ALEXIS SUIRA-TOPUNIVERSIDADES MUNDIAL- EJEMPLO 01 DEL VIDEO 03
Alexis Suira
2024-12-21
library(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2) # Cargamsos el dataset desde un archivo
url <- "https://raw.githubusercontent.com/Suiralexis/QM206-2024S/refs/heads/main/topuniversities.csv"
universities_data <- read.csv(url, stringsAsFactors = FALSE)
# Mostrar las primeras filas del dataframe cargado
universities_data## Rank Ordinal.Rank
## 1 1 1
## 2 2 2
## 3 3 3
## 4 4 4
## 5 5 5
## 6 6 6
## 7 7 7
## 8 8 8
## 9 9 9
## 10 10 10
## 11 11 11
## 12 12 12
## 13 13 13
## 14 14 14
## 15 15 15
## 16 16 16
## 17 17 17
## 18 18 18
## 19 19 19
## 20 20 20
## 21 21 21
## 22 22 22
## 23 23 23
## 24 24 24
## 25 25 25
## 26 26 26
## 27 27 26
## 28 28 28
## 29 29 29
## 30 30 30
## 31 31 31
## 32 32 32
## 33 33 32
## 34 34 34
## 35 35 35
## 36 36 36
## 37 37 37
## 38 38 38
## 39 39 39
## 40 40 40
## 41 41 41
## 42 42 42
## 43 43 43
## 44 44 44
## 45 45 45
## 46 46 46
## 47 47 47
## 48 48 48
## 49 49 48
## 50 50 48
## 51 51 51
## 52 52 52
## 53 53 53
## 54 54 54
## 55 55 55
## 56 56 56
## 57 57 57
## 58 58 58
## 59 59 59
## 60 60 60
## 61 61 61
## 62 62 62
## 63 63 63
## 64 64 64
## 65 65 65
## 66 66 66
## 67 67 67
## 68 68 67
## 69 69 67
## 70 70 70
## 71 71 71
## 72 72 72
## 73 73 73
## 74 74 74
## 75 75 75
## 76 76 76
## 77 77 76
## 78 78 78
## 79 79 79
## 80 80 79
## 81 81 81
## 82 82 81
## 83 83 83
## 84 84 84
## 85 85 84
## 86 86 86
## 87 87 87
## 88 88 88
## 89 89 89
## 90 90 90
## 91 91 91
## 92 92 92
## 93 93 92
## 94 94 94
## 95 95 95
## 96 96 96
## 97 97 97
## 98 98 98
## 99 99 99
## 100 100 100
## 101 101 101
## 102 102 101
## 103 103 103
## 104 104 104
## 105 105 105
## 106 106 106
## 107 107 107
## 108 108 108
## 109 109 109
## 110 110 110
## 111 111 111
## 112 112 112
## 113 113 112
## 114 114 114
## 115 115 115
## 116 116 116
## 117 117 117
## 118 118 117
## 119 119 119
## 120 120 119
## 121 121 121
## 122 122 122
## 123 123 123
## 124 124 124
## 125 125 125
## 126 126 126
## 127 127 127
## 128 128 128
## 129 129 128
## 130 130 130
## 131 131 130
## 132 132 132
## 133 133 133
## 134 134 133
## 135 135 133
## 136 136 133
## 137 137 137
## 138 138 137
## 139 139 139
## 140 140 139
## 141 141 141
## 142 142 142
## 143 143 143
## 144 144 144
## 145 145 144
## 146 146 146
## 147 147 147
## 148 148 148
## 149 149 149
## 150 150 150
## 151 151 151
## 152 152 152
## 153 153 152
## 154 154 154
## 155 155 154
## 156 156 154
## 157 157 154
## 158 158 154
## 159 159 159
## 160 160 160
## 161 161 160
## 162 162 162
## 163 163 163
## 164 164 164
## 165 165 164
## 166 166 166
## 167 167 167
## 168 168 168
## 169 169 169
## 170 170 169
## 171 171 171
## 172 172 172
## 173 173 173
## 174 174 174
## 175 175 174
## 176 176 174
## 177 177 174
## 178 178 178
## 179 179 179
## 180 180 180
## 181 181 181
## 182 182 182
## 183 183 183
## 184 184 184
## 185 185 185
## 186 186 186
## 187 187 187
## 188 188 188
## 189 189 189
## 190 190 190
## 191 191 191
## 192 192 192
## 193 193 192
## 194 194 194
## 195 195 195
## 196 196 195
## 197 197 195
## 198 198 198
## 199 199 198
## 200 200 200
## 201 201 201
## 202 202 201
## 203 203 203
## 204 204 204
## 205 205 205
## 206 206 206
## 207 207 207
## 208 208 208
## 209 209 208
## 210 210 210
## 211 211 211
## 212 212 212
## 213 213 213
## 214 214 213
## 215 215 215
## 216 216 216
## 217 217 217
## 218 218 218
## 219 219 219
## 220 220 219
## 221 221 221
## 222 222 222
## 223 223 222
## 224 224 222
## 225 225 225
## 226 226 226
## 227 227 227
## 228 228 227
## 229 229 229
## 230 230 230
## 231 231 231
## 232 232 231
## 233 233 231
## 234 234 231
## 235 235 235
## 236 236 236
## 237 237 237
## 238 238 237
## 239 239 237
## 240 240 240
## 241 241 241
## 242 242 241
## 243 243 243
## 244 244 244
## 245 245 244
## 246 246 246
## 247 247 246
## 248 248 246
## 249 249 249
## 250 250 249
## 251 251 249
## 252 252 249
## 253 253 249
## 254 254 254
## 255 255 255
## 256 256 255
## 257 257 255
## 258 258 258
## 259 259 258
## 260 260 260
## 261 261 261
## 262 262 261
## 263 263 263
## 264 264 263
## 265 265 263
## 266 266 266
## 267 267 267
## 268 268 268
## 269 269 269
## 270 270 269
## 271 271 271
## 272 272 271
## 273 273 273
## 274 274 273
## 275 275 275
## 276 276 275
## 277 277 277
## 278 278 277
## 279 279 277
## 280 280 280
## 281 281 280
## 282 282 282
## 283 283 283
## 284 284 284
## 285 285 284
## 286 286 284
## 287 287 287
## 288 288 288
## 289 289 288
## 290 290 290
## 291 291 290
## 292 292 290
## 293 293 293
## 294 294 294
## 295 295 294
## 296 296 296
## 297 297 296
## 298 298 298
## 299 299 299
## 300 300 300
## University.Name
## 1 Peking University
## 2 The University of Hong Kong
## 3 National University of Singapore (NUS)
## 4 Nanyang Technological University
## 5 Fudan University
## 6 The Chinese University of Hong Kong (CUHK)
## 7 Tsinghua University
## 8 Zhejiang University
## 9 Yonsei University
## 10 City University of Hong Kong (CityUHK)
## 11 The Hong Kong University of Science and Technology
## 12 Universiti Malaya (UM)
## 13 Korea University
## 14 Shanghai Jiao Tong University
## 15 KAIST - Korea Advanced Institute of Science & Technology
## 16 Sungkyunkwan University (SKKU)
## 17 The Hong Kong Polytechnic University
## 18 Seoul National University
## 19 Hanyang University
## 20 Universiti Putra Malaysia (UPM)
## 21 The University of Tokyo
## 22 Pohang University of Science And Technology (POSTECH)
## 23 Kyoto University
## 24 Nanjing University
## 25 Tohoku University
## 26 National Taiwan University (NTU)
## 27 Universiti Kebangsaan Malaysia (UKM)
## 28 Universiti Teknologi Malaysia
## 29 Al-Farabi Kazakh National University
## 30 Tokyo Institute of Technology (Tokyo Tech)
## 31 University of Science and Technology of China
## 32 Nagoya University
## 33 Osaka University
## 34 Kyushu University
## 35 Hokkaido University
## 36 Taylor's University
## 37 Universiti Sains Malaysia (USM)
## 38 Wuhan University
## 39 National Tsing Hua University - NTHU
## 40 Kyung Hee University
## 41 National Cheng Kung University (NCKU)
## 42 National Yang Ming Chiao Tung University (NYCU)
## 43 Tongji University
## 44 Indian Institute of Technology Delhi (IITD)
## 45 UCSI University
## 46 Universitas Indonesia
## 47 Chulalongkorn University
## 48 Indian Institute of Technology Bombay (IITB)
## 49 Keio University
## 50 Sun Yat-sen University
## 51 Waseda University
## 52 Airlangga University
## 53 Gadjah Mada University
## 54 Universiti Teknologi PETRONAS (UTP)
## 55 Mahidol University
## 56 Indian Institute of Technology Madras (IITM)
## 57 Tianjin University
## 58 Harbin Institute of Technology
## 59 Bandung Institute of Technology (ITB)
## 60 Indian Institute of Technology Kharagpur (IIT-KGP)
## 61 Beijing Normal University
## 62 Indian Institute of Science
## 63 University of Tsukuba
## 64 Beijing Institute of Technology
## 65 L.N. Gumilyov Eurasian National University (ENU)
## 66 National Taiwan University of Science and Technology (Taiwan Tech)
## 67 Indian Institute of Technology Kanpur (IITK)
## 68 National University of Sciences & Technology (NUST) Islamabad
## 69 Huazhong University of Science and Technology
## 70 Ewha Womans University
## 71 Hong Kong Baptist University
## 72 Xi’an Jiaotong University
## 73 Chung-Ang University (CAU)
## 74 Sunway University
## 75 Shandong University
## 76 Kobe University
## 77 Xiamen University
## 78 Universiti Brunei Darussalam (UBD)
## 79 National Taiwan Normal University
## 80 Universiti Utara Malaysia (UUM)
## 81 Pusan National University
## 82 University of Delhi
## 83 National Sun Yat-sen University
## 84 Quaid-i-Azam University
## 85 Shanghai University
## 86 University of the Philippines
## 87 University of Tehran
## 88 Ulsan National Institute of Science and