 0.000
31 37 Colombia 6.260 0.960 1.439
32 82 Croatia 5.321 1.115 1.161
33 78 Serbia 5.398 0.975 1.369
34 99 Cameroon 4.975 0.535 0.891
35 79 Greece 5.358 1.154 1.202
36 143 Madagascar 3.774 0.262 0.908
37 55 Mauritius 5.891 1.090 1.387
38 116 Sri Lanka 4.471 0.918 1.314
39 51 Slovenia 5.948 1.219 1.506
40 57 South Korea 5.875 1.266 1.204
41 89 Macedonia 5.185 0.959 1.239
42 102 Venezuela 4.806 0.996 1.469
43 103 Gabon 4.758 1.036 1.164
44 132 Congo (Kinshasa) 4.245 0.069 1.136
45 113 Sierra Leone 4.571 0.256 0.813
46 29 Argentina 6.388 1.073 1.468
47 62 Bolivia 5.752 0.751 1.223
48 124 Kenya 4.410 0.493 1.048
49 105 South Africa 4.724 0.940 1.410
50 118 Mali 4.447 0.370 1.233
51 35 Malaysia 6.322 1.161 1.258
52 131 Chad 4.301 0.358 0.907
53 25 Chile 6.476 1.131 1.331
54 135 Uganda 4.161 0.322 1.090
55 142 Angola 3.795 0.730 1.125
56 27 Panama 6.430 1.112 1.438
57 111 Tunisia 4.592 0.900 0.906
58 42 Poland 6.123 1.176 1.448
59 119 Namibia 4.441 0.874 1.281
60 152 Yemen 3.355 0.442 1.073
61 120 Cambodia 4.433 0.549 1.088
62 53 Latvia 5.933 1.148 1.454
63 136 Benin 4.141 0.378 0.372
64 30 Guatemala 6.382 0.781 1.268
65 36 Spain 6.310 1.251 1.538
66 64 Paraguay 5.681 0.835 1.522
67 72 Honduras 5.504 0.620 1.205
68 85 Morocco 5.254 0.779 0.797
69 104 Palestinian Territories 4.743 0.642 1.217
70 156 Burundi 2.905 0.091 0.627
71 114 Congo (Brazzaville) 4.559 0.682 0.811
72 26 Taiwan 6.441 1.365 1.436
73 101 Nepal 4.880 0.425 1.228
74 95 Vietnam 5.103 0.715 1.365
75 147 Malawi 3.587 0.186 0.541
76 81 Montenegro 5.347 1.017 1.279
77 11 United Kingdom 7.190 1.244 1.433
78 40 El Salvador 6.167 0.806 1.231
79 109 Senegal 4.631 0.429 1.117
80 125 Zambia 4.377 0.562 1.047
81 137 Sudan 4.139 0.605 1.240
82 28 Brazil 6.419 0.986 1.474
83 49 Belize 5.956 0.807 1.101
84 146 Botswana 3.590 1.017 1.174
85 126 Mauritania 4.356 0.557 1.245
86 133 India 4.190 0.721 0.747
87 140 Guinea 3.964 0.344 0.792
88 24 Mexico 6.488 1.038 1.252
89 117 Iraq 4.456 1.010 0.971
90 153 Tanzania 3.303 0.455 0.991
91 134 Niger 4.166 0.131 0.867
92 144 Zimbabwe 3.692 0.357 1.094
93 13 Costa Rica 7.072 1.010 1.459
94 139 Togo 3.999 0.259 0.474
95 86 China 5.246 0.989 1.142
96 107 Ivory Coast 4.671 0.541 0.872
97 148 Haiti 3.582 0.315 0.714
98 71 Philippines 5.524 0.775 1.312
99 45 Kuwait 6.083 1.474 1.301
100 83 Dominican Republic 5.302 0.982 1.441
101 154 South Sudan 3.254 0.337 0.608
102 122 Egypt 4.419 0.885 1.025
103 74 Turkey 5.483 1.148 1.380
