Use the given code below to answer the questions.

## ── Attaching packages ─────────────────────── tidyverse 1.2.1 ──
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## ✔ readr   1.3.1     ✔ forcats 0.4.0
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## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## Loading required package: lubridate
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date
## Loading required package: PerformanceAnalytics
## Loading required package: xts
## Loading required package: zoo
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## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## Registered S3 method overwritten by 'xts':
##   method     from
##   as.zoo.xts zoo
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## Attaching package: 'xts'
## The following objects are masked from 'package:dplyr':
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##     first, last
## 
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
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##     legend
## Loading required package: quantmod
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
## Version 0.4-0 included new data defaults. See ?getSymbols.
## # A tibble: 922 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2016-01-04 103.  105.  102   105.  67649400     98.7
##  2 2016-01-05 106.  106.  102.  103.  55791000     96.3
##  3 2016-01-06 101.  102.   99.9 101.  68457400     94.4
##  4 2016-01-07  98.7 100.   96.4  96.4 81094400     90.4
##  5 2016-01-08  98.6  99.1  96.8  97.0 70798000     90.9
##  6 2016-01-11  99.0  99.1  97.3  98.5 49739400     92.4
##  7 2016-01-12 101.  101.   98.8 100.0 49154200     93.7
##  8 2016-01-13 100.  101.   97.3  97.4 62439600     91.3
##  9 2016-01-14  98.0 100.   95.7  99.5 63170100     93.3
## 10 2016-01-15  96.2  97.7  95.4  97.1 79833900     91.0
## # … with 912 more rows
## Observations: 922
## Variables: 7
## $ date     <date> 2016-01-04, 2016-01-05, 2016-01-06, 2016-01-07, 2016-0…
## $ open     <dbl> 102.61, 105.75, 100.56, 98.68, 98.55, 98.97, 100.55, 10…
## $ high     <dbl> 105.37, 105.85, 102.37, 100.13, 99.11, 99.06, 100.69, 1…
## $ low      <dbl> 102.00, 102.41, 99.87, 96.43, 96.76, 97.34, 98.84, 97.3…
## $ close    <dbl> 105.35, 102.71, 100.70, 96.45, 96.96, 98.53, 99.96, 97.…
## $ volume   <dbl> 67649400, 55791000, 68457400, 81094400, 70798000, 49739…
## $ adjusted <dbl> 98.74225, 96.26781, 94.38389, 90.40047, 90.87848, 92.35…

Q1 Get Microsoft stock prices, instead of Apple.

Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. Replace the ticker symbol for Microsoft. You may find the ticker symbol for Microsoft from Yahoo Finance.

## # A tibble: 922 x 7
##    date        open  high   low close   volume adjusted
##    <date>     <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 2016-01-04  54.3  54.8  53.4  54.8 53778000     50.7
##  2 2016-01-05  54.9  55.4  54.5  55.0 34079700     50.9
##  3 2016-01-06  54.3  54.4  53.6  54.0 39518900     50.0
##  4 2016-01-07  52.7  53.5  52.1  52.2 56564900     48.3
##  5 2016-01-08  52.4  53.3  52.2  52.3 48754000     48.4
##  6 2016-01-11  52.5  52.8  51.5  52.3 36943800     48.4
##  7 2016-01-12  52.8  53.1  52.1  52.8 36095500     48.8
##  8 2016-01-13  53.8  54.1  51.3  51.6 66883600     47.8
##  9 2016-01-14  52    53.4  51.6  53.1 52381900     49.1
## 10 2016-01-15  51.3  52.0  50.3  51.0 71820700     47.2
## # … with 912 more rows

Q2 How many columns (variables) are there?

Hint: Insert a new code chunk below and type in the code, using the glimpse() function above.

## Observations: 922
## Variables: 7
## $ date     <date> 2016-01-04, 2016-01-05, 2016-01-06, 2016-01-07, 2016-0…
## $ open     <dbl> 54.32, 54.93, 54.32, 52.70, 52.37, 52.51, 52.76, 53.80,…
## $ high     <dbl> 54.80, 55.39, 54.40, 53.49, 53.28, 52.85, 53.10, 54.07,…
## $ low      <dbl> 53.39, 54.54, 53.64, 52.07, 52.15, 51.46, 52.06, 51.30,…
## $ close    <dbl> 54.80, 55.05, 54.05, 52.17, 52.33, 52.30, 52.78, 51.64,…
## $ volume   <dbl> 53778000, 34079700, 39518900, 56564900, 48754000, 36943…
## $ adjusted <dbl> 50.70846, 50.93979, 50.01446, 48.27483, 48.42288, 48.39…

Q3 What are the variables?

date, open, high, low, close, volume, adjusted

Q4 What type of data are they? What are other basic data types?

Hint: Watch the video, “Basic Data Types”, in DataCamp: Introduction to R for Finance: Ch1 The Basics.

numeric data, character data, logical data

Q5 How many rows are there?

922

Q6 What does the row represent?

Q7 Create a line plot for the data.

Hint: Insert a new code chunk below and type in the code, using the ggplot() function above. For more information on the ggplot() function, refer to Ch2 Introduction to ggplot2 in one of our e-textbooks, Data Visualization with R.

Q8 Hide the messages and warings but display the code and results of the code on the webpage.

Hint: Change message, warning, collapse, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.