ASSIGNMENT #1 - Tidyverse & Int. to R

Use gapminder package and data set to answer the following questions. Please write your commands under each question.

1. Get the data for 2002. Assign a name to that data.

data_2002 <- gapminder %>% filter(year == 2002)
data_2002

## # A tibble: 142 × 6
##    country     continent  year lifeExp       pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Afghanistan Asia       2002    42.1  25268405      727.
##  2 Albania     Europe     2002    75.7   3508512     4604.
##  3 Algeria     Africa     2002    71.0  31287142     5288.
##  4 Angola      Africa     2002    41.0  10866106     2773.
##  5 Argentina   Americas   2002    74.3  38331121     8798.
##  6 Australia   Oceania    2002    80.4  19546792    30688.
##  7 Austria     Europe     2002    79.0   8148312    32418.
##  8 Bahrain     Asia       2002    74.8    656397    23404.
##  9 Bangladesh  Asia       2002    62.0 135656790     1136.
## 10 Belgium     Europe     2002    78.3  10311970    30486.
## # ℹ 132 more rows

2. Get the data for Germany in 2002.

germany_2002 <- gapminder %>% filter(country == "Germany", year == 2002)
germany_2002

## # A tibble: 1 × 6
##   country continent  year lifeExp      pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Germany Europe     2002    78.7 82350671    30036.

3. Find which country has the lowest lifeExp overall.

lowest_lifeExp_overall <- gapminder %>% arrange(lifeExp) %>% slice(1)
lowest_lifeExp_overall

## # A tibble: 1 × 6
##   country continent  year lifeExp     pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>   <int>     <dbl>
## 1 Rwanda  Africa     1992    23.6 7290203      737.

4. Find which country has the lowest lifeExp in 2002.

lowest_lifeExp_2002 <- data_2002 %>% arrange(lifeExp) %>% slice(1)
lowest_lifeExp_2002

## # A tibble: 1 × 6
##   country continent  year lifeExp      pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Zambia  Africa     2002    39.2 10595811     1072.

5. Find the lifeExp in Germany in 2002.

germany_lifeExp_2002 <- germany_2002$lifeExp
germany_lifeExp_2002

## [1] 78.67

6. Find the countries whose lifeExp is higher than 80 in 2002.

countries_lifeExp_above_80 <- data_2002 %>% filter(lifeExp > 80) %>% select(country, lifeExp)
countries_lifeExp_above_80

## # A tibble: 7 × 2
##   country          lifeExp
##   <fct>              <dbl>
## 1 Australia           80.4
## 2 Hong Kong, China    81.5
## 3 Iceland             80.5
## 4 Italy               80.2
## 5 Japan               82  
## 6 Sweden              80.0
## 7 Switzerland         80.6

7. Find the countries whose lifeExp is more than 70 and less than 80

countries_lifeExp_70_to_80 <- data_2002 %>% filter(lifeExp > 70, lifeExp < 80) %>% select(country, lifeExp)
countries_lifeExp_70_to_80

## # A tibble: 68 × 2
##    country                lifeExp
##    <fct>                    <dbl>
##  1 Albania                   75.7
##  2 Algeria                   71.0
##  3 Argentina                 74.3
##  4 Austria                   79.0
##  5 Bahrain                   74.8
##  6 Belgium                   78.3
##  7 Bosnia and Herzegovina    74.1
##  8 Brazil                    71.0
##  9 Bulgaria                  72.1
## 10 Canada                    79.8
## # ℹ 58 more rows

8. Find the lifeExp in Europe across the years. Which year is the highest lifeExp in Europe?

gapminder %>% 
  filter(continent == "Europe") %>% 
  group_by(year) %>% 
  summarise(avg_lifeExp = mean(lifeExp)) %>% 
  arrange(desc(avg_lifeExp))

## # A tibble: 12 × 2
##     year avg_lifeExp
##    <int>       <dbl>
##  1  2007        77.6
##  2  2002        76.7
##  3  1997        75.5
##  4  1992        74.4
##  5  1987        73.6
##  6  1982        72.8
##  7  1977        71.9
##  8  1972        70.8
##  9  1967        69.7
## 10  1962        68.5
## 11  1957        66.7
## 12  1952        64.4

