问题
I have a data frame df1 that summarises the depth of different fishes over time at different places.
On the other hand, I have df2 that summarises the intensity of the currents over time (EVERY THREE HOURS) from the surface to 39 meters depth at intervals of 8 meters (m0-7, m8-15, m16-23, m24-31 and m32-39) in a specific place. As an example:
df1<-data.frame(Datetime=c("2016-08-01 15:34:07","2016-08-01 16:25:16","2016-08-01 17:29:16","2016-08-01 18:33:16","2016-08-01 20:54:16","2016-08-01 22:48:16"),Site=c("BD","HG","BD","BD","BD","BD"),Ind=c(16,17,19,16,17,16), Depth=c(5.3,24,36.4,42,NA,22.1))
df1$Datetime<-as.POSIXct(df1$Datetime, format="%Y-%m-%d %H:%M:%S",tz="UTC")
> df1
Datetime Site Ind Depth
1 2016-08-01 15:34:07 BD 16 5.3
2 2016-08-01 16:25:16 HG 17 24.0
3 2016-08-01 17:29:16 BD 19 36.4
4 2016-08-01 18:33:16 BD 16 42.0
5 2016-08-01 20:54:16 BD 17 NA
6 2016-08-01 22:48:16 BD 16 22.1
df2<-data.frame(Datetime=c("2016-08-01 12:00:00","2016-08-01 15:00:00","2016-08-01 18:00:00","2016-08-01 21:00:00","2016-08-02 00:00:00"), Site=c("BD","BD","BD","BD","BD"),var1=c(2.75,4,6.75,2.25,4.3),var2=c(3,4,4.75,3,2.1),var3=c(2.75,4,5.75,2.25,1.4),var4=c(3.25,3,6.5,2.75,3.4),var5=c(3,4,4.75,3,1.7))
df2$Datetime<-as.POSIXct(df2$Datetime, format="%Y-%m-%d %H:%M:%S",tz="UTC")
colnames(df2)<-c("Datetime","Site","m0-7","m8-15","m16-23","m24-31","m32-39")
> df2
Datetime Site m0-7 m8-15 m16-23 m24-31 m32-39
1 2016-08-01 12:00:00 BD 2.75 3.00 2.75 3.25 3.00
2 2016-08-01 15:00:00 BD 4.00 4.00 4.00 3.00 4.00
3 2016-08-01 18:00:00 BD 6.75 4.75 5.75 6.50 4.75
4 2016-08-01 21:00:00 BD 2.25 3.00 2.25 2.75 3.00
5 2016-08-02 00:00:00 BD 4.30 2.10 1.40 3.40 1.70
I want to create a variable in df1 that reflects the mean current for the depth layers in which the fish WASN'T. For instance, if fish is at 20 meters depth, which corresponds to the layer m16-23, I want to know the mean current for the layers m0-7, m8-15, m24-31 and m32-39.
Note1: if my fish was to a depth higher than 39 meters, I consider it as if it was at the deepest layer (m32-39). An example of this in row 4 of df1.
Note2: since the current records are every three hours, every hour indicated in df2$Datetime represents one hour and a half more, and one hour and a half less. That is, the current intensity pointed out in df2 at 21:00:00 reflects the currents between 19:30:00 and 22:30:00. The same with the rest of the hours.
I would expect this:
> df1
Datetime Site Ind Depth current.Mean
1 2016-08-01 15:34:07 BD 16 5.3 3.75
2 2016-08-01 16:25:16 HG 17 24.0 NA
3 2016-08-01 17:29:16 BD 19 36.4 5.94
4 2016-08-01 18:33:16 BD 16 42.0 5.94
5 2016-08-01 20:54:16 BD 17 NA NA
6 2016-08-01 22:48:16 BD 16 22.1 2.87
Does anyone know how to do it?
回答1:
This question comprises interesting challenges:
- The OP is asking for a "partial anti-join", i.e., the OP wants to aggregate current data in
df2whereDatetimeandSiteare matching but the depth layer does not. - The current data
df2are given in a look-up table where each value is associated with a depth range (depth layer) and a time range of 3 hours. So, the measuredDepthandDatetimeindf1need to be mapped onto the respective ranges.
