Code
library(tidyverse)
library(lubridate)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Mutating dataset using lubridate, stringr: hotel_bookings.csv
challenge_4
hotel_bookings.csv
readr
lubridate, stringr
Read in data
Code
library(readr)
hotel_bookings_data <- read_csv("_data/hotel_bookings.csv")
View(hotel_bookings_data)Code
# Preview the first few rows of the dataset
head(hotel_bookings_data)# A tibble: 6 × 32
hotel is_ca…¹ lead_…² arriv…³ arriv…⁴ arriv…⁵ arriv…⁶ stays…⁷ stays…⁸ adults
<chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Resort… 0 342 2015 July 27 1 0 0 2
2 Resort… 0 737 2015 July 27 1 0 0 2
3 Resort… 0 7 2015 July 27 1 0 1 1
4 Resort… 0 13 2015 July 27 1 0 1 1
5 Resort… 0 14 2015 July 27 1 0 2 2
6 Resort… 0 14 2015 July 27 1 0 2 2
# … with 22 more variables: children <dbl>, babies <dbl>, meal <chr>,
# country <chr>, market_segment <chr>, distribution_channel <chr>,
# is_repeated_guest <dbl>, previous_cancellations <dbl>,
# previous_bookings_not_canceled <dbl>, reserved_room_type <chr>,
# assigned_room_type <chr>, booking_changes <dbl>, deposit_type <chr>,
# agent <chr>, company <chr>, days_in_waiting_list <dbl>,
# customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>, …Code
# Understanding the dimensions of the dataset
dim(hotel_bookings_data)[1] 119390 32Code
# Identifying the column names of the dataset
colnames(hotel_bookings_data) [1] "hotel" "is_canceled"
[3] "lead_time" "arrival_date_year"
[5] "arrival_date_month" "arrival_date_week_number"
[7] "arrival_date_day_of_month" "stays_in_weekend_nights"
[9] "stays_in_week_nights" "adults"
[11] "children" "babies"
[13] "meal" "country"
[15] "market_segment" "distribution_channel"
[17] "is_repeated_guest" "previous_cancellations"
[19] "previous_bookings_not_canceled" "reserved_room_type"
[21] "assigned_room_type" "booking_changes"
[23] "deposit_type" "agent"
[25] "company" "days_in_waiting_list"
[27] "customer_type" "adr"
[29] "required_car_parking_spaces" "total_of_special_requests"
[31] "reservation_status" "reservation_status_date" Code
# Changing column name to make it more readable
colnames(hotel_bookings_data)[28] <- "average_daily_rate"
# Identifying the data types of the columns
sapply(hotel_bookings_data, class) hotel is_canceled
"character" "numeric"
lead_time arrival_date_year
"numeric" "numeric"
arrival_date_month arrival_date_week_number
"character" "numeric"
arrival_date_day_of_month stays_in_weekend_nights
"numeric" "numeric"
stays_in_week_nights adults
"numeric" "numeric"
children babies
"numeric" "numeric"
meal country
"character" "character"
market_segment distribution_channel
"character" "character"
is_repeated_guest previous_cancellations
"numeric" "numeric"
previous_bookings_not_canceled reserved_room_type
"numeric" "character"
assigned_room_type booking_changes
"character" "numeric"
deposit_type agent
"character" "character"
company days_in_waiting_list
"character" "numeric"
customer_type average_daily_rate
"character" "numeric"
required_car_parking_spaces total_of_special_requests
"numeric" "numeric"
reservation_status reservation_status_date
"character" "Date" Code
table(sapply(hotel_bookings_data, function(x) typeof(x)))
character double
13 19 Code
sapply(hotel_bookings_data, function(x) n_distinct(x)) hotel is_canceled
2 2
lead_time arrival_date_year
479 3
arrival_date_month arrival_date_week_number
12 53
arrival_date_day_of_month stays_in_weekend_nights
31 17
stays_in_week_nights adults
35 14
children babies
6 5
meal country
5 178
