Unit1-IntrotoAnalytics
Raka
9/21/2020
Getting started with R: Vector and dataframe
I would like to document all of my learning journey for this specific course on EDX: MITx Analytics Edge, offered to students who are interesting in using R as the data science engine. For those who are interested, go to: https://www.edx.org/course/the-analytics-edge for details.
c(2,5,1,6,3)## [1] 2 5 1 6 3country <- c("Brazil", "Indonesia", "China", "USA")
country## [1] "Brazil" "Indonesia" "China" "USA"lifeExpectancy <- c(78,75,72,81)
# Getting specific element. ex: Indonesia, number 2
country[2]## [1] "Indonesia"Creating sequence of numbers
We can also create a sequence of numbers using seq() function. See below.
seq(0,100,2)## [1] 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
## [20] 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74
## [39] 76 78 80 82 84 86 88 90 92 94 96 98 100Data frame in R
countryData <- data.frame(country, lifeExpectancy)
countryData## country lifeExpectancy
## 1 Brazil 78
## 2 Indonesia 75
## 3 China 72
## 4 USA 81Adding data into the dataframe
Bear in mind, I just randomly created these numbers without checking their actual values.
countryData$population <- c(10000,22222,64545454,11101)
countryData## country lifeExpectancy population
## 1 Brazil 78 10000
## 2 Indonesia 75 22222
## 3 China 72 64545454
## 4 USA 81 11101Combining/ appending dataframe
For instance, we have 2 extra countries – to be addded as rows.
country <- c("Switzerland", "France")
lifeExpectancy <- c(88,81)
population <- c(9000000,1191919)
newCountryData <- data.frame(country, lifeExpectancy, population)
# Adding this df to old df
AllCountrydata <- rbind(countryData, newCountryData)
AllCountrydata## country lifeExpectancy population
## 1 Brazil 78 10000
## 2 Indonesia 75 22222
## 3 China 72 64545454
## 4 USA 81 11101
## 5 Switzerland 88 9000000
## 6 France 81 1191919Reading data from a CSV file
Let’s now read a CSV file in our folder. Check your directory to see the generated file :).
WHO <- read.csv("WHO.csv")
WHO## Country Region Population
## 1 Afghanistan Eastern Mediterranean 29825
## 2 Albania Europe 3162
## 3 Algeria Africa 38482
## 4 Andorra Europe 78
## 5 Angola Africa 20821
## 6 Antigua and Barbuda Americas 89
## 7 Argentina Americas 41087
## 8 Armenia Europe 2969
## 9 Australia Western Pacific 23050
## 10 Austria Europe 8464
## 11 Azerbaijan Europe 9309
## 12 Bahamas Americas 372
## 13 Bahrain Eastern Mediterranean 1318
## 14 Bangladesh South-East Asia 155000
## 15 Barbados Americas 283
## 16 Belarus Europe 9405
## 17 Belgium Europe 11060
## 18 Belize Americas 324
## 19 Benin Africa 10051
## 20 Bhutan South-East Asia 742
## 21 Bolivia (Plurinational State of) Americas 10496
## 22 Bosnia and Herzegovina Europe 3834
## 23 Botswana Africa 2004
## 24 Brazil Americas 199000
## 25 Brunei Darussalam Western Pacific 412
## 26 Bulgaria Europe 7278
## 27 Burkina Faso Africa 16460
## 28 Burundi Africa 9850
## 29 Cambodia Western Pacific 14865
## 30 Cameroon Africa 21700
## 31 Canada Americas 34838
## 32 Cape Verde Africa 494
## 33 Central African Republic Africa 4525
## 34 Chad Africa 12448
## 35 Chile Americas 17465
## 36 China Western Pacific 1390000
## 37 Colombia Americas 47704
## 38 Comoros Africa 718
## 39 Congo Africa 4337
## 40 Cook Islands Western Pacific 21
## 41 Costa Rica Americas 4805
## 42 Ivory Coast Africa 19840
## 43 Croatia Europe 4307
## 44 Cuba Americas 11271
## 45 Cyprus Europe 1129
## 46 Czech Republic Europe 10660
## 47 Democratic People's Republic of Korea South-East Asia 24763
## 48 Democratic Republic of the Congo Africa 65705
## 49 Denmark Europe 5598
## 50 Djibouti Eastern Mediterranean 860
## 51 Dominica Americas 72
## 52 Dominican Republic Americas 10277
## 53 Ecuador Americas 15492
## 54 Egypt Eastern Mediterranean 80722
## 55 El Salvador Americas 6297
## 56 Equatorial Guinea Africa 736
## 57 Eritrea Africa 6131
## 58 Estonia Europe 1291
## 59 Ethiopia Africa 91729
## 60 Fiji Western Pacific 875
## 61 Finland Europe 5408
## 62 France Europe 63937
## 63 Gabon Africa 1633
## 64 Gambia Africa 1791
## 65 Georgia Europe 4358
## 66 Germany Europe 82800
## 67 Ghana Africa 25366
## 68 Greece Europe 11125
## 69 Grenada Americas 105
## 70 Guatemala Americas 15083
## 71 Guinea Africa 11451
## 72 Guinea-Bissau Africa 1664
## 73 Guyana Americas 795
## 74 Haiti Americas 10174
## 75 Honduras Americas 7936
## 76 Hungary Europe 9976
## 77 Iceland Europe 326
## 78 India South-East Asia 1240000
## 79 Indonesia South-East Asia 247000
## 80 Iran (Islamic Republic of) Eastern Mediterranean 76424
## 81 Iraq Eastern Mediterranean 32778
## 82 Ireland Europe 4576
## 83 Israel Europe 7644
## 84 Italy Europe 60885
## 85 Jamaica Americas 2769
## 86 Japan Western Pacific 127000
## 87 Jordan Eastern Mediterranean 7009
## 88 Kazakhstan Europe 16271
## 89 Kenya Africa 43178
## 90 Kiribati Western Pacific 101
## 91 Kuwait Eastern Mediterranean 3250
## 92 Kyrgyzstan Europe 5474
## 93 Lao People's Democratic Republic Western Pacific 6646
## 94 Latvia Europe 2060
## 95 Lebanon Eastern Mediterranean 4647
## 96 Lesotho Africa 2052
## 97 Liberia Africa 4190
## 98 Libya Eastern Mediterranean 6155
## 99 Lithuania Europe 3028
## 100 Luxembourg Europe 524
## 101 Madagascar Africa 22294
## 102 Malawi Africa 15906
## 103 Malaysia Western Pacific 29240
## 104 Maldives South-East Asia 338
## 105 Mali Africa 14854
## 106 Malta Europe 428
## 107 Marshall Islands Western Pacific 