Use the given code below to answer the questions.
## # A tibble: 1,032 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 98.2
## 2 2016-01-05 106. 106. 102. 103. 55791000 95.8
## 3 2016-01-06 101. 102. 99.9 101. 68457400 93.9
## 4 2016-01-07 98.7 100. 96.4 96.4 81094400 89.9
## 5 2016-01-08 98.6 99.1 96.8 97.0 70798000 90.4
## 6 2016-01-11 99.0 99.1 97.3 98.5 49739400 91.9
## 7 2016-01-12 101. 101. 98.8 100. 49154200 93.2
## 8 2016-01-13 100. 101. 97.3 97.4 62439600 90.8
## 9 2016-01-14 98.0 100. 95.7 99.5 63170100 92.8
## 10 2016-01-15 96.2 97.7 95.4 97.1 79833900 90.6
## # … with 1,022 more rows
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: 772 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2017-01-13 131. 134. 131. 134. 10515000 134.
## 2 2017-01-17 135. 135. 132. 133. 12220200 133.
## 3 2017-01-18 133. 134. 131. 133. 16168600 133.
## 4 2017-01-19 142. 143. 138. 138. 23203400 138.
## 5 2017-01-20 139. 141. 138. 139. 9497400 139.
## 6 2017-01-23 139. 139. 137. 137. 7433900 137.
## 7 2017-01-24 138. 141. 137. 140. 7754700 140.
## 8 2017-01-25 141. 141. 139. 140. 7238100 140.
## 9 2017-01-26 140. 141. 139. 139. 6038300 139.
## 10 2017-01-27 139. 142. 139 142. 8323900 142.
## # … with 762 more rows
Hint: Watch the video, “Basic Data Types”, in DataCamp: Introduction to R for Finance: Ch1 The Basics.
Example of Characeter data are words and letters.
Example of Logical is numerical data.
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.
Netflix had done pretty good for stocks in 2019 they kept between 300 and 400.
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.
## Import data
stocks <- tq_get(c("NFLX", "AMZN") , get = "stock.prices", from = "2017-01-13")
stocks
## # A tibble: 1,544 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NFLX 2017-01-13 131. 134. 131. 134. 10515000 134.
## 2 NFLX 2017-01-17 135. 135. 132. 133. 12220200 133.
## 3 NFLX 2017-01-18 133. 134. 131. 133. 16168600 133.
## 4 NFLX 2017-01-19 142. 143. 138. 138. 23203400 138.
## 5 NFLX 2017-01-20 139. 141. 138. 139. 9497400 139.
## 6 NFLX 2017-01-23 139. 139. 137. 137. 7433900 137.
## 7 NFLX 2017-01-24 138. 141. 137. 140. 7754700 140.
## 8 NFLX 2017-01-25 141. 141. 139. 140. 7238100 140.
## 9 NFLX 2017-01-26 140. 141. 139. 139. 6038300 139.
## 10 NFLX 2017-01-27 139. 142. 139 142. 8323900 142.
## # … with 1,534 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.