Technology (UNIST)
## 89 Satbayev University
## 90 Jilin University
## 91 National Taipei University of Technology
## 92 IPB University (aka Bogor Agricultural University)
## 93 Sogang University
## 94 Sichuan University
## 95 Daegu Gyeongbuk Institute of Science and Technology (DGIST)
## 96 Hiroshima University
## 97 Sharif University of Technology
## 98 Nankai University
## 99 University of Macau
## 100 Sejong University
## 101 Kyungpook National University
## 102 Universiti Teknologi MARA - UiTM
## 103 Renmin (People's) University of China
## 104 Indian Institute of Technology Guwahati (IITG)
## 105 Gwangju Institute of Science and Technology (GIST)
## 106 University of Chinese Academy of Sciences (UCAS)
## 107 Southern University of Science and Technology (SUSTech)
## 108 Chiang Mai University
## 109 Indian Institute of Technology Roorkee (IITR)
## 110 Jawaharlal Nehru University
## 111 East China Normal University
## 112 National Central University
## 113 University of Dhaka
## 114 Institut Teknologi Sepuluh Nopember (ITS Surabaya)
## 115 Lahore University of Management Sciences (LUMS)
## 116 Inha University
## 117 Dongguk University
## 118 Jeonbuk National University
## 119 Chandigarh University
## 120 National Chung Hsing University
## 121 Taipei Medical University (TMU)
## 122 National Chengchi University
## 123 Ajou University
## 124 HUFS - Hankuk (Korea) University of Foreign Studies
## 125 Beihang University (former BUAA)
## 126 Duy Tan University
## 127 Thammasat University
## 128 COMSATS University Islamabad
## 129 Management and Science University
## 130 Abai Kazakh National Pedagogical University (Abai University)
## 131 Universiti Tunku Abdul Rahman (UTAR)
## 132 International Islamic University Malaysia (IIUM)
## 133 Diponegoro University
## 134 Southeast University
## 135 Universitas Padjadjaran (UNPAD)
## 136 Universiti Teknologi Brunei
## 137 Auezov South Kazakhstan University (SKU)
## 138 University of the Punjab
## 139 Konkuk University
## 140 Singapore Management University
## 141 Krirk University
## 142 Ateneo de Manila University
## 143 Hitotsubashi University
## 144 Amirkabir University of Technology
## 145 Ritsumeikan University
## 146 Universiti Tenaga Nasional (UNITEN)
## 147 South China University of Technology
## 148 UPES
## 149 Kazakh National Agrarian Research University (KazNARU)
## 150 Vellore Institute of Technology (VIT)
## 151 Iran University of Science and Technology
## 152 China Medical University
## 153 Dalian University of Technology
## 154 Singapore University of Technology and Design
## 155 University of Science and Technology Beijing
## 156 Kasetsart University
## 157 North South University
## 158 Shenzhen University
## 159 Bangladesh University of Engineering and Technology
## 160 Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME)-National Research University
## 161 Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA)
## 162 Vietnam National University
## 163 Hunan University
## 164 De La Salle University
## 165 University of Engineering & Technology (UET) Lahore
## 166 Universiti Pendidikan Sultan Idris (UPSI)
## 167 University of Seoul
## 168 Karaganda Buketov University
## 169 Central South University
## 170 Shoolini University of Biotechnology and Management Sciences
## 171 INTI International University
## 172 Birla Institute of Technology and Science
## 173 Universitas Brawijaya
## 174 Northwestern Polytechnical University
## 175 Savitribai Phule Pune University
## 176 Chiba University
## 177 Tokyo University of Science
## 178 Anna University
## 179 Tokyo Medical and Dental University (TMDU)
## 180 University of Calcutta
## 181 Zhengzhou University
## 182 University of Santo Tomas
## 183 The University of Lahore
## 184 Amity University
## 185 Viet Nam National University Ho Chi Minh City (VNU-HCM)
## 186 Lincoln University College
## 187 Khon Kaen University
## 188 Chongqing University
## 189 Jamia Millia Islamia
## 190 Lingnan University
## 191 National Chung Cheng University
## 192 Asia Pacific University of Technology and Innovation (APU) Malaysia
## 193 China Agricultural University
## 194 Tokyo University of Agriculture and Technology
## 195 Chungnam National University
## 196 Khoja Akhmet Yassawi International Kazakh-Turkish University
## 197 Universiti Malaysia Terengganu (UMT)
## 198 Isfahan University of Technology
## 199 Manipal Academy of Higher Education
## 200 Ton Duc Thang University
## 201 Chonnam National University
## 202 Jinan University (China)
## 203 University of Agriculture
## 204 Banaras Hindu University
## 205 Bina Nusantara University (BINUS)
## 206 University of Peshawar
## 207 Prince of Songkla University
## 208 Multimedia University (MMU)
## 209 Osaka Metropolitan University
## 210 Shiraz University
## 211 Kanazawa University
## 212 Jadavpur University
## 213 Sophia University
## 214 The Education University of Hong Kong
## 215 Lanzhou University
## 216 East China University of Science and Technology
## 217 Symbiosis International (Deemed University)
## 218 University of Electronic Science and Technology of China
## 219 Ferdowsi University of Mashhad
## 220 Jiangsu University
## 221 Soochow University
## 222 Bharathiar University
## 223 SRM Institute of Science and Technology
## 224 Universitas Sebelas Maret
## 225 King Mongkut's University of Technology Thonburi
## 226 University of Tabriz
## 227 China University of Geosciences
## 228 Lovely Professional University (LPU)
## 229 Macau University of Science and Technology
## 230 Universiti Malaysia Sarawak (UNIMAS)
## 231 Beijing University of Technology
## 232 Kazakh-British Technical University
## 233 Kyrgyz-Turkish Manas University
## 234 Okayama University
## 235 Shahid Beheshti University (SBU)
## 236 Pakistan Institute of Engineering and Applied Sciences (PIEAS)
## 237 Nagasaki University
## 238 Nanjing University of Science and Technology
## 239 Universitas Hasanuddin
## 240 University of Hyderabad
## 241 Chang Gung University
## 242 Tokyo Metropolitan University
## 243 National University of Uzbekistan named after Mirzo Ulugbek
## 244 International Islamic University
## 245 Universiti Malaysia Sabah (UMS)
## 246 Beijing Jiaotong University
## 247 Nanjing University of Aeronautics and Astronautics
## 248 University of Mumbai
## 249 Asia University Taiwan
## 250 Nanjing Normal University
## 251 Universiti Tun Hussein Onn Malaysia (UTHM)
## 252 University of Ulsan
## 253 Wuhan University of Technology
## 254 BRAC University
## 255 Indian Institute of Technology Indore
## 256 Kumamoto University
## 257 Yokohama National University
## 258 Kalinga Institute of Industrial Technology (KIIT) University
## 259 Yeungnam University
## 260 Northwest Agriculture and Forestry University
## 261 American University of Central Asia
## 262 Osh State University
## 263 China University of Mining and Technology
## 264 Thapar Institute of Engineering & Technology
## 265 Tunghai University
## 266 Karaganda State Technical University
## 267 The Catholic University of Korea
## 268 Saveetha Institute of Medical And Technical Sciences (SIMATS)
## 269 China University of Political Science and Law
## 270 University of Karachi
## 271 NJSC KIMEP University
## 272 Panjab University
## 273 O.P. Jindal Global University (JGU)
## 274 Yangzhou University
## 275 Indian Institute of Technology BHU Varanasi (IIT BHU Varanasi)
## 276 Sookmyung Women's University
## 277 Aga Khan University
## 278 Jiangnan University
## 279 University of Colombo
## 280 Aligarh Muslim University
## 281 Siksha ‘O’ Anusandhan (Deemed to be University)
## 282 Daffodil International University
## 283 Saken Seifullin Kazakh Agrotechnical University
## 284 Beijing University of Chemical Technology
## 285 Qingdao University
## 286 Telkom University
## 287 Alagappa