I have a data with the following columns:
CaseID, Time, Value.
The 'time' column values are not at regular intervals of 1. I am trying to add the missing values of time with 'NA' for the rest of the columns except CaseID.
Case Value Time
1 100 07:52:00
1 110 07:53:00
1 120 07:55:00
2 10 08:35:00
2 11 08:36:00
2 12 08:38:00
Desired output:
Case Value Time
1 100 07:52:00
1 110 07:53:00
1 NA 07:54:00
1 120 07:55:00
2 10 08:35:00
2 11 08:36:00
2 NA 08:37:00
2 12 08:38:00
I tried dt[CJ(unique(CaseID),seq(min(Time),max(Time),"min"))] but it gives the following error:
Error in vecseq(f__, len__, if (allow.cartesian || notjoin) NULL else as.integer(max(nrow(x), :
Join results in 9827315 rows; more than 9620640 = max(nrow(x),nrow(i)). Check for duplicate key values in i, each of which join to the same group in x over and over again. If that's ok, try including `j` and dropping `by` (by-without-by) so that j runs for each group to avoid the large allocation. If you are sure you wish to proceed, rerun with allow.cartesian=TRUE. Otherwise, please search for this error message in the FAQ, Wiki, Stack Overflow and datatable-help for advice.
I cannot able to make it work..any help would be appreciated.
Like this??
dt[,Time:=as.POSIXct(Time,format="%H:%M:%S")]
result <- dt[,list(Time=seq(min(Time),max(Time),by="1 min")),by=Case]
setkey(result,Case,Time)
setkey(dt,Case,Time)
result <- dt[result][,Time:=format(Time,"%H:%M:%S")]
result
# Case Value Time
# 1: 1 100 07:52:00
# 2: 1 110 07:53:00
# 3: 1 NA 07:54:00
# 4: 1 120 07:55:00
# 5: 2 10 08:35:00
# 6: 2 11 08:36:00
# 7: 2 NA 08:37:00
# 8: 2 12 08:38:00
Another way:
dt[, Time := as.POSIXct(Time, format = "%H:%M:%S")]
setkey(dt, Time)
dt[, .SD[J(seq(min(Time), max(Time), by='1 min'))], by=Case]
We group by Case and join on Time on each group using .SD (hence setting key on Time). From here you can use format() as shown above.
Related
I have a data table containing daily data. From this data table I want to extract weekly data points obtained each Wednesday. If Wednesday is a holiday, i.e. not available in the data table, the next available data point should be taken.
Here a MWE:
library(data.table)
df <- data.table(date=as.Date(c("2012-06-25","2012-06-26","2012-06-27","2012-06-28","2012-06-29","2012-07-02","2012-07-03","2012-07-05","2012-07-06","2012-07-09","2012-07-10","2012-07-11","2012-07-12","2012-07-13","2012-07-16","2012-07-17","2012-07-18","2012-07-19","2012-07-20")))
df[,weekday:=strftime(date,'%u')]
with output:
date weekday
1: 2012-06-25 1
2: 2012-06-26 2
3: 2012-06-27 3
4: 2012-06-28 4
5: 2012-06-29 5
6: 2012-07-02 1
7: 2012-07-03 2
8: 2012-07-05 4 #here the 4th of July was skipped
9: 2012-07-06 5
10: 2012-07-09 1
11: 2012-07-10 2
12: 2012-07-11 3
13: 2012-07-12 4
14: 2012-07-13 5
15: 2012-07-16 1
16: 2012-07-17 2
17: 2012-07-18 3
18: 2012-07-19 4
19: 2012-07-20 5
My desired result, in this case would be:
date weekday
2012-06-27 3
2012-07-05 4
2012-07-11 3
2012-07-18 3
Is there a more efficient way of obtaining this than going week-by-week via for loop and checking whether the Wednesday data point is included in the data or not? I feel that there must be a better way, so any advice would be highly appreciated!
Working solution (following Imo's suggestion):
df[,weekday:=wday(date)] #faster way to get weekdays, careful: numbers increased by 1 vs strftime
df[,numweek:=floor(as.numeric(date-date[1])/7+1)] #get continuous week numbers extending over end of years
df[df[,.I[which.min(abs(weekday-4.25))],by=.(numweek)]$V1] #gets result
Here is one method using a join on a data.table that finds the position (using .I) of the closest value to 3 (that is not 2, using which.min(abs(as.integer(weekday)-3.25))) by week using.
df[df[, .I[which.min(abs(as.integer(weekday)-3.25))], by=week(date)]$V1]
date weekday
1: 2012-06-27 3
2: 2012-07-05 4
3: 2012-07-11 3
4: 2012-07-18 3
Note that if your real data spans years, then you need to use by=.(week(date), year(date)).