market_segment distribution_channel
8 5
is_repeated_guest previous_cancellations
2 15
previous_bookings_not_canceled reserved_room_type
73 10
assigned_room_type booking_changes
12 21
deposit_type agent
3 334
company days_in_waiting_list
353 128
customer_type average_daily_rate
4 8879
required_car_parking_spaces total_of_special_requests
5 6
reservation_status reservation_status_date
3 926 Code
# Identifying unique values for columns - hotel, country, distribution_channel
unique(hotel_bookings_data$hotel)[1] "Resort Hotel" "City Hotel" Code
unique(hotel_bookings_data$country) [1] "PRT" "GBR" "USA" "ESP" "IRL" "FRA" "NULL" "ROU" "NOR" "OMN"
[11] "ARG" "POL" "DEU" "BEL" "CHE" "CN" "GRC" "ITA" "NLD" "DNK"
[21] "RUS" "SWE" "AUS" "EST" "CZE" "BRA" "FIN" "MOZ" "BWA" "LUX"
[31] "SVN" "ALB" "IND" "CHN" "MEX" "MAR" "UKR" "SMR" "LVA" "PRI"
[41] "SRB" "CHL" "AUT" "BLR" "LTU" "TUR" "ZAF" "AGO" "ISR" "CYM"
[51] "ZMB" "CPV" "ZWE" "DZA" "KOR" "CRI" "HUN" "ARE" "TUN" "JAM"
[61] "HRV" "HKG" "IRN" "GEO" "AND" "GIB" "URY" "JEY" "CAF" "CYP"
[71] "COL" "GGY" "KWT" "NGA" "MDV" "VEN" "SVK" "FJI" "KAZ" "PAK"
[81] "IDN" "LBN" "PHL" "SEN" "SYC" "AZE" "BHR" "NZL" "THA" "DOM"
[91] "MKD" "MYS" "ARM" "JPN" "LKA" "CUB" "CMR" "BIH" "MUS" "COM"
[101] "SUR" "UGA" "BGR" "CIV" "JOR" "SYR" "SGP" "BDI" "SAU" "VNM"
[111] "PLW" "QAT" "EGY" "PER" "MLT" "MWI" "ECU" "MDG" "ISL" "UZB"
[121] "NPL" "BHS" "MAC" "TGO" "TWN" "DJI" "STP" "KNA" "ETH" "IRQ"
[131] "HND" "RWA" "KHM" "MCO" "BGD" "IMN" "TJK" "NIC" "BEN" "VGB"
[141] "TZA" "GAB" "GHA" "TMP" "GLP" "KEN" "LIE" "GNB" "MNE" "UMI"
[151] "MYT" "FRO" "MMR" "PAN" "BFA" "LBY" "MLI" "NAM" "BOL" "PRY"
[161] "BRB" "ABW" "AIA" "SLV" "DMA" "PYF" "GUY" "LCA" "ATA" "GTM"
[171] "ASM" "MRT" "NCL" "KIR" "SDN" "ATF" "SLE" "LAO" Code
unique(hotel_bookings_data$distribution_channel)[1] "Direct" "Corporate" "TA/TO" "Undefined" "GDS" Briefly describe the data
This dataset gives the information about hotel reservations. The dataset has 11930 rows and 32 columns. Based on the above, we can infer that there are 13 columns(variables) are of character datatype and 19 variables are of double datatype. Almost all the columns are readable for the user, except adr(column 28) which stands for average daily rate. The hotel reservation data is provided for two types of hotels(Resort, City hotels) in various countries(178 countries) in years 2015, 2016 and 2017. Each observation in the dataset provides information of hotel category, reservation of the hotel information like arrival date(date, month, year, week), number of visitors(adult, children), country, number of days staying, meal, previous cancellations, reserved room types, customer type, number of special requests etc.
Tidy Data (as needed)
Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.
In the process of tidying the data we can check if there are any NULL values in the data, and if the class type and values present in a particular column are easy to identify or not. For example, a class can be defined as a character, but there might be numeric values in it, which makes it hard for the user in analyzing the dataset.
While analyzing the dataset, I have found that few of the columns like country, company has NULL values in it. I will remove the country cases where there is a NULL value since it doesn’t help in data analysis. I will also change the class of company and agent to numeric since they have numeric values in it but are defined as character classes. I will make the NULL values in these two columns as NA.
