Project 2a - Flags of the World
FILL IN YOUR NAME
Image by Gordon Johnson from Pixabay
Data Import
Download flag.csv and flag.names to your working directory. Make sure to set your working directory appropriately!
Let’s look at some information about this file. Open flag.names in RStudio by double clicking it in the files pane in bottom left. Read through this file.
Who is the donor of this data? Richard S. Forsyth from England.
Is there any missing data? According to Item 8, there are no missing data.
- Import the flag.csv data into R. Store it in a data.frame named flag_df.
# fill in your code here
getwd()## [1] "C:/Users/Jerome/Documents/0000_Work_Files/0000_Montgomery_College/Data_Science_101/Data_101_Fall_2022/Homework_8_Due_31Oct2022/project2a files"flag_df <- read.csv("flag.csv")- Check to make sure the class of flag_df is data.frame. Then find the dimensions of flag_df.
# fill in your code here
class(flag_df)## [1] "data.frame"dim(flag_df)## [1] 194 31- Print out the first 5 lines and the last 5 lines of flag_df.
# fill in your code here
head(flag_df,5)## X name landmass zone area population language religion bars stripes
## 1 1 Afghanistan 5 1 648 16 10 2 0 3
## 2 2 Albania 3 1 29 3 6 6 0 0
## 3 3 Algeria 4 1 2388 20 8 2 2 0
## 4 4 American-Samoa 6 3 0 0 1 1 0 0
## 5 5 Andorra 3 1 0 0 6 0 3 0
## colours red green blue gold white black orange mainhue circles crosses
## 1 5 1 1 0 1 1 1 0 green 0 0
## 2 3 1 0 0 1 0 1 0 red 0 0
## 3 3 1 1 0 0 1 0 0 green 0 0
## 4 5 1 0 1 1 1 0 1 blue 0 0
## 5 3 1 0 1 1 0 0 0 gold 0 0
## saltires quarters sunstars crescent triangle icon animate text topleft
## 1 0 0 1 0 0 1 0 0 black
## 2 0 0 1 0 0 0 1 0 red
## 3 0 0 1 1 0 0 0 0 green
## 4 0 0 0 0 1 1 1 0 blue
## 5 0 0 0 0 0 0 0 0 blue
## botright
## 1 green
## 2 red
## 3 white
## 4 red
## 5 redtail(flag_df,5)## X name landmass zone area population language religion bars
## 190 190 Western-Samoa 6 3 3 0 1 1 0
## 191 191 Yugoslavia 3 1 256 22 6 6 0
## 192 192 Zaire 4 2 905 28 10 5 0
## 193 193 Zambia 4 2 753 6 10 5 3
## 194 194 Zimbabwe 4 2 391 8 10 5 0
## stripes colours red green blue gold white black orange mainhue circles
## 190 0 3 1 0 1 0 1 0 0 red 0
## 191 3 4 1 0 1 1 1 0 0 red 0
## 192 0 4 1 1 0 1 0 0 1 green 1
## 193 0 4 1 1 0 0 0 1 1 green 0
## 194 7 5 1 1 0 1 1 1 0 green 0
## crosses saltires quarters sunstars crescent triangle icon animate text
## 190 0 0 1 5 0 0 0 0 0
## 191 0 0 0 1 0 0 0 0 0
## 192 0 0 0 0 0 0 1 1 0
## 193 0 0 0 0 0 0 0 1 0
## 194 0 0 0 1 0 1 1 1 0
## topleft botright
## 190 blue red
## 191 blue red
## 192 green green
## 193 green brown
## 194 green green- Print out the summary statistics of each variable of flag_df.
# fill in your code here
summary(flag_df)## X name landmass zone
## Min. : 1.00 Length:194 Min. :1.000 Min. :1.000
## 1st Qu.: 49.25 Class :character 1st Qu.:3.000 1st Qu.:1.000
## Median : 97.50 Mode :character Median :4.000 Median :2.000
## Mean : 97.50 Mean :3.572 Mean :2.211
## 3rd Qu.:145.75 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :194.00 Max. :6.000 Max. :4.000
## area population language religion
## Min. : 0.0 Min. : 0.00 Min. : 1.00 Min. :0.000
## 1st Qu.: 9.0 1st Qu.: 0.00 1st Qu.: 2.00 1st Qu.:1.000
## Median : 111.0 Median : 4.00 Median : 6.00 Median :1.000
## Mean : 700.0 Mean : 23.27 Mean : 5.34 Mean :2.191
## 3rd Qu.: 471.2 3rd Qu.: 14.00 3rd Qu.: 9.00 3rd Qu.:4.000
## Max. :22402.0 Max. :1008.00 Max. :10.00 Max. :7.000
## bars stripes colours red
## Min. :0.0000 Min. : 0.000 Min. :1.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:3.000 1st Qu.:1.0000
## Median :0.0000 Median : 0.000 Median :3.000 Median :1.0000
## Mean :0.4536 Mean : 1.552 Mean :3.464 Mean :0.7887
## 3rd Qu.:0.0000 3rd Qu.: 3.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :5.0000 Max. :14.000 Max. :8.000 Max. :1.0000
## green blue gold white
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median :0.0000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.4691 Mean :0.5103 Mean :0.4691 Mean :0.7526
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## black orange mainhue circles
## Min. :0.000 Min. :0.000 Length:194 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.000 Class :character 1st Qu.:0.0000
## Median :0.000 Median :0.000 Mode :character Median :0.0000
## Mean :0.268 Mean :0.134 Mean :0.1701
## 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.000 Max. :4.0000
## crosses saltires quarters sunstars
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.: 0.000
## Median :0.0000 Median :0.00000 Median :0.0000 Median : 0.000
## Mean :0.1495 Mean :0.09278 Mean :0.1495 Mean : 1.387
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.: 1.000
## Max. :2.0000 Max. :1.00000 Max. :4.0000 Max. :50.000
## crescent triangle icon animate
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.0567 Mean :0.1392 Mean :0.2526 Mean :0.201
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.7500 3rd Qu.:0.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
## text topleft botright
## Min. :0.00000 Length:194 Length:194
## 1st Qu.:0.00000 Class :character Class :character
## Median :0.00000 Mode :character Mode :character
## Mean :0.08247
## 3rd Qu.:0.00000
## Max. :1.00000- Print out the structure of flag_df.
# fill in your code here
str(flag_df)## 'data.frame': 194 obs. of 31 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ name : chr "Afghanistan" "Albania" "Algeria" "American-Samoa" ...
## $ landmass : int 5 3 4 6 3 4 1 1 2 2 ...
## $ zone : int 1 1 1 3 1 2 4 4 3 3 ...
## $ area : int 648 29 2388 0 0 1247 0 0 2777 2777 ...
## $ population: int 16 3 20 0 0 7 0 0 28 28 ...
## $ language : int 10 6 8 1 6 10 1 1 2 2 ...
## $ religion : int 2 6 2 1 0 5 1 1 0 0 ...
## $ bars : int 0 0 2 0 3 0 0 0 0 0 ...
## $ stripes : int 3 0 0 0 0 2 1 1 3 3 ...
## $ colours : int 5 3 3 5 3 3 3 5 2 3 ...
## $ red : int 1 1 1 1 1 1 0 1 0 0 ...
## $ green : int 1 0 1 0 0 0 0 0 0 0 ...
## $ blue : int 0 0 0 1 1 0 1 1 1 1 ...
## $ gold : int 1 1 0 1 1 1 0 1 0 1 ...
## $ white : int 1 0 1 1 0 0 1 1 1 1 ...
## $ black : int 1 1 0 0 0 1 0 1 0 0 ...
## $ orange : int 0 0 0 1 0 0 1 0 0 0 ...
## $ mainhue : chr "green" "red" "green" "blue" ...
## $ circles : int 0 0 0 0 0 0 0 0 0 0 ...
## $ crosses : int 0 0 0 0 0 0 0 0 0 0 ...
## $ saltires : int 0 0 0 0 0 0 0 0 0 0 ...
## $ quarters : int 0 0 0 0 0 0 0 0 0 0 ...
## $ sunstars : int 1 1 1 0 0 1 0 1 0 1 ...
## $ crescent : int 0 0 1 0 0 0 0 0 0 0 ...
## $ triangle : int 0 0 0 1 0 0 0 1 0 0 ...
## $ icon : int 1 0 0 1 0 1 0 0 0 0 ...
## $ animate : int 0 1 0 1 0 0 1 0 0 0 ...
## $ text : int 0 0 0 0 0 0 0 0 0 0 ...
## $ topleft : chr "black" "red" "green" "blue" ...
## $ botright : chr "green" "red" "white" "red" ...Data Cleaning/Management
We are going to use the dplyr package.
- Load the tidyverse and convert the type of flag_df to tibble.
# fill in your code here
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()as_tibble(flag_df)## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 1 Afgh… 5 1 648 16 10 2 0 3 5
## 2 2 Alba… 3 1 29 3 6 6 0 0 3
## 3 3 Alge… 4 1 2388 20 8 2 2 0 3
## 4 4 Amer… 6 3 0 0 1 1 0 0 5
## 5 5 Ando… 3 1 0 0 6 0 3 0 3
## 6 6 Ango… 4 2 1247 7 10 5 0 2 3
## 7 7 Angu… 1 4 0 0 1 1 0 1 3
## 8 8 Anti… 1 4 0 0 1 1 0 1 5
## 9 9 Arge… 2 3 2777 28 2 0 0 3 2
## 10 10 Arge… 2 3 2777 28 2 0 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religion- Find the variable (column) names of flag_df.
# fill in your code here
colnames(flag_df)## [1] "X" "name" "landmass" "zone" "area"
## [6] "population" "language" "religion" "bars" "stripes"
## [11] "colours" "red" "green" "blue" "gold"
## [16] "white" "black" "orange" "mainhue" "circles"
## [21] "crosses" "saltires" "quarters" "sunstars" "crescent"
## [26] "triangle" "icon" "animate" "text" "topleft"
## [31] "botright"Something should look strange about the first column name. Let’s investigate this.
- Print out the first column.
# fill in your code here
print(flag_df$x)## NULLWhat is in this first column? Row numbers
Do we really need it? No, because the row numberis provided when flag_df is displayed when it’s selected by clicking on it.
- Delete the first column of flag_df.
