Cost of Living in Big Cities

Overview:

This data processing report is a part of task series to complete my course education in Full Stack Academy - Algoritma Data Science Education, Jakarta, Indonesia. This second task is about practising competencies for data visualization mostly with dplyr and ggplot libraries set of commands. The data itself can be downloaded from Kaggle.com.

Data source: https://www.kaggle.com/stephenofarrell/cost-of-living

Business Objective

This report is aimed to explore preliminary possibilities to setup “budget accomodation business - Capsul apartment/ hostel” in some most expensive cities measured by cost of living. Some indicators that will be analyzed are:

Cost of Mc Donalds Combo Meal
Cost of Meals in Expensive Restaurants
Cost of Coke & Pepsi
Apartment cost in the city areas
Cost of internet

Some indicators outside those 5 indicators above still displayed in a short glance in order to show the process of pre processing and exploratory steps, but not to be described in the form of graphs.

Last, some of summaries will be explained in short sentence to emphasize the report.

Read Data

library(tidyverse)
cost <- read.csv("cost-of-living.csv")

Inspect Data

01. Inspect data using glimpse

glimpse(cost)

## Observations: 55
## Variables: 161
## $ X                                  <fct> "Meal, Inexpensive Restaurant", ...
## $ Saint.Petersburg..Russia           <dbl> 7.34, 29.35, 4.40, 2.20, 2.20, 0...
## $ Istanbul..Turkey                   <dbl> 4.58, 15.28, 3.82, 3.06, 3.06, 0...
## $ Izmir..Turkey                      <dbl> 3.06, 12.22, 3.06, 2.29, 2.75, 0...
## $ Helsinki..Finland                  <dbl> 12.00, 65.00, 8.00, 6.50, 6.75, ...
## $ Chisinau..Moldova                  <dbl> 4.67, 20.74, 4.15, 1.04, 1.43, 0...
## $ Milan..Italy                       <dbl> 15.00, 60.00, 8.00, 5.00, 5.00, ...
## $ Cairo..Egypt                       <dbl> 3.38, 17.48, 4.51, 1.69, 2.82, 0...
## $ Banja.Luka..Bosnia.And.Herzegovina <dbl> 3.58, 22.99, 3.58, 1.02, 1.53, 1...
## $ Baku..Azerbaijan                   <dbl> 5.27, 23.73, 4.22, 0.84, 2.11, 0...
## $ Guadalajara..Mexico                <dbl> 5.25, 23.86, 4.25, 1.43, 2.39, 0...
## $ Kathmandu..Nepal                   <dbl> 1.99, 11.92, 5.56, 2.38, 3.18, 0...
## $ Hanoi..Vietnam                     <dbl> 1.94, 15.52, 3.88, 0.78, 1.55, 0...
## $ Ho.Chi.Minh.City..Vietnam          <dbl> 1.94, 17.50, 3.87, 0.78, 1.36, 0...
## $ Mexico.City..Mexico                <dbl> 4.77, 23.86, 4.77, 1.91, 2.86, 0...
## $ Rome..Italy                        <dbl> 15.00, 55.00, 8.00, 5.00, 4.00, ...
## $ Monterrey..Mexico                  <dbl> 5.75, 23.94, 4.31, 1.68, 2.39, 0...
## $ Yekaterinburg..Russia              <dbl> 5.88, 29.42, 4.12, 1.47, 2.94, 0...
## $ Sarajevo..Bosnia.And.Herzegovina   <dbl> 3.57, 20.42, 4.08, 1.79, 1.79, 1...
## $ Kharkiv..Ukraine                   <dbl> 4.50, 18.76, 3.75, 0.88, 1.29, 0...
## $ Kiev..Ukraine                      <dbl> 5.63, 22.51, 3.75, 1.13, 1.80, 0...
## $ Calgary..Canada                    <dbl> 13.75, 42.97, 6.87, 4.81, 5.50, ...
## $ Tunis..Tunisia                     <dbl> 1.92, 12.78, 3.19, 1.60, 1.60, 0...
## $ Edmonton..Canada                   <dbl> 13.75, 48.12, 6.53, 4.12, 4.12, ...
## $ Amsterdam..Netherlands             <dbl> 15.00, 65.00, 8.00, 5.00, 4.00, ...
## $ Belgrade..Serbia                   <dbl> 5.39, 24.62, 5.09, 1.70, 1.95, 1...
## $ Odessa..Ukraine                    <dbl> 5.65, 20.67, 3.67, 0.94, 1.20, 0...
## $ Paris..France                      <dbl> 15.00, 50.00, 8.70, 7.00, 6.00, ...