104 141 Lesotho 3.808 0.472 1.215
105 75 Pakistan 5.472 0.652 0.810
106 48 Ecuador 5.973 0.889 1.330
107 60 Kazakhstan 5.790 1.143 1.516
108 43 Bahrain 6.105 1.338 1.366
109 33 Saudi Arabia 6.371 1.379 1.331
110 41 Nicaragua 6.141 0.668 1.319
111 121 Burkina Faso 4.424 0.314 1.097
112 106 Iran 4.707 1.059 0.771
113 18 United States 6.886 1.398 1.471
114 90 Jordan 5.161 0.822 1.265
115 84 Algeria 5.295 0.979 1.154
116 80 Lebanon 5.358 0.965 1.179
117 4 Iceland 7.495 1.343 1.644
118 22 Malta 6.627 1.270 1.525
119 115 Bangladesh 4.500 0.532 0.850
120 150 Syria 3.462 0.689 0.382
121 127 Ethiopia 4.350 0.308 0.950
122 70 Libya 5.566 0.985 1.350
123 54 Japan 5.915 1.294 1.462
124 58 Northern Cyprus 5.835 1.229 1.211
125 73 Belarus 5.483 1.039 1.498
126 31 Uruguay 6.379 1.093 1.459
127 123 Mozambique 4.417 0.198 0.902
128 32 Qatar 6.374 1.649 1.303
129 97 Bhutan 5.082 0.796 1.335
130 63 Estonia 5.739 1.200 1.532
131 110 Laos 4.623 0.720 1.034
132 23 France 6.489 1.293 1.466
133 87 Azerbaijan 5.201 1.024 1.161
134 130 Myanmar 4.308 0.682 1.174
135 128 Georgia 4.340 0.853 0.592
136 12 Austria 7.139 1.341 1.504
137 16 Belgium 6.927 1.324 1.483
138 44 Uzbekistan 6.096 0.719 1.584
139 19 Israel 6.814 1.301 1.559
140 15 Germany 6.965 1.340 1.474
141 98 Somalia 4.982 0.000 0.712
142 7 Canada 7.328 1.330 1.532
143 76 Hong Kong 5.430 1.405 1.290
144 6 Netherlands 7.441 1.361 1.488
145 10 Australia 7.272 1.340 1.573
146 14 Ireland 6.977 1.448 1.583
147 17 Luxembourg 6.910 1.576 1.520
148 2 Norway 7.594 1.456 1.582
149 5 Switzerland 7.487 1.420 1.549
150 9 Sweden 7.314 1.355 1.501
151 8 New Zealand 7.324 1.268 1.601
152 1 Finland 7.632 1.305 1.592
153 3 Denmark 7.555 1.351 1.590
154 151 Rwanda 3.408 0.332 0.896
155 34 Singapore 6.343 1.529 1.451
156 20 United Arab Emirates 6.774 2.096 0.776
Life.expectancy Freedom Generosity Trust year
1 0.620 0.232 0.171 0.000 2018
2 0.758 0.280 0.216 0.000 2018
3 0.726 0.528 0.088 0.001 2018
4 0.716 0.350 0.026 0.006 2018
5 0.712 0.359 0.064 0.009 2018
6 0.609 0.163 0.187 0.011 2018
7 0.776 0.354 0.118 0.014 2018
8 0.884 0.562 0.055 0.017 2018
9 0.522 0.538 0.484 0.018 2018
10 0.564 0.575 0.171 0.019 2018
11 0.732 0.259 0.061 0.022 2018
12 0.578 0.448 0.274 0.023 2018
13 0.599 0.399 0.065 0.025 2018
14 0.946 0.281 0.137 0.028 2018
15 0.666 0.260 0.077 0.028 2018
16 0.707 0.637 0.364 0.029 2018
17 0.337 0.499 0.212 0.029 2018
18 0.267 0.419 0.206 0.030 2018
19 0.693 0.575 0.096 0.031 2018