# fill in your code here
select(flag_df, -1)## name landmass zone area population language religion
## 1 Afghanistan 5 1 648 16 10 2
## 2 Albania 3 1 29 3 6 6
## 3 Algeria 4 1 2388 20 8 2
## 4 American-Samoa 6 3 0 0 1 1
## 5 Andorra 3 1 0 0 6 0
## 6 Angola 4 2 1247 7 10 5
## 7 Anguilla 1 4 0 0 1 1
## 8 Antigua-Barbuda 1 4 0 0 1 1
## 9 Argentina 2 3 2777 28 2 0
## 10 Argentine 2 3 2777 28 2 0
## 11 Australia 6 2 7690 15 1 1
## 12 Austria 3 1 84 8 4 0
## 13 Bahamas 1 4 19 0 1 1
## 14 Bahrain 5 1 1 0 8 2
## 15 Bangladesh 5 1 143 90 6 2
## 16 Barbados 1 4 0 0 1 1
## 17 Belgium 3 1 31 10 6 0
## 18 Belize 1 4 23 0 1 1
## 19 Benin 4 1 113 3 3 5
## 20 Bermuda 1 4 0 0 1 1
## 21 Bhutan 5 1 47 1 10 3
## 22 Bolivia 2 3 1099 6 2 0
## 23 Botswana 4 2 600 1 10 5
## 24 Brazil 2 3 8512 119 6 0
## 25 British-Virgin-Isles 1 4 0 0 1 1
## 26 Brunei 5 1 6 0 10 2
## 27 Bulgaria 3 1 111 9 5 6
## 28 Burkina 4 4 274 7 3 5
## 29 Burma 5 1 678 35 10 3
## 30 Burundi 4 2 28 4 10 5
## 31 Cameroon 4 1 474 8 3 1
## 32 Canada 1 4 9976 24 1 1
## 33 Cape-Verde-Islands 4 4 4 0 6 0
## 34 Cayman-Islands 1 4 0 0 1 1
## 35 Central-African-Republic 4 1 623 2 10 5
## 36 Chad 4 1 1284 4 3 5
## 37 Chile 2 3 757 11 2 0
## 38 China 5 1 9561 1008 7 6
## 39 Colombia 2 4 1139 28 2 0
## 40 Comorro-Islands 4 2 2 0 3 2
## 41 Congo 4 2 342 2 10 5
## 42 Cook-Islands 6 3 0 0 1 1
## 43 Costa-Rica 1 4 51 2 2 0
## 44 Cuba 1 4 115 10 2 6
## 45 Cyprus 3 1 9 1 6 1
## 46 Czechoslovakia 3 1 128 15 5 6
## 47 Denmark 3 1 43 5 6 1
## 48 Djibouti 4 1 22 0 3 2
## 49 Dominica 1 4 0 0 1 1
## 50 Dominican-Republic 1 4 49 6 2 0
## 51 Ecuador 2 3 284 8 2 0
## 52 Egypt 4 1 1001 47 8 2
## 53 El-Salvador 1 4 21 5 2 0
## 54 Equatorial-Guinea 4 1 28 0 10 5
## 55 Ethiopia 4 1 1222 31 10 1
## 56 Faeroes 3 4 1 0 6 1
## 57 Falklands-Malvinas 2 3 12 0 1 1
## 58 Fiji 6 2 18 1 1 1
## 59 Finland 3 1 337 5 9 1
## 60 France 3 1 547 54 3 0
## 61 French-Guiana 2 4 91 0 3 0
## 62 French-Polynesia 6 3 4 0 3 0
## 63 Gabon 4 2 268 1 10 5
## 64 Gambia 4 4 10 1 1 5
## 65 Germany-DDR 3 1 108 17 4 6
## 66 Germany-FRG 3 1 249 61 4 1
## 67 Ghana 4 4 239 14 1 5
## 68 Gibraltar 3 4 0 0 1 1
## 69 Greece 3 1 132 10 6 1
## 70 Greenland 1 4 2176 0 6 1
## 71 Grenada 1 4 0 0 1 1
## 72 Guam 6 1 0 0 1 1
## 73 Guatemala 1 4 109 8 2 0
## 74 Guinea 4 4 246 6 3 2
## 75 Guinea-Bissau 4 4 36 1 6 5
## 76 Guyana 2 4 215 1 1 4
## 77 Haiti 1 4 28 6 3 0
## 78 Honduras 1 4 112 4 2 0
## 79 Hong-Kong 5 1 1 5 7 3
## 80 Hungary 3 1 93 11 9 6
## 81 Iceland 3 4 103 0 6 1
## 82 India 5 1 3268 684 6 4
## 83 Indonesia 6 2 1904 157 10 2
## 84 Iran 5 1 1648 39 6 2
## 85 Iraq 5 1 435 14 8 2
## 86 Ireland 3 4 70 3 1 0
## 87 Israel 5 1 21 4 10 7
## 88 Italy 3 1 301 57 6 0
## 89 Ivory-Coast 4 4 323 7 3 5
## 90 Jamaica 1 4 11 2 1 1
## 91 Japan 5 1 372 118 9 7
## 92 Jordan 5 1 98 2 8 2
## 93 Kampuchea 5 1 181 6 10 3
## 94 Kenya 4 1 583 17 10 5
## 95 Kiribati 6 1 0 0 1 1
## 96 Kuwait 5 1 18 2 8 2
## 97 Laos 5 1 236 3 10 6
## 98 Lebanon 5 1 10 3 8 2
## 99 Lesotho 4 2 30 1 10 5
## 100 Liberia 4 4 111 1 10 5
## 101 Libya 4 1 1760 3 8 2
## 102 Liechtenstein 3 1 0 0 4 0
## 103 Luxembourg 3 1 3 0 4 0
## 104 Malagasy 4 2 587 9 10 1
## 105 Malawi 4 2 118 6 10 5
## 106 Malaysia 5 1 333 13 10 2
## 107 Maldive-Islands 5 1 0 0 10 2
## 108 Mali 4 4 1240 7 3 2
## 109 Malta 3 1 0 0 10 0
## 110 Marianas 6 1 0 0 10 1
## 111 Mauritania 4 4 1031 2 8 2
## 112 Mauritius 4 2 2 1 1 4
## 113 Mexico 1 4 1973 77 2 0
## 114 Micronesia 6 1 1 0 10 1
## 115 Monaco 3 1 0 0 3 0
## 116 Mongolia 5 1 1566 2 10 6
## 117 Montserrat 1 4 0 0 1 1
## 118 Morocco 4 4 447 20 8 2
## 119 Mozambique 4 2 783 12 10 5
## 120 Nauru 6 2 0 0 10 1
## 121 Nepal 5 1 140 16 10 4
## 122 Netherlands 3 1 41 14 6 1
## 123 Netherlands-Antilles 1 4 0 0 6 1
## 124 New-Zealand 6 2 268 2 1 1
## 125 Nicaragua 1 4 128 3 2 0
## 126 Niger 4 1 1267 5 3 2
## 127 Nigeria 4 1 925 56 10 2
## 128 Niue 6 3 0 0 1 1
## 129 North-Korea 5 1 121 18 10 6
## 130 North-Yemen 5 1 195 9 8 2
## 131 Norway 3 1 324 4 6 1
## 132 Oman 5 1 212 1 8 2
## 133 Pakistan 5 1 804 84 6 2
## 134 Panama 2 4 76 2 2 0
## 135 Papua-New-Guinea 6 2 463 3 1 5
## 136 Parguay 2 3 407 3 2 0
## 137 Peru 2 3 1285 14 2 0
## 138 Philippines 6 1 300 48 10 0
## 139 Poland 3 1 313 36 5 6
## 140 Portugal 3 4 92 10 6 0
## 141 Puerto-Rico 1 4 9 3 2 0
## 142 Qatar 5 1 11 0 8 2
## 143 Romania 3 1 237 22 6 6
## 144 Rwanda 4 2 26 5 10 5
## 145 San-Marino 3 1 0 0 6 0
## 146 Sao-Tome 4 1 0 0 6 0
## 147 Saudi-Arabia 5 1 2150 9 8 2
## 148 Senegal 4 4 196 6 3 2
## 149 Seychelles 4 2 0 0 1 1
## 150 Sierra-Leone 4 4 72 3 1 5
## 151 Singapore 5 1 1 3 7 3
## 152 Soloman-Islands 6 2 30 0 1 1
## 153 Somalia 4 1 637 5 10 2
## 154 South-Africa 4 2 1221 29 6 1
## 155 South-Korea 5 1 99 39 10 7
## 156 South-Yemen 5 1 288 2 8 2
## 157 Spain 3 4 505 38 2 0
## 158 Sri-Lanka 5 1 66 15 10 3
## 159 St-Helena 4 3 0 0 1 1
## 160 St-Kitts-Nevis 1 4 0 0 1 1
## 161 St-Lucia 1 4 0 0 1 1
## 162 St-Vincent 1 4 0 0 1 1
## 163 Sudan 4 1 2506 20 8 2
## 164 Surinam 2 4 63 0 6 1
## 165 Swaziland 4 2 17 1 10 1
## 166 Sweden 3 1 450 8 6 1
## 167 Switzerland 3 1 41 6 4 1
## 168 Syria 5 1 185 10 8 2
## 169 Taiwan 5 1 36 18 7 3
## 170 Tanzania 4 2 945 18 10 5
## 171 Thailand 5 1 514 49 10 3
## 172 Togo 4 1 57 2 3 7
## 173 Tonga 6 2 1 0 10 1
## 174 Trinidad-Tobago 2 4 5 1 1 1
## 175 Tunisia 4 1 164 7 8 2
## 176 Turkey 5 1 781 45 9 2
## 177 Turks-Cocos-Islands 1 4 0 0 1 1
## 178 Tuvalu 6 2 0 0 1 1
## 179 UAE 5 1 84 1 8 2
## 180 Uganda 4 1 236 13 10 5
## 181 UK 3 4 245 56 1 1
## 182 Uruguay 2 3 178 3 2 0
## 183 US-Virgin-Isles 1 4 0 0 1 1
## 184 USA 1 4 9363 231 1 1
## 185 USSR 5 1 22402 274 5 6
## 186 Vanuatu 6 2 15 0 6 1
## 187 Vatican-City 3 1 0 0 6 0
## 188 Venezuela 2 4 912 15 2 0
## 189 Vietnam 5 1 333 60 10 6
## 190 Western-Samoa 6 3 3 0 1 1
## 191 Yugoslavia 3 1 256 22 6 6
## 192 Zaire 4 2 905 28 10 5
## 193 Zambia 4 2 753 6 10 5
## 194 Zimbabwe 4 2 391 8 10 5
## bars stripes colours red green blue gold white black orange mainhue circles
## 1 0 3 5 1 1 0 1 1 1 0 green 0
## 2 0 0 3 1 0 0 1 0 1 0 red 0
## 3 2 0 3 1 1 0 0 1 0 0 green 0
## 4 0 0 5 1 0 1 1 1 0 1 blue 0
## 5 3 0 3 1 0 1 1 0 0 0 gold 0
## 6 0 2 3 1 0 0 1 0 1 0 red 0
## 7 0 1 3 0 0 1 0 1 0 1 white 0
## 8 0 1 5 1 0 1 1 1 1 0 red 0
## 9 0 3 2 0 0 1 0 1 0 0 blue 0
## 10 0 3 3 0 0 1 1 1 0 0 blue 0
## 11 0 0 3 1 0 1 0 1 0 0 blue 0
## 12 0 3 2 1 0 0 0 1 0 0 red 0
## 13 0 3 3 0 0 1 1 0 1 0 blue 0
## 14 0 0 2 1 0 0 0 1 0 0 red 0
## 15 0 0 2 1 1 0 0 0 0 0 green 1
## 16 3 0 3 0 0 1 1 0 1 0 blue 0
## 17 3 0 3 1 0 0 1 0 1 0 gold 0
## 18 0 2 8 1 1 1 1 1 1 1 blue 1
## 19 0 0 2 1 1 0 0 0 0 0 green 0
## 20 0 0 6 1 1 1 1 1 1 0 red 1
## 21 0 0 4 1 0 0 0 1 1 1 orange 4
## 22 0 3 3 1 1 0 1 0 0 0 red 0
## 23 0 5 3 0 0 1 0 1 1 0 blue 0
## 24 0 0 4 0 1 1 1 1 0 0 green 1
## 25 0 0 6 1 1 1 1 1 0 1 blue 0
## 26 0 0 4 1 0 0 1 1 1 0 gold 0
## 27 0 3 5 1 1 1 1 1 0 0 red 0
## 28 0 2 3 1 1 0 1 0 0 0 red 0
## 29 0 0 3 1 0 1 0 1 0 0 red 0
## 30 0 0 3 1 1 0 0 1 0 0 red 1
## 31 3 0 3 1 1 0 1 0 0 0 gold 0
## 32 2 0 2 1 0 0 0 1 0 0 red 0
## 33 1 2 5 1 1 0 1 0 1 1 gold 0
## 34 0 0 6 1 1 1 1 1 0 1 blue 1
## 35 1 0 5 1 1 1 1 1 0 0 gold 0
## 36 3 0 3 1 0 1 1 0 0 0 gold 0
## 37 0 2 3 1 0 1 0 1 0 0 red 0
## 38 0 0 2 1 0 0 1 0 0 0 red 0
## 39 0 3 3 1 0 1 1 0 0 0 gold 0
## 40 0 0 2 0 1 0 0 1 0 0 green 0
## 41 0 0 3 1 1 0 1 0 0 0 red 0
## 42 0 0 4 1 0 1 0 1 0 0 blue 1
## 43 0 5 3 1 0 1 0 1 0 0 blue 0
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## 46 0 0 3 1 0 1 0 1 0 0 white 0
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## 48 0 0 4 1 1 1 0 1 0 0 blue 0
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## 50 0 0 3 1 0 1 0 1 0 0 blue 0
## 51 0 3 3 1 0 1 1 0 0 0 gold 0
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## 53 0 3 2 0 0 1 0 1 0 0 blue 0
## 54 0 3 4 1 1 1 0 1 0 0 green 0
## 55 0 3 3 1 1 0 1 0 0 0 green 0
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## 57 0 0 6 1 1 1 1 1 0 0 blue 1
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## 61 3 0 3 1 0 1 0 1 0 0 white 0
## 62 0 3 5 1 0 1 1 1 1 0 red 1
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## 71 0 0 3 1 1 0 1 0 0 0 gold 1
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## 74 3 0 3 1 1 0 1 0 0 0 gold 0
## 75 1 2 4 1 1 0 1 0 1 0 gold 0
## 76 0 0 5 1 1 0 1 1 1 0 green 0
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## 79 0 0 6 1 1 1 1 1 0 1 blue 1
## 80 0 3 3 1 1 0 0 1 0 0 red 0
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## 82 0 3 4 0 1 1 0 1 0 1 orange 1
## 83 0 2 2 1 0 0 0 1 0 0 red 0
## 84 0 3 3 1 1 0 0 1 0 0 red 0
## 85 0 3 4 1 1 0 0 1 1 0 red 0
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## 101 0 0 1 0 1 0 0 0 0 0 green 0
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## 108 3 0 3 1 1 0 1 0 0 0 gold 0
## 109 2 0 3 1 0 0 0 1 1 0 red 0
## 110 0 0 3 0 0 1 0 1 0 0 blue 0
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## 115 0 2 2 1 0 0 0 1 0 0 red 0