53
## 108 Mauritania Africa 3796
## 109 Mauritius Africa 1240
## 110 Mexico Americas 121000
## 111 Micronesia (Federated States of) Western Pacific 103
## 112 Monaco Europe 38
## 113 Mongolia Western Pacific 2796
## 114 Montenegro Europe 621
## 115 Morocco Eastern Mediterranean 32521
## 116 Mozambique Africa 25203
## 117 Myanmar South-East Asia 52797
## 118 Namibia Africa 2259
## 119 Nauru Western Pacific 10
## 120 Nepal South-East Asia 27474
## 121 Netherlands Europe 16714
## 122 New Zealand Western Pacific 4460
## 123 Nicaragua Americas 5992
## 124 Niger Africa 17157
## 125 Nigeria Africa 169000
## 126 Niue Western Pacific 1
## 127 Norway Europe 4994
## 128 Oman Eastern Mediterranean 3314
## 129 Pakistan Eastern Mediterranean 179000
## 130 Palau Western Pacific 21
## 131 Panama Americas 3802
## 132 Papua New Guinea Western Pacific 7167
## 133 Paraguay Americas 6687
## 134 Peru Americas 29988
## 135 Philippines Western Pacific 96707
## 136 Poland Europe 38211
## 137 Portugal Europe 10604
## 138 Qatar Eastern Mediterranean 2051
## 139 Republic of Korea Western Pacific 49003
## 140 Republic of Moldova Europe 3514
## 141 Romania Europe 21755
## 142 Russian Federation Europe 143000
## 143 Rwanda Africa 11458
## 144 Saint Kitts and Nevis Americas 54
## 145 Saint Lucia Americas 181
## 146 Saint Vincent and the Grenadines Americas 109
## 147 Samoa Western Pacific 189
## 148 San Marino Europe 31
## 149 Sao Tome and Principe Africa 188
## 150 Saudi Arabia Eastern Mediterranean 28288
## 151 Senegal Africa 13726
## 152 Serbia Europe 9553
## 153 Seychelles Africa 92
## 154 Sierra Leone Africa 5979
## 155 Singapore Western Pacific 5303
## 156 Slovakia Europe 5446
## 157 Slovenia Europe 2068
## 158 Solomon Islands Western Pacific 550
## 159 Somalia Eastern Mediterranean 10195
## 160 South Africa Africa 52386
## 161 South Sudan Eastern Mediterranean 10838
## 162 Spain Europe 46755
## 163 Sri Lanka South-East Asia 21098
## 164 Sudan Eastern Mediterranean 37195
## 165 Suriname Americas 535
## 166 Swaziland Africa 1231
## 167 Sweden Europe 9511
## 168 Switzerland Europe 7997
## 169 Syrian Arab Republic Eastern Mediterranean 21890
## 170 Tajikistan Europe 8009
## 171 Thailand South-East Asia 66785
## 172 The former Yugoslav Republic of Macedonia Europe 2106
## 173 Timor-Leste South-East Asia 1114
## 174 Togo Africa 6643
## 175 Tonga Western Pacific 105
## 176 Trinidad and Tobago Americas 1337
## 177 Tunisia Eastern Mediterranean 10875
## 178 Turkey Europe 73997
## 179 Turkmenistan Europe 5173
## 180 Tuvalu Western Pacific 10
## 181 Uganda Africa 36346
## 182 Ukraine Europe 45530
## 183 United Arab Emirates Eastern Mediterranean 9206
## 184 United Kingdom Europe 62783
## 185 United Republic of Tanzania Africa 47783
## 186 United States of America Americas 318000
## 187 Uruguay Americas 3395
## 188 Uzbekistan Europe 28541
## 189 Vanuatu Western Pacific 247
## 190 Venezuela (Bolivarian Republic of) Americas 29955
## 191 Viet Nam Western Pacific 90796
## 192 Yemen Eastern Mediterranean 23852
## 193 Zambia Africa 14075
## 194 Zimbabwe Africa 13724
## Under15 Over60 FertilityRate LifeExpectancy ChildMortality
## 1 47.42 3.82 5.40 60 98.5
## 2 21.33 14.93 1.75 74 16.7
## 3 27.42 7.17 2.83 73 20.0
## 4 15.20 22.86 NA 82 3.2
## 5 47.58 3.84 6.10 51 163.5
## 6 25.96 12.35 2.12 75 9.9
## 7 24.42 14.97 2.20 76 14.2
## 8 20.34 14.06 1.74 71 16.4
## 9 18.95 19.46 1.89 82 4.9
## 10 14.51 23.52 1.44 81 4.0
## 11 22.25 8.24 1.96 71 35.2
## 12 21.62 11.24 1.90 75 16.9
## 13 20.16 3.38 2.12 79 9.6
## 14 30.57 6.89 2.24 70 40.9
## 15 18.99 15.78 1.84 78 18.4
## 16 15.10 19.31 1.47 71 5.2
## 17 16.88 23.81 1.85 80 4.2
## 18 34.40 5.74 2.76 74 18.3
## 19 42.95 4.54 5.01 57 89.5
## 20 28.53 6.90 2.32 67 44.6
## 21 35.23 7.28 3.31 67 41.4
## 22 16.35 20.52 1.26 76 6.7
## 23 33.75 5.63 2.71 66 53.3
## 24 24.56 10.81 1.82 74 14.4
## 25 25.75 7.03 2.03 77 8.0
## 26 13.53 26.11 1.51 74 12.1
## 27 45.66 3.88 5.78 56 102.4
## 28 44.20 3.87 6.21 53 104.3
## 29 31.23 7.67 2.93 65 39.7
## 30 43.08 4.89 4.94 53 94.9
## 31 16.37 20.82 1.66 82 5.3
## 32 30.17 7.05 2.38 72 22.2
## 33 40.07 5.74 4.54 48 128.6
## 34 48.52 3.80 6.49 51 149.8
## 35 21.38 13.80 1.84 79 9.1
## 36 17.95 13.42 1.66 76 14.0
## 37 28.03 9.19 2.35 78 17.6
## 38 42.17 4.50 4.85 62 77.6
## 39 42.37 5.13 5.05 58 96.0
## 40 30.61 9.07 NA 77 10.6
## 41 23.94 10.15 1.83 79 9.9
## 42 41.48 5.10 4.91 56 107.6
## 43 14.98 24.69 1.48 77 4.7
## 44 16.58 17.95 1.46 78 5.5
## 45 17.16 16.92 1.47 81 3.2
## 46 14.56 23.23 1.53 78 3.8
## 47 21.98 12.74 2.00 69 28.8
## 48 45.11 4.51 6.15 49 145.7
## 49 17.66 23.90 1.88 79 3.7
## 50 33.72 5.96 3.53 58 80.9
## 51 25.96 12.35 NA 74 12.6
## 52 30.53 8.97 2.55 73 27.1
## 53 30.29 9.21 2.62 76 23.3
## 54 31.25 8.62 2.85 73 21.0
## 55 30.62 9.64 2.24 72 15.9
## 56 38.95 4.53 5.04 54 100.3
## 57 43.10 3.73 4.88 61 51.8
## 58 15.69 23.92 1.62 76 3.6
## 59 43.29 5.17 4.77 60 68.3
## 60 28.88 8.38 2.64 70 22.4
## 61 16.42 25.90 1.85 81 2.9
## 62 18.26 23.82 1.98 82 4.1
## 63 38.49 7.38 4.18 62 62.0
## 64 45.90 3.72 5.79 58 72.9
## 65 17.62 19.47 1.82 72 19.9
## 66 13.17 26.72 1.40 81 4.1
## 67 38.59 5.40 3.99 64 72.0
## 68 14.60 25.41 1.51 81 4.8
## 69 26.96 9.72 2.22 74 13.5
## 70 40.80 6.56 3.91 69 32.0
## 71 42.46 5.03 5.09 55 101.2
## 72 41.55 5.06 5.05 50 129.1
## 73 36.77 5.18 2.64 63 35.2
## 74 35.35 6.70 3.28 63 75.6
## 75 35.72 6.41 3.10 74 22.9
## 76 14.62 23.41 1.38 75 6.2
## 77 20.71 17.62 2.11 