Univeristy
## 288 Donghua University
## 289 Guru Nanak Dev University
## 290 D. Serikbayev East Kazakhstan Technical University
## 291 Soonchunhyang University
## 292 University of Isfahan
## 293 Institute of Business Administration (IBA)
## 294 Amrita Vishwa Vidyapeetham
## 295 Jahangirnagar University
## 296 National Chinyi University of Technology
## 297 Sungshin Women's University
## 298 Northwest University (China)
## 299 Ocean University of China
## 300 Huazhong Agricultural University
## Overall.Score City Country Citations.per.Paper
## 1 100.0 Beijing China 96.4
## 2 99.7 Pokfulam Hong Kong 99.5
## 3 98.9 Singapore Singapore 99.9
## 4 98.3 Singapore Singapore 100.0
## 5 97.2 Shanghai China 92.1
## 6 96.7 Sha Tin Hong Kong 99.6
## 7 96.3 Beijing China 98.6
## 8 96.0 Hangzhou China 86.0
## 9 95.4 Seoul South Korea 78.2
## 10 95.3 Kowloon Hong Kong 99.8
## 11 95.1 Clear Water Bay Hong Kong 99.9
## 12 94.8 Kuala Lumpur Malaysia 67.5
## 13 94.5 Seoul South Korea 89.4
## 14 93.2 Shanghai China 76.7
## 15 92.4 Daejeon South Korea 89.1
## 16 92.1 Suwon South Korea 80.5
## 17 92.0 Kowloon Hong Kong 99.9
## 18 91.8 Seoul South Korea 69.3
## 19 89.1 Seoul South Korea 80.0
## 20 89.0 Serdang Malaysia 42.7
## 21 88.6 Tokyo Japan 35.8
## 22 88.1 Pohang South Korea 92.3
## 23 88.0 Kyoto Japan 42.0
## 24 86.5 Nanjing China 97.4
## 25 86.4 Sendai City Japan 20.1
## 26 85.5 Taipei Taiwan 61.2
## 27 85.5 Bangi Malaysia 29.8
## 28 84.8 Skudai Malaysia 47.4
## 29 84.6 Almaty Kazakhstan 2.0
## 30 84.2 Tokyo Japan 19.1
## 31 84.0 Hefei China 98.0
## 32 82.6 Nagoya Japan 21.7
## 33 82.6 Osaka City Japan 20.4
## 34 82.0 Fukuoka City Japan 24.2
## 35 81.8 Sapporo Japan 27.8
## 36 81.3 Subang Jaya Malaysia 95.7
## 37 80.8 Gelugor Malaysia 27.5
## 38 80.5 Wuhan China 93.7
## 39 78.4 Hsinchu City Taiwan 44.6
## 40 77.5 Seoul South Korea 72.9
## 41 77.4 Tainan City Taiwan 37.6
## 42 76.9 Hsinchu City Taiwan 28.7
## 43 76.2 Shanghai China 79.2
## 44 75.4 New Delhi India 26.9
## 45 75.1 Kuala Lumpur Malaysia 48.4
## 46 74.7 Depok Indonesia 1.5
## 47 73.7 Bangkok Thailand 31.2
## 48 73.1 Mumbai India 16.9
## 49 73.1 Tokyo Japan 18.0
## 50 73.1 Guangzhou China 91.2
## 51 72.8 Tokyo Japan 10.7
## 52 70.5 Surabaya Indonesia 1.4
## 53 70.2 Yogyakarta Indonesia 1.5
## 54 70.2 Seri Iskandar Malaysia 49.1
## 55 70.1 Nakhon Pathom Thailand 19.8
## 56 69.6 Chennai India 16.5
## 57 69.5 Tianjin China 89.9
## 58 69.3 Harbin China 78.6
## 59 69.1 Bandung Indonesia 1.8
## 60 68.8 Kharagpur India 36.1
## 61 68.7 Beijing China 86.9
## 62 68.4 Bangalore India 28.6
## 63 68.3 Tsukuba City Japan 16.2
## 64 68.0 Beijing China 77.1
## 65 67.5 Astana Kazakhstan 2.2
## 66 67.3 Taipei City Taiwan 55.4
## 67 66.7 Kanpur India 22.7
## 68 66.7 Islamabad Pakistan 56.4
## 69 66.3 Wuhan China 96.1
## 70 66.1 Seoul South Korea 60.0
## 71 65.3 Kowloon Hong Kong 96.9
## 72 64.5 Xi'an China 71.2
## 73 64.4 Seoul South Korea 52.0
## 74 62.9 Petaling Jaya Malaysia 77.9
## 75 62.8 Jinan China 69.6
## 76 62.7 Kobe City Japan 12.9
## 77 62.7 Xiamen China 95.4
## 78 62.3 Bandar Seri Begawan Brunei 26.4
## 79 62.2 Taipei City Taiwan 26.4
## 80 62.2 Sintok Malaysia 38.1
## 81 61.9 Busan South Korea 36.0
## 82 61.9 New Delhi India 13.3
## 83 61.7 Kaohsiung City Taiwan 24.2
## 84 61.3 Islamabad Pakistan 82.7
## 85 61.3 Shanghai China 69.0
## 86 60.6 Quezon City Philippines 6.4
## 87 60.5 Tehran Iran 70.5
## 88 59.9 Ulsan South Korea 99.7
## 89 59.8 Almaty Kazakhstan 1.5
## 90 59.6 Changchun China 78.3
## 91 59.5 Taipei City Taiwan 42.5
## 92 58.6 Bogor Indonesia 1.4
## 93 58.6 Seoul South Korea 44.6
## 94 58.5 Chengdu China 76.0
## 95 57.9 Daegu South Korea 87.6
## 96 57.7 Higashihiroshima City Japan 9.7
## 97 57.5 Tehran Iran 71.0
## 98 57.2 Tianjin China 99.8
## 99 56.8 Macau Macau SAR 97.4
## 100 56.7 Seoul South Korea 99.1
## 101 56.6 Daegu South Korea 40.1
## 102 56.6 Shah Alam Malaysia 3.3
## 103 56.5 Beijing China 71.5
## 104 56.2 Guwahati India 33.8
## 105 55.9 Gwangju South Korea 75.8
## 106 55.8 Beijing China 88.1
## 107 55.6 Shenzhen China 98.8
## 108 55.3 Chiang Mai Thailand 11.6
## 109 55.3 Roorkee India 31.9
## 110 55.2 New Delhi India 38.7
## 111 54.6 Shanghai China 90.8
## 112 54.0 Taoyuan City Taiwan 18.5
## 113 54.0 Dhaka Bangladesh 51.4
## 114 53.2 Surabaya Indonesia 1.5
## 115 53.1 Lahore Pakistan 58.6
## 116 52.9 Incheon South Korea 64.2
## 117 52.7 Seoul South Korea 90.2
## 118 52.7 Jeonju South Korea 61.7
## 119 52.5 Mohali India 3.6
## 120 52.5 Taichung City Taiwan 22.9
## 121 52.3 Taipei Taiwan 39.7
## 122 52.2 Taipei City Taiwan 8.1
## 123 52.0 Suwon South Korea 61.8
## 124 52.0 Seoul South Korea 25.2
## 125 51.6 Beijing China 64.5
## 126 50.9 Da Nang Vietnam 100.0
## 127 50.8 Bangkok Thailand 5.7
## 128 50.6 Islamabad Pakistan 74.9
## 129 50.6 Shah Alam Malaysia 6.7
## 130 50.2 Almaty Kazakhstan 1.3
## 131 50.2 Kampar Malaysia 52.0
## 132 50.0 Kuala Lumpur Malaysia 4.2
## 133 49.8 Semarang Indonesia 1.2
## 134 49.8 Nanjing China 60.2
## 135 49.8 Bandung Indonesia 1.4
## 136 49.8 Bandar Seri Begawan Brunei 53.4
## 137 49.5 Shymkent Kazakhstan 1.1
## 138 49.5 Lahore Pakistan 30.7
## 139 49.1 Seoul South Korea 71.1
## 140 49.1 Singapore Singapore 84.1
## 141 48.9 Bangkok Thailand 1.3
## 142 49.0 Quezon City Philippines 5.8
## 143 48.8 Kunitachi City Japan 4.2
## 144 48.2 Tehran Iran 54.9
## 145 48.2 Kyoto Japan 9.5
## 146 48.0 Kajang Malaysia 45.7
## 147 47.8 Guangzhou China 97.2
## 148 47.5 Dehradun India 10.1
## 149 47.4 Almaty Kazakhstan 1.3
## 150 46.4 Vellore India 21.7
## 151 46.1 Tehran Iran 64.0
## 152 45.4 Taichung Taiwan 64.5
## 153 45.5 Dalian China 71.8
## 154 45.4 Singapore Singapore 99.9
## 155 45.4 Beijing China 74.1
## 156 45.3 Bangkok Thailand 10.2
## 157 45.3 Dhaka Bangladesh 60.6
## 158 45.3 Shenzhen China 96.9
## 159 45.1 Dhaka Bangladesh 28.3
## 160 44.9 Tashkent Uzbekistan 5.4
## 161 44.9 Kuantan Malaysia 41.3
## 162 44.8 Hanoi Vietnam 36.0
## 163 44.5 Changsha China 100.0
## 164 44.4 Manila Philippines 4.5
## 165 44.4 Lahore Pakistan 44.5
## 166 44.0 Tanjong Malim Malaysia 6.2
## 167 43.9 Seoul South Korea 55.9
## 168 43.8 Karaganda Kazakhstan 1.4
## 169 43.5 Changsha China 87.2
## 170 43.5 Solan India 99.8
## 171 43.4 Nilai Malaysia 3.9
## 172 43.2 Pilani India 28.0
## 173 43.0 Malang Indonesia 1.3
## 174 42.8 Xi'an China 76.6
## 175 42.8 Pune India 7.4
## 176 42.6 Chiba City Japan 11.5
## 177 42.6 Kagurazaka Japan 7.6
## 178 42.5 Chennai India 10.7
## 179 42.5 Tokyo Japan 24.3
## 180 42.4 Kolkata India 8.0
## 181 42.2 Zhengzhou China 96.6
## 182 42.0 Manila Philippines 5.1
## 183 41.9 Lahore Pakistan 44.0
## 184 41.8 Noida India 8.6
## 185 41.7 Ho Chi Minh City Vietnam 11.6
## 186 41.6 Petaling Jaya Malaysia 4.2
## 187 41.5 Khon Kaen Thailand 9.8
## 188 41.4 Chongqing China 78.7
## 189 41.3 New Delhi India 72.7
## 190 41.3 Tuen Mun Hong Kong 51.1
## 191 41.2 Minxiong Township Taiwan 13.1
## 192 41.0 Kuala Lumpur Malaysia 5.7
## 193 41.0 Beijing China 79.6
## 194 40.9 Tokyo Japan 7.9
## 195 40.8 Daejeon South Korea 19.4
## 196 40.8 Turkestan Kazakhstan 1.4
## 197 40.6 Kuala Terengganu Malaysia 27.2
## 198 40.4 Isfahan Iran 66.9
## 199 40.4 Manipal India 8.3
## 200 40.3 Ho Chi Minh City Vietnam 100.0
## 201 40.0 Gwangju South Korea 50.0
## 202 40.0 Guangzhou China 91.3
## 203 39.6 Faisalabad Pakistan 87.4
## 204 39.5 Varanasi India 22.5
## 205 39.2 Jakarta Indonesia 1.3
## 206 38.8 Peshawar Pakistan 66.3
## 207 38.6 Hat Yai Thailand 14.5
## 208 38.4 Cyberjaya Malaysia 11.9
## 209 38.4 Osaka Japan 8.5
## 210 37.8 Shiraz Iran 49.3
## 211 37.6 Kanazawa Japan 17.3
## 212 37.1 Kolkata India 14.7
## 213 37.0 Tokyo Japan 5.7
## 214 37.0 Tai Po Hong Kong 97.0
## 215 36.9 Lanzhou China 75.2
## 216 36.8 Shanghai China 88.1
## 217 36.7 Pune India 7.7
## 218 36.5 