I have the following R data frame:
zed
# A tibble: 10 x 3
jersey_number first_name statistics.minutes
<chr> <chr> <chr>
1 20 Marques 8:20
2 53 Brennan 00:00
3 35 Marvin 40:00
4 50 Justin 00:00
5 14 Jordan 00:00
6 1 Trevon 31:00
7 15 Alex 2:00
8 51 Mike 00:00
9 12 Javin 17:00
10 3 Grayson 38:00
> dput(zed)
structure(list(jersey_number = c("20", "53", "35", "50", "14",
"1", "15", "51", "12", "3"), first_name = c("Marques", "Brennan",
"Marvin", "Justin", "Jordan", "Trevon", "Alex", "Mike", "Javin",
"Grayson"), statistics.minutes = c("8:20", "00:00", "40:00",
"00:00", "00:00", "31:00", "2:00", "00:00", "17:00", "38:00")), row.names = c(NA,
-10L), class = c("tbl_df", "tbl", "data.frame"))
This is the format of the data from the API I am receiving it from. All of the columns (there are ~100 cols) are initially of class character. To convert everything, I use readr::type_convert(), but the following error happens:
> zed %>% readr::type_convert()
Parsed with column specification:
cols(
jersey_number = col_integer(),
first_name = col_character(),
statistics.minutes = col_time(format = "")
)
# A tibble: 10 x 3
jersey_number first_name statistics.minutes
<int> <chr> <time>
1 20 Marques 08:20
2 53 Brennan 00:00
3 35 Marvin NA
4 50 Justin 00:00
5 14 Jordan 00:00
6 1 Trevon NA
7 15 Alex 02:00
8 51 Mike 00:00
9 12 Javin 17:00
10 3 Grayson NA
Instead of throwing errors and messing up the conversion, I would like it if this minutes column instead turned into class == numeric. If a row shows '8:20' for this column, I'd like this to simply be converted into 8.33.
Any thoughts on how I can do this - preferably something that allows me to continue using type_convert.
library(lubridate)
Read in df with no alterations (your dput code).
Add hours to the minute-seconds:
df$statistics.minutes <- paste0("00:", df$statistics.minutes)
Convert to a time type:
df$statistics.minutes <- lubridate::hms(df$statistics.minutes)
Divide by 60:
period_to_seconds(df$statistics.minutes) / 60
Result:
[1] 8.333333 0.000000 40.000000 0.000000 0.000000
[6] 31.000000 2.000000 0.000000 17.000000 38.000000
Replace in df, if desired:
df$statistics.minutes <- period_to_seconds(df$statistics.minutes) / 60
[OP's addition] :-)
I've created the following helper function - based on this result - so I can fix the issue without breaking my pipe chain:
fixMinutes <- function(raw.data) {
new.raw.data <- raw.data %>%
dplyr::mutate(statistics.minutes = paste0("00:", statistics.minutes)) %>%
dplyr::mutate(statistics.minutes = lubridate::hms(statistics.minutes)) %>%
dplyr::mutate(statistics.minutes = lubridate::period_to_seconds(statistics.minutes) / 60)
return(new.raw.data)
}
zed %>%
... %>%
fixMinutes() %>%
... %>%