Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 2
challenge_2
railroads
faostat
hotel_bookings
Challenge Overview
Today’s challenge is to
- read in a data set, and describe the data using both words and any supporting information (e.g., tables, etc)
- provide summary statistics for different interesting groups within the data, and interpret those statistics
Read in the Data
Read in one (or more) of the following data sets, available in the posts/_data folder, using the correct R package and command.
- railroad*.csv or StateCounty2012.xls ⭐
- FAOstat*.csv or birds.csv ⭐⭐⭐
- hotel_bookings.csv ⭐⭐⭐⭐
Code
hotel1 <- read_csv("_data/hotel_bookings.csv")
hotel1# A tibble: 119,390 × 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 Resor… 0 342 2015 July 27 1 0 0 2
2 Resor… 0 737 2015 July 27 1 0 0 2
3 Resor… 0 7 2015 July 27 1 0 1 1
4 Resor… 0 13 2015 July 27 1 0 1 1
5 Resor… 0 14 2015 July 27 1 0 2 2
6 Resor… 0 14 2015 July 27 1 0 2 2
7 Resor… 0 0 2015 July 27 1 0 2 2
8 Resor… 0 9 2015 July 27 1 0 2 2
9 Resor… 1 85 2015 July 27 1 0 3 2
10 Resor… 1 75 2015 July 27 1 0 3 2
# … with 119,380 more rows, 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>, …Add any comments or documentation as needed. More challenging data may require additional code chunks and documentation.
Describe the data
Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).
After using the read_csv function we can see that the data has 119320 rows and 32 columns. After using the summary function we can see that: - The dates span from 2015 to 2017 for this data set. - In one booking 55 adults booked a room when we check the max of the adults column. - In one booking 10 children and in another booking 10 babies based on max values of the children and the babies column respectively. - A booking was on waiting list for a maximum of 391 days!! - The maximum number of 5 special requests was made on one booking. - The maximum number of 8 parking spaces were requested on one booking, looks like it was the time 55 people came to the place.XD
Code
summary(hotel1) hotel is_canceled lead_time arrival_date_year
Length:119390 Min. :0.0000 Min. : 0 Min. :2015
Class :character 1st Qu.:0.0000 1st Qu.: 18 1st Qu.:2016
Mode :character Median :0.0000 Median : 69 Median :2016
Mean :0.3704 Mean :104 Mean :2016
3rd Qu.:1.0000 3rd Qu.:160 3rd Qu.:2017
Max. :1.0000 Max. :737 Max. :2017
arrival_date_month arrival_date_week_number arrival_date_day_of_month
Length:119390 Min. : 1.00 Min. : 1.0
Class :character 1st Qu.:16.00 1st Qu.: 8.0
Mode :character Median :28.00 Median :16.0
Mean :27.17 Mean :15.8
3rd Qu.:38.00 3rd Qu.:23.0
Max. :53.00 Max. :31.0
stays_in_weekend_nights stays_in_week_nights adults
Min. : 0.0000 Min. : 0.0 Min. : 0.000
1st Qu.: 0.0000 1st Qu.: 1.0 1st Qu.: 2.000
Median : 1.0000 Median : 2.0 Median : 2.000
Mean : 0.9276 Mean : 2.5 Mean : 1.856
3rd Qu.: 2.0000 3rd Qu.: 3.0 3rd Qu.: 2.000
Max. :19.0000 Max. :50.0 Max. :55.000
children babies meal country
Min. : 0.0000 Min. : 0.000000 Length:119390 Length:119390
1st Qu.: 0.0000 1st Qu.: 0.000000 Class :character Class :character
Median : 0.0000 Median : 0.000000 Mode :character Mode :character
Mean : 0.1039 Mean : 0.007949
3rd Qu.: 0.0000 3rd Qu.: 0.000000
Max. :10.0000 Max. :10.000000
NA's :4
