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!
CodePudding user response：
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))
}
))]
CodePudding user response：
I suggest a detailed step-by-step solution (using data.table and lubridate libraries), probably a bit academic, but which tries not to lose the reader. So, please find below a reprex.
Reprex
1. DATA PREPARATION
library(data.table)
library(lubridate)
# Convert the dataframe 'df1' into data.table and add 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"))
# What df1 looks like:
df1
#> Place Group_Id FollowUp exprmt StartTime Time
#> 1: PlaceA A1 start 1 <NA> 2018-01-15
#> 2: PlaceA A1 week 5 1 2018-01-15 2018-02-03
#> 3: PlaceA A1 week 9 1 2018-02-03 2018-02-27
#> 4: PlaceA A1 week 10 1 2018-02-27 2018-03-10
#> 5: PlaceA A1 week 11 1 2018-03-10 2018-03-18
#> 6: PlaceA A1 start 2 <NA> 2019-02-02
#> 7: PlaceA A1 week 9 2 2019-02-02 2019-03-01
#> 8: PlaceA ....
# Convert 'StartTime' and 'Time' columns into class 'PosiXct'
sel_cols <- c("StartTime", "Time")
df1[, (sel_cols) := lapply(.SD, as.POSIXct, tz = "GMT"), .SDcols = sel_cols]
# Convert the dataframes 'data_A' and 'data_B' into data.tables
setDT(data_A)
setDT(data_B)
2. JOINS
# Merge 'data_A' and 'data_B' on 't'
data_merge <- merge(data_A, data_B, by = 't')
# Join 'df1' and 'data_merge' with Time > t >= StartTime, and remove unnecessary columns
DF_join_1 <- df1[data_merge, on = .(StartTime <= t,Time > t)
][, `:=` (Place = NULL, Group_Id = NULL, FollowUp = NULL, exprmt = NULL, Time = NULL)
][]
# Join 'DF_join_1' and 'df1' on StartTime, then remove the dummy variable StartTime and reorder columns
DF_join_2 <- DF_join_1[df1, on = .(StartTime)
][, StartTime := NULL
][]
setcolorder(DF_join_2, c("Place", "Group_Id", "Time", "exprmt", "FollowUp", "temp_A", "temp_B"))
3. ADD A COLUMN 'TEMP'
# Create a column 'temp' filled with 'temp_A' values when 'Place == PlaceA' and 'temp_B' values when 'Place == PlaceB'
DF_results <- DF_join_2[, temp := fcase(Place == "PlaceA", temp_A,
Place == "PlaceB", temp_B)
][, `:=` (temp_A = NULL, temp_B = NULL)
][]
4. SUMMARIZE TO GET THE DESIRED OUTPUT
# Summarize DF_results to get the mean of 'temp' by group in the 'expected' variable
DF_results[, .(expected = mean(temp, na.rm = TRUE)), by = .(Place, Group_Id, exprmt, Time, FollowUp)]
#> Place Group_Id exprmt Time FollowUp expected
#> 1: PlaceA A1 1 2018-01-15 start NaN
#> 2: PlaceA A1 1 2018-02-03 week 5 10.618465
#> 3: PlaceA A1 1 2018-02-27 week 9 15.997990
#> 4: PlaceA A1 1 2018-03-10 week 10 14.874170
#> 5: PlaceA A1 1 2018-03-18 week 11 8.005203
#> 6: PlaceA A1 2 2019-02-02 start NaN
#> 7: PlaceA A1 2 2019-03-01 week 9 17.768572
#> 8: PlaceA A1 2 2019-03-15 week 11 8.525002
#> 9: PlaceA A1 2 2019-03-28 week 13 20.948760
#> 10: PlaceA A1 2 2019-04-05 week 14 16.898529
#> 11: PlaceA A1 2 2019-04-12 week 15 7.172799
#> 12: PlaceA A2 3 2018-02-01 start NaN
#> 13: PlaceA A2 3 2018-03-01 week 9 17.521202
#> 14: PlaceA A2 3 2018-04-07 week 14 21.653708
#> 15: PlaceB B1 4 2018-01-17 start NaN
#> 16: PlaceB B1 4 2018-01-27 week 4 22.622165
#> 17: PlaceB B1 4 2018-02-17 week 7 22.462456
#> 18: PlaceB B1 4 2018-03-03 week 9 10.210829
#> 19: PlaceB B1 4 2018-04-02 week 14 19.731544
#> 20: PlaceB B1 4 2018-04-25 week 17 25.700109
#> 21: PlaceB B2 5 2018-03-03 start NaN
#> 22: PlaceB B2 5 2018-03-18 week 11 19.731544
#> 23: PlaceB B2 5 2018-04-08 week 14 16.757186
#> 24: PlaceB B2 5 2018-04-20 week 16 5.248006
#> 25: PlaceB B2 6 2019-01-23 start NaN
#> 26: PlaceB B2 6 2019-02-09 week 6 7.720195
#> 27: PlaceB B2 6 2019-02-27 week 9 13.185666
#> 28: PlaceB B2 6 2019-03-12 week 11 9.706857
#> 29: PlaceB B2 6 2019-03-30 week 13 10.022071
#> Place Group_Id exprmt Time FollowUp expected
Created on 2021-11-19 by the reprex package (v2.0.1)