Note also that there is a data.table function wday that will returns the integer day of the week directly. It is 1 greater than the character integer value returned by strftime, so an adjustment would be required if you wanted to use it directly.
From your data.table with a single variable, you'd do
df[, weekday := wday(date)]
df[df[, .I[which.min(abs(weekday-4.25))], by=week(date)]$V1]
date weekday
1: 2012-06-27 4
2: 2012-07-05 5
3: 2012-07-11 4
4: 2012-07-18 4
Note that the dates match those above.
I have a data.table containing two date variables. The data set was read into R from a .csv file (was originally an .xlsx file) as a data.frame and the two variables then converted to date format using as.Date() so that they display as below:
df
id specdate recdate
1 1 2014-08-12 2014-08-17
2 2 2014-08-15 2014-08-20
3 3 2014-08-21 2014-08-26
4 4 <NA> 2014-08-28
5 5 2014-08-25 2014-08-30
6 6 <NA> <NA>
I then converted the data.frame to a data.table:
df <- data.table(df)
I then wanted to create a third variable, that would include "specdate" if present, but replace it with "recdate" if "specdate" was missing (NA). This is where I'm having some difficulty, as it seems that no matter how I approach this, data.table displays dates in date format only if a complete variable that is already in date format is copied. Otherwise, individual values are displayed as a number (even when using as.IDate) and I gather that an origin date is needed to correct this. Is there any way to avoid supplying an origin date but display the dates as dates in data.table?
Below is my attempt to fill the NAs of specdate with the recdate dates:
# Function to fill NAs:
fillnas <- function(dataref, lookupref, nacol, replacecol, replacelist=NULL) {
nacol <- as.character(nacol)
if(!is.null(replacelist)) nacol <- factor(ifelse(dataref==lookupref & (is.na(nacol) | nacol %in% replacelist), replacecol, nacol))
else nacol <- factor(ifelse(dataref==lookupref & is.na(nacol), replacecol, nacol))
nacol
}
# Fill the NAs in specdate with the function:
df[, finaldate := fillnas(dataref=id, lookupref=id, nacol=specdate, replacecol=as.IDate(recdate, format="%Y-%m-%d"))]
Here is what happens:
> df
id specdate recdate finaldate
1: 1 2014-08-12 2014-08-17 2014-08-12
2: 2 2014-08-15 2014-08-20 2014-08-15
3: 3 2014-08-21 2014-08-26 2014-08-21
4: 4 <NA> 2014-08-28 16310
5: 5 2014-08-25 2014-08-30 2014-08-25
6: 6 <NA> <NA> NA
The display problem is compounded if I create the new variable from scratch by using ifelse:
df[, finaldate := ifelse(!is.na(specdate), specdate, recdate)]
This gives:
> df
id specdate recdate finaldate
1: 1 2014-08-12 2014-08-17 16294
2: 2 2014-08-15 2014-08-20 16297
3: 3 2014-08-21 2014-08-26 16303
4: 4 <NA> 2014-08-28 16310
5: 5 2014-08-25 2014-08-30 16307
6: 6 <NA> <NA> NA
Alternately if I try a find-and-replace type approach, I get an error about the number of items to replace not matching the replacement length (I'm guessing this is because that approach is not vectorised?), the values from recdate are recycled and end up in the wrong place:
> df$finaldate <- df$specdate
> df$finaldate[is.na(df$specdate)] <- df$recdate
Warning message:
In NextMethod(.Generic) :
number of items to replace is not a multiple of replacement length
> df
id specdate recdate finaldate
1: 1 2014-08-12 2014-08-17 2014-08-12
2: 2 2014-08-15 2014-08-20 2014-08-15
3: 3 2014-08-21 2014-08-26 2014-08-21
4: 4 <NA> 2014-08-28 2014-08-17
5: 5 2014-08-25 2014-08-30 2014-08-25
6: 6 <NA> <NA> 2014-08-20
So in conclusion - the function I applied gets me closest to what I want, except that where NAs have been replaced, the replacement value is displayed as a number and not in date format. Once displayed as a number, the origin is required to again display it as a date (and I would like to avoid supplying the origin since I usually don't know it and it seems unnecessarily repetitive to have to supply it when the date was originally in the correct format).
Any insights as to where I'm going wrong would be much appreciated.