## 116 3 0 3 1 0 1 1 0 0 0 red 2
## 117 0 0 7 1 1 1 1 1 1 0 blue 0
## 118 0 0 2 1 1 0 0 0 0 0 red 0
## 119 0 5 5 1 1 0 1 1 1 0 gold 0
## 120 0 3 3 0 0 1 1 1 0 0 blue 0
## 121 0 0 3 0 0 1 0 1 0 1 brown 0
## 122 0 3 3 1 0 1 0 1 0 0 red 0
## 123 0 1 3 1 0 1 0 1 0 0 white 0
## 124 0 0 3 1 0 1 0 1 0 0 blue 0
## 125 0 3 2 0 0 1 0 1 0 0 blue 0
## 126 0 3 3 0 1 0 0 1 0 1 orange 1
## 127 3 0 2 0 1 0 0 1 0 0 green 0
## 128 0 0 4 1 0 1 1 1 0 0 gold 1
## 129 0 5 3 1 0 1 0 1 0 0 blue 1
## 130 0 3 4 1 1 0 0 1 1 0 red 0
## 131 0 0 3 1 0 1 0 1 0 0 red 0
## 132 0 2 3 1 1 0 0 1 0 0 red 0
## 133 1 0 2 0 1 0 0 1 0 0 green 0
## 134 0 0 3 1 0 1 0 1 0 0 red 0
## 135 0 0 4 1 0 0 1 1 1 0 black 0
## 136 0 3 6 1 1 1 1 1 1 0 red 1
## 137 3 0 2 1 0 0 0 1 0 0 red 0
## 138 0 0 4 1 0 1 1 1 0 0 blue 0
## 139 0 2 2 1 0 0 0 1 0 0 white 0
## 140 0 0 5 1 1 1 1 1 0 0 red 1
## 141 0 5 3 1 0 1 0 1 0 0 red 0
## 142 0 0 2 0 0 0 0 1 0 1 brown 0
## 143 3 0 7 1 1 1 1 1 0 1 red 0
## 144 3 0 4 1 1 0 1 0 1 0 red 0
## 145 0 2 2 0 0 1 0 1 0 0 white 0
## 146 0 3 4 1 1 0 1 0 1 0 green 0
## 147 0 0 2 0 1 0 0 1 0 0 green 0
## 148 3 0 3 1 1 0 1 0 0 0 green 0
## 149 0 0 3 1 1 0 0 1 0 0 red 0
## 150 0 3 3 0 1 1 0 1 0 0 green 0
## 151 0 2 2 1 0 0 0 1 0 0 white 0
## 152 0 0 4 0 1 1 1 1 0 0 green 0
## 153 0 0 2 0 0 1 0 1 0 0 blue 0
## 154 0 3 5 1 1 1 0 1 0 1 orange 0
## 155 0 0 4 1 0 1 0 1 1 0 white 1
## 156 0 3 4 1 0 1 0 1 1 0 red 0
## 157 0 3 2 1 0 0 1 0 0 0 red 0
## 158 2 0 4 0 1 0 1 0 0 1 gold 0
## 159 0 0 7 1 1 1 1 1 0 1 blue 0
## 160 0 0 5 1 1 0 1 1 1 0 green 0
## 161 0 0 4 0 0 1 1 1 1 0 blue 0
## 162 5 0 4 0 1 1 1 1 0 0 green 0
## 163 0 3 4 1 1 0 0 1 1 0 red 0
## 164 0 5 4 1 1 0 1 1 0 0 red 0
## 165 0 5 7 1 0 1 1 1 1 1 blue 0
## 166 0 0 2 0 0 1 1 0 0 0 blue 0
## 167 0 0 2 1 0 0 0 1 0 0 red 0
## 168 0 3 4 1 1 0 0 1 1 0 red 0
## 169 0 0 3 1 0 1 0 1 0 0 red 1
## 170 0 0 4 0 1 1 1 0 1 0 green 0
## 171 0 5 3 1 0 1 0 1 0 0 red 0
## 172 0 5 4 1 1 0 1 1 0 0 green 0
## 173 0 0 2 1 0 0 0 1 0 0 red 0
## 174 0 0 3 1 0 0 0 1 1 0 red 0
## 175 0 0 2 1 0 0 0 1 0 0 red 1
## 176 0 0 2 1 0 0 0 1 0 0 red 0
## 177 0 0 6 1 1 1 1 1 0 1 blue 0
## 178 0 0 5 1 0 1 1 1 0 0 blue 0
## 179 1 3 4 1 1 0 0 1 1 0 green 0
## 180 0 6 5 1 0 0 1 1 1 0 gold 1
## 181 0 0 3 1 0 1 0 1 0 0 red 0
## 182 0 9 3 0 0 1 1 1 0 0 white 0
## 183 0 0 6 1 1 1 1 1 0 0 white 0
## 184 0 13 3 1 0 1 0 1 0 0 white 0
## 185 0 0 2 1 0 0 1 0 0 0 red 0
## 186 0 0 4 1 1 0 1 0 1 0 red 0
## 187 2 0 4 1 0 0 1 1 1 0 gold 0
## 188 0 3 7 1 1 1 1 1 1 1 red 0
## 189 0 0 2 1 0 0 1 0 0 0 red 0
## 190 0 0 3 1 0 1 0 1 0 0 red 0
## 191 0 3 4 1 0 1 1 1 0 0 red 0
## 192 0 0 4 1 1 0 1 0 0 1 green 1
## 193 3 0 4 1 1 0 0 0 1 1 green 0
## 194 0 7 5 1 1 0 1 1 1 0 green 0
## crosses saltires quarters sunstars crescent triangle icon animate text
## 1 0 0 0 1 0 0 1 0 0
## 2 0 0 0 1 0 0 0 1 0
## 3 0 0 0 1 1 0 0 0 0
## 4 0 0 0 0 0 1 1 1 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 1 0 0 1 0 0
## 7 0 0 0 0 0 0 0 1 0
## 8 0 0 0 1 0 1 0 0 0
## 9 0 0 0 0 0 0 0 0 0
## 10 0 0 0 1 0 0 0 0 0
## 11 1 1 1 6 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 1 0 0 0
## 14 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 1 0 0
## 17 0 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 1 1 1
## 19 0 0 0 1 0 0 0 0 0
## 20 1 1 1 0 0 0 1 1 0
## 21 0 0 0 0 0 0 0 1 0
## 22 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0
## 24 0 0 0 22 0 0 0 0 1
## 25 1 1 1 0 0 0 1 1 1
## 26 0 0 0 0 0 1 1 1 1
## 27 0 0 0 1 0 0 1 1 0
## 28 0 0 0 1 0 0 0 0 0
## 29 0 0 1 14 0 0 1 1 0
## 30 0 1 0 3 0 0 0 0 0
## 31 0 0 0 1 0 0 0 0 0
## 32 0 0 0 0 0 0 0 1 0
## 33 0 0 0 1 0 0 0 1 0
## 34 1 1 1 4 0 0 1 1 1
## 35 0 0 0 1 0 0 0 0 0
## 36 0 0 0 0 0 0 0 0 0
## 37 0 0 1 1 0 0 0 0 0
## 38 0 0 0 5 0 0 0 0 0
## 39 0 0 0 0 0 0 0 0 0
## 40 0 0 0 4 1 0 0 0 0
## 41 0 0 0 1 0 0 1 1 0
## 42 1 1 1 15 0 0 0 0 0
## 43 0 0 0 0 0 0 0 0 0
## 44 0 0 0 1 0 1 0 0 0
## 45 0 0 0 0 0 0 1 1 0
## 46 0 0 0 0 0 1 0 0 0
## 47 1 0 0 0 0 0 0 0 0
## 48 0 0 0 1 0 1 0 0 0
## 49 0 0 0 10 0 0 0 1 0
## 50 1 0 0 0 0 0 0 0 0
## 51 0 0 0 0 0 0 0 0 0
## 52 0 0 0 0 0 0 0 1 1
## 53 0 0 0 0 0 0 0 0 0
## 54 0 0 0 0 0 1 0 0 0
## 55 0 0 0 0 0 0 0 0 0
## 56 1 0 0 0 0 0 0 0 0
## 57 1 1 1 0 0 0 1 1 1
## 58 2 1 1 0 0 0 1 1 0
## 59 1 0 0 0 0 0 0 0 0
## 60 0 0 0 0 0 0 0 0 0
## 61 0 0 0 0 0 0 0 0 0
## 62 0 0 0 1 0 0 1 0 0
## 63 0 0 0 0 0 0 0 0 0
## 64 0 0 0 0 0 0 0 0 0
## 65 0 0 0 0 0 0 1 0 0
## 66 0 0 0 0 0 0 0 0 0
## 67 0 0 0 1 0 0 0 0 0
## 68 0 0 0 0 0 0 1 0 0
## 69 1 0 1 0 0 0 0 0 0
## 70 0 0 0 0 0 0 0 0 0
## 71 0 0 0 7 0 1 0 1 0
## 72 0 0 0 0 0 0 1 1 1
## 73 0 0 0 0 0 0 0 0 0
## 74 0 0 0 0 0 0 0 0 0
## 75 0 0 0 1 0 0 0 0 0
## 76 0 0 0 0 0 1 0 0 0
## 77 0 0 0 0 0 0 0 0 0
## 78 0 0 0 5 0 0 0 0 0
## 79 1 1 1 0 0 0 1 1 1
## 80 0 0 0 0 0 0 0 0 0
## 81 1 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 1 0 0
## 83 0 0 0 0 0 0 0 0 0
## 84 0 0 0 0 0 0 1 0 1
## 85 0 0 0 3 0 0 0 0 0
## 86 0 0 0 0 0 0 0 0 0
## 87 0 0 0 1 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0
## 89 0 0 0 0 0 0 0 0 0
## 90 0 1 0 0 0 1 0 0 0
## 91 0 0 0 1 0 0 0 0 0
## 92 0 0 0 1 0 1 0 0 0
## 93 0 0 0 0 0 0 1 0 0
## 94 0 0 0 0 0 0 1 0 0
## 95 0 0 0 1 0 0 1 1 0
## 96 0 0 0 0 0 0 0 0 0
## 97 0 0 0 0 0 0 0 0 0
## 98 0 0 0 0 0 0 0 1 0
## 99 0 0 0 0 0 0 1 0 0
## 100 0 0 1 1 0 0 0 0 0
## 101 0 0 0 0 0 0 0 0 0
## 102 0 0 0 0 0 0 1 0 0
## 103 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0
## 105 0 0 0 1 0 0 0 0 0
## 106 0 0 1 1 1 0 0 0 0
## 107 0 0 0 0 1 0 0 0 0
## 108 0 0 0 0 0 0 0 0 0
## 109 1 0 0 0 0 0 1 0 0
## 110 0 0 0 1 0 0 1 0 0
## 111 0 0 0 1 1 0 0 0 0
## 112 0 0 0 0 0 0 0 0 0
## 113 0 0 0 0 0 0 0 1 0
## 114 0 0 0 4 0 0 0 0 0
## 115 0 0 0 0 0 0 0 0 0
## 116 0 0 0 1 1 1 1 0 0
## 117 2 1 1 0 0 0 1 1 0
## 118 0 0 0 1 0 0 0 0 0
## 119 0 0 0 1 0 1 1 0 0
## 120 0 0 0 1 0 0 0 0 0
## 121 0 0 0 2 1 0 0 0 0
## 122 0 0 0 0 0 0 0 0 0
## 123 0 0 0 6 0 0 0 0 0
## 124 1 1 1 4 0 0 0 0 0
## 125 0 0 0 0 0 0 0 0 0
## 126 0 0 0 0 0 0 0 0 0
## 127 0 0 0 0 0 0 0 0 0
## 128 1 1 1 5 0 0 0 0 0
## 129 0 0 0 1 0 0 0 0 0
## 130 0 0 0 1 0 0 0 0 0
## 131 1 0 0 0 0 0 0 0 0
## 132 0 0 0 0 0 0 1 0 0
## 133 0 0 0 1 1 0 0 0 0
## 134 0 0 4 2 0 0 0 0 0
## 135 0 0 0 5 0 1 0 1 0
## 136 0 0 0 1 0 0 1 1 1
## 137 0 0 0 0 0 0 0 0 0
## 138 0 0 0 4 0 1 0 0 0
## 139 0 0 0 0 0 0 0 0 0
## 140 0 0 0 0 0 0 1 0 0
## 141 0 0 0 1 0 1 0 0 0
## 142 0 0 0 0 0 0 0 0 0
## 143 0 0 0 2 0 0 1 1 1
## 144 0 0 0 0 0 0 0 0 1
## 145 0 0 0 0 0 0 0 0 0
## 146 0 0 0 2 0 1 0 0 0
## 147 0 0 0 0 0 0 1 0 1
## 148 0 0 0 1 0 0 0 0 0
## 149 0 0 0 0 0 0 0 0 0
## 150 0 0 0 0 0 0 0 0 0
## 151 0 0 0 5 1 0 0 0 0
## 152 0 0 0 5 0 1 0 0 0
## 153 0 0 0 1 0 0 0 0 0
## 154 1 1 0 0 0 0 0 0 0
## 155 0 0 0 0 0 0 1 0 0
## 156 0 0 0 1 0 1 0 0 0
## 157 0 0 0 0 0 0 0 0 0
## 158 0 0 0 0 0 0 1 1 0
## 159 1 1 1 0 0 0 1 0 0
## 160 0 0 0 2 0 1 0 0 0
## 161 0 0 0 0 0 1 0 0 0
## 162 0 0 0 0 0 0 1 1 1
## 163 0 0 0 0 0 1 0 0 0
## 164 0 0 0 1 0 0 0 0 0
## 165 0 0 0 0 0 0 1 0 0
## 166 1 0 0 0 0 0 0 0 0
## 167 1 0 0 0 0 0 0 0 0
## 168 0 0 0 2 0 0 0 0 0
## 169 0 0 1 1 0 0 0 0 0
## 170 0 0 0 0 0 1 0 0 0
## 171 0 0 0 0 0 0 0 0 0
## 172 0 0 1 1 0 0 0 0 0
## 173 1 0 1 0 0 0 0 0 0
## 174 0 0 0 0 0 1 0 0 0
## 175 0 0 0 1 1 0 0 0 0
## 176 0 0 0 1 1 0 0 0 0
## 177 1 1 1 0 0 0 1 1 0
## 178 1 1 1 9 0 0 0 0 0
## 179 0 0 0 0 0 0 0 0 0
## 180 0 0 0 0 0 0 0 1 0
## 181 1 1 0 0 0 0 0 0 0
## 182 0 0 1 1 0 0 0 0 0
## 183 0 0 0 0 0 0 1 1 1
## 184 0 0 1 50 0 0 0 0 0
## 185 0 0 0 1 0 0 1 0 0
## 186 0 0 0 0 0 1 0 1 0
## 187 0 0 0 0 0 0 1 0 0
## 188 0 0 0 7 0 0 1 1 0
## 189 0 0 0 1 0 0 0 0 0
## 190 0 0 1 5 0 0 0 0 0
## 191 0 0 0 1 0 0 0 0 0
## 192 0 0 0 0 0 0 1 1 0
## 193 0 0 0 0 0 0 0 1 0
## 194 0 0 0 1 0 1 1 1 0
## topleft botright
## 1 black green
## 2 red red
## 3 green white
## 4 blue red
## 5 blue red
## 6 red black
## 7 white blue
## 8 black red
## 9 blue blue
## 10 blue blue
## 11 white blue
## 12 red red
## 13 blue blue
## 14 white red
## 15 green green
## 16 blue blue
## 17 black red
## 18 red red
## 19 green green
## 20 white red
## 21 orange red
## 22 red green
## 23 blue blue
## 24 green green
## 25 white blue
## 26 white gold
## 27 white red
## 28 red green
## 29 blue red
## 30 white white
## 31 green gold
## 32 red red
## 33 red green
## 34 white blue
## 35 blue gold
## 36 blue red
## 37 blue red
## 38 red red
## 39 gold red
## 40 green green
## 41 red red
## 42 white blue
## 43 blue blue
## 44 blue blue
## 45 white white
## 46 white red
## 47 red red
## 48 white green
## 49 green green
## 50 blue blue
## 51 gold red
## 52 red black
## 53 blue blue
## 54 green red
## 55 green red
## 56 white white
## 57 white blue
## 58 white blue
## 59 white white
## 60 blue red
## 61 blue red
## 62 red red
## 63 green blue
## 64 red green
## 65 black gold
## 66 black gold
## 67 red green
## 68 white red
## 69 blue blue
## 70 white red
## 71 red red
## 72 red red
## 73 blue blue
## 74 red green
## 75 red green
## 76 black green
## 77 black red
## 78 blue blue
## 79 white blue
## 80 red green
## 81 blue blue
## 82 orange green
## 83 red white
## 84 green red
## 85 red black
## 86 green orange
## 87 blue blue
## 88 green red
## 89 red green
## 90 gold gold
## 91 white white
## 92 black green
## 93 red red
## 94 black green
## 95 red blue
## 96 green red
## 97 red red
## 98 red red
## 99 green blue
## 100 blue red
## 101 green green
## 102 blue red
## 103 red blue
## 104 white green
## 105 black green
## 106 blue white
## 107 red red
## 108 green red
## 109 white red
## 110 blue blue
## 111 green green
## 112 red green
## 113 green red
## 114 blue blue
## 115 red white
## 116 red red
## 117 white blue
## 118 red red
## 119 green gold
## 120 blue blue
## 121 blue blue
## 122 red blue
## 123 white white
## 124 white blue
## 125 blue blue
## 126 orange green
## 127 green green
## 128 white gold
## 129 blue blue
## 130 red black
## 131 red red
## 132 red green
## 133 white green
## 134 white white
## 135 red black
## 136 red blue
## 137 red red
## 138 blue red
## 139 white red
## 140 green red
## 141 red red
## 142 white brown
## 143 blue red
## 144 red green
## 145 white blue
## 146 green green
## 147 green green
## 148 green red
## 149 red green
## 150 green blue
## 151 red white
## 152 blue green
## 153 blue blue
## 154 orange blue
## 155 white white
## 156 red black
## 157 red red
## 158 gold gold
## 159 white blue
## 160 green red
## 161 blue blue
## 162 blue green
## 163 red black
## 164 green green
## 165 blue blue
## 166 blue blue
## 167 red red
## 168 red black
## 169 blue red
## 170 green blue
## 171 red red
## 172 red green
## 173 white red
## 174 white white
## 175 red red
## 176 red red
## 177 white blue
## 178 white blue
## 179 red black
## 180 black red
## 181 white red
## 182 white white
## 183 white white
## 184 blue red
## 185 red red
## 186 black green
## 187 gold white
## 188 gold red
## 189 red red
## 190 blue red
## 191 blue red
## 192 green green
## 193 green brown
## 194 green green- Verify that there are no missing values in flag_df.
# fill in your code here
is.na(flag_df)## X name landmass zone area population language religion bars
