Rolling join in data.table is using wrong time zone - r
I am trying to join two data tables using a rolling join. My time/date stamps are POSIXct specified as eastern standard time zone (EST). During the join, values using GMT are instead retrieved, i.e., the value from EST - 5 hours. Additionally, it's making corrections for daylight savings time, retrieving values from EST - 4 hours during daylight savings time.
I've tried using fuzzyjoin and that works correctly but suffers from being very slow even for small joins and runs out of resources for larger problems (I'm guessing because it's doing a full cartesian join then filtering) so won't run my real job. I've looked through stackoverlow and I haven't seen this problem described
In the example below, the flow data is stream flow measured at 15-minute intervals (in cubic meters per second) and the chem data are results from analyzing grab samples. What I want is to find the value of stream flow (Q.cms) that is closest in time to the time the grab sample was collected. In the result, Q.cms retrieved from the flow data should match flow.EST and not flow.GMT (flow.EST and flow.GMT were obtained using the vlookup function in Excel). You can look at the resulting table or use the ggplot to visualize the results. The black symbols should fall on the 1:1 line not the red symbols. Some of this follows the examples given here https://r-norberg.blogspot.com/2016/06/understanding-datatable-rolling-joins.html. In the roll statement, I've also tried "-Inf" and "T" - neither help.
library(tidyverse)
library(data.table)
NHchem <- tibble(SampleDateTime = list("2012-03-07 15:15:00",
"2012-03-07 07:57:00",
"2012-03-07 09:00:00",
"2012-03-07 08:31:00",
"2012-03-07 08:00:00"),
Parameter = rep("HgD", 5),
Value = c(11.83, 9.09, 8.98, 10.91, 10.91),
flow.EST = c(1.734292, 1.730299, 1.711665, 1.721448, 1.729299),
flow.GMT = c(1.729082, 1.855115, 1.818062, 1.838623, 1.855263)
)
NHchem$SampleDateTime <- lubridate::ymd_hms(NHchem$SampleDateTime, tz = "EST")
NHflow <- tibble(DateTime = list("2012-03-07 00:00:00", "2012-03-07 00:15:00", "2012-03-07 00:30:00",
"2012-03-07 00:45:00", "2012-03-07 01:00:00", "2012-03-07 01:15:00",
"2012-03-07 01:30:00", "2012-03-07 01:45:00", "2012-03-07 02:00:00",
"2012-03-07 02:15:00", "2012-03-07 02:30:00", "2012-03-07 02:45:00",
"2012-03-07 03:00:00", "2012-03-07 03:15:00", "2012-03-07 03:30:00",
"2012-03-07 03:45:00", "2012-03-07 04:00:00", "2012-03-07 04:15:00",
"2012-03-07 04:30:00", "2012-03-07 04:45:00", "2012-03-07 05:00:00",
"2012-03-07 05:15:00", "2012-03-07 05:30:00", "2012-03-07 05:45:00",
"2012-03-07 06:00:00", "2012-03-07 06:15:00", "2012-03-07 06:30:00",
"2012-03-07 06:45:00", "2012-03-07 07:00:00", "2012-03-07 07:15:00",
"2012-03-07 07:30:00", "2012-03-07 07:45:00", "2012-03-07 08:00:00",
"2012-03-07 08:15:00", "2012-03-07 08:30:00", "2012-03-07 08:45:00",
"2012-03-07 09:00:00", "2012-03-07 09:15:00", "2012-03-07 09:30:00",
"2012-03-07 09:45:00", "2012-03-07 10:00:00", "2012-03-07 10:15:00",
"2012-03-07 10:30:00", "2012-03-07 10:45:00", "2012-03-07 11:00:00",
"2012-03-07 11:15:00", "2012-03-07 11:30:00", "2012-03-07 11:45:00",
"2012-03-07 12:00:00", "2012-03-07 12:15:00", "2012-03-07 12:30:00",
"2012-03-07 12:45:00", "2012-03-07 13:00:00", "2012-03-07 13:15:00",
"2012-03-07 13:30:00", "2012-03-07 13:45:00", "2012-03-07 14:00:00",