## $ Eindhoven..Netherlands             <dbl> 15.00, 56.50, 8.00, 5.00, 3.00, ...
## $ Plovdiv..Bulgaria                  <dbl> 5.10, 20.42, 4.59, 1.28, 1.53, 0...
## $ Thessaloniki..Greece               <dbl> 10.00, 35.00, 6.50, 3.50, 4.50, ...
## $ Ottawa..Canada                     <dbl> 11.00, 55.00, 6.87, 4.81, 4.81, ...
## $ Sofia..Bulgaria                    <dbl> 6.14, 25.57, 4.60, 1.53, 1.97, 0...
## $ Rotterdam..Netherlands             <dbl> 13.00, 50.00, 8.00, 4.00, 4.00, ...
## $ Varna..Bulgaria                    <dbl> 6.12, 20.42, 4.08, 1.02, 1.53, 0...
## $ Novi.Sad..Serbia                   <dbl> 4.84, 17.85, 4.42, 1.50, 1.80, 1...
## $ Utrecht..Netherlands               <dbl> 15.00, 65.00, 8.00, 5.00, 4.50, ...
## $ Berlin..Germany                    <dbl> 8.50, 40.00, 7.00, 3.50, 3.10, 2...
## $ Beirut..Lebanon                    <dbl> 8.98, 53.91, 7.19, 3.22, 4.17, 0...
## $ Austin..TX..United.States          <dbl> 13.48, 44.92, 7.19, 4.49, 5.39, ...
## $ Singapore..Singapore               <dbl> 8.33, 39.98, 5.33, 6.66, 8.00, 1...
## $ Toronto..Canada                    <dbl> 13.75, 55.00, 7.56, 5.16, 5.50, ...
## $ Auckland..New.Zealand              <dbl> 11.95, 50.78, 6.57, 5.68, 5.38, ...
## $ Podgorica..Montenegro              <dbl> 5.00, 25.00, 3.00, 1.50, 1.90, 1...
## $ Vancouver..Canada                  <dbl> 11.69, 55.00, 6.87, 4.81, 4.81, ...
## $ Tokyo..Japan                       <dbl> 8.19, 40.97, 5.70, 4.10, 4.92, 1...
## $ Victoria..Canada                   <dbl> 12.05, 49.93, 6.89, 4.82, 4.82, ...
## $ Winnipeg..Canada                   <dbl> 10.31, 41.25, 6.80, 4.12, 4.81, ...
## $ Boston..MA..United.States          <dbl> 13.47, 62.85, 7.18, 6.29, 6.73, ...
## $ Chicago..IL..United.States         <dbl> 13.47, 53.87, 7.18, 4.94, 6.29, ...
## $ Almaty..Kazakhstan                 <dbl> 4.77, 23.84, 3.81, 1.07, 1.43, 0...
## $ Oslo..Norway                       <dbl> 18.70, 80.88, 11.12, 9.00, 9.10,...
## $ Frankfurt..Germany                 <dbl> 10.00, 50.00, 8.00, 4.00, 3.50, ...
## $ Bratislava..Slovakia               <dbl> 6.00, 30.00, 6.00, 2.00, 2.00, 1...
## $ Dallas..TX..United.States          <dbl> 13.48, 44.92, 6.51, 4.49, 5.39, ...
## $ Zagreb..Croatia                    <dbl> 6.72, 33.58, 5.37, 2.01, 2.42, 1...
## $ Hamburg..Germany                   <dbl> 11.00, 50.00, 8.00, 4.00, 3.50, ...
## $ Krakow..Cracow...Poland            <dbl> 5.91, 23.62, 4.72, 2.13, 2.36, 1...
## $ Riga..Latvia                       <dbl> 7.00, 40.00, 5.00, 2.90, 2.20, 1...
## $ Gdansk..Poland                     <dbl> 5.91, 23.62, 4.25, 2.13, 2.36, 1...
## $ Santiago..Chile                    <dbl> 6.94, 34.68, 5.20, 2.95, 3.47, 0...
## $ Nairobi..Kenya                     <dbl> 4.43, 26.58, 6.20, 2.04, 2.66, 0...
## $ Abu.Dhabi..United.Arab.Emirates    <dbl> 6.12, 48.92, 6.24, 9.78, 9.78, 0...
## $ Houston..TX..United.States         <dbl> 13.48, 53.91, 6.74, 5.39, 5.84, ...
## $ Tbilisi..Georgia                   <dbl> 4.67, 18.69, 4.05, 0.93, 1.25, 0...
## $ Dubai..United.Arab.Emirates        <dbl> 7.33, 48.89, 6.11, 11.00, 11.00,...
## $ Bogota..Colombia                   <dbl> 3.30, 19.24, 4.40, 0.96, 2.20, 0...
## $ Brno..Czech.Republic               <dbl> 5.95, 25.19, 5.16, 1.39, 1.59, 1...
## $ Munich..Germany                    <dbl> 12.00, 60.00, 8.00, 3.80, 3.90, ...
## $ Poznan..Poland                     <dbl> 5.90, 23.61, 4.72, 2.01, 2.36, 0...
## $ Las.Vegas..NV..United.States       <dbl> 13.47, 53.87, 7.18, 5.39, 6.29, ...
## $ London..United.Kingdom             <dbl> 17.49, 64.15, 7.00, 5.83, 5.25, ...
## $ Los.Angeles..CA..United.States     <dbl> 13.47, 58.36, 7.18, 5.39, 6.29, ...
