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Let's say I have the following dataset

df <- read.table(text="UTCDate UTCTime White Black
 2018.01.01 03:49:40 JL XN
 2018.01.01 03:52:01 XN JL
 2018.01.01 03:54:16 JL XN
 2018.01.01 03:55:58 XN JL
 2018.01.01 03:57:59 JL XN
 2018.01.01 04:00:27 XN JL
 2018.01.01 04:01:48 JL XN
 2018.01.01 04:03:43 XN JL
 2018.01.01 04:06:12 JL XN
 2018.01.01 09:21:16 JL OC
 2018.01.01 09:22:28 OC JL
 2018.01.01 09:24:16 JL OC
 2018.01.01 09:24:58 OC JL", header=T)

I am trying to create a column that tracks "consecutive games" where a consecutive game is defined as, say, any entry that is less than 10 minutes from the preceding entry. Here, consecutive game is defined from the perspective of some player (e.g. JL) so rows 1 through 9 would read (1:9) in consec_games column and rows 10 through 13 would be (1:4).

The column "consecutive_games" should only apply to a single player (e.g. JL). So it would be JL_consecutive_games. This is a large dataset where multiple player may be playing others at the same time. Therefore, the consecutive games column has to apply only to some pre-specified player (in this case, JL).

I have seen answers like this: Consecutive count by groups which take advantage of the lag function, but I'm not sure how to apply a lag to this dataset.

What about a convenience data.table function:

consecutive_plays <- function(df, player, date_var = "UTCDate", time_var = "UTCTime", white_var = "White", black_var = "Black", diff_time = 10, unit_time = "mins") get(black_var) == player, paste0(player, "_consecutive_games") := seq_len(.N), by = .(Player_Present, cumsum(time_diff))][
 , c("TimeDimension", "time_diff", "Player_Present") := NULL
 ]

That you can call for any desired player:

df <- consecutive_plays(df, player = "JL")

And get the output with the corresponding column:

 UTCDate UTCTime White Black JL_consecutive_games
 1: 2018.01.01 03:49:40 JL XN 1
 2: 2018.01.01 03:52:01 XN JL 2
 3: 2018.01.01 03:54:16 JL XN 3
 4: 2018.01.01 03:55:58 XN JL 4
 5: 2018.01.01 03:57:59 JL XN 5
 6: 2018.01.01 04:00:27 XN JL 6
 7: 2018.01.01 04:01:48 JL XN 7
 8: 2018.01.01 04:03:43 XN JL 8
 9: 2018.01.01 04:06:12 JL XN 9
10: 2018.01.01 09:21:16 JL OC 1
11: 2018.01.01 09:22:28 OC JL 2
12: 2018.01.01 09:24:16 JL OC 3
13: 2018.01.01 09:24:58 OC JL 4

If the player is not there for any of the games, the rows would be set to NA:

df <- consecutive_plays(df, player = "XN")

 UTCDate UTCTime White Black JL_consecutive_games XN_consecutive_games
 1: 2018.01.01 03:49:40 JL XN 1 1
 2: 2018.01.01 03:52:01 XN JL 2 2
 3: 2018.01.01 03:54:16 JL XN 3 3
 4: 2018.01.01 03:55:58 XN JL 4 4
 5: 2018.01.01 03:57:59 JL XN 5 5
 6: 2018.01.01 04:00:27 XN JL 6 6
 7: 2018.01.01 04:01:48 JL XN 7 7
 8: 2018.01.01 04:03:43 XN JL 8 8
 9: 2018.01.01 04:06:12 JL XN 9 9
10: 2018.01.01 09:21:16 JL OC 1 NA
11: 2018.01.01 09:22:28 OC JL 2 NA
12: 2018.01.01 09:24:16 JL OC 3 NA
13: 2018.01.01 09:24:58 OC JL 4 NA

It doesn't matter much how many players you have. You can easily quickly get the columns for all of them:

players <- unique(c(as.character(df$White), as.character(df$Black)))

for (player in players) df <- consecutive_plays(df, player = player) 

Output:

 UTCDate UTCTime White Black JL_consecutive_games XN_consecutive_games OC_consecutive_games
 1: 2018.01.01 03:49:40 JL XN 1 1 NA
 2: 2018.01.01 03:52:01 XN JL 2 2 NA
 3: 2018.01.01 03:54:16 JL XN 3 3 NA
 4: 2018.01.01 03:55:58 XN JL 4 4 NA
 5: 2018.01.01 03:57:59 JL XN 5 5 NA
 6: 2018.01.01 04:00:27 XN JL 6 6 NA
 7: 2018.01.01 04:01:48 JL XN 7 7 NA
 8: 2018.01.01 04:03:43 XN JL 8 8 NA
 9: 2018.01.01 04:06:12 JL XN 9 9 NA
10: 2018.01.01 09:21:16 JL OC 1 NA 1
11: 2018.01.01 09:22:28 OC JL 2 NA 2
12: 2018.01.01 09:24:16 JL OC 3 NA 3
13: 2018.01.01 09:24:58 OC JL 4 NA 4

Eventually, you can also then configure other parameters, e.g. if you would like to change the diff_time to more than 10 minutes, or if you would like to change the unit_time to hours, days or weeks, etc.

In base R only.