82 2.3
## 78 29.43 8.10 2.53 65 56.3
## 79 29.27 7.86 2.40 69 31.0
## 80 23.68 7.82 1.91 73 17.6
## 81 40.51 4.95 4.15 69 34.4
## 82 21.54 16.59 2.00 81 4.0
## 83 27.53 15.15 2.92 82 4.2
## 84 14.04 26.97 1.45 82 3.8
## 85 27.78 10.98 2.31 75 16.8
## 86 13.12 31.92 1.39 83 3.0
## 87 34.13 5.30 3.39 74 19.1
## 88 25.46 10.04 2.52 67 18.7
## 89 42.37 4.25 4.54 60 72.9
## 90 30.10 8.84 3.01 67 59.9
## 91 24.90 3.80 2.65 80 11.0
## 92 30.21 6.34 3.03 69 26.6
## 93 35.61 5.76 3.20 68 71.8
## 94 14.57 24.24 1.57 74 8.7
## 95 21.64 12.03 1.50 74 9.3
## 96 36.75 6.31 3.15 50 99.6
## 97 43.06 4.76 4.95 59 74.8
## 98 29.45 6.96 2.47 65 15.4
## 99 15.13 20.57 1.49 74 5.4
## 100 17.46 19.15 1.65 82 2.2
## 101 42.72 4.45 4.59 66 58.2
## 102 45.44 4.92 5.55 58 71.0
## 103 26.65 8.21 1.99 74 8.5
## 104 29.03 6.65 2.31 77 10.5
## 105 47.14 4.29 6.85 51 128.0
## 106 14.98 22.87 1.37 80 6.8
## 107 30.10 8.84 NA 60 37.9
## 108 40.22 4.94 4.78 59 84.0
## 109 20.17 13.23 1.51 74 15.1
## 110 29.02 9.18 2.25 75 16.2
## 111 35.81 6.67 3.40 69 38.5
## 112 18.26 23.82 NA 82 3.8
## 113 27.05 5.80 2.45 68 27.5
## 114 19.01 18.58 1.69 76 5.9
## 115 27.85 7.61 2.65 72 31.1
## 116 45.38 5.01 5.34 53 89.7
## 117 25.28 8.15 1.98 65 52.3
## 118 36.59 5.38 3.17 65 38.7
## 119 30.10 8.84 NA 71 37.1
## 120 35.58 7.65 2.50 68 41.6
## 121 17.21 23.02 1.76 81 4.1
## 122 20.26 19.01 2.10 81 5.7
## 123 33.37 6.59 2.59 73 24.4
## 124 49.99 4.26 7.58 56 113.5
## 125 44.23 4.49 6.02 53 123.7
## 126 30.61 9.07 NA 72 25.1
## 127 18.64 21.41 1.93 81 2.8
## 128 24.19 3.99 2.90 72 11.6
## 129 34.31 6.44 3.35 67 85.9
## 130 30.10 8.84 NA 72 20.8
## 131 28.65 10.13 2.52 77 18.5
## 132 38.37 4.79 3.90 63 63.0
## 133 32.78 8.01 2.93 75 22.0
## 134 29.18 9.12 2.48 77 18.2
## 135 34.53 6.21 3.11 69 29.8
## 136 14.91 20.48 1.39 76 5.0
## 137 14.92 24.39 1.33 80 3.6
## 138 13.28 1.73 2.06 82 7.4
## 139 15.25 16.58 1.29 81 3.8
## 140 16.52 16.72 1.47 71 17.6
## 141 15.05 20.66 1.39 74 12.2
## 142 15.45 18.60 1.51 69 10.3
## 143 43.56 3.94 4.73 60 55.0
## 144 25.96 12.35 NA 74 9.2
## 145 24.31 12.13 1.96 75 17.5
## 146 25.70 9.92 2.05 74 23.4
## 147 37.88 7.39 4.28 73 17.8
## 148 14.04 26.97 NA 83 3.3
## 149 41.60 4.76 4.22 63 53.2
## 150 29.69 4.59 2.76 76 8.6
## 151 43.54 4.57 5.02 61 59.6
## 152 16.45 20.52 1.37 74 6.6
## 153 21.95 10.05 2.23 74 13.1
## 154 41.74 4.41 4.86 47 181.6
## 155 16.48 15.13 1.27 82 2.9
## 156 15.00 18.60 1.37 76 7.5
## 157 14.16 23.16 1.49 80 3.1
## 158 40.37 5.10 4.17 70 31.1
## 159 47.35 4.46 6.77 50 147.4
## 160 29.53 8.44 2.44 58 44.6
## 161 42.28 5.26 5.10 54 104.0
## 162 15.20 22.86 1.47 82 4.5
## 163 25.15 12.40 2.35 75 9.6
## 164 41.48 4.99 4.56 62 73.1
## 165 27.83 9.55 2.32 72 20.8
## 166 38.05 5.34 3.48 50 79.7
## 167 16.71 25.32 1.93 82 2.9
## 168 14.79 23.25 1.51 83 4.3
## 169 35.35 6.09 3.04 75 15.1
## 170 35.75 4.80 3.81 68 58.3
## 171 18.47 13.96 1.43 74 13.2
## 172 16.89 17.56 1.44 75 7.4
## 173 46.33 5.16 6.11 64 56.7
## 174 41.89 4.44 4.75 56 95.5
## 175 37.33 7.96 3.86 72 12.8
## 176 20.73 13.18 1.80 71 20.7
## 177 23.22 10.49 2.04 76 16.1
## 178 26.00 10.56 2.08 76 14.2
## 179 28.65 6.30 2.38 63 52.8
## 180 30.61 9.07 NA 64 29.7
## 181 48.54 3.72 6.06 56 68.9
## 182 14.18 20.76 1.45 71 10.7
## 183 14.41 0.81 1.84 76 8.4
## 184 17.54 23.06 1.90 80 4.8
## 185 44.85 4.89 5.36 59 54.0
## 186 19.63 19.31 2.00 79 7.1
## 187 22.05 18.59 2.07 77 7.2
## 188 28.90 6.38 2.38 68 39.6
## 189 37.37 6.02 3.46 72 17.9
## 190 28.84 9.17 2.44 75 15.3
## 191 22.87 9.32 1.79 75 23.0
## 192 40.72 4.54 4.35 64 60.0
## 193 46.73 3.95 5.77 55 88.5
## 194 40.24 5.68 3.64 54 89.8
## CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale
## 1 54.26 NA 1140 NA
## 2 96.39 NA 8820 NA
## 3 98.99 NA 8310 98.2
## 4 75.49 NA NA 78.4
## 5 48.38 70.1 5230 93.1
## 6 196.41 99.0 17900 91.1
## 7 134.92 97.8 17130 NA
## 8 103.57 99.6 6100 NA
## 9 108.34 NA 38110 96.9
## 10 154.78 NA 42050 NA
## 11 108.75 NA 8960 85.3
## 12 86.06 NA NA NA
## 13 127.96 91.9 NA NA
## 14 56.06 56.8 1940 NA
## 15 127.01 NA NA NA
## 16 111.88 NA 14460 NA
## 17 116.61 NA 39190 98.9
## 18 69.96 NA 6090 NA
## 19 85.33 42.4 1620 NA
## 20 65.58 NA 5570 88.3
## 21 82.82 NA 4890 91.2
## 22 84.52 97.9 9190 86.5
## 23 142.82 84.5 14550 NA
## 24 124.26 NA 11420 NA
## 25 109.17 95.2 NA NA
## 26 140.68 NA 14160 99.3
## 27 45.27 NA 1300 60.7
## 28 22.33 67.2 610 NA
## 29 96.17 NA 2230 96.4
## 30 52.35 NA 2330 99.6
## 31 79.73 NA 39660 NA
## 32 79.19 84.3 3980 94.6
## 33 40.65 56.0 810 81.3
## 34 31.80 34.5 1360 NA
## 35 129.71 NA 16330 94.3
## 36 73.19 94.3 8390 NA
## 37 98.45 93.4 9560 91.7
## 38 28.71 74.9 1110 NA
## 39 93.84 NA 3240 92.3
## 40 NA NA NA 97.6
## 41 92.20 96.2 11860 NA
## 42 86.06 56.2 1710 NA
## 43 116.37 98.8 18760 94.8
## 44 11.69 99.8 NA 100.0
## 45 97.71 98.3 NA 99.1
## 46 123.44 NA 24370 NA
## 47 4.09 NA NA NA
## 48 23.09 66.8 340 NA
## 49 128.47 NA 41900 94.8
## 50 21.32 NA NA NA
## 51 164.02 NA 13000 NA
## 52 87.22 89.5 9420 95.5
## 53 104.55 91.9 8510 NA
## 54 101.08 72.0 6120 NA
## 55 133.54 84.5 6640 95.2
## 56 59.15 93.9 25620 56.5
## 57 4.47 67.8 580 37.2
## 58 138.98 99.8 20850 97.7
## 59 16.67 NA 1110 84.8
## 60 83.72 NA 4610 NA
## 61 166.02 NA 37670 97.7
## 62 94.79 NA 35910 99.1
## 63 117.32 88.4 13740 NA
## 64 78.89 50.0 1750 68.2
## 65 102.31 99.7 5350 NA
## 66 132.30 NA 40230 NA
## 67 84.78 67.3 1810 NA
## 68 106.48 97.2 25100 98.8
## 69 NA NA 10350 NA
## 70 140.38 75.2 4760 98.6
## 71 44.02 41.0 