## $ Panama.City..Panama                <dbl> 8.98, 35.94, 6.06, 2.58, 3.59, 1...
## $ Seoul..South.Korea                 <dbl> 6.42, 31.11, 5.06, 3.11, 4.67, 1...
## $ Warsaw..Poland                     <dbl> 5.91, 28.35, 4.72, 2.36, 2.36, 0...
## $ Prague..Czech.Republic             <dbl> 5.95, 29.75, 5.55, 1.59, 1.98, 1...
## $ Wroclaw..Poland                    <dbl> 5.91, 25.99, 4.72, 1.89, 2.36, 0...
## $ Kuala.Lumpur..Malaysia             <dbl> 2.87, 16.60, 3.32, 3.32, 4.42, 0...
## $ New.York..NY..United.States        <dbl> 17.97, 76.37, 8.09, 6.29, 7.19, ...
## $ Copenhagen..Denmark                <dbl> 17.40, 80.29, 10.04, 6.69, 5.35,...
## $ Ljubljana..Slovenia                <dbl> 8.00, 35.00, 5.00, 2.60, 3.40, 2...
## $ Chandigarh..India                  <dbl> 2.54, 10.16, 3.30, 1.90, 3.17, 0...
## $ Colombo..Sri.Lanka                 <dbl> 1.49, 14.90, 4.22, 1.49, 2.48, 0...
## $ Noida..India                       <dbl> 3.80, 12.66, 3.16, 1.39, 2.53, 0...
## $ Kaunas..Lithuania                  <dbl> 6.00, 30.00, 5.00, 2.70, 3.00, 1...
## $ Athens..Greece                     <dbl> 10.00, 40.00, 6.00, 4.00, 4.00, ...
## $ Phoenix..AZ..United.States         <dbl> 10.77, 53.87, 6.73, 3.59, 4.49, ...
## $ Hong.Kong..Hong.Kong               <dbl> 5.78, 46.26, 4.05, 5.78, 5.78, 1...
## $ Portland..OR..United.States        <dbl> 12.58, 44.92, 6.74, 4.49, 4.49, ...
## $ Lisbon..Portugal                   <dbl> 8.50, 35.00, 6.00, 2.00, 2.50, 1...
## $ Beijing..China                     <dbl> 3.91, 26.04, 4.56, 1.95, 3.06, 0...
## $ Cape.Town..South.Africa            <dbl> 8.91, 35.81, 3.74, 2.18, 2.49, 0...
## $ Tirana..Albania                    <dbl> 4.10, 24.62, 4.76, 1.27, 2.00, 1...
## $ Porto..Portugal                    <dbl> 7.00, 30.00, 6.00, 2.00, 2.50, 1...
## $ Durban..South.Africa               <dbl> 7.47, 25.53, 3.74, 1.62, 1.68, 0...
## $ Budapest..Hungary                  <dbl> 5.99, 29.96, 4.79, 1.50, 1.80, 1...
## $ Vilnius..Lithuania                 <dbl> 7.00, 35.00, 5.00, 3.00, 3.00, 1...
## $ Johannesburg..South.Africa         <dbl> 8.47, 31.37, 3.45, 1.73, 2.20, 0...
## $ Barcelona..Spain                   <dbl> 11.00, 40.00, 7.00, 2.50, 3.00, ...
## $ San.Diego..CA..United.States       <dbl> 13.48, 53.91, 6.74, 5.39, 6.29, ...
## $ San.Francisco..CA..United.States   <dbl> 15.95, 71.88, 8.98, 6.29, 7.19, ...
## $ Lima..Peru                         <dbl> 3.26, 21.71, 4.21, 1.90, 2.71, 0...
## $ Seattle..WA..United.States         <dbl> 13.48, 62.89, 7.19, 5.39, 5.84, ...
## $ Brasov..Romania                    <dbl> 6.27, 20.91, 4.18, 1.25, 1.88, 1...
## $ Bucharest..Romania                 <dbl> 6.28, 27.21, 4.19, 1.78, 2.09, 1...
## $ Tashkent..Uzbekistan               <dbl> 3.59, 15.72, 2.70, 0.90, 1.80, 0...
## $ Ahmedabad..India                   <dbl> 1.90, 8.86, 3.16, 1.90, 3.80, 0....
## $ Cluj.Napoca..Romania               <dbl> 5.23, 23.00, 4.18, 1.57, 2.09, 1...
## $ Madrid..Spain                      <dbl> 12.00, 40.00, 8.00, 3.00, 3.00, ...
## $ Tallinn..Estonia                   <dbl> 8.00, 45.00, 6.00, 4.00, 4.00, 1...
## $ Bangalore..India                   <dbl> 2.53, 12.66, 3.16, 1.90, 3.80, 0...
## $ Iasi..Romania                      <dbl> 5.23, 20.93, 4.19, 1.26, 1.88, 1...
## $ Chennai..India                     <dbl> 1.52, 7.59, 3.35, 1.90, 3.16, 0....
## $ Doha..Qatar                        <dbl> 6.18, 49.47, 6.18, 11.13, 11.13,...
## $ Delhi..India                       <dbl> 3.80, 15.19, 3.16, 1.90, 3.10, 0...
## $ Gurgaon..India                     <dbl> 3.80, 12.66, 3.16, 1.96, 3.16, 0...
