Preamble

Standard packages

### Load packages
library(tidyverse) # Collection of all the good stuff like dplyr, ggplot2 ect.

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.4     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   2.0.1     ✓ forcats 0.5.1
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(magrittr) # For extra-piping operators (eg. %<>%)


Attaching package: ‘magrittr’

The following object is masked from ‘package:purrr’:

    set_names

The following object is masked from ‘package:tidyr’:

    extract

Load data

Trips

trips <- read_csv('https://sds-aau.github.io/SDS-master/M1/data/trips.csv')

New names:
* `` -> ...1

Rows: 46510 Columns: 11
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (6): username, country, country_code, country_slug, place, place_slug
dbl  (3): ...1, latitude, longitude
date (2): date_end, date_start

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

trips %>% glimpse()

Rows: 46,510
Columns: 11
$ ...1         <dbl> 0, 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, 4…
$ username     <chr> "@lewellenmichael", "@lewellenmichael", "@lewellenmichael", "@lewellenmichael", "@waylandchin", "@waylandchin", "@waylandchin", "@waylandchin", "@waylandchin", "@waylandch…
$ country      <chr> "Mexico", "Mexico", "Mexico", "Jordan", "China", "Vietnam", "Hong Kong", "China", "China", "China", "Thailand", "Malaysia", "Cambodia", "Vietnam", "India", "India", "India…
$ country_code <chr> "MX", "MX", "MX", "JO", "CN", "VN", "HK", "CN", "CN", "CN", "TH", "MY", "KH", "VN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "CN", "CN", "US", "US", "US", "US", "AE…
$ country_slug <chr> "mexico", "mexico", "mexico", "jordan", "china", "vietnam", "hong-kong", "china", "china", "china", "thailand", "malaysia", "cambodia", "vietnam", "india", "india", "india…
$ date_end     <date> 2018-06-15, 2018-06-03, 2017-11-05, 2017-08-07, 2017-03-18, 2017-02-16, 2016-09-01, 2016-08-02, 2016-07-31, 2016-07-03, 2016-06-03, 2016-05-22, 2016-04-14, 2016-02-15, 20…
$ date_start   <date> 2018-06-04, 2018-05-31, 2017-11-01, 2017-07-24, 2017-02-17, 2016-09-02, 2016-08-02, 2016-07-31, 2016-07-03, 2016-06-03, 2016-05-22, 2016-04-19, 2016-02-14, 2015-11-15, 20…
$ latitude     <dbl> 21, 19, 21, 31, 40, 10, 22, 22, 22, 18, 7, 3, 11, 10, 13, 26, 27, 27, 28, 28, 19, 11, 22, 22, 38, 43, 45, 42, 25, 1, 34, 55, 57, 57, 56, 55, 52, 50, 52, 15, 52, 53, 50, 53…
$ longitude    <dbl> -101, -99, -86, 35, 122, 106, 114, 114, 113, 109, 98, 101, 104, 106, 80, 75, 78, 78, 77, 77, 72, 79, 114, 114, -77, -89, -69, -71, 55, 103, -119, -4, -5, -4, -5, -3, 5, 4,…
$ place        <chr> "Guanajuato", "Mexico City", "Cancun", "Amman", "Yingkou", "Ho Chi Minh City", "Shenzhen", "Hong Kong", "Zhuhai", "Sanya", "Phuket", "Kuala Lumpur", "Phnom Penh", "Ho Chi …
$ place_slug   <chr> "mexico", "mexico-city-mexico", "cancun-mexico", "amman-jordan", "china", "ho-chi-minh-city-vietnam", "hong-kong", "hong-kong-china", "zhuhai-china", "china", "phuket-thai…

People

people <- read_csv('https://sds-aau.github.io/SDS-master/M1/data/people.csv')

New names:
* `` -> ...1

Rows: 4016 Columns: 6
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): username, work_raw, education_raw
dbl (3): ...1, followers, following

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

people %>% glimpse()