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## stripes colours red green blue gold white black orange mainhue
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [186,] FALSE FALSE FALSE FALSE
## [187,] FALSE FALSE FALSE FALSE
## [188,] FALSE FALSE FALSE FALSE
## [189,] FALSE FALSE FALSE FALSE
## [190,] FALSE FALSE FALSE FALSE
## [191,] FALSE FALSE FALSE FALSE
## [192,] FALSE FALSE FALSE FALSE
## [193,] FALSE FALSE FALSE FALSE
## [194,] FALSE FALSE FALSE FALSEAt this point, we know there are no missing values in the dataset so we will use dplyr to make the dataset a bit more readable to us. Look at the flag.names file again. Under “Attribute Information” look at the variables landmass, zone, language, religion.
Instead of encoding these categories using numbers, we would like to just use the categories in the variables. For example, in the zone column, we want our data to be “NE”, “SE”, “SW”, “NW”, instead of 1, 2, 3, 4.
- Change each of the columns landmass, zone, language, and religion to hold their actual categorical data (not their encoded numbers). The type of each of these columns should be Factor.
# fill in your code here
as.character(flag_df$zone)## [1] "1" "1" "1" "3" "1" "2" "4" "4" "3" "3" "2" "1" "4" "1" "1" "4" "1" "4"
## [19] "1" "4" "1" "3" "2" "3" "4" "1" "1" "4" "1" "2" "1" "4" "4" "4" "1" "1"
## [37] "3" "1" "4" "2" "2" "3" "4" "4" "1" "1" "1" "1" "4" "4" "3" "1" "4" "1"
## [55] "1" "4" "3" "2" "1" "1" "4" "3" "2" "4" "1" "1" "4" "4" "1" "4" "4" "1"
## [73] "4" "4" "4" "4" "4" "4" "1" "1" "4" "1" "2" "1" "1" "4" "1" "1" "4" "4"
## [91] "1" "1" "1" "1" "1" "1" "1" "1" "2" "4" "1" "1" "1" "2" "2" "1" "1" "4"
## [109] "1" "1" "4" "2" "4" "1" "1" "1" "4" "4" "2" "2" "1" "1" "4" "2" "4" "1"
## [127] "1" "3" "1" "1" "1" "1" "1" "4" "2" "3" "3" "1" "1" "4" "4" "1" "1" "2"
## [145] "1" "1" "1" "4" "2" "4" "1" "2" "1" "2" "1" "1" "4" "1" "3" "4" "4" "4"
## [163] "1" "4" "2" "1" "1" "1" "1" "2" "1" "1" "2" "4" "1" "1" "4" "2" "1" "1"
## [181] "4" "3" "4" "4" "1" "2" "1" "4" "1" "3" "1" "2" "2" "2"class(flag_df$zone)## [1] "integer"flag_df <- mutate(flag_df,
zone = recode (zone, '1' = 'NE', '2' = 'SE', '3' = 'SW', '4' = 'NW'))
as.factor(flag_df$zone)## [1] NE NE NE SW NE SE NW NW SW SW SE NE NW NE NE NW NE NW NE NW NE SW SE SW NW
## [26] NE NE NW NE SE NE NW NW NW NE NE SW NE NW SE SE SW NW NW NE NE NE NE NW NW
## [51] SW NE NW NE NE NW SW SE NE NE NW SW SE NW NE NE NW NW NE NW NW NE NW NW NW
## [76] NW NW NW NE NE NW NE SE NE NE NW NE NE NW NW NE NE NE NE NE NE NE NE SE NW
## [101] NE NE NE SE SE NE NE NW NE NE NW SE NW NE NE NE NW NW SE SE NE NE NW SE NW
## [126] NE NE SW NE NE NE NE NE NW SE SW SW NE NE NW NW NE NE SE NE NE NE NW SE NW
## [151] NE SE NE SE NE NE NW NE SW NW NW NW NE NW SE NE NE NE NE SE NE NE SE NW NE
## [176] NE NW SE NE NE NW SW NW NW NE SE NE NW NE SW NE SE SE SE
## Levels: NE NW SE SWstr(flag_df$zone)## chr [1:194] "NE" "NE" "NE" "SW" "NE" "SE" "NW" "NW" "SW" "SW" "SE" "NE" ...flag_df <- mutate_at(flag_df, vars(landmass, zone, language, religion), as.factor)
class(flag_df$ landmass)## [1] "factor"class(flag_df$zone)## [1] "factor"class(flag_df$religion)## [1] "factor"class(flag_df$language)## [1] "factor"# code for Question 6 Continued (that chunk was big enough)
as.character(flag_df$landmass)## [1] "5" "3" "4" "6" "3" "4" "1" "1" "2" "2" "6" "3" "1" "5" "5" "1" "3" "1"
## [19] "4" "1" "5" "2" "4" "2" "1" "5" "3" "4" "5" "4" "4" "1" "4" "1" "4" "4"
## [37] "2" "5" "2" "4" "4" "6" "1" "1" "3" "3" "3" "4" "1" "1" "2" "4" "1" "4"
## [55] "4" "3" "2" "6" "3" "3" "2" "6" "4" "4" "3" "3" "4" "3" "3" "1" "1" "6"
## [73] "1" "4" "4" "2" "1" "1" "5" "3" "3" "5" "6" "5" "5" "3" "5" "3" "4" "1"
## [91] "5" "5" "5" "4" "6" "5" "5" "5" "4" "4" "4" "3" "3" "4" "4" "5" "5" "4"
## [109] "3" "6" "4" "4" "1" "6" "3" "5" "1" "4" "4" "6" "5" "3" "1" "6" "1" "4"
## [127] "4" "6" "5" "5" "3" "5" "5" "2" "6" "2" "2" "6" "3" "3" "1" "5" "3" "4"
## [145] "3" "4" "5" "4" "4" "4" "5" "6" "4" "4" "5" "5" "3" "5" "4" "1" "1" "1"
## [163] "4" "2" "4" "3" "3" "5" "5" "4" "5" "4" "6" "2" "4" "5" "1" "6" "5" "4"
## [181] "3" "2" "1" "1" "5" "6" "3" "2" "5" "6" "3" "4" "4" "4"flag_df <- mutate(flag_df,
landmass = recode(
landmass, '1'= 'N America', '2' = 'S America', '3' = 'Europe', '4' =
'Africa', '5' = 'Asia', '6' = 'Oceania'
))#Code for Question 6 continued
flag_df <- mutate(flag_df,
language = recode(
language, '1' = 'English', '2' = 'Spanish', '3' = 'French', '4' = 'German',
'5' = 'Slavic', '6' = 'Other I-E', '7' = 'Chinese', '8' = 'Arabic', '9' = 'J-T-F-M',
'10' = 'Other'))# Code for Question 6 continued
flag_df <- mutate(flag_df,
religion = recode(
religion, '0' = 'Catholic', '1' = 'Other Christian', '2' = 'Muslim', '3' = 'Buddhist',
'4' = 'Hindu', '5' = 'Ethnic', '6' = 'Marxist', '7' = 'Other'
))Notice from our earlier structure command that the data types for columns red, green, blue, gold, white, black, orange, crescent, triangle, icon, animate, text are all integer. Looking at flag.names these integer variables are really just an encoding for true (1) or false (0). We don’t want to compute with these 1s and 0s (for example find a mean). So we should change these to logicals.
- Change the column type to logical for the following columns: red, green, blue, gold, white, black, orange, crescent, triangle, icon, animate, and text.
# fill in your code here
#install.packages("hablar")
library(hablar)##
## Attaching package: 'hablar'## The following object is masked from 'package:forcats':
##
## fct## The following object is masked from 'package:dplyr':
##
## na_if## The following object is masked from 'package:tibble':
##
## numflag_df %>%
convert(lgl(red))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <lgl>, green <int>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(green))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <lgl>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(blue))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <lgl>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(gold))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <lgl>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(white))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <lgl>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(black))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <int>, black <lgl>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(orange))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <lgl>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(crescent))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <lgl>, triangle <int>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(triangle))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <lgl>, icon <int>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(icon))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <lgl>, animate <int>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(animate))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <lgl>,
## # text <int>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionflag_df %>%
convert(lgl(text))## # A tibble: 194 × 31
## X name landm…¹ zone area popul…² langu…³ relig…⁴ bars stripes colours
## <int> <chr> <fct> <fct> <int> <int> <fct> <fct> <int> <int> <int>
## 1 1 Afgh… Asia NE 648 16 Other Muslim 0 3 5
## 2 2 Alba… Europe NE 29 3 Other … Marxist 0 0 3
## 3 3 Alge… Africa NE 2388 20 Arabic Muslim 2 0 3
## 4 4 Amer… Oceania SW 0 0 English Other … 0 0 5
## 5 5 Ando… Europe NE 0 0 Other … Cathol… 3 0 3
## 6 6 Ango… Africa SE 1247 7 Other Ethnic 0 2 3
## 7 7 Angu… N Amer… NW 0 0 English Other … 0 1 3
## 8 8 Anti… N Amer… NW 0 0 English Other … 0 1 5
## 9 9 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 2
## 10 10 Arge… S Amer… SW 2777 28 Spanish Cathol… 0 3 3
## # … with 184 more rows, 20 more variables: red <int>, green <int>, blue <int>,
## # gold <int>, white <int>, black <int>, orange <int>, mainhue <chr>,
## # circles <int>, crosses <int>, saltires <int>, quarters <int>,
## # sunstars <int>, crescent <int>, triangle <int>, icon <int>, animate <int>,
## # text <lgl>, topleft <chr>, botright <chr>, and abbreviated variable names
## # ¹landmass, ²population, ³language, ⁴religionNow that our data is clean, let’s answer some questions about it!
Data Investigation
- Print out how many countries have each “mainhue” category.