20 0.048 0.462 0.201 0.032 2018
21 0.575 0.395 0.253 0.032 2018
22 0.790 0.419 0.149 0.032 2018
23 0.854 0.543 0.064 0.034 2018
24 0.674 0.530 0.092 0.034 2018
25 0.598 0.292 0.187 0.034 2018
26 0.909 0.423 0.202 0.035 2018
27 0.594 0.540 0.281 0.035 2018
28 0.255 0.085 0.191 0.036 2018
29 0.517 0.417 0.199 0.037 2018
30 0.010 0.305 0.218 0.038 2018
31 0.635 0.531 0.099 0.039 2018
32 0.737 0.380 0.120 0.039 2018
33 0.685 0.288 0.134 0.043 2018
34 0.182 0.454 0.183 0.043 2018
35 0.879 0.131 0.000 0.044 2018
36 0.402 0.221 0.155 0.049 2018
37 0.684 0.584 0.245 0.050 2018
38 0.672 0.585 0.307 0.050 2018
39 0.856 0.633 0.160 0.051 2018
40 0.955 0.244 0.175 0.051 2018
41 0.691 0.394 0.173 0.052 2018
42 0.657 0.133 0.056 0.052 2018
43 0.404 0.356 0.032 0.052 2018
44 0.204 0.312 0.197 0.052 2018
45 0.000 0.355 0.238 0.053 2018
46 0.744 0.570 0.062 0.054 2018
47 0.508 0.606 0.141 0.054 2018
48 0.454 0.504 0.352 0.055 2018
49 0.330 0.516 0.103 0.056 2018
50 0.152 0.367 0.139 0.056 2018
51 0.669 0.356 0.311 0.059 2018
52 0.053 0.189 0.181 0.060 2018
53 0.808 0.431 0.197 0.061 2018
54 0.237 0.450 0.259 0.061 2018
55 0.269 0.000 0.079 0.061 2018
56 0.759 0.597 0.125 0.063 2018
57 0.690 0.271 0.040 0.063 2018
58 0.781 0.546 0.108 0.064 2018
59 0.365 0.519 0.051 0.064 2018
60 0.343 0.244 0.083 0.064 2018
61 0.457 0.696 0.256 0.065 2018
62 0.671 0.363 0.092 0.066 2018
63 0.240 0.440 0.163 0.067 2018
64 0.608 0.604 0.179 0.071 2018
65 0.965 0.449 0.142 0.074 2018
66 0.615 0.541 0.162 0.074 2018
67 0.622 0.459 0.197 0.074 2018
68 0.669 0.460 0.026 0.074 2018
69 0.602 0.266 0.086 0.076 2018
70 0.145 0.065 0.149 0.076 2018
71 0.343 0.514 0.091 0.077 2018
72 0.857 0.418 0.151 0.078 2018
73 0.539 0.526 0.302 0.078 2018
74 0.702 0.618 0.177 0.079 2018
75 0.306 0.531 0.210 0.080 2018
76 0.729 0.259 0.111 0.081 2018
77 0.888 0.464 0.262 0.082 2018
78 0.639 0.461 0.065 0.082 2018
79 0.433 0.406 0.138 0.082 2018
80 0.295 0.503 0.221 0.082 2018
81 0.312 0.016 0.134 0.082 2018
82 0.675 0.493 0.110 0.088 2018
83 0.474 0.593 0.183 0.089 2018
84 0.417 0.557 0.042 0.092 2018
85 0.292 0.129 0.134 0.093 2018
86 0.485 0.539 0.172 0.093 2018
87 0.211 0.394 0.185 0.094 2018
88 0.761 0.479 0.069 0.095 2018
89 0.536 0.304 0.148 0.095 2018
90 0.381 0.481 0.270 0.097 2018
91 0.221 0.390 0.175 0.099 2018
92 0.248 0.406 0.132 0.099 2018
93 0.817 0.632 0.143 0.101 2018
94 0.253 0.434 0.158 0.101 2018
95 0.799 0.597 0.029 0.103 2018
96 0.080 0.467 0.146 0.103 2018
