Mata
Reputation: 665
Include in data frame 1 averaged values from several other data frames and based on varying time intervals
I have a data frame with several variables, and whose first columns look like this:
Place <- c(rep("PlaceA",14),rep("PlaceB",15))
Group_Id <- c(rep("A1",5),rep("A1",6),rep("A2",3),rep("B1",6),rep("B2",4),rep("B2",5))
Time <- as.Date(c("2018-01-15","2018-02-03","2018-02-27","2018-03-10","2018-03-18","2019-02-02","2019-03-01","2019-03-15","2019-03-28","2019-04-05","2019-04-12","2018-02-01",
"2018-03-01","2018-04-07","2018-01-17","2018-01-27","2018-02-17","2018-03-03","2018-04-02","2018-04-25","2018-03-03","2018-03-18","2018-04-08","2018-04-20",
"2019-01-23","2019-02-09","2019-02-27","2019-03-12","2019-03-30"))
FollowUp <- c("start",paste("week",week(ymd(Time[2:5]))),"start",paste("week",week(ymd(Time[7:11]))),"start",paste("week",week(ymd(Time[13:14]))),"start",paste("week",week(ymd(Time[16:20]))),"start",paste("week",week(ymd(Time[22:24]))),"start",paste("week",week(ymd(Time[26:29]))))
exprmt <- c(rep(1,5),rep(2,6),rep(3,3),rep(4,6),rep(5,4),rep(6,5))
> df1
Place Group_Id Time exprmt FollowUp
1 PlaceA A1 2018-01-15 1 start
2 PlaceA A1 2018-02-03 1 week 5
3 PlaceA A1 2018-02-27 1 week 9
4 PlaceA A1 2018-03-10 1 week 10
5 PlaceA A1 2018-03-18 1 week 11
6 PlaceA A1 2019-02-02 2 start
7 PlaceA A1 2019-03-01 2 week 9
8 PlaceA A1 2019-03-15 2 week 11
9 PlaceA A1 2019-03-28 2 week 13
10 PlaceA A1 2019-04-05 2 week 14
11 PlaceA A1 2019-04-12 2 week 15
12 PlaceA A2 2018-02-01 3 start
13 PlaceA A2 2018-03-01 3 week 9
14 PlaceA A2 2018-04-07 3 week 14
15 PlaceB B1 2018-01-17 4 start
16 PlaceB B1 2018-01-27 4 week 4
17 PlaceB B1 2018-02-17 4 week 7
18 PlaceB B1 2018-03-03 4 week 9
19 PlaceB B1 2018-04-02 4 week 14
20 PlaceB B1 2018-04-25 4 week 17
21 PlaceB B2 2018-03-03 5 start
22 PlaceB B2 2018-03-18 5 week 11
23 PlaceB B2 2018-04-08 5 week 14
24 PlaceB B2 2018-04-20 5 week 16
25 PlaceB B2 2019-01-23 6 start
26 PlaceB B2 2019-02-09 6 week 6
27 PlaceB B2 2019-02-27 6 week 9
28 PlaceB B2 2019-03-12 6 week 11
29 PlaceB B2 2019-03-30 6 week 13
For each Place (more than 2 in my actual data), I have a separate data frame with temperature records by hours. For example:
set.seed(1032)
t <- c(seq.POSIXt(from = ISOdate(2018,01,01),to = ISOdate(2018,06,01), by = "hour"),seq.POSIXt(from = ISOdate(2019,01,01),to = ISOdate(2019,06,01), by = "hour"))
temp_A <- runif(length(t),min = 5, max = 25)
temp_B <- runif(length(t),min = 3, max = 32)
data_A <- data.frame(t,temp_A)
data_B <- data.frame(t,temp_B)
> head(data_A)
t temp_A
1 2018-01-01 12:00:00 14.24961
2 2018-01-01 13:00:00 21.64925
3 2018-01-01 14:00:00 21.77058
4 2018-01-01 15:00:00 13.31673
5 2018-01-01 16:00:00 16.10350
6 2018-01-01 17:00:00 17.64567
I need to add a column in df1 with average temperature for the time interval by Place, group_Id and exprmt: the first of each group_byshould be a NaN, than I would need the average for each time interval. Knowing that for each Place, the data are also in a separate data frame.