Chengdu China 68.0
## 219 36.3 Mashhad Iran 30.5
## 220 36.3 Zhenjiang China 90.4
## 221 36.2 Suzhou China 99.2
## 222 36.1 Coimbatore India 27.6
## 223 36.1 Chennai India 11.2
## 224 36.1 Surakarta Indonesia 1.3
## 225 35.9 Bangkok Thailand 33.7
## 226 35.7 Tabriz Iran 82.6
## 227 35.6 Wuhan China 70.2
## 228 35.6 Chaheru India 28.9
## 229 35.6 Macau Macau SAR 96.3
## 230 35.6 Kuching Malaysia 6.8
## 231 35.3 Beijing China 39.4
## 232 35.3 Almaty Kazakhstan 2.9
## 233 35.3 Bishkek Kyrgystan 3.6
## 234 35.3 Okayama City Japan 10.5
## 235 35.2 Tehran Iran 25.9
## 236 34.9 Islamabad Pakistan 63.2
## 237 34.8 Nagasaki City Japan 12.3
## 238 34.8 Nanjing China 59.8
## 239 34.8 Makassar Indonesia 1.2
## 240 34.7 Hyderabad India 27.1
## 241 34.6 Taoyuan City Taiwan 38.0
## 242 34.6 Tokyo Japan 24.6
## 243 34.5 Tashkent Uzbekistan 1.4
## 244 34.3 Islamabad Pakistan 71.7
## 245 34.3 Kota Kinabalu Malaysia 4.6
## 246 34.2 Beijing China 20.0
## 247 34.2 Nanjing China 29.2
## 248 34.2 Mumbai India 3.8
## 249 33.7 Taichung City Taiwan 79.6
## 250 33.7 Nanjing China 97.7
## 251 33.7 Batu Pahat Malaysia 5.5
## 252 33.7 Ulsan South Korea 63.7
## 253 33.7 Wuhan China 84.7
## 254 33.6 Dhaka Bangladesh 15.2
## 255 33.5 Indore India 42.4
## 256 33.5 Kumamoto City Japan 27.0
## 257 33.5 Yokohama City Japan 5.0
## 258 33.4 Bhubaneswar India 8.3
## 259 33.4 Gyeongsan South Korea 82.0
## 260 33.2 Yangling China 87.6
## 261 32.6 Bishkek Kyrgystan 6.1
## 262 32.8 Osh Kyrgystan 1.0
## 263 32.4 Xuzhou China 59.3
## 264 32.4 Patiala India 82.8
## 265 32.4 Taichung City Taiwan 72.8
## 266 32.1 Karaganda Kazakhstan 1.0
## 267 31.8 Gyeonggi South Korea 26.5
## 268 31.7 Tiruvallur India 5.3
## 269 31.6 Beijing China 3.5
## 270 31.6 Karachi Pakistan 12.1
## 271 31.3 Almaty Kazakhstan 49.7
## 272 31.3 Chandigarh India 35.1
## 273 31.2 Sonepat India 9.6
## 274 31.2 Yangzhou China 84.8
## 275 30.9 Varanasi India 44.9
## 276 30.9 Seoul South Korea 72.6
## 277 30.8 Karachi Pakistan 16.1
## 278 30.8 Wuxi China 71.3
## 279 30.8 Colombo Sri Lanka 16.4
## 280 30.7 Aligarh India 39.7
## 281 30.7 Bhubaneswar India 7.8
## 282 30.6 Dhaka Bangladesh 18.5
## 283 30.4 Nur-Sultan Kazakhstan 1.3
## 284 30.3 Beijing China 99.8
## 285 30.3 Qingdao China 96.7
## 286 30.3 Bandung Indonesia 1.3
## 287 30.2 Karaikudi India 74.3
## 288 30.1 Shanghai China 94.1
## 289 30.1 Amritsar India 64.4
## 290 30.0 Ust-Kamenogorsk Kazakhstan 1.3
## 291 30.0 Asan South Korea 38.2
## 292 30.0 Isfahan Iran 38.7
## 293 29.9 Karachi Pakistan 57.4
## 294 29.8 Amritapuri India 3.5
## 295 29.8 Dhaka Bangladesh 38.2
## 296 29.5 Taichung City Taiwan 25.8
## 297 29.5 Seoul South Korea 9.1
## 298 29.2 Xi'an China 64.5
## 299 29.1 Qingdao China 48.1
## 300 29.0 Wuhan China 91.5
## Papers.per.Faculty Academic.Reputation Faculty.Student.Ratio Staff.with.PhD
## 1 79.8 100.0 98.6 90.7
## 2 55.0 100.0 93.3 97.4
## 3 57.4 100.0 85.8 82.5
## 4 53.8 100.0 93.0 67
## 5 63.1 99.8 92.5 73.4
## 6 61.5 99.9 84.0 80.3
## 7 80.8 100.0 99.0 96.9
## 8 96.2 99.3 54.2 95.1
## 9 45.3 99.8 94.8 98.4
## 10 91.4 96.6 97.7 97.7
## 11 77.8 99.7 76.6 94.3
## 12 37.0 99.7 92.1 84.9
## 13 41.8 99.5 87.7 93.7
## 14 96.3 99.8 79.1 61.9
## 15 83.1 99.9 79.5 98.3
## 16 42.0 96.2 92.9 97.9
## 17 60.3 98.2 83.1 64.9
## 18 69.0 100.0 94.2 79
## 19 34.3 89.4 91.1 79.7
## 20 41.9 96.2 77.5 96.8
## 21 72.6 100.0 97.0 87.8
## 22 82.2 91.1 100.0 98.6
## 23 55.8 100.0 98.7 90.4
## 24 69.1 95.4 61.6 74.3
## 25 51.4 99.0 99.7 89.3
## 26 52.0 100.0 43.1 85.1
## 27 35.6 96.7 89.8 91
## 28 54.4 90.7 66.9 85.6
## 29 5.3 94.4 99.9 95.5
## 30 73.7 99.2 94.7 71.1
## 31 77.0 92.4 99.8 41.3
## 32 52.2 97.4 95.0 89.3
## 33 81.9 99.7 49.3 93.3
## 34 53.6 95.8 90.5 95.1
## 35 49.1 96.1 90.5 87.2
## 36 15.1 73.3 98.8 14
## 37 44.3 96.9 63.1 54.2
## 38 72.8 85.0 26.2 88.5
## 39 72.7 94.7 46.5 94.4
## 40 22.3 66.8 93.7 94.4
## 41 37.3 93.5 53.1 81.8
## 42 74.5 78.7 71.7 96.8
## 43 89.8 77.4 47.8 76.1
## 44 95.2 95.0 27.2 94.7
## 45 10.9 72.4 97.6 21
## 46 17.4 95.2 85.9 10.6
## 47 18.5 98.3 51.1 78
## 48 96.2 96.6 17.9 96.3
## 49 15.5 93.8 77.0 3.3
## 50 42.3 77.4 67.2 93.3
## 51 13.4 96.5 40.4 18.1
## 52 10.0 85.1 82.4 9.7
## 53 10.7 95.1 77.3 8.8
## 54 90.2 58.2 66.8 46.2
## 55 14.0 88.8 93.3 81.8
## 56 98.1 88.7 30.5 99.9
## 57 92.4 53.3 69.8 56
## 58 96.9 71.2 45.9 77.4
## 59 19.4 88.5 71.0 79.8
## 60 94.8 80.3 14.8 99.9
## 61 78.9 79.8 41.5 92.4
## 62 100.0 92.7 49.8 99.9
## 63 44.2 82.6 74.8 88.2
## 64 70.2 65.0 64.2 64
## 65 5.0 86.5 94.8 54.2
## 66 41.2 71.9 64.3 58.2
## 67 91.1 83.7 26.2 99.9
## 68 29.8 60.1 83.8 12.7
## 69 94.6 59.9 15.1 81.5
## 70 13.6 65.4 90.6 95.3
## 71 44.7 55.6 80.0 52.1
## 72 83.1 57.2 24.1 71.6
## 73 18.4 53.3 85.1 91.6
## 74 28.7 48.9 71.9 14.9
## 75 82.8 52.1 30.5 71.5
## 76 34.7 73.0 59.8 76.3
## 77 53.3 65.7 34.0 57.1
## 78 30.8 59.6 98.1 94.3
## 79 14.6 80.6 81.5 65.5
## 80 21.6 63.0 59.3 83.3
## 81 36.9 58.9 79.8 93.4
## 82 60.9 84.6 9.5 71.1
## 83 46.1 70.3 51.4 96
## 84 82.6 54.0 9.5 51.7
## 85 37.8 59.8 71.4 84.2
## 86 6.3 83.0 47.9 1
## 87 78.0 61.0 5.7 62.7
## 88 71.2 45.2 98.8 99.8
## 89 4.7 81.8 98.7 17.2
## 90 32.4 41.2 57.8 41.9
## 91 57.6 45.6 41.6 99.9
## 92 10.2 67.2 94.3 50.6
## 93 11.6 57.4 76.6 52.3
## 94 95.1 59.9 20.0 67.8
## 95 96.6 36.7 94.7 98.4
## 96 31.1 66.4 90.4 68.5
## 97 99.1 40.4 8.6 99.9
## 98 49.2 56.1 43.0 93.2
## 99 84.6 40.8 15.7 89.1
## 100 52.4 38.7 22.5 74.6
## 101 36.7 38.4 84.5 91.8
## 102 13.6 76.0 36.6 11.5
## 103 14.0 58.6 30.0 87.2
## 104 98.9 58.0 11.5 99.2
## 105 99.8 48.3 59.0 99.9
## 106 100.0 60.7 7.1 97.5
## 107 86.0 32.8 94.2 96.5
## 108 14.9 80.8 31.5 55.4
## 109 97.4 58.8 13.5 99.3
## 110 47.9 76.8 19.6 99.5
## 111 52.0 44.3 29.7 83.3
## 112 37.2 62.0 39.6 85.5
## 113 9.8 65.4 14.6 2.6
## 114 8.2 56.8 85.8 1
## 115 21.0 53.4 45.4 12.1
## 116 18.9 42.2 93.6 41.2
## 117 14.2 29.7 94.5 62.1
## 118 18.8 49.8 80.3 60.8
## 119 6.0 54.2 52.9 3
## 120 29.2 61.3 42.9 90.1
## 121 34.8 37.0 97.4 94.1
## 122 7.1 59.3 31.2 79.4
## 123 27.7 45.3 85.9 67.8
## 124 3.3 39.5 93.3 80.7
## 125 76.9 41.1 71.2 66.5
## 126 37.2 38.3 7.8 1.8
## 127 7.8 67.3 44.6 59.3
## 128 33.7 40.2 25.3 4.2
## 129 7.6 52.5 68.2 14.4
## 130 2.5 77.1 79.7 4.6
## 131 14.2 46.0 27.6 13.2
## 132 14.0 67.1 25.3 16.5
## 133 9.0 60.8 54.9 1.3
## 134 81.0 44.7 36.7 62.5
## 135 7.1 59.9 76.1 4.5
## 136 20.8 49.8 96.7 63.5
## 137 2.0 65.8 97.1 6.9
## 138 30.0 50.0 7.9 5.3
## 139 29.2 27.8 80.7 64.6
## 140 23.5 41.5 19.8 27.7
## 141 1.8 43.1 65.6 54.8
## 142 7.3 64.2 41.7 1.8
## 143 8.3 47.0 50.0 33.5
## 144 99.8 20.6 9.1 99.1
## 145 10.8 53.1 15.4 31
## 146 32.7 29.4 93.0 11.8
## 147 78.9 41.1 35.5 29.8
## 148 17.0 72.8 60.1 6.7
## 149 3.0 63.6 99.3 27.6
## 150 42.9 45.1 16.5 80.9
## 151 98.2 28.9 5.1 98.3
## 152 96.4 25.6 40.9 65.5
## 153 81.4 34.2 32.6 87.9
## 154 37.3 27.4 99.9 null
## 155 96.5 40.2 19.8 67.6
## 156 8.3 68.3 12.9 12.2
## 157 9.1 44.4 13.4 6.6
## 158 69.9 28.4 43.7 87.7
## 159 40.9 52.7 22.8 27.7
## 160 9.7 52.2 86.2 47.7
## 161 56.4 37.5 29.1 94.7
## 162 5.7 49.9 12.6 26.5
## 163 52.0 29.0 30.3 45.9
## 164 15.4 64.4 13.9 1.6
## 165 18.7 35.4 33.6 1.4
## 166 12.8 43.8 48.7 97.5
## 167 17.1 38.0 56.9 79.2
## 168 2.3 53.0 76.2 1.2
## 169 95.1 31.0 10.9 66.9
## 170 42.1 28.5 17.4 29
## 171 6.3 34.8 79.7 19.6
## 172 43.8 40.2 17.7 98
## 173 7.5 60.2 22.9 1.3
## 174 59.8 22.2 65.6 71.3
## 175 82.1 32.0 69.5 36.8
## 176 29.4 52.0 83.9 35.7
## 177 37.2 36.4 18.4 80.4
## 178 100.0 38.0 11.0 76.2
## 179 39.4 40.3 100.0 87.7
## 180 62.6 53.1 10.9 95.7
## 181 54.9 20.7 9.9 11.5
## 182 2.7 43.3 20.9 1.2
## 183 13.1 40.2 22.1 1.1
## 184 34.3 37.3 20.6 66.6
## 185 7.1 57.8 8.5 1.8
## 186 3.9 31.5 76.8 75.6
## 187 16.6 53.4 14.7 70.9
## 188 74.0 28.0 23.2 60.3
## 189 37.6 35.3 53.4 81.8
## 190 20.1 35.0 18.5 90.9
## 191 16.4 42.2 