Calculate maximum values for three consecutive days of tibble in R - r

Question

I am doing time series analysis. Part of my data is as follow:
# A tibble: 6 x 3
time DOY Value
<dttm> <dbl> <dbl>
1 2015-01-08 12:30:00 8 0.664
2 2015-01-08 13:00:00 8 0.647
3 2015-01-11 14:00:00 11 0.669
4 2015-01-11 15:00:00 11 0.644
5 2015-02-04 12:30:00 35 0.664
6 2015-02-04 13:00:00 35 0.647
I would like to calculate the maximum values of 7 consecutive days of the data. For example:
# A tibble: 6 x 4
time DOY Value Max
<dttm> <dbl> <dbl> <dbl>
1 2015-01-08 12:30:00 8 0.664 11.669
2 2015-01-08 13:00:00 8 0.647 11.669
3 2015-01-11 14:00:00 11 0.669 11.669
4 2015-01-11 15:00:00 11 0.644 11.669
5 2015-02-04 12:30:00 35 0.664 35.664
6 2015-02-04 13:00:00 35 0.647 35.664

Answer 1

welcome to R and Stackoverflow. As mentioned above, you will find many friends here, if you provide a reproducible example, and explain what you have done and/or where things go wrong for you. This helps others to help you.
Based on your data fragment, I do some basic operations that I think might help you. Still you may need to adapt the principles to your problem case.
data
I turned your example into a tibble. Please note, when you work with date, times, date-times I recommend you use the respective variable type. This will give you access to helpful functions, etc.
Please also note you mentioned 8*3 tibble above. In fact your data structure is already a 4 column tibble with Date, time, DOY, and value!
library(dplyr) # basic dataframe/tibble operations
library(lubridate) # for datetime handling
df <- tribble(
~Date, ~time, ~DOY, ~Value
,"2015-01-08", "12:30:00", 8, 0.664
,"2015-01-08", "13:00:00", 8, 0.647
,"2015-01-11", "14:00:00", 11, 0.669
,"2015-01-11", "15:00:00", 11, 0.644
,"2015-02-04", "12:30:00", 35, 0.664
,"2015-02-04", "13:00:00", 35, 0.647
)
df <- df %>%
mutate(timestamp = ymd_hms(paste(Date, time))
This yields:
df
# A tibble: 6 x 5
Date time DOY Value timestamp
<chr> <chr> <dbl> <dbl> <dttm>
1 2015-01-08 12:30:00 8 0.664 2015-01-08 12:30:00
2 2015-01-08 13:00:00 8 0.647 2015-01-08 13:00:00
3 2015-01-11 14:00:00 11 0.669 2015-01-11 14:00:00
4 2015-01-11 15:00:00 11 0.644 2015-01-11 15:00:00
5 2015-02-04 12:30:00 35 0.664 2015-02-04 12:30:00
6 2015-02-04 13:00:00 35 0.647 2015-02-04 13:00:00
Note: timestamp is now a datetime objet dttm.
binning of data
It is not fully clear what your consecutive 7 days are and/or how you "group" them.
I assume you want to pick 7 days of a week.
As datetime is dttm, we can use the power of {lubridate} and extract the week from the datetime.
Note: you may want to bin/group your data differently. Think about what you want to achieve here and adpat this accordingly.
df <- df %>% mutate(bin = week(timestamp))
df
# A tibble: 6 x 6
Date time DOY Value timestamp bin
<chr> <chr> <dbl> <dbl> <dttm> <dbl>
1 2015-01-08 12:30:00 8 0.664 2015-01-08 12:30:00 2
2 2015-01-08 13:00:00 8 0.647 2015-01-08 13:00:00 2
3 2015-01-11 14:00:00 11 0.669 2015-01-11 14:00:00 2
4 2015-01-11 15:00:00 11 0.644 2015-01-11 15:00:00 2
5 2015-02-04 12:30:00 35 0.664 2015-02-04 12:30:00 5
6 2015-02-04 13:00:00 35 0.647 2015-02-04 13:00:00 5
If you want to work on "7 consecutive days" you will need to identify the groups of 7 days. Again, there are different ways to do this, check what the modulo operator does and how to apply this to your DOY.
operating on your groups/bins
You describe looking for the maximum per bin (7 days ~ week).
{dplyr} offers for such problems grouped operations. Read up on them:
df %>%
group_by(bin) %>%
summarise(MaxValue = max(Value) # we create a new variable and assing the max of