Rows: 4,016
Columns: 6
$ ...1          <dbl> 0, 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, …
$ username      <chr> "@lewellenmichael", "@waylandchin", "@karan", "@skaboss217", "@apwn", "@samcalma", "@paulbremer", "@jtompl", "@jezfx", "@markcaggiano", "@ndbroadbent", "@tedavery", "@wal…
$ followers     <dbl> 1, 0, 2, 0, 17, 3, 4, 2, 17, 2, 11, 11, 5, 8, 0, 9, 3, 5, 25, 1, 1, 1, 61, 2, 11, 0, 1, 2, 93, 3, 2, 6, 9, 14, 27, 0, 9, 0, 4, 5, 5, 8, 6, 5, 4, 12, 6, 10, 4, 1, 5, 1, 3,…
$ following     <dbl> 2, 2, 1, 1, 426, 3, 9, 3, 23, 2, 17, 6, 9, 7, 1, 6, 3, 34, 23, 4, 4, 4, 120, 2, 10, 2, 2, 5, 10, 4, 2, 1, 16, 14, 33, 1, 14, 2, 25, 15, 2, 7, 5, 11, 2, 9, 6, 11, 7, 2, 7,…
$ work_raw      <chr> "Software Dev, Startup Founder, Finance, Crypto, Product Manager, Education, Data, Ecommerce", NA, NA, NA, "Web Dev", NA, NA, "Web Dev, Software Dev, Startup Founder, Pro…
$ education_raw <chr> "High School, Bachelor's Degree", NA, NA, NA, NA, NA, NA, "High School, Bachelor's Degree, Master's Degree", NA, NA, NA, NA, NA, NA, "Master's Degree", "High School, Bach…

Countries

countries <- read_csv( 'https://sds-aau.github.io/SDS-master/M1/data/countrylist.csv')

Rows: 249 Columns: 3
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (3): alpha_2, region, sub_region

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

countries %>% glimpse()

Rows: 249
Columns: 3
$ alpha_2    <chr> "AF", "AX", "AL", "DZ", "AS", "AD", "AO", "AI", "AQ", "AG", "AR", "AM", "AW", "AU", "AT", "AZ", "BS", "BH", "BD", "BB", "BY", "BE", "BZ", "BJ", "BM", "BT", "BO", "BQ", "BA",…
$ region     <chr> "Asia", "Europe", "Europe", "Africa", "Oceania", "Europe", "Africa", "Americas", NA, "Americas", "Americas", "Asia", "Americas", "Oceania", "Europe", "Asia", "Americas", "As…
$ sub_region <chr> "Southern Asia", "Northern Europe", "Southern Europe", "Northern Africa", "Polynesia", "Southern Europe", "Sub-Saharan Africa", "Latin America and the Caribbean", NA, "Latin…

1: Preprocessing

a. Trips: transform dates into timestamps

trips %>% select(date_start, date_end) %>%
  glimpse()

Rows: 46,510
Columns: 2
$ date_start <date> 2018-06-04, 2018-05-31, 2017-11-01, 2017-07-24, 2017-02-17, 2016-09-02, 2016-08-02, 2016-07-31, 2016-07-03, 2016-06-03, 2016-05-22, 2016-04-19, 2016-02-14, 2015-11-15, 2017…
$ date_end   <date> 2018-06-15, 2018-06-03, 2017-11-05, 2017-08-07, 2017-03-18, 2017-02-16, 2016-09-01, 2016-08-02, 2016-07-31, 2016-07-03, 2016-06-03, 2016-05-22, 2016-04-14, 2016-02-15, 2017…

readr is smart, so if you loaded the data with read_csv, then this is already taken care of. Otherwise:

# To demonstrate, I transform it back to a string.
trips %<>% mutate(date_start = date_start %>% as.character(),
                  date_end = date_end %>% as.character())

trips %>% select(date_start, date_end) %>%
  glimpse()

Rows: 46,510
Columns: 2
$ date_start <chr> "2018-06-04", "2018-05-31", "2017-11-01", "2017-07-24", "2017-02-17", "2016-09-02", "2016-08-02", "2016-07-31", "2016-07-03", "2016-06-03", "2016-05-22", "2016-04-19", "2016…
$ date_end   <chr> "2018-06-15", "2018-06-03", "2017-11-05", "2017-08-07", "2017-03-18", "2017-02-16", "2016-09-01", "2016-08-02", "2016-07-31", "2016-07-03", "2016-06-03", "2016-05-22", "2016…

In case it is a string but well formated, we can use the lubridate packages.

library(lubridate) # This is tidyverse's datetime package


Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

    date, intersect, setdiff, union

trips %<>% mutate(date_start = date_start %>% as_date(),
                  date_end = date_end %>% as_date())

trips %>% select(date_start, date_end) %>%
  glimpse()