23.7 74.8
## 192 6.1 38.5 78.5 1.2
## 193 89.1 30.6 30.0 81.4
## 194 75.9 45.6 24.5 93.2
## 195 29.1 32.3 67.1 73.6
## 196 3.4 57.1 62.9 2
## 197 42.0 36.6 25.6 80
## 198 87.3 19.6 10.2 66.9
## 199 19.1 38.8 63.6 49.5
## 200 45.7 19.6 10.1 1.1
## 201 23.3 34.0 66.2 83.2
## 202 45.7 20.3 17.6 61.3
## 203 56.9 24.0 2.6 25.8
## 204 25.2 45.8 22.6 73.5
## 205 25.9 44.2 35.3 2.8
## 206 10.2 25.9 84.0 null
## 207 10.9 62.2 30.1 38.9
## 208 16.3 41.5 21.0 20.5
## 209 24.7 41.8 71.9 35.5
## 210 69.8 26.1 9.2 95.4
## 211 33.4 36.7 75.2 80.3
## 212 75.8 54.9 16.2 52.5
## 213 5.9 42.7 38.2 5.2
## 214 18.5 22.5 99.3 null
## 215 34.6 29.6 56.3 5.6
## 216 57.7 25.8 22.6 81.9
## 217 6.2 21.0 43.3 27
## 218 96.9 20.4 16.2 2.2
## 219 50.2 26.7 6.8 92.5
## 220 29.0 11.8 86.4 8.5
## 221 40.5 24.7 29.3 25.9
## 222 99.8 18.7 89.5 93.5
## 223 15.6 35.3 26.5 11.1
## 224 9.4 47.0 41.2 10.1
## 225 20.1 43.9 25.2 44
## 226 86.6 22.9 4.7 null
## 227 42.0 19.7 73.9 5.1
## 228 13.1 29.7 22.0 5.2
## 229 42.2 17.3 6.9 73.3
## 230 15.0 44.1 26.6 19.5
## 231 54.0 28.4 58.6 64.4
## 232 7.6 46.9 27.6 2
## 233 7.6 53.5 20.7 38.5
## 234 26.8 32.1 80.3 26.5
## 235 58.7 29.8 8.2 2.9
## 236 53.6 24.4 65.3 36.1
## 237 21.2 25.7 93.2 63
## 238 87.1 21.9 16.9 82
## 239 5.2 50.3 62.5 3.6
## 240 66.1 44.1 27.0 97.6
## 241 60.1 23.7 87.7 40.3
## 242 28.4 43.1 56.3 9.1
## 243 5.3 57.8 18.7 15.8
## 244 14.1 26.1 6.9 3.4
## 245 19.8 47.6 16.9 35.3
## 246 45.0 34.4 39.0 37
## 247 79.3 21.3 36.2 63.3
## 248 90.6 32.6 14.2 34.6
## 249 65.8 22.4 7.1 49.5
## 250 26.6 19.4 29.1 42.7
## 251 37.8 36.0 19.3 61.8
## 252 42.2 19.6 79.3 72.9
## 253 27.6 25.9 33.7 16
## 254 5.9 39.9 17.1 1
## 255 96.7 23.3 42.3 83.2
## 256 29.7 26.5 75.3 66.9
## 257 29.4 33.2 32.9 18.2
## 258 9.1 15.3 65.4 77.7
## 259 30.8 15.3 25.3 61.2
## 260 41.1 17.8 47.1 23.5
## 261 4.2 30.2 85.3 2.9
## 262 1.2 54.0 24.6 1.1
## 263 71.6 14.0 48.5 41.3
## 264 48.0 19.6 20.2 59.1
## 265 13.9 24.5 9.1 63
## 266 3.6 36.5 98.3 10.9
## 267 34.4 17.7 99.8 79.3
## 268 48.4 21.6 37.4 9.1
## 269 2.4 11.1 15.8 95.5
## 270 16.9 39.7 2.6 2.3
## 271 12.8 22.4 20.1 7.3
## 272 43.1 28.7 15.6 76.8
## 273 7.1 35.6 74.1 6.5
## 274 28.0 8.8 54.6 14.9
## 275 95.8 22.7 7.7 99.9
## 276 4.5 17.4 58.7 48.6
## 277 21.1 18.2 100.0 1
## 278 59.4 17.5 28.5 11
## 279 9.7 44.1 4.7 8.1
## 280 27.3 38.9 25.8 9.4
## 281 24.8 36.6 54.2 59.8
## 282 12.0 34.9 7.6 1.1
## 283 3.3 35.4 71.1 15
## 284 38.5 17.9 43.4 1.1
## 285 36.3 12.2 20.5 42.5
## 286 10.9 33.6 34.1 1.6
## 287 51.0 13.0 43.5 93
## 288 41.1 15.3 43.1 12.4
## 289 36.8 15.2 92.1 3
## 290 3.1 41.2 85.0 1.1
## 291 13.5 21.0 82.7 34.2
## 292 36.8 20.6 3.9 100
## 293 17.7 18.6 3.7 null
## 294 26.7 22.0 51.3 11.8
## 295 15.7 34.4 7.8 null
## 296 15.9 31.9 12.9 52.1
## 297 2.1 21.4 93.0 18.6
## 298 27.4 16.1 69.5 17.5
## 299 60.5 20.8 13.9 33
## 300 52.2 16.7 12.1 61
## International.Research.Center International.Students Outbound.Exchange
## 1 98.0 69.1 100.0
## 2 98.4 100 100.0
## 3 99.9 99.2 97.6
## 4 99.7 98.8 97.9
## 5 92.1 81 94.9
## 6 96.0 99.4 96.1
## 7 97.9 51.7 59.6
## 8 99.9 66.4 100.0
## 9 97.7 90.9 68.4
## 10 88.9 100 100.0
## 11 70.6 99.8 100.0
## 12 99.9 94.2 94.3
## 13 91.1 85.7 81.4
## 14 95.8 67.6 65.9
## 15 70.6 45.5 67.3
## 16 80.3 83.6 99.4
## 17 97.8 99.6 90.4
## 18 95.6 56.4 40.4
## 19 89.0 91.6 73.7
## 20 98.2 99.7 100.0
## 21 99.5 76 37.8
## 22 36.5 14 75.5
## 23 99.3 62.3 10.5
## 24 92.0 35.6 92.0
## 25 92.9 46.9 76.6
## 26 94.7 72.4 38.6
## 27 98.9 97.5 37.1
## 28 98.8 99.1 73.7
## 29 90.3 90.4 100.0
## 30 87.4 76.8 9.0
## 31 96.7 26.3 91.9
## 32 90.4 63.3 11.8
## 33 92.8 48.9 42.4
## 34 93.8 56.6 6.5
## 35 97.4 48.6 6.5
## 36 62.5 100 100.0
## 37 99.6 99.8 40.8
## 38 97.5 25.1 91.9
## 39 68.1 51.2 33.3
## 40 80.6 91.8 97.5
## 41 85.1 52.8 34.1
## 42 79.7 62.6 48.1
## 43 95.4 72.6 100.0
## 44 89.2 5.3 9.7
## 45 56.3 99.9 100.0
## 46 55.5 31.6 100.0
## 47 98.0 15.8 28.2
## 48 78.0 3.6 6.4
## 49 83.8 43.7 30.5
## 50 97.3 18.8 5.4
## 51 91.5 74.3 46.6
## 52 60.7 31.9 86.9
## 53 57.0 14.8 47.4
## 54 89.7 64.1 88.6
## 55 83.6 22.2 48.9
## 56 56.1 6.3 30.5
## 57 90.7 69.5 97.6
## 58 93.0 27.5 40.9
## 59 35.8 18 26.7
## 60 93.5 2.3 3.8
## 61 94.3 65.9 38.4
## 62 58.8 5.2 6.7
## 63 91.9 62.8 16.5
## 64 93.0 32.5 51.2
## 65 29.4 35.3 28.0
## 66 42.4 71.4 43.5
## 67 58.2 2 5.6
## 68 95.7 21.1 13.6
## 69 97.9 14 38.6
## 70 45.1 68.6 90.7
## 71 55.2 100 97.0
## 72 98.2 19.8 27.2
## 73 42.5 83.1 94.5
## 74 76.5 75.9 15.9
## 75 89.8 16.2 26.5
## 76 81.1 28.3 13.6
## 77 88.6 16.4 27.6
## 78 52.4 90.6 100.0
## 79 30.2 71 60.4
## 80 50.1 98.5 60.8
## 81 84.1 52.4 40.7
## 82 96.4 5.5 1.1
## 83 52.8 45.4 61.7
## 84 90.5 8.2 14.8
## 85 85.0 27.1 32.7
## 86 87.1 2.1 3.3
## 87 99.4 8.9 1.3
## 88 33.1 42.2 10.7
## 89 15.8 53.8 7.7
## 90 88.3 9.8 24.9
## 91 26.7 64.8 65.9
## 92 22.7 18.9 29.4
## 93 13.8 63.3 45.1
## 94 94.6 25.4 4.6
## 95 11.6 17.5 99.7
## 96 93.9 50.6 8.4
## 97 87.6 3.2 1.4
## 98 76.5 13.7 22.3
## 99 83.3 100 33.0
## 100 89.9 76.5 34.3
## 101 77.6 66.3 45.5
## 102 93.1 10 5.0
## 103 32.2 12.1 53.6
## 104 76.6 2.7 6.5
## 105 37.2 39.6 96.8
## 106 100.0 10 1.3
## 107 72.7 4.7 61.4
## 108 88.5 22.1 1.5
## 109 67.1 8.9 9.7
## 110 71.5 4 1.7
## 111 95.0 45.5 74.5
## 112 43.5 43.3 25.9
## 113 77.6 1.4 2.8
## 114 11.2 27.2 88.7
## 115 23.5 7.5 15.7
## 116 31.5 52.3 97.0
## 117 54.3 69.3 72.6
## 118 52.2 32.9 77.7
## 119 55.9 54.4 32.8
## 120 46.7 35.7 17.0
## 121 29.6 52.2 100.0
## 122 13.1 66.6 85.8
## 123 22.2 65.6 27.2
## 124 12.1 89.3 100.0
## 125 76.7 23.2 37.8
## 126 98.9 4.7 9.6
## 127 61.8 7 41.4
## 128 99.6 4.6 1.5
## 129 11.4 100 100.0
## 130 2.5 19.5 45.1
## 131 68.7 23.8 38.3
## 132 88.5 93.3 6.5
## 133 37.9 15.8 48.9
## 134 93.4 18.1 42.4
## 135 23.6 10.8 34.2
## 136 17.2 29.2 97.1
## 137 2.4 57.5 48.3
## 138 88.3 3.4 3.2
## 139 70.7 55.3 75.9
## 140 25.7 93.9 100.0
## 141 1.7 100 80.0
## 142 29.7 12.5 14.4
## 143 5.5 63.9 45.6
## 144 94.0 14.4 2.9
## 145 44.4 41.9 15.3
## 146 48.0 92.7 92.4
## 147 76.7 24 26.7
## 148 83.2 2.5 10.4
## 149 4.5 67.7 17.3
## 150 97.1 15.2 2.9
## 151 79.3 5.4 2.8
## 152 96.5 34.2 63.6
## 153 82.1 15.2 58.0
## 154 44.9 null 97.9
## 155 83.1 14.4 50.7
## 156 77.4 3.3 10.5
## 157 32.5 2.3 33.8
## 158 90.7 4.6 5.5
## 159 33.6 1.3 1.2
## 160 6.7 21.3 43.9
## 161 80.8 38.9 55.4
## 162 87.2 2.5 19.3
## 163 70.3 7.8 28.8
## 164 28.2 7.5 5.4
## 165 62.8 9.6 1.9
## 166 26.3 77.1 98.8
## 167 31.6 33.2 50.4
## 168 5.5 26.7 96.3
## 169 92.3 21 34.0
## 170 67.7 6.8 14.0
## 171 4.9 99.6 100.0
## 172 56.3 12.3 25.2
## 173 23.6 4.9 20.8
## 174 79.3 26.6 49.6
## 175 66.8 12.5 1.0
## 176 40.7 22.5 5.5
## 177 24.8 19.2 1.9
## 178 99.7 4 1.6
## 179 27.7 51.7 12.8
## 180 82.3 2 3.8
## 181 82.6 11.6 1.3
## 182 21.1 29.3 99.8
## 183 87.6 15.4 1.0
## 184 87.7 65.6 99.9
## 185 69.1 1.9 1.7
## 186 2.3 100 100.0
## 187 76.2 11.5 18.9
## 188 86.0 10 14.5
## 189 65.0 25.5 10.4
## 190 16.8 100 98.3
## 191 26.9 18.6 36.6
## 192 4.7 100 99.9
## 193 77.9 5.8 14.2
## 194 61.0 34.1 14.7
## 195 47.6 40.9 44.5
## 196 1.9 57.6 13.2
## 197 75.4 43.5 68.2
## 198 65.7 4.7 3.9
## 199 87.4 36.1 12.2
## 200 99.8 4.8 59.4
## 201 40.0 34.7 44.1
## 202 78.6 99 43.9
## 203 91.8 1.8 1.7
## 204 82.7 6.7 2.3
## 205 9.4 22.6 100.0
## 206 64.9 4.6 1.0
## 207 60.8 8.1 4.8
## 208 23.6 73.7 49.3
## 209 41.4 16.3 2.5
## 210 80.3 10.7 2.0
## 211 46.2 28.9 13.0
## 212 47.3 1.3 1.6
## 213 6.7 45.4 54.1
## 214 42.6 null 22.9
## 215 89.8 5.4 6.8
## 216 59.6 7.2 16.1
## 217 18.6 27.9 23.9
## 218 95.6 12.3 69.2
## 219 94.8 34.2 1.2
## 220 91.8 28.3 18.9
## 221 71.5 12.7 34.6
## 222 54.1 2.8 15.8
## 223 82.5 6 10.4
## 224 15.6 14.2 79.6
## 225 55.1 16.8 4.5
## 226 94.9 23.9 1.2
## 227 92.0 13.9 36.7
## 228 79.7 19.7 25.4
## 229 35.6 100 19.8
## 230 40.7 44.5 59.9
## 231 46.2 7.6 8.8
## 232 13.9 12.2 19.4
## 233 