Use the given code below to answer the questions.
Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. Replace the ticker symbol. Find ticker symbols from Yahoo Finance.
## # A tibble: 781 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017-01-03 125. 128. 124. 127. 9437900 127.
## 2 2017-01-04 127. 130. 127. 129. 7843600 129.
## 3 2017-01-05 129. 133. 129. 132. 10185500 132.
## 4 2017-01-06 132. 134. 130. 131. 10657900 131.
## 5 2017-01-09 131. 132. 130. 131. 5771800 131.
## 6 2017-01-10 131. 132. 129. 130. 5985800 130.
## 7 2017-01-11 131. 132. 129. 130. 5615100 130.
## 8 2017-01-12 131. 131. 128. 129. 5388900 129.
## 9 2017-01-13 131. 134. 131. 134. 10515000 134.
## 10 2017-01-17 135. 135. 132. 133. 12220200 133.
## # … with 771 more rows
Hint: Watch the video, “Basic Data Types”, in DataCamp: Introduction to R for Finance: Ch1 The Basics.
An example for character data would be “Hello” and an examplefor logical data is would be true or false.
Hint: Insert a new code chunk below and type in the code, using the ggplot() function above. Revise the code so that it maps close to the y-axis, instead of adjusted.
For more information on the ggplot() function, refer to Ch2 Introduction to ggplot2 in one of our e-textbooks, Data Visualization with R.
It had a big increase and started to stay steady around the 350 range. Then towards the end of 2019 it had a big decrease but it started to increase again going into 2020.
Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. You may refer to the manual of the tidyquant r package. Or, simply Google the tq_get function and see examples of the function’s usage.
## # A tibble: 1,033 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016-01-04 109 110 105. 110. 20794800 110.
## 2 2016-01-05 110. 111. 106. 108. 17664600 108.
## 3 2016-01-06 105. 118. 105. 118. 33045700 118.
## 4 2016-01-07 116. 122. 112. 115. 33636700 115.
## 5 2016-01-08 116. 118. 111. 111. 18067100 111.
## 6 2016-01-11 112. 117. 111. 115. 21920400 115.
## 7 2016-01-12 116. 118. 115. 117. 15133500 117.
## 8 2016-01-13 114. 114. 105. 107. 24921600 107.
## 9 2016-01-14 106. 109. 101. 107. 23664800 107.
## 10 2016-01-15 102. 106. 102. 104. 19775100 104.
## # … with 1,023 more rows
## # A tibble: 1,033 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016-01-04 656. 658. 628. 637. 9314500 637.
## 2 2016-01-05 647. 647. 628. 634. 5822600 634.
## 3 2016-01-06 622 640. 620. 633. 5329200 633.
## 4 2016-01-07 622. 630 605. 608. 7074900 608.
## 5 2016-01-08 620. 624. 606 607. 5512900 607.
## 6 2016-01-11 612. 620. 599. 618. 4891600 618.
## 7 2016-01-12 625. 626. 612. 618. 4724100 618.
## 8 2016-01-13 621. 621. 579. 582. 7655200 582.
## 9 2016-01-14 580. 602. 570. 593 7238000 593
## 10 2016-01-15 572. 585. 565. 570. 7784500 570.
## # … with 1,023 more rows
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.
Hint: Use echo and results in the chunk option. Note that this question only applies to the individual code chunk of Q6.