# fill in your code here
# black blue brown gold green orange red white
# 5 40 2 19 31 4 71 22
table(flag_df$mainhue)##
## black blue brown gold green orange red white
## 5 40 2 19 31 4 71 22- How many countries have the three colors red, white, and blue in their flags? How many countries have ONLY the three colors red, white, and blue in their flags?
# fill in your code here
# 63 Countries have red, white, and blue in their flags
filter(flag_df, red == T & white == T & blue == T)## X name landmass zone area population language
## 1 4 American-Samoa Oceania SW 0 0 English
## 2 8 Antigua-Barbuda N America NW 0 0 English
## 3 11 Australia Oceania SE 7690 15 English
## 4 18 Belize N America NW 23 0 English
## 5 20 Bermuda N America NW 0 0 English
## 6 25 British-Virgin-Isles N America NW 0 0 English
## 7 27 Bulgaria Europe NE 111 9 Slavic
## 8 29 Burma Asia NE 678 35 Other
## 9 34 Cayman-Islands N America NW 0 0 English
## 10 35 Central-African-Republic Africa NE 623 2 Other
## 11 37 Chile S America SW 757 11 Spanish
## 12 42 Cook-Islands Oceania SW 0 0 English
## 13 43 Costa-Rica N America NW 51 2 Spanish
## 14 44 Cuba N America NW 115 10 Spanish
## 15 46 Czechoslovakia Europe NE 128 15 Slavic
## 16 48 Djibouti Africa NE 22 0 French
## 17 49 Dominica N America NW 0 0 English
## 18 50 Dominican-Republic N America NW 49 6 Spanish
## 19 54 Equatorial-Guinea Africa NE 28 0 Other
## 20 56 Faeroes Europe NW 1 0 Other I-E
## 21 57 Falklands-Malvinas S America SW 12 0 English
## 22 58 Fiji Oceania SE 18 1 English
## 23 60 France Europe NE 547 54 French
## 24 61 French-Guiana S America NW 91 0 French
## 25 62 French-Polynesia Oceania SW 4 0 French
## 26 64 Gambia Africa NW 10 1 English
## 27 72 Guam Oceania NE 0 0 English
## 28 79 Hong-Kong Asia NE 1 5 Chinese
## 29 81 Iceland Europe NW 103 0 Other I-E
## 30 95 Kiribati Oceania NE 0 0 English
## 31 97 Laos Asia NE 236 3 Other
## 32 99 Lesotho Africa SE 30 1 Other
## 33 100 Liberia Africa NW 111 1 Other
## 34 103 Luxembourg Europe NE 3 0 German
## 35 106 Malaysia Asia NE 333 13 Other
## 36 117 Montserrat N America NW 0 0 English
## 37 122 Netherlands Europe NE 41 14 Other I-E
## 38 123 Netherlands-Antilles N America NW 0 0 Other I-E
## 39 124 New-Zealand Oceania SE 268 2 English
## 40 128 Niue Oceania SW 0 0 English
## 41 129 North-Korea Asia NE 121 18 Other
## 42 131 Norway Europe NE 324 4 Other I-E
## 43 134 Panama S America NW 76 2 Spanish
## 44 136 Parguay S America SW 407 3 Spanish
## 45 138 Philippines Oceania NE 300 48 Other
## 46 140 Portugal Europe NW 92 10 Other I-E
## 47 141 Puerto-Rico N America NW 9 3 Spanish
## 48 143 Romania Europe NE 237 22 Other I-E
## 49 154 South-Africa Africa SE 1221 29 Other I-E
## 50 155 South-Korea Asia NE 99 39 Other
## 51 156 South-Yemen Asia NE 288 2 Arabic
## 52 159 St-Helena Africa SW 0 0 English
## 53 165 Swaziland Africa SE 17 1 Other
## 54 169 Taiwan Asia NE 36 18 Chinese
## 55 171 Thailand Asia NE 514 49 Other
## 56 177 Turks-Cocos-Islands N America NW 0 0 English
## 57 178 Tuvalu Oceania SE 0 0 English
## 58 181 UK Europe NW 245 56 English
## 59 183 US-Virgin-Isles N America NW 0 0 English
## 60 184 USA N America NW 9363 231 English
## 61 188 Venezuela S America NW 912 15 Spanish
## 62 190 Western-Samoa Oceania SW 3 0 English
## 63 191 Yugoslavia Europe NE 256 22 Other I-E
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## 2 Other Christian 0 1 5 1 0 1 1 1 1 0
## 3 Other Christian 0 0 3 1 0 1 0 1 0 0
## 4 Other Christian 0 2 8 1 1 1 1 1 1 1
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## 21 Other Christian 0 0 6 1 1 1 1 1 0 0
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## 26 Ethnic 0 5 4 1 1 1 0 1 0 0
## 27 Other Christian 0 0 7 1 1 1 1 1 0 1
## 28 Buddhist 0 0 6 1 1 1 1 1 0 1
## 29 Other Christian 0 0 3 1 0 1 0 1 0 0
## 30 Other Christian 0 0 4 1 0 1 1 1 0 0
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## 35 Muslim 0 14 4 1 0 1 1 1 0 0
## 36 Other Christian 0 0 7 1 1 1 1 1 1 0
## 37 Other Christian 0 3 3 1 0 1 0 1 0 0
## 38 Other Christian 0 1 3 1 0 1 0 1 0 0
## 39 Other Christian 0 0 3 1 0 1 0 1 0 0
## 40 Other Christian 0 0 4 1 0 1 1 1 0 0
## 41 Marxist 0 5 3 1 0 1 0 1 0 0
## 42 Other Christian 0 0 3 1 0 1 0 1 0 0
## 43 Catholic 0 0 3 1 0 1 0 1 0 0
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## 47 Catholic 0 5 3 1 0 1 0 1 0 0
## 48 Marxist 3 0 7 1 1 1 1 1 0 1
## 49 Other Christian 0 3 5 1 1 1 0 1 0 1
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## 51 Muslim 0 3 4 1 0 1 0 1 1 0
## 52 Other Christian 0 0 7 1 1 1 1 1 0 1
## 53 Other Christian 0 5 7 1 0 1 1 1 1 1
## 54 Buddhist 0 0 3 1 0 1 0 1 0 0
## 55 Buddhist 0 5 3 1 0 1 0 1 0 0
## 56 Other Christian 0 0 6 1 1 1 1 1 0 1
## 57 Other Christian 0 0 5 1 0 1 1 1 0 0
## 58 Other Christian 0 0 3 1 0 1 0 1 0 0
## 59 Other Christian 0 0 6 1 1 1 1 1 0 0
## 60 Other Christian 0 13 3 1 0 1 0 1 0 0
## 61 Catholic 0 3 7 1 1 1 1 1 1 1
## 62 Other Christian 0 0 3 1 0 1 0 1 0 0
## 63 Marxist 0 3 4 1 0 1 1 1 0 0
## mainhue circles crosses saltires quarters sunstars crescent triangle icon
## 1 blue 0 0 0 0 0 0 1 1
## 2 red 0 0 0 0 1 0 1 0
## 3 blue 0 1 1 1 6 0 0 0
## 4 blue 1 0 0 0 0 0 0 1
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## 7 red 0 0 0 0 1 0 0 1
## 8 red 0 0 0 1 14 0 0 1
## 9 blue 1 1 1 1 4 0 0 1
## 10 gold 0 0 0 0 1 0 0 0
## 11 red 0 0 0 1 1 0 0 0
## 12 blue 1 1 1 1 15 0 0 0
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## 17 green 1 0 0 0 10 0 0 0
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## 42 red 0 1 0 0 0 0 0 0
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## 46 red 1 0 0 0 0 0 0 1
## 47 red 0 0 0 0 1 0 1 0
## 48 red 0 0 0 0 2 0 0 1
## 49 orange 0 1 1 0 0 0 0 0
## 50 white 1 0 0 0 0 0 0 1
## 51 red 0 0 0 0 1 0 1 0
## 52 blue 0 1 1 1 0 0 0 1
## 53 blue 0 0 0 0 0 0 0 1
## 54 red 1 0 0 1 1 0 0 0
## 55 red 0 0 0 0 0 0 0 0
## 56 blue 0 1 1 1 0 0 0 1
## 57 blue 0 1 1 1 9 0 0 0
## 58 red 0 1 1 0 0 0 0 0
## 59 white 0 0 0 0 0 0 0 1
## 60 white 0 0 0 1 50 0 0 0
## 61 red 0 0 0 0 7 0 0 1
## 62 red 0 0 0 1 5 0 0 0
## 63 red 0 0 0 0 1 0 0 0
## animate text topleft botright
## 1 1 0 blue red
## 2 0 0 black red
## 3 0 0 white blue
## 4 1 1 red red
## 5 1 0 white red
## 6 1 1 white blue
## 7 1 0 white red
## 8 1 0 blue red
## 9 1 1 white blue
## 10 0 0 blue gold
## 11 0 0 blue red
## 12 0 0 white blue
## 13 0 0 blue blue
## 14 0 0 blue blue
## 15 0 0 white red
## 16 0 0 white green
## 17 1 0 green green
## 18 0 0 blue blue
## 19 0 0 green red
## 20 0 0 white white
## 21 1 1 white blue
## 22 1 0 white blue
## 23 0 0 blue red
## 24 0 0 blue red
## 25 0 0 red red
## 26 0 0 red green
## 27 1 1 red red
## 28 1 1 white blue
## 29 0 0 blue blue
## 30 1 0 red blue
## 31 0 0 red red
## 32 0 0 green blue
## 33 0 0 blue red
## 34 0 0 red blue
## 35 0 0 blue white
## 36 1 0 white blue
## 37 0 0 red blue
## 38 0 0 white white
## 39 0 0 white blue
## 40 0 0 white gold
## 41 0 0 blue blue
## 42 0 0 red red
## 43 0 0 white white
## 44 1 1 red blue
## 45 0 0 blue red
## 46 0 0 green red
## 47 0 0 red red
## 48 1 1 blue red
## 49 0 0 orange blue
## 50 0 0 white white
## 51 0 0 red black
## 52 0 0 white blue
## 53 0 0 blue blue
## 54 0 0 blue red
## 55 0 0 red red
## 56 1 0 white blue
## 57 0 0 white blue
## 58 0 0 white red
## 59 1 1 white white
## 60 0 0 blue red
## 61 1 0 gold red
## 62 0 0 blue red
## 63 0 0 blue red# 27 Countries only have red, white, and blue in their flags
filter(flag_df, red == T & white == T & blue == T & orange == F & black == F & green == F & gold == F)## X name landmass zone area population language
## 1 11 Australia Oceania SE 7690 15 English
## 2 29 Burma Asia NE 678 35 Other
## 3 37 Chile S America SW 757 11 Spanish
## 4 42 Cook-Islands Oceania SW 0 0 English
## 5 43 Costa-Rica N America NW 51 2 Spanish
## 6 44 Cuba N America NW 115 10 Spanish
## 7 46 Czechoslovakia Europe NE 128 15 Slavic
## 8 50 Dominican-Republic N America NW 49 6 Spanish
## 9 56 Faeroes Europe NW 1 0 Other I-E
## 10 60 France Europe NE 547 54 French
## 11 61 French-Guiana S America NW 91 0 French
## 12 81 Iceland Europe NW 103 0 Other I-E
## 13 97 Laos Asia NE 236 3 Other
## 14 100 Liberia Africa NW 111 1 Other
## 15 103 Luxembourg Europe NE 3 0 German
## 16 122 Netherlands Europe NE 41 14 Other I-E
## 17 123 Netherlands-Antilles N America NW 0 0 Other I-E
## 18 124 New-Zealand Oceania SE 268 2 English
## 19 129 North-Korea Asia NE 121 18 Other
## 20 131 Norway Europe NE 324 4 Other I-E
## 21 134 Panama S America NW 76 2 Spanish
## 22 141 Puerto-Rico N America NW 9 3 Spanish
## 23 169 Taiwan Asia NE 36 18 Chinese
## 24 171 Thailand Asia NE 514 49 Other
## 25 181 UK Europe NW 245 56 English
## 26 184 USA N America NW 9363 231 English
## 27 190 Western-Samoa Oceania SW 3 0 English
## religion bars stripes colours red green blue gold white black orange
## 1 Other Christian 0 0 3 1 0 1 0 1 0 0
## 2 Buddhist 0 0 3 1 0 1 0 1 0 0
## 3 Catholic 0 2 3 1 0 1 0 1 0 0
## 4 Other Christian 0 0 4 1 0 1 0 1 0 0
## 5 Catholic 0 5 3 1 0 1 0 1 0 0
## 6 Marxist 0 5 3 1 0 1 0 1 0 0
## 7 Marxist 0 0 3 1 0 1 0 1 0 0
## 8 Catholic 0 0 3 1 0 1 0 1 0 0
## 9 Other Christian 0 0 3 1 0 1 0 1 0 0
## 10 Catholic 3 0 3 1 0 1 0 1 0 0
## 11 Catholic 3 0 3 1 0 1 0 1 0 0
## 12 Other Christian 0 0 3 1 0 1 0 1 0 0
## 13 Marxist 0 3 3 1 0 1 0 1 0 0
## 14 Ethnic 0 11 3 1 0 1 0 1 0 0
## 15 Catholic 0 3 3 1 0 1 0 1 0 0
## 16 Other Christian 0 3 3 1 0 1 0 1 0 0
## 17 Other Christian 0 1 3 1 0 1 0 1 0 0
## 18 Other Christian 0 0 3 1 0 1 0 1 0 0
## 19 Marxist 0 5 3 1 0 1 0 1 0 0
## 20 Other Christian 0 0 3 1 0 1 0 1 0 0
## 21 Catholic 0 0 3 1 0 1 0 1 0 0
## 22 Catholic 0 5 3 1 0 1 0 1 0 0
## 23 Buddhist 0 0 3 1 0 1 0 1 0 0
## 24 Buddhist 0 5 3 1 0 1 0 1 0 0
## 25 Other Christian 0 0 3 1 0 1 0 1 0 0
## 26 Other Christian 0 13 3 1 0 1 0 1 0 0
## 27 Other Christian 0 0 3 1 0 1 0 1 0 0
## mainhue circles crosses saltires quarters sunstars crescent triangle icon
## 1 blue 0 1 1 1 6 0 0 0
## 2 red 0 0 0 1 14 0 0 1
## 3 red 0 0 0 1 1 0 0 0
## 4 blue 1 1 1 1 15 0 0 0
## 5 blue 0 0 0 0 0 0 0 0
## 6 blue 0 0 0 0 1 0 1 0
## 7 white 0 0 0 0 0 0 1 0
## 8 blue 0 1 0 0 0 0 0 0
## 9 white 0 1 0 0 0 0 0 0
## 10 white 0 0 0 0 0 0 0 0
## 11 white 0 0 0 0 0 0 0 0
## 12 blue 0 1 0 0 0 0 0 0
## 13 red 1 0 0 0 0 0 0 0
## 14 red 0 0 0 1 1 0 0 0
## 15 red 0 0 0 0 0 0 0 0
## 16 red 0 0 0 0 0 0 0 0
## 17 white 0 0 0 0 6 0 0 0
## 18 blue 0 1 1 1 4 0 0 0
## 19 blue 1 0 0 0 1 0 0 0
## 20 red 0 1 0 0 0 0 0 0
## 21 red 0 0 0 4 2 0 0 0
## 22 red 0 0 0 0 1 0 1 0
## 23 red 1 0 0 1 1 0 0 0
## 24 red 0 0 0 0 0 0 0 0
## 25 red 0 1 1 0 0 0 0 0
## 26 white 0 0 0 1 50 0 0 0
## 27 red 0 0 0 1 5 0 0 0
## animate text topleft botright
## 1 0 0 white blue
## 2 1 0 blue red
## 3 0 0 blue red
## 4 0 0 white blue
## 5 0 0 blue blue
## 6 0 0 blue blue
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## 10 0 0 blue red
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## 12 0 0 blue blue
## 13 0 0 red red
## 14 0 0 blue red
## 15 0 0 red blue
## 16 0 0 red blue
## 17 0 0 white white
## 18 0 0 white blue
## 19 0 0 blue blue
## 20 0 0 red red
## 21 0 0 white white
## 22 0 0 red red
## 23 0 0 blue red
## 24 0 0 red red
## 25 0 0 white red
## 26 0 0 blue red
## 27 0 0 blue red- Print out the data observations for the 10 countries with the largest populations. The 10 data observations should be printed out in descending order according to population.