5.1 63.4 10.8
## 234 78.3 31 5.1
## 235 90.3 6.5 7.2
## 236 15.3 1.4 4.6
## 237 74.0 24.4 14.7
## 238 58.1 14.6 62.7
## 239 17.3 4.7 8.0
## 240 37.3 9.2 1.0
## 241 38.5 23.4 2.9
## 242 41.8 23.4 9.9
## 243 10.9 12.6 18.2
## 244 75.4 30.2 2.0
## 245 39.3 24.5 14.0
## 246 41.1 42.2 97.4
## 247 59.8 23.1 99.6
## 248 21.3 9.1 1.0
## 249 83.5 16.9 14.8
## 250 79.2 21.9 30.5
## 251 66.2 31.9 9.8
## 252 40.5 11.7 4.9
## 253 76.4 12.3 13.3
## 254 26.8 4.8 2.6
## 255 30.9 2.2 10.7
## 256 51.9 18.2 3.8
## 257 36.3 36.4 6.3
## 258 71.5 47.5 26.1
## 259 66.4 35.4 9.9
## 260 85.2 4.5 9.7
## 261 2.7 99.9 52.1
## 262 1.1 97.9 15.8
## 263 78.1 9 18.3
## 264 44.8 7.9 31.0
## 265 32.3 39.8 41.9
## 266 1.5 60.5 31.2
## 267 9.1 59.6 38.3
## 268 87.0 23.4 87.2
## 269 4.1 5.1 99.7
## 270 54.9 1.6 1.0
## 271 1.5 56.5 53.5
## 272 44.4 6.9 1.1
## 273 6.5 3.1 36.1
## 274 69.3 35.3 96.8
## 275 32.6 1.9 6.7
## 276 3.0 10.4 69.2
## 277 49.1 4.6 1.0
## 278 67.1 5.9 19.3
## 279 23.1 3.1 1.1
## 280 69.6 4.6 1.0
## 281 31.0 10.9 1.0
## 282 27.9 14.6 16.4
## 283 1.6 21.9 56.2
## 284 55.6 7.1 12.4
## 285 75.3 6.6 11.9
## 286 8.5 26.3 60.2
## 287 55.6 1.9 2.2
## 288 44.7 16.3 62.1
## 289 36.0 2 1.0
## 290 1.5 12.5 6.9
## 291 10.8 20.7 10.2
## 292 75.9 4.9 1.2
## 293 5.6 null 1.0
## 294 59.7 6.3 27.3
## 295 41.7 null 1.2
## 296 11.5 29.8 3.4
## 297 2.5 22.5 85.5
## 298 49.7 13.3 47.9
## 299 76.4 6.5 27.3
## 300 45.6 6.6 11.5
## Inbound.Exchange International.Faculty Employer.Reputation
## 1 88.5 83.2 100.0
## 2 99.8 100 96.8
## 3 93.4 100 99.9
## 4 90.5 100 98.8
## 5 99.5 98.9 99.5
## 6 100.0 100 91.5
## 7 15.9 46 100.0
## 8 100.0 99.7 99.9
## 9 100.0 50.5 100.0
## 10 100.0 100 74.0
## 11 98.8 100 92.3
## 12 99.0 89.4 99.9
## 13 100.0 40.6 99.9
## 14 100.0 43.2 99.4
## 15 81.1 51.2 99.7
## 16 100.0 44.9 96.8
## 17 73.0 100 78.8
## 18 78.2 30.5 100.0
## 19 97.1 44.8 98.3
## 20 100.0 70.9 94.3
## 21 24.9 29.5 100.0
## 22 91.8 60.8 98.3
## 23 24.8 42 100.0
## 24 95.8 90.2 73.4
## 25 57.0 38.5 99.0
## 26 73.0 43.4 99.8
## 27 29.6 79.7 95.7
## 28 90.1 38.4 93.4
## 29 98.8 82.3 98.7
## 30 22.2 54.9 99.9
## 31 33.6 33 66.3
## 32 27.9 39.3 95.5
## 33 46.8 55.6 99.4
## 34 6.2 47.2 95.7
## 35 6.2 47.2 94.7
## 36 92.1 91.6 98.5
## 37 29.0 56.1 96.6
## 38 38.6 74 82.8
## 39 30.3 28 96.9
## 40 98.8 38.8 80.1
## 41 13.0 42.8 97.6
## 42 46.0 48.7 94.5
## 43 98.4 86 54.1
## 44 2.7 9.1 99.0
## 45 100.0 99 94.9
## 46 100.0 98.8 98.0
## 47 26.2 47.1 79.0
## 48 6.9 13.1 99.5
## 49 42.9 27.8 99.8
## 50 1.2 5.9 70.9
## 51 36.6 61.2 99.9
## 52 93.1 88.2 97.8
## 53 76.7 75.1 96.7
## 54 98.3 98 70.9
## 55 76.2 15.1 62.4
## 56 5.9 11.7 95.0
## 57 99.1 56.9 53.0
## 58 4.4 24.1 63.4
## 59 56.9 99.6 96.5
## 60 1.5 1.2 89.5
## 61 12.8 20.2 40.5
## 62 8.4 11.1 68.6
## 63 28.1 34.6 63.5
## 64 15.6 17.7 65.6
## 65 31.1 93.3 94.8
## 66 78.7 48.9 80.7
## 67 5.8 7.1 92.7
## 68 3.3 13 97.0
## 69 17.6 16 71.1
## 70 92.8 21.3 61.6
## 71 86.7 100 43.7
## 72 7.8 11.7 82.0
## 73 92.3 38.5 75.2
## 74 35.6 70.6 81.8
## 75 35.4 24.7 78.7
## 76 36.0 21.3 76.5
## 77 84.5 15.7 49.9
## 78 100.0 100 42.9
## 79 68.7 39.1 60.5
## 80 99.1 48.4 67.6
## 81 74.8 30.4 53.4
## 82 1.7 2.4 78.4
## 83 46.2 53.8 66.9
## 84 3.4 21.4 84.3
## 85 3.3 75.6 46.1
## 86 2.2 3.4 94.4
## 87 2.0 5 75.5
## 88 12.6 63 47.1
## 89 8.9 92.5 82.6
## 90 1.0 33 86.8
## 91 83.5 49 91.4
## 92 63.4 85.2 83.3
## 93 89.0 21.5 86.3
## 94 5.6 21.7 49.8
## 95 42.8 20.3 56.4
## 96 8.1 36.4 44.5
## 97 2.5 8.4 82.1
## 98 2.0 34.5 38.0
## 99 23.8 100 39.8
## 100 39.7 55.4 52.6
## 101 88.3 31.8 53.7
## 102 15.5 3.6 82.8
## 103 15.5 20.1 80.3
## 104 1.8 6 72.0
## 105 24.1 47 35.9
## 106 1.0 59.3 22.7
## 107 99.5 72.3 11.4
## 108 28.4 14.8 54.5
## 109 4.9 2.2 70.3
## 110 3.2 null 49.0
## 111 99.1 55.9 21.6
## 112 23.6 32.9 71.0
## 113 6.6 4.2 86.4
## 114 92.1 81.1 84.6
## 115 1.6 34.3 97.4
## 116 93.6 28.4 49.0
## 117 85.8 26.7 40.0
## 118 41.0 23.5 41.4
## 119 28.8 72.4 90.5
## 120 13.9 26.2 64.2
## 121 100.0 42.2 45.0
## 122 68.7 59.3 80.7
## 123 72.9 27.3 50.5
## 124 100.0 66.4 61.6
## 125 24.2 21.3 32.4
## 126 7.6 72.5 64.1
## 127 32.5 26.9 58.6
## 128 1.1 3.1 73.7
## 129 99.6 99.9 67.5
## 130 21.0 72.7 63.9
## 131 52.5 35.4 74.1
## 132 8.6 53.9 54.3
## 133 47.2 61.2 78.8
## 134 17.6 38.9 28.3
## 135 36.9 69.5 78.6
## 136 10.5 100 31.9
## 137 16.2 35.9 68.8
## 138 1.9 9.5 90.2
## 139 65.7 28 33.2
## 140 100.0 100 48.7
## 141 16.6 100 86.7
## 142 15.1 8.4 93.6
## 143 67.0 49.1 97.4
## 144 1.4 56.6 64.4
## 145 14.4 61.9 91.9
## 146 98.0 45.8 42.7
## 147 6.2 31 26.9
## 148 2.4 46.2 30.5
## 149 30.7 48.6 53.2
## 150 2.0 26.4 52.4
## 151 1.5 33.9 50.9
## 152 57.7 12.8 18.9
## 153 18.3 32.7 17.8
## 154 89.4 null 23.6
## 155 11.1 9.2 19.5
## 156 8.1 13 57.3
## 157 3.2 23.9 87.9
## 158 12.1 31.6 14.9
## 159 1.0 null 79.7
## 160 62.3 51.4 53.3
## 161 44.5 52.8 23.8
## 162 30.8 16.9 57.3
## 163 12.7 14.2 39.5
## 164 5.3 3.7 88.2
## 165 1.3 1.4 84.8
## 166 100.0 51.5 38.4
## 167 73.2 16.9 37.8
## 168 52.2 36.4 63.7
## 169 3.5 19.7 18.9
## 170 2.7 63.6 46.7
## 171 100.0 99.2 57.6
## 172 1.2 5.7 56.9
## 173 32.7 37.5 79.8
## 174 1.8 43.5 15.2
## 175 1.1 2.5 55.7
## 176 26.5 22.7 34.3
## 177 4.3 35 89.6
## 178 1.1 null 43.2
## 179 8.2 6.4 28.0
## 180 1.1 56.8 27.9
## 181 1.0 null 60.4
## 182 79.9 69.6 79.4
## 183 1.1 12.2 58.6
## 184 15.4 11.2 38.8
## 185 2.5 12.7 66.5
## 186 100.0 99.9 42.7
## 187 21.5 16.5 40.4
## 188 2.9 51.6 21.2
## 189 16.4 9.3 14.3
## 190 90.7 100 28.2
## 191 48.3 14.6 66.9
## 192 100.0 99.3 44.8
## 193 1.4 10.8 12.3
## 194 13.0 16.5 31.0
## 195 89.3 21.9 32.3
## 196 3.3 21.7 65.8
## 197 78.8 31.8 19.7
## 198 1.1 6.1 54.4
## 199 7.5 4.4 33.6
## 200 75.7 27.1 28.3
## 201 29.0 26.8 21.3
## 202 30.5 68.7 12.7
## 203 1.1 7 42.9
## 204 1.8 1.8 32.9
## 205 96.9 69.3 57.8
## 206 1.1 null 38.5
## 207 3.3 16.2 24.9
## 208 100.0 62 51.1
## 209 5.8 14.1 42.8
## 210 1.6 1.6 31.5
## 211 24.2 24.5 18.2
## 212 3.5 null 26.0
## 213 57.2 69.1 59.4
## 214 9.9 null 17.8
## 215 3.6 6.3 11.5
## 216 1.6 6.9 15.7
## 217 41.9 39.8 86.8
## 218 1.3 9.3 20.5
## 219 1.1 2.3 29.7
## 220 1.7 13.8 7.0
## 221 11.2 19.2 12.3
## 222 38.4 null 6.4
## 223 6.2 3.1 52.4
## 224 46.6 44.2 47.9
## 225 13.5 27.8 27.5
## 226 1.5 3.5 22.5
## 227 11.8 3.1 5.1
## 228 26.4 26.3 46.4
## 229 42.8 100 15.7
## 230 67.5 69.5 30.7
## 231 8.8 14.4 22.4
## 232 1.8 18.5 69.8
## 233 9.2 99.2 42.2
## 234 9.3 18.7 17.4
## 235 100.0 3.3 33.9
## 236 1.1 null 37.5
## 237 14.7 17.8 10.6
## 238 25.7 15.7 11.7
## 239 10.1 26.2 44.7
## 240 1.1 4.6 13.8
## 241 2.0 15.7 20.0
## 242 9.4 22.1 24.5
## 243 18.8 87.1 43.2
## 244 1.1 12.8 40.5
## 245 56.2 29.6 35.6
## 246 10.5 10.9 24.0
## 247 22.7 16.3 15.9
## 248 1.1 4.8 64.1
## 249 7.8 28.8 7.5
## 250 1.8 null 7.8
## 251 37.7 19.5 27.1
## 252 5.1 5.1 10.4
## 253 3.1 9.6 11.7
## 254 1.6 5.1 70.6
## 255 3.5 5.3 22.5
## 256 26.9 21.8 13.2
## 257 14.1 29.5 53.1
## 258 72.0 36.2 22.3
## 259 12.8 68.8 13.5
## 260 2.5 8.3 5.4
## 261 40.8 71.3 31.4
## 262 3.5 8.7 48.8
## 263 38.3 6.4 6.6
## 264 1.7 15.9 20.2
## 265 56.9 25 24.1
## 266 2.5 29.2 32.1
## 267 38.4 11.4 13.6
## 268 20.7 98.6 13.7
## 269 39.1 11.2 81.6
## 270 1.1 10.6 52.6
## 271 26.3 95.8 48.8
## 272 1.9 null 30.7
## 273 4.4 45.8 38.7
## 274 7.4 15.6 4.2
## 275 1.1 2.5 23.3
## 276 58.5 12 25.0
## 277 1.1 2.3 33.2
## 278 3.4 4.5 18.3
## 279 1.3 1.5 56.1
## 280 1.1 1 13.3
## 281 1.1 5.8 24.4
## 282 11.9 15.4 59.2
## 283 26.3 29.5 36.6
## 284 2.8 6.8 7.9
## 285 1.1 7.8 9.8
## 286 72.5 70.3 43.5
## 287 2.3 null 4.1
## 288 19.5 5.7 6.3
## 289 1.1 null 17.0
## 290 1.3 39 31.9
## 291 55.7 16.9 23.7
## 292 1.1 null 21.0
## 293 1.1 null 79.4
## 294 94.5 63.3 21.4
## 295 1.0 null 45.4
## 296 17.2 5.1 46.7
## 297 44.6 7.7 33.9
## 298 6.1 10.6 5.3
## 299 2.8 8.5 12.4
## 300 1.5 11.7 9.3# Verificamos los nombres de las columnas
colnames(universities_data)## [1] "Rank" "Ordinal.Rank"