9. Define gdp as it is equal to to gdpPercap * pop/10000 . Find the gdp of Europe in 2002.

gdp_europe_2002 <- data_2002 %>%
  mutate(gdp = gdpPercap * pop / 10000) %>%
  filter(continent == "Europe") %>%
  summarize(total_gdp = sum(gdp))
gdp_europe_2002

## # A tibble: 1 × 1
##     total_gdp
##         <dbl>
## 1 1309346445.

10. Which country has the highest gdp in Europe in 2002 ?

highest_gdp_country <- data_2002 %>%
  mutate(gdp = gdpPercap * pop / 10000) %>%
  filter(continent == "Europe") %>%
  arrange(desc(gdp)) %>%
  slice(1)
highest_gdp_country

## # A tibble: 1 × 7
##   country continent  year lifeExp      pop gdpPercap        gdp
##   <fct>   <fct>     <int>   <dbl>    <int>     <dbl>      <dbl>
## 1 Germany Europe     2002    78.7 82350671    30036. 247346845.

11. Save the data in 2002 in Europe. Call it data_2002.

data_Europe <- data_2002 %>% filter(continent == "Europe")
data_Europe

## # A tibble: 30 × 6
##    country                continent  year lifeExp      pop gdpPercap
##    <fct>                  <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Albania                Europe     2002    75.7  3508512     4604.
##  2 Austria                Europe     2002    79.0  8148312    32418.
##  3 Belgium                Europe     2002    78.3 10311970    30486.
##  4 Bosnia and Herzegovina Europe     2002    74.1  4165416     6019.
##  5 Bulgaria               Europe     2002    72.1  7661799     7697.
##  6 Croatia                Europe     2002    74.9  4481020    11628.
##  7 Czech Republic         Europe     2002    75.5 10256295    17596.
##  8 Denmark                Europe     2002    77.2  5374693    32167.
##  9 Finland                Europe     2002    78.4  5193039    28205.
## 10 France                 Europe     2002    79.6 59925035    28926.
## # ℹ 20 more rows

12. Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp.

ggplot(data_Europe, aes(x = gdpPercap, y = lifeExp)) + geom_point() + theme_minimal()

13. Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp by continent (color)

ggplot(data_2002, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point()

14. Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp by continent and pop (color and size)

ggplot(data_2002, aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) +
geom_point()

15. Get data for Europe in 2002. Call it data_Europe

data_Europe <- gapminder %>% filter(year == 2002, continent == "Europe")
data_Europe

## # A tibble: 30 × 6
##    country                continent  year lifeExp      pop gdpPercap
##    <fct>                  <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Albania                Europe     2002    75.7  3508512     4604.
##  2 Austria                Europe     2002    79.0  8148312    32418.
##  3 Belgium                Europe     2002    78.3 10311970    30486.
##  4 Bosnia and Herzegovina Europe     2002    74.1  4165416     6019.
##  5 Bulgaria               Europe     2002    72.1  7661799     7697.
##  6 Croatia                Europe     2002    74.9  4481020    11628.
##  7 Czech Republic         Europe     2002    75.5 10256295    17596.
##  8 Denmark                Europe     2002    77.2  5374693    32167.
##  9 Finland                Europe     2002    78.4  5193039    28205.
## 10 France                 Europe     2002    79.6 59925035    28926.
## # ℹ 20 more rows

16. Use data_Europe. Use ggplot. Plot pop vs gdpPercap.

ggplot(data_Europe, aes(x = pop, y = gdpPercap)) +
  geom_point() +
  labs(title = "Population vs GDP Per Capita", x = "Population", y = "GDP Per Capita")

17. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10

ggplot(data_Europe, aes(x = log10(pop), y = gdpPercap)) +
  geom_point() +
  labs(title = "Log-Scaled Population vs GDP Per Capita", x = "Log10(Population)", y = "GDP Per Capita")

18. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10. Color the data by country.

ggplot(data_Europe, aes(x = log10(pop), y = gdpPercap, color = country)) +
  geom_point() +
  labs(title = "Log-Scaled Population vs GDP Per Capita by Country", x = "Log10(Population)", y = "GDP Per Capita")

19. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10. Color the data by country and size it by lifeExp.

ggplot(data_Europe, aes(x = log10(pop), y = gdpPercap, color = country, size = lifeExp)) +
  geom_point() +
  labs(title = "Log-Scaled Population vs GDP Per Capita by Country and Life Expectancy", x = "Log10(Population)", y = "GDP Per Capita")

20. See the attached file in excel, namely,tourism.xls. Create a folder and give a name FORECASTING.

1) Save the tourism excel file in that FORECASTING directory.

