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(year == 2002, country == "Germany")
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 %>% 
  filter(lifeExp == min(lifeExp)) %>%
  select(country, lifeExp)
lowest_lifeExp_overall

## # A tibble: 1 × 2
##   country lifeExp
##   <fct>     <dbl>
## 1 Rwanda     23.6

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

lowest_lifeExp_2002 <- gapminder %>% 
  filter(year == 2002) %>%
  filter(lifeExp == min(lifeExp)) %>%
  select(country, lifeExp)
lowest_lifeExp_2002

## # A tibble: 1 × 2
##   country lifeExp
##   <fct>     <dbl>
## 1 Zambia     39.2

5. Find the lifeExp in Germany in 2002.

lifeExp_germany_2002 <- gapminder %>% 
  filter(year == 2002, country == "Germany") %>%
  select(country,lifeExp)
lifeExp_germany_2002

## # A tibble: 1 × 2
##   country lifeExp
##   <fct>     <dbl>
## 1 Germany    78.7

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

high_lifeExp_countries_2002 <- gapminder %>% 
  filter(year == 2002, lifeExp > 80) %>%
  select(country, lifeExp)
high_lifeExp_countries_2002

## # 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

mid_lifeExp_countries <- gapminder %>% 
  filter(lifeExp > 70 & lifeExp < 80) %>%
  select(country, lifeExp)
mid_lifeExp_countries

## # A tibble: 471 × 2
##    country   lifeExp
##    <fct>       <dbl>
##  1 Albania      70.4
##  2 Albania      72  
##  3 Albania      71.6
##  4 Albania      73.0
##  5 Albania      75.7
##  6 Albania      76.4
##  7 Algeria      71.0
##  8 Algeria      72.3
##  9 Argentina    70.8
## 10 Argentina    71.9
## # ℹ 461 more rows

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

europe_lifeExp <- gapminder %>% 
  filter(continent == "Europe") %>%
  group_by(year) %>%
  summarize(avg_lifeExp = mean(lifeExp))

highest_lifeExp_year <- europe_lifeExp %>% 
  filter(avg_lifeExp == max(avg_lifeExp))
highest_lifeExp_year

## # A tibble: 1 × 2
##    year avg_lifeExp
##   <int>       <dbl>
## 1  2007        77.6

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

data_2002 <- data_2002 %>%
  mutate(gdp = gdpPercap * pop / 10000)

europe_gdp_2002 <- data_2002 %>% 
  filter(continent == "Europe") %>%
  summarize(total_gdp = sum(gdp))
europe_gdp_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 %>% 
  filter(continent == "Europe") %>%
  filter(gdp == max(gdp)) %>%
  select(country, gdp)
highest_gdp_country

## # A tibble: 1 × 2
##   country        gdp
##   <fct>        <dbl>
## 1 Germany 247346845.

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

data_2002 <- gapminder %>%
  filter(year == 2002, continent == "Europe")
data_2002

## # 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_2002, aes(x = gdpPercap, y = lifeExp)) +
  geom_point() +
  labs(title = "GDP per Capita vs Life Expectancy in 2002",
       x = "GDP per Capita",
       y = "Life Expectancy")

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() +
  labs(title = "GDP per Capita vs Life Expectancy in 2002 by Continent",
       x = "GDP per Capita",
       y = "Life Expectancy")

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() +
  labs(title = "GDP per Capita vs Life Expectancy in 2002 by Continent and Population",
       x = "GDP per Capita",
       y = "Life Expectancy")

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 in Europe (2002)",
       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(Population) vs GDP per Capita in Europe (2002)",
       x = "Log(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(Population) vs GDP per Capita in Europe (2002) by Country",
       x = "Log(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(Population) vs GDP per Capita in Europe (2002) by Country and Life Expectancy",
       x = "Log(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)

## Warning: package 'readxl' was built under R version 4.3.2

setwd("C://FORECASTING")
mydata <- read_excel("C://FORECASTING//tourism.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 ...

mydata$Region <- as.factor(mydata$Region)
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 : Factor w/ 76 levels "Adelaide","Adelaide Hills",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ 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 ...

ASSIGNMENT #1 - Tidyverse & Int. to R

Akshitha Varada avara3@unh.newhaven.edu

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.