Rows: 46,510
Columns: 2
$ date_start <date> 2018-06-04, 2018-05-31, 2017-11-01, 2017-07-24, 2017-02-17, 2016-09-02, 2016-08-02, 2016-07-31, 2016-07-03, 2016-06-03, 2016-05-22, 2016-04-19, 2016-02-14, 2015-11-15, 2017…
$ date_end   <date> 2018-06-15, 2018-06-03, 2017-11-05, 2017-08-07, 2017-03-18, 2017-02-16, 2016-09-01, 2016-08-02, 2016-07-31, 2016-07-03, 2016-06-03, 2016-05-22, 2016-04-14, 2016-02-15, 2017…

b. Calculate trip duration in days

trips %<>% mutate(trip_duration = date_end - date_start)

# Test if it works
trips %>% 
  select(trip_duration, date_start, date_end) %>%
  head()

Seems to work fine :)

c. Filter extreme (fake?) observations for durations as well as dates - start and end

Lets inspect:

trips %>% 
  select(trip_duration, date_start, date_end) %>%
  summary()

 trip_duration       date_start            date_end         
 Length:46510      Min.   :0003-11-12   Min.   :0012-07-12  
 Class :difftime   1st Qu.:2015-08-29   1st Qu.:2015-09-29  
 Mode  :numeric    Median :2016-09-23   Median :2016-10-15  
                   Mean   :2015-09-28   Mean   :2014-07-10  
                   3rd Qu.:2017-08-26   3rd Qu.:2017-09-14  
                   Max.   :2106-06-19   Max.   :2222-01-01  
                   NA's   :14           NA's   :189

We clearly see that some observations areunrealistic (trip in Jesus’s times or in the future etc.). Lets look at the distribution

trips %>%
  ggplot(aes(x = date_start)) +
  geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

trips %>%
  ggplot(aes(x = date_end)) +
  geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

There are many ways to deal with outliers. To make it simple:

1: We could filter by some minimum / maximum date set manually 2: We could just delete extreme values using percentage_rank (deleting the 1 percent of obs with highest/lowest values). We will demonstrate this here:

trips %<>%
  mutate(date_start_pct = date_start %>% as.numeric() %>% percent_rank(),
         date_end_pct = date_end %>% as.numeric() %>% percent_rank()) %>%
  filter(date_start_pct >= 0.01 & date_start_pct <= 0.99) %>%
  filter(date_end_pct >= 0.01 & date_end_pct <= 0.99)

Lets check how it looks now:

trips %>% 
  select(trip_duration, date_start, date_end) %>%
  summary()

 trip_duration       date_start            date_end         
 Length:45176      Min.   :2002-09-11   Min.   :2003-06-08  
 Class :difftime   1st Qu.:2015-09-08   1st Qu.:2015-10-10  
 Mode  :numeric    Median :2016-09-20   Median :2016-10-14  
                   Mean   :2016-03-09   Mean   :2016-04-10  
                   3rd Qu.:2017-08-16   3rd Qu.:2017-09-06  
                   Max.   :2018-09-01   Max.   :2018-10-12

We clearly see that some observations areunrealistic (trip in Jesus’s times or in the future etc.). Lets look at the distribution

trips %>%
  ggplot(aes(x = date_start)) +
  geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

trips %>%
  ggplot(aes(x = date_end)) +
  geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Way more realistic, right?

d. Join the countrylist data to the trips data-frame using the countrycode as a key

A simple left join.Be only aware of the different variable names

trips %<>%
  left_join(countries, by = c("country_code" = "alpha_2"))

trips %>% head()

New variables are in, seems to work. Lets check if there are some trips that could not match:

trips %>% filter(is.na(region) | is.na(sub_region))

trips %>% filter(is.na(region) | is.na(sub_region)) %>%
  count(country_code, sort = TRUE)

Ok, we see some country codes did not match. We dont bother for most small numbers, but one thing we might take a look at: UK did not match, since it is coded GB in the countries dataframe (Just inspect it). Lets delete the newly matched variables and start over again.

trips %<>% 
  select(-region, -sub_region)

Lets replace UK with GB

trips %<>% 
  mutate(country_code = country_code %>% str_replace(pattern = 'UK', replacement = 'GB'))

trips %>%
  filter(country_code == 'UK' |country_code == 'GB') %>%
  count(country_code)