Code
table(hotel_bookings_data$country)
ABW AGO AIA ALB AND ARE ARG ARM ASM ATA ATF AUS AUT
2 362 1 12 7 51 214 8 1 2 1 426 1263
AZE BDI BEL BEN BFA BGD BGR BHR BHS BIH BLR BOL BRA
17 1 2342 3 1 12 75 5 1 13 26 10 2224
BRB BWA CAF CHE CHL CHN CIV CMR CN COL COM CPV CRI
4 1 5 1730 65 999 6 10 1279 71 2 24 19
CUB CYM CYP CZE DEU DJI DMA DNK DOM DZA ECU EGY ESP
8 1 51 171 7287 1 1 435 14 103 27 32 8568
EST ETH FIN FJI FRA FRO GAB GBR GEO GGY GHA GIB GLP
83 3 447 1 10415 5 4 12129 22 3 4 18 2
GNB GRC GTM GUY HKG HND HRV HUN IDN IMN IND IRL IRN
9 128 4 1 29 1 100 230 35 2 152 3375 83
IRQ ISL ISR ITA JAM JEY JOR JPN KAZ KEN KHM KIR KNA
14 57 669 3766 6 8 21 197 19 6 2 1 2
KOR KWT LAO LBN LBY LCA LIE LKA LTU LUX LVA MAC MAR
133 16 2 31 8 1 3 7 81 287 55 16 259
MCO MDG MDV MEX MKD MLI MLT MMR MNE MOZ MRT MUS MWI
4 1 12 85 10 1 18 1 5 67 1 7 2
MYS MYT NAM NCL NGA NIC NLD NOR NPL NULL NZL OMN PAK
28 2 1 1 34 1 2104 607 1 488 74 18 14
PAN PER PHL PLW POL PRI PRT PRY PYF QAT ROU RUS RWA
9 29 40 1 919 12 48590 4 1 15 500 632 2
SAU SDN SEN SGP SLE SLV SMR SRB STP SUR SVK SVN SWE
48 1 11 39 1 2 1 101 2 5 65 57 1024
SYC SYR TGO THA TJK TMP TUN TUR TWN TZA UGA UKR UMI
2 3 2 59 9 3 39 248 51 5 2 68 1
URY USA UZB VEN VGB VNM ZAF ZMB ZWE
32 2097 4 26 1 8 80 2 4 There are 488 rows with country value as NULL.
Code
table(hotel_bookings_data$company)
10 100 101 102 103 104 105 106 107 108 109
1 1 1 1 16 1 8 2 9 11 1
11 110 112 113 115 116 118 12 120 122 126
1 52 13 36 4 6 7 14 14 18 1
127 130 132 135 137 139 14 140 142 143 144
15 12 1 66 4 3 9 1 1 17 27
146 148 149 150 153 154 158 159 16 160 163
3 37 5 19 215 133 2 6 5 1 17
165 167 168 169 174 178 179 18 180 183 184
3 7 2 65 149 27 24 1 5 16 1
185 186 192 193 195 197 20 200 202 203 204
4 12 4 16 38 47 50 3 38 13 34
207 209 210 212 213 215 216 217 218 219 22
9 19 2 1 1 8 21 2 43 141 6
220 221 222 223 224 225 227 229 230 232 233
4 27 2 784 3 7 24 1 3 2 114
234 237 238 240 242 243 245 246 250 251 253
1 1 33 3 62 2 3 3 2 18 1
254 255 257 258 259 260 263 264 268 269 270
10 6 1 1 2 3 14 2 14 33 43
271 272 273 274 275 277 278 279 28 280 281
2 3 1 14 3 5 2 8 5 48 138
282 284 286 287 288 289 29 290 291 292 293
4 1 21 5 1 2 2 17 12 18 5
297 301 302 304 305 307 308 309 31 311 312
7 1 5 2 1 36 33 1 17 2 3
313 314 316 317 318 319 32 320 321 323 324
1 1 2 9 1 3 1 1 2 10 9
325 329 330 331 332 333 334 337 338 34 341
2 12 4 61 2 11 3 25 12 8 5
342 343 346 347 348 349 35 350 351 352 353
48 29 14 1 59 2 1 3 2 1 4
355 356 357 358 360 361 362 364 365 366 367
13 10 5 7 12 2 2 6 29 24 14
368 369 37 370 371 372 373 376 377 378 379
1 5 10 2 11 3 1 1 5 3 9
38 380 382 383 384 385 386 388 39 390 391
51 12 5 6 9 30 1 7 8 13 2
392 393 394 395 396 397 398 399 40 400 401
4 1 6 4 18 15 1 11 927 2 1
402 403 405 407 408 409 410 411 412 413 415
1 2 119 22 15 12 5 2 1 1 1
416 417 418 419 42 420 421 422 423 424 425
1 1 25 1 5 1 9 1 2 24 1
426 428 429 43 433 435 436 437 439 442 443
4 13 2 29 2 12 2 7 6 1 5