I'd approach it like this, maybe :
DT <- data.table(df)
DT[, finaldate := specdata]
DT[is.na(specdata), finaldate := recdate]
It seems you want to add a new column so you can can retain the original columns as well. I do that as well a lot. Sometimes, I just update in place :
DT <- data.table(df)
DT[!is.na(specdate), specdate:=recdate]
setnames(DT, "specdate", "finaldate")
Using i like that avoids creating a whole new column's worth of data which might be very large. Depends on how important retaining the original columns is to you and how many of them there are and your data size. (Note that a whole column's worth of data is still created by the is.na() call and then again by ! but at least there isn't a third column's worth for the new finaldate. Would be great to optimize i=!is.na() in future (#1386) and if you use data.table this way now you won't need to change your code in future to benefit.)
It seems that you might have various "NA" strings that you're replacing. Note that fread in v1.9.6 on CRAN has a fix for that. From README :
correctly handles na.strings argument for all types of columns - it detect possible NA values without coercion to character, like in base read.table. fixes #504. Thanks to #dselivanov for the PR. Also closes #1314, which closes this issue completely, i.e., na.strings = c("-999", "FALSE") etc. also work.
Btw, you've made one of the top 3 mistakes mentioned here : https://github.com/Rdatatable/data.table/wiki/Support
Works for me. You may want to test to be sure that your NA values are not strings or factors "<NA>"; they will look like real NA values:
dt[, finaldate := ifelse(is.na(specdate), recdate, specdate)][
,finaldate := as.POSIXct(finaldate*86400, origin="1970-01-01", tz="UTC")]
# id specdate recdate finaldate
# 1: 1 2014-08-12 2014-08-17 2014-08-12
# 2: 2 2014-08-15 2014-08-20 2014-08-15
# 3: 3 2014-08-21 2014-08-26 2014-08-21
# 4: 4 NA 2014-08-28 2014-08-28
# 5: 5 2014-08-25 2014-08-30 2014-08-25
# 6: 6 NA NA NA
Data
df <- read.table(text=" id specdate recdate
1 1 2014-08-12 2014-08-17
2 2 2014-08-15 2014-08-20
3 3 2014-08-21 2014-08-26
4 4 NA 2014-08-28
5 5 2014-08-25 2014-08-30
6 6 NA NA", header=T, stringsAsFactors=F)
dt <- as.data.table(df)
I have two data.tables that are each 5-10GB in size. They look similar to the following.
library(data.table)
A <- data.table(
person = c(1,1,1,2,3,3,3,3,4,4),
datetime = c(
'2015-04-06 14:22:18',
'2015-04-07 02:55:32',
'2015-11-21 10:16:05',
'2015-10-03 13:37:29',
'2015-02-26 23:51:56',
'2015-05-16 18:21:44',
'2015-06-02 04:07:43',
'2015-11-28 15:22:36',
'2015-01-19 04:10:22',
'2015-01-24 02:18:11'
)
)
B <- data.table(
person = c(1,1,3,4,4,5),
datetime2 = c(
'2015-04-06 14:24:59',
'2015-11-28 15:22:36',
'2015-06-02 04:07:43',
'2015-01-19 06:10:22',
'2015-01-24 02:18:18',
'2015-04-06 14:22:18'
)
)
A$datetime <- as.POSIXct(A$datetime)
B$datetime2 <- as.POSIXct(B$datetime2)
The idea is to find rows in B where the datetime is within 0-10 minutes of a matching row in A (matching is done by person) and mark them in A. The question is how can I do it most efficiently using data.table?
One plan is to join the two data tables based on [I]person[/I] only, then calculate the time difference and find rows where the time difference is between 0 and 600 seconds, and finally outer join the latter with A:
setkey(A,person)
AB <- A[B,.(datetime,
datetime2,
diff = difftime(datetime2, datetime, units = "secs"))
, by = .EACHI]
M <- AB[diff < 600 & diff > 0]
setkey(A, person, datetime)
setkey(M, person, datetime)
M[A,]
Which gives us the correct result:
person datetime datetime2 diff
1: 1 2015-04-06 14:22:18 2015-04-06 14:24:59 161 secs
2: 1 2015-04-07 02:55:32 <NA> NA secs
3: 1 2015-11-21 10:16:05 <NA> NA secs
4: 2 2015-10-03 13:37:29 <NA> NA secs
5: 3 2015-02-26 23:51:56 <NA> NA secs
6: 3 2015-05-16 18:21:44 <NA> NA secs
7: 3 2015-06-02 04:07:43 <NA> NA secs
8: 3 2015-11-28 15:22:36 <NA> NA secs
9: 4 2015-01-19 04:10:22 <NA> NA secs
10: 4 2015-01-24 02:18:11 2015-01-24 02:18:18 7 secs
However, I am not sure if this is the most efficient way. Specifically, I am using AB[diff < 600 & diff > 0] which I assume will run a vector search not a binary search, but I cannot think of how to do it using a binary search.