Ok, no more UK present… lets join again.

trips %<>%
  left_join(countries, by = c("country_code" = "alpha_2"))

trips %>% filter(is.na(region) | is.na(sub_region)) %>%
  count(country_code, sort = TRUE)

Ok, the rest seems negligible… we just delete these observations…

trips %<>% drop_na(region)

2: People

people %>% glimpse()

Rows: 4,016
Columns: 6
$ ...1          <dbl> 0, 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, …
$ username      <chr> "@lewellenmichael", "@waylandchin", "@karan", "@skaboss217", "@apwn", "@samcalma", "@paulbremer", "@jtompl", "@jezfx", "@markcaggiano", "@ndbroadbent", "@tedavery", "@wal…
$ followers     <dbl> 1, 0, 2, 0, 17, 3, 4, 2, 17, 2, 11, 11, 5, 8, 0, 9, 3, 5, 25, 1, 1, 1, 61, 2, 11, 0, 1, 2, 93, 3, 2, 6, 9, 14, 27, 0, 9, 0, 4, 5, 5, 8, 6, 5, 4, 12, 6, 10, 4, 1, 5, 1, 3,…
$ following     <dbl> 2, 2, 1, 1, 426, 3, 9, 3, 23, 2, 17, 6, 9, 7, 1, 6, 3, 34, 23, 4, 4, 4, 120, 2, 10, 2, 2, 5, 10, 4, 2, 1, 16, 14, 33, 1, 14, 2, 25, 15, 2, 7, 5, 11, 2, 9, 6, 11, 7, 2, 7,…
$ work_raw      <chr> "Software Dev, Startup Founder, Finance, Crypto, Product Manager, Education, Data, Ecommerce", NA, NA, NA, "Web Dev", NA, NA, "Web Dev, Software Dev, Startup Founder, Pro…
$ education_raw <chr> "High School, Bachelor's Degree", NA, NA, NA, NA, NA, NA, "High School, Bachelor's Degree, Master's Degree", NA, NA, NA, NA, NA, NA, "Master's Degree", "High School, Bach…

a: How many people have a least a “High School” diploma?

Lets see what educations we have in the data

people %>% count(education_raw)

Ok, that seems easy. Since all educations include Highschool (or higher), we can just assume that everybody that has all people with a non-missing education field have at least a highschool degree.

sum(!is.na(people$education_raw))

[1] 451

However, for the rest we just dont know…

b. How many “Startup Founders” have attained a “Master’s Degree”?

people %>% count(work_raw, sort = TRUE)

We cannot just filter for the string, since it is contained in multiple categories. We have to instead detect all strings where “Spartup Founder” appears. HEre we need the stringr package.

people %>% 
  filter(work_raw %>% str_detect('Startup Founder')) %>% 
  head()

people %>% 
  filter(education_raw %>% str_detect('Master\'s Degree')) %>% head()


# Notice the needed escape sign \ before the '

Putting it together and counting

people %>% 
  filter(work_raw %>% str_detect('Startup Founder') & education_raw %>% str_detect('Master\'s Degree') ) %>%
  summarise(n = n())

NA

Its 53…

3. Trips

trips %>% glimpse()

