Please answer what you can, trying to learn/understand this material. Sample data for this problem can be found above. (Note: This is only a portion of the data, there are 24,000 line items in Excel, showing the beginning and ending half of the data set). Thank you in advance!

Use gapminder package and data set to answer the following questions. Please write your commands under each question. 1. Find the data for 2002. 2. Find the data for Germany in 2002. 3. Find which country has the lowest lifeExp. 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 higher 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 continent has the highest gdp in Europe in 2002 ? 11. Save the data in 2002. 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. 18. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10 19. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10. Color the data by country. 20. Use data_Europe. Use ggplot. Plot pop vs gdpPercap. Scale population by log10. Color the data by country and size it by lifeExp. 21. Save the data for Americas. Call it data_Americas. 22. Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country. 23. Use data_Americas. Plot year vs gdpPercap. Scale gdpPercap by log10. Color the data by country and size by pop. 24. See the attached file in excel, namely,tourism.xls. Set your working directory (\& then save the tourism.xlsx file in that folder). Import this file in R-studio. Write the command below. Give an another name to your dataset, For example: mydata options (digits =3, scipen =9999, stringaspactors = FALSE) * make sure characters are not factors. The lst column, Quarter, needs to be Nor factor. 25. Check the structure of your dataset by str0 function. Change Region column from character to factor. Use as.factor() function