market_segment distribution_channel is_repeated_guest
Length:119390 Length:119390 Min. :0.00000
Class :character Class :character 1st Qu.:0.00000
Mode :character Mode :character Median :0.00000
Mean :0.03191
3rd Qu.:0.00000
Max. :1.00000
previous_cancellations previous_bookings_not_canceled reserved_room_type
Min. : 0.00000 Min. : 0.0000 Length:119390
1st Qu.: 0.00000 1st Qu.: 0.0000 Class :character
Median : 0.00000 Median : 0.0000 Mode :character
Mean : 0.08712 Mean : 0.1371
3rd Qu.: 0.00000 3rd Qu.: 0.0000
Max. :26.00000 Max. :72.0000
assigned_room_type booking_changes deposit_type agent
Length:119390 Min. : 0.0000 Length:119390 Length:119390
Class :character 1st Qu.: 0.0000 Class :character Class :character
Mode :character Median : 0.0000 Mode :character Mode :character
Mean : 0.2211
3rd Qu.: 0.0000
Max. :21.0000
company days_in_waiting_list customer_type adr
Length:119390 Min. : 0.000 Length:119390 Min. : -6.38
Class :character 1st Qu.: 0.000 Class :character 1st Qu.: 69.29
Mode :character Median : 0.000 Mode :character Median : 94.58
Mean : 2.321 Mean : 101.83
3rd Qu.: 0.000 3rd Qu.: 126.00
Max. :391.000 Max. :5400.00
required_car_parking_spaces total_of_special_requests reservation_status
Min. :0.00000 Min. :0.0000 Length:119390
1st Qu.:0.00000 1st Qu.:0.0000 Class :character
Median :0.00000 Median :0.0000 Mode :character
Mean :0.06252 Mean :0.5714
3rd Qu.:0.00000 3rd Qu.:1.0000
Max. :8.00000 Max. :5.0000
reservation_status_date
Min. :2014-10-17
1st Qu.:2016-02-01
Median :2016-08-07
Mean :2016-07-30
3rd Qu.:2017-02-08
Max. :2017-09-14
Provide Grouped Summary Statistics
Conduct some exploratory data analysis, using dplyr commands such as group_by(), select(), filter(), and summarise(). Find the central tendency (mean, median, mode) and dispersion (standard deviation, mix/max/quantile) for different subgroups within the data set.
Code
hotel2 <- hotel1 %>% group_by(hotel, arrival_date_month, arrival_date_year)
hotel3 <- hotel2 %>% summarise(Babies = sum(babies), Children = sum(children, na.rm = TRUE), Adults = sum(adults))
hotel3# A tibble: 52 × 6
# Groups: hotel, arrival_date_month [24]
hotel arrival_date_month arrival_date_year Babies Children Adults
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 City Hotel April 2016 11 339 6671
2 City Hotel April 2017 13 448 7619
3 City Hotel August 2015 16 165 4697
4 City Hotel August 2016 26 730 6904
5 City Hotel August 2017 19 604 6240
6 City Hotel December 2015 23 120 2907
7 City Hotel December 2016 15 378 4702
8 City Hotel February 2016 13 194 4124
9 City Hotel February 2017 18 305 4722
10 City Hotel January 2016 16 69 2200
# … with 42 more rowsCode
x <- filter(hotel3, arrival_date_year == 2015)
y <- filter(hotel3, arrival_date_year == 2016)
z <- filter(hotel3, arrival_date_year == 2017)
x1 <- filter(x, hotel == "City Hotel")
x1# A tibble: 6 × 6
# Groups: hotel, arrival_date_month [6]
hotel arrival_date_month arrival_date_year Babies Children Adults
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 City Hotel August 2015 16 165 4697
2 City Hotel December 2015 23 120 2907
3 City Hotel July 2015 2 14 2671
4 City Hotel November 2015 8 25 1918
5 City Hotel October 2015 21 98 5910
6 City Hotel September 2015 14 90 6282Code
y1 <- filter(y, hotel == "City Hotel")