"2012-03-07 14:15:00", "2012-03-07 14:30:00", "2012-03-07 14:45:00",
"2012-03-07 15:00:00", "2012-03-07 15:15:00", "2012-03-07 15:30:00",
"2012-03-07 15:45:00", "2012-03-07 16:00:00", "2012-03-07 16:15:00",
"2012-03-07 16:30:00", "2012-03-07 16:45:00", "2012-03-07 17:00:00",
"2012-03-07 17:15:00", "2012-03-07 17:30:00", "2012-03-07 17:45:00",
"2012-03-07 18:00:00", "2012-03-07 18:15:00", "2012-03-07 18:30:00",
"2012-03-07 18:45:00", "2012-03-07 19:00:00", "2012-03-07 19:15:00",
"2012-03-07 19:30:00", "2012-03-07 19:45:00", "2012-03-07 20:00:00",
"2012-03-07 20:15:00", "2012-03-07 20:30:00", "2012-03-07 20:45:00",
"2012-03-07 21:00:00", "2012-03-07 21:15:00", "2012-03-07 21:30:00",
"2012-03-07 21:45:00", "2012-03-07 22:00:00", "2012-03-07 22:15:00",
"2012-03-07 22:30:00", "2012-03-07 22:45:00", "2012-03-07 23:00:00",
"2012-03-07 23:15:00", "2012-03-07 23:30:00", "2012-03-07 23:45:00"
),
Q.cms = c(1.9183428976162, 1.90884554590853, 1.90659614969849, 1.89946370922413,
1.89667381849492, 1.88823268972675, 1.89009845845496, 1.88925133710857,
1.89175644143891, 1.88902902166044, 1.88199493586431, 1.87318875971247,
1.86917038183388, 1.86071830324102, 1.85932613686066, 1.86093401873381,
1.87101369024615, 1.87296981545807, 1.8762632040545, 1.86625922525504,
1.86445655629997, 1.86091440792692, 1.86261797589624, 1.85849714690774,
1.85809949483732, 1.85365423835426, 1.86769219811402, 1.87137568188775,
1.86957715377645, 1.86178854932717, 1.85842153505713, 1.85511511281169,
1.85526345829864, 1.84606992374992, 1.8386232796469, 1.8306698161104,
1.81806190831676, 1.80972662643993, 1.80671560966769, 1.79692071214778,
1.79246415779217, 1.78383758090983, 1.77911151556738, 1.77540849330392,
1.76977546791765, 1.76704647349273, 1.75506646810193, 1.75116656853259,
1.74116032441497, 1.73694852237565, 1.73539800459245, 1.73029929958069,
1.72929873358336, 1.72145096214292, 1.72144822481096, 1.7137286373663,
1.71166508485562, 1.72512015058183, 1.72801058727519, 1.71946126373971,
1.72908219540057, 1.73003063754833, 1.73433620637326, 1.74704929729279,
1.75374511203315, 1.75751451176466, 1.74550984385788, 1.7374425791972,
1.73028285052801, 1.73164553811319, 1.71538821542136, 1.71111583998635,
1.69850042893639, 1.7018112620745, 1.70068743638687, 1.70624871061042,
1.70513766128531, 1.70189855875322, 1.71388172504149, 1.70687667156756,
1.72965507980508, 1.73429231190258, 1.73062829040751, 1.74161628160761,
1.74502884750137, 1.74764319440737, 1.76775550519358, 1.74926846992512,
1.75406991794209, 1.75302950118107, 1.75219840078475, 1.75821138471585,
1.74749746409036, 1.75047602988317, 1.76192575356191, 1.76360735465132)
)
NHflow$DateTime <- lubridate::ymd_hms(NHflow$DateTime, tz = "EST")
NHflowDT <- setDT(NHflow)
NHchemDT <- setDT(NHchem)
NHflowDT[, join_time := DateTime]
NHchemDT[, join_time := SampleDateTime]
setkey(NHflowDT, join_time)
setkey(NHchemDT, join_time)
NHflow_chem <- NHflowDT[ NHchemDT, roll = 'nearest']
NHflow_chem
ggplot(data = NHflow_chem, mapping = aes(x = flow.EST, y = Q.cms)) +
geom_point(size = 4) +
geom_point(aes(x = flow.GMT, y = Q.cms), color = "red", size = 4) +
geom_abline(slope = 1, intercept = 0) +
labs(x = "flow.EST (black), flow.GMT (red)")
I'm not sure what your exact problem is, but the rolling update join below seems to work fine when I look at your sample data sets...