I have tried different approaches but I ended up with the one below which does not make assumptions about the aggregate function. So, mean() can be called directly.
library(data.table)
library(magrittr)
# reshape df2 from wide to long format
currents <- melt(setDT(df2), id.vars = c("Datetime", "Site"),
variable.name = "layer", value.name = "current")
# create columns to join on
labels <- names(df2) %>% stringr::str_subset("^m")
breaks <- c(seq(0, 32, 8), Inf)
setDT(df1)[, layer := cut(Depth, breaks = breaks, labels = labels)]
df1[, current.dt := df2[df1, on = .(Site, Datetime),
roll = "nearest", x.Datetime]]
# "partial anti-join" to compute mean of other layers
currents_other_layers <-
currents[df1, on = .(Site, Datetime = current.dt)][
layer != i.layer, mean(current), by = .(i.Datetime, Site)]
# append result column
df1[currents_other_layers, on = .(Site, Datetime = i.Datetime), current.mean := i.V1]
df1
Datetime Site Ind Depth layer current.dt current.mean 1: 2016-08-01 15:34:07 BD 16 5.3 m0-7 2016-08-01 15:00:00 3.7500 2: 2016-08-01 16:25:16 HG 17 24.0 m16-23 <NA> NA 3: 2016-08-01 17:29:16 BD 19 36.4 m32-39 2016-08-01 18:00:00 5.9375 4: 2016-08-01 18:33:16 BD 16 42.0 m32-39 2016-08-01 18:00:00 5.9375 5: 2016-08-01 20:54:16 BD 17 NA <NA> 2016-08-01 21:00:00 NA 6: 2016-08-01 22:48:16 BD 16 22.1 m16-23 2016-08-02 00:00:00 2.8750
This reproduces OP's expected result.
Explanation
df2 is reshaped from wide to long format. This allows for joining / anti-joining on the layer column.
currents
Datetime Site layer current 1: 2016-08-01 12:00:00 BD m0-7 2.75 2: 2016-08-01 15:00:00 BD m0-7 4.00 3: 2016-08-01 18:00:00 BD m0-7 6.75 4: 2016-08-01 21:00:00 BD m0-7 2.25 5: 2016-08-02 00:00:00 BD m0-7 4.30 6: 2016-08-01 12:00:00 BD m8-15 3.00 7: 2016-08-01 15:00:00 BD m8-15 4.00 8: 2016-08-01 18:00:00 BD m8-15 4.75 9: 2016-08-01 21:00:00 BD m8-15 3.00 10: 2016-08-02 00:00:00 BD m8-15 2.10 11: 2016-08-01 12:00:00 BD m16-23 2.75 12: 2016-08-01 15:00:00 BD m16-23 4.00 13: 2016-08-01 18:00:00 BD m16-23 5.75 14: 2016-08-01 21:00:00 BD m16-23 2.25 15: 2016-08-02 00:00:00 BD m16-23 1.40 16: 2016-08-01 12:00:00 BD m24-31 3.25 17: 2016-08-01 15:00:00 BD m24-31 3.00 18: 2016-08-01 18:00:00 BD m24-31 6.50 19: 2016-08-01 21:00:00 BD m24-31 2.75 20: 2016-08-02 00:00:00 BD m24-31 3.40 21: 2016-08-01 12:00:00 BD m32-39 3.00 22: 2016-08-01 15:00:00 BD m32-39 4.00 23: 2016-08-01 18:00:00 BD m32-39 4.75 24: 2016-08-01 21:00:00 BD m32-39 3.00 25: 2016-08-02 00:00:00 BD m32-39 1.70 Datetime Site layer current
Now, df1 has to be amended to include columns which correspond to layer and Datetime in currents.
For Depth, the cut() function is used. The last layer level m32-39 is extended to Inf so all depths greater 32 m are included in this level as requested by the OP.
For Datetime, a rolling join to the nearest Datetime in df2 is used. This is possible because df2$Datetime denotes the mid-point of the 3 hour time range.