97 0.289 0.025 0.392 0.104 2018
98 0.513 0.643 0.120 0.105 2018
99 0.675 0.554 0.167 0.106 2018
100 0.614 0.578 0.120 0.106 2018
101 0.177 0.112 0.224 0.106 2018
102 0.553 0.312 0.092 0.107 2018
103 0.686 0.324 0.106 0.109 2018
104 0.079 0.423 0.116 0.112 2018
105 0.424 0.334 0.216 0.113 2018
106 0.736 0.556 0.114 0.120 2018
107 0.631 0.454 0.148 0.121 2018
108 0.698 0.594 0.243 0.123 2018
109 0.633 0.509 0.098 0.127 2018
110 0.700 0.527 0.208 0.128 2018
111 0.254 0.312 0.175 0.128 2018
112 0.691 0.459 0.282 0.129 2018
113 0.819 0.547 0.291 0.133 2018
114 0.645 0.468 0.130 0.134 2018
115 0.687 0.077 0.055 0.135 2018
116 0.785 0.503 0.214 0.136 2018
117 0.914 0.677 0.353 0.138 2018
118 0.884 0.645 0.376 0.142 2018
119 0.579 0.580 0.153 0.144 2018
120 0.539 0.088 0.376 0.144 2018
121 0.391 0.452 0.220 0.146 2018
122 0.553 0.496 0.116 0.148 2018
123 0.988 0.553 0.079 0.150 2018
124 0.909 0.495 0.179 0.154 2018
125 0.700 0.307 0.101 0.154 2018
126 0.771 0.625 0.130 0.155 2018
127 0.173 0.531 0.206 0.158 2018
128 0.748 0.654 0.256 0.171 2018
129 0.527 0.541 0.364 0.171 2018
130 0.737 0.553 0.086 0.174 2018
131 0.441 0.626 0.230 0.174 2018
132 0.908 0.520 0.098 0.176 2018
133 0.603 0.430 0.031 0.176 2018
134 0.429 0.580 0.598 0.178 2018
135 0.643 0.375 0.038 0.215 2018
136 0.891 0.617 0.242 0.224 2018
137 0.894 0.583 0.188 0.240 2018
138 0.605 0.724 0.328 0.259 2018
139 0.883 0.533 0.354 0.272 2018
140 0.861 0.586 0.273 0.280 2018
141 0.115 0.674 0.238 0.282 2018
142 0.896 0.653 0.321 0.291 2018
143 1.030 0.524 0.246 0.291 2018
144 0.878 0.638 0.333 0.295 2018
145 0.910 0.647 0.361 0.302 2018
146 0.876 0.614 0.307 0.306 2018
147 0.896 0.632 0.196 0.321 2018
148 0.861 0.686 0.286 0.340 2018
149 0.927 0.660 0.256 0.357 2018
150 0.913 0.659 0.285 0.383 2018
151 0.876 0.669 0.365 0.389 2018
152 0.874 0.681 0.202 0.393 2018
153 0.868 0.683 0.284 0.408 2018
154 0.400 0.636 0.200 0.444 2018
155 1.008 0.631 0.261 0.457 2018
156 0.670 0.284 0.186 <NA> 2018
'data.frame': 156 obs. of 10 variables:
$ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Country : chr "Finland" "Norway" "Denmark" "Iceland" ...
$ Score : num 7.63 7.59 7.55 7.5 7.49 ...
$ GDP.per.capita : num 1.3 1.46 1.35 1.34 1.42 ...
$ Social.support : num 1.59 1.58 1.59 1.64 1.55 ...
$ Life.expectancy: num 0.874 0.861 0.868 0.914 0.927 0.878 0.896 0.876 0.913 0.91 ...
$ Freedom : num 0.681 0.686 0.683 0.677 0.66 0.638 0.653 0.669 0.659 0.647 ...