NHflowDT <- setDT(NHflow)
NHchemDT <- setDT(NHchem)
NHchemDT[, c("nearest_flow_time", "Q.cms") :=
NHflowDT[NHchemDT, .(x.DateTime, x.Q.cms),
on = .(DateTime = SampleDateTime),
roll = "nearest"] ][]
output
SampleDateTime Parameter Value flow.EST flow.GMT nearest_flow_time Q.cms
1: 2012-03-07 15:15:00 HgD 11.83 1.734292 1.729082 2012-03-07 15:15:00 1.730031
2: 2012-03-07 07:57:00 HgD 9.09 1.730299 1.855115 2012-03-07 08:00:00 1.855263
3: 2012-03-07 09:00:00 HgD 8.98 1.711665 1.818062 2012-03-07 09:00:00 1.818062
4: 2012-03-07 08:31:00 HgD 10.91 1.721448 1.838623 2012-03-07 08:30:00 1.838623
5: 2012-03-07 08:00:00 HgD 10.91 1.729299 1.855263 2012-03-07 08:00:00 1.855263
> attributes( NHchemDT$SampleDateTime)
$class
[1] "POSIXct" "POSIXt"
$tzone
[1] "EST"
> attributes( NHchemDT$nearest_flow_time)
$class
[1] "POSIXct" "POSIXt"
$tzone
[1] "EST"
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Plot timelines without date
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How to change the x-values displayed in ggplot and ggplotly in R
I have a timeseries X, with related timestamps, and i want to provide a graph with the hourly values. The X-axis should not show the timestamp but rather the hour, however the plot should be generated with the timestamp as the x-axis
I've already tried to plot the X with the related timestamps (hours from 0-23) using the scale_x_datetime function. However the problem arises when trying to get the x-values to show 1-24. A problem arises when you go over midnight and end up with duplicated x-values
Timestamp <- c("2019-07-30 23:00:00", "2019-07-31 00:00:00", "2019-07-31 01:00:00", "2019-07-31 02:00:00", "2019-07-31 03:00:00", "2019-07-31 04:00:00", "2019-07-31 05:00:00", "2019-07-31 06:00:00",
"2019-07-31 07:00:00", "2019-07-31 08:00:00", "2019-07-31 09:00:00", "2019-07-31 10:00:00", "2019-07-31 11:00:00", "2019-07-31 12:00:00",
"2019-07-31 13:00:00", "2019-07-31 14:00:00", "2019-07-31 15:00:00", "2019-07-31 16:00:00", "2019-07-31 17:00:00", "2019-07-31 18:00:00",
"2019-07-31 19:00:00", "2019-07-31 20:00:00", "2019-07-31 21:00:00", "2019-07-31 22:00:00", "2019-07-31 23:00:00", "2019-08-01 00:00:00","2019-08-01 01:00:00")
col <- c(110,100,105,100,105,100,110,100,110,100,110,100,110,100,110,100,
110,100,110,100,110,100,110,105,110,105,110)
hour <- c(23,24,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,1)
Timestamp <- as.POSIXct(Timestamp, tz = "GMT")
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library(lubridate)
library(scales)
library(ggplot2)
data <- data.frame(Timestamp,col,hour)
data$Timestamp <- as.POSIXct(data$Timestamp)
ggplot(data = data, aes(x = Timestamp))+
geom_line(aes(y = col))+
scale_x_datetime(date_breaks = "1 hours",
date_minor_breaks = "1 hour",
labels = date_format("%H"))
The desired output is a ggplot/ggplotly with the hours displayed as the x-axis (1-24 and not 0-23).
