R lapply with several dynamic arguments
Question
I'm trying to run an lapply function using inputs from a data frame (example below).
Here below the example of the csv file, which I called holdings.csv :
ticker,buy_date,shares_hold,buy_price,sell_date
"AAPL","2019-11-25",0.07,264.5714,
"MSFT","2019-11-29",0.195,151.85,
"MSFT","2020-04-08",0.6,165.17,
"DIS","2020-01-16",0.15,144.6,
"AMZN","2020-04-08",0.048,2017.5,
"BRK-B","2020-04-09",0.75,195.28,
Then I imported that data into a data frame with:
lots <- read_csv("holdings.csv")
> lots
# A tibble: 6 x 5
ticker buy_date shares_hold buy_price sell_date
<chr> <date> <dbl> <dbl> <lgl>
1 AAPL 2019-11-25 0.07 265. NA
2 MSFT 2019-11-29 0.195 152. NA
3 MSFT 2020-04-08 0.6 165. NA
4 DIS 2020-01-16 0.15 145. NA
5 AMZN 2020-04-08 0.048 2018. NA
6 BRK-B 2020-04-09 0.75 195. NA
Then I run the following code using the tq_get function from the tidyquant package:
prices <- lapply(lots$ticker, FUN = tq_get,
from = lots$buy_date,
get = "stock.prices",
to = today())
The lapply function gets and uses the first argument ( ticker vector) properly (going row by row in the data frame column defined), but it assumes the second argument ( buy_date vector) as an static value (not going row by row).
Basically, I want to get stock prices from different companies (first argument) using a different start date (second argument), but it looks for the stock prices for all different companies but from the same date.
Here below the output, where there are 6 lists (same as the one for the input data frame) but each list has the same number of rows (because all were gotten from the same starting date).
> prices
[[1]]
# A tibble: 94 x 8
symbol date open high low close volume adjusted
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 AAPL 2019-11-25 263. 266. 263. 266. 21005100 266.
2 AAPL 2019-11-26 267. 267. 262. 264. 26301900 264.
3 AAPL 2019-11-27 266. 268. 265. 268. 16308900 267.
4 AAPL 2019-11-29 267. 268 266. 267. 11654400 267.
5 AAPL 2019-12-02 267. 268. 263. 264. 23621800 264.
6 AAPL 2019-12-03 258. 260. 256. 259. 28607600 259.
7 AAPL 2019-12-04 261. 263. 261. 262. 16795400 261.
8 AAPL 2019-12-05 264. 266. 263. 266. 18606100 265.
9 AAPL 2019-12-06 267. 271 267. 271. 26518900 270.
10 AAPL 2019-12-09 270 271. 265. 267. 32010600 266.
# ... with 84 more rows
[[2]]
# A tibble: 94 x 8
symbol date open high low close volume adjusted
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 MSFT 2019-11-25 150 151. 150. 151. 22420900 151.
2 MSFT 2019-11-26 151. 152. 151. 152. 24620100 152.
3 MSFT 2019-11-27 152. 152. 152. 152. 15184400 152.
4 MSFT 2019-11-29 152. 152. 151. 151. 11977300 151.
5 MSFT 2019-12-02 152. 152. 148. 150. 27418400 149.
6 MSFT 2019-12-03 147. 149. 147. 149. 24066000 149.
7 MSFT 2019-12-04 150. 150. 149. 150. 17574700 149.
8 MSFT 2019-12-05 150. 150. 149. 150. 17869100 150.
9 MSFT 2019-12-06 151. 152. 150. 152. 16403500 151.
10 MSFT 2019-12-09 151. 152. 151. 151. 16687400 151.
# ... with 84 more rows
[[3]]
# A tibble: 94 x 8
symbol date open high low close volume adjusted
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 MSFT 2019-11-25 150 151. 150. 151. 22420900 151.
2 MSFT 2019-11-26 151. 152. 151. 152. 24620100 152.
3 MSFT 2019-11-27 152. 152. 152. 152. 15184400 152.
4 MSFT 2019-11-29 152. 152. 151. 151. 11977300 151.
5 MSFT 2019-12-02 152. 152. 148. 150. 27418400 149.
6 MSFT 2019-12-03 147. 149. 147. 149. 24066000 149.
7 MSFT 2019-12-04 150. 150. 149. 150. 17574700 149.
8 MSFT 2019-12-05 150. 150. 149. 150. 17869100 150.
9 MSFT 2019-12-06 151. 152. 150. 152. 16403500 151.
10 MSFT 2019-12-09 151. 152. 151. 151. 16687400 151.
# ... with 84 more rows
[[4]]
# A tibble: 94 x 8
symbol date open high low close volume adjusted
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 DIS 2019-11-25 149. 150. 148. 150. 11316800 149.