$ Generosity : num 0.202 0.286 0.284 0.353 0.256 0.333 0.321 0.365 0.285 0.361 ...
$ Trust : num 0.393 0.34 0.408 0.138 0.357 0.295 0.291 0.389 0.383 0.302 ...
$ year : num 2018 2018 2018 2018 2018 ...
After solving the issues, we try the bind again:
'data.frame': 782 obs. of 12 variables:
$ Country : chr "Switzerland" "Iceland" "Denmark" "Norway" ...
$ Region : chr "Western Europe" "Western Europe" "Western Europe" "Western Europe" ...
$ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Score : num 7.59 7.56 7.53 7.52 7.43 ...
$ GDP.per.capita : num 1.4 1.3 1.33 1.46 1.33 ...
$ Family : num 1.35 1.4 1.36 1.33 1.32 ...
$ Life.expectancy: num 0.941 0.948 0.875 0.885 0.906 ...
$ Freedom : num 0.666 0.629 0.649 0.67 0.633 ...
$ Trust : num 0.42 0.141 0.484 0.365 0.33 ...
$ Generosity : num 0.297 0.436 0.341 0.347 0.458 ...
$ year : num 2015 2015 2015 2015 2015 ...
$ Social.support : num NA NA NA NA NA NA NA NA NA NA ...
Now we can look at specific countries or years using filter() function. Make plots, summaries etc.
Question: How would you create a table for Country, Happiness Score and GDP per capita for only two countries (Afghanistan and Switzerland) for only 2016?
Country GDP.per.capita Score
1 Switzerland 1.52733 7.509
2 Afghanistan 0.38227 3.360
Column bindI would not use this if I were you. Instead use join functions.
The match() function
In some cases, you might have several variables, too many maybe, and you may only need to focus on a subset of these variables.
The function can be used to select columns of a data frame that you want to focus on or similarly omitting columns you don’t need.
Rank Country Score
1 1 Finland 7.769
2 2 Denmark 7.600
Rank Score Social.support
1 1 7.769 1.587
2 2 7.600 1.573
Country Score GDP.per.capita Social.support Life.expectancy Freedom
1 Finland 7.769 1.340 1.587 0.986 0.596
2 Denmark 7.600 1.383 1.573 0.996 0.592
Generosity Trust year
1 0.153 0.393 2019
2 0.252 0.410 2019
You can also select columns based on specific criteria with:
starts_with() = Select columns that start with a character string ends_with() = Select columns that end with a character string contains() = Select columns that contain a character string matches() = Select columns that match a regular expression one_of() = Select columns names that are from a group of names or there is the grepl function!
'data.frame': 156 obs. of 1 variable:
$ Freedom: num 0.596 0.592 0.603 0.591 0.557 0.572 0.574 0.585 0.584 0.532 ...
'data.frame': 156 obs. of 2 variables:
$ GDP.per.capita: num 1.34 1.38 1.49 1.38 1.4 ...
$ Generosity : num 0.153 0.252 0.271 0.354 0.322 0.263 0.267 0.33 0.285 0.244 ...
The filter() function
Suppose we wanted to extract the rows of the happiness data frame where the levels of happiness (Score) are greater than 7, we could do:
'data.frame': 156 obs. of 10 variables:
$ Rank : int 1 2 3 4 5 6 7 8 9 10 ...
$ Country : chr "Finland" "Denmark" "Norway" "Iceland" ...
$ Score : num 7.77 7.6 7.55 7.49 7.49 ...
$ GDP.per.capita : num 1.34 1.38 1.49 1.38 1.4 ...
$ Social.support : num 1.59 1.57 1.58 1.62 1.52 ...
$ Life.expectancy: num 0.986 0.996 1.028 1.026 0.999 ...
$ Freedom : num 0.596 0.592 0.603 0.591 0.557 0.572 0.574 0.585 0.584 0.532 ...
$ Generosity : num 0.153 0.252 0.271 0.354 0.322 0.263 0.267 0.33 0.285 0.244 ...