Future work would also a timeseries with 1 minute resolution to be included in the plot
edited: added working code. I want the x-axis to be 1-24 and start over at 1 after 24
Here i found the answer. It was an evolution and addition to the scale_x_datetime function
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"2019-07-31 07:00:00", "2019-07-31 08:00:00", "2019-07-31 09:00:00", "2019-07-31 10:00:00", "2019-07-31 11:00:00", "2019-07-31 12:00:00",
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"2019-07-31 19:00:00", "2019-07-31 20:00:00", "2019-07-31 21:00:00", "2019-07-31 22:00:00", "2019-07-31 23:00:00", "2019-08-01 00:00:00","2019-08-01 01:00:00")
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library(lubridate)
library(scales)
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geom_line(aes(y = col))+
scale_x_datetime(date_breaks = "1 hours",
date_minor_breaks = "1 hour",
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To merge list's members with differing number of rows [duplicate]
This question already has answers here:
Simultaneously merge multiple data.frames in a list
(9 answers)
Closed 8 years ago.
Here is my list that you can run in your console (please, tell me if it's too long for example purposes, I can amend it):
my_list = list(structure(list(PX_LAST = c(0.398, 0.457, 0.4, 0.159, 0.126,
0.108, 0.26, 0.239, 0.222, 0.191, 0.184)), .Names = "PX_LAST", row.names = c("2014-04-28 00:00:00",
"2014-04-29 00:00:00", "2014-04-30 00:00:00", "2014-05-02 00:00:00",
"2014-05-05 00:00:00", "2014-05-06 00:00:00", "2014-05-07 00:00:00",
"2014-05-08 00:00:00", "2014-05-09 00:00:00", "2014-05-12 00:00:00",
"2014-05-13 00:00:00"), class = "data.frame"), structure(list(
PX_LAST = c(1.731, 1.706, 1.7095, 1.69, 1.713, 1.711, 1.724,
1.699, 1.702, 1.705, 1.649, 1.611)), .Names = "PX_LAST", row.names = c("2014-04-29 00:00:00",
"2014-04-30 00:00:00", "2014-05-01 00:00:00", "2014-05-02 00:00:00",
"2014-05-05 00:00:00", "2014-05-06 00:00:00", "2014-05-07 00:00:00",
"2014-05-08 00:00:00", "2014-05-09 00:00:00", "2014-05-12 00:00:00",
"2014-05-13 00:00:00", "2014-05-14 00:00:00"), class = "data.frame"),
structure(list(PX_LAST = c(0.481, 0.456, 0.448, 0.439, 0.436,
0.448, 0.458, 0.466, 0.432, 0.437, 0.441, 0.417, 0.4035)), .Names = "PX_LAST", row.names = c("2014-04-28 00:00:00",
"2014-04-29 00:00:00", "2014-04-30 00:00:00", "2014-05-01 00:00:00",
"2014-05-02 00:00:00", "2014-05-05 00:00:00", "2014-05-06 00:00:00",
"2014-05-07 00:00:00", "2014-05-08 00:00:00", "2014-05-09 00:00:00",
"2014-05-12 00:00:00", "2014-05-13 00:00:00", "2014-05-14 00:00:00"
), class = "data.frame"), structure(list(PX_LAST = c(1.65,
1.65, 1.64, 1.65, 1.662, 1.6595, 1.665, 1.6595, 1.6625, 1.652,
1.645, 1.6245, 1.627, 1.633)), .Names = "PX_LAST", row.names = c("2014-04-25 00:00:00",
"2014-04-28 00:00:00", "2014-04-29 00:00:00", "2014-04-30 00:00:00",
"2014-05-01 00:00:00", "2014-05-02 00:00:00", "2014-05-05 00:00:00",
"2014-05-06 00:00:00", "2014-05-07 00:00:00", "2014-05-08 00:00:00",
"2014-05-09 00:00:00", "2014-05-12 00:00:00", "2014-05-13 00:00:00",
"2014-05-14 00:00:00"), class = "data.frame"))
My question is: how can I use do.call() on that list to merge all the data according to their date?
Consider either merge and cbind return errors that I am not able to manage:
> do.call(what = merge, args = my_list)
Error in fix.by(by.x, x) :
'by' must specify column(s) as numbers, names or logical
> do.call(what = cbind, args = my_list)
Error in data.frame(..., check.names = FALSE) :
arguments imply differing number of rows: 11, 12, 13, 14
I would like to get a single data matrix (whose possibly missing/not matching data are replaced by NAs) equal to the one I would get using merge() on the elements of my_list.