First make a data/time object with the two separate columns UTCDate and UTCTime. Then use a cumsum trick to get the groups. Finally ave applies a function (seq_along) to each of the vectors defined by d.

UTC <- paste(df$UTCDate, df$UTCTime)
UTC <- as.POSIXct(UTC, format = "%Y.%m.%d %H:%M:%S")

d <- c(0, difftime(UTC[-1], UTC[-length(UTC)], units = "mins"))
d <- cumsum(d > 10)

ave(seq_len(nrow(df)), d, FUN = seq_along)
#[1] 1 2 3 4 5 6 7 8 9 1 2 3 4

df$Games <- ave(seq_len(nrow(df)), d, FUN = seq_along)

rm(UTC, d) # Tidy up

Here is a solution using the tidyverse that gives you the result for all players:

library(tidyverse)
library(magrittr)
library(lubridate)

df %<>% 
 gather("color", "player", 3:4) %>%
 unite(datetime, 1:2) %>%
 arrange(datetime) %>%
 mutate(name = player)

df$datetime %<>% parse_datetime("%Y.%m.%d_%H:%M:%S")

nested <- df %>%
 mutate(cond = difftime(df$datetime, lag(df$datetime)) < 600) %>% 
 group_by(player) %>%
 nest()

get_cons <- function(df) 
 df$consecutive_games[1] <- 1
 for(i in 2:nrow(df)) 
 if(df$cond[i] == T) 
 df$consecutive_games[i] <- df$consecutive_games[i - 1] + 1
 
 df$cond[i] <- 1
 
 df %>%
 select(- cond)


options(tibble.print_max = Inf) # to show entire tibble

map_df(nested$data, get_cons)

# A tibble: 26 x 4
 datetime color name consecutive_games
 <dttm> <chr> <chr> <dbl>
 1 2018-01-01 03:49:40 White JL 1
 2 2018-01-01 03:52:01 Black JL 2
 3 2018-01-01 03:54:16 White JL 3
 4 2018-01-01 03:55:58 Black JL 4
 5 2018-01-01 03:57:59 White JL 5
 6 2018-01-01 04:00:27 Black JL 6
 7 2018-01-01 04:01:48 White JL 7
 8 2018-01-01 04:03:43 Black JL 8
 9 2018-01-01 04:06:12 White JL 9
10 2018-01-01 09:21:16 White JL 1
11 2018-01-01 09:22:28 Black JL 2
12 2018-01-01 09:24:16 White JL 3
13 2018-01-01 09:24:58 Black JL 4
14 2018-01-01 03:49:40 Black XN 1
15 2018-01-01 03:52:01 White XN 2
16 2018-01-01 03:54:16 Black XN 3
17 2018-01-01 03:55:58 White XN 4
18 2018-01-01 03:57:59 Black XN 5
19 2018-01-01 04:00:27 White XN 6
20 2018-01-01 04:01:48 Black XN 7
21 2018-01-01 04:03:43 White XN 8
22 2018-01-01 04:06:12 Black XN 9
23 2018-01-01 09:21:16 Black OC 1
24 2018-01-01 09:22:28 White OC 2
25 2018-01-01 09:24:16 Black OC 3
26 2018-01-01 09:24:58 White OC 4

And if you prefer it in wide format:

map_df(nested$data, get_cons) %>%
 rownames_to_column(var = "id") %>%
 mutate_at(vars(id), funs(stringi::stri_pad_left(., width = 2, pad = "0"))) %>%
 spread(name, consecutive_games)

# A tibble: 26 x 6
 id datetime color JL OC XN
 <chr> <dttm> <chr> <dbl> <dbl> <dbl>
 1 01 2018-01-01 03:49:40 White 1 NA NA
 2 02 2018-01-01 03:52:01 Black 2 NA NA
 3 03 2018-01-01 03:54:16 White 3 NA NA
 4 04 2018-01-01 03:55:58 Black 4 NA NA
 5 05 2018-01-01 03:57:59 White 5 NA NA
 6 06 2018-01-01 04:00:27 Black 6 NA NA
 7 07 2018-01-01 04:01:48 White 7 NA NA
 8 08 2018-01-01 04:03:43 Black 8 NA NA
 9 09 2018-01-01 04:06:12 White 9 NA NA
10 10 2018-01-01 09:21:16 White 1 NA NA
11 11 2018-01-01 09:22:28 Black 2 NA NA
12 12 2018-01-01 09:24:16 White 3 NA NA
13 13 2018-01-01 09:24:58 Black 4 NA NA
14 14 2018-01-01 03:49:40 Black NA NA 1
15 15 2018-01-01 03:52:01 White NA NA 2
16 16 2018-01-01 03:54:16 Black NA NA 3
17 17 2018-01-01 03:55:58 White NA NA 4
18 18 2018-01-01 03:57:59 Black NA NA 5
19 19 2018-01-01 04:00:27 White NA NA 6
20 20 2018-01-01 04:01:48 Black NA NA 7
21 21 2018-01-01 04:03:43 White NA NA 8
22 22 2018-01-01 04:06:12 Black NA NA 9
23 23 2018-01-01 09:21:16 Black NA 1 NA
24 24 2018-01-01 09:22:28 White NA 2 NA
25 25 2018-01-01 09:24:16 Black NA 3 NA
26 26 2018-01-01 09:24:58 White NA 4 NA