1020 85.2
## 72 56.18 54.2 1240 76.7
## 73 69.94 NA NA 82.4
## 74 41.49 NA 1180 NA
## 75 103.97 84.8 3820 94.8
## 76 117.30 99.0 20310 97.8
## 77 106.08 NA 31020 98.8
## 78 72.00 NA 3590 NA
## 79 103.09 NA 4500 NA
## 80 74.93 NA NA NA
## 81 78.12 78.2 3750 NA
## 82 108.41 NA 34180 99.4
## 83 121.66 NA 27110 97.0
## 84 157.93 98.9 32400 99.6
## 85 108.12 86.6 NA 83.4
## 86 104.95 NA 35330 NA
## 87 118.20 92.6 5930 90.8
## 88 155.74 99.7 11250 NA
## 89 67.49 87.4 1710 NA
## 90 13.64 NA 3300 NA
## 91 175.09 NA NA NA
## 92 116.40 NA 2180 95.5
## 93 87.16 NA 2580 98.1
## 94 102.94 99.8 17700 95.0
## 95 78.65 NA 14470 93.5
## 96 56.17 89.6 2050 72.2
## 97 49.17 60.8 540 NA
## 98 155.70 89.2 NA NA
## 99 151.30 99.7 19640 95.6
## 100 148.27 NA 64260 93.6
## 101 40.65 NA 950 NA
## 102 25.69 74.8 870 NA
## 103 127.04 93.1 15650 NA
## 104 165.72 NA 7430 96.5
## 105 68.32 31.1 1040 70.6
## 106 124.86 NA NA 93.3
## 107 NA NA NA NA
## 108 93.60 58.0 2400 72.8
## 109 99.04 88.5 14330 NA
## 110 82.38 93.1 15390 99.2
## 111 NA NA 3580 NA
## 112 89.73 NA NA NA
## 113 105.08 97.4 4290 99.6
## 114 NA 98.4 13700 NA
## 115 113.26 NA 4880 NA
## 116 32.83 56.1 970 94.6
## 117 2.57 92.3 NA NA
## 118 96.39 88.8 6560 83.8
## 119 65.00 NA NA NA
## 120 43.81 60.3 1260 NA
## 121 NA NA 43140 NA
## 122 109.19 NA NA 99.3
## 123 82.15 NA 3730 93.2
## 124 29.52 NA 720 64.2
## 125 58.58 61.3 2290 60.1
## 126 NA NA NA NA
## 127 115.62 NA 61460 99.1
## 128 168.97 NA NA NA
## 129 61.61 NA 2870 81.3
## 130 74.94 NA 11080 NA
## 131 188.60 94.1 14510 99.1
## 132 34.22 60.6 2570 NA
## 133 99.40 93.9 5390 84.4
## 134 110.41 NA 9440 97.8
## 135 99.30 NA 4140 NA
## 136 130.97 99.5 20430 96.9
## 137 115.39 95.2 24440 99.1
## 138 123.11 96.3 86440 95.7
## 139 108.50 NA 30370 99.3
## 140 104.80 98.5 3640 90.1
## 141 109.16 97.7 15120 87.9
## 142 179.31 99.6 20560 NA
## 143 40.63 71.1 1270 NA
## 144 NA NA 16470 85.8
## 145 123.00 NA 11220 90.2
## 146 120.52 NA 10440 NA
## 147 NA 98.8 4270 93.2
## 148 111.75 NA NA NA
## 149 68.26 89.2 2080 NA
## 150 191.24 86.6 24700 96.7
## 151 73.25 NA 1940 75.9
## 152 125.39 97.9 11540 94.7
## 153 145.71 91.8 25140 NA
## 154 35.63 42.1 840 NA
## 155 150.24 95.9 59380 NA
## 156 109.35 NA 22130 NA
## 157 106.56 99.7 26510 97.7
## 158 49.77 NA 2350 87.7
## 159 6.85 NA NA NA
## 160 126.83 NA 10710 NA
## 161 NA NA NA NA
## 162 113.22 97.7 31400 99.7
## 163 87.05 91.2 5520 93.9
## 164 56.14 71.1 2120 NA
## 165 178.88 94.7 NA NA
## 166 63.70 87.4 5930 NA
## 167 118.57 NA 42200 99.7
## 168 131.43 NA 52570 98.9
## 169 63.17 83.4 NA NA
## 170 90.64 99.7 2300 99.5
## 171 111.63 NA 8360 NA
## 172 107.24 97.3 11090 97.3
## 173 53.23 58.3 NA 86.2
## 174 50.45 NA 1040 NA
## 175 52.63 NA 5000 NA
## 176 135.64 98.8 NA 97.7
## 177 116.93 NA 9030 NA
## 178 88.70 NA 16940 99.5
## 179 68.77 99.6 8690 NA
## 180 21.63 NA NA NA
## 181 48.38 73.2 1310 89.7
## 182 122.98 99.7 7040 90.8
## 183 148.62 NA 47890 NA
## 184 130.75 NA 36010 99.8
## 185 55.53 73.2 1500 NA
## 186 92.72 NA 48820 95.4
## 187 140.75 98.1 14640 NA
## 188 91.65 99.4 3420 93.3
## 189 55.76 82.6 4330 NA
## 190 97.78 NA 12430 94.7
## 191 143.39 93.2 3250 NA
## 192 47.05 63.9 2170 85.5
## 193 60.59 71.2 1490 91.4
## 194 72.13 92.2 NA NA
## PrimarySchoolEnrollmentFemale
## 1 NA
## 2 NA
## 3 96.4
## 4 79.4
## 5 78.2
## 6 84.5
## 7 NA
## 8 NA
## 9 97.5
## 10 NA
## 11 84.1
## 12 NA
## 13 NA
## 14 NA
## 15 NA
## 16 NA
## 17 99.2
## 18 NA
## 19 NA
## 20 91.5
## 21 91.5
## 22 88.4
## 23 NA
## 24 NA
## 25 NA
## 26 99.7
## 27 55.9
## 28 NA
## 29 95.4
## 30 87.4
## 31 NA
## 32 92.4
## 33 60.6
## 34 NA
## 35 94.4
## 36 NA
## 37 91.3
## 38 NA
## 39 89.3
## 40 99.3
## 41 NA
## 42 NA
## 43 97.0
## 44 99.7
## 45 99.5
## 46 NA
## 47 NA
## 48 NA
## 49 96.9
## 50 NA
## 51 NA
## 52 90.4
## 53 NA
## 54 NA
## 55 95.5
## 56 56.0
## 57 32.5
## 58 97.0
## 59 79.5
## 60 NA
## 61 97.9
## 62 99.3
## 63 NA
## 64 70.4
## 65 NA
## 66 NA
## 67 NA
## 68 99.3
## 69 NA
## 70 97.5
## 71 72.1
## 72 73.3
## 73 85.9
## 74 NA
## 75 97.0
## 76 98.3
## 77 99.2
## 78 NA
## 79 NA
## 80 NA
## 81 NA
## 82 100.0
## 83 97.8
## 84 98.5
## 85 81.4
## 86 NA
## 87 90.7
## 88 NA
## 89 NA
## 90 NA
## 91 NA
## 92 95.1
## 93 95.4
## 94 96.8
## 95 92.9
## 96 75.3
## 97 NA
## 98 NA
## 99 95.8
## 100 95.7
## 101 NA
## 102 NA
## 103 NA
## 104 96.5
## 105 60.8
## 106 94.3
## 107 NA
## 108 76.0
## 109 NA
## 110 99.9
## 111 NA
## 112 NA
## 113 98.5
## 114 NA
## 115 NA
## 116 89.4
## 117 NA
## 118 88.5
## 119 NA
## 120 NA
## 121 NA
## 122 99.6
## 123 94.5
## 124 52.0
## 125 54.8
## 126 NA
## 127 99.2
## 128 NA
## 129 66.5
## 130 NA
## 131 98.2
## 132 NA
## 133 83.9
## 134 98.5
## 135 NA
## 136 96.7
## 137 99.7
## 138 96.6
## 139 98.4
## 140 90.1
## 141 87.3
## 142 NA
## 143 NA
## 144 86.2
## 145 89.2
## 146 NA
## 147 97.1
## 148 NA
## 149 NA
## 150 96.5
## 151 80.2
## 152 94.4
## 153 NA
## 154 NA
## 155 NA
## 156 NA
## 157 97.3
## 158 87.3
## 159 NA
## 160 NA
## 161 NA
## 162 99.8
## 163 94.4
## 164 NA
## 165 NA
## 166 NA
## 167 99.0
## 168 99.5
## 169 NA
## 170 96.0
## 171 NA
## 172 99.2
## 173 85.6
## 174 NA
## 175 NA
## 176 97.0
## 177 NA
## 178 98.3
## 179 NA
## 180 NA
## 181 92.3
## 182 91.5
## 183 NA
## 184 99.6
## 185 NA
## 186 96.1
## 187 NA
## 188 91.0
## 189 NA
## 190 95.1
## 191 NA
## 192 70.5
## 193 93.9
## 194 NAstr(WHO)## 'data.frame': 194 obs. of 13 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ Region : chr "Eastern Mediterranean" "Europe" "Africa" "Europe" ...