This would be a bit easier if you were not merging by row names, But you could do this with the Reduce function which will sequentially apply a function along a list of values (in this case data.frames`. Try
Reduce(function(x,y) {
dd<-merge(x,y,by=0); rownames(dd)<-dd$Row.names; dd[-1]
}, my_list)
This will merge all matching rows. You can add all=T to the match if you like as well or customize how you would if you were using a regular merge().
You will get a warning about column names because each of your columns has an identical name so when you merge into multiple columns, merge doesn't know what you name them. You could rename them with something like
my_new_list <- Map(
function(x,n) {
names(x)<-n; x
},
my_list,
paste("PX_LAST",1:length(my_list), sep="_")
)
then
Reduce(function(x,y) {
dd<-merge(x,y,by=0); rownames(dd)<-dd$Row.names; dd[-1]
}, my_new_list)
won't complain.
Here is a solution using data.table and reshape2:
# Load libraries
library(data.table)
library(reshape2)
# Setup new list object
my_list.2 <- vector(length(my_list), mode="list")
# Add time stamps as variable and add ID variable
for(i in 1:length(my_list)){
my_list.2[[i]] <- cbind(time=rownames(my_list[[i]]), my_list[[i]], id=rep(paste0("list_",i), id=nrow(my_list[[i]])))
}
# Collapse all lists in one data table
d.temp <- rbindlist(my_list.2)
# Transform the data
d.final <- dcast(time~id, value.var="PX_LAST", data=d.temp)
# > d.final
# time list_1 list_2 list_3 list_4
# 1 2014-04-28 00:00:00 0.398 NA 0.4810 1.6500
# 2 2014-04-29 00:00:00 0.457 1.7310 0.4560 1.6400
# 3 2014-04-30 00:00:00 0.400 1.7060 0.4480 1.6500
# 4 2014-05-02 00:00:00 0.159 1.6900 0.4360 1.6595
# 5 2014-05-05 00:00:00 0.126 1.7130 0.4480 1.6650
# 6 2014-05-06 00:00:00 0.108 1.7110 0.4580 1.6595
# 7 2014-05-07 00:00:00 0.260 1.7240 0.4660 1.6625
# 8 2014-05-08 00:00:00 0.239 1.6990 0.4320 1.6520
# 9 2014-05-09 00:00:00 0.222 1.7020 0.4370 1.6450
# 10 2014-05-12 00:00:00 0.191 1.7050 0.4410 1.6245
# 11 2014-05-13 00:00:00 0.184 1.6490 0.4170 1.6270
# 12 2014-05-01 00:00:00 NA 1.7095 0.4390 1.6620
# 13 2014-05-14 00:00:00 NA 1.6110 0.4035 1.6330
# 14 2014-04-25 00:00:00 NA NA NA 1.6500
Extracting and averaging monthly data in R using grep?
In advance I have to say that I'm truly sorry for poor formatting! I just can not seem to get a hang of it...
I am also an R noob.
So,
I have a 80 files containing information on 80 individual fish's movements;
> head(PAVdata)
X meanlat meanlong meandepth mindepth maxdepth depthrange rec numhits datetime mon X.1 Y Z
1 23 58.39601 8.708952 6.493529 5.72 7.47 1.75 4 17 2012-05-11 11:15:00 1 482986.7 6472838 6.493529
2 24 58.39618 8.710993 4.035588 1.76 7.47 5.71 6 34 2012-05-11 11:45:00 1 483106.1 6472855 4.035588
3 25 58.39615 8.710919 6.037692 3.08 8.35 5.27 6 26 2012-05-11 12:15:00 1 483101.8 6472853 6.037692
4 26 58.39556 8.710654 3.987500 2.20 6.16 3.96 5 16 2012-05-11 12:45:00 1 483086.0 6472787 3.987500
5 27 58.39617 8.711363 2.944615 2.20 3.96 1.76 6 26 2012-05-11 13:15:00 1 483127.8 6472855 2.944615
6 28 58.39628 8.710904 2.860000 2.20 3.52 1.32 6 24 2012-05-11 13:45:00 1 483101.0 6472867 2.860000
But the columns in interest is datetime and Z, where Z is depth.
