ওভারল্যাপিং রেকর্ডগুলি সনাক্ত করার জন্য একটি data.tableসমাধান এখানে দেওয়া foverlapsহয়েছে (যেমন ইতিমধ্যে @ জেনারাস দ্বারা উল্লিখিত)। ওভারল্যাপিং রেকর্ডগুলি সর্বাধিক দিয়ে রেকর্ড ফিল্টার করার জন্য গ্রুপগুলিকে বরাদ্দ করা হয়। গ্রুপে অগ্রাধিকার। এই পদ্ধতিটি তিন বা ততোধিক ওভারল্যাপিং রেকর্ডের জন্যও কাজ করছে তা দেখানোর জন্য আমি আপনার উদাহরণের ডেটাতে আরও দুটি রেকর্ড যুক্ত করেছি:
সম্পাদনা করুন: আমি @ পিজকুডাহির সমাধানটি সংশোধন ও অনুবাদ করেছি data.tableযা আরও দ্রুত কোড দেয়:
library(data.table)
library(lubridate)
times_df <- data.frame(
start = as_datetime(
c(
"2019-10-05 14:05:25",
"2019-10-05 17:30:20",
"2019-10-05 17:37:00",
"2019-10-06 04:43:55",
"2019-10-06 04:53:45",
"2019-10-06 04:53:46",
"2019-10-06 04:53:47"
)
),
stop = as_datetime(
c(
"2019-10-05 14:19:20",
"2019-10-05 17:45:15",
"2019-10-05 17:50:45",
"2019-10-06 04:59:00",
"2019-10-06 05:07:10",
"2019-10-06 05:07:11",
"2019-10-06 05:07:12"
)
),
priority = c(5, 3, 4, 3, 4, 5, 6)
)
resultDT <- setDT(times_df, key="start")[!(stop >= shift(start, type="lead", fill = TRUE) & priority > shift(priority, type="lead", fill = TRUE)) &
!(start <= shift(stop, type="lag", fill = FALSE) & priority > shift(priority, type="lag", fill = TRUE))]
# old approach ------------------------------------------------------------
# times_dt <- as.data.table(times_df)
# setkey(times_dt, start, stop)[, index := .I]
# overlaps_dt <- foverlaps(times_dt, times_dt, type = "any", which = TRUE)[xid != yid][, group := fifelse(xid > yid, yes = paste0(yid, "_", xid), no = paste0(xid, "_", yid))]
# overlaps_merged <- merge(times_dt, overlaps_dt, by.x = "index", by.y = "xid")[, .(delete_index = index[priority == max(priority)]), by = "group"]
# result_dt <- times_dt[!unique(overlaps_merged$delete_index)][, index := NULL]
আরও তথ্যের জন্য দয়া করে দেখুন ?foverlaps- ওভারল্যাপ হিসাবে বিবেচনা করা হয় এমন নিয়ন্ত্রণ করতে কিছু আরও কার্যকর বৈশিষ্ট্য প্রয়োগ করা হয়েছে যেমন maxgap, minoverlapবা type(যে কোনও, এর মধ্যে, শুরু, শেষ এবং সমান)।
আপডেট - নতুন মানদণ্ড
Unit: microseconds
expr min lq mean median uq max neval
Paul 25572.550 26105.2710 30183.930 26514.342 29614.272 153810.600 100
MKa 5100.447 5276.8350 6508.333 5401.275 5832.270 23137.879 100
pgcudahy 3330.243 3474.4345 4284.640 3556.802 3748.203 21241.260 100
ismirsehregal 711.084 913.3475 1144.829 1013.096 1433.427 2316.159 100
বেঞ্চমার্ক কোড:
#### library ----
library(dplyr)
library(lubridate)
library(igraph)
library(data.table)
library(microbenchmark)
#### data ----
times_df <- data.frame(
start = as_datetime(
c(
"2019-10-05 14:05:25",
"2019-10-05 17:30:20",
"2019-10-05 17:37:00",
"2019-10-06 04:43:55",
"2019-10-06 04:53:45",
"2019-10-06 04:53:46",
"2019-10-06 04:53:47"
)
),
stop = as_datetime(
c(
"2019-10-05 14:19:20",
"2019-10-05 17:45:15",
"2019-10-05 17:50:45",
"2019-10-06 04:59:00",
"2019-10-06 05:07:10",
"2019-10-06 05:07:11",
"2019-10-06 05:07:12"
)
),
priority = c(5, 3, 4, 3, 4, 5, 6)
)
times_tib <- as_tibble(times_df)
times_dt <- as.data.table(times_df)