$ Trust : num 0.393 0.41 0.341 0.118 0.298 0.343 0.373 0.38 0.308 0.226 ...
$ year : num 2019 2019 2019 2019 2019 ...
Rank Country Score GDP.per.capita Social.support Life.expectancy Freedom
1 1 Finland 7.769 1.340 1.587 0.986 0.596
2 2 Denmark 7.600 1.383 1.573 0.996 0.592
3 3 Norway 7.554 1.488 1.582 1.028 0.603
4 4 Iceland 7.494 1.380 1.624 1.026 0.591
5 5 Netherlands 7.488 1.396 1.522 0.999 0.557
6 6 Switzerland 7.480 1.452 1.526 1.052 0.572
Generosity Trust year
1 0.153 0.393 2019
2 0.252 0.410 2019
3 0.271 0.341 2019
4 0.354 0.118 2019
5 0.322 0.298 2019
6 0.263 0.343 2019
We can place an arbitrarily complex logical sequence inside of filter(), say filtering observations where Score is greater than 7 and GDP.per.capita >1.5:
Rank Country Score GDP.per.capita Social.support Life.expectancy Freedom
1 14 Luxembourg 7.09 1.609 1.479 1.012 0.526
Generosity Trust year
1 0.194 0.316 2019
The arrange() function
Here we can order the rows of the data frame by GDP.per.capita, so that the first row is the least developed observation and the last row is the most developed observation. Currently the data is ordered according to Happiness Score.
Rank Country Score GDP.per.capita Social.support
1 112 Somalia 4.668 0.000 0.698
2 155 Central African Republic 3.083 0.026 0.000
3 145 Burundi 3.775 0.046 0.447
4 141 Liberia 3.975 0.073 0.922
5 127 Congo (Kinshasa) 4.418 0.094 1.125
6 114 Niger 4.628 0.138 0.774
Life.expectancy Freedom Generosity Trust year
1 0.268 0.559 0.243 0.270 2019
2 0.105 0.225 0.235 0.035 2019
3 0.380 0.220 0.176 0.180 2019
4 0.443 0.370 0.233 0.033 2019
5 0.357 0.269 0.212 0.053 2019
6 0.366 0.318 0.188 0.102 2019
The rename() function
Renaming variable names can be difficult in R. For example, you may want to rename GDP.per.capita as gdppercapita, all in small letters and no dots in between.
Rank Country Score gdppercapita Social.support Life.expectancy Freedom
1 1 Finland 7.769 1.340 1.587 0.986 0.596
2 2 Denmark 7.600 1.383 1.573 0.996 0.592
3 3 Norway 7.554 1.488 1.582 1.028 0.603
4 4 Iceland 7.494 1.380 1.624 1.026 0.591
5 5 Netherlands 7.488 1.396 1.522 0.999 0.557
6 6 Switzerland 7.480 1.452 1.526 1.052 0.572
Generosity Trust year
1 0.153 0.393 2019
2 0.252 0.410 2019
3 0.271 0.341 2019
4 0.354 0.118 2019
5 0.322 0.298 2019
6 0.263 0.343 2019
The mutate() function
The mutate() function exists to compute transformations of variables in a data frame. Often, you want to create new variables that are derived from existing variables and mutate() provides a clean interface for doing that.
Note: mutate() is order aware. So you can chain multiple mutates in a single call.
For example, you may want to standardize GDP.per.capita or apply a log transformation, or create a column for years:
Boolean, logical and conditional operators all work well with mutate() too.
The Pipeline Operator
The pipeline operator strings together multiple dplyr functions in a sequence.
> third(second(first(x)))
Instead of creating difficult to read and manage functions, we will use the pipeline:
> first(x) |> second |> third
Let us assume we wanted to select gdppercapita column, then filter those countries with less than 1.5, and log transform the values:
[1] GDP.per.capita loggdp
<0 rows> (or 0-length row.names)
The group_by() function
The group_by() function is used to generate summary statistics from the data frame within strata defined by a variable.