What I am aiming to do is;
Get a daily and monthly average of depth
Get a daily and monthly min & max (look at amplitude)
What I've tried so far is;
june<-c(PAVdata$datetime[grep("^2012-07",as.character(PAVdata$datetime))])
Which yield something thats completely farfetched to me
head(june)
[1] 2390 2391 2392 2393 2394 2395
I was reading about subset and aggregate and POSIXlt and tapply and zoo and wanted to try these, but I seemed to get the error that there was an length diff ..
Heres an attempt with the POSIXlt;
PAVdata$datetime <- strftime(as.POSIXlt(PAVdata$datetime,format="%Y-%m-%d %H:%M:%S"),format='%b')
june_Monthly_Avg <- ave(PAVdata$Z,PAVdata$datetime,FUN=mean)
head(june_Monthly_Avg)
[1] 9.341862 9.341862 9.341862 9.341862 9.341862 9.341862
And the whole thing consisted of the same numbers...
Think I'm most def using the commando very wrong.
But then I tried something simpler;
juni_Monthly_Avg <- ave(PAVdata$Z,PAVdata$datetime,FUN=mean)
head(juni_Monthly_Avg)
[1] 6.493529 4.035588 6.037692 3.987500 2.944615 2.860000
Confusing to me, seeing that for June I should only get ONE value.
So far I reckon grep looks most promising, but I can't seem to get my head around it.
Been trawling The R Book and the interwebs and of course SO for a couple days now and decided to cry out for help before I pull out all my hair (and before consulting my supervisors).
Thanks ^_^
> dput(head(PAVdata))
structure(list(X = 23:28, meanlat = c(58.3960117647059, 58.3961761764706,
58.3961507692308, 58.395558125, 58.3961726923077, 58.3962804166667
), meanlong = c(8.70895176470588, 8.71099294117647, 8.71091923076923,
8.71065375, 8.71136346153846, 8.71090416666667), meandepth = c(6.49352941176471,
4.03558823529412, 6.03769230769231, 3.9875, 2.94461538461538,
2.86), mindepth = c(5.72, 1.76, 3.08, 2.2, 2.2, 2.2), maxdepth = c(7.47,
7.47, 8.35, 6.16, 3.96, 3.52), depthrange = c(1.75, 5.71, 5.27,
3.96, 1.76, 1.32), rec = c(4L, 6L, 6L, 5L, 6L, 6L), numhits = c(17L,
34L, 26L, 16L, 26L, 24L), datetime = structure(1:6, .Label = c("2012-06-01 00:15:00", "2012-06-01 00:45:00",
"2012-06-01 01:15:00", "2012-06-01 01:45:00", "2012-06-01 02:15:00",
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"2012-07-10 23:45:00"), class = "factor"), mon = c(1L, 1L, 1L,
1L, 1L, 1L), X.1 = c(482986.747312444, 483106.142664631, 483101.821796104,
483086.019405256, 483127.799620954, 483101.003261731), Y = c(6472837.55691427,
6472855.34802117, 6472852.53770682, 6472786.61976395, 6472854.86710904,
6472866.97636095), Z = c(6.49352941176471, 4.03558823529412,
6.03769230769231, 3.9875, 2.94461538461538, 2.86)), .Names = c("X",
"meanlat", "meanlong", "meandepth", "mindepth", "maxdepth", "depthrange",
"rec", "numhits", "datetime", "mon", "X.1", "Y", "Z"), row.names = c(NA,
6L), class = "data.frame")
>
You look that You tray many things but you don't try one thing in a good way. For example using POSIXlt, dat here is your PAVdata object ll <- as.POSIXlt(dat$datetime) dat$hours <- ll$hour ## I create a variable hour dat$days <- ll$mday ## I create a variable day dat$months <- ll$mon ## I create a variable month Now i can use this variable to average by group. here a solution using plyr package. ddply(dat,.(months,days),summarise, m = mean(meandepth)) ## I group by months and days months days m 1 4 11 4.393154 > ddply(dat,.(months,days,hours),summarise, m = mean(meandepth)) months days hours m 1 4 11 11 5.264558 2 4 11 12 5.012596 3 4 11 13 2.902308 Whatever you use, try to use one thing in a good way. Generally there are many method to do such things in R.