After df1 has been prepared, we can do the "partial anti-join". Unfortunately, data.table's non-equi joins does not accept the != operator. So, we cannot write
currents[df1, on = .(Datetime = current.dt, Site, layer != layer)]
directly but have to use a work-around where we first pick the rows where we expect matches and then do an anti-join:
currents[df1, on = .(Datetime = current.dt, Site)][
!df1, on = .(Datetime = current.dt, Site, layer)]
Datetime Site layer current i.Datetime Ind Depth i.layer 1: 2016-08-01 15:00:00 BD m8-15 4.00 2016-08-01 15:34:07 16 5.3 m0-7 2: 2016-08-01 15:00:00 BD m16-23 4.00 2016-08-01 15:34:07 16 5.3 m0-7 3: 2016-08-01 15:00:00 BD m24-31 3.00 2016-08-01 15:34:07 16 5.3 m0-7 4: 2016-08-01 15:00:00 BD m32-39 4.00 2016-08-01 15:34:07 16 5.3 m0-7 5: 2016-08-01 18:00:00 BD m0-7 6.75 2016-08-01 17:29:16 19 36.4 m32-39 6: 2016-08-01 18:00:00 BD m8-15 4.75 2016-08-01 17:29:16 19 36.4 m32-39 7: 2016-08-01 18:00:00 BD m16-23 5.75 2016-08-01 17:29:16 19 36.4 m32-39 8: 2016-08-01 18:00:00 BD m24-31 6.50 2016-08-01 17:29:16 19 36.4 m32-39 9: 2016-08-01 18:00:00 BD m0-7 6.75 2016-08-01 18:33:16 16 42.0 m32-39 10: 2016-08-01 18:00:00 BD m8-15 4.75 2016-08-01 18:33:16 16 42.0 m32-39 11: 2016-08-01 18:00:00 BD m16-23 5.75 2016-08-01 18:33:16 16 42.0 m32-39 12: 2016-08-01 18:00:00 BD m24-31 6.50 2016-08-01 18:33:16 16 42.0 m32-39 13: 2016-08-01 21:00:00 BD m0-7 2.25 2016-08-01 20:54:16 17 NA <NA> 14: 2016-08-01 21:00:00 BD m8-15 3.00 2016-08-01 20:54:16 17 NA <NA> 15: 2016-08-01 21:00:00 BD m16-23 2.25 2016-08-01 20:54:16 17 NA <NA> 16: 2016-08-01 21:00:00 BD m24-31 2.75 2016-08-01 20:54:16 17 NA <NA> 17: 2016-08-01 21:00:00 BD m32-39 3.00 2016-08-01 20:54:16 17 NA <NA> 18: 2016-08-02 00:00:00 BD m0-7 4.30 2016-08-01 22:48:16 16 22.1 m16-23 19: 2016-08-02 00:00:00 BD m8-15 2.10 2016-08-01 22:48:16 16 22.1 m16-23 20: 2016-08-02 00:00:00 BD m24-31 3.40 2016-08-01 22:48:16 16 22.1 m16-23 21: 2016-08-02 00:00:00 BD m32-39 1.70 2016-08-01 22:48:16 16 22.1 m16-23 22: <NA> HG <NA> NA 2016-08-01 16:25:16 17 24.0 m16-23 Datetime Site layer current i.Datetime Ind Depth i.layer
This can be aggregated as desired by an arbitrary aggregation function (no need to manually add single columns selectively):
currents_other_layers <-
currents[df1, on = .(Datetime = current.dt, Site)][
!df1, on = .(Datetime = current.dt, Site, layer)][
!is.na(Depth), mean(current), by = .(i.Datetime, Site)]
currents_other_layers
i.Datetime Site V1 1: 2016-08-01 15:34:07 BD 3.7500 2: 2016-08-01 17:29:16 BD 5.9375 3: 2016-08-01 18:33:16 BD 5.9375 4: 2016-08-01 22:48:16 BD 2.8750 5: 2016-08-01 16:25:16 HG NA
This result contains the average currents of all other layers except the observed layer. Note that grouping is by i.Datetime which refers to df1$Datetime and Site. Rows where Depth is missing in df1 are omitted to meet OP's expected result.
A final update join appends the result column to df1.
回答2:
I'd approach this in two steps:
- make a lookup table with avg_speed_elsewhere for each Datetime, Site, and Depth in df2.
- Join to df1.
Here's a lookup table:
library(tidyverse)
