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 mean weight by sector. Which sector has the greatest weight on S&P600 on average?# Load packages
library(tidyquant)
library(tidyverse) dividends Import dividends of Walmart and Target since 2010.tg_divs <- tq_get("TGT", get = "dividends", from = "2010-01-01")
tg_divs## # A tibble: 45 x 3
## symbol date value
## <chr> <date> <dbl>
## 1 TGT 2010-02-17 0.17
## 2 TGT 2010-05-18 0.17
## 3 TGT 2010-08-18 0.25
## 4 TGT 2010-11-17 0.25
## 5 TGT 2011-02-14 0.25
## 6 TGT 2011-05-16 0.25
## 7 TGT 2011-08-16 0.3
## 8 TGT 2011-11-14 0.3
## 9 TGT 2012-02-13 0.3
## 10 TGT 2012-05-14 0.3
## # ... with 35 more rowswm_divs <- tq_get("WMT", get = "dividends", from = "2010-01-01")
wm_divs## # A tibble: 46 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 36 more rowseconomic data Import the U.S. Unemployment Rate (Percent, Monthly, Seasonally Adjusted) since 2010.Hint: Find the symbol in FRED.
us_un <- tq_get("UNRATE", get = "economic.data", from = "2010-01-01")
us_un## # A tibble: 136 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 126 more rowsstock prices Import stock prices of Facebook, Apple, Netflix and Google since 2019.tq_get(c("FB", "NFLX", "GOOGL"), get = "stock.prices", from = "2019-01-01")## # A tibble: 1,773 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 1,763 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.
Index <- tq_index("SP600")mutate 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.
weight <- Index %>%
select(weight) %>%
mutate(
weight = weight * 100,
)
weight## # A tibble: 602 x 1
## weight
## <dbl>
## 1 0.840
## 2 0.662
## 3 0.595
## 4 0.574
## 5 0.526
## 6 0.515
## 7 0.513
## 8 0.468
## 9 0.467
## 10 0.462
## # ... with 592 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.
summarize(Index, weight = sum(weight))## # A tibble: 1 x 1
## weight
## <dbl>
## 1 1.00group_by and summarize Calculate the mean weight by sector. Which sector has the greatest weight on S&P600 on average?Hint: You may Google “dplyr::group_by” to find its documentation.
Index %>% group_by(sector) %>% summarize(weight = sum(weight))## # A tibble: 11 x 2
## sector weight
## <chr> <dbl>
## 1 Communication Services 0.0186
## 2 Consumer Discretionary 0.163
## 3 Consumer Staples 0.0401
## 4 Energy 0.0385
## 5 Financials 0.179
## 6 Health Care 0.109
## 7 Industrials 0.182
## 8 Information Technology 0.125
## 9 Materials 0.0541
## 10 Real Estate 0.0761
## 11 Utilities 0.0155The Industrials sector has the greatest weight on the S&P600 with a weight of 0.182.
Hint: Refer to the RMarkdown Reference Guide.