Filter through a data frame of stocks using a search pattern. The pattern is split up by spaces, and each resulting pattern is matched to the values of the stock name and ticker.
Arguments
- x
The data frame of stocks (e.g. the default_stock_data).
- pattern
The pattern to use to filter
x.
Examples
search_stocks(default_stock_data, "Al p")
#> # A tibble: 59 × 100
#> symbol company…¹ excha…² indus…³ website descr…⁴ ceo secur…⁵ sector prima…⁶
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 APD Air Prod… NEW YO… "Indus… www.ai… "Air P… Seif… Air Pr… Manuf… 2813
#> 2 ALK Alaska A… NEW YO… "Sched… www.al… "Alask… Brad… Alaska… Trans… 4512
#> 3 ALB Albemarl… NEW YO… "Plast… www.al… "Albem… Jerr… Albema… Manuf… 2821
#> 4 ALLE Allegion… NEW YO… "Secur… www.al… "Alleg… NA Allegi… Admin… 7381
#> 5 LNT Alliant … NASDAQ "Nucle… https:… "Allia… John… Allian… Utili… 4931
#> 6 ALL Allstate… NEW YO… "Direc… www.al… "The A… Thom… Allsta… Finan… 6331
#> 7 GOOGL Alphabet… NASDAQ "All O… abc.xyz "Larry… Sund… Alphab… Infor… 7375
#> 8 MO Altria G… NEW YO… "Tobac… https:… "Altri… Will… Altria… Manuf… 2111
#> 9 AAL American… NASDAQ "Sched… www.aa… "Ameri… Thom… Americ… Trans… 4512
#> 10 AIG American… NEW YO… "Third… www.ai… "Ameri… Bria… Americ… Finan… 6331
#> # … with 49 more rows, 90 more variables: employees <dbl>, address <chr>,
#> # state <chr>, city <chr>, ZIP <chr>, country <chr>, phone <dbl>,
#> # capital_expenditures <dbl>, cash_change <dbl>, cash_flow <dbl>,
#> # cash_flow_financing <dbl>, changes_in_inventories <dbl>,
#> # changes_in_receivables <dbl>, currency <chr>, depreciation <dbl>,
#> # filing_type <chr>, fiscal_date <dbl>, net_borrowings <dbl>,
#> # net_income <dbl>, report_date <dbl>, total_investing_cash_flows <dbl>, …