library(pacman)
p_load(tidyverse, fpp3)
Stat. 474 Quiz 1
Instructions:
The quiz investigate the dataset global_economy. The dataset contains yearly historial economic data for all countries on Earth.
Question 1
How many countries are there one Earth? How may Countries are there in the dataset? Why do the numbers differ?
Answer
<<< Write your answer here. >>>
Provide your code here.
data(global_economy)
global_economy
# A tsibble: 15,150 x 9 [1Y]
# Key: Country [263]
Country Code Year GDP Growth CPI Imports Exports Population
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan AFG 1960 537777811. NA NA 7.02 4.13 8996351
2 Afghanistan AFG 1961 548888896. NA NA 8.10 4.45 9166764
3 Afghanistan AFG 1962 546666678. NA NA 9.35 4.88 9345868
4 Afghanistan AFG 1963 751111191. NA NA 16.9 9.17 9533954
5 Afghanistan AFG 1964 800000044. NA NA 18.1 8.89 9731361
6 Afghanistan AFG 1965 1006666638. NA NA 21.4 11.3 9938414
7 Afghanistan AFG 1966 1399999967. NA NA 18.6 8.57 10152331
8 Afghanistan AFG 1967 1673333418. NA NA 14.2 6.77 10372630
9 Afghanistan AFG 1968 1373333367. NA NA 15.2 8.90 10604346
10 Afghanistan AFG 1969 1408888922. NA NA 15.0 10.1 10854428
# … with 15,140 more rows
%>% distinct(Country) %>%
global_economy count()
# A tibble: 1 × 1
n
<int>
1 263
Question 2
Create a new variable GDP_per_capita. Show the first few values of the new variable.
Answer
<<< Write your answer here. >>>
Provide your code here.
<- global_economy %>%
gdppc mutate(GDP_per_capita = GDP / Population)
%>% select(GDP_per_capita) %>%
gdppc head()
# A tsibble: 6 x 3 [1Y]
# Key: Country [1]
GDP_per_capita Year Country
<dbl> <dbl> <fct>
1 59.8 1960 Afghanistan
2 59.9 1961 Afghanistan
3 58.5 1962 Afghanistan
4 78.8 1963 Afghanistan
5 82.2 1964 Afghanistan
6 101. 1965 Afghanistan
Question 3
Plot the time series data for Population for each of these countries: United States, Brasil, Canada, Mexico, Russia, Israel, and Japan. What do you notice about the population of Russian and Japan?
Answer
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Provide your code here.
%>% filter(Code %in% c("USA", "BRA", "CAN", "MEX", "RUS", "ISR", "JPN")) %>%
gdppc autoplot(Population)
Question 4
Plot the time series data for GDP for each of these countries: United States, Brasil, Canada, Mexico, Russia, Israel, and Japan. How does the GDP of Japan compare to the GDP of the United States?
Answer
<<< Write your answer here. >>>
Provide your code here.
%>% filter(Code %in% c("USA", "BRA", "CAN", "MEX", "RUS", "ISR", "JPN")) %>%
gdppc autoplot(GDP)
Warning: Removed 29 row(s) containing missing values (geom_path).
Question 5
Plot the time series data for GDP_per_capita for each of these countries: United States, Brasil, Canada, Mexico, Russia, Israel, and Japan. How does the GDP per capita differ for Russia, Mexico and Brasil?
Answer
<<< Write your answer here. >>>
Provide your code here.
%>% filter(Code %in% c("USA", "BRA", "CAN", "MEX", "RUS", "ISR", "JPN")) %>%
gdppc autoplot(GDP_per_capita)
Warning: Removed 29 row(s) containing missing values (geom_path).
Question 6
Remake all of your plots including China. How does China compare to the United States in each plot?
Answer
<<< Write your answer here. >>>
Provide your code here.
Question 7
Does it make sense to run a Seasonal Decomposition with these data? Why or why not, explain.
Answer
<<< Write your answer here. >>>
Question 8
R questions.
- Explain what a tsibble is?
<<< Write your answer here. >>>
- Explain what a mable is?
<<< Write your answer here. >>>
- Explain what a fable is?
<<< Write your answer here. >>>