444 445 446 447 448 45 450 451 452 454 455
5 4 1 2 4 250 10 6 4 1 1
456 457 458 459 46 460 461 465 466 47 470
2 3 2 5 26 3 1 12 3 72 5
477 478 479 48 481 482 483 484 485 486 487
23 2 1 5 1 2 2 2 14 2 1
489 49 490 491 492 494 496 497 498 499 501
1 5 5 2 2 4 1 1 58 1 1
504 506 507 51 511 512 513 514 515 516 518
11 1 23 99 6 3 2 2 6 1 2
52 520 521 523 525 528 53 530 531 534 539
2 1 7 19 15 2 8 5 1 2 2
54 541 543 59 6 61 62 64 65 67 68
1 1 2 7 1 2 47 1 1 267 46
71 72 73 76 77 78 8 80 81 82 83
2 30 3 1 1 22 1 1 23 14 9
84 85 86 88 9 91 92 93 94 96 99
3 2 32 22 37 48 13 3 87 1 12
NULL
112593 Code
table(hotel_bookings_data$agent)
1 10 103 104 105 106 107 11 110 111 112 114 115
7191 260 21 53 14 2 2 395 12 16 15 1 225
117 118 119 12 121 122 126 127 128 129 13 132 133
1 69 304 578 37 2 14 45 23 14 82 143 56
134 135 138 139 14 141 142 143 144 146 147 148 149
482 2 287 8 3640 6 137 172 1 124 156 4 28
15 150 151 152 153 154 155 156 157 158 159 16 162
402 5 56 183 25 193 94 190 61 1 89 246 37
163 165 167 168 17 170 171 173 174 175 177 179 180
7 1 3 184 241 93 607 29 22 195 347 2 4
181 182 183 184 185 187 19 191 192 193 195 196 197
59 8 45 52 78 24 1061 198 41 15 193 301 1
2 20 201 205 208 21 210 211 213 214 215 216 219
162 540 42 27 173 875 7 2 1 5 15 1 13
22 220 223 227 229 23 232 234 235 236 24 240 241
382 104 18 2 786 25 2 128 29 247 22 13922 1721
242 243 244 245 247 248 249 25 250 251 252 253 254
780 514 4 37 1 131 51 3 2870 220 29 87 29
256 257 258 26 261 262 265 267 269 27 270 273 275
24 24 3 401 38 22 1 1 2 450 6 349 8
276 278 28 280 281 282 283 285 286 287 288 289 29
8 1 1666 1 82 2 2 1 45 8 14 1 683
290 291 294 295 296 298 299 3 30 300 301 302 303
19 1 1 4 42 472 1 1336 484 1 1 3 2
304 305 306 307 308 31 310 313 314 315 32 321 323
1 45 35 14 54 162 25 36 927 284 15 3 25
324 325 326 327 328 33 330 331 332 333 334 335 336
9 6 165 20 9 31 125 2 55 1 28 4 23
337 339 34 341 344 346 348 35 350 352 354 355 358
1 77 294 4 8 1 22 109 28 1 14 4 1
359 36 360 363 364 367 368 37 370 371 375 378 38
21 100 15 6 19 1 45 1230 3 4 40 36 274
384 385 387 388 39 390 391 393 394 397 4 40 403
2 60 32 1 127 57 2 13 33 1 47 1039 4
404 405 406 408 41 410 411 414 416 418 42 420 423
2 5 1 1 75 133 16 2 1 8 211 3 19
425 426 427 429 430 431 432 433 434 436 438 44 440
16 3 3 5 4 1 1 1 33 49 2 292 56
441 444 446 449 45 450 451 453 454 455 459 461 464
7 1 1 2 32 1 1 1 2 19 16 2 98
467 468 469 47 472 474 475 476 479 480 481 483 484
39 49 2 50 1 17 8 2 32 1 8 1 11
492 493 495 497 5 50 502 508 509 510 52 526 527
28 35 57 1 330 20 24 6 10 2 137 10 35
53 531 535 54 55 56 57 58 59 6 60 61 63
18 68 3 1 16 375 28 335 1 3290 19 2 29
64 66 67 68 69 7 70 71 72 73 74 75 77
23 44 127 211 90 3539 1 73 6 1 20 73 33
78 79 8 81 82 83 85 86 87 88 89 9 90
37 47 1514 6 77 696 554 338 77 19 99 31961 1
91 92 93 94 95 96 98 99 NULL
58 7 1 114 135 537 124 68 16340 There are 16340 rows as NULL in agent column and 112593 rows as NULL in company column.