## $ Population : int 29825 3162 38482 78 20821 89 41087 2969 23050 8464 ...
## $ Under15 : num 47.4 21.3 27.4 15.2 47.6 ...
## $ Over60 : num 3.82 14.93 7.17 22.86 3.84 ...
## $ FertilityRate : num 5.4 1.75 2.83 NA 6.1 2.12 2.2 1.74 1.89 1.44 ...
## $ LifeExpectancy : int 60 74 73 82 51 75 76 71 82 81 ...
## $ ChildMortality : num 98.5 16.7 20 3.2 163.5 ...
## $ CellularSubscribers : num 54.3 96.4 99 75.5 48.4 ...
## $ LiteracyRate : num NA NA NA NA 70.1 99 97.8 99.6 NA NA ...
## $ GNI : num 1140 8820 8310 NA 5230 ...
## $ PrimarySchoolEnrollmentMale : num NA NA 98.2 78.4 93.1 91.1 NA NA 96.9 NA ...
## $ PrimarySchoolEnrollmentFemale: num NA NA 96.4 79.4 78.2 84.5 NA NA 97.5 NA ...# We can even call a summary function
summary(WHO)## Country Region Population Under15
## Length:194 Length:194 Min. : 1 Min. :13.12
## Class :character Class :character 1st Qu.: 1696 1st Qu.:18.72
## Mode :character Mode :character Median : 7790 Median :28.65
## Mean : 36360 Mean :28.73
## 3rd Qu.: 24535 3rd Qu.:37.75
## Max. :1390000 Max. :49.99
##
## Over60 FertilityRate LifeExpectancy ChildMortality
## Min. : 0.81 Min. :1.260 Min. :47.00 Min. : 2.200
## 1st Qu.: 5.20 1st Qu.:1.835 1st Qu.:64.00 1st Qu.: 8.425
## Median : 8.53 Median :2.400 Median :72.50 Median : 18.600
## Mean :11.16 Mean :2.941 Mean :70.01 Mean : 36.149
## 3rd Qu.:16.69 3rd Qu.:3.905 3rd Qu.:76.00 3rd Qu.: 55.975
## Max. :31.92 Max. :7.580 Max. :83.00 Max. :181.600
## NA's :11
## CellularSubscribers LiteracyRate GNI
## Min. : 2.57 Min. :31.10 Min. : 340
## 1st Qu.: 63.57 1st Qu.:71.60 1st Qu.: 2335
## Median : 97.75 Median :91.80 Median : 7870
## Mean : 93.64 Mean :83.71 Mean :13321
## 3rd Qu.:120.81 3rd Qu.:97.85 3rd Qu.:17558
## Max. :196.41 Max. :99.80 Max. :86440
## NA's :10 NA's :91 NA's :32
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## Min. : 37.20 Min. : 32.50
## 1st Qu.: 87.70 1st Qu.: 87.30
## Median : 94.70 Median : 95.10
## Mean : 90.85 Mean : 89.63
## 3rd Qu.: 98.10 3rd Qu.: 97.90
## Max. :100.00 Max. :100.00
## NA's :93 NA's :93Taking only a small subset of DF
What about taking smaller fractions of our data? Yes we can, using subset function. Afterward, we output this into “WHO_Europe.csv” file.
WHO_Europe <- subset(WHO, Region == "Europe")
str(WHO_Europe)## 'data.frame': 53 obs. of 13 variables:
## $ Country : chr "Albania" "Andorra" "Armenia" "Austria" ...
## $ Region : chr "Europe" "Europe" "Europe" "Europe" ...
## $ Population : int 3162 78 2969 8464 9309 9405 11060 3834 7278 4307 ...
## $ Under15 : num 21.3 15.2 20.3 14.5 22.2 ...
## $ Over60 : num 14.93 22.86 14.06 23.52 8.24 ...
## $ FertilityRate : num 1.75 NA 1.74 1.44 1.96 1.47 1.85 1.26 1.51 1.48 ...
## $ LifeExpectancy : int 74 82 71 81 71 71 80 76 74 77 ...
## $ ChildMortality : num 16.7 3.2 16.4 4 35.2 5.2 4.2 6.7 12.1 4.7 ...
## $ CellularSubscribers : num 96.4 75.5 103.6 154.8 108.8 ...
## $ LiteracyRate : num NA NA 99.6 NA NA NA NA 97.9 NA 98.8 ...
## $ GNI : num 8820 NA 6100 42050 8960 ...
## $ PrimarySchoolEnrollmentMale : num NA 78.4 NA NA 85.3 NA 98.9 86.5 99.3 94.8 ...
## $ PrimarySchoolEnrollmentFemale: num NA 79.4 NA NA 84.1 NA 99.2 88.4 99.7 97 ...# Storing this subset into a separate csv
write.csv(WHO_Europe, "WHO_Eur.csv")
# This line is used to check our variables
ls()## [1] "AllCountrydata" "country" "countryData" "lifeExpectancy"
## [5] "newCountryData" "population" "WHO" "WHO_Europe"# We can also remove a specific variable by using rm()
rm(countyrdata) #terribly misspelled## Warning in rm(countyrdata): object 'countyrdata' not foundDoing basic data analysis
We can practice some data analysis in this exercise such as mean, etc.
WHO$Under15## [1] 47.42 21.33 27.42 15.20 47.58 25.96 24.42 20.34 18.95 14.51 22.25 21.62
## [13] 20.16 30.57 18.99 15.10 16.88 34.40 42.95 28.53 35.23 16.35 33.75 24.56
## [25] 25.75 13.53 45.66 44.20 31.23 43.08 16.37 30.17 40.07 48.52 21.38 17.95
## [37] 28.03 42.17 42.37 30.61 23.94 41.48 14.98 16.58 17.16 14.56 21.98 45.11
## [49] 17.66 33.72 25.96 30.53 30.29 31.25 30.62 38.95 43.10 15.69 43.29 28.88
## [61] 16.42 18.26 38.49 45.90 17.62 13.17 38.59 14.60 26.96 40.80 42.46 41.55
## [73] 36.77 35.35 35.72 14.62 20.71 29.43 29.27 23.68 40.51 21.54 27.53 14.04
## [85] 27.78 13.12 34.13 25.46 42.37 30.10 24.90 30.21 35.61 14.57 21.64 36.75
## [97] 43.06 29.45 15.13 17.46 42.72 45.44 26.65 29.03 47.14 14.98 30.10 40.22
## [109] 20.17 29.02 35.81 18.26 27.05 19.01 27.85 45.38 25.28 36.59 30.10 35.58
## [121] 17.21 20.26 33.37 49.99 44.23 30.61 18.64 24.19 34.31 30.10 28.65 38.37
## [133] 32.78 29.18 34.53 14.91 14.92 13.28 15.25 16.52 15.05 15.45 43.56 25.96
## [145] 24.31 25.70 37.88 14.04 41.60 29.69 43.54 16.45 21.95 41.74 16.48 15.00
## [157] 14.16 40.37 47.35 29.53 42.28 15.20 25.15 41.48 27.83 38.05 16.71 14.79
## [169] 35.35 35.75 18.47 16.89 46.33 41.89 37.33 20.73 23.22 26.00 28.65 30.61
## [181] 48.54 14.18 14.41 17.54 44.85 19.63 22.05 28.90 37.37 28.84 22.87 40.72
## [193] 46.73 40.24mean(WHO$Under15)## [1] 28.73242# and std dev
sd(WHO$Under15)## [1] 10.53457# or specific variable
summary(WHO$Under15)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.12 18.72 28.65 28.73 37.75 49.99Can we index to a specific value? YES!
For instance, can we identify, which country has the the lowest or highest value in Under15? Japan is the answer.
which.min(WHO$Under15)## [1] 86which.max(WHO$Under15)## [1] 124# Now we use this as indexing
WHO$Country[which.min(WHO$Under15)]## [1] "Japan"WHO$Country[which.max(WHO$Under15)]## [1] "Niger"Plotting world in R
So, let’s start with the scatter plot: the mother of all plots.
plot(x = WHO$GNI, y = WHO$FertilityRate)
## Managing outliers in dataframe, after seeing the plot
Can we spot the outliers from figure above? yep, of course!
outliers <- subset(WHO, GNI > 10000 & FertilityRate > 2.5)
nrow(outliers) ## [1] 7# Then we output them
outliers[c("Country", "GNI", "FertilityRate")]## Country GNI FertilityRate
## 23 Botswana 14550 2.71
## 56 Equatorial Guinea 25620 5.04
## 63 Gabon 13740 4.18
## 83 Israel 27110 2.92
## 88 Kazakhstan 11250 2.52
## 131 Panama 14510 2.52
## 150 Saudi Arabia 24700 2.76Answering some questions on the website
These are just some lines to answer questions after the video. Feel free to skip.
colnames(WHO)## [1] "Country" "Region"
## [3] "Population" "Under15"
## [5] "Over60" "FertilityRate"
## [7] "LifeExpectancy" "ChildMortality"
## [9] "CellularSubscribers" "LiteracyRate"
## [11] "GNI" "PrimarySchoolEnrollmentMale"
## [13] "PrimarySchoolEnrollmentFemale"mean(WHO$Over60)## [1] 11.16366WHO$Country[which.min(WHO$Over60)]## [1] "United Arab Emirates"WHO$Country[which.max(WHO$LiteracyRate)]## [1] "Cuba"Fun parts: Playing with statistical distributions on R.