I tried something like this, but it is not working:
df1 <- df1 %>% group_by(Place,Group_Id,exprmt) %>% mutate(
temp = case_when(FollowUp == "start" & Place == "PlaceA" ~ NA,
FollowUp == FollowUp[c(2:n())] & Place == "PlaceA" ~ mean(temp_A[c(which(date(temp_A$t))==lag(Time,1):which(date(temp_A$t))==Time),2]),
)
)
I found information on how calculate averages over multiple dataframes (e.g. this or this), but this is not what I am looking for. I would like to do it without a loop. My expected results is (etc stand for and so on..):
> df1
Place Group_Id Time exprmt FollowUp expected
1 PlaceA A1 2018-01-15 1 start NaN
2 PlaceA A1 2018-02-03 1 week 5 mean temp_A between 2018-01-15 and 2018-02-03
3 PlaceA A1 2018-02-27 1 week 9 mean temp_A between 2018-02-03 and 2018-02-27
4 PlaceA A1 2018-03-10 1 week 10 mean temp_A between 2018-02-27 and 2018-03-10
5 PlaceA A1 2018-03-18 1 week 11 mean temp_A between 2018-03-10 and 2018-03-18
6 PlaceA A1 2019-02-02 2 start NaN
7 PlaceA A1 2019-03-01 2 week 9 mean temp_A between 2019-02-02 and 2019-03-01
8 PlaceA A1 2019-03-15 2 week 11 etc
9 PlaceA A1 2019-03-28 2 week 13 etc
10 PlaceA A1 2019-04-05 2 week 14 etc
11 PlaceA A1 2019-04-12 2 week 15 etc
12 PlaceA A2 2018-02-01 3 start etc
13 PlaceA A2 2018-03-01 3 week 9 etc
14 PlaceA A2 2018-04-07 3 week 14 etc
15 PlaceB B1 2018-01-17 4 start NaN
16 PlaceB B1 2018-01-27 4 week 4 mean temp_B between 2018-01-17 and 2018-01-27
17 PlaceB B1 2018-02-17 4 week 7 etc
18 PlaceB B1 2018-03-03 4 week 9 etc
19 PlaceB B1 2018-04-02 4 week 14 etc
20 PlaceB B1 2018-04-25 4 week 17 etc
21 PlaceB B2 2018-03-03 5 start etc
22 PlaceB B2 2018-03-18 5 week 11 etc
23 PlaceB B2 2018-04-08 5 week 14 etc
24 PlaceB B2 2018-04-20 5 week 16 etc
25 PlaceB B2 2019-01-23 6 start etc
26 PlaceB B2 2019-02-09 6 week 6 etc
27 PlaceB B2 2019-02-27 6 week 9 etc
28 PlaceB B2 2019-03-12 6 week 11 etc
29 PlaceB B2 2019-03-30 6 week 13 etc
Any help will be appreciated!
Upvotes: 1
Views: 121
Answers (2)
lovalery
Reputation: 4662
I suggest a detailed step-by-step solution (using data.table, lubridate and gtools libraries) which tries not to lose the reader. So, please find below a reprex.
Reprex
1. DATA PREPARATION
library(data.table)
library(lubridate)
library(gtools)
# Convert the dataframe 'df1' into data.table and add of the dummy variable 'StartTime'
setDT(df1)[, StartTime := shift(Time,1), by = .(Place, Group_Id, exprmt)][]
setcolorder(df1, c("Place", "Group_Id", "FollowUp", "exprmt", "StartTime", "Time"))
# Convert 'StartTime' and 'Time' columns into class 'PosiXct' and into ymd_hms format
# with the function 'ymd_TO_ymd_hms'
ymd_TO_ymd_hms <- function(x,y) as_datetime(as.double(as.POSIXct(x)+3600), tz = y)
sel_cols <- c("StartTime", "Time")
df1[, (sel_cols) := lapply(.SD, ymd_TO_ymd_hms, "GMT"), .SDcols = sel_cols][, Time := Time - 3600]
# Here is to what 'df1' looks like:
df1
#> Place Group_Id FollowUp exprmt StartTime Time
#> 1: PlaceA A1 start 1 <NA> 2018-01-14 23:00:00
#> 2: PlaceA A1 week 5 1 2018-01-15 00:00:00 2018-02-02 23:00:00
#> 3: PlaceA A1 week 9 1 2018-02-03 00:00:00 2018-02-26 23:00:00
#> 4: PlaceA A1 week 10 1 2018-02-27 00:00:00 2018-03-09 23:00:00
#> 5: PlaceA A1 week 11 1 2018-03-10 00:00:00 2018-03-17 23:00:00
#> 6: PlaceA A1 start 2 <NA> 2019-02-01 23:00:00
#> 7: PlaceA A1 week 9 2 2019-02-02 00:00:00 2019-02-28 23:00:00
#> 8: PlaceA A1 week 11 2 2019-03-01 00:00:00 2019-03-14 23:00:00
#> 9: PlaceA A1 week 13 2 2019-03-15 00:00:00 2019-03-27 23:00:00
#> 10: PlaceA A1 ...
