reviews <- read.csv("Hotel_Reviews.csv", stringsAsFactors = FALSE)library(stringr)
reviews$Reviewer_Nationality <-
str_trim(reviews$Reviewer_Nationality, side = 'both')country_neg <- function(comp_country){
library(dplyr)
country_comp <- reviews %>%
filter(Reviewer_Nationality==comp_country) %>%
tally()
country_comp[1,1]
guest_comp <- reviews %>%
filter(Reviewer_Nationality==comp_country) %>%
filter(Negative_Review!='No Negative') %>%
tally()
guest_comp[1,1] / country_comp[1,1]
}nationality <- unique(reviews$Reviewer_Nationality)
library(dplyr)
guest_comp_ratio <- sapply(nationality, country_neg)
df_comp <- data.frame(nationality, guest_comp_ratio)df_comp %>% arrange(-guest_comp_ratio) %>%
top_n(80)## Selecting by guest_comp_ratio## nationality guest_comp_ratio
## 1 Burundi 1.0000000
## 2 Papua New Guinea 1.0000000
## 3 Montserrat 1.0000000
## 4 Yemen 1.0000000
## 5 Belize 1.0000000
## 6 Liberia 1.0000000
## 7 Lesotho 1.0000000
## 8 Svalbard Jan Mayen 1.0000000
## 9 Djibouti 1.0000000
## 10 Saint Barts 1.0000000
## 11 Kiribati 1.0000000
## 12 Cook Islands 1.0000000
## 13 Congo 1.0000000
## 14 Somalia 1.0000000
## 15 Saint Vincent Grenadines 1.0000000
## 16 San Marino 1.0000000
## 17 Vatican City 1.0000000
## 18 Anguilla 1.0000000
## 19 Northern Mariana Islands 1.0000000
## 20 Tuvalu 1.0000000
## 21 Guinea 1.0000000
## 22 American Samoa 1.0000000
## 23 Palau 1.0000000
## 24 Turks Caicos Islands 0.9285714
## 25 Laos 0.8888889
## 26 British Virgin Islands 0.8750000
## 27 Faroe Islands 0.8750000
## 28 Afghanistan 0.8750000
## 29 United States Minor Outlying Islands 0.8513514
## 30 Uganda 0.8510638
## 31 Liechtenstein 0.8500000
## 32 Oman 0.8433283
## 33 Zambia 0.8378378
## 34 Saudi Arabia 0.8343202
## 35 Haiti 0.8333333
## 36 Kuwait 0.8327236
## 37 Jamaica 0.8292683
## 38 Tanzania 0.8275862
## 39 Qatar 0.8200943
## 40 Syria 0.8181818
## 41 Bahrain 0.8153266
## 42 Seychelles 0.8095238
## 43 Kyrgyzstan 0.8095238
## 44 Egypt 0.8086265
## 45 Bangladesh 0.8079470
## 46 Ghana 0.8068966
## 47 Nigeria 0.8065507
## 48 Azerbaijan 0.8050542
## 49 Cura ao 0.8048780
## 50 Sudan 0.8039216
## 51 United Arab Emirates 0.8038105
## 52 Uzbekistan 0.8000000
## 53 Malawi 0.8000000
## 54 Guyana 0.8000000
## 55 Mali 0.8000000
## 56 Turkmenistan 0.8000000
## 57 Kenya 0.7918216
## 58 Pakistan 0.7860262
## 59 Botswana 0.7857143
## 60 Cayman Islands 0.7857143
## 61 Antigua Barbuda 0.7857143
## 62 Guam 0.7857143
## 63 Jersey 0.7856315
## 64 India 0.7814955
## 65 Montenegro 0.7812500
## 66 Ethiopia 0.7812500
## 67 Iran 0.7780847
## 68 Abkhazia Georgia 0.7777778
## 69 Saint Kitts and Nevis 0.7777778
## 70 South Korea 0.7736549
## 71 Isle of Man 0.7728395
## 72 Jordan 0.7714663
## 73 Mozambique 0.7714286
## 74 Singapore 0.7712714
## 75 Spain 0.7701077
## 76 Mongolia 0.7692308
## 77 Indonesia 0.7667959
## 78 Portugal 0.7667210
## 79 Romania 0.7666960
## 80 Netherlands 0.7666439