For the data screenshot sent $,$ I need Code in R for the following questions

## PART A:

### A$1$: Get tourism data as given below from the link. Then convert the data into tsibble format by using tsibble functions. Save it as my$\_$tourism

### A$2.$ To analyze the data, first view my$\_$tourism set, and see the dates of the data.

#### $1.$ Is it annual or quarterly data? Which year does it start from?

#### $2.$ Use table$($State$,$Purpose$)$ command to see the cross$-$table. How many States are there in the data? How many Purpose of trips category?

#### $3.$ Group the data by Region and Purpose by using group$()$ command.To eliminate time effect, use tibble format. $($i$.$e as$\_$tibble$()\%>\%$ group$\_$by$($Region$,$ Purpose$))$

#### $4.$ After grouping the data in #$3,$ use summarize$()$ function to get the average of Trips for each combination, and assign it as Trips$($ i$.$e$.,$ summarise$($Trips $=$ mean$($Trips$)).$

#### $5.$ Find which region has Purpose had the maximum number of overnight trips on average. ungroup the data to find the result$($ i$.$e ungroup$()\%>\%$ filter$($Trips $==$ max$($Trips$)))$

### A$3.$Question: Create a new tsibble which combines the Purposes and Regions,and just has total trips by State. $($i$.$e$.$ group$\_$by$($State$)\%>\%$summarise$($Trips $=$ sum$($Trips$))).$ Then ungroup$()$ the data. Save it as state$\_$tourism.

## PART B:

### B$1.$Question: Create plots of the following time series. Analyze it visually. What do you see? comment on the below in Answer.

## PART C:

### C$1.$ Question: Use my$\_$tourism data you created as tsibble in A$1.$ Filter Region for Snowy$\_$Mountains and save it as snowy.

### C$2.$ Question: Use autoplot$(),$ gg$\_$season$()$ and gg$\_$subseries$()$ to explore the snowy data. What do you observe? What type of pattern do you see. Write your comment on Answer below

## PART D:

### D$1.$Question: Use these two functions $1)$ gg$\_$lag $2)$ ACF to explore the following time series: i$)$Bricks from aus$\_$production ii$)$Lynx from pelt iii$)$ Victorian Electricity Demand from aus$\_$elec. Write your comments about each graphs you created

### D$2.$Question: after using these functions, Can you spot any seasonality, cyclicity and trend? What do you learn about the series? Write your comments below for each series:

#### i$)$ Bricks

#### ii$)$ Lynx from pelt:

#### iii$)$ Electricity

## PART E: See the data below for Google Stock price from the gafa$\_$stock data set.

### E$1$: Calculate the first difference of the "goog" series.

### E$2.$ Question: Does "diff" $($difference of the series$)$ look like white noise? Recall the definition of white noise. Recall what function do you use to check if a series is white noise or not. Use the necessary graph that shows if a series is white noise? Comment based on the graph.