# fill in your code here
flag_df1 <- arrange(flag_df,desc(population))
flag_df1[1:10]## X name landmass zone area population language
## 1 38 China Asia NE 9561 1008 Chinese
## 2 82 India Asia NE 3268 684 Other I-E
## 3 185 USSR Asia NE 22402 274 Slavic
## 4 184 USA N America NW 9363 231 English
## 5 83 Indonesia Oceania SE 1904 157 Other
## 6 24 Brazil S America SW 8512 119 Other I-E
## 7 91 Japan Asia NE 372 118 J-T-F-M
## 8 15 Bangladesh Asia NE 143 90 Other I-E
## 9 133 Pakistan Asia NE 804 84 Other I-E
## 10 113 Mexico N America NW 1973 77 Spanish
## 11 66 Germany-FRG Europe NE 249 61 German
## 12 189 Vietnam Asia NE 333 60 Other
## 13 88 Italy Europe NE 301 57 Other I-E
## 14 127 Nigeria Africa NE 925 56 Other
## 15 181 UK Europe NW 245 56 English
## 16 60 France Europe NE 547 54 French
## 17 171 Thailand Asia NE 514 49 Other
## 18 138 Philippines Oceania NE 300 48 Other
## 19 52 Egypt Africa NE 1001 47 Arabic
## 20 176 Turkey Asia NE 781 45 J-T-F-M
## 21 84 Iran Asia NE 1648 39 Other I-E
## 22 155 South-Korea Asia NE 99 39 Other
## 23 157 Spain Europe NW 505 38 Spanish
## 24 139 Poland Europe NE 313 36 Slavic
## 25 29 Burma Asia NE 678 35 Other
## 26 55 Ethiopia Africa NE 1222 31 Other
## 27 154 South-Africa Africa SE 1221 29 Other I-E
## 28 9 Argentina S America SW 2777 28 Spanish
## 29 10 Argentine S America SW 2777 28 Spanish
## 30 39 Colombia S America NW 1139 28 Spanish
## 31 192 Zaire Africa SE 905 28 Other
## 32 32 Canada N America NW 9976 24 English
## 33 143 Romania Europe NE 237 22 Other I-E
## 34 191 Yugoslavia Europe NE 256 22 Other I-E
## 35 3 Algeria Africa NE 2388 20 Arabic
## 36 118 Morocco Africa NW 447 20 Arabic
## 37 163 Sudan Africa NE 2506 20 Arabic
## 38 129 North-Korea Asia NE 121 18 Other
## 39 169 Taiwan Asia NE 36 18 Chinese
## 40 170 Tanzania Africa SE 945 18 Other
## 41 65 Germany-DDR Europe NE 108 17 German
## 42 94 Kenya Africa NE 583 17 Other
## 43 1 Afghanistan Asia NE 648 16 Other
## 44 121 Nepal Asia NE 140 16 Other
## 45 11 Australia Oceania SE 7690 15 English
## 46 46 Czechoslovakia Europe NE 128 15 Slavic
## 47 158 Sri-Lanka Asia NE 66 15 Other
## 48 188 Venezuela S America NW 912 15 Spanish
## 49 67 Ghana Africa NW 239 14 English
## 50 85 Iraq Asia NE 435 14 Arabic
## 51 122 Netherlands Europe NE 41 14 Other I-E
## 52 137 Peru S America SW 1285 14 Spanish
## 53 106 Malaysia Asia NE 333 13 Other
## 54 180 Uganda Africa NE 236 13 Other
## 55 119 Mozambique Africa SE 783 12 Other
## 56 37 Chile S America SW 757 11 Spanish
## 57 80 Hungary Europe NE 93 11 J-T-F-M
## 58 17 Belgium Europe NE 31 10 Other I-E
## 59 44 Cuba N America NW 115 10 Spanish
## 60 69 Greece Europe NE 132 10 Other I-E
## 61 140 Portugal Europe NW 92 10 Other I-E
## 62 168 Syria Asia NE 185 10 Arabic
## 63 27 Bulgaria Europe NE 111 9 Slavic
## 64 104 Malagasy Africa SE 587 9 Other
## 65 130 North-Yemen Asia NE 195 9 Arabic
## 66 147 Saudi-Arabia Asia NE 2150 9 Arabic
## 67 12 Austria Europe NE 84 8 German
## 68 31 Cameroon Africa NE 474 8 French
## 69 51 Ecuador S America SW 284 8 Spanish
## 70 73 Guatemala N America NW 109 8 Spanish
## 71 166 Sweden Europe NE 450 8 Other I-E
## 72 194 Zimbabwe Africa SE 391 8 Other
## 73 6 Angola Africa SE 1247 7 Other
## 74 28 Burkina Africa NW 274 7 French
## 75 89 Ivory-Coast Africa NW 323 7 French
## 76 108 Mali Africa NW 1240 7 French
## 77 175 Tunisia Africa NE 164 7 Arabic
## 78 22 Bolivia S America SW 1099 6 Spanish
## 79 50 Dominican-Republic N America NW 49 6 Spanish
## 80 74 Guinea Africa NW 246 6 French
## 81 77 Haiti N America NW 28 6 French
## 82 93 Kampuchea Asia NE 181 6 Other
## 83 105 Malawi Africa SE 118 6 Other
## 84 148 Senegal Africa NW 196 6 French
## 85 167 Switzerland Europe NE 41 6 German
## 86 193 Zambia Africa SE 753 6 Other
## 87 47 Denmark Europe NE 43 5 Other I-E
## 88 53 El-Salvador N America NW 21 5 Spanish
## 89 59 Finland Europe NE 337 5 J-T-F-M
## 90 79 Hong-Kong Asia NE 1 5 Chinese
## 91 126 Niger Africa NE 1267 5 French
## 92 144 Rwanda Africa SE 26 5 Other
## 93 153 Somalia Africa NE 637 5 Other
## 94 30 Burundi Africa SE 28 4 Other
## 95 36 Chad Africa NE 1284 4 French
## 96 78 Honduras N America NW 112 4 Spanish
## 97 87 Israel Asia NE 21 4 Other
## 98 131 Norway Europe NE 324 4 Other I-E
## 99 2 Albania Europe NE 29 3 Other I-E
## 100 19 Benin Africa NE 113 3 French
## 101 86 Ireland Europe NW 70 3 English
## 102 97 Laos Asia NE 236 3 Other
## 103 98 Lebanon Asia NE 10 3 Arabic
## 104 101 Libya Africa NE 1760 3 Arabic
## 105 125 Nicaragua N America NW 128 3 Spanish
## 106 135 Papua-New-Guinea Oceania SE 463 3 English
## 107 136 Parguay S America SW 407 3 Spanish
## 108 141 Puerto-Rico N America NW 9 3 Spanish
## 109 150 Sierra-Leone Africa NW 72 3 English
## 110 151 Singapore Asia NE 1 3 Chinese
## 111 182 Uruguay S America SW 178 3 Spanish
## 112 35 Central-African-Republic Africa NE 623 2 Other
## 113 41 Congo Africa SE 342 2 Other
## 114 43 Costa-Rica N America NW 51 2 Spanish
## 115 90 Jamaica N America NW 11 2 English
## 116 92 Jordan Asia NE 98 2 Arabic
## 117 96 Kuwait Asia NE 18 2 Arabic
## 118 111 Mauritania Africa NW 1031 2 Arabic
## 119 116 Mongolia Asia NE 1566 2 Other
## 120 124 New-Zealand Oceania SE 268 2 English
## 121 134 Panama S America NW 76 2 Spanish
## 122 156 South-Yemen Asia NE 288 2 Arabic
## 123 172 Togo Africa NE 57 2 French
## 124 21 Bhutan Asia NE 47 1 Other
## 125 23 Botswana Africa SE 600 1 Other
## 126 45 Cyprus Europe NE 9 1 Other I-E
## 127 58 Fiji Oceania SE 18 1 English
## 128 63 Gabon Africa SE 268 1 Other
## 129 64 Gambia Africa NW 10 1 English
## 130 75 Guinea-Bissau Africa NW 36 1 Other I-E
## 131 76 Guyana S America NW 215 1 English
## 132 99 Lesotho Africa SE 30 1 Other
## 133 100 Liberia Africa NW 111 1 Other
## 134 112 Mauritius Africa SE 2 1 English
## 135 132 Oman Asia NE 212 1 Arabic
## 136 165 Swaziland Africa SE 17 1 Other
## 137 174 Trinidad-Tobago S America NW 5 1 English
## 138 179 UAE Asia NE 84 1 Arabic
## 139 4 American-Samoa Oceania SW 0 0 English
## 140 5 Andorra Europe NE 0 0 Other I-E
## 141 7 Anguilla N America NW 0 0 English
## 142 8 Antigua-Barbuda N America NW 0 0 English
## 143 13 Bahamas N America NW 19 0 English
## 144 14 Bahrain Asia NE 1 0 Arabic
## 145 16 Barbados N America NW 0 0 English
## 146 18 Belize N America NW 23 0 English
## 147 20 Bermuda N America NW 0 0 English
## 148 25 British-Virgin-Isles N America NW 0 0 English
## 149 26 Brunei Asia NE 6 0 Other
## 150 33 Cape-Verde-Islands Africa NW 4 0 Other I-E
## 151 34 Cayman-Islands N America NW 0 0 English
## 152 40 Comorro-Islands Africa SE 2 0 French
## 153 42 Cook-Islands Oceania SW 0 0 English
## 154 48 Djibouti Africa NE 22 0 French
## 155 49 Dominica N America NW 0 0 English
## 156 54 Equatorial-Guinea Africa NE 28 0 Other
## 157 56 Faeroes Europe NW 1 0 Other I-E
## 158 57 Falklands-Malvinas S America SW 12 0 English
## 159 61 French-Guiana S America NW 91 0 French
## 160 62 French-Polynesia Oceania SW 4 0 French
## 161 68 Gibraltar Europe NW 0 0 English
## 162 70 Greenland N America NW 2176 0 Other I-E
## 163 71 Grenada N America NW 0 0 English
## 164 72 Guam Oceania NE 0 0 English
## 165 81 Iceland Europe NW 103 0 Other I-E
## 166 95 Kiribati Oceania NE 0 0 English
## 167 102 Liechtenstein Europe NE 0 0 German
## 168 103 Luxembourg Europe NE 3 0 German
## 169 107 Maldive-Islands Asia NE 0 0 Other
## 170 109 Malta Europe NE 0 0 Other
## 171 110 Marianas Oceania NE 0 0 Other
## 172 114 Micronesia Oceania NE 1 0 Other
## 173 115 Monaco Europe NE 0 0 French
## 174 117 Montserrat N America NW 0 0 English
## 175 120 Nauru Oceania SE 0 0 Other
## 176 123 Netherlands-Antilles N America NW 0 0 Other I-E
## 177 128 Niue Oceania SW 0 0 English
## 178 142 Qatar Asia NE 11 0 Arabic
## 179 145 San-Marino Europe NE 0 0 Other I-E
## 180 146 Sao-Tome Africa NE 0 0 Other I-E
## 181 149 Seychelles Africa SE 0 0 English
## 182 152 Soloman-Islands Oceania SE 30 0 English
## 183 159 St-Helena Africa SW 0 0 English
## 184 160 St-Kitts-Nevis N America NW 0 0 English
## 185 161 St-Lucia N America NW 0 0 English
## 186 162 St-Vincent N America NW 0 0 English
## 187 164 Surinam S America NW 63 0 Other I-E
## 188 173 Tonga Oceania SE 1 0 Other
## 189 177 Turks-Cocos-Islands N America NW 0 0 English
## 190 178 Tuvalu Oceania SE 0 0 English
## 191 183 US-Virgin-Isles N America NW 0 0 English
## 192 186 Vanuatu Oceania SE 15 0 Other I-E
## 193 187 Vatican-City Europe NE 0 0 Other I-E
## 194 190 Western-Samoa Oceania SW 3 0 English
## religion bars stripes
## 1 Marxist 0 0
## 2 Hindu 0 3
## 3 Marxist 0 0
## 4 Other Christian 0 13
## 5 Muslim 0 2
## 6 Catholic 0 0
## 7 Other 0 0
## 8 Muslim 0 0
## 9 Muslim 1 0
## 10 Catholic 3 0
## 11 Other Christian 0 3
## 12 Marxist 0 0
## 13 Catholic 