# A tibble: 1 × 2
happy mean_gdp
<chr> <dbl>
1 happy 1.36
Family of join operationsSometimes you may collect data from different environments and may need to merge the datasets according a particular variable.
One of the main advantages of dplyr is that you can join data frames easily. There are several functions one can use to merge, or join datasets. But the dplyr is the easiest and fastest.
# A tibble: 336,776 × 19
year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
<int> <int> <int> <int> <int> <dbl> <int> <int>
1 2013 1 1 517 515 2 830 819
2 2013 1 1 533 529 4 850 830
3 2013 1 1 542 540 2 923 850
4 2013 1 1 544 545 -1 1004 1022
5 2013 1 1 554 600 -6 812 837
6 2013 1 1 554 558 -4 740 728
7 2013 1 1 555 600 -5 913 854
8 2013 1 1 557 600 -3 709 723
9 2013 1 1 557 600 -3 838 846
10 2013 1 1 558 600 -2 753 745
# ℹ 336,766 more rows
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
# tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
# hour <dbl>, minute <dbl>, time_hour <dttm>
# A tibble: 3,322 × 9
tailnum year type manufacturer model engines seats speed engine
<chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
1 N10156 2004 Fixed wing multi… EMBRAER EMB-… 2 55 NA Turbo…
2 N102UW 1998 Fixed wing multi… AIRBUS INDU… A320… 2 182 NA Turbo…
3 N103US 1999 Fixed wing multi… AIRBUS INDU… A320… 2 182 NA Turbo…
4 N104UW 1999 Fixed wing multi… AIRBUS INDU… A320… 2 182 NA Turbo…
5 N10575 2002 Fixed wing multi… EMBRAER EMB-… 2 55 NA Turbo…
6 N105UW 1999 Fixed wing multi… AIRBUS INDU… A320… 2 182 NA Turbo…
7 N107US 1999 Fixed wing multi… AIRBUS INDU… A320… 2 182 NA Turbo…
8 N108UW 1999 Fixed wing multi… AIRBUS INDU… A320… 2 182 NA Turbo…
9 N109UW 1999 Fixed wing multi… AIRBUS INDU… A320… 2 182 NA Turbo…
10 N110UW 1999 Fixed wing multi… AIRBUS INDU… A320… 2 182 NA Turbo…
# ℹ 3,312 more rows
inner_joinThis join will take only the matching values based on the specified columns.
# A tibble: 4,630 × 10
year month day dep_time arr_time carrier flight tailnum type model
<int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <chr>
1 2013 1 18 1846 2156 UA 1292 N37465 Fixed wing … 737-…
2 2013 10 1 647 744 B6 318 N355JB Fixed wing … ERJ …
3 2013 10 1 652 921 UA 1439 N37471 Fixed wing … 737-…
4 2013 10 1 755 954 9E 3538 N292PQ Fixed wing … CL-6…
5 2013 10 1 813 1050 B6 281 N354JB Fixed wing … ERJ …
6 2013 10 1 925 1025 B6 116 N373JB Fixed wing … ERJ …
7 2013 10 1 1113 1215 B6 1307 N374JB Fixed wing … ERJ …
8 2013 10 1 1426 1535 B6 286 N368JB Fixed wing … ERJ …
9 2013 10 1 1446 1635 US 1995 N156UW Fixed wing … A321…
10 2013 10 1 1454 1751 B6 575 N374JB Fixed wing … ERJ …
# ℹ 4,620 more rows
left_joinWhat do we mean by left join?
This join will take all of the values from the table we specify as left (e.g., the first one) and match them to records from the table on the right (e.g. the second one). If there isn’t a match in the second table, then it will return NULL for the row in question.