df2_long <- df2 %>%
gather(depth_rng, speed, `m0-7`:`m32-39`) %>%
separate(depth_rng, c("min_depth", "max_depth")) %>%
mutate_at(vars(matches("depth")), parse_number) %>%
# EDIT -- added to make deep category cover >39 too
mutate(max_depth = if_else(max_depth == 39, 10000, max_depth)) %>%
group_by(Datetime, Site) %>%
# Avg Speed elsewhere is the sum of all speeds, minus this speed, all divided by 4.
mutate(avg_speed_elsewhere = (sum(speed) - speed) / 4)
> df2_long
# A tibble: 25 x 6
# Groups: Datetime, Site [5]
Datetime Site min_depth max_depth speed avg_speed_elsewhere
<dttm> <fct> <dbl> <dbl> <dbl> <dbl>
1 2016-08-18 12:00:00 BD 0 7 2.75 3
2 2016-08-18 15:00:00 BD 0 7 4 3.75
3 2016-08-18 18:00:00 BD 0 7 6.75 5.44
4 2016-08-18 21:00:00 BD 0 7 2.25 2.75
5 2016-08-19 00:00:00 BD 0 7 4.3 2.15
6 2016-08-18 12:00:00 BD 8 15 3 2.94
7 2016-08-18 15:00:00 BD 8 15 4 3.75
8 2016-08-18 18:00:00 BD 8 15 4.75 5.94
9 2016-08-18 21:00:00 BD 8 15 3 2.56
10 2016-08-19 00:00:00 BD 8 15 2.1 2.7
# ... with 15 more rows
I expect this will work, but your provided data doesn't overlap so I'm not sure:
df1 %>%
# EDIT - replaced floor_date with round_date
mutate(Datetime_3hr = lubridate::round_date(Datetime, "3 hour")) %>%
left_join(df2_long, by = c("Site", "Datetime_3hr" = "Datetime")) %>%
filter(Depth >= min_depth & Depth < max_depth + 1 | is.na(Depth))
回答3:
Using data.table you can do a rolling join between your two databases in order to associate your depth variables with your current variables even though the times don't match. What the rolling join does is associate one table with the other by which time is the closest (according to your chosen options). I changed a bit of your data so that the the days match
library(data.table)
df1<-data.frame(Datetime=c("2016-08-01 15:34:07","2016-08-01 16:25:16","2016-08-01 17:29:16","2016-08-01 18:33:16","2016-08-01 20:54:16","2016-08-01 22:48:16"),Site=c("BD","HG","BD","BD","BD","BD"),Ind=c(16,17,19,16,17,16), Depth=c(5.3,24,36.4,42,NA,22.1))
df1$Datetime<-as.POSIXct(df1$Datetime, format="%Y-%m-%d %H:%M:%S",tz="UTC")
df2<-data.frame(Datetime=c("2016-08-01 12:00:00","2016-08-01 15:00:00","2016-08-01 18:00:00","2016-08-01 21:00:00","2016-08-02 00:00:00"), Site=c("BD","BD","BD","BD","BD"),var1=c(2.75,4,6.75,2.25,4.3),var2=c(3,4,4.75,3,2.1),var3=c(2.75,4,5.75,2.25,1.4),var4=c(3.25,3,6.5,2.75,3.4),var5=c(3,4,4.75,3,1.7))
df2$Datetime<-as.POSIXct(df2$Datetime, format="%Y-%m-%d %H:%M:%S",tz="UTC")
colnames(df2)<-c("Datetime","Site","m0-7","m8-15","m16-23","m24-31","m32-39")
setDT(df1)
setDT(df2)
setkey(df1, Site, Datetime)
setkey(df2, Site, Datetime)
df_merge = df2[df1, roll = Inf]
Then I use dplyr's case_when to calculate the currents for other depths
library(dplyr)
df_merge[, current_elsewhere := case_when(
is.na(Depth) ~ NA_real_,
Depth < 7 ~ (`m8-15` + `m16-23` + `m24-31` + `m32-39`)/4,
Depth < 15 ~ (`m0-7` + `m16-23` + `m24-31` + `m32-39`)/4,
Depth < 23 ~ (`m0-7` + `m8-15` + `m24-31` + `m32-39`)/4,
Depth < 31 ~ (`m0-7` + `m8-15` + `m16-23` + `m32-39`)/4,
T ~ (`m0-7` + `m8-15` + `m16-23` + `m24-31`)/4)]
df_merge
Datetime Site m0-7 m8-15 m16-23 m24-31 m32-39 Ind Depth current_elsewhere
1: 2016-08-01 15:34:07 BD 4.00 4.00 4.00 3.00 4.00 16 5.3 3.7500
2: 2016-08-01 17:29:16 BD 4.00 4.00 4.00 3.00 4.00 19 36.4 3.7500
3: 2016-08-01 18:33:16 BD 6.75 4.75 5.75 6.50 4.75 16 42.0 5.9375
4: 2016-08-01 20:54:16 BD 6.75 4.75 5.75 6.50 4.75 17 NA NA
5: 2016-08-01 22:48:16 BD 2.25 3.00 2.25 2.75 3.00 16 22.1 2.7500
6: 2016-08-01 16:25:16 HG NA NA NA NA NA 17 24.0 NA
来源：https://stackoverflow.com/questions/56191446/calculate-the-mean-between-several-columns-of-df2-that-can-vary-according-to-the