Rows: 45,124
Columns: 16
$ ...1           <dbl> 0, 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,…
$ username       <chr> "@lewellenmichael", "@lewellenmichael", "@lewellenmichael", "@lewellenmichael", "@waylandchin", "@waylandchin", "@waylandchin", "@waylandchin", "@waylandchin", "@wayland…
$ country        <chr> "Mexico", "Mexico", "Mexico", "Jordan", "China", "Vietnam", "Hong Kong", "China", "China", "China", "Thailand", "Malaysia", "Cambodia", "Vietnam", "India", "India", "Ind…
$ country_code   <chr> "MX", "MX", "MX", "JO", "CN", "VN", "HK", "CN", "CN", "CN", "TH", "MY", "KH", "VN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "IN", "CN", "CN", "US", "US", "US", "US", "…
$ country_slug   <chr> "mexico", "mexico", "mexico", "jordan", "china", "vietnam", "hong-kong", "china", "china", "china", "thailand", "malaysia", "cambodia", "vietnam", "india", "india", "ind…
$ date_end       <date> 2018-06-15, 2018-06-03, 2017-11-05, 2017-08-07, 2017-03-18, 2017-02-16, 2016-09-01, 2016-08-02, 2016-07-31, 2016-07-03, 2016-06-03, 2016-05-22, 2016-04-14, 2016-02-15, …
$ date_start     <date> 2018-06-04, 2018-05-31, 2017-11-01, 2017-07-24, 2017-02-17, 2016-09-02, 2016-08-02, 2016-07-31, 2016-07-03, 2016-06-03, 2016-05-22, 2016-04-19, 2016-02-14, 2015-11-15, …
$ latitude       <dbl> 21, 19, 21, 31, 40, 10, 22, 22, 22, 18, 7, 3, 11, 10, 13, 26, 27, 27, 28, 28, 19, 11, 22, 22, 38, 43, 45, 42, 25, 1, 34, 55, 57, 57, 56, 55, 52, 50, 52, 15, 52, 53, 50, …
$ longitude      <dbl> -101, -99, -86, 35, 122, 106, 114, 114, 113, 109, 98, 101, 104, 106, 80, 75, 78, 78, 77, 77, 72, 79, 114, 114, -77, -89, -69, -71, 55, 103, -119, -4, -5, -4, -5, -3, 5, …
$ place          <chr> "Guanajuato", "Mexico City", "Cancun", "Amman", "Yingkou", "Ho Chi Minh City", "Shenzhen", "Hong Kong", "Zhuhai", "Sanya", "Phuket", "Kuala Lumpur", "Phnom Penh", "Ho Ch…
$ place_slug     <chr> "mexico", "mexico-city-mexico", "cancun-mexico", "amman-jordan", "china", "ho-chi-minh-city-vietnam", "hong-kong", "hong-kong-china", "zhuhai-china", "china", "phuket-th…
$ trip_duration  <drtn> 11 days, 3 days, 4 days, 14 days, 29 days, 167 days, 30 days, 2 days, 28 days, 30 days, 12 days, 33 days, 60 days, 92 days, 29 days, 2 days, 1 days, 1 days, 4 days, 4 d…
$ date_start_pct <dbl> 0.95692010, 0.95414561, 0.80311861, 0.72446500, 0.60333369, 0.48489085, 0.46589956, 0.46402839, 0.44695129, 0.42716421, 0.41881923, 0.39642972, 0.35259705, 0.28917088, 0…
$ date_end_pct   <dbl> 0.9515976, 0.9441062, 0.7909758, 0.7200777, 0.6090242, 0.5862047, 0.4690415, 0.4508636, 0.4485967, 0.4315415, 0.4113774, 0.4026339, 0.3758636, 0.3352332, 0.5750000, 0.55…
$ region         <chr> "Americas", "Americas", "Americas", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia…
$ sub_region     <chr> "Latin America and the Caribbean", "Latin America and the Caribbean", "Latin America and the Caribbean", "Western Asia", "Eastern Asia", "South-eastern Asia", "Eastern A…

a. Which country received the highest number of trips? – And which the lowest?

Thats easy…

trips %>%
  count(country_code) %>%
  arrange(desc(n)) %>%
  head()

The US recieves the most trips.

trips %>%
  count(country_code) %>%
  arrange(n) %>%
  head()

Hmm, all some weird country codes… we could no filter them for only official ones… but lets leave it like that for now…

b. Which region received the highest number of trips in 2017? Use the start of trips as a time reference.

Since the dates are already datetimes, we can just extract the year with the year() function of lubridate.

trips %>%
  filter(year(date_start) == 2017) %>%
  count(country_code, sort = TRUE)

Its again the US.

c. Which country in “Western Europe” did travelers spent least time? – Provide visualization

trips %>%
  filter(sub_region == 'Western Europe') %>%
#  count(country_code, sort = TRUE) %>%
  ggplot(aes(x = country_code)) +
  geom_bar()

Could be done pettier, though

d. Do nomad Startup Founders tend to have shorter or longer trips on average?

Here, we first need to do a join with the people dataframe.
Then we only have to create a founder dummy variable and summarize.

trips %>% 
  left_join(people %>% select(username, work_raw), by = 'username') %>%
  mutate(founder = work_raw %>% str_detect('Startup Founder')) %>%
  group_by(founder) %>%
  summarize(duration_mean = trip_duration %>% mean(na.rm = TRUE))

Indeed, it seems they on average have the shortest trips… busy people….

