dividends Import dividends of General Motors and Ford since 2010.economic data Import New Hampshire monthly civilian unemployment rate (seasonally adjusted) since 2010.stock prices Import stock prices of NASDAQ, Dow Jones Industrial Average, and S&P500 since 2019.stock index Get all stocks in the S&P500 Index using tq_index(). Save the imported data under “Index” for the following questions.mutate Convert weight in decimals to percentages.summarize You would think all values in weight should sum to 100%. Calculate the sum of weight to confirm.group_by and summarize Calculate the total weight by sector. Which sector has the largest weight on the Index in sum?# Load packages
library(tidyquant)
library(tidyverse) dividends Import dividends of General Motors and Ford since 2010.mult_stocks <- tq_get(c("F", "GM"),
get = "dividends",
from = "2010-01-01")
mult_stocks## # A tibble: 58 x 3
## symbol date value
## <chr> <date> <dbl>
## 1 F 2012-01-27 0.05
## 2 F 2012-04-30 0.05
## 3 F 2012-08-01 0.05
## 4 F 2012-10-31 0.05
## 5 F 2013-01-28 0.1
## 6 F 2013-05-01 0.1
## 7 F 2013-07-31 0.1
## 8 F 2013-10-30 0.1
## 9 F 2014-01-29 0.125
## 10 F 2014-04-30 0.125
## # ... with 48 more rowseconomic data Import New Hampshire monthly civilian unemployment rate (seasonally adjusted) since 2010.Hint: Find the symbol in FRED.
econ <- tq_get("NHUR", get = "economic.data", from = "2010-01-01")
econ## # A tibble: 133 x 3
## symbol date price
## <chr> <date> <dbl>
## 1 NHUR 2010-01-01 6.7
## 2 NHUR 2010-02-01 6.6
## 3 NHUR 2010-03-01 6.5
## 4 NHUR 2010-04-01 6.3
## 5 NHUR 2010-05-01 6.1
## 6 NHUR 2010-06-01 6
## 7 NHUR 2010-07-01 5.9
## 8 NHUR 2010-08-01 5.8
## 9 NHUR 2010-09-01 5.8
## 10 NHUR 2010-10-01 5.8
## # ... with 123 more rowsstock prices Import stock prices of NASDAQ, Dow Jones Industrial Average, and S&P500 since 2019.mult_stocks <- tq_get (c("NASDAQCOM", "DJIA","SP500"),
get = "economic.data",
from = "2019-01-01")
mult_stocks## # A tibble: 1,734 x 3
## symbol date price
## <chr> <date> <dbl>
## 1 NASDAQCOM 2019-01-01 NA
## 2 NASDAQCOM 2019-01-02 6666.
## 3 NASDAQCOM 2019-01-03 6464.
## 4 NASDAQCOM 2019-01-04 6739.
## 5 NASDAQCOM 2019-01-07 6823.
## 6 NASDAQCOM 2019-01-08 6897
## 7 NASDAQCOM 2019-01-09 6957.
## 8 NASDAQCOM 2019-01-10 6986.
## 9 NASDAQCOM 2019-01-11 6971.
## 10 NASDAQCOM 2019-01-14 6906.
## # ... with 1,724 more rowsstock index Get all stocks in the S&P500 Index using tq_index(). Save the imported data under “Index” for the following questions.Hint: Google tidyquant r package to find its manual and look for tq_index function on the manual. Alternatively, Google tq_index on the Web to find its documentation.
tq_index_options()## [1] "DOW" "DOWGLOBAL" "SP400" "SP500" "SP600"Index <- tq_index("SP500")
Index## # A tibble: 505 x 8
## symbol company identifier sedol weight sector shares_held local_currency
## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 AAPL Apple Inc. 03783310 20462~ 0.0582 Inform~ 163588830 USD
## 2 MSFT Microsoft~ 59491810 25881~ 0.0527 Inform~ 77389240 USD
## 3 AMZN Amazon.co~ 02313510 20000~ 0.0390 Consum~ 4365411 USD
## 4 FB Facebook ~ 30303M10 B7TL8~ 0.0203 Commun~ 24607110 USD
## 5 GOOGL Alphabet ~ 02079K30 BYVY8~ 0.0184 Commun~ 3077364 USD
## 6 GOOG Alphabet ~ 02079K10 BYY88~ 0.0179 Commun~ 2971282 USD
## 7 TSLA Tesla Inc 88160R10 B616C~ 0.0150 Consum~ 7762131 USD
## 8 BRK.B Berkshire~ 08467070 20733~ 0.0149 Financ~ 19920620 USD
## 9 JPM JPMorgan ~ 46625H10 21903~ 0.0145 Financ~ 31201408 USD
## 10 JNJ Johnson &~ 47816010 24758~ 0.0128 Health~ 26946668 USD
## # ... with 495 more rowsmutate Convert weight in decimals to percentages.Hint: Multiply weight by 100 and save it under the same name, weight. You may Google “dplyr::mutate” to find its documentation.
mutate(Index, weight = weight * 100)## # A tibble: 505 x 8
## symbol company identifier sedol weight sector shares_held local_currency
## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 AAPL Apple Inc. 03783310 20462~ 5.82 Inform~ 163588830 USD
## 2 MSFT Microsoft~ 59491810 25881~ 5.27 Inform~ 77389240 USD
## 3 AMZN Amazon.co~ 02313510 20000~ 3.90 Consum~ 4365411 USD
## 4 FB Facebook ~ 30303M10 B7TL8~ 2.03 Commun~ 24607110 USD
## 5 GOOGL Alphabet ~ 02079K30 BYVY8~ 1.84 Commun~ 3077364 USD
## 6 GOOG Alphabet ~ 02079K10 BYY88~ 1.79 Commun~ 2971282 USD
## 7 TSLA Tesla Inc 88160R10 B616C~ 1.50 Consum~ 7762131 USD
## 8 BRK.B Berkshire~ 08467070 20733~ 1.49 Financ~ 19920620 USD
## 9 JPM JPMorgan ~ 46625H10 21903~ 1.45 Financ~ 31201408 USD
## 10 JNJ Johnson &~ 47816010 24758~ 1.28 Health~ 26946668 USD
## # ... with 495 more rowssummarize You would think all values in weight should sum to 100%. Calculate the sum of weight to confirm.Hint: You may Google “dplyr::summarize” to find its documentation.
Index %>%
summarise(sum_of_weight = sum(weight))## # A tibble: 1 x 1
## sum_of_weight
## <dbl>
## 1 1group_by and summarize Calculate the total weight by sector. Which sector has the largest weight on the Index in sum?Hint: You may Google “dplyr::group_by” to find its documentation.
Index %>%
group_by(sector) %>%
summarise(total_weight_by_sector = sum(weight))## # A tibble: 11 x 2
## sector total_weight_by_sector
## * <chr> <dbl>
## 1 Communication Services 0.111
## 2 Consumer Discretionary 0.123
## 3 Consumer Staples 0.0605
## 4 Energy 0.0284
## 5 Financials 0.117
## 6 Health Care 0.130
## 7 Industrials 0.0883
## 8 Information Technology 0.264
## 9 Materials 0.0272
## 10 Real Estate 0.0247
## 11 Utilities 0.0260Hint: Refer to the RMarkdown Reference Guide.