Code
# filtering out NULL values in country column
hotel_bookings_data <- hotel_bookings_data %>%
filter(!(country == "NULL"))
table(hotel_bookings_data$country)
ABW AGO AIA ALB AND ARE ARG ARM ASM ATA ATF AUS AUT
2 362 1 12 7 51 214 8 1 2 1 426 1263
AZE BDI BEL BEN BFA BGD BGR BHR BHS BIH BLR BOL BRA
17 1 2342 3 1 12 75 5 1 13 26 10 2224
BRB BWA CAF CHE CHL CHN CIV CMR CN COL COM CPV CRI
4 1 5 1730 65 999 6 10 1279 71 2 24 19
CUB CYM CYP CZE DEU DJI DMA DNK DOM DZA ECU EGY ESP
8 1 51 171 7287 1 1 435 14 103 27 32 8568
EST ETH FIN FJI FRA FRO GAB GBR GEO GGY GHA GIB GLP
83 3 447 1 10415 5 4 12129 22 3 4 18 2
GNB GRC GTM GUY HKG HND HRV HUN IDN IMN IND IRL IRN
9 128 4 1 29 1 100 230 35 2 152 3375 83
IRQ ISL ISR ITA JAM JEY JOR JPN KAZ KEN KHM KIR KNA
14 57 669 3766 6 8 21 197 19 6 2 1 2
KOR KWT LAO LBN LBY LCA LIE LKA LTU LUX LVA MAC MAR
133 16 2 31 8 1 3 7 81 287 55 16 259
MCO MDG MDV MEX MKD MLI MLT MMR MNE MOZ MRT MUS MWI
4 1 12 85 10 1 18 1 5 67 1 7 2
MYS MYT NAM NCL NGA NIC NLD NOR NPL NZL OMN PAK PAN
28 2 1 1 34 1 2104 607 1 74 18 14 9
PER PHL PLW POL PRI PRT PRY PYF QAT ROU RUS RWA SAU
29 40 1 919 12 48590 4 1 15 500 632 2 48
SDN SEN SGP SLE SLV SMR SRB STP SUR SVK SVN SWE SYC
1 11 39 1 2 1 101 2 5 65 57 1024 2
SYR TGO THA TJK TMP TUN TUR TWN TZA UGA UKR UMI URY
3 2 59 9 3 39 248 51 5 2 68 1 32
USA UZB VEN VGB VNM ZAF ZMB ZWE
2097 4 26 1 8 80 2 4 Identify variables that need to be mutated
Are there any variables that require mutation to be usable in your analysis stream? For example, are all time variables correctly coded as dates? Are all string variables reduced and cleaned to sensible categories? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?
I have observed that each of the date, month, year are presented in a different column, instead of this we can combine all three columns and have 1 column as date to track the reservations.
Code
# Mutating the month, date, year columns to 1 column - date arrived and mutating adults, children, babies to total_number_of_guests and mutating stays_in_weekend_nights, stays_in_week_nights to total_days_stay
hotel_bookings_data_m <- hotel_bookings_data %>%
mutate(date_arrived = str_c(arrival_date_month, arrival_date_day_of_month, arrival_date_year, sep = "/"),
date_arrived = mdy(date_arrived),
total_days_stay = stays_in_weekend_nights + stays_in_week_nights,
total_number_of_guests = adults + children + babies) %>%
select(-c(arrival_date_month, arrival_date_day_of_month, arrival_date_year))
hotel_bookings_data_m# A tibble: 118,902 × 32
hotel is_ca…¹ lead_…² arriv…³ stays…⁴ stays…⁵ adults child…⁶ babies meal
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 Resort H… 0 342 27 0 0 2 0 0 BB
2 Resort H… 0 737 27 0 0 2 0 0 BB
3 Resort H… 0 7 27 0 1 1 0 0 BB
4 Resort H… 0 13 27 0 1 1 0 0 BB
5 Resort H… 0 14 27 0 2 2 0 0 BB
6 Resort H… 0 14 27 0 2 2 0 0 BB
7 Resort H… 0 0 27 0 2 2 0 0 BB
8 Resort H… 0 9 27 0 2 2 0 0 FB
9 Resort H… 1 85 27 0 3 2 0 0 BB
10 Resort H… 1 75 27 0 3 2 0 0 HB
# … with 118,892 more rows, 22 more variables: country <chr>,
# market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
# reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>,
# deposit_type <chr>, agent <chr>, company <chr>, days_in_waiting_list <dbl>,
# customer_type <chr>, average_daily_rate <dbl>,
# required_car_parking_spaces <dbl>, total_of_special_requests <dbl>, …We can see that date_arrived, total_days_stay, total_number_of_guests columns are present after the mutation. Let’s understand the statistics of these columns.
