Any problem in `R`

can be approached with advantage using the basic idea of school algebra: f(x) = y where the three objects are

x, called an argument, which is what is at hand, in this case a vector of strings representing HH:MM:SS

y the object that is desired

f a function, which may be composed to transform x to y.

There are regression methods meant to work with time series specifically that be considered if the time data are more variable. The `Time`

variable might, in this case of ordinary least square regression be treated as a categorical variable since it seems, a priori, unlikely that Activity varies continuously with the passage of time. However, the conversion below assumes that seconds is the appropriate measure.

```
# LIBRARIES
suppressPackageStartupMessages({
library(dplyr)
library(purrr)
library(stringr)
})
# FUNCTIONS
mk_secs <- function(x) x[1]*3600 + x[2] * 60 + x[3] * 1
# DATA
first_time <- rep("10:10:00",10)
second_time <- rep("10:11:00",10)
the_times <- c(first_time,second_time)
# MAIN
str_split(the_times,":") %>%
map(., as.numeric) %>%
map(., mk_secs) %>%
unlist(.)
#> [1] 36600 36600 36600 36600 36600 36600 36600 36600 36600 36600 36660 36660
#> [13] 36660 36660 36660 36660 36660 36660 36660 36660
```

^{Created on 2020-10-23 by the reprex package (v0.3.0.9001)}