dividends Import dividends of Walmart and Target since 2010.economic data Import the U.S. Unemployment Rate (Percent, Monthly, Seasonally Adjusted) since 2010.stock prices Import stock prices of Facebook, Apple, Netflix and Google since 2019.stock index Get all stocks in the S&P600 Index using tq_index().mutate Convert weight in decimals to percentages in S&P600.summarize You want to know what the mean weight is. Calculate the mean weight of S&P600.group_by and summarize Calculate the sum of weights by sector. Which sector has the greatest weight on S&P600 in aggregate?# Load packages
library(tidyquant)
library(tidyverse) dividends Import dividends of Walmart and Target since 2010.mult_stocks <- tq_get(c("WMT", "TGT"),
get = "dividends",
from = "2010-01-01")
mult_stocks## # A tibble: 88 x 3
## symbol date value
## <chr> <date> <dbl>
## 1 WMT 2010-03-10 0.303
## 2 WMT 2010-05-12 0.303
## 3 WMT 2010-08-11 0.303
## 4 WMT 2010-12-08 0.303
## 5 WMT 2011-03-09 0.365
## 6 WMT 2011-05-11 0.365
## 7 WMT 2011-08-10 0.365
## 8 WMT 2011-12-07 0.365
## 9 WMT 2012-03-08 0.398
## 10 WMT 2012-05-09 0.398
## # ... with 78 more rowseconomic data Import the U.S. Unemployment Rate (Percent, Monthly, Seasonally Adjusted) since 2010.Hint: Find the symbol in FRED.
econdata <- tq_get(c("UNRATE"),
get = "economic.data",
from = "2010-01-01")
econdata## # A tibble: 130 x 3
## symbol date price
## <chr> <date> <dbl>
## 1 UNRATE 2010-01-01 9.8
## 2 UNRATE 2010-02-01 9.8
## 3 UNRATE 2010-03-01 9.9
## 4 UNRATE 2010-04-01 9.9
## 5 UNRATE 2010-05-01 9.6
## 6 UNRATE 2010-06-01 9.4
## 7 UNRATE 2010-07-01 9.4
## 8 UNRATE 2010-08-01 9.5
## 9 UNRATE 2010-09-01 9.5
## 10 UNRATE 2010-10-01 9.4
## # ... with 120 more rowsstock prices Import stock prices of Facebook, Apple, Netflix and Google since 2019.stock_prices <- tq_get(c("FB", "AAPL", "NFLX", "GOOG"),
get = "stock.prices",
from = "2019-01-01")
stock_prices## # A tibble: 1,908 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FB 2019-01-02 129. 138. 129. 136. 28146200 136.
## 2 FB 2019-01-03 135. 137. 131. 132. 22717900 132.
## 3 FB 2019-01-04 134. 138 134. 138. 29002100 138.
## 4 FB 2019-01-07 138. 139. 136. 138. 20089300 138.
## 5 FB 2019-01-08 140. 143. 140. 143. 26263800 143.
## 6 FB 2019-01-09 143. 145. 141. 144. 22205900 144.
## 7 FB 2019-01-10 143. 145. 141. 144. 16125000 144.
## 8 FB 2019-01-11 143. 145. 143. 144. 12908000 144.
## 9 FB 2019-01-14 142 147. 141. 145. 20520300 145.
## 10 FB 2019-01-15 146. 151. 146. 149. 24069000 149.
## # ... with 1,898 more rowsstock index Get all stocks in the S&P600 Index using tq_index().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.
SP <- tq_index("SP600")
SP## # A tibble: 601 x 8
## symbol company identifier sedol weight sector shares_held local_currency
## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 BRKS Brooks Au~ 11434010 2145~ 0.00637 Inform~ 110008 USD
## 2 CPRI Capri Hol~ G1890L10 BJ1N~ 0.00630 Consum~ 224119 USD
## 3 KNSL Kinsale C~ 49714P10 BD1M~ 0.00622 Financ~ 31695 USD
## 4 NEO NeoGenomi~ 64049M20 2598~ 0.00594 Health~ 164738 USD
## 5 YETI YETI Hold~ 98585X10 BGR7~ 0.00567 Consum~ 111567 USD
## 6 SAIA Saia Inc. 78709Y10 2982~ 0.00559 Indust~ 39044 USD
## 7 OMCL Omnicell ~ 68213N10 2789~ 0.00555 Health~ 63791 USD
## 8 EXPO Exponent ~ 30214U10 2330~ 0.00536 Indust~ 76950 USD
## 9 POWI Power Int~ 73927610 2133~ 0.00518 Inform~ 89183 USD
## 10 CROX Crocs Inc. 22704610 B0T7~ 0.00494 Consum~ 100643 USD
## # ... with 591 more rowsmutate Convert weight in decimals to percentages in S&P600.Hint: Multiply weight by 100 and save it under the same name, weight. You may Google “dplyr::mutate” to find its documentation.
SP <-
SP %>%
mutate(weight = weight * 100)
SP## # A tibble: 601 x 8
## symbol company identifier sedol weight sector shares_held local_currency
## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 BRKS Brooks Au~ 11434010 2145~ 0.637 Informa~ 110008 USD
## 2 CPRI Capri Hol~ G1890L10 BJ1N~ 0.630 Consume~ 224119 USD
## 3 KNSL Kinsale C~ 49714P10 BD1M~ 0.622 Financi~ 31695 USD
## 4 NEO NeoGenomi~ 64049M20 2598~ 0.594 Health ~ 164738 USD
## 5 YETI YETI Hold~ 98585X10 BGR7~ 0.567 Consume~ 111567 USD
## 6 SAIA Saia Inc. 78709Y10 2982~ 0.559 Industr~ 39044 USD
## 7 OMCL Omnicell ~ 68213N10 2789~ 0.555 Health ~ 63791 USD
## 8 EXPO Exponent ~ 30214U10 2330~ 0.536 Industr~ 76950 USD
## 9 POWI Power Int~ 73927610 2133~ 0.518 Informa~ 89183 USD
## 10 CROX Crocs Inc. 22704610 B0T7~ 0.494 Consume~ 100643 USD
## # ... with 591 more rowssummarize You want to know what the mean weight is. Calculate the mean weight of S&P600.Hint: You may Google “dplyr::summarize” to find its documentation.
SP %>%
summarize(sum = sum(weight))## # A tibble: 1 x 1
## sum
## <dbl>
## 1 100group_by and summarize Calculate the sum of weights by sector. Which sector has the greatest weight on S&P600 in aggregate?Hint: You may Google “dplyr::group_by” to find its documentation.
SP %>%
group_by(sector) %>%
summarize(sum = sum(weight))## # A tibble: 11 x 2
## sector sum
## <chr> <dbl>
## 1 Communication Services 2.43
## 2 Consumer Discretionary 15.1
## 3 Consumer Staples 3.50
## 4 Energy 3.03
## 5 Financials 17.4
## 6 Health Care 11.7
## 7 Industrials 17.5
## 8 Information Technology 14.0
## 9 Materials 5.53
## 10 Real Estate 8.15
## 11 Utilities 1.66Hint: Refer to the RMarkdown Reference Guide.