Also, I am not sure if converting to POSIXct is the most efficient way of calculating time differences.
Any ideas on how to improve efficiency are high appreciated.
data.table's rolling join is perfect for this task:
B[, datetime := datetime2]
setkey(A,person,datetime)
setkey(B,person,datetime)
B[A,roll=-600]
person datetime2 datetime
1: 1 2015-04-06 14:24:59 1428319338
2: 1 NA 1428364532
3: 1 NA 1448090165
4: 2 NA 1443868649
5: 3 NA 1424983916
6: 3 NA 1431789704
7: 3 2015-06-02 04:07:43 1433207263
8: 3 NA 1448713356
9: 4 NA 1421629822
10: 4 2015-01-24 02:18:18 1422055091
The only difference with your expected output is that it checks timedifference as less or equal to 10 minutes (<=). If that is bad for you you can just delete equal matches
I have created the following 2 dummy datasets as follows:
id<-c(8,8,50,87,141,161,192,216,257,282)
date<-c("2011-03-03","2011-12-12","2010-08-18","2009-04-28","2010-11-29","2012-04-02","2013-01-08","2007-01-22","2009-06-03","2009-12-02")
data<-data.frame(cbind(id,date))
id<-c(3,8,11,11,11,11,11,11,19,19,19,19,19,50,50,50,50,50,87,87,87,87,87,87,282,282,282,282,282,282,282,282,282,282,288,288,288,288,288,288,288,288,288,288,288,288,288)
date<-c("2010-11-04","2011-02-25","2009-07-26","2009-07-27","2009-08-09","2009-08-10","2009-08-30","2004-01-20","2006-02-13","2006-07-18","2007-04-20","2008-05-12","2008-05-29","2009-06-10","2010-08-17","2010-08-15","2011-05-13","2011-06-08","2007-08-09","2008-01-19","2008-02-19","2009-04-28","2009-05-16","2009-05-20","2005-05-14","2007-04-15","2007-07-25","2007-10-12","2007-10-23","2007-10-27","2007-11-20","2009-11-28","2012-08-16","2012-08-16","2008-11-17","2009-10-23","2009-10-27","2009-10-27","2009-10-27","2009-10-27","2009-10-28","2010-06-15","2010-06-17","2010-06-23","2010-07-27","2010-07-27","2010-07-28")
ns<-data.frame(cbind(id,date))
Note that only some of the id in data are included in ns and viceversa.
For each of the values in data$id I am trying to find if there is a ns$date that is 14 days before the data$date where data$id==ns$id and report the number of days difference.
The output I need is a vector/column ("received") of the same number of rows of data, with a TRUE/FALSE whre ns$date[ns$id==data$id] is less than 14 days before the respective data$date and a similar vector with the actual number of days where "received" is TRUE. I hope this makes sense now.
This is where I got so far
# convert dates
data$date <- ymd(data$date)
ns$date <- ymd(ns$date)
# left join datasets
tmp <- merge(data, ns, by="id", all.x=TRUE)
#NOTE THAT this will automatically rename data$date as date.x and tmp$date as date.y
# create variable to say when there is a date difference less than 14 days
tmp$received <- with(tmp, difftime(date.x, date.y, units="days")<14&difftime(date.x, date.y, units="days")>0)
#create a variable that reports the days difference
tmp$dif<-ifelse(tmp$received==TRUE,difftime(tmp$date.x,tmp$date.y, units="days"),NA)
This link Find if date is within 14 days if id matches between datasets in R provides an idea but the result does not include the number of days difference in tmp$dif.
In the result table I need only the lowest difference for each data$id for those cases were tmp$received was TRUE.
Hope this makes more sense now? If not please let me know what needs further clarification.