## $ Valencia..Spain                    <dbl> 8.75, 35.00, 7.00, 2.00, 2.50, 1...
## $ Vienna..Austria                    <dbl> 10.00, 45.00, 7.25, 4.00, 4.00, ...
## $ Hyderabad..India                   <dbl> 1.90, 8.86, 3.80, 1.52, 2.53, 0....
## $ Montevideo..Uruguay                <dbl> 8.41, 31.23, 6.96, 1.92, 2.40, 1...
## $ Tel.Aviv.Yafo..Israel              <dbl> 15.54, 64.75, 12.95, 7.77, 7.77,...
## $ Timisoara..Romania                 <dbl> 5.23, 20.91, 4.18, 1.25, 1.67, 0...
## $ Taipei..Taiwan                     <dbl> 3.61, 24.04, 3.91, 1.77, 2.10, 0...
## $ Kolkata..India                     <dbl> 2.53, 10.76, 3.79, 1.87, 3.48, 0...
## $ Skopje..Macedonia                  <dbl> 4.87, 16.25, 3.25, 1.62, 1.95, 1...
## $ Shanghai..China                    <dbl> 4.56, 26.04, 4.56, 1.30, 2.93, 0...
## $ Bangkok..Thailand                  <dbl> 1.78, 23.74, 5.04, 2.37, 4.45, 0...
## $ Mumbai..India                      <dbl> 3.80, 15.19, 3.80, 2.28, 3.92, 0...
## $ Reykjavik..Iceland                 <dbl> 17.96, 109.16, 11.64, 8.73, 7.28...
## $ Amman..Jordan                      <dbl> 6.34, 38.02, 6.34, 6.34, 6.34, 0...
## $ Pune..India                        <dbl> 3.16, 12.66, 3.16, 2.28, 3.54, 0...
## $ Stockholm..Sweden                  <dbl> 11.40, 66.47, 7.60, 6.55, 6.65, ...
## $ Buenos.Aires..Argentina            <dbl> 5.99, 20.97, 4.94, 1.50, 1.95, 0...
## $ Minsk..Belarus                     <dbl> 8.93, 27.95, 4.49, 1.80, 2.70, 0...
## $ San.Jose..Costa.Rica               <dbl> 6.29, 42.47, 6.29, 2.36, 3.81, 1...
## $ Casablanca..Morocco                <dbl> 4.45, 20.63, 5.16, 2.58, 2.81, 0...
## $ Lodz..Poland                       <dbl> 4.72, 23.61, 4.25, 1.77, 1.65, 0...
## $ Montreal..Canada                   <dbl> 10.31, 44.69, 6.87, 4.81, 5.50, ...
## $ Sao.Paulo..Brazil                  <dbl> 6.58, 28.51, 5.92, 1.97, 3.29, 1...
## $ Gothenburg..Sweden                 <dbl> 10.45, 75.97, 7.60, 6.31, 6.17, ...
## $ Dublin..Ireland                    <dbl> 15.00, 60.00, 8.00, 5.80, 5.50, ...
## $ Moscow..Russia                     <dbl> 8.80, 36.69, 4.40, 2.93, 2.57, 0...
## $ Santo.Domingo..Dominican.Republic  <dbl> 5.10, 34.01, 5.10, 1.70, 2.55, 0...
## $ Adelaide..Australia                <dbl> 11.19, 40.39, 6.84, 4.97, 5.59, ...
## $ Zurich..Switzerland                <dbl> 23.12, 92.46, 12.94, 6.24, 5.55,...
## $ Yerevan..Armenia                   <dbl> 4.70, 22.54, 4.60, 1.13, 1.69, 0...
## $ Manila..Philippines                <dbl> 3.56, 17.82, 2.67, 1.25, 2.32, 0...
## $ Brisbane..Australia                <dbl> 12.38, 49.54, 7.43, 4.95, 5.57, ...
## $ Jakarta..Indonesia                 <dbl> 2.62, 16.40, 3.28, 2.62, 3.44, 0...
## $ Ankara..Turkey                     <dbl> 3.82, 15.28, 3.51, 2.75, 3.06, 0...
## $ Lviv..Ukraine                      <dbl> 3.75, 18.76, 3.56, 1.50, 1.50, 0...
## $ Novosibirsk..Russia                <dbl> 5.72, 22.01, 3.67, 1.10, 2.20, 0...
## $ Bursa..Turkey                      <dbl> 3.82, 11.47, 3.06, 2.37, 3.06, 0...
## $ Brussels..Belgium                  <dbl> 15.00, 60.00, 8.20, 4.00, 4.00, ...
## $ Jerusalem..Israel                  <dbl> 15.56, 62.24, 12.97, 7.26, 7.26,...
## $ Melbourne..Australia               <dbl> 10.22, 49.54, 7.12, 5.57, 5.57, ...
## $ Perth..Australia                   <dbl> 12.43, 56.55, 7.32, 5.90, 5.59, ...
## $ Sydney..Australia                  <dbl> 11.81, 54.37, 7.15, 4.97, 4.97, ...
## $ Alexandria..Egypt                  <dbl> 2.81, 14.06, 3.38, 1.69, 2.81, 0...
## $ Quito..Ecuador                     <dbl> 3.59, 31.45, 5.39, 1.35, 2.70, 0...