# Convert the dataframes 'data_A' and 'data_B' into data.tables
setDT(data_A)
setDT(data_B)
2. EXPAND ROWS OF 'df1' BY DATE RANGE USING 'StartTime' and 'Time'
df1_time_seq <- df1[!is.na(StartTime) # remove rows where StartTime = NA
][ ,.(Place = Place, Group_Id = Group_Id, FollowUp = FollowUp, exprmt = exprmt, Time_seq = seq(from = StartTime, to = Time, by = "hour")), by = 1:nrow(df1[!is.na(StartTime)])]
df1_time_seq
#> nrow Place Group_Id FollowUp exprmt Time_seq
#> 1: 1 PlaceA A1 week 5 1 2018-01-15 00:00:00
#> 2: 1 PlaceA A1 week 5 1 2018-01-15 01:00:00
#> 3: 1 PlaceA A1 week 5 1 2018-01-15 02:00:00
#> 4: 1 PlaceA A1 week 5 1 2018-01-15 03:00:00
#> 5: 1 PlaceA A1 week 5 1 2018-01-15 04:00:00
#> ---
#> 9784: 23 PlaceB B2 week 13 6 2019-03-29 19:00:00
#> 9785: 23 PlaceB B2 week 13 6 2019-03-29 20:00:00
#> 9786: 23 PlaceB B2 week 13 6 2019-03-29 21:00:00
#> 9787: 23 PlaceB B2 week 13 6 2019-03-29 22:00:00
#> 9788: 23 PlaceB B2 week 13 6 2019-03-29 23:00:00
3. JOINS
# Merge 'data_A' and 'data_B' on 't'
data_merge <- merge(data_A, data_B, by = 't')
# Merge 'df1_time_seq' and 'data_merge' on 'Time_seq' = 't' and add a column 'temp' filled with 'temp_A' values when 'Place == PlaceA' and 'temp_B' values when 'Place == PlaceB'
df1_time_seq_merge <- merge(df1_time_seq, data_merge, by.x = "Time_seq", by.y = "t")[, temp := fcase(Place == "PlaceA", temp_A,
Place == "PlaceB", temp_B)
][, `:=` (temp_A = NULL, temp_B = NULL)
][]
df1_time_seq_merge
#> Time_seq nrow Place Group_Id FollowUp exprmt temp
#> 1: 2018-01-15 00:00:00 1 PlaceA A1 week 5 1 10.618465
#> 2: 2018-01-15 01:00:00 1 PlaceA A1 week 5 1 16.156850
#> 3: 2018-01-15 02:00:00 1 PlaceA A1 week 5 1 6.806842
#> 4: 2018-01-15 03:00:00 1 PlaceA A1 week 5 1 21.036855
#> 5: 2018-01-15 04:00:00 1 PlaceA A1 week 5 1 21.578569
#> ---
#> 9784: 2019-04-11 18:00:00 9 PlaceA A1 week 15 2 16.646570
#> 9785: 2019-04-11 19:00:00 9 PlaceA A1 week 15 2 12.362436
#> 9786: 2019-04-11 20:00:00 9 PlaceA A1 week 15 2 24.853746
#> 9787: 2019-04-11 21:00:00 9 PlaceA A1 week 15 2 22.553074
#> 9788: 2019-04-11 22:00:00 9 PlaceA A1 week 15 2 21.020600
4. SUMMARIZE 'df1_time_seq_merge'
# Summarize df1_time_seq_merge to get the mean of 'temp' by group in the 'expected' variable
df1_mean <- df1_time_seq_merge[, .(expected = mean(temp)), by = .(Place, Group_Id, exprmt, FollowUp)]
df1_mean
#> Place Group_Id exprmt FollowUp expected
#> 1: PlaceA A1 1 week 5 15.17243
#> 2: PlaceB B1 4 week 4 19.26662
#> 3: PlaceB B1 4 week 7 17.32940
#> 4: PlaceA A2 3 week 9 14.92409
#> 5: PlaceA A1 1 week 9 14.86734
#> 6: PlaceB B1 4 week 9 18.36255
#> 7: PlaceA A1 1 week 10 14.75482
#> 8: PlaceA A2 3 week 14 14.86063
#> 9: PlaceB B1 4 week 14 17.35101
#> 10: PlaceB B2 5 week 11 17.93565
#> 11: PlaceA A1 1 week 11 14.86273
#> 12: PlaceB B2 5 week 14 16.77532
#> 13: PlaceB B1 4 week 17 18.00866
#> 14: PlaceB B2 5 week 16 18.15545
#> 15: PlaceB B2 6 week 6 17.95428
#> 16: PlaceA A1 2 week 9 14.96347
#> 17: PlaceB B2 6 week 9 16.85704
#> 18: PlaceB B2 6 week 11 17.23744
#> 19: PlaceA A1 2 week 11 15.22046
#> 20: PlaceB B2 6 week 13 17.33922
#> 21: PlaceA A1 2 week 13 14.58677
#> 22: PlaceA A1 2 week 14 15.24341
#> 23: PlaceA A1 2 week 15 15.87080
#> Place Group_Id exprmt FollowUp expected
5. FINAL JOIN BETWEEN 'df1' AND 'df1_MEAN'
DF_Results <- merge(df1, df1_mean, by = c("Place", "Group_Id", "exprmt", "FollowUp"), all.x = TRUE)[, Time := Time + 3600][]
