Analyzing US Census Data in R
Hillary Oliver
February 7, 2019
- Q1. How many columns (variables) are there?
- Q2. What are the variables?
- Q3. Add Microsoft stock prices, in addition to Apple.
- Q4. How many variables are there now? Any new variable?
- Q5. On how many days either of the two stocks closed higher than $200 per share?
- Q6. On how many days Apple stock closed higher than $200 per share?
- Q7. Create a new variable, MC, market capitalization.
- Q8. Keep only three variables symbol, date, close and market capitalization and drop the other variables.
- Q9 Plot daily closing stock prices for both stocks by mapping symbol to color.
Using the given code, answer the questions below.
library(tidyquant)
library(tidyverse)
stocks <- tq_get("AAPL", get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 782 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 99.5
## 2 2016-01-05 106. 106. 102. 103. 55791000 97.0
## 3 2016-01-06 101. 102. 99.9 101. 68457400 95.1
## 4 2016-01-07 98.7 100. 96.4 96.4 81094400 91.1
## 5 2016-01-08 98.6 99.1 96.8 97.0 70798000 91.6
## 6 2016-01-11 99.0 99.1 97.3 98.5 49739400 93.1
## 7 2016-01-12 101. 101. 98.8 100.0 49154200 94.4
## 8 2016-01-13 100. 101. 97.3 97.4 62439600 92.0
## 9 2016-01-14 98.0 100. 95.7 99.5 63170100 94.0
## 10 2016-01-15 96.2 97.7 95.4 97.1 79010000 91.7
## # … with 772 more rows
stocks %>%
ggplot(aes(x = date, y = close)) +
geom_line()
Q1. How many columns (variables) are there?
There are seven variables.
Q2. What are the variables?
THe variables are date, open, high, low, close, volume, and adusted.
Q3. Add Microsoft stock prices, in addition to Apple.
stocks <- tq_get(c("AAPL", "MSFT"), get = "stock.prices", from = "2016-01-01")
stocks
## # A tibble: 1,564 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 99.5
## 2 AAPL 2016-01-05 106. 106. 102. 103. 55791000 97.0
## 3 AAPL 2016-01-06 101. 102. 99.9 101. 68457400 95.1
## 4 AAPL 2016-01-07 98.7 100. 96.4 96.4 81094400 91.1
## 5 AAPL 2016-01-08 98.6 99.1 96.8 97.0 70798000 91.6
## 6 AAPL 2016-01-11 99.0 99.1 97.3 98.5 49739400 93.1
## 7 AAPL 2016-01-12 101. 101. 98.8 100.0 49154200 94.4
## 8 AAPL 2016-01-13 100. 101. 97.3 97.4 62439600 92.0
## 9 AAPL 2016-01-14 98.0 100. 95.7 99.5 63170100 94.0
## 10 AAPL 2016-01-15 96.2 97.7 95.4 97.1 79010000 91.7
## # … with 1,554 more rowsQ4. How many variables are there now? Any new variable?
There are eight variables nnow, the new variable is symbol.
Q7. Create a new variable, MC, market capitalization.
Hint: Use dplyr::mutate. Market cap is given by the formula, MC = N × P, where MC is the market capitalization, N is the number of shares outstanding, and P is the closing price per share.
stocks <- stocks %>%
mutate(MC = volume * close)Q8. Keep only three variables symbol, date, close and market capitalization and drop the other variables.
Hint: Use dplyr::select.
stocks %>% select(symbol, date, close, MC)
## # A tibble: 1,564 x 4
## symbol date close MC
## <chr> <date> <dbl> <dbl>
## 1 AAPL 2016-01-04 105. 7126864155.
## 2 AAPL 2016-01-05 103. 5730293554.
## 3 AAPL 2016-01-06 101. 6893659975.
## 4 AAPL 2016-01-07 96.4 7821554637.
## 5 AAPL 2016-01-08 97.0 6864574009.
## 6 AAPL 2016-01-11 98.5 4900823032.
## 7 AAPL 2016-01-12 100.0 4913453783.
## 8 AAPL 2016-01-13 97.4 6080992582.
## 9 AAPL 2016-01-14 99.5 6286688162.
## 10 AAPL 2016-01-15 97.1 7674241063.
## # … with 1,554 more rowsQ9 Plot daily closing stock prices for both stocks by mapping symbol to color.
Hint: Use ggplot2::ggplot.
stocks %>%
ggplot(aes(x = date, y = close, color = symbol)) +
geom_line()