02. Check missing values using colSums

#colSums(is.na(cost)) - not be displayed in rpbus, because it takes a very long line
anyNA(cost)

## [1] FALSE

Exploratory & Graphs

03 Transpose Columns to Row with t()

library(data.table)
library(janitor)

cost_t <- data.frame(t(cost))%>%
  row_to_names(row_number = 1) 
  names(cost_t)[5] <- "Imported Beer (0.33 litre bottles)"
setDT(cost_t, keep.rownames = "City in Country")

head(cost_t)

03. Converting data type in columns from factor to number with mutate function

# Convert all data to numeric and remove variable 'City in Country'

cos_n <- cost_t %>% 
 
  mutate_if(is.factor, as.character) %>% 
  mutate_if(is.character, as.numeric) %>% 
  select(c(-`City in Country`)) %>% 
  mutate(id = row_number())

## Warning: NAs introduced by coercion

head(cos_n)

# Assigning only 'City in Country' variable'

cos_ci <- cost_t %>% 
  select(c(`City in Country`)) %>% 
  mutate(id = row_number()) 
head(cos_ci)

# Joining data 'City in Country' variable with other numeric variables

cos <- merge(cos_ci,cos_n, by="id") %>% 
select(c(-id))
head(cos)

04. Selecting data type to be analyzed

a. Cost of food

cost.food <-cos %>% 
  select(`City in Country`, `McMeal at McDonalds (or Equivalent Combo Meal)`, `Meal, Inexpensive Restaurant`, `Meal for 2 People, Mid-range Restaurant, Three-course`, `Loaf of Fresh White Bread (500g)`, `Eggs (regular) (12)`, `Local Cheese (1kg)`, `Chicken Breasts (Boneless, Skinless), (1kg)`, `Beef Round (1kg) (or Equivalent Back Leg Red Meat)`, `Rice (white), (1kg)`, `Potato (1kg)`, `Onion (1kg)` )
glimpse(cost.food)

## Observations: 160
## Variables: 12
## $ `City in Country`                                       <chr> "Saint.Pete...
## $ `McMeal at McDonalds (or Equivalent Combo Meal)`        <dbl> 4.40, 3.82,...
## $ `Meal, Inexpensive Restaurant`                          <dbl> 7.34, 4.58,...
## $ `Meal for 2 People, Mid-range Restaurant, Three-course` <dbl> 29.35, 15.2...
## $ `Loaf of Fresh White Bread (500g)`                      <dbl> 0.71, 0.36,...
## $ `Eggs (regular) (12)`                                   <dbl> 1.18, 1.62,...
## $ `Local Cheese (1kg)`                                    <dbl> 7.60, 5.32,...
## $ `Chicken Breasts (Boneless, Skinless), (1kg)`           <dbl> 3.96, 3.50,...
## $ `Beef Round (1kg) (or Equivalent Back Leg Red Meat)`    <dbl> 7.18, 9.73,...
## $ `Rice (white), (1kg)`                                   <dbl> 0.92, 1.30,...
## $ `Potato (1kg)`                                          <dbl> 0.56, 0.59,...
## $ `Onion (1kg)`                                           <dbl> 0.48, 0.62,...

cofmcd <- cost.food %>%
  arrange(desc(`McMeal at McDonalds (or Equivalent Combo Meal)`)) 
  

  ggplot((head(cofmcd,15)), mapping = aes(x = reorder(`City in Country`, `McMeal at McDonalds (or Equivalent Combo Meal)` ) , `McMeal at McDonalds (or Equivalent Combo Meal)`)) +
  
  geom_bar(stat="identity", fill ="#06697A") +
  coord_flip() +
  
  theme(plot.background=element_rect(fill = "light gray")) +
  theme(panel.background=element_blank()) +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=9,colour="black")) +
  
  scale_x_discrete(name="")+
  ylab("Cost") +
  ggtitle("Cost of McDonalds Combo Meal in big cities") +
  
  theme(plot.title = element_text(size= 12, face = "bold"))

Summaries:

Top 5 Cost of food meal measured by Mc Donalds Combo Meal in big cities:

Jerussalem Israel
Tel. Aviv Israel
Zurich Switzerland
Reykjavic Iceland
Oslo Norway

cofmim <- cost.food %>%
  arrange(desc(`Meal, Inexpensive Restaurant`)) 
  

  ggplot((head(cofmim,15)) , mapping = aes(x = reorder(`City in Country`, `Meal, Inexpensive Restaurant` ) ,`Meal, Inexpensive Restaurant`  )) +
  
  geom_bar(stat="identity", fill ="#06697A") +
  coord_flip() +
  
  theme(plot.background=element_rect(fill = "light gray")) +
  theme(panel.background=element_blank()) +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=9,colour="black")) +
  
  scale_x_discrete(name="")+
  ylab("Cost ") +
  ggtitle("Cost of Inexpensive meals in big cities") +
  
  theme(plot.title = element_text(size= 12, face = "bold"))

Summaries:

Top 5 Cost of food meal measured by Cost of meals in Inexpensive restaurants in big cities:

Zurich Switzerland
Oslo Norway
New York USA
Rekyavic Iceland
London UK

b. cost of fruit & vegetables

cost.fruit.vegetables <- cos %>% 
  select(`City in Country`, `Apples (1kg)`,`Oranges (1kg)`, `Banana (1kg)`, `Lettuce (1 head)`, `Tomato (1kg)`)  
  glimpse(cost.fruit.vegetables)

## Observations: 160
## Variables: 6
## $ `City in Country`  <chr> "Saint.Petersburg..Russia", "Istanbul..Turkey", ...
## $ `Apples (1kg)`     <dbl> 1.29, 0.85, 0.77, 2.10, 0.70, 2.13, 1.31, 0.86, ...
## $ `Oranges (1kg)`    <dbl> 1.25, 0.86, 0.73, 1.75, 1.22, 2.05, 0.39, 1.18, ...
## $ `Banana (1kg)`     <dbl> 0.89, 1.91, 1.78, 1.61, 1.37, 1.99, 0.66, 1.06, ...
## $ `Lettuce (1 head)` <dbl> 0.86, 0.61, 0.57, 2.30, 0.84, 1.27, 0.28, 0.55, ...
## $ `Tomato (1kg)`     <dbl> 1.91, 0.80, 0.70, 2.91, 1.56, 2.45, 0.35, 0.94, ...

c. cost of drinks

cost.drinks <- cos %>% 
  select ( `City in Country`,`Water (0.33 liter bottle) `, `Water (1.5 liter bottle)`, `Milk (regular), (1 liter)`, `Coke/Pepsi (0.33 liter bottle)`, `Cappuccino (regular)`, `Domestic Beer (0.5 liter draught)`, `Domestic Beer (0.5 liter bottle)`, `Imported Beer (0.33 liter bottle)`, `Imported Beer (0.33 litre bottles)`, `Bottle of Wine (Mid-Range)`,  )
glimpse(cost.drinks)

## Observations: 160
## Variables: 11
## $ `City in Country`                    <chr> "Saint.Petersburg..Russia", "I...
## $ `Water (0.33 liter bottle) `         <dbl> 0.53, 0.24, 0.22, 1.89, 0.44, ...
## $ `Water (1.5 liter bottle)`           <dbl> 0.63, 0.33, 0.29, 1.54, 0.59, ...
## $ `Milk (regular), (1 liter)`          <dbl> 0.98, 0.71, 0.65, 0.96, 0.68, ...
## $ `Coke/Pepsi (0.33 liter bottle)`     <dbl> 0.76, 0.64, 0.61, 2.66, 0.64, ...
## $ `Cappuccino (regular)`               <dbl> 1.96, 1.84, 1.56, 3.87, 1.25, ...
## $ `Domestic Beer (0.5 liter draught)`  <dbl> 2.20, 3.06, 2.29, 6.50, 1.04, ...
## $ `Domestic Beer (0.5 liter bottle)`   <dbl> 0.88, 1.79, 1.63, 2.23, 0.77, ...
## $ `Imported Beer (0.33 liter bottle)`  <dbl> 1.89, 2.48, 2.09, 2.95, 1.38, ...
## $ `Imported Beer (0.33 litre bottles)` <dbl> 2.20, 3.06, 2.75, 6.75, 1.43, ...
## $ `Bottle of Wine (Mid-Range)`         <dbl> 5.87, 7.64, 6.11, 12.00, 3.61,...

cofcoke <- cost.drinks %>%
  arrange(desc(`Coke/Pepsi (0.33 liter bottle)`)) 
  

  ggplot((head(cofcoke,15)) , mapping = aes(x = reorder(`City in Country`, `Coke/Pepsi (0.33 liter bottle)` ) ,`Coke/Pepsi (0.33 liter bottle)`  )) +
  
  geom_bar(stat="identity", fill ="#06697A") +
  coord_flip() +
  
  theme(plot.background=element_rect(fill = "light gray")) +
  theme(panel.background=element_blank()) +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=9,colour="black")) +
  theme(axis.text = element_text(colour = "black")) +
  theme(axis.ticks = element_line(size = 2)) +
  
  scale_x_discrete(name="")+
  ylab("Cost") +
  ggtitle("Cost of Coke/Pepsi in big cities") +
  
  theme(plot.title = element_text(size= 12, face = "bold"))

Summaries:

Top 5 Cost of drinks measured by Cost Coke & Pepsi in big cities:

Zurich Switzerland
Paris France
Copenhagen Denmark
Oslo Norway
Vienna Austria

d. cost of transportation

cost.transport <- cos %>% 
  select(`City in Country`, `One-way Ticket (Local Transport)`, `Taxi Start (Normal Tariff)`, `Taxi 1km (Normal Tariff)`, `Taxi 1hour Waiting (Normal Tariff)`, `Volkswagen Golf`, `Toyota Corolla 1.6l 97kW Comfort (Or Equivalent New Car)`, `Gasoline (1 liter)`)
glimpse(cost.transport)

## Observations: 160
## Variables: 8
## $ `City in Country`                                          <chr> "Saint.P...
## $ `One-way Ticket (Local Transport)`                         <dbl> 0.59, 0....
## $ `Taxi Start (Normal Tariff)`                               <dbl> 1.47, 0....
## $ `Taxi 1km (Normal Tariff)`                                 <dbl> 0.26, 0....
## $ `Taxi 1hour Waiting (Normal Tariff)`                       <dbl> 4.40, 3....
## $ `Volkswagen Golf`                                          <dbl> 19289.39...
## $ `Toyota Corolla 1.6l 97kW Comfort (Or Equivalent New Car)` <dbl> 19305.29...
## $ `Gasoline (1 liter)`                                       <dbl> 0.67, 1....

e. cost of clothing

cost.clothing <- cos %>% 
  select( `City in Country`, `1 Pair of Nike Running Shoes (Mid-Range)`, `1 Pair of Men Leather Business Shoes` , `1 Pair of Jeans (Levis 501 Or Similar)`, `1 Summer Dress in a Chain Store (Zara, H&M, ...)`)
glimpse(cost.clothing)

## Observations: 160
## Variables: 5
## $ `City in Country`                                  <chr> "Saint.Petersbur...
## $ `1 Pair of Nike Running Shoes (Mid-Range)`         <dbl> 74.88, 61.31, 52...
## $ `1 Pair of Men Leather Business Shoes`             <dbl> 100.72, 50.58, 4...
## $ `1 Pair of Jeans (Levis 501 Or Similar)`           <dbl> 71.86, 36.15, 33...
## $ `1 Summer Dress in a Chain Store (Zara, H&M, ...)` <dbl> 38.25, 25.91, 22...

f. apartment cost

cost.apartment <- cos %>% 
  select(`City in Country`, `Apartment (1 bedroom) Outside of Centre`, `Apartment (1 bedroom) in City Centre`, `Apartment (3 bedrooms) Outside of Centre`, `Apartment (3 bedrooms) in City Centre`, `Price per Square Meter to Buy Apartment Outside of Centre`, `Price per Square Meter to Buy Apartment in City Centre`)  
glimpse(cost.apartment)

## Observations: 160
## Variables: 7
## $ `City in Country`                                           <chr> "Saint....
## $ `Apartment (1 bedroom) Outside of Centre`                   <dbl> 344.27,...
## $ `Apartment (1 bedroom) in City Centre`                      <dbl> 524.45,...
## $ `Apartment (3 bedrooms) Outside of Centre`                  <dbl> 615.19,...
## $ `Apartment (3 bedrooms) in City Centre`                     <dbl> 1012.53...
## $ `Price per Square Meter to Buy Apartment Outside of Centre` <dbl> 1507.70...
## $ `Price per Square Meter to Buy Apartment in City Centre`    <dbl> 2476.05...

coa <- cost.apartment %>%
  arrange(desc(`Apartment (1 bedroom) in City Centre`)) 
  

  ggplot((head(coa,15)) , mapping = aes(x = reorder(`City in Country`, `Apartment (1 bedroom) in City Centre` ) ,`Apartment (1 bedroom) in City Centre`  ) ) +
  
  geom_bar(stat="identity", fill ="#06697A") +
  coord_flip() +
  
  theme(plot.background=element_rect(fill = "light gray")) +
  theme(panel.background=element_blank()) +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=9,colour="black")) +
  theme(axis.text = element_text(colour = "black")) +
  theme(axis.ticks = element_line(size = 2)) +
  
  scale_x_discrete(name="")+
  ylab("Cost ") +
  
    
  ggtitle("Apartment cost with 1 bedroom in City Centre") +
  
  theme(plot.title = element_text(size= 12, face = "bold"))

Summaries:

Top 5 Apartment cost with 1 bedroom in city centre of big cities:

San Fransisco USA
New York USA
Boston USA
Hongkong Hongkong
London UK

coa3 <- cost.apartment %>%
  arrange(desc(`Apartment (3 bedrooms) in City Centre`)) 
  

  ggplot((head(coa,15)) , mapping = aes(x = reorder(`City in Country`, `Apartment (3 bedrooms) in City Centre` ) ,`Apartment (3 bedrooms) in City Centre`  )) +
  
  geom_bar(stat="identity", fill ="#06697A") +
  coord_flip() +
  
  theme(plot.background=element_rect(fill = "light gray")) +
  theme(panel.background=element_blank()) +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=9,colour="black")) +
  theme(axis.text = element_text(colour = "black")) +
  theme(axis.ticks = element_line(size = 2)) +
    
    
  scale_x_discrete(name="")+
  ylab("Cost ") +
  ggtitle("Apartment Cost with 3 bedroom in City Centre")+
  
  
  theme(plot.title = element_text(size= 12, face = "bold"))

