library(yfR)

ticker <-"IBM"

begdate<- "2023-1-1"
enddate <-"2023-12-31"

x<- yf_get(ticker,begdate,enddate)


head(x)

d(x)
# A tibble: 6 × 11
  ticker ref_date   price_open price_high price_low price_close  volume price_adjusted ret_adjusted_prices
  <chr>  <date>          <dbl>      <dbl>     <dbl>       <dbl>   <dbl>          <dbl>               <dbl>
1 IBM    2023-01-03       141.       142.      140.        142. 3338600           132.            NA      
2 IBM    2023-01-04       142.       144.      141.        143. 3869200           133.             0.00742
3 IBM    2023-01-05       142.       142.      140.        141. 2866600           132.            -0.0104 
4 IBM    2023-01-06       142.       144.      142.        144. 3574000           134.             0.0184 
5 IBM    2023-01-09       144.       145.      143.        144. 3987700           134.            -0.00104
6 IBM    2023-01-10       144.       145.      143.        145. 2152100           135.             0.00871
# ℹ 2 more variables: ret_closing_prices <dbl>, cumret_adjusted_prices <dbl>
> x2<-data.frame(x)
> head(x2)
  ticker   ref_date price_open price_high price_low price_close  volume price_adjusted ret_adjusted_prices
1    IBM 2023-01-03     141.10     141.90    140.48      141.55 3338600       132.3207                  NA
2    IBM 2023-01-04     142.07     143.62    141.37      142.60 3869200       133.3022         0.007417749
3    IBM 2023-01-05     142.44     142.50    140.01      141.11 2866600       131.9094        -0.010448721
4    IBM 2023-01-06     142.38     144.25    141.58      143.70 3574000       134.3305         0.018354361
5    IBM 2023-01-09     144.08     145.47    143.40      143.55 3987700       134.1903        -0.001043678
6    IBM 2023-01-10     143.61     144.85    142.90      144.80 2152100       135.3588         0.008707805
  ret_closing_prices cumret_adjusted_prices
1                 NA              1.0000000
2        0.007417895              1.0074177
3       -0.010448846              0.9968915
4        0.018354449              1.0151888
5       -0.001043799              1.0141293
6        0.008707767              1.0229601