y1# A tibble: 12 × 6
# Groups: hotel, arrival_date_month [12]
hotel arrival_date_month arrival_date_year Babies Children Adults
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 City Hotel April 2016 11 339 6671
2 City Hotel August 2016 26 730 6904
3 City Hotel December 2016 15 378 4702
4 City Hotel February 2016 13 194 4124
5 City Hotel January 2016 16 69 2200
6 City Hotel July 2016 21 605 6231
7 City Hotel June 2016 17 222 7012
8 City Hotel March 2016 15 279 5599
9 City Hotel May 2016 7 254 6671
10 City Hotel November 2016 14 147 5412
11 City Hotel October 2016 13 336 7755
12 City Hotel September 2016 27 261 7287Code
z1 <- filter(z, hotel == "City Hotel")
z1# A tibble: 8 × 6
# Groups: hotel, arrival_date_month [8]
hotel arrival_date_month arrival_date_year Babies Children Adults
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 City Hotel April 2017 13 448 7619
2 City Hotel August 2017 19 604 6240
3 City Hotel February 2017 18 305 4722
4 City Hotel January 2017 14 244 4178
5 City Hotel July 2017 12 574 7073
6 City Hotel June 2017 11 347 7507
7 City Hotel March 2017 16 165 6259
8 City Hotel May 2017 10 235 8287Code
x2 <- filter(x, hotel == "Resort Hotel")
x2# A tibble: 6 × 6
# Groups: hotel, arrival_date_month [6]
hotel arrival_date_month arrival_date_year Babies Children Adults
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Resort Hotel August 2015 51 263 2848
2 Resort Hotel December 2015 23 96 2301
3 Resort Hotel July 2015 24 252 2752
4 Resort Hotel November 2015 13 47 1796
5 Resort Hotel October 2015 11 94 2950
6 Resort Hotel September 2015 17 93 3230Code
y2 <- filter(y, hotel == "Resort Hotel")
y2# A tibble: 12 × 6
# Groups: hotel, arrival_date_month [12]
hotel arrival_date_month arrival_date_year Babies Children Adults
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Resort Hotel April 2016 13 114 3323
2 Resort Hotel August 2016 55 438 3447
3 Resort Hotel December 2016 17 142 2472
4 Resort Hotel February 2016 14 128 2742
5 Resort Hotel January 2016 7 47 1496
6 Resort Hotel July 2016 38 319 2887
7 Resort Hotel June 2016 24 201 2625
8 Resort Hotel March 2016 19 164 3185
9 Resort Hotel May 2016 25 152 3311
10 Resort Hotel November 2016 10 60 2362
11 Resort Hotel October 2016 17 175 3664
12 Resort Hotel September 2016 29 154 2882Code
z2 <- filter(z, hotel == "Resort Hotel")
z2# A tibble: 8 × 6
# Groups: hotel, arrival_date_month [8]
hotel arrival_date_month arrival_date_year Babies Children Adults
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Resort Hotel April 2017 16 240 3193
2 Resort Hotel August 2017 29 580 3659
3 Resort Hotel February 2017 14 163 2862
4 Resort Hotel January 2017 11 92 2150
5 Resort Hotel July 2017 24 558 3550
6 Resort Hotel June 2017 19 287 3209
7 Resort Hotel March 2017 7 92 2632
8 Resort Hotel May 2017 30 204 3270Code
summary(x) hotel arrival_date_month arrival_date_year Babies
Length:12 Length:12 Min. :2015 Min. : 2.00
Class :character Class :character 1st Qu.:2015 1st Qu.:12.50
Mode :character Mode :character Median :2015 Median :16.50
Mean :2015 Mean :18.58
3rd Qu.:2015 3rd Qu.:23.00
Max. :2015 Max. :51.00
Children Adults
Min. : 14.00 Min. :1796
1st Qu.: 79.25 1st Qu.:2578
Median : 95.00 Median :2878
Mean :113.08 Mean :3355
3rd Qu.:131.25 3rd Qu.:3597
Max. :263.00 Max. :6282 Code