#### group_interval function ----
# buffer to take a form similar to: days(1), weeks(2), etc.
group_interval <- function(start, end, buffer = 0) {
dat <- tibble(rid = 1:length(start),
start = start,
end = end,
intervals = case_when(!is.na(start) & !is.na(end) ~ interval(start, end),
is.na(start) ~ interval(end, end),
is.na(end) ~ interval(start, start),
TRUE ~ interval(NA, NA)))
# apply buffer period to intervals
int_start(dat$intervals) <- int_start(dat$intervals) - buffer + seconds(0.01)
int_end(dat$intervals) <- int_end(dat$intervals) + buffer - seconds(0.01)
df_overlap <- bind_cols(
expand.grid(dat$rid, dat$rid), # make a 2 col table with every combination of id numbers
expand.grid(dat$intervals, dat$intervals)) %>% # make a combination of every interval
mutate(overlap = int_overlaps(.data$Var11, .data$Var21)) %>% # determine if intervals overlap
rename("row" = "Var1", "col" = "Var2")
# Find groups via graph theory See igraph package
dat_graph <- graph_from_data_frame(filter(df_overlap, overlap) %>% select(row, col))
groups <- components(dat_graph)$membership[df_overlap$row]
# create a 2 column df with row (index) and group number, arrange on row number and return distinct values
df_groups <- tibble(row = as.integer(names(groups)), group = groups) %>%
unique()
# returns
left_join(select(dat, rid), df_groups, by = c("rid" = "row"))$group
}
#### benchmark ----
library(igraph)
library(data.table)
library(dplyr)
library(lubridate)
library(microbenchmark)
df_Paul <- df_MKa <- df_pgcudahy <- df_ismirsehregal <- times_df <- data.frame(
start = as_datetime(
c(
"2019-10-05 14:05:25",
"2019-10-05 17:30:20",
"2019-10-05 17:37:00",
"2019-10-06 04:43:55",
"2019-10-06 04:53:45",
"2019-10-06 04:53:46",
"2019-10-07 06:00:00",
"2019-10-07 06:10:00",
"2019-10-07 06:20:00",
"2019-10-08 06:00:00",
"2019-10-08 06:10:00",
"2019-10-08 06:20:00",
"2019-10-09 03:00:00",
"2019-10-09 03:10:00",
"2019-10-10 03:00:00",
"2019-10-10 03:10:00",
"2019-10-11 05:00:00",
"2019-10-11 05:00:00")
),
stop = as_datetime(
c(
"2019-10-05 14:19:20",
"2019-10-05 17:45:15",
"2019-10-05 17:50:45",
"2019-10-06 04:59:00",
"2019-10-06 05:07:10",
"2019-10-06 05:07:11",
"2019-10-07 06:18:00",
"2019-10-07 06:28:00",
"2019-10-07 06:38:00",
"2019-10-08 06:18:00",
"2019-10-08 06:28:00",
"2019-10-08 06:38:00",
"2019-10-09 03:30:00",
"2019-10-09 03:20:00",
"2019-10-10 03:30:00",
"2019-10-10 03:20:00",
"2019-10-11 05:40:00",
"2019-10-11 05:40:00")
),
priority = c(5, 3, 4, 3, 4, 5, 4, 3, 4, 3, 4, 3, 1, 2, 2, 1, 3, 4)
)
benchmarks <- microbenchmark(Paul = {
group_interval <- function(start, end, buffer = 0) {
dat <- tibble(rid = 1:length(start),
start = start,
end = end,
intervals = case_when(!is.na(start) & !is.na(end) ~ interval(start, end),
is.na(start) ~ interval(end, end),
is.na(end) ~ interval(start, start),
TRUE ~ interval(NA, NA)))