Here comes the interesting journey in R, how easy it is to build plots (somewhat complex in EXCEL) with only 1 - 2 lines of code. Embrace!
hist(WHO$CellularSubscribers)
boxplot(WHO$LifeExpectancy ~ WHO$Region)
# we fix some labelling and title.
boxplot(WHO$LifeExpectancy ~ WHO$Region,
xlab = "", ylab = "Life Expectancy",
main = "Life Expectancy of Countries by Region")
# Creating a summary output in a table
We can also create a table from our dataframe as the summary output.
table(WHO$Region)##
## Africa Americas Eastern Mediterranean
## 46 35 22
## Europe South-East Asia Western Pacific
## 53 11 27Can we use it to find aggregated results? Ex: the percentage of pop over 60 in these regions. HOW?
Yes, we use the function called tapply :) Behold.
tapply(WHO$Over60, WHO$Region, mean)## Africa Americas Eastern Mediterranean
## 5.220652 10.943714 5.620000
## Europe South-East Asia Western Pacific
## 19.774906 8.769091 10.162963# Lets then find other results
tapply(WHO$LiteracyRate, WHO$Region, min, na.rm = TRUE)## Africa Americas Eastern Mediterranean
## 31.1 75.2 63.9
## Europe South-East Asia Western Pacific
## 95.2 56.8 60.6Answering questions from the course, part 2
If you like, you can skip again this part.
tapply(WHO$ChildMortality, WHO$Region, min, na.rm = TRUE)## Africa Americas Eastern Mediterranean
## 13.1 5.3 7.4
## Europe South-East Asia Western Pacific
## 2.2 9.6 2.9The start of Recitation: USDA Nutritional Education, Understanding food
Allright folks, now we enter the recitation part of this week. Are you ready? Yes, I am so pumped too. Let’s get started! Of course, the first thing to do is to load the data
USDA <- read.csv("USDA.csv")
#and somwewhat mandatory steps in any EDA, Str and Summary
str(USDA)## 'data.frame': 7058 obs. of 16 variables:
## $ ID : int 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ...
## $ Description : chr "BUTTER,WITH SALT" "BUTTER,WHIPPED,WITH SALT" "BUTTER OIL,ANHYDROUS" "CHEESE,BLUE" ...
## $ Calories : int 717 717 876 353 371 334 300 376 403 387 ...
## $ Protein : num 0.85 0.85 0.28 21.4 23.24 ...
## $ TotalFat : num 81.1 81.1 99.5 28.7 29.7 ...
## $ Carbohydrate: num 0.06 0.06 0 2.34 2.79 0.45 0.46 3.06 1.28 4.78 ...
## $ Sodium : int 714 827 2 1395 560 629 842 690 621 700 ...
## $ SaturatedFat: num 51.4 50.5 61.9 18.7 18.8 ...
## $ Cholesterol : int 215 219 256 75 94 100 72 93 105 103 ...
## $ Sugar : num 0.06 0.06 0 0.5 0.51 0.45 0.46 NA 0.52 NA ...
## $ Calcium : int 24 24 4 528 674 184 388 673 721 643 ...
## $ Iron : num 0.02 0.16 0 0.31 0.43 0.5 0.33 0.64 0.68 0.21 ...
## $ Potassium : int 24 26 5 256 136 152 187 93 98 95 ...
## $ VitaminC : num 0 0 0 0 0 0 0 0 0 0 ...
## $ VitaminE : num 2.32 2.32 2.8 0.25 0.26 0.24 0.21 NA 0.29 NA ...
## $ VitaminD : num 1.5 1.5 1.8 0.5 0.5 0.5 0.4 NA 0.6 NA ...summary(USDA)## ID Description Calories Protein
## Min. : 1001 Length:7058 Min. : 0.0 Min. : 0.00
## 1st Qu.: 8387 Class :character 1st Qu.: 85.0 1st Qu.: 2.29
## Median :13294 Mode :character Median :181.0 Median : 8.20
## Mean :14260 Mean :219.7 Mean :11.71
## 3rd Qu.:18337 3rd Qu.:331.0 3rd Qu.:20.43
## Max. :93600 Max. :902.0 Max. :88.32
## NA's :1 NA's :1
## TotalFat Carbohydrate Sodium SaturatedFat
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0.72 1st Qu.: 0.00 1st Qu.: 37.0 1st Qu.: 0.172
## Median : 4.37 Median : 7.13 Median : 79.0 Median : 1.256
## Mean : 10.32 Mean : 20.70 Mean : 322.1 Mean : 3.452
## 3rd Qu.: 12.70 3rd Qu.: 28.17 3rd Qu.: 386.0 3rd Qu.: 4.028
## Max. :100.00 Max. :100.00 Max. :38758.0 Max. :95.600
## NA's :1 NA's :1 NA's :84 NA's :301
## Cholesterol Sugar Calcium Iron
## Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 9.00 1st Qu.: 0.520
## Median : 3.00 Median : 1.395 Median : 19.00 Median : 1.330
## Mean : 41.55 Mean : 8.257 Mean : 73.53 Mean : 2.828
## 3rd Qu.: 69.00 3rd Qu.: 7.875 3rd Qu.: 56.00 3rd Qu.: 2.620
## Max. :3100.00 Max. :99.800 Max. :7364.00 Max. :123.600
## NA's :288 NA's :1910 NA's :136 NA's :123
## Potassium VitaminC VitaminE VitaminD
## Min. : 0.0 Min. : 0.000 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 135.0 1st Qu.: 0.000 1st Qu.: 0.120 1st Qu.: 0.0000
## Median : 250.0 Median : 0.000 Median : 0.270 Median : 0.0000
## Mean : 301.4 Mean : 9.436 Mean : 1.488 Mean : 0.5769
## 3rd Qu.: 348.0 3rd Qu.: 3.100 3rd Qu.: 0.710 3rd Qu.: 0.1000
## Max. :16500.0 Max. :2400.000 Max. :149.400 Max. :250.0000
## NA's :409 NA's :332 NA's :2720 NA's :2834There is a weird number!
The amount of sodium (max value) that summary function gives us is way too high. Let’s find out what that is.
names(USDA)## [1] "ID" "Description" "Calories" "Protein" "TotalFat"
## [6] "Carbohydrate" "Sodium" "SaturatedFat" "Cholesterol" "Sugar"
## [11] "Calcium" "Iron" "Potassium" "VitaminC" "VitaminE"
## [16] "VitaminD"USDA$Description[which.max(USDA$Sodium)]## [1] "SALT,TABLE"What about high containing sodium?
We can create a subset of data, using subset function indeed :)
HiSodium <- subset(USDA, Sodium > 10000)
nrow(HiSodium)## [1] 10HiSodium$Description## [1] "SALT,TABLE"
## [2] "SOUP,BF BROTH OR BOUILLON,PDR,DRY"
## [3] "SOUP,BEEF BROTH,CUBED,DRY"
## [4] "SOUP,CHICK BROTH OR BOUILLON,DRY"
## [5] "SOUP,CHICK BROTH CUBES,DRY"
## [6] "GRAVY,AU JUS,DRY"
## [7] "ADOBO FRESCO"
## [8] "LEAVENING AGENTS,BAKING PDR,DOUBLE-ACTING,NA AL SULFATE"
## [9] "LEAVENING AGENTS,BAKING SODA"
## [10] "DESSERTS,RENNIN,TABLETS,UNSWTND"Finding the sodium value of “CAVIAR”
Yes, we can also filter using ‘string’ as input command.
match("CAVIAR", USDA$Description)## [1] 4154USDA$Sodium[match("CAVIAR", USDA$Description)]## [1] 1500# descriptive stas
mean(USDA$Sodium, na.rm = T)## [1] 322.0592sd(USDA$Sodium, na.rm = T)## [1] 1045.417Entering the visualization part of week 1 USDA DATA
Here we go, my favorite part totally!
plot(USDA$Protein, USDA$TotalFat)
plot(USDA$Protein, USDA$TotalFat, xlab = "Protein", ylab = "Fat",
main = "Protein vs Fat", col = "red")
# Histogram of VItC Levels
hist(USDA$VitaminC, xlab = "Vit C (mg)", main = "Histogram of Vit C level")
hist(USDA$VitaminC, xlab = "Vit C (mg)", main = "Histogram of Vit C level", xlim = c(0,100), breaks = 2000)
Plotting in boxplot
We look at the sugar level now :)
boxplot(USDA$Sugar, main = "Boxplot of Sugar Levels", ylab = "Sugar (g)")
## How about creating variables using logical data as results? Sure thing bro
We can assign our logical results as numeric. See below.