# A tibble: 336,776 × 10
year month day dep_time arr_time carrier flight tailnum type model
<int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <chr>
1 2013 1 1 517 830 UA 1545 N14228 <NA> <NA>
2 2013 1 1 533 850 UA 1714 N24211 <NA> <NA>
3 2013 1 1 542 923 AA 1141 N619AA <NA> <NA>
4 2013 1 1 544 1004 B6 725 N804JB <NA> <NA>
5 2013 1 1 554 812 DL 461 N668DN <NA> <NA>
6 2013 1 1 554 740 UA 1696 N39463 <NA> <NA>
7 2013 1 1 555 913 B6 507 N516JB <NA> <NA>
8 2013 1 1 557 709 EV 5708 N829AS <NA> <NA>
9 2013 1 1 557 838 B6 79 N593JB <NA> <NA>
10 2013 1 1 558 753 AA 301 N3ALAA <NA> <NA>
# ℹ 336,766 more rows
If you do not provide the columns to be joined, dplyr will joing the datasets based on the columns that have the same name. At the end of the operator, it will tell us which columns are used to join.
Joining, by = c("year", "tailnum")
Now, what if the joining columns don’t have same name, or the meaning of the same named columns is different, then you need to specify the joining columns with the by = argument:
# A tibble: 336,776 × 10
year month day dep_time arr_time carrier flight tailnum type model
<int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <chr>
1 2013 1 1 517 830 UA 1545 N14228 <NA> <NA>
2 2013 1 1 533 850 UA 1714 N24211 <NA> <NA>
3 2013 1 1 542 923 AA 1141 N619AA <NA> <NA>
4 2013 1 1 544 1004 B6 725 N804JB <NA> <NA>
5 2013 1 1 554 812 DL 461 N668DN <NA> <NA>
6 2013 1 1 554 740 UA 1696 N39463 <NA> <NA>
7 2013 1 1 555 913 B6 507 N516JB <NA> <NA>
8 2013 1 1 557 709 EV 5708 N829AS <NA> <NA>
9 2013 1 1 557 838 B6 79 N593JB <NA> <NA>
10 2013 1 1 558 753 AA 301 N3ALAA <NA> <NA>
# ℹ 336,766 more rows
right_joinBasically takes the second data frame’s observations into account and the new joint will take all of its values from the second dataframe.
full_joinThe full outer join returns all of the records in a new table, whether it matches on either the left or right tables. If the table rows match, then a join will be executed, otherwise it will return NULL in places where a matching row does not exist.
semi_joinA semi join creates a new table where it will return all rows from the first table where there is a corresponding matching value in second, but instead of the new table combining both the first and second tables, it only contains data from the first table.
A very common filter() use case is identifying (or removing) missing data cases.
[1] Rank Country Score GDP.per.capita
[5] Social.support Life.expectancy Freedom Generosity
[9] Trust year loggdppercapita happy
<0 rows> (or 0-length row.names)
How would we remove missing observations? Try this yourself.
Arranging the data based on a variable (either numeric or categorical)
Rank Country Score GDP.per.capita Social.support Life.expectancy Freedom
1 1 Finland 7.769 1.340 1.587 0.986 0.596
2 2 Denmark 7.600 1.383 1.573 0.996 0.592
Generosity Trust year loggdppercapita happy
1 0.153 0.393 2019 0.1271048 happy
2 0.252 0.410 2019 0.1408222 happy
Arranging on a character-based column (i.e. strings) will sort alphabetically. Try this yourself by arranging according to the “Country.or.region” column.
We can also arrange items in descending order using arrange(desc()).
Rank Country Score GDP.per.capita Social.support Life.expectancy Freedom
1 2 Denmark 7.600 1.383 1.573 0.996 0.592
2 1 Finland 7.769 1.340 1.587 0.986 0.596
Generosity Trust year loggdppercapita happy
1 0.252 0.410 2019 0.1408222 happy
2 0.153 0.393 2019 0.1271048 happy
Once you learn the dplyr grammar there are a few additional benefits. dplyr can work with other data frame “backends”” such as SQL databases. There is an SQL interface for relational databases via the DBI package dplyr can be integrated with the data.table package for large fast tables
