Quiz 1
Patrick Macdonald
5/8/20
- Q1 Import Microsoft stock prices, instead of Apple.
- Q2 How many shares of the stock were traded on January 13, 2018?
- Q3 Stock prices in this data would be a good example of numeric data. Character and logical are two other basic data types in R. List one example of character data and one example of logical data.
- Q4 Plot the adjusted closing price in a line chart.
- Q5 From the chart you created in Q4, briefly describe how the Microsoft stock has performed since the beginning of 2019.
- Q6 Import two stocks: Microsoft and Amazon for the same time period.
- Q7 Hide the messages and the results of the code, but display the code on the webpage.
- Q8 Make an exception to the code chunk in Q6 by displaying both the code and its results.
- Q9 Display the title and your name correctly at the top of the webpage.
- Q10 Use the correct slug.
Use the given code below to answer the questions.
## Load package
library(tidyverse) # for cleaning, plotting, etc## ── Attaching packages ────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.0 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ───────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(tidyquant) # for financial analysis## Loading required package: lubridate##
## Attaching package: 'lubridate'## The following objects are masked from 'package:dplyr':
##
## intersect, setdiff, union## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union## Loading required package: PerformanceAnalytics## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:dplyr':
##
## first, last##
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:graphics':
##
## 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.## ══ Need to Learn tidyquant? ═══════════════════════════════════════════════════════
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>## Import data
stocks <- tq_get("AAPL", get = "stock.prices", from = "2016-01-01")
stocks## # A tibble: 1,094 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2016-01-04 103. 105. 102 105. 67649400 98.2
## 2 AAPL 2016-01-05 106. 106. 102. 103. 55791000 95.8
## 3 AAPL 2016-01-06 101. 102. 99.9 101. 68457400 93.9
## 4 AAPL 2016-01-07 98.7 100. 96.4 96.4 81094400 89.9
## 5 AAPL 2016-01-08 98.6 99.1 96.8 97.0 70798000 90.4
## 6 AAPL 2016-01-11 99.0 99.1 97.3 98.5 49739400 91.9
## 7 AAPL 2016-01-12 101. 101. 98.8 100. 49154200 93.2
## 8 AAPL 2016-01-13 100. 101. 97.3 97.4 62439600 90.8
## 9 AAPL 2016-01-14 98.0 100. 95.7 99.5 63170100 92.8
## 10 AAPL 2016-01-15 96.2 97.7 95.4 97.1 79833900 90.6
## # … with 1,084 more rows## Visualize
stocks %>%
ggplot(aes(x = date, y = close)) +
geom_line()
Q1 Import 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. Find ticker symbols from Yahoo Finance.
stocks <- tq_get("MSFT", get = "stock.prices", from = "2018-01-01")
stocks## # A tibble: 591 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MSFT 2018-01-02 86.1 86.3 85.5 85.9 22483800 83.0
## 2 MSFT 2018-01-03 86.1 86.5 86.0 86.3 26061400 83.4
## 3 MSFT 2018-01-04 86.6 87.7 86.6 87.1 21912000 84.2
## 4 MSFT 2018-01-05 87.7 88.4 87.4 88.2 23407100 85.2
## 5 MSFT 2018-01-08 88.2 88.6 87.6 88.3 22113000 85.3
## 6 MSFT 2018-01-09 88.7 88.7 87.9 88.2 19484300 85.2
## 7 MSFT 2018-01-10 87.9 88.2 87.4 87.8 18652200 84.8
## 8 MSFT 2018-01-11 88.1 88.1 87.2 88.1 17808900 85.1
## 9 MSFT 2018-01-12 88.7 89.8 88.4 89.6 24271500 86.6
## 10 MSFT 2018-01-16 90.1 90.8 88.0 88.3 36599700 85.3
## # … with 581 more rowsQ3 Stock prices in this data would be a good example of numeric data. Character and logical are two other basic data types in R. List one example of character data and one example of logical data.
Hint: Watch the video, “Basic Data Types”, in DataCamp: Introduction to R for Finance: Ch1 The Basics.
Logical data is true or false data. Character data is data such as favorite colors, survery takers names, and addresses.
Q4 Plot the adjusted closing price in a line chart.
Hint: Insert a new code chunk below and type in the code, using the ggplot() function above. Revise the code so that it maps adjusted to the y-axis, instead of close.
knitr::opts_chunk$set(echo = TRUE, message = FALSE, results = 'markup')
stocks %>%
ggplot(aes(x = date, y = `adjusted`)) +
geom_line()
For more information on the ggplot() function, refer to Ch2 Introduction to ggplot2 in one of our e-textbooks, Data Visualization with R.
Q5 From the chart you created in Q4, briefly describe how the Microsoft stock has performed since the beginning of 2019.
Since the beginning of 2019, Microsoft stock has increased reaching at a high point a few months into 2020.
Q6 Import two stocks: Microsoft and Amazon for the same time period.
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. Do this by using the tq_get() function once, not twice.
mult_stocks <- tq_get(c("MSFT", "AMZN"),
get = "stock.prices",
from = "2016-01-01",
to = "2017-01-01")
mult_stocks## # A tibble: 504 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 MSFT 2016-01-04 54.3 54.8 53.4 54.8 53778000 50.4
## 2 MSFT 2016-01-05 54.9 55.4 54.5 55.0 34079700 50.6
## 3 MSFT 2016-01-06 54.3 54.4 53.6 54.0 39518900 49.7
## 4 MSFT 2016-01-07 52.7 53.5 52.1 52.2 56564900 48.0
## 5 MSFT 2016-01-08 52.4 53.3 52.2 52.3 48754000 48.1
## 6 MSFT 2016-01-11 52.5 52.8 51.5 52.3 36943800 48.1
## 7 MSFT 2016-01-12 52.8 53.1 52.1 52.8 36095500 48.5
## 8 MSFT 2016-01-13 53.8 54.1 51.3 51.6 66883600 47.5
## 9 MSFT 2016-01-14 52 53.4 51.6 53.1 52381900 48.8
## 10 MSFT 2016-01-15 51.3 52.0 50.3 51.0 71820700 46.9
## # … with 494 more rowsQ7 Hide the messages and the results of the code, but display the code on the webpage.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.
Q8 Make an exception to the code chunk in Q6 by displaying both the code and its results.
Hint: Use echo and results in the chunk option. Note that this question only applies to the individual code chunk of Q6.