HighSodium <- USDA$Sodium > mean(USDA$Sodium, na.rm = TRUE)
HighSodium## [1] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [13] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [37] FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
## [85] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
## [133] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [145] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [157] FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [181] TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [217] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [265] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [277] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE
## [397] TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [409] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [445] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE
## [457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
## [757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [781] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [793] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [805] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [853] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [865] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [877] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [889] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [901] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [913] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [925] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [937] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [949] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [961] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
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## [1105] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [1285] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE
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## [1309] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1321] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1333] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1345] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [1369] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [1393] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1405] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [1441] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1453] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1465] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1477] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [1489] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [1501] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1513] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1525] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [1537] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [1549] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
## [1561] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [1573] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
## [1585] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
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## [1669] FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE
## [1681] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
## [1693] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE
## [1705] TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1717] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE
## [1729] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## [1741] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [1753] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
## [1765] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [1777] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [1789] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE
## [1801] TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
## [1813] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
## [1825] TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
## [1837] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1849] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [1861] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
## [1873] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
## [1885] FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [1897] FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [1909] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [1921] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1933] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1945] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1957] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1969] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1981] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1993] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2017] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2041] FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE
## [2053] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2065] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2077] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
## [2089] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2101] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2113] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2137] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2149] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2161] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2173] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2185] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2197] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2233] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2245] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2257] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2269] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2281] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2293] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2305] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2317] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2329] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2341] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2353] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2365] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2377] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2389] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [2401] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
## [2413] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2425] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2437] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2449] FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [2461] TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
## [2473] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2485] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2497] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2509] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2521] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [2533] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [2545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [2557] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2569] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2581] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2593] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2605] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [2629] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2641] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2653] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2665] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2677] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2701] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2713] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2725] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2737] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2749] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2761] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2773] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [2785] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2797] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [2809] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [2821] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2833] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [2845] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2857] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [2869] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [2881] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
## [2893] FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE
## [2905] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2917] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [2929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2941] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2953] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2965] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2977] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2989] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [3001] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3025] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3037] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3049] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3061] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
## [3073] FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [3085] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3097] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3121] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [3145] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [3157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3181] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [3193] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [3205] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [3229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3253] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3265] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [3277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [3289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [3301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [3313] FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
## [3325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [3337] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3349] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [3361] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [3445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [3481] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
## [3493] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3553] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## [3565] FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
## [3577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA
## [3901] NA NA NA NA NA NA NA FALSE NA NA NA NA
## [3913] FALSE NA NA FALSE NA FALSE NA NA NA FALSE NA NA
## [3925] NA FALSE FALSE NA NA FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3937] FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE NA NA FALSE
## [3949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE
## [3961] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3973] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
## [4009] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4021] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [4033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4045] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4057] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [4081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4093] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4105] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4117] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4141] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4153] FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
## [4165] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
## [4177] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
## [4189] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [4201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [4213] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [4225] FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [4237] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [4249] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4261] TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [4273] FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
## [4285] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
## [4297] TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [4309] FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [4321] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [4333] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4345] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4357] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE
## [4369] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4381] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
## [4393] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [4405] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [4417] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4429] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4441] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
## [4453] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [4465] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4477] FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE
## [4489] TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
## [4501] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [4513] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [4525] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE
## [4537] TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
## [4549] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [4561] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4573] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4585] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4597] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4609] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4621] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4633] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4645] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4657] FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
## [4669] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [4681] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4693] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4705] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4717] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [4741] TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [4753] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4777] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4789] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4801] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4813] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4825] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4837] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
## [4849] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4861] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4885] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE
## [4897] FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE
## [4909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4921] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4933] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4945] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4957] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4969] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4981] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4993] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5017] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5041] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5053] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5065] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5077] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5089] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5101] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [5113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5125] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [5137] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5149] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5161] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
## [5173] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
## [5185] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [5197] TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE
## [5209] TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
## [5221] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5233] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
## [5245] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
## [5257] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [5269] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5281] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5293] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5305] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [5317] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
## [5329] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [5341] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [5353] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
## [5365] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [5377] FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE
## [5389] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE
## [5401] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
## [5413] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5425] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5437] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE
## [5449] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [5461] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5473] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
## [5485] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [5497] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
## [5509] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
## [5521] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
## [5533] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [5545] TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
## [5557] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [5569] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5581] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5593] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5605] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5629] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
## [5641] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5653] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [5665] TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
## [5677] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [5689] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [5701] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5713] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [5725] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5737] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [5749] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [5761] TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
## [5773] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [5785] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [5797] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5809] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE
## [5821] FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
## [5833] TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
## [5845] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
## [5857] TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [5869] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [5881] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5893] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5905] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5917] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5929] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5941] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5953] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5965] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [5977] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5989] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [6001] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6013] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [6025] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6037] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6049] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
## [6061] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6073] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6085] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6097] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE
## [6109] TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
## [6121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
## [6133] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
## [6145] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE
## [6157] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6181] TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [6193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6577] FALSE FALSE FALSE NA TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE
## [6589] FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
## [6601] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
## [6613] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
## [6625] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [6637] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
## [6649] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [6661] FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [6673] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6685] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE NA NA FALSE NA
## [6697] FALSE NA NA FALSE FALSE NA NA NA NA NA FALSE NA
## [6709] NA NA NA FALSE NA NA FALSE FALSE NA FALSE NA FALSE
## [6721] FALSE NA FALSE FALSE NA FALSE NA NA NA NA NA FALSE
## [6733] NA NA NA NA NA NA NA TRUE NA NA NA FALSE
## [6745] NA FALSE NA NA FALSE NA NA NA NA NA NA NA
## [6757] NA NA NA FALSE NA NA NA FALSE FALSE FALSE FALSE FALSE
## [6769] FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6781] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
## [6793] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6805] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
## [6817] FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE FALSE
## [6829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE
## [6841] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE
## [6853] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
## [6865] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [6877] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE
## [6889] TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [6901] TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [6913] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE
## [6925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
## [6937] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6949] TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
## [6961] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
## [6973] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
## [6985] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6997] TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE
## [7009] FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
## [7021] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [7033] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE
## [7045] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
## [7057] FALSE FALSE# Assign them as numeric results: 1 or 0
HighSodium <- as.numeric(USDA$Sodium > mean(USDA$Sodium, na.rm = TRUE))