---
title: "Assignment: Data Manipulation, EDA and Viz - Nomad Dataset (Example Solution)"
author: "Daniel S. Hain (dsh@business.aau.dk)"
date: "Updated `r format(Sys.time(), '%B %d, %Y')`"
output:
  html_notebook:
    code_folding: show
    df_print: paged
    toc: true
    toc_depth: 2
    toc_float:
      collapsed: false
    theme: flatly
---

```{r setup, include=FALSE}
# Knitr options
### Generic preamble
Sys.setenv(LANG = "en") # For english language
options(scipen = 5) # To deactivate annoying scientific number notation

# rm(list=ls()); graphics.off() # get rid of everything in the workspace
if (!require("knitr")) install.packages("knitr"); library(knitr) # For display of the markdown

### Knitr options
knitr::opts_chunk$set(warning=FALSE,
                     message=FALSE,
                     fig.align="center"
                     )
```

# Preamble

## Standard packages

```{r}
### Load packages
library(tidyverse) # Collection of all the good stuff like dplyr, ggplot2 ect.
library(magrittr) # For extra-piping operators (eg. %<>%)
```

## Load data

Trips

```{r}
trips <- read_csv('https://sds-aau.github.io/SDS-master/M1/data/trips.csv')
```

```{r}
trips %>% glimpse()
```

People

```{r}
people <- read_csv('https://sds-aau.github.io/SDS-master/M1/data/people.csv')
```

```{r}
people %>% glimpse()
```

Countries

```{r}
countries <- read_csv( 'https://sds-aau.github.io/SDS-master/M1/data/countrylist.csv')
```

```{r}
countries %>% glimpse()
```

# 1: Preprocessing

## a. Trips: transform dates into timestamps

```{r}
trips %>% select(date_start, date_end) %>%
  glimpse()
```

`readr` is smart, so if you loaded the data with `read_csv`, then this is already taken care of. Otherwise:

```{r}
# To demonstrate, I transform it back to a string.
trips %<>% mutate(date_start = date_start %>% as.character(),
                  date_end = date_end %>% as.character())
```

```{r}
trips %>% select(date_start, date_end) %>%
  glimpse()
```

In case it is a string but well formated, we can use the `lubridate` packages.

```{r}
library(lubridate) # This is tidyverse's datetime package

trips %<>% mutate(date_start = date_start %>% as_date(),
                  date_end = date_end %>% as_date())
```

```{r}
trips %>% select(date_start, date_end) %>%
  glimpse()
```

## b. Calculate trip duration in days

```{r}
trips %<>% mutate(trip_duration = date_end - date_start)
```

```{r}
# Test if it works
trips %>% 
  select(trip_duration, date_start, date_end) %>%
  head()
```

Seems to work fine :)

## c. Filter extreme (fake?) observations for durations as well as dates - start and end

Lets inspect:

```{r}
trips %>% 
  select(trip_duration, date_start, date_end) %>%
  summary()
```

We clearly see that some observations areunrealistic (trip in Jesus's times or in the future etc.). Lets look at the distribution

```{r}
trips %>%
  ggplot(aes(x = date_start)) +
  geom_histogram()
```

```{r}
trips %>%
  ggplot(aes(x = date_end)) +
  geom_histogram()
```

There are many ways to deal with outliers. To make it simple:

1: We could filter by some minimum / maximum date set manually
2: We could just delete extreme values using `percentage_rank` (deleting the 1 percent of obs with highest/lowest values). We will demonstrate this here:

```{r}
trips %<>%
  mutate(date_start_pct = date_start %>% as.numeric() %>% percent_rank(),
         date_end_pct = date_end %>% as.numeric() %>% percent_rank()) %>%
  filter(date_start_pct >= 0.01 & date_start_pct <= 0.99) %>%
  filter(date_end_pct >= 0.01 & date_end_pct <= 0.99) 
```

Lets check how it looks now:

```{r}
trips %>% 
  select(trip_duration, date_start, date_end) %>%
  summary()
```

We clearly see that some observations areunrealistic (trip in Jesus's times or in the future etc.). Lets look at the distribution

```{r}
trips %>%
  ggplot(aes(x = date_start)) +
  geom_histogram()
```
```{r}
trips %>%
  ggplot(aes(x = date_end)) +
  geom_histogram()
```

Way more realistic, right?