2 DIS 2019-11-26 152. 153. 151. 152. 24949900 151.
3 DIS 2019-11-27 152. 153. 151. 151. 6155400 151.
4 DIS 2019-11-29 151. 152. 151. 152. 6284900 151.
5 DIS 2019-12-02 153. 153. 149. 151. 10351000 150.
6 DIS 2019-12-03 148. 149. 147. 149. 9273800 148.
7 DIS 2019-12-04 149. 149. 148. 148. 7684800 147.
8 DIS 2019-12-05 149. 149. 147. 147. 7363300 147.
9 DIS 2019-12-06 148. 149. 147. 148. 7084900 147.
10 DIS 2019-12-09 148. 149. 145. 146. 11515000 145.
# ... with 84 more rows
[[5]]
# A tibble: 94 x 8
symbol date open high low close volume adjusted
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 AMZN 2019-11-25 1753. 1777. 1753. 1774. 3486200 1774.
2 AMZN 2019-11-26 1780. 1797. 1778. 1797. 3181200 1797.
3 AMZN 2019-11-27 1801 1824. 1797. 1819. 3025600 1819.
4 AMZN 2019-11-29 1818. 1825. 1801. 1801. 1923400 1801.
5 AMZN 2019-12-02 1804. 1806. 1763. 1782. 3925600 1782.
6 AMZN 2019-12-03 1760 1773. 1747. 1770. 3380900 1770.
7 AMZN 2019-12-04 1774. 1789. 1760. 1761. 2670100 1761.
8 AMZN 2019-12-05 1764. 1764. 1740 1740. 2823800 1740.
9 AMZN 2019-12-06 1751. 1754. 1740. 1752. 3117400 1752.
10 AMZN 2019-12-09 1751. 1767. 1746. 1750. 2442800 1750.
# ... with 84 more rows
[[6]]
# A tibble: 94 x 8
symbol date open high low close volume adjusted
<chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 BRK-B 2019-11-25 219. 219. 218. 219. 3648500 219.
2 BRK-B 2019-11-26 219. 219. 218. 219. 3717800 219.
3 BRK-B 2019-11-27 219. 221. 218. 220. 3945500 220.
4 BRK-B 2019-11-29 220. 221. 220. 220. 2256500 220.
5 BRK-B 2019-12-02 221. 221. 220. 220. 3888100 220.
6 BRK-B 2019-12-03 219. 219. 216. 218. 4480100 218.
7 BRK-B 2019-12-04 218. 219. 218. 218. 2667700 218.
8 BRK-B 2019-12-05 219. 220. 218. 220. 2319300 220.
9 BRK-B 2019-12-06 222. 223 221. 223. 3447700 223.
10 BRK-B 2019-12-09 223. 223. 221. 221. 2601000 221.
# ... with 84 more rows
I've tried mapply but it doesn't give me the result that I need.
prices <- mapply(lots$ticker, FUN = tq_get,
from = lots$buy_date,
get = "stock.prices",
to = today())
> prices
AAPL MSFT MSFT DIS AMZN BRK-B
symbol Character,94 Character,91 Character,2 Character,59 Character,2 Character,2
date Numeric,94 Numeric,91 Numeric,2 Numeric,59 Numeric,2 Numeric,2
open Numeric,94 Numeric,91 Numeric,2 Numeric,59 Numeric,2 Numeric,2
high Numeric,94 Numeric,91 Numeric,2 Numeric,59 Numeric,2 Numeric,2
low Numeric,94 Numeric,91 Numeric,2 Numeric,59 Numeric,2 Numeric,2
close Numeric,94 Numeric,91 Numeric,2 Numeric,59 Numeric,2 Numeric,2
volume Numeric,94 Numeric,91 Numeric,2 Numeric,59 Numeric,2 Numeric,2
adjusted Numeric,94 Numeric,91 Numeric,2 Numeric,59 Numeric,2 Numeric,2
1 answers
solution1
1 ACCPTED 2020-04-12 19:30:21
Your result from mapply() is because the function tries to convert the output to a matrix with the simplify argument by default. You can get your desired output with the following:
prices <- mapply(lots$ticker, FUN = tq_get,
from = lots$buy_date,
get = "stock.prices",
to = today(), SIMPLIFY = FALSE)
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