How can I convert date to dttm in R?-CodePudding

I have a dataset downloaded from yahoo in R :


library(tidyquant)
symbol = c("DOW","TWTR","FB","GOOG","TSLA","NOK","AMZN","AAPL")
ASSETS = tq_get(symbol, 
                from = "2017-01-01", 
                to = "2022-02-01")
DATAWEB = ASSETS%>%
  select(symbol,date,close,volume)

that look like this :


   symbol date       close   volume
   <chr>  <date>     <dbl>    <dbl>
 1 DOW    2019-03-20  49.8  2350800
 2 DOW    2019-03-21  49.0  1764700
 3 DOW    2019-03-22  48.6   844700
 4 DOW    2019-03-25  49.2   440900
 5 DOW    2019-03-26  48.8   504700
 6 DOW    2019-03-27  50.1  1788600
 7 DOW    2019-03-28  50.8   585400
 8 DOW    2019-03-29  51.6  1769000
 9 DOW    2019-04-01  53.5 19663400
10 DOW    2019-04-02  56.2 17414200
# ... with 9,667 more rows
# i Use `print(n = ...)` to see more rows

I want to convert the date column form date class object to dttm. How can I do it? Any help?

CodePudding user response：

as.POSIXct converts it to dttm object like this:

library(tidyquant)
library(dplyr)
symbol = c("DOW","TWTR","FB","GOOG","TSLA","NOK","AMZN","AAPL")
ASSETS = tq_get(symbol, 
                from = "2017-01-01", 
                to = "2022-02-01")
DATAWEB = ASSETS%>%
  select(symbol,date,close,volume) %>%
  mutate(date = as.POSIXct(date))
DATAWEB
#> # A tibble: 9,677 × 4
#>    symbol date                close   volume
#>    <chr>  <dttm>              <dbl>    <dbl>
#>  1 DOW    2019-03-20 01:00:00  49.8  2350800
#>  2 DOW    2019-03-21 01:00:00  49.0  1764700
#>  3 DOW    2019-03-22 01:00:00  48.6   844700
#>  4 DOW    2019-03-25 01:00:00  49.2   440900
#>  5 DOW    2019-03-26 01:00:00  48.8   504700
#>  6 DOW    2019-03-27 01:00:00  50.1  1788600
#>  7 DOW    2019-03-28 01:00:00  50.8   585400
#>  8 DOW    2019-03-29 01:00:00  51.6  1769000
#>  9 DOW    2019-04-01 02:00:00  53.5 19663400
#> 10 DOW    2019-04-02 02:00:00  56.2 17414200
#> # … with 9,667 more rows
#> # ℹ Use `print(n = ...)` to see more rows

^{Created on 2022-07-22 by the reprex package (v2.0.1)}

CodePudding user response：

A possible solution, based on lubridate::as_datetime:

library(dplyr)

DATAWEB %>% 
  mutate(date = lubridate::as_datetime(date))

#> # A tibble: 9,677 × 4
#>    symbol date                close   volume
#>    <chr>  <dttm>              <dbl>    <dbl>
#>  1 DOW    2019-03-20 00:00:00  49.8  2350800
#>  2 DOW    2019-03-21 00:00:00  49.0  1764700
#>  3 DOW    2019-03-22 00:00:00  48.6   844700
#>  4 DOW    2019-03-25 00:00:00  49.2   440900
#>  5 DOW    2019-03-26 00:00:00  48.8   504700
#>  6 DOW    2019-03-27 00:00:00  50.1  1788600
#>  7 DOW    2019-03-28 00:00:00  50.8   585400
#>  8 DOW    2019-03-29 00:00:00  51.6  1769000
#>  9 DOW    2019-04-01 00:00:00  53.5 19663400
#> 10 DOW    2019-04-02 00:00:00  56.2 17414200
#> # … with 9,667 more rows
#> # ℹ Use `print(n = ...)` to see more rows