summary(y) hotel arrival_date_month arrival_date_year Babies
Length:24 Length:24 Min. :2016 Min. : 7.00
Class :character Class :character 1st Qu.:2016 1st Qu.:13.00
Mode :character Mode :character Median :2016 Median :16.50
Mean :2016 Mean :19.29
3rd Qu.:2016 3rd Qu.:24.25
Max. :2016 Max. :55.00
Children Adults
Min. : 47.0 Min. :1496
1st Qu.:145.8 1st Qu.:2847
Median :197.5 Median :3556
Mean :246.2 Mean :4374
3rd Qu.:323.2 3rd Qu.:6341
Max. :730.0 Max. :7755 Code
summary(z) hotel arrival_date_month arrival_date_year Babies
Length:16 Length:16 Min. :2017 Min. : 7.00
Class :character Class :character 1st Qu.:2017 1st Qu.:11.75
Mode :character Mode :character Median :2017 Median :15.00
Mean :2017 Mean :16.44
3rd Qu.:2017 3rd Qu.:19.00
Max. :2017 Max. :30.00
Children Adults
Min. : 92.0 Min. :2150
1st Qu.:194.2 1st Qu.:3205
Median :265.5 Median :3918
Mean :321.1 Mean :4776
3rd Qu.:475.5 3rd Qu.:6462
Max. :604.0 Max. :8287 Code
summary(x1) hotel arrival_date_month arrival_date_year Babies
Length:6 Length:6 Min. :2015 Min. : 2.00
Class :character Class :character 1st Qu.:2015 1st Qu.: 9.50
Mode :character Mode :character Median :2015 Median :15.00
Mean :2015 Mean :14.00
3rd Qu.:2015 3rd Qu.:19.75
Max. :2015 Max. :23.00
Children Adults
Min. : 14.00 Min. :1918
1st Qu.: 41.25 1st Qu.:2730
Median : 94.00 Median :3802
Mean : 85.33 Mean :4064
3rd Qu.:114.50 3rd Qu.:5607
Max. :165.00 Max. :6282 Code
summary(y1) hotel arrival_date_month arrival_date_year Babies
Length:12 Length:12 Min. :2016 Min. : 7.00
Class :character Class :character 1st Qu.:2016 1st Qu.:13.00
Mode :character Mode :character Median :2016 Median :15.00
Mean :2016 Mean :16.25
3rd Qu.:2016 3rd Qu.:18.00
Max. :2016 Max. :27.00
Children Adults
Min. : 69.0 Min. :2200
1st Qu.:215.0 1st Qu.:5234
Median :270.0 Median :6451
Mean :317.8 Mean :5881
3rd Qu.:348.8 3rd Qu.:6931
Max. :730.0 Max. :7755 Code
summary(z1) hotel arrival_date_month arrival_date_year Babies
Length:8 Length:8 Min. :2017 Min. :10.00
Class :character Class :character 1st Qu.:2017 1st Qu.:11.75
Mode :character Mode :character Median :2017 Median :13.50
Mean :2017 Mean :14.12
3rd Qu.:2017 3rd Qu.:16.50
Max. :2017 Max. :19.00
Children Adults
Min. :165.0 Min. :4178
1st Qu.:241.8 1st Qu.:5860
Median :326.0 Median :6666
Mean :365.2 Mean :6486
3rd Qu.:479.5 3rd Qu.:7535
Max. :604.0 Max. :8287 Code
summary(x2) hotel arrival_date_month arrival_date_year Babies
Length:6 Length:6 Min. :2015 Min. :11.00
Class :character Class :character 1st Qu.:2015 1st Qu.:14.00
Mode :character Mode :character Median :2015 Median :20.00
Mean :2015 Mean :23.17
3rd Qu.:2015 3rd Qu.:23.75
Max. :2015 Max. :51.00
Children Adults
Min. : 47.00 Min. :1796
1st Qu.: 93.25 1st Qu.:2414
Median : 95.00 Median :2800
Mean :140.83 Mean :2646
3rd Qu.:213.00 3rd Qu.:2924
Max. :263.00 Max. :3230 Code
summary(y2) hotel arrival_date_month arrival_date_year Babies
Length:12 Length:12 Min. :2016 Min. : 7.00
Class :character Class :character 1st Qu.:2016 1st Qu.:13.75
Mode :character Mode :character Median :2016 Median :18.00
Mean :2016 Mean :22.33
3rd Qu.:2016 3rd Qu.:26.00
Max. :2016 Max. :55.00
Children Adults
Min. : 47.0 Min. :1496
1st Qu.:124.5 1st Qu.:2587
Median :153.0 Median :2884
Mean :174.5 Mean :2866
3rd Qu.:181.5 3rd Qu.:3314