## [3] "University.Name" "Overall.Score"
## [5] "City" "Country"
## [7] "Citations.per.Paper" "Papers.per.Faculty"
## [9] "Academic.Reputation" "Faculty.Student.Ratio"
## [11] "Staff.with.PhD" "International.Research.Center"
## [13] "International.Students" "Outbound.Exchange"
## [15] "Inbound.Exchange" "International.Faculty"
## [17] "Employer.Reputation"# Verificamos los valores únicos en la columna 'Country'
unique(universities_data$Country)## [1] "China" "Hong Kong" "Singapore" "South Korea" "Malaysia"
## [6] "Japan" "Taiwan" "Kazakhstan" "India" "Indonesia"
## [11] "Thailand" "Pakistan" "Brunei" "Philippines" "Iran"
## [16] "Macau SAR" "Bangladesh" "Vietnam" "Uzbekistan" "Kyrgystan"
## [21] "Sri Lanka"# Filtrar datos por país específico, ejemplo: China
df <- universities_data %>% filter(Country == "China")
# Mostrar las primeras filas del dataframe filtrado
df## Rank Ordinal.Rank University.Name
## 1 1 1 Peking University
## 2 5 5 Fudan University
## 3 7 7 Tsinghua University
## 4 8 8 Zhejiang University
## 5 14 14 Shanghai Jiao Tong University
## 6 24 24 Nanjing University
## 7 31 31 University of Science and Technology of China
## 8 38 38 Wuhan University
## 9 43 43 Tongji University
## 10 50 48 Sun Yat-sen University
## 11 57 57 Tianjin University
## 12 58 58 Harbin Institute of Technology
## 13 61 61 Beijing Normal University
## 14 64 64 Beijing Institute of Technology
## 15 69 67 Huazhong University of Science and Technology
## 16 72 72 Xi’an Jiaotong University
## 17 75 75 Shandong University
## 18 77 76 Xiamen University
## 19 85 84 Shanghai University
## 20 90 90 Jilin University
## 21 94 94 Sichuan University
## 22 98 98 Nankai University
## 23 103 103 Renmin (People's) University of China
## 24 106 106 University of Chinese Academy of Sciences (UCAS)
## 25 107 107 Southern University of Science and Technology (SUSTech)
## 26 111 111 East China Normal University
## 27 125 125 Beihang University (former BUAA)
## 28 134 133 Southeast University
## 29 147 147 South China University of Technology
## 30 153 152 Dalian University of Technology
## 31 155 154 University of Science and Technology Beijing
## 32 158 154 Shenzhen University
## 33 163 163 Hunan University
## 34 169 169 Central South University
## 35 174 174 Northwestern Polytechnical University
## 36 181 181 Zhengzhou University
## 37 188 188 Chongqing University
## 38 193 192 China Agricultural University
## 39 202 201 Jinan University (China)
## 40 215 215 Lanzhou University
## 41 216 216 East China University of Science and Technology
## 42 218 218 University of Electronic Science and Technology of China
## 43 220 219 Jiangsu University
## 44 221 221 Soochow University
## 45 227 227 China University of Geosciences
## 46 231 231 Beijing University of Technology
## 47 238 237 Nanjing University of Science and Technology
## 48 246 246 Beijing Jiaotong University
## 49 247 246 Nanjing University of Aeronautics and Astronautics
## 50 250 249 Nanjing Normal University
## 51 253 249 Wuhan University of Technology
## 52 260 260 Northwest Agriculture and Forestry University
## 53 263 263 China University of Mining and Technology
## 54 269 269 China University of Political Science and Law
## 55 274 273 Yangzhou University
## 56 278 277 Jiangnan University
## 57 284 284 Beijing University of Chemical Technology
## 58 285 284 Qingdao University
## 59 288 288 Donghua University
## 60 298 298 Northwest University (China)
## 61 299 299 Ocean University of China
## 62 300 300 Huazhong Agricultural University
## Overall.Score City Country Citations.per.Paper Papers.per.Faculty
## 1 100.0 Beijing China 96.4 79.8
## 2 97.2 Shanghai China 92.1 63.1
## 3 96.3 Beijing China 98.6 80.8
## 4 96.0 Hangzhou China 86.0 96.2
## 5 93.2 Shanghai China 76.7 96.3
## 6 86.5 Nanjing China 97.4 69.1
## 7 84.0 Hefei China 98.0 77.0
## 8 80.5 Wuhan China 93.7 72.8
## 9 76.2 Shanghai China 79.2 89.8
## 10 73.1 Guangzhou China 91.2 42.3
## 11 69.5 Tianjin China 89.9 92.4
## 12 69.3 Harbin China 78.6 96.9
## 13 68.7 Beijing China 86.9 78.9
## 14 68.0 Beijing China 77.1 70.2
## 15 66.3 Wuhan China 96.1 94.6
## 16 64.5 Xi'an China 71.2 83.1
## 17 62.8 Jinan China 69.6 82.8
## 18 62.7 Xiamen China 95.4 53.3
## 19 61.3 Shanghai China 69.0 37.8
## 20 59.6 Changchun China 78.3 32.4
## 21 58.5 Chengdu China 76.0 95.1
## 22 57.2 Tianjin China 99.8 49.2
## 23 56.5 Beijing China 71.5 14.0
## 24 55.8 Beijing China 88.1 100.0
## 25 55.6 Shenzhen China 98.8 86.0
## 26 54.6 Shanghai China 90.8 52.0
## 27 51.6 Beijing China 64.5 76.9
## 28 49.8 Nanjing China 60.2 81.0
## 29 47.8 Guangzhou China 97.2 78.9
## 30 45.5 Dalian China 71.8 81.4
## 31 45.4 Beijing China 74.1 96.5
## 32 45.3 Shenzhen China 96.9 69.9
## 33 44.5 Changsha China 100.0 52.0
## 34 43.5 Changsha China 87.2 95.1
## 35 42.8 Xi'an China 76.6 59.8
## 36 42.2 Zhengzhou China 96.6 54.9
## 37 41.4 Chongqing China 78.7 74.0
## 38 41.0 Beijing China 79.6 89.1
## 39 40.0 Guangzhou China 91.3 45.7
## 40 36.9 Lanzhou China 75.2 34.6
## 41 36.8 Shanghai China 88.1 57.7
## 42 36.5 Chengdu China 68.0 96.9
## 43 36.3 Zhenjiang China 90.4 29.0
## 44 36.2 Suzhou China 99.2 40.5
## 45 35.6 Wuhan China 70.2 42.0
## 46 35.3 Beijing China 39.4 54.0
## 47 34.8 Nanjing China 59.8 87.1
## 48 34.2 Beijing China 20.0 45.0
## 49 34.2 Nanjing China 29.2 79.3
## 50 33.7 Nanjing China 97.7 26.6
## 51 33.7 Wuhan China 84.7 27.6
## 52 33.2 Yangling China 87.6 41.1
## 53 32.4 Xuzhou China 59.3 71.6
## 54 31.6 Beijing China 3.5 2.4
## 55 31.2 Yangzhou China 84.8 28.0
## 56 30.8 Wuxi China 71.3 59.4
## 57 30.3 Beijing China 99.8 38.5
## 58 30.3 Qingdao China 96.7 36.3
## 59 30.1 Shanghai China 94.1 41.1
## 60 29.2 Xi'an China 64.5 27.4
## 61 29.1 Qingdao China 48.1 60.5
## 62 29.0 Wuhan China 91.5 52.2
## Academic.Reputation Faculty.Student.Ratio Staff.with.PhD
## 1 100.0 98.6 90.7
## 2 99.8 92.5 73.4
## 3 100.0 99.0 96.9
## 4 99.3 54.2 95.1
## 5 99.8 79.1 61.9
## 6 95.4 61.6 74.3
## 7 92.4 99.8 41.3
## 8 85.0 26.2 88.5
## 9 77.4 47.8 76.1
## 10 77.4 67.2 93.3
## 11 53.3 69.8 56
## 12 71.2 45.9 77.4
## 13 79.8 41.5 92.4
## 14 65.0 64.2 64
## 15 59.9 15.1 81.5
## 16 57.2 24.1 71.6
## 17 52.1 30.5 71.5
## 18 65.7 34.0 57.1
## 19 59.8 71.4 84.2
## 20 41.2 57.8 41.9
## 21 59.9 20.0 67.8
## 22 56.1 43.0 93.2
## 23 58.6 30.0 87.2
## 24 60.7 7.1 97.5
## 25 32.8 94.2 96.5
## 26 44.3 29.7 83.3
## 27 41.1 71.2 66.5
## 28 44.7 36.7 62.5
## 29 41.1 35.5 29.8
## 30 34.2 32.6 87.9
## 31 40.2 19.8 67.6
## 32 28.4 43.7 87.7
## 33 29.0 30.3 45.9
## 34 31.0 10.9 66.9
## 35 22.2 65.6 71.3
## 36 20.7 9.9 11.5
## 37 28.0 23.2 60.3
## 38 30.6 30.0 81.4
## 39 20.3 17.6 61.3
## 40 29.6 56.3 5.6
## 41 25.8 22.6 81.9
## 42 20.4 16.2 2.2
## 43 11.8 86.4 8.5
## 44 24.7 29.3 25.9
## 45 19.7 73.9 5.1
## 46 28.4 58.6 64.4
## 47 21.9 16.9 82
## 48 34.4 39.0 37
## 49 21.3 36.2 63.3
## 50 19.4 29.1 42.7
## 51 25.9 33.7 16
## 52 17.8 47.1 23.5
## 53 14.0 48.5 41.3
## 54 11.1 15.8 95.5
## 55 8.8 54.6 14.9
## 56 17.5 28.5 11
## 57 17.9 43.4 1.1
## 58 12.2 20.5 42.5
## 59 15.3 43.1 12.4
## 60 16.1 69.5 17.5
## 61 20.8 13.9 33
## 62 16.7 12.1 61
## International.Research.Center International.Students Outbound.Exchange
## 1 98.0 69.1 100.0
## 2 92.1 81 94.9
## 3 97.9 51.7 59.6
## 4 99.9 66.4 100.0
## 5 95.8 67.6 65.9
## 6 92.0 35.6 92.0
## 7 96.7 26.3 91.9
## 8 97.5 25.1 91.9
## 9 95.4 72.6 100.0
## 10 97.3 18.8 5.4
## 11 90.7 69.5 97.6
## 12 93.0 27.5 40.9
## 13 94.3 65.9 38.4
## 14 93.0 32.5 51.2
## 15 97.9 14 38.6
## 16 98.2 19.8 27.2
## 17 89.8 16.2 26.5
## 18 88.6 16.4 27.6
## 19 85.0 27.1 32.7
## 20 88.3 9.8 24.9
## 21 94.6 25.4 4.6
## 22 76.5 13.7 22.3
## 23 32.2 12.1 53.6
## 24 100.0 10 1.3
## 25 72.7 4.7 61.4
## 26 95.0 45.5 74.5
## 27 76.7 23.2 37.8
## 28 93.4 