```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ## Section 1: Vector manipulation For this section, you will be working on the following vector ```{r} L = c(3, 1, 1.3, 2, '2', c(2,8), TRUE, FALSE, FALSE, TRUE, 2, 3, 3, 9,2, "banana", "tree", 'bee', 'this is a long string') ``` ### Q1. Write the code to show the **data type** of the **6th element** in the vector L (Note: R uses the 1-based index). ```{r} ``` ### Q2. What is the **data type** of the 4th (`2`) and 5th element (`'2'`) in the vector L respectively? ```{r} ``` ### Q3. Find the **total number of elements** in the vector L. ```{r} ``` ### Q4. Find the **length (number of characters)** of the **last element** in the vector L. ```{r} ``` ### Q5. Use vector slicing to access the **4 boolean values** in the vector L. ```{r} ``` ### Q6. Print the vector in **reverse order** without changing it. ```{r} ``` ### Q7. Write code to calculate the **sum of the first three numbers** in the vector L. ```{r} ``` ### Q8. Write code to count **the frequency of 3** in the vector L. ```{r} ``` ### Q9. Add the following 2 elements to the vector L: `“illini”, “spring”`. ```{r} ``` ### Q10. Change the **last element** in the vector L to `“this is a short string”`. ```{r} ``` ## Section 2: Data Frame In the following session, we will deal with a data frame. You can access the CSV file in the following link: https://drmaggiezhang.com/files/ElonMuskTweets.csv This file contains all tweets posted by Elon Musk in the past 2 years from Jan/01/2021 to Jan/01/2023. I did the data collection using Twitter API. By the end of 1st module, you will also be able to do it in just a few minutes. ### Q11. Read in the CSV file into a `data frame` format. How many **rows and columns** of this data frame? ```{r} ``` ### Q12. Find the most liked tweet with the **maximum** `like_count`. What is the **id** of that tweet? ```{r} ``` ### Q13. What is the **average** `retweet_count` of Elon Musk’s tweets? ```{r} ``` ### Q14. What is the total **length** of tweet `text`? ```{r} ``` ### Q15. Write a function that takes in 3 parameters as input: `retweet_count`, `reply_count`, and `like_count`. Calculate their **average** as the output. Name the function as **`CalculateEngagement`** ```{r} ``` ### Q16. Using the **`CalculateEngagement`** functions you just wrote, create a new column in the data frame called **engagement_index**. ```{r} ``` ### Q17. Write another function that takes in 2 parameters: `retweet_count`, and `reply_count`. Calculate the reply-to-retweet ratio as the output (`reply_count/retweet_count`). Name the function as **`CalculateRatio`**. ```{r} ``` ### Q18. Similarly, using the **CalculateRatio** functions you just wrote, create a new column in the data frame called `ratio`. **Note**: there are some tweets whose `retweet_count` is 0. Therefore you need to write a **conditional statement**. If the `retweet_count` is 0, set the `ratio` to 0. ```{r} ``` ### Q19. Get the full vector of **all hashtags** that appeared in Elon Musk’s tweets. Note: you might need to combine the **for loop** and **conditional statement**, as well as the **vector combination**. The logic goes as follows: 1. create an empty vector (named `all_hashtags`) to store all hashtags. 2. For each item in the `ElonMuskTweets[‘hashtags’]`, if it is not empty, it means there is a vector of hashtags in that tweet. 3.Append this vector to the `all_hashtags` vector you created. 4.Use `nchar() == 0` to see if an element is empty. Similarly use `nchar() != 0` to detect non empty element. ```{r} ``` ### Q20. Save the current data frame (with 2 additional columns) as a new CSV file. Upload it to Canvas. ```{r} ``` 