My First RMarkdown with Code
Braedon Gold
8/28/2019
- Q1 Get Microsoft stock prices, instead of Apple.
- Q2 How many columns (variables) are there?
- Q3 What are the variables?
- Q4 What type of data are they? What are other basic data types?
- Q5 How many rows are there?
- Q6 What does the row represent?
- Q7 Create a line plot for the data.
- Q8 Hide the messages and warings but display the code and results of the code on the webpage.
- 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.
## ── Attaching packages ───────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 0.8.3 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0## ── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ 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':
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## date## 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## Registered S3 method overwritten by 'xts':
## method from
## as.zoo.xts zoo##
## 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.
Q1 Get Microsoft stock prices, instead of Apple.
## Load package
library(tidyverse) # for cleaning, plotting, etc
library(tidyquant) # for financial analysis
## Import data
stocks <- tq_get("MSFT", get = "stock.prices", from = "2016-01-01")
stocks## # 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 rowsQ2 How many columns (variables) are there?
glimpse(stocks)## 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?
data,open,high,low,close,volume,adjusted
Q4 What type of data are they? What are other basic data types?
numerics, integers, logical, characters.
Q5 How many rows are there?
922Q6 What does the row represent?
stocks
Q7 Create a line plot for the data.
## Visualize
stocks %>%
ggplot(aes(x = date, y = close)) +
geom_line()