# Load packages
library(tidyquant)
library(tidyverse) # for count() function
# Import S&P500 Stock Index
SP500 <- tq_index("SP500")
SP500
## # 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~ 0.0667 Inform~ 174650100 USD
## 2 MSFT Microsoft~ 59491810 25881~ 0.0558 Inform~ 81274030 USD
## 3 AMZN Amazon.co~ 02313510 20000~ 0.0479 Consum~ 4490004 USD
## 4 FB Facebook ~ 30303M10 B7TL8~ 0.0237 Commun~ 25768718 USD
## 5 GOOGL Alphabet ~ 02079K30 BYVY8~ 0.0166 Commun~ 3218310 USD
## 6 GOOG Alphabet ~ 02079K10 BYY88~ 0.0163 Commun~ 3134044 USD
## 7 BRK.B Berkshire~ 08467070 20733~ 0.0154 Financ~ 20853808 USD
## 8 JNJ Johnson &~ 47816010 24758~ 0.0141 Health~ 28258660 USD
## 9 V Visa Inc.~ 92826C83 B2PZN~ 0.0123 Inform~ 18095848 USD
## 10 PG Procter &~ 74271810 27044~ 0.0122 Consum~ 26545804 USD
## # ... with 495 more rows
505 rows
8 columns
505 companies
Apple, Microsoft, and Amazon have the greatest influence on ups and downs.
Four of the sectors represented are Information Technology, Consumer Discretionary, Communication Services, and Health Care.
NFLX
count(SP500, sector, sort = T)
## # A tibble: 11 x 2
## sector n
## <chr> <int>
## 1 Industrials 73
## 2 Information Technology 71
## 3 Financials 66
## 4 Health Care 62
## 5 Consumer Discretionary 61
## 6 Consumer Staples 33
## 7 Real Estate 31
## 8 Materials 28
## 9 Utilities 28
## 10 Communication Services 26
## 11 Energy 26
As shown from the data, Industrials have the largest representation based on number of stocks.