Summaries:

Top 5 Apartment cost with 3 bedroom in city centre of big cities:

San Fransisco USA
New York USA
Hongkong Hongkong
London UK
Los Angeles USA

g. cost of utilities

cost.utilities <- cos %>% 
  select(`City in Country`, `Basic (Electricity, Heating, Cooling, Water, Garbage) for 85m2 Apartment`, `1 min. of Prepaid Mobile Tariff Local (No Discounts or Plans)`, `Internet (60 Mbps or More, Unlimited Data, Cable/ADSL)`)
glimpse(cost.utilities)

## Observations: 160
## Variables: 4
## $ `City in Country`                                                          <chr> ...
## $ `Basic (Electricity, Heating, Cooling, Water, Garbage) for 85m2 Apartment` <dbl> ...
## $ `1 min. of Prepaid Mobile Tariff Local (No Discounts or Plans)`            <dbl> ...
## $ `Internet (60 Mbps or More, Unlimited Data, Cable/ADSL)`                   <dbl> ...

coi <- cost.utilities %>%
  arrange(desc(`Internet (60 Mbps or More, Unlimited Data, Cable/ADSL)`)) 
  

  ggplot((head(coi,15)) , mapping = aes(x = reorder(`City in Country`, `Internet (60 Mbps or More, Unlimited Data, Cable/ADSL)` ) ,`Internet (60 Mbps or More, Unlimited Data, Cable/ADSL)`  )) +
  
  geom_bar(stat="identity", fill ="#06697A") +
  coord_flip() +
  
  theme(plot.background=element_rect(fill = "light gray")) +
  theme(panel.background=element_blank()) +
  theme(panel.grid = element_blank()) +
  theme(text=element_text(size=9,colour="black")) +
  theme(axis.text = element_text(colour = "black")) +
  theme(axis.ticks = element_line(size = 2)) +
    
    
  scale_x_discrete(name="")+
  ylab("Cost ")  +
  ggtitle("Cost of Internet Unlimited Data/Cable/ASDL in Big cities") +
  
  theme(plot.title = element_text(size= 12, face = "bold"))

Summaries:

Top 5 Internet cost with unlimited data/Cable/ASDL in big cities:

Abu Dhabi UAE
Dubai UAE
Dhoha Qatar
Phoenix AZ USA
San Jose Costarica

h. cost of leisures, health & education

cost.lhe <- cos %>% 
  select(`City in Country`,`Cigarettes 20 Pack (Marlboro)`, `Fitness Club, Monthly Fee for 1 Adult`, `Tennis Court Rent (1 Hour on Weekend)`, `Cinema, International Release, 1 Seat`, `Preschool (or Kindergarten), Full Day, Private, Monthly for 1 Child`, `International Primary School, Yearly for 1 Child` )
glimpse(cost.lhe)

## Observations: 160
## Variables: 7
## $ `City in Country`                                                     <chr> ...
## $ `Cigarettes 20 Pack (Marlboro)`                                       <dbl> ...
## $ `Fitness Club, Monthly Fee for 1 Adult`                               <dbl> ...
## $ `Tennis Court Rent (1 Hour on Weekend)`                               <dbl> ...
## $ `Cinema, International Release, 1 Seat`                               <dbl> ...
## $ `Preschool (or Kindergarten), Full Day, Private, Monthly for 1 Child` <dbl> ...
## $ `International Primary School, Yearly for 1 Child`                    <dbl> ...

i. Income & mortgage interest rates

income.and.mortgage <- cos %>% 
  select ( `City in Country`, `Average Monthly Net Salary (After Tax)`, `Mortgage Interest Rate in Percentages (%), Yearly, for 20 Years Fixed-Rate`)

glimpse(income.and.mortgage)

## Observations: 160
## Variables: 3
## $ `City in Country`                                                            <chr> ...
## $ `Average Monthly Net Salary (After Tax)`                                     <dbl> ...
## $ `Mortgage Interest Rate in Percentages (%), Yearly, for 20 Years Fixed-Rate` <dbl> ...

Recommendations

From preliminary reports above we could recommend some highest cost of living measured by those 5 indicators especially in apartment cost & cost of food to have potentials in establishing capsule hotels/apartment/ stays such as: San Fransisco USA, New York USA, Boston USA,Hongkong Hongkong, London UK, Los Angeles USA and Zurich Switzerland.

In order to have more certain conclusion about the analysis we should do more analysis with more comprehensive data and prediction to anticipate upcoming trends.

Cost of Living in Big Cities

Yufy Firdiansyah | Jupyter Night A - Algoritma Data Science Education

March 15, 2020

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