18.1 42.4
## 29 76.7 24 26.7
## 30 82.1 15.2 58.0
## 31 83.1 14.4 50.7
## 32 90.7 4.6 5.5
## 33 70.3 7.8 28.8
## 34 92.3 21 34.0
## 35 79.3 26.6 49.6
## 36 82.6 11.6 1.3
## 37 86.0 10 14.5
## 38 77.9 5.8 14.2
## 39 78.6 99 43.9
## 40 89.8 5.4 6.8
## 41 59.6 7.2 16.1
## 42 95.6 12.3 69.2
## 43 91.8 28.3 18.9
## 44 71.5 12.7 34.6
## 45 92.0 13.9 36.7
## 46 46.2 7.6 8.8
## 47 58.1 14.6 62.7
## 48 41.1 42.2 97.4
## 49 59.8 23.1 99.6
## 50 79.2 21.9 30.5
## 51 76.4 12.3 13.3
## 52 85.2 4.5 9.7
## 53 78.1 9 18.3
## 54 4.1 5.1 99.7
## 55 69.3 35.3 96.8
## 56 67.1 5.9 19.3
## 57 55.6 7.1 12.4
## 58 75.3 6.6 11.9
## 59 44.7 16.3 62.1
## 60 49.7 13.3 47.9
## 61 76.4 6.5 27.3
## 62 45.6 6.6 11.5
## Inbound.Exchange International.Faculty Employer.Reputation
## 1 88.5 83.2 100.0
## 2 99.5 98.9 99.5
## 3 15.9 46 100.0
## 4 100.0 99.7 99.9
## 5 100.0 43.2 99.4
## 6 95.8 90.2 73.4
## 7 33.6 33 66.3
## 8 38.6 74 82.8
## 9 98.4 86 54.1
## 10 1.2 5.9 70.9
## 11 99.1 56.9 53.0
## 12 4.4 24.1 63.4
## 13 12.8 20.2 40.5
## 14 15.6 17.7 65.6
## 15 17.6 16 71.1
## 16 7.8 11.7 82.0
## 17 35.4 24.7 78.7
## 18 84.5 15.7 49.9
## 19 3.3 75.6 46.1
## 20 1.0 33 86.8
## 21 5.6 21.7 49.8
## 22 2.0 34.5 38.0
## 23 15.5 20.1 80.3
## 24 1.0 59.3 22.7
## 25 99.5 72.3 11.4
## 26 99.1 55.9 21.6
## 27 24.2 21.3 32.4
## 28 17.6 38.9 28.3
## 29 6.2 31 26.9
## 30 18.3 32.7 17.8
## 31 11.1 9.2 19.5
## 32 12.1 31.6 14.9
## 33 12.7 14.2 39.5
## 34 3.5 19.7 18.9
## 35 1.8 43.5 15.2
## 36 1.0 null 60.4
## 37 2.9 51.6 21.2
## 38 1.4 10.8 12.3
## 39 30.5 68.7 12.7
## 40 3.6 6.3 11.5
## 41 1.6 6.9 15.7
## 42 1.3 9.3 20.5
## 43 1.7 13.8 7.0
## 44 11.2 19.2 12.3
## 45 11.8 3.1 5.1
## 46 8.8 14.4 22.4
## 47 25.7 15.7 11.7
## 48 10.5 10.9 24.0
## 49 22.7 16.3 15.9
## 50 1.8 null 7.8
## 51 3.1 9.6 11.7
## 52 2.5 8.3 5.4
## 53 38.3 6.4 6.6
## 54 39.1 11.2 81.6
## 55 7.4 15.6 4.2
## 56 3.4 4.5 18.3
## 57 2.8 6.8 7.9
## 58 1.1 7.8 9.8
## 59 19.5 5.7 6.3
## 60 6.1 10.6 5.3
## 61 2.8 8.5 12.4
## 62 1.5 11.7 9.3# Crear una nueva columna: clasificar universidades por rango (ejemplo: Top 100, Top 200)
universities_data <- universities_data %>%
mutate(
Rank_Category = case_when(
Rank <= 100 ~ "Top 100",
Rank <= 200 ~ "Top 200",
TRUE ~ "Below 200"
)
)
# Mostrar las primeras filas después de la clasificación
df <- head(universities_data)
df## Rank Ordinal.Rank University.Name Overall.Score
## 1 1 1 Peking University 100.0
## 2 2 2 The University of Hong Kong 99.7
## 3 3 3 National University of Singapore (NUS) 98.9
## 4 4 4 Nanyang Technological University 98.3
## 5 5 5 Fudan University 97.2
## 6 6 6 The Chinese University of Hong Kong (CUHK) 96.7
## City Country Citations.per.Paper Papers.per.Faculty
## 1 Beijing China 96.4 79.8
## 2 Pokfulam Hong Kong 99.5 55.0
## 3 Singapore Singapore 99.9 57.4
## 4 Singapore Singapore 100.0 53.8
## 5 Shanghai China 92.1 63.1
## 6 Sha Tin Hong Kong 99.6 61.5
## Academic.Reputation Faculty.Student.Ratio Staff.with.PhD
## 1 100.0 98.6 90.7
## 2 100.0 93.3 97.4
## 3 100.0 85.8 82.5
## 4 100.0 93.0 67
## 5 99.8 92.5 73.4
## 6 99.9 84.0 80.3
## International.Research.Center International.Students Outbound.Exchange
## 1 98.0 69.1 100.0
## 2 98.4 100 100.0
## 3 99.9 99.2 97.6
## 4 99.7 98.8 97.9
## 5 92.1 81 94.9
## 6 96.0 99.4 96.1
## Inbound.Exchange International.Faculty Employer.Reputation Rank_Category
## 1 88.5 83.2 100.0 Top 100
## 2 99.8 100 96.8 Top 100
## 3 93.4 100 99.9 Top 100
## 4 90.5 100 98.8 Top 100
## 5 99.5 98.9 99.5 Top 100
## 6 100.0 100 91.5 Top 100# Agrupar por país y calcular estadísticas de interés
stats_by_country <- universities_data %>%
group_by(Country) %>%
summarize(
Avg_Score = mean(Overall.Score, na.rm = TRUE), # Usa el nombre correcto de la columna
Total_Universities = n()
) %>%
arrange(desc(Total_Universities))
# Mostrar los datos agrupados
df <- head(stats_by_country)
df## # A tibble: 6 × 3
## Country Avg_Score Total_Universities
## <chr> <dbl> <int>
## 1 China 51.9 62
## 2 India 43.9 41
## 3 South Korea 58.9 32
## 4 Japan 57.8 27
## 5 Malaysia 57.6 25
## 6 Taiwan 55.4 18# Creamos un gráfico de barras para los países con más universidades en el ranking
df <- stats_by_country %>% filter(Total_Universities > 10) # Seleccionar países con más de 10 universidades
ggplot(df, aes(x = reorder(Country, -Total_Universities), y = Total_Universities)) +
geom_bar(stat = "identity", fill = "steelblue") +
theme_minimal() +
labs(title = "Países con más Universidades en el Ranking", x = "País", y = "Cantidad de Universidades") +
coord_flip()
# Creamos un histograma de los puntajes generales
ggplot(universities_data, aes(x = Overall.Score)) +
geom_histogram(binwidth = 5, fill = "lightblue", color = "black") +
theme_minimal() +
labs(title = "Distribución de Puntajes Generales", x = "Puntaje General", y = "Frecuencia")
# Creamos un scatter plot de Overall Score vs Rank
ggplot(universities_data, aes(x = Rank, y = Overall.Score)) +
geom_point(alpha = 0.6, color = "darkgreen") +
theme_minimal() +
labs(title = "Relación entre Puntaje General y Ranking", x = "Ranking", y = "Puntaje General")
# Filtramos las universidades en el Top 10
df <- universities_data %>% filter(Rank <= 10)
df## Rank Ordinal.Rank University.Name Overall.Score
## 1 1 1 Peking University 100.0
## 2 2 2 The University of Hong Kong 99.7
## 3 3 3 National University of Singapore (NUS) 98.9
## 4 4 4 Nanyang Technological University 98.3
## 5 5 5 Fudan University 97.2
## 6 6 6 The Chinese University of Hong Kong (CUHK) 96.7
## 7 7 7 Tsinghua University 96.3
## 8 8 8 Zhejiang University 96.0
## 9 9 9 Yonsei University 95.4
## 10 10 10 City University of Hong Kong (CityUHK) 95.3
## City Country Citations.per.Paper Papers.per.Faculty
## 1 Beijing China 96.4 79.8
## 2 Pokfulam Hong Kong 99.5 55.0
## 3 Singapore Singapore 99.9 57.4
## 4 Singapore Singapore 100.0 53.8
## 5 Shanghai China 92.1 63.1
## 6 Sha Tin Hong Kong 99.6 61.5
## 7 Beijing China 98.6 80.8
## 8 Hangzhou China 86.0 96.2
## 9 Seoul South Korea 78.2 45.3
## 10 Kowloon Hong Kong 99.8 91.4
## Academic.Reputation Faculty.Student.Ratio Staff.with.PhD
## 1 100.0 98.6 90.7
## 2 100.0 93.3 97.4
## 3 100.0 85.8 82.5
## 4 100.0 93.0 67
## 5 99.8 92.5 73.4
## 6 99.9 84.0 80.3
## 7 100.0 99.0 96.9
## 8 99.3 54.2 95.1
## 9 99.8 94.8 98.4
## 10 96.6 97.7 97.7
## International.Research.Center International.Students Outbound.Exchange
## 1 98.0 69.1 100.0
## 2 98.4 100 100.0
## 3 99.9 99.2 97.6
## 4 99.7 98.8 97.9
## 5 92.1 81 94.9
## 6 96.0 99.4 96.1
## 7 97.9 51.7 59.6
## 8 99.9 66.4 100.0
## 9 97.7 90.9 68.4
## 10 88.9 100 100.0
## Inbound.Exchange International.Faculty Employer.Reputation Rank_Category
## 1 88.5 83.2 100.0 Top 100
## 2 99.8 100 96.8 Top 100
## 3 93.4 100 99.9 Top 100
## 4 90.5 100 98.8 Top 100
## 5 99.5 98.9 99.5 Top 100
## 6 100.0 100 91.5 Top 100
## 7 15.9 46 100.0 Top 100
## 8 100.0 99.7 99.9 Top 100
## 9 100.0 50.5 100.0 Top 100
## 10 100.0 100 74.0 Top 100# Agrupamos universidades por ciudad y sumamos puntajes generales
city_stats <- universities_data %>%
group_by(City) %>%
summarize(
Total_Score = sum(Overall.Score, na.rm = TRUE),
University_Count = n()
) %>%
arrange(desc(Total_Score))
# Mostrar los datos agrupados por ciudad
df <- head(city_stats)
df## # A tibble: 6 × 3
## City Total_Score University_Count
## <chr> <dbl> <int>
## 1 Seoul 952. 15
## 2 Beijing 715. 13
## 3 Tokyo 474. 8
## 4 Shanghai 449. 7
## 5 Almaty 309. 6
## 6 Singapore 292. 4# Creamos un gráfico de barras para las ciudades con mayor puntaje total
df <- city_stats %>% filter(University_Count > 3) # Ciudades con más de 3 universidades
ggplot(df, aes(x = reorder(City, -Total_Score), y = Total_Score)) +
geom_bar(stat = "identity", fill = "coral") +
theme_minimal() +
labs(title = "Ciudades con Mayor Puntaje Total", x = "Ciudad", y = "Puntaje Total") +
coord_flip()