3 0
## 14 Muslim 3 0
## 15 Other Christian 0 0
## 16 Catholic 3 0
## 17 Buddhist 0 5
## 18 Catholic 0 0
## 19 Muslim 0 3
## 20 Muslim 0 0
## 21 Muslim 0 3
## 22 Other 0 0
## 23 Catholic 0 3
## 24 Marxist 0 2
## 25 Buddhist 0 0
## 26 Other Christian 0 3
## 27 Other Christian 0 3
## 28 Catholic 0 3
## 29 Catholic 0 3
## 30 Catholic 0 3
## 31 Ethnic 0 0
## 32 Other Christian 2 0
## 33 Marxist 3 0
## 34 Marxist 0 3
## 35 Muslim 2 0
## 36 Muslim 0 0
## 37 Muslim 0 3
## 38 Marxist 0 5
## 39 Buddhist 0 0
## 40 Ethnic 0 0
## 41 Marxist 0 3
## 42 Ethnic 0 5
## 43 Muslim 0 3
## 44 Hindu 0 0
## 45 Other Christian 0 0
## 46 Marxist 0 0
## 47 Buddhist 2 0
## 48 Catholic 0 3
## 49 Ethnic 0 3
## 50 Muslim 0 3
## 51 Other Christian 0 3
## 52 Catholic 3 0
## 53 Muslim 0 14
## 54 Ethnic 0 6
## 55 Ethnic 0 5
## 56 Catholic 0 2
## 57 Marxist 0 3
## 58 Catholic 3 0
## 59 Marxist 0 5
## 60 Other Christian 0 9
## 61 Catholic 0 0
## 62 Muslim 0 3
## 63 Marxist 0 3
## 64 Other Christian 1 2
## 65 Muslim 0 3
## 66 Muslim 0 0
## 67 Catholic 0 3
## 68 Other Christian 3 0
## 69 Catholic 0 3
## 70 Catholic 3 0
## 71 Other Christian 0 0
## 72 Ethnic 0 7
## 73 Ethnic 0 2
## 74 Ethnic 0 2
## 75 Ethnic 3 0
## 76 Muslim 3 0
## 77 Muslim 0 0
## 78 Catholic 0 3
## 79 Catholic 0 0
## 80 Muslim 3 0
## 81 Catholic 2 0
## 82 Buddhist 0 0
## 83 Ethnic 0 3
## 84 Muslim 3 0
## 85 Other Christian 0 0
## 86 Ethnic 3 0
## 87 Other Christian 0 0
## 88 Catholic 0 3
## 89 Other Christian 0 0
## 90 Buddhist 0 0
## 91 Muslim 0 3
## 92 Ethnic 3 0
## 93 Muslim 0 0
## 94 Ethnic 0 0
## 95 Ethnic 3 0
## 96 Catholic 0 3
## 97 Other 0 2
## 98 Other Christian 0 0
## 99 Marxist 0 0
## 100 Ethnic 0 0
## 101 Catholic 3 0
## 102 Marxist 0 3
## 103 Muslim 0 2
## 104 Muslim 0 0
## 105 Catholic 0 3
## 106 Ethnic 0 0
## 107 Catholic 0 3
## 108 Catholic 0 5
## 109 Ethnic 0 3
## 110 Buddhist 0 2
## 111 Catholic 0 9
## 112 Ethnic 1 0
## 113 Ethnic 0 0
## 114 Catholic 0 5
## 115 Other Christian 0 0
## 116 Muslim 0 3
## 117 Muslim 0 3
## 118 Muslim 0 0
## 119 Marxist 3 0
## 120 Other Christian 0 0
## 121 Catholic 0 0
## 122 Muslim 0 3
## 123 Other 0 5
## 124 Buddhist 0 0
## 125 Ethnic 0 5
## 126 Other Christian 0 0
## 127 Other Christian 0 0
## 128 Ethnic 0 3
## 129 Ethnic 0 5
## 130 Ethnic 1 2
## 131 Hindu 0 0
## 132 Ethnic 2 0
## 133 Ethnic 0 11
## 134 Hindu 0 4
## 135 Muslim 0 2
## 136 Other Christian 0 5
## 137 Other Christian 0 0
## 138 Muslim 1 3
## 139 Other Christian 0 0
## 140 Catholic 3 0
## 141 Other Christian 0 1
## 142 Other Christian 0 1
## 143 Other Christian 0 3
## 144 Muslim 0 0
## 145 Other Christian 3 0
## 146 Other Christian 0 2
## 147 Other Christian 0 0
## 148 Other Christian 0 0
## 149 Muslim 0 0
## 150 Catholic 1 2
## 151 Other Christian 0 0
## 152 Muslim 0 0
## 153 Other Christian 0 0
## 154 Muslim 0 0
## 155 Other Christian 0 0
## 156 Ethnic 0 3
## 157 Other Christian 0 0
## 158 Other Christian 0 0
## 159 Catholic 3 0
## 160 Catholic 0 3
## 161 Other Christian 0 1
## 162 Other Christian 0 0
## 163 Other Christian 0 0
## 164 Other Christian 0 0
## 165 Other Christian 0 0
## 166 Other Christian 0 0
## 167 Catholic 0 2
## 168 Catholic 0 3
## 169 Muslim 0 0
## 170 Catholic 2 0
## 171 Other Christian 0 0
## 172 Other Christian 0 0
## 173 Catholic 0 2
## 174 Other Christian 0 0
## 175 Other Christian 0 3
## 176 Other Christian 0 1
## 177 Other Christian 0 0
## 178 Muslim 0 0
## 179 Catholic 0 2
## 180 Catholic 0 3
## 181 Other Christian 0 0
## 182 Other Christian 0 0
## 183 Other Christian 0 0
## 184 Other Christian 0 0
## 185 Other Christian 0 0
## 186 Other Christian 5 0
## 187 Other Christian 0 5
## 188 Other Christian 0 0
## 189 Other Christian 0 0
## 190 Other Christian 0 0
## 191 Other Christian 0 0
## 192 Other Christian 0 0
## 193 Catholic 2 0
## 194 Other Christian 0 0Let’s see if we can find any patterns in the data.
- Group the flags by landmass and find the following for each group:
- the mode mainhue
- the median number of sun or star symbols
- the number of flags with animate images
- the percentage of flags with animate images
Your output should be a data frame with each row corresponding to a group. There will be five columns.
Repeat this process except group by zone, language, and religion.
#This code suggested by Professor Iapalucci to ensure the landmass column is factor. It worked.
flag_df <- mutate_at(flag_df, vars(landmass), as.factor)
str(flag_df)## 'data.frame': 194 obs. of 31 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ name : chr "Afghanistan" "Albania" "Algeria" "American-Samoa" ...
## $ landmass : Factor w/ 6 levels "N America","S America",..: 5 3 4 6 3 4 1 1 2 2 ...
## $ zone : Factor w/ 4 levels "NE","NW","SE",..: 1 1 1 4 1 3 2 2 4 4 ...
## $ area : int 648 29 2388 0 0 1247 0 0 2777 2777 ...
## $ population: int 16 3 20 0 0 7 0 0 28 28 ...
## $ language : Factor w/ 10 levels "English","Spanish",..: 10 6 8 1 6 10 1 1 2 2 ...
## $ religion : Factor w/ 8 levels "Catholic","Other Christian",..: 3 7 3 2 1 6 2 2 1 1 ...
## $ bars : int 0 0 2 0 3 0 0 0 0 0 ...
## $ stripes : int 3 0 0 0 0 2 1 1 3 3 ...
## $ colours : int 5 3 3 5 3 3 3 5 2 3 ...
## $ red : int 1 1 1 1 1 1 0 1 0 0 ...
## $ green : int 1 0 1 0 0 0 0 0 0 0 ...
## $ blue : int 0 0 0 1 1 0 1 1 1 1 ...
## $ gold : int 1 1 0 1 1 1 0 1 0 1 ...
## $ white : int 1 0 1 1 0 0 1 1 1 1 ...
## $ black : int 1 1 0 0 0 1 0 1 0 0 ...
## $ orange : int 0 0 0 1 0 0 1 0 0 0 ...
## $ mainhue : chr "green" "red" "green" "blue" ...
## $ circles : int 0 0 0 0 0 0 0 0 0 0 ...
## $ crosses : int 0 0 0 0 0 0 0 0 0 0 ...
## $ saltires : int 0 0 0 0 0 0 0 0 0 0 ...
## $ quarters : int 0 0 0 0 0 0 0 0 0 0 ...
## $ sunstars : int 1 1 1 0 0 1 0 1 0 1 ...
## $ crescent : int 0 0 1 0 0 0 0 0 0 0 ...
## $ triangle : int 0 0 0 1 0 0 0 1 0 0 ...
## $ icon : int 1 0 0 1 0 1 0 0 0 0 ...
## $ animate : int 0 1 0 1 0 0 1 0 0 0 ...
## $ text : int 0 0 0 0 0 0 0 0 0 0 ...
## $ topleft : chr "black" "red" "green" "blue" ...
## $ botright : chr "green" "red" "white" "red" ...# fill in your code here
#This chunk calculates the median # of flags w/ sunstars for each of the 6 land masses in the landmass column.Asia has a median of 1; Oceania has a median of 2.5. The other continents have medians of zero.
flag_df_landmass <- group_by(flag_df, landmass)
flag_df_landmass %>%
summarise(median = median(sunstars)) ## # A tibble: 6 × 2
## landmass median
## <fct> <dbl>
## 1 N America 0
## 2 S America 0
## 3 Europe 0
## 4 Africa 0
## 5 Asia 1
## 6 Oceania 2.5#Use this chunk to create a separate data file for landmass North America. Use this to calculate the mode & the # of
# flags w/ Animate for the North American landmass. I can't figure out the code to make it work w/ the group_by file.
### In the North America group, there are 13 sunstars (41.9% of the total in North America); blue is the mode.
lm_NA <- filter(flag_df, landmass == 'N America')
which.max(table(lm_NA$mainhue)) %>% names()## [1] "blue"table(lm_NA$animate)##
## 0 1
## 18 1313/31## [1] 0.4193548#This code was run to check the calculation of the mode. The count of Blue in this table matched the calculated
#mode in a prior step.
table(lm_NA$mainhue)##
## black blue gold green red white
## 1 15 1 5 4 5#Use this chunk to create a separate data file for landmass South America. Use this to calculate the mode & the # of
# flags w/ Animate for the South American landmass.
#The mode color for flags in the South American landmass is red. 3 of the 17 (17.6%) flags in S. America have Animate.
lm_SA <- filter(flag_df, landmass == 'S America')
which.max(table(lm_SA$mainhue)) %>% names()## [1] "red"table(lm_SA$animate)##
## 0 1
## 14 33/17## [1] 0.1764706#Use this chunk to create a separate data file for landmass Europe. Use this to calculate the mode & the # of
# flags w/ Animate for the European landmass.
#The mode color for flags in the European landmass is red. 4 of the 35 (11.4%) flags in Europe have Animate.
lm_E <- filter(flag_df, landmass == 'Europe')
which.max(table(lm_E$mainhue)) %>% names()## [1] "red"table(lm_E$animate)##
## 0 1
## 31 44/35## [1] 0.1142857#Use this chunk to create a separate data file for landmass Africa. Use this to calculate the mode & the # of
# flags w/ Animate for the African landmass.
#The mode color for flags in the African landmass is green. 7 of the 52 (13.5%) flags in Africa have Animate.
lm_AF <- filter(flag_df, landmass == 'Africa')
which.max(table(lm_AF$mainhue)) %>% names()## [1] "green"table(lm_AF$animate)##
## 0 1
## 45 77/52## [1] 0.1346154#Use this chunk to create a separate data file for landmass Asia. Use this to calculate the mode & the # of
# flags w/ Animate for the Asian landmass.
#The mode color for flags in the Asian landmass is red. 6 of the 39 (15.3%) flags in Asia have Animate.