## d. Join the countrylist data to the trips data-frame using the countrycode as a key

A simple left join.Be only aware of the different variable names

```{r}
trips %<>%
  left_join(countries, by = c("country_code" = "alpha_2"))
```

```{r}
trips %>% head()
```

New variables are in, seems to work. Lets check if there are some trips that could not match:

```{r}
trips %>% filter(is.na(region) | is.na(sub_region))
```

```{r}
trips %>% filter(is.na(region) | is.na(sub_region)) %>%
  count(country_code, sort = TRUE)
```

Ok, we see some country codes did not match. We dont bother for most small numbers, but one thing we might take a look at: UK did not match, since it is coded GB in the countries dataframe (Just inspect it). Lets delete the newly matched variables and start over again.

```{r}
trips %<>% 
  select(-region, -sub_region)
```

Lets replace UK with GB

```{r}
trips %<>% 
  mutate(country_code = country_code %>% str_replace(pattern = 'UK', replacement = 'GB'))
```

```{r}
trips %>%
  filter(country_code == 'UK' |country_code == 'GB') %>%
  count(country_code)
```

Ok, no more UK present... lets join again.

```{r}
trips %<>%
  left_join(countries, by = c("country_code" = "alpha_2"))
```

```{r}
trips %>% filter(is.na(region) | is.na(sub_region)) %>%
  count(country_code, sort = TRUE)
```

Ok, the rest seems negligible... we just delete these observations...

```{r}
trips %<>% drop_na(region)
```

# 2: People

```{r}
people %>% glimpse()
```


## a: How many people have a least a “High School” diploma?

Lets see what educations we have in the data

```{r}
people %>% count(education_raw)
```

Ok, that seems easy. Since all educations include Highschool (or higher), we can just assume that everybody that has all people with a non-missing education field have at least a highschool degree.

```{r}
sum(!is.na(people$education_raw))
```

However, for the rest we just dont know...

## b. How many “Startup Founders” have attained a “Master’s Degree”?

```{r}
people %>% count(work_raw, sort = TRUE)
```


We cannot just filter for the string, since it is contained in multiple categories. We have to instead detect all strings where "Spartup Founder" appears. HEre we need the `stringr` package.

```{r}
people %>% 
  filter(work_raw %>% str_detect('Startup Founder')) %>% 
  head()
```

```{r}
people %>% 
  filter(education_raw %>% str_detect('Master\'s Degree')) %>% head()

# Notice the needed escape sign \ before the '
```

Putting it together and counting

```{r}
people %>% 
  filter(work_raw %>% str_detect('Startup Founder') & education_raw %>% str_detect('Master\'s Degree') ) %>%
  summarise(n = n())

```

Its 53...

# 3. Trips

```{r}
trips %>% glimpse()
```


## a. Which country received the highest number of trips? – And which the lowest?

Thats easy...

```{r}
trips %>%
  count(country_code) %>%
  arrange(desc(n)) %>%
  head()
```

The US recieves the most trips.

```{r}
trips %>%
  count(country_code) %>%
  arrange(n) %>%
  head()
```

Hmm, all some weird country codes... we could no filter them for only official ones... but lets leave it like that for now...

## b. Which region received the highest number of trips in 2017? Use the start of trips as a time reference.

Since the dates are already datetimes, we can just extract the year with the year() function of lubridate.

```{r}
trips %>%
  filter(year(date_start) == 2017) %>%
  count(country_code, sort = TRUE)
```
Its again the US.

## c. Which country in “Western Europe” did travelers spent least time? – Provide visualization

```{r}
trips %>%
  filter(sub_region == 'Western Europe') %>%
#  count(country_code, sort = TRUE) %>%
  ggplot(aes(x = country_code)) +
  geom_bar()
```

Could be done pettier, though


## d. Do nomad Startup Founders tend to have shorter or longer trips on average?

* Here, we first need to do a join with the people dataframe.
* Then we only have to create a founder dummy variable and summarize.

```{r}
trips %>% 
  left_join(people %>% select(username, work_raw), by = 'username') %>%
  mutate(founder = work_raw %>% str_detect('Startup Founder')) %>%
  group_by(founder) %>%
  summarize(duration_mean = trip_duration %>% mean(na.rm = TRUE))
```
Indeed, it seems they on average have the shortest trips... busy people....





Assignment: Data Manipulation, EDA and Viz - Nomad Dataset (Example Solution)

Daniel S. Hain (dsh@business.aau.dk)

Updated September 20, 2021