M
PS: as requested I added what the desired output should look like (same number of rows of data = 10 - no rows for data in ns not in data). Should have thought this might help earlier.
id date received dif
1 8 2011-03-03 TRUE 6
2 8 2011-12-12 FALSE NA
3 50 2010-08-18 TRUE 1
4 87 2009-04-28 TRUE 0
5 141 2010-11-29 NA NA
6 161 2012-04-02 NA NA
7 192 2013-01-08 NA NA
8 216 2007-01-22 NA NA
9 257 2009-06-03 NA NA
10 282 2009-12-02 TRUE 4
Here's a data.table approach
Converting to data.table objects
library(data.table)
setkey(setDT(data), id)
setkey(setDT(ns), id)
Merging
ns <- ns[data]
Converting to Date class
ns[, c("date", "date.1") := lapply(.SD, as.Date), .SDcols = c("date", "date.1")]
Computing days differences and TRUE/FALSE
ns[, `:=`(timediff = date.1 - date,
Logical = (date.1 - date) < 14)]
Taking only the rows we are interested in
res <- ns[is.na(timediff) | timediff >= 0, list(received = any(Logical), dif = timediff[Logical]), by = list(id, date.1)]
Sorting by id and date
res[, id := as.numeric(as.character(id))]
setkey(res, id, date.1)
Subsetting by minimum dstance
res[, list(diff = min(dif)), by = list(id, date.1, received)]
# id date.1 received diff
# 1: 8 2011-03-03 TRUE 6 days
# 2: 8 2011-12-12 FALSE NA days
# 3: 50 2010-08-18 TRUE 1 days
# 4: 87 2009-04-28 TRUE 0 days
# 5: 141 2010-11-29 NA NA days
# 6: 161 2012-04-02 NA NA days
# 7: 192 2013-01-08 NA NA days
# 8: 216 2007-01-22 NA NA days
# 9: 257 2009-06-03 NA NA days
# 10: 282 2009-12-02 TRUE 4 days
I am loading a data.table from CSV file that has date, orders, amount etc. fields.
The input file occasionally does not have data for all dates. For example, as shown below:
> NADayWiseOrders
date orders amount guests
1: 2013-01-01 50 2272.55 149
2: 2013-01-02 3 64.04 4
3: 2013-01-04 1 18.81 0
4: 2013-01-05 2 77.62 0
5: 2013-01-07 2 35.82 2
In the above 03-Jan and 06-Jan do not have any entries.
Would like to fill the missing entries with default values (say, zero for orders, amount etc.), or carry the last vaue forward (e.g, 03-Jan will reuse 02-Jan values and 06-Jan will reuse the 05-Jan values etc..)
What is the best/optimal way to fill-in such gaps of missing dates data with such default values?
The answer here suggests using allow.cartesian = TRUE, and expand.grid for missing weekdays - it may work for weekdays (since they are just 7 weekdays) - but not sure if that would be the right way to go about dates as well, especially if we are dealing with multi-year data.
The idiomatic data.table way (using rolling joins) is this:
setkey(NADayWiseOrders, date)
all_dates <- seq(from = as.Date("2013-01-01"),
to = as.Date("2013-01-07"),
by = "days")
NADayWiseOrders[J(all_dates), roll=Inf]
date orders amount guests
1: 2013-01-01 50 2272.55 149
2: 2013-01-02 3 64.04 4
3: 2013-01-03 3 64.04 4
4: 2013-01-04 1 18.81 0
5: 2013-01-05 2 77.62 0
6: 2013-01-06 2 77.62 0
7: 2013-01-07 2 35.82 2
Here is how you fill in the gaps within subgroup
# a toy dataset with gaps in the time series
dt <- as.data.table(read.csv(textConnection('"group","date","x"
"a","2017-01-01",1
"a","2017-02-01",2
"a","2017-05-01",3
"b","2017-02-01",4
"b","2017-04-01",5')))
dt[,date := as.Date(date)]
# the desired dates by group
indx <- dt[,.(date=seq(min(date),max(date),"months")),group]
# key the tables and join them using a rolling join
setkey(dt,group,date)
setkey(indx,group,date)
dt[indx,roll=TRUE]
#> group date x
#> 1: a 2017-01-01 1
#> 2: a 2017-02-01 2
#> 3: a 2017-03-01 2
#> 4: a 2017-04-01 2
#> 5: a 2017-05-01 3
#> 6: b 2017-02-01 4
#> 7: b 2017-03-01 4
#> 8: b 2017-04-01 5
Not sure if it's the fastest, but it'll work if there are no NAs in the data:
# just in case these aren't Dates.
NADayWiseOrders$date <- as.Date(NADayWiseOrders$date)
# all desired dates.
alldates <- data.table(date=seq.Date(min(NADayWiseOrders$date), max(NADayWiseOrders$date), by="day"))
# merge
dt <- merge(NADayWiseOrders, alldates, by="date", all=TRUE)
# now carry forward last observation (alternatively, set NA's to 0)
require(xts)
na.locf(dt)