Max. :438.0 Max. :3664 Code
summary(z2) hotel arrival_date_month arrival_date_year Babies
Length:8 Length:8 Min. :2017 Min. : 7.00
Class :character Class :character 1st Qu.:2017 1st Qu.:13.25
Mode :character Mode :character Median :2017 Median :17.50
Mean :2017 Mean :18.75
3rd Qu.:2017 3rd Qu.:25.25
Max. :2017 Max. :30.00
Children Adults
Min. : 92.0 Min. :2150
1st Qu.:145.2 1st Qu.:2804
Median :222.0 Median :3201
Mean :277.0 Mean :3066
3rd Qu.:354.8 3rd Qu.:3340
Max. :580.0 Max. :3659 Code
sum(hotel3$Babies, na.rm=TRUE)[1] 949Code
sum(hotel3$Children, na.rm=TRUE)[1] 12403Code
sum(hotel3$Adults, na.rm=TRUE)[1] 221636Code
sum(x1$Babies, na.rm = TRUE)[1] 84Code
sum(x1$Children, na.rm = TRUE)[1] 512Code
sum(x1$Adults, na.rm = TRUE)[1] 24385Code
sum(y1$Babies, na.rm = TRUE)[1] 195Code
sum(y1$Children, na.rm = TRUE)[1] 3814Code
sum(y1$Adults, na.rm = TRUE)[1] 70568Code
sum(z1$Babies, na.rm = TRUE)[1] 113Code
sum(z1$Children, na.rm = TRUE)[1] 2922Code
sum(z1$Adults, na.rm = TRUE)[1] 51885Code
sum(x2$Babies, na.rm = TRUE)[1] 139Code
sum(x2$Children, na.rm = TRUE)[1] 845Code
sum(x2$Adults, na.rm = TRUE)[1] 15877Code
sum(y2$Babies, na.rm = TRUE)[1] 268Code
sum(y2$Children, na.rm = TRUE)[1] 2094Code
sum(y2$Adults, na.rm = TRUE)[1] 34396Code
sum(z2$Babies, na.rm = TRUE)[1] 150Code
sum(z2$Children, na.rm = TRUE)[1] 2216Code
sum(z2$Adults, na.rm = TRUE)[1] 24525Explain and Interpret
Be sure to explain why you choose a specific group. Comment on the interpretation of any interesting differences between groups that you uncover. This section can be integrated with the exploratory data analysis, just be sure it is included.
So the data analysis that I have done is in regard to how many babies, children and adults stayed in each hotel for the three different years which are city hotel and resort hotel for the years 2015, 2016 and 2017 respectively.
From the analysis we can see that for city hotel the analysis is:
Babies: 2015 ==> 84, 2016 ==> 195, 2017 ==> 113
Children: 2015 ==> 512, 2016 ==> 3814, 2017 ==> 2922
Adults: 2015 ==> 24385, 2016 ==> 70568, 2017 ==> 51885
For the resort hotel it is:
Babies: 2015 ==> 139, 2016 ==> 845, 2017 ==> 15877
Children: 2015 ==> 268, 2016 ==> 2094, 2017 ==> 34396
Adults: 2015 ==> 150, 2016 ==> 2216, 2017 ==> 24525
When we take the analysis for both hotels together, the total number of babies, children and adults are 949, 12403 and 221636 respectively.
The minimum and maximum number of babies for the city hotel in a month for the years are:
City Hotel:
Max 2015 ==>23, 2016 ==>27, 2017 ==>19 Min 2015 ==> 2, 2016 ==> 7, 2017 ==>10
Resort Hotel: Max 2015 ==>51, 2016 ==>55, 2017 ==>30 Min 2015 ==>11, 2016 ==> 7, 2017 ==>7
The minimum and maximum number of children for the city hotel in a month for the years are:
City Hotel:
Max 2015 ==> 165, 2016 ==> 730, 2017 ==> 604 Min 2015 ==> 14, 2016 ==> 69, 2017 ==> 165
Resort Hotel: Max 2015 ==> 263, 2016 ==> 438, 2017 ==> 580 Min 2015 ==> 47, 2016 ==> 47, 2017 ==> 92
The minimum and maximum number of adults for the city hotel in a month for the years are:
City Hotel:
Max 2015 ==> 6282, 2016 ==> 7755, 2017 ==> 8287 Min 2015 ==> 1918, 2016 ==> 2200, 2017 ==> 4178
Resort Hotel: Max 2015 ==> 3230, 2016 ==> 3664, 2017 ==> 3659 Min 2015 ==> 1796, 2016 ==> 1496, 2017 ==> 2150