int_start(dat$intervals) <- int_start(dat$intervals) - buffer + seconds(0.01)
int_end(dat$intervals) <- int_end(dat$intervals) + buffer - seconds(0.01)
df_overlap <- bind_cols(
expand.grid(dat$rid, dat$rid), # make a 2 col table with every combination of id numbers
expand.grid(dat$intervals, dat$intervals)) %>% # make a combination of every interval
mutate(overlap = int_overlaps(.data$Var11, .data$Var21)) %>% # determine if intervals overlap
rename("row" = "Var1", "col" = "Var2")
dat_graph <- graph_from_data_frame(filter(df_overlap, overlap) %>% select(row, col))
groups <- components(dat_graph)$membership[df_overlap$row]
df_groups <- tibble(row = as.integer(names(groups)), group = groups) %>%
unique()
left_join(select(dat, rid), df_groups, by = c("rid" = "row"))$group
}
times_tib <- as_tibble(df_Paul)
mutate(times_tib, group = group_interval(start, stop)) %>%
group_by(group) %>%
top_n(1, desc(priority)) %>%
ungroup() %>%
select(-group)
},
MKa = {
df_MKa$id <- 1:nrow(df_MKa)
# Create consolidated df which we will use to check if stop date is in between start and stop
my_df <- bind_rows(replicate(n = nrow(df_MKa), expr = df_MKa, simplify = FALSE))
my_df$stop_chk <- rep(df_MKa$stop, each = nrow(df_MKa))
# Flag if stop date sits in between start and stop
my_df$chk <- my_df$stop_chk >= my_df$start & my_df$stop_chk <= my_df$stop
my_df$chk_id <- df_MKa[match(my_df$stop_chk, df_MKa$stop), "id"]
# Using igrpah to cluster ids to create unique groups
# this will identify any overlapping groups
library(igraph)
g <- graph.data.frame(my_df[my_df$chk == TRUE, c("id", "chk_id")])
df_g <- data.frame(clusters(g)$membership)
df_g$chk_id <- row.names(df_g)
# copy the unique groups to the df
my_df$new_id <- df_g[match(my_df$chk_id, df_g$chk_id), "clusters.g..membership"]
my_df %>%
filter(chk == TRUE) %>%
arrange(priority) %>%
filter(!duplicated(new_id)) %>%
select(start, stop, priority) %>%
arrange(start)
}, pgcudahy = {
df_pgcudahy %>%
arrange(start) %>%
mutate(remove1 = ifelse((stop >= lead(start, default=FALSE)) &
(priority > lead(priority, default=(max(priority) + 1))), TRUE, FALSE)) %>%
mutate(remove2 = ifelse((start <= lag(stop, default=FALSE)) &
(priority > lag(priority, default=(max(priority) + 1))), TRUE, FALSE)) %>%
filter(remove1 == FALSE & remove2 == FALSE) %>%
select(1:3)
}, ismirsehregal = {
setDT(df_ismirsehregal, key="start")[!(stop >= shift(start, type="lead", fill = TRUE) & priority > shift(priority, type="lead", fill = TRUE)) &
!(start <= shift(stop, type="lag", fill = FALSE) & priority > shift(priority, type="lag", fill = TRUE))]
})
benchmarks
combn, যদিও আপনার অনেকগুলি সারি পাওয়া গেলে এটি ব্যয়বহুল হতে পারে।times_df %>% mutate(interval = interval(start, stop)) %>% {combn(nrow(.), 2, function(x) if (int_overlaps(.$interval[x[1]], .$interval[x[2]])) x[which.min(.$priority[x])], simplify = FALSE)} %>% unlist() %>% {slice(times_df, -.)}