How to get returns from a DF - r

Question

So I'm using the quantmod library to calculate historical returns, but while I can get the past prices, how can I calculate the returns and add it on to the dataframe???
My code looks like this
tickers <- c('KO', 'AAPL')
getSymbols(tickers, from = '2020-07-01', to = '2021-07-01')
history <- cbind(KO$KO.Close,AAPL$AAPL.Close)

Answer 1

First I did a way to better import and structure data
Import
library(quantmod)
library(tidyverse)
tickers <- c('KO', 'AAPL')
df <-
map_df(
.x = tickers,
.f = function(x){
getSymbols(x, from = '2020-07-01', to = '2021-07-01',auto.assign = FALSE) %>%
as_tibble() %>%
set_names(c("open","high","low","close","volume","adjusted")) %>%
mutate(symbol = x)
}
)
# A tibble: 504 x 7
open high low close volume adjusted symbol
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 44.9 45.5 44.7 44.8 14316500 43.1 KO
2 45.3 45.4 44.8 44.9 15111900 43.2 KO
3 45.1 45.3 44.6 45.2 15146000 43.5 KO
4 45 45.5 44.8 45.2 13043600 43.5 KO
5 45.1 45.2 44.5 45.1 13851200 43.3 KO
6 45.0 45.0 43.8 43.9 16087100 42.2 KO
7 43.9 45.2 43.9 45.2 15627800 43.4 KO
8 45.5 45.7 45.0 45.2 16705300 43.5 KO
9 44.9 45.9 44.7 45.9 17080100 44.1 KO
10 46.3 47.2 46.2 46.4 23738000 44.6 KO
Return
I do not know if this is the right formula for return, but you can change later inside mutate
df %>%
group_by(symbol) %>%
mutate(return = 100*((open/lag(open))-1))
# A tibble: 504 x 8
# Groups: symbol [2]
open high low close volume adjusted symbol return
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 44.9 45.5 44.7 44.8 14316500 43.1 KO NA
2 45.3 45.4 44.8 44.9 15111900 43.2 KO 0.801
3 45.1 45.3 44.6 45.2 15146000 43.5 KO -0.331
4 45 45.5 44.8 45.2 13043600 43.5 KO -0.310
5 45.1 45.2 44.5 45.1 13851200 43.3 KO 0.311
6 45.0 45.0 43.8 43.9 16087100 42.2 KO -0.199
7 43.9 45.2 43.9 45.2 15627800 43.4 KO -2.60
8 45.5 45.7 45.0 45.2 16705300 43.5 KO 3.76
9 44.9 45.9 44.7 45.9 17080100 44.1 KO -1.36
10 46.3 47.2 46.2 46.4 23738000 44.6 KO 3.10
# ... with 494 more rows

Answer 2

Assuming the return you're looking for as today's value/yesterday's value, and using the tidyverse:
library(tidyverse)
library(timetk)
tickers <- c('KO', 'AAPL')
quantmod::getSymbols(tickers, from = '2020-07-01', to = '2021-07-01')
# Convert to a tibble to keep the dates
equity1 <- tk_tbl(KO) %>%
select(date = index, 5)
equity2 <- tk_tbl(AAPL) %>%
select(date = index, 5)
# Combine the series using a join, in case dates don't line up exactly.
history <- full_join(equity1, equity2, by = "date")
# Make data long, group by equity, do the calculation, turn back into wide data:
return <- history %>%
pivot_longer(-date) %>%
group_by(name) %>%
mutate(return = value/lag(value)-1) %>%
ungroup() %>%
pivot_wider(names_from = name, values_from = c(value, return))
# A tibble: 252 x 5
date value_KO.Close value_AAPL.Close return_KO.Close return_AAPL.Close
<date> <dbl> <dbl> <dbl> <dbl>
1 2020-07-01 44.8 91.0 NA NA
2 2020-07-02 44.9 91.0 0.00134 0
3 2020-07-06 45.2 93.5 0.00780 0.0268
4 2020-07-07 45.2 93.2 -0.000442 -0.00310
5 2020-07-08 45.1 95.3 -0.00310 0.0233
6 2020-07-09 43.9 95.8 -0.0257 0.00430
7 2020-07-10 45.2 95.9 0.0282 0.00175
8 2020-07-13 45.2 95.5 0.00221 -0.00461
9 2020-07-14 45.9 97.1 0.0137 0.0165
10 2020-07-15 46.4 97.7 0.0116 0.00688
# ... with 242 more rows