Code
# Calculating summaries of date_arrived, total_days_stay, total_number_of_guests columns
summary(hotel_bookings_data_m$date_arrived) Min. 1st Qu. Median Mean 3rd Qu. Max.
"2015-07-01" "2016-03-14" "2016-09-07" "2016-08-29" "2017-03-19" "2017-08-31" Code
summary(hotel_bookings_data_m$total_days_stay) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 2.000 3.000 3.431 4.000 57.000 Code
summary(hotel_bookings_data_m$total_number_of_guests) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.000 2.000 1.971 2.000 55.000 4 Based on the above summary, we can say that the reservation data from July 1st 2015 to 31st August 2017 are present and the minimum and maximum number of days stayed are 0, 57. The minimum and maximum total number of guests stayed are 0 and 55.
Mutating the class of agent and company fields to numeric
Code
# Mutating the class of agent and company fields to numeric and updating NULL values as NA
hotel_bookings_data_m <- hotel_bookings_data_m %>%
mutate(across(c(company, agent), ~ replace(.,str_detect(.,"NULL"), NA))) %>% mutate_at(vars(company, agent), as.numeric)
# verifying whether the classes are updated as numeric for company and agent columns
is.numeric(hotel_bookings_data_m$company)[1] TRUECode
is.numeric(hotel_bookings_data_m$agent)[1] TRUECode
sapply(hotel_bookings_data_m, class) hotel is_canceled
"character" "numeric"
lead_time arrival_date_week_number
"numeric" "numeric"
stays_in_weekend_nights stays_in_week_nights
"numeric" "numeric"
adults children
"numeric" "numeric"
babies meal
"numeric" "character"
country market_segment
"character" "character"
distribution_channel is_repeated_guest
"character" "numeric"
previous_cancellations previous_bookings_not_canceled
"numeric" "numeric"
reserved_room_type assigned_room_type
"character" "character"
booking_changes deposit_type
"numeric" "character"
agent company
"numeric" "numeric"
days_in_waiting_list customer_type
"numeric" "character"
average_daily_rate required_car_parking_spaces
"numeric" "numeric"
total_of_special_requests reservation_status
"numeric" "character"
reservation_status_date date_arrived
"Date" "Date"
total_days_stay total_number_of_guests
"numeric" "numeric" Code
hotel_bookings_data_m# A tibble: 118,902 × 32
hotel is_ca…¹ lead_…² arriv…³ stays…⁴ stays…⁵ adults child…⁶ babies meal
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 Resort H… 0 342 27 0 0 2 0 0 BB
2 Resort H… 0 737 27 0 0 2 0 0 BB
3 Resort H… 0 7 27 0 1 1 0 0 BB
4 Resort H… 0 13 27 0 1 1 0 0 BB
5 Resort H… 0 14 27 0 2 2 0 0 BB
6 Resort H… 0 14 27 0 2 2 0 0 BB
7 Resort H… 0 0 27 0 2 2 0 0 BB
8 Resort H… 0 9 27 0 2 2 0 0 FB
9 Resort H… 1 85 27 0 3 2 0 0 BB
10 Resort H… 1 75 27 0 3 2 0 0 HB
# … with 118,892 more rows, 22 more variables: country <chr>,
# market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
# reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>,
# deposit_type <chr>, agent <dbl>, company <dbl>, days_in_waiting_list <dbl>,
# customer_type <chr>, average_daily_rate <dbl>,
# required_car_parking_spaces <dbl>, total_of_special_requests <dbl>, …