HighSodium## [1] 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1
## [25] 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1
## [49] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [73] 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
## [97] 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [121] 1 1 1 1 0 0 0 0 0 1 0 1 0 0 0 1 1 1 1 1 1 1 0 1
## [145] 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 1 1 0 1 1 1 1 1 0
## [169] 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 0 0 0
## [193] 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## [217] 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [241] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [265] 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0
## [289] 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
## [313] 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0
## [337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## [385] 0 0 0 1 1 0 1 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1
## [409] 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## [433] 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 1 1 0 1 1
## [457] 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0
## [481] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [601] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [673] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [697] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [745] 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
## [769] 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0
## [793] 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
## [817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [841] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
## [865] 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1
## [889] 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1
## [913] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [937] 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [961] 0 1 1 1 1 1 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 0 0 0
## [985] 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1009] 0 1 1 1 0 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1
## [1033] 0 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1057] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## [1081] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1
## [1105] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 0 0 0 0 1
## [1129] 1 0 1 1 1 0 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 0 0
## [1153] 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1
## [1177] 1 0 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 0 0
## [1201] 1 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## [1225] 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0
## [1249] 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1
## [1273] 0 1 0 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1
## [1297] 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1321] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1345] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1369] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1393] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1417] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1441] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1465] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## [1489] 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1513] 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1
## [1537] 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1 0 1 1
## [1561] 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1
## [1585] 1 1 0 1 1 0 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0
## [1609] 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0
## [1633] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1657] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 1 1 0
## [1681] 1 1 1 1 0 1 0 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 0 1
## [1705] 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0
## [1729] 1 1 1 1 0 1 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1
## [1753] 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 1 1 1
## [1777] 1 1 1 1 0 1 1 1 1 1 0 0 1 0 0 0 1 1 1 1 0 1 0 1
## [1801] 1 0 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1
## [1825] 1 1 0 1 1 0 0 0 0 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0
## [1849] 0 1 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 0 1
## [1873] 1 1 1 1 0 1 1 0 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0
## [1897] 0 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1
## [1921] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [1993] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2017] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2041] 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2065] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0
## [2089] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2113] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2137] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2161] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2185] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2209] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2233] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2257] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2281] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2305] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2329] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
## [2377] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0
## [2401] 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2425] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2449] 0 0 0 1 0 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 0 1 1 1
## [2473] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2497] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2521] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0
## [2545] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## [2569] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2617] 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## [2641] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2665] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2689] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2713] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2737] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2761] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## [2785] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## [2809] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2833] 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2857] 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1
## [2881] 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 0
## [2905] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## [2929] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2953] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [2977] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## [3001] 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3025] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3049] 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0
## [3073] 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3097] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3121] 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## [3145] 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3169] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## [3193] 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## [3217] 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [3265] 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0
## [3289] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## [3313] 0 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## [3337] 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0
## [3361] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3433] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
## [3481] 1 1 1 1 1 1 0 1 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0
## [3505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3553] 0 0 0 0 1 1 1 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 0 0
## [3577] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3601] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3625] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3649] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3673] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3697] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3721] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3745] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3769] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3817] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3841] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3865] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [3889] 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA NA NA NA NA 0 NA NA NA NA
## [3913] 0 NA NA 0 NA 0 NA NA NA 0 NA NA NA 0 0 NA NA 0 0 0 0 0 0 0
## [3937] 0 0 0 NA 0 0 0 0 0 NA NA 0 0 0 0 0 0 0 0 1 1 0 1 1
## [3961] 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [3985] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
## [4009] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0
## [4033] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [4057] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## [4081] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4105] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4129] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## [4153] 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0
## [4177] 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
## [4201] 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0
## [4225] 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [4249] 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0 0
## [4273] 0 0 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 0 1 1
## [4297] 1 1 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 0 0 1 1 0 0 0
## [4321] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4345] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1
## [4369] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
## [4393] 1 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 0 0 1 0 0 0
## [4417] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## [4441] 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1
## [4465] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 1
## [4489] 1 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
## [4513] 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1
## [4537] 1 0 1 0 0 0 0 1 0 1 1 1 0 1 0 1 1 1 1 1 0 0 0 0
## [4561] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4585] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4609] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4633] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## [4657] 0 1 1 0 0 1 0 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1
## [4681] 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4705] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4729] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 0 0 0 0
## [4753] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4777] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4801] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4825] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
## [4849] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4873] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0 0 0
## [4897] 0 0 0 0 0 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4921] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4945] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [4993] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5017] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5041] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5065] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5089] 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
## [5113] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1
## [5137] 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5161] 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 0 1 1 0 0
## [5185] 0 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 1 1 0 0 1 1
## [5209] 1 0 1 0 1 0 1 1 1 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1
## [5233] 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1
## [5257] 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1
## [5281] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1
## [5305] 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 0 0 1
## [5329] 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1
## [5353] 1 1 1 0 0 0 0 1 0 0 0 1 1 0 1 1 1 1 1 1 1 1 1 0
## [5377] 0 1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1 1 0 1 1 1 0 1
## [5401] 1 1 1 1 1 0 1 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1
## [5425] 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1
## [5449] 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1
## [5473] 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1
## [5497] 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 0
## [5521] 1 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 1
## [5545] 1 0 1 0 0 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0
## [5569] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5617] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## [5641] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0
## [5665] 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0
## [5689] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5713] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5737] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## [5761] 1 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0
## [5785] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5809] 1 1 0 0 0 0 1 1 0 1 1 1 0 0 1 0 1 1 0 1 1 1 1 1
## [5833] 1 0 0 1 1 0 0 0 1 1 1 0 1 1 0 1 1 1 0 1 0 0 0 1
## [5857] 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## [5881] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [5905] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## [5929] 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0
## [5953] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
## [5977] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0
## [6001] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0
## [6025] 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6049] 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0
## [6073] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## [6097] 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 1 1
## [6121] 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 1 1 0 1 0
## [6145] 1 1 0 0 0 1 1 1 0 0 1 0 1 0 0 0 1 1 1 1 1 1 1 1
## [6169] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 0 0
## [6193] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6241] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6313] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6337] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6385] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6409] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6433] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6481] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6505] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6553] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [6577] 0 0 0 NA 1 1 0 0 1 1 1 0 0 1 0 0 1 1 0 1 0 0 0 1
## [6601] 1 1 1 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0
## [6625] 0 0 1 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0
## [6649] 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1
## [6673] 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 NA NA 0 NA
## [6697] 0 NA NA 0 0 NA NA NA NA NA 0 NA NA NA NA 0 NA NA 0 0 NA 0 NA 0
## [6721] 0 NA 0 0 NA 0 NA NA NA NA NA 0 NA NA NA NA NA NA NA 1 NA NA NA 0
## [6745] NA 0 NA NA 0 NA NA NA NA NA NA NA NA NA NA 0 NA NA NA 0 0 0 0 0
## [6769] 0 0 0 0 NA 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 1
## [6793] 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0
## [6817] 0 0 0 0 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA 0 0
## [6841] 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 0 1 0 1 0 0
## [6865] 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 1 0
## [6889] 1 0 1 0 1 0 0 1 1 1 1 1 1 0 0 1 0 0 0 1 1 0 0 0
## [6913] 1 1 1 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1
## [6937] 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0
## [6961] 0 0 0 1 1 0 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 1 1
## [6985] 1 1 1 0 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 1 1 0
## [7009] 0 1 0 1 0 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0
## [7033] 1 1 1 0 0 0 0 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 1 1
## [7057] 0 0# Then insert this into a new column
USDA$HiSodium <- as.numeric(USDA$Sodium > mean(USDA$Sodium, na.rm = TRUE))
str(USDA)## 'data.frame': 7058 obs. of 17 variables:
## $ ID : int 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ...
## $ Description : chr "BUTTER,WITH SALT" "BUTTER,WHIPPED,WITH SALT" "BUTTER OIL,ANHYDROUS" "CHEESE,BLUE" ...
## $ Calories : int 717 717 876 353 371 334 300 376 403 387 ...
## $ Protein : num 0.85 0.85 0.28 21.4 23.24 ...
## $ TotalFat : num 81.1 81.1 99.5 28.7 29.7 ...
## $ Carbohydrate: num 0.06 0.06 0 2.34 2.79 0.45 0.46 3.06 1.28 4.78 ...
## $ Sodium : int 714 827 2 1395 560 629 842 690 621 700 ...
## $ SaturatedFat: num 51.4 50.5 61.9 18.7 18.8 ...
## $ Cholesterol : int 215 219 256 75 94 100 72 93 105 103 ...
## $ Sugar : num 0.06 0.06 0 0.5 0.51 0.45 0.46 NA 0.52 NA ...
## $ Calcium : int 24 24 4 528 674 184 388 673 721 643 ...
## $ Iron : num 0.02 0.16 0 0.31 0.43 0.5 0.33 0.64 0.68 0.21 ...
## $ Potassium : int 24 26 5 256 136 152 187 93 98 95 ...
## $ VitaminC : num 0 0 0 0 0 0 0 0 0 0 ...
## $ VitaminE : num 2.32 2.32 2.8 0.25 0.26 0.24 0.21 NA 0.29 NA ...
## $ VitaminD : num 1.5 1.5 1.8 0.5 0.5 0.5 0.4 NA 0.6 NA ...
## $ HiSodium : num 1 1 0 1 1 1 1 1 1 1 ...# and do the same for other variables
USDA$HiProtein <- as.numeric(USDA$Protein > mean(USDA$Protein, na.rm = TRUE))
USDA$HiFat <- as.numeric(USDA$TotalFat > mean(USDA$TotalFat, na.rm = TRUE))
USDA$HiCarbs <- as.numeric(USDA$Carbohydrate > mean(USDA$Carbohydrate, na.rm = TRUE))
str(USDA)## 'data.frame': 7058 obs. of 20 variables:
## $ ID : int 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ...
## $ Description : chr "BUTTER,WITH SALT" "BUTTER,WHIPPED,WITH SALT" "BUTTER OIL,ANHYDROUS" "CHEESE,BLUE" ...
## $ Calories : int 717 717 876 353 371 334 300 376 403 387 ...
## $ Protein : num 0.85 0.85 0.28 21.4 23.24 ...
## $ TotalFat : num 81.1 81.1 99.5 28.7 29.7 ...
## $ Carbohydrate: num 0.06 0.06 0 2.34 2.79 0.45 0.46 3.06 1.28 4.78 ...
## $ Sodium : int 714 827 2 1395 560 629 842 690 621 700 ...
## $ SaturatedFat: num 51.4 50.5 61.9 18.7 18.8 ...
## $ Cholesterol : int 215 219 256 75 94 100 72 93 105 103 ...
## $ Sugar : num 0.06 0.06 0 0.5 0.51 0.45 0.46 NA 0.52 NA ...
## $ Calcium : int 24 24 4 528 674 184 388 673 721 643 ...
## $ Iron : num 0.02 0.16 0 0.31 0.43 0.5 0.33 0.64 0.68 0.21 ...
## $ Potassium : int 24 26 5 256 136 152 187 93 98 95 ...
## $ VitaminC : num 0 0 0 0 0 0 0 0 0 0 ...
## $ VitaminE : num 2.32 2.32 2.8 0.25 0.26 0.24 0.21 NA 0.29 NA ...
## $ VitaminD : num 1.5 1.5 1.8 0.5 0.5 0.5 0.4 NA 0.6 NA ...
## $ HiSodium : num 1 1 0 1 1 1 1 1 1 1 ...
## $ HiProtein : num 0 0 0 1 1 1 1 1 1 1 ...
## $ HiFat : num 1 1 1 1 1 1 1 1 1 1 ...
## $ HiCarbs : num 0 0 0 0 0 0 0 0 0 0 ...Creating a summary table from our previous results
We can now summarize our analysis and present them in a chart.
table(USDA$HiSodium, USDA$HiFat)##
## 0 1
## 0 3529 1355
## 1 1378 712Looking for specifics: Food that contains iron, can we calculate their average iron contents based on two groups of Hi Protein?
Yes, why not! We use tapply for that.
tapply(USDA$Iron, USDA$HiProtein, mean, na.rm = TRUE)## 0 1
## 2.558945 3.197294# With the same approach, to find max vit C between hi carb/ lo carb food
tapply(USDA$VitaminC , USDA$HiCarbs, max, na.rm = TRUE)## 0 1
## 1677.6 2400.0# we can even use summary!
tapply(USDA$VitaminC , USDA$HiCarbs, summary, na.rm = TRUE)## $`0`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 0.000 6.364 2.800 1677.600 248
##
## $`1`
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.00 0.20 16.31 4.50 2400.00 83