-
Notifications
You must be signed in to change notification settings - Fork 1
/
Hotel_data.R
209 lines (152 loc) · 8.79 KB
/
Hotel_data.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
library(tidyr)
library(dplyr)
#===============================================================================================================
# Hotel Occupancy Rate
#================================================================================================================
# hotel website link yearly for Occupancy Rate
#==============================================
# First 10 provinces
url_link1 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=8&MS=1&GA=1,2,3,4,5,6,7,8,9,10&PR=&PSR="
# next 10 cities
url_link2 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=8&MS=1&GA=&PR=1,2,3,4,5,6,&PSR=a0,a1,b0,b1,"
# next 10 cities
url_link3 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=8&MS=1&GA=&PR=7,&PSR=b2,b3,b4,b5,b6,c0,c1,c2,c3,"
# next 10 cities
url_link4 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=8&MS=1&GA=&PR=8,9,10,11,&PSR=d0,d1,e0,e1,e2,f0,"
#next 10 cities
url_link5 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=8&MS=1&GA=&PR=12,13,14,&PSR=f1,g0,g1,h0,h1,h2,i0,"
#last 5 cities
url_link6 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=8&MS=1&GA=&PR=15,16,17,18,&PSR=i1,"
dt1 <- read.csv(url(url_link1),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt1)[1] <- "Year"
dt1 <- dt1 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt2 <- read.csv(url(url_link2),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt2)[1] <- "Year"
dt2 <- dt2 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt3 <- read.csv(url(url_link3),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt3)[1] <- "Year"
dt3 <- dt3 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt4 <- read.csv(url(url_link4),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt4)[1] <- "Year"
dt4 <- dt4 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt5 <- read.csv(url(url_link5),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt5)[1] <- "Year"
dt5 <- dt5 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt6 <- read.csv(url(url_link6),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt6)[1] <- "Year"
dt6 <- dt6 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
Hotel_occupancy_rate <- rbind(dt1, dt2, dt3, dt4, dt5, dt6)%>% dplyr::mutate("Type" = "Hotel Occupancy Rate")
#===============================================================================================================
# Hotel Average Daily Rate
#================================================================================================================
# hotel website link for monthly average daily rate
#=======================================================
# First 10 provinces
url_link1 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=8&MS=2&GA=1,2,3,4,5,6,7,8,9,10&PR=&PSR="
# next 10 cities
url_link2 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=2&GA=&PR=1,2,3,4,5,6,&PSR=a0,a1,b0,b1,"
# next 10 cities
url_link3 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=2&GA=&PR=7,&PSR=b2,b3,b4,b5,b6,c0,c1,c2,c3,"
# next 10 cities
url_link4 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=2&GA=&PR=8,9,10,11,&PSR=d0,d1,e0,e1,e2,f0,"
#next 10 cities
url_link5 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=2&GA=&PR=12,13,14,&PSR=f1,g0,g1,h0,h1,h2,i0,"
#last 5 cities
url_link6 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=2&GA=&PR=15,16,17,18,&PSR=i1,"
dt1 <- read.csv(url(url_link1),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt1)[1] <- "Year"
dt1 <- dt1 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt2 <- read.csv(url(url_link2),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt2)[1] <- "Year"
dt2 <- dt2 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt3 <- read.csv(url(url_link3),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt3)[1] <- "Year"
dt3 <- dt3 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt4 <- read.csv(url(url_link4),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt4)[1] <- "Year"
dt4 <- dt4 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt5 <- read.csv(url(url_link5),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt5)[1] <- "Year"
dt5 <- dt5 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt6 <- read.csv(url(url_link6),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt6)[1] <- "Year"
dt6 <- dt6 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
Average_daily_rate <- rbind(dt1, dt2, dt3, dt4, dt5, dt6)%>% dplyr::mutate("Type" = "Average Daily Rate")
#===============================================================================================================
# Hotel Revenue per Available Room
#================================================================================================================
# hotel website link for monthly revenue per available Room
#===========================================================
# First 10 provinces
url_link1 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=3&GA=1,2,3,4,5,6,7,8,9,10&PR=&PSR="
# next 10 cities
url_link2 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=3&GA=&PR=1,2,3,4,5,6,&PSR=a0,a1,b0,b1,"
# next 10 cities
url_link3 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=3&GA=&PR=7,&PSR=b2,b3,b4,b5,b6,c0,c1,c2,c3,"
# next 10 cities
url_link4 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=3&GA=&PR=8,9,10,11,&PSR=d0,d1,e0,e1,e2,f0,"
#next 10 cities
url_link5 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=3&GA=&PR=12,13,14,&PSR=f1,g0,g1,h0,h1,h2,i0,"
#last 5 cities
url_link6 <- "http://www.mtc-currentperformance.com/HotelDataXML.aspx?querytype=1&type=csv&sy=2001&sm=1&ey=2022&em=12&MS=3&GA=&PR=15,16,17,18,&PSR=i1,"
dt1 <- read.csv(url(url_link1),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt1)[1] <- "Year"
dt1 <- dt1 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt2 <- read.csv(url(url_link2),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt2)[1] <- "Year"
dt2 <- dt2 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt3 <- read.csv(url(url_link3),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt3)[1] <- "Year"
dt3 <- dt3 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt4 <- read.csv(url(url_link4),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt4)[1] <- "Year"
dt4 <- dt4 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt5 <- read.csv(url(url_link5),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt5)[1] <- "Year"
dt5 <- dt5 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
dt6 <- read.csv(url(url_link6),header = T, check.names = F)%>%
select(where(~ any(!is.na(.))))
colnames(dt6)[1] <- "Year"
dt6 <- dt6 %>%
tidyr::pivot_longer(!c(Year,Month), names_to = "GEO", values_to = "Value")
Revenue_per_room <- rbind(dt1, dt2, dt3, dt4, dt5, dt6)%>% dplyr::mutate("Type" = "Revenue per Available Room")
# Combine all three datasets
#============================
Hotel_data <- rbind(Hotel_occupancy_rate, Average_daily_rate, Revenue_per_room)
# save data
#==========
write.csv(Hotel_data, file = paste0("Data/Hotel_data.csv") , row.names = F)