R Tidyquant adding a price difference column to a tibble-CodePudding

I'm trying to add a column to a Tidyquant tibble. Here's the code:

library(tidyquant)
symbol <- 'AAPL'

start_date <- as.Date('2022-01-01')  
end_date <- as.Date('2022-03-31')    

prices <- tq_get(symbol,
                 from = start_date,
                 to = end_date,
                 get = 'stock.prices')

head(prices)

# A tibble: 6 x 8
  symbol date        open  high   low close    volume adjusted
  <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
1 AAPL   2022-01-03  178.  183.  178.  182. 104487900     182.
2 AAPL   2022-01-04  183.  183.  179.  180.  99310400     179.
3 AAPL   2022-01-05  180.  180.  175.  175.  94537600     175.
4 AAPL   2022-01-06  173.  175.  172.  172   96904000     172.
5 AAPL   2022-01-07  173.  174.  171.  172.  86709100     172.
6 AAPL   2022-01-10  169.  172.  168.  172. 106765600     172.

Now, I'm attempting to add the change_on_day column (that's just the difference in the 'adjusted' prices between one day and the next) using the following:

prices$change_on_day <- diff(prices$adjusted)

The error message is:

Error: Assigned data `diff(prices$adjusted)` must be compatible with existing data.
x Existing data has 61 rows.
x Assigned data has 60 rows.
i Only vectors of size 1 are recycled.

How would I add this price difference column?

Thanks!

CodePudding user response：

If you are trying to get today's value from the previous date value then you should be able to do that with the lag() function

prices %>% 
  mutate(change_on_day=adjusted-lag(adjusted,1))

CodePudding user response：

We can use tq_transmute with quantmod::periodReturn setting the period argument to 'daily' in order to calculate daily returns.

library(tidyquant)

symbol <- "AAPL"

start_date <- as.Date("2022-01-01")
end_date <- as.Date("2022-03-31")

prices <- tq_get(symbol,
  from = start_date,
  to = end_date,
  get = "stock.prices"
)


stock_returns_monthly <- prices %>%
  tq_transmute(
    select = adjusted,
    mutate_fun = periodReturn,
    period = "daily",
    col_rename = "change_on_day"
  )
stock_returns_monthly
#> # A tibble: 61 × 2
#>    date       change_on_day
#>    <date>             <dbl>
#>  1 2022-01-03      0       
#>  2 2022-01-04     -0.0127  
#>  3 2022-01-05     -0.0266  
#>  4 2022-01-06     -0.0167  
#>  5 2022-01-07      0.000988
#>  6 2022-01-10      0.000116
#>  7 2022-01-11      0.0168  
#>  8 2022-01-12      0.00257 
#>  9 2022-01-13     -0.0190  
#> 10 2022-01-14      0.00511 
#> # … with 51 more rows

^{Created on 2022-04-18 by the reprex package (v2.0.1)}

For more information check this vignette