6. CLEANING 'DF_Results' TO GET THE DESIRED OUTPUT
ymd_hms_TO_ymd <- function(x) as_date(as.POSIXct(x))
DF_Results[, `:=` (StartTime = NULL, Time = lapply(Time, ymd_hms_TO_ymd))]
setcolorder(DF_Results, c("Place", "Group_Id", "exprmt", "Time", "FollowUp", "expected"))
DF_Results <- DF_Results[gtools::mixedorder(FollowUp, decreasing = FALSE)]
setorder(DF_Results, Place, Group_Id, exprmt)
DF_Results
#> Place Group_Id exprmt Time FollowUp expected
#> 1: PlaceA A1 1 2018-01-15 start NA
#> 2: PlaceA A1 1 2018-02-03 week 5 15.17243
#> 3: PlaceA A1 1 2018-02-27 week 9 14.86734
#> 4: PlaceA A1 1 2018-03-10 week 10 14.75482
#> 5: PlaceA A1 1 2018-03-18 week 11 14.86273
#> 6: PlaceA A1 2 2019-02-02 start NA
#> 7: PlaceA A1 2 2019-03-01 week 9 14.96347
#> 8: PlaceA A1 2 2019-03-15 week 11 15.22046
#> 9: PlaceA A1 2 2019-03-28 week 13 14.58677
#> 10: PlaceA A1 2 2019-04-04 week 14 15.24341
#> 11: PlaceA A1 2 2019-04-11 week 15 15.87080
#> 12: PlaceA A2 3 2018-02-01 start NA
#> 13: PlaceA A2 3 2018-03-01 week 9 14.92409
#> 14: PlaceA A2 3 2018-04-06 week 14 14.86063
#> 15: PlaceB B1 4 2018-01-17 start NA
#> 16: PlaceB B1 4 2018-01-27 week 4 19.26662
#> 17: PlaceB B1 4 2018-02-17 week 7 17.32940
#> 18: PlaceB B1 4 2018-03-03 week 9 18.36255
#> 19: PlaceB B1 4 2018-04-01 week 14 17.35101
#> 20: PlaceB B1 4 2018-04-24 week 17 18.00866
#> 21: PlaceB B2 5 2018-03-03 start NA
#> 22: PlaceB B2 5 2018-03-18 week 11 17.93565
#> 23: PlaceB B2 5 2018-04-07 week 14 16.77532
#> 24: PlaceB B2 5 2018-04-19 week 16 18.15545
#> 25: PlaceB B2 6 2019-01-23 start NA
#> 26: PlaceB B2 6 2019-02-09 week 6 17.95428
#> 27: PlaceB B2 6 2019-02-27 week 9 16.85704
#> 28: PlaceB B2 6 2019-03-12 week 11 17.23744
#> 29: PlaceB B2 6 2019-03-30 week 13 17.33922
#> Place Group_Id exprmt Time FollowUp expected
Created on 2021-11-24 by the reprex package (v2.0.1)
Upvotes: 1
Isa
Reputation: 496
Sharing the results with temperature data of 2 places. You can always generalize the same either by joining and creating a single data object (if total places are less) or use an ifelse statement.
library(data.table)
setDT(df1)
setDT(data_A) # converting to data.table
setDT(data_B) # converting to data.table
Merged temperature to have a single data object
data_AB <- merge(data_A, data_B, by = 't')
Create a lag column of Time variable based on Place, Group_Id, exprmt
df1[,':='(LAG_DATE = shift(Time, type = 'lag')), by = .(Place, Group_Id, exprmt)]
Using apply function and user defined function to subset the temperature data based on consecutive time periods and also using data.table functionality along with lapply to get the mean for those subsets
Here I have assumed Place column can somehow be joined/mapped on some condition with the temperature data. Like in the example shared temp_A/temp_B can be formed by concatenating 'temp_' and 6th character of Place column
df1[,':='(EXPECTED = apply(cbind(LAG_DATE, Time, Place), 1, function(x) {
x1 <- as.Date(as.numeric(x[1]), origin = '1970-01-01')
x2 <- as.Date(as.numeric(x[2]), origin = '1970-01-01')
Place <- as.character(x[3])
Mean_Value <- ifelse(is.na(x1), NaN, data_AB[as.Date(t) >= x1 &
as.Date(t) <= x2, lapply(.SD, mean), .SDcols = paste('temp_', substr(Place, 6,
6), sep = '')])
return(as.numeric(Mean_Value))
}
))]
Upvotes: 1
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