2) Set your working directory as FORECASTING

3) Import tourism excel file into R-studio.

4) Assign a different name to this data, such as “mydata”

5) Check the structure of your dataset by str() function. Change Region column from character to factor. Use as.factor() function.

options(digits = 3, scipen = 9999, stringasFactors = FALSE)
# make sure characters are not factors. The 1st column, Quarter, needs to be NOT factor.

library(readxl)
library(readxl)
setwd("C:/Users/pooja/OneDrive/Desktop/RAssignment2")
# Load dataset
mydata <- read_excel("tourism-3.xlsx")
str(mydata)

## tibble [24,320 × 5] (S3: tbl_df/tbl/data.frame)
##  $ Quarter: chr [1:24320] "1998-01-01" "1998-04-01" "1998-07-01" "1998-10-01" ...
##  $ Region : chr [1:24320] "Adelaide" "Adelaide" "Adelaide" "Adelaide" ...
##  $ State  : chr [1:24320] "South Australia" "South Australia" "South Australia" "South Australia" ...
##  $ Purpose: chr [1:24320] "Business" "Business" "Business" "Business" ...
##  $ Trips  : num [1:24320] 135 110 166 127 137 ...

# Convert Region column to factor
mydata$Region <- as.factor(mydata$Region)
summary(mydata)

##    Quarter                                 Region         State          
##  Length:24320       Adelaide                  :  320   Length:24320      
##  Class :character   Adelaide Hills            :  320   Class :character  
##  Mode  :character   Alice Springs             :  320   Mode  :character  
##                     Australia's Coral Coast   :  320                     
##                     Australia's Golden Outback:  320                     
##                     Australia's North West    :  320                     
##                     (Other)                   :22400                     
##    Purpose              Trips    
##  Length:24320       Min.   :  0  
##  Class :character   1st Qu.:  7  
##  Mode  :character   Median : 28  
##                     Mean   : 71  
##                     3rd Qu.: 78  
##                     Max.   :985  
##

ASSIGNMENT #1 - Tidyverse & Int. to R

Your Name and E-mail Here!

Use gapminder package and data set to answer the following questions. Please write your commands under each question.

1. Get the data for 2002. Assign a name to that data.

2. Get the data for Germany in 2002.

3. Find which country has the lowest lifeExp overall.

4. Find which country has the lowest lifeExp in 2002.

5. Find the lifeExp in Germany in 2002.

6. Find the countries whose lifeExp is higher than 80 in 2002.

7. Find the countries whose lifeExp is more than 70 and less than 80

8. Find the lifeExp in Europe across the years. Which year is the highest lifeExp in Europe?

9. Define gdp as it is equal to to gdpPercap * pop/10000 . Find the gdp of Europe in 2002.

10. Which country has the highest gdp in Europe in 2002 ?

11. Save the data in 2002 in Europe. Call it data_2002.

12. Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp.

13. Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp by continent (color)

14. Use data_2002. Use ggplot. Plot gdpPercap vs lifeExp by continent and pop (color and size)

15. Get data for Europe in 2002. Call it data_Europe

16. Use data_Europe. Use ggplot. Plot pop vs gdpPercap.

17. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10

18. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10. Color the data by country.

19. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10. Color the data by country and size it by lifeExp.

20. See the attached file in excel, namely,tourism.xls. Create a folder and give a name FORECASTING.

1) Save the tourism excel file in that FORECASTING directory.

2) Set your working directory as FORECASTING

3) Import tourism excel file into R-studio.

4) Assign a different name to this data, such as “mydata”

5) Check the structure of your dataset by str() function. Change Region column from character to factor. Use as.factor() function.