lm_AS <- filter(flag_df, landmass == 'Asia')
which.max(table(lm_AS$mainhue)) %>% names()## [1] "red"table(lm_AS$animate)##
## 0 1
## 33 66/39## [1] 0.1538462#Use this chunk to create a separate data file for landmass Oceania. Use this to calculate the mode & the # of
# flags w/ Animate for the Oceanian landmass.
#The mode color for flags in the Ocenian landmass is blue. 6 of the 20 (30%) flags in Oceania have Animate.
lm_OC <- filter(flag_df, landmass == 'Oceania')
which.max(table(lm_OC$mainhue)) %>% names()## [1] "blue"table(lm_OC$animate)##
## 0 1
## 14 66/20## [1] 0.3# fill in your code here
#This chunk calculates the median # of flags w/ sunstars for each of the 4 zones in the zone column.SW has a median of 1. The other zones have medians of zero.
flag_df_zone <- group_by(flag_df, zone)
flag_df_zone %>%
summarise(median = median(sunstars)) ## # A tibble: 4 × 2
## zone median
## <fct> <dbl>
## 1 NE 0
## 2 NW 0
## 3 SE 0
## 4 SW 1#Use this chunk to create a separate data file for Zone NE. Use this to calculate the mode & the # of
# flags w/ Animate for the NE zone. In the NE zone, there are 14 flags with Animate (15.4%) of the total in the NE Zone; red is the mode.
zone_NE <- filter(flag_df, zone == 'NE')
which.max(table(zone_NE$mainhue)) %>% names()## [1] "red"table(zone_NE$animate)##
## 0 1
## 77 1414/91## [1] 0.1538462#Use this chunk to create a separate data file for Zone SE. Use this to calculate the mode & the # of
# flags w/ Animate for the SE zone. In the SE zone, there are 7 flags with Animate (24.1%) of the total in the SE Zone; red is the mode.
zone_SE <- filter(flag_df, zone == 'SE')
which.max(table(zone_SE$mainhue)) %>% names()## [1] "red"table(zone_SE$animate)##
## 0 1
## 22 77/29## [1] 0.2413793#Use this chunk to create a separate data file for Zone SW. Use this to calculate the mode & the # of
# flags w/ Animate for the SW zone. In the SW zone, there are 3 flags with Animate (18.75%) of the total in the SW Zone; blue is the mode.
zone_SW <- filter(flag_df, zone == 'SW')
which.max(table(zone_SW$mainhue)) %>% names()## [1] "blue"table(zone_SW$animate)##
## 0 1
## 13 33/16## [1] 0.1875#Use this chunk to create a separate data file for Zone NW. Use this to calculate the mode & the # of
# flags w/ Animate for the NW zone. In the NW zone, there are 3 flags with Animate (25.9%) of the total in the NW Zone; blue is the mode.
zone_NW <- filter(flag_df, zone == 'NW')
which.max(table(zone_NW$mainhue)) %>% names()## [1] "blue"table(zone_NW$animate)##
## 0 1
## 43 1515/58## [1] 0.2586207# fill in your code here
#This chunk calculates the median # of flags w/ sunstars for each of the 10 language groups in the language column. Chinese has a median of 3. Slavic and the Japanese/Turkish/Finnish/Magyar group have medians of 0.5. The other languages have medians of zero.
flag_df_lang <- group_by(flag_df, language)
flag_df_lang %>%
summarise(median = median(sunstars)) ## # A tibble: 10 × 2
## language median
## <fct> <dbl>
## 1 English 0
## 2 Spanish 0
## 3 French 0
## 4 German 0
## 5 Slavic 0.5
## 6 Other I-E 0
## 7 Chinese 3
## 8 Arabic 0
## 9 J-T-F-M 0.5
## 10 Other 0#Use this chunk to create a separate data file for the English language. Use this to calculate the mode & the # of
# flags w/ Animate for the English language countries. In the English language countries, there are 18 flags with Animate (41.9%) of the total in the English language countries; blue is the mode.
lang_Eng <- filter(flag_df, language == 'English')
which.max(table(lang_Eng$mainhue)) %>% names()## [1] "blue"table(lang_Eng$animate)##
## 0 1
## 25 1818/43## [1] 0.4186047#Use this chunk to create a separate data file for the Spansh language. Use this to calculate the mode & the # of
# flags w/ Animate for the Spanish language countries. In the Spanish language countries, there are 3 flags with Animate (14.2%) of the total in the Spanish language countries; blue is the mode.
lang_Spa <- filter(flag_df, language == 'Spanish')
which.max(table(lang_Spa$mainhue)) %>% names()## [1] "blue"table(lang_Spa$animate)##
## 0 1
## 18 33/21## [1] 0.1428571#Use this chunk to create a separate data file for the French language. Use this to calculate the mode & the # of
# flags w/ Animate for the Francophone countries. In the Francophone countries, there are no flags with Animate; gold is the mode.
lang_FRA <- filter(flag_df, language == 'French')
which.max(table(lang_FRA$mainhue)) %>% names()## [1] "gold"table(lang_FRA$animate)##
## 0
## 17#Use this chunk to create a separate data file for the German language. Use this to calculate the mode & the # of
# flags w/ Animate for the German language countries. In the German language countries, there are no flags with Animate; red is the mode.
lang_GER <- filter(flag_df, language == 'German')
which.max(table(lang_GER$mainhue)) %>% names()## [1] "red"table(lang_GER$animate)##
## 0
## 6#Use this chunk to create a separate data file for the Slavic language countries. Use this to calculate the mode & the # of flags w/ Animate for the Slavic language countries. In the Slavic language countries, there is 1 flag with Animate (25%) of the total in the Slavic language countries; red is the mode.
lang_SLA <- filter(flag_df, language == 'Slavic')
which.max(table(lang_SLA$mainhue)) %>% names()## [1] "red"table(lang_SLA$animate)##
## 0 1
## 3 11/4## [1] 0.25#Use this chunk to create a separate data file for the Other Indo-European language countries. Use this to calculate the mode & the # of flags w/ Animate for the other Indo-European language countries. In the Other Indo-European language countries, there are 5 flags with Animate (16.67%) of the total in the Other Indo-European language countries; red is the mode.
lang_OIE <- filter(flag_df, language == 'Other I-E')
which.max(table(lang_OIE$mainhue)) %>% names()## [1] "red"table(lang_OIE$animate)##
## 0 1
## 25 55/30## [1] 0.1666667#Use this chunk to create a separate data file for the Chinese language countries. Use this to calculate the mode & the # of flags w/ Animate for the Chinese language countries. In the Chinese language countries, there is 1 flag with Animate (25%) of the total in the Chinese language countries; red is the mode.
lang_CHI <- filter(flag_df, language == 'Chinese')
which.max(table(lang_CHI$mainhue)) %>% names()## [1] "red"table(lang_CHI$animate)##
## 0 1
## 3 11/4## [1] 0.25#Use this chunk to create a separate data file for the Arabic language countries. Use this to calculate the mode & the # of flags w/ Animate for the Arabic language countries. In the Arabic language countries, there are 2 flags with Animate (10.5%) of the total in the Arabic language countries; red is the mode.
lang_ARA <- filter(flag_df, language == 'Arabic')
which.max(table(lang_ARA$mainhue)) %>% names()## [1] "red"table(lang_ARA$animate)##
## 0 1
## 17 22/19## [1] 0.1052632#Use this chunk to create a separate data file for the Japanese/Turkish/Finnish/Magyar (J/T/F/M) language countries. Use this to calculate the mode & the # of flags w/ Animate for the J/T/F/M language countries. In the J/T/F/M language countries, there are zero flags with Animate; red is the mode.
lang_JTFM <- filter(flag_df, language == 'J-T-F-M')
which.max(table(lang_JTFM$mainhue)) %>% names()## [1] "red"table(lang_JTFM$animate)##
## 0
## 4#Use this chunk to create a separate data file for the Other language countries. Use this to calculate the mode & the # of flags w/ Animate for the Other language countries. In the Other language countries, there are 9 out of 46 (19.6%) flags with Animate; red is the mode.
lang_OTH <- filter(flag_df, language == 'Other')
which.max(table(lang_OTH$mainhue)) %>% names()## [1] "red"table(lang_OTH$animate)##
## 0 1
## 37 99/46## [1] 0.1956522# fill in your code here
#This chunk calculates the median # of flags w/ sunstars for each of the 7 religions in the religion column.Marxism and Other have medians of 1; the other continents have medians of zero.
flag_df_religion <- group_by(flag_df, religion)
flag_df_religion %>%
summarise(median = median(sunstars)) ## # A tibble: 8 × 2
## religion median
## <fct> <dbl>
## 1 Catholic 0
## 2 Other Christian 0
## 3 Muslim 0
## 4 Buddhist 0
## 5 Hindu 0
## 6 Ethnic 0
## 7 Marxist 1
## 8 Other 1#Use this chunk to create a separate data file for religion - Catholic. Use this to calculate the mode & the # of
# flags w/ Animate for the Catholic countries. I can't figure out the code to make it work w/ the group_by file. In the Catholic group, there are 4 of 40 countries (10%) with Animate; red is the mode.
rel_RC <- filter(flag_df, religion == 'Catholic')
which.max(table(rel_RC$mainhue)) %>% names()## [1] "red"table(rel_RC$animate)##
## 0 1
## 36 44/40## [1] 0.1#Use this chunk to create a separate data file for religion - Other Christian. Use this to calculate the mode & the # of # flags w/ Animate for the Other Christian countries. In the Other Christian group of countries, there are 19 of 60 countries (31.67%) with Animate; blue is the mode.
rel_OC <- filter(flag_df, religion == 'Other Christian')
which.max(table(rel_OC$mainhue)) %>% names()## [1] "blue"table(rel_OC$animate)##
## 0 1
## 41 1919/60## [1] 0.3166667#Use this chunk to create a separate data file for religion - Muslim. Use this to calculate the mode & the # of # flags w/ Animate for the Muslim countries. In the Muslim group of countries, there are 3 of36 countries (8.33%) with Animate; red is the mode.
rel_MU <- filter(flag_df, religion == 'Muslim')
which.max(table(rel_MU$mainhue)) %>% names()## [1] "red"table(rel_MU$animate)##
## 0 1
## 33 33/36## [1] 0.08333333#Use this chunk to create a separate data file for religion - Buddhist. Use this to calculate the mode & the # of # flags w/ Animate for the Buddhist countries. In the Buddhist group of countries, there are 4 of8 countries (50%) with Animate; red is the mode.
rel_BU <- filter(flag_df, religion == 'Buddhist')
which.max(table(rel_BU$mainhue)) %>% names()## [1] "red"table(rel_BU$animate)##
## 0 1
## 4 44/8## [1] 0.5#Use this chunk to create a separate data file for religion - Hindu. Use this to calculate the mode & the # of # flags w/ Animate for the Hindu countries. In the Hindu group of countries, there no countries with Animate; brown is the mode.
rel_HI <- filter(flag_df, religion == 'Hindu')
which.max(table(rel_HI$mainhue)) %>% names()## [1] "brown"table(rel_HI$animate)##
## 0
## 4#Use this chunk to create a separate data file for religion - Ethnic. Use this to calculate the mode & the # of # flags w/ Animate for the Ethnic religion countries. In the Ethnic religion group of countries, there are 6 countries (22.2%) with Animate; red is the mode.
rel_ET <- filter(flag_df, religion == 'Ethnic')
which.max(table(rel_ET$mainhue)) %>% names()## [1] "red"table(rel_ET$animate)##
## 0 1
## 21 66/27## [1] 0.2222222#Use this chunk to create a separate data file for religion - Marxist. Use this to calculate the mode & the # of # flags w/ Animate for the Marxist countries. In the Marxist countries, there are 3 countries (20%) with Animate; red is the mode.
rel_MA <- filter(flag_df, religion == 'Marxist')
which.max(table(rel_MA$mainhue)) %>% names()## [1] "red"table(rel_MA$animate)##
## 0 1
## 12 33/15## [1] 0.2#Use this chunk to create a separate data file for religion - Other. Use this to calculate the mode & the # of # flags w/ Animate for the Other religion countries. In the Other religion countries, there are zero countries with Animate; white is the mode.
rel_OTH <- filter(flag_df, religion == 'Other')
which.max(table(rel_OTH$mainhue)) %>% names()## [1] "white"table(rel_OTH$animate)##
## 0
## 4Do you see any patterns in flag mainhue, sun or star symbols, and animate images? If so, describe these patterns. (Hint: you should see patterns! Look at the trends when grouping by landmass, zone, language, and religion.) Write a paragraph to answer this question.
FILL IN YOUR ANSWER HERE
### The patterns revealed by the data are interesting, and in some cases what one would expect, but in other cases, they are not. Of the 28 data categories (6 landmasses, 4 zones, 10 languages & 8 religions), only 8 had median values > zero for the suns & stars categories. The highest mode was 3 (Chinese language), with the Oceania landmass 2nd at 2.5. No other median was > 1, and one was 0.5.
### Red was the mode of color. Of the 28 categories, red predominated in 17, with blue a distant 2nd at 7. Gold, brown, green, and white each had one. The colors largely were consistent among the various groups; for example, North America's predominate color was blue; blue predominated among English language speakers. The Muslim religious group's predominate color was red as was the Arabic language group's. Christians were divided as to the color of their flags; Catholic countries preferred red flags, but the Other Christian countries had mostly blue flags. The most interesting anomolie in this regard was the Spanish language group. Its predominate color was blue, but the Catholic's predominate color was red. This strikes me as being an aberration.
### Finally, flags w/ animate objects were concentrated in North America (41.9%) and in English speaking countries (again, 41.9%). The only group w/ a higher percentage of animate objects was the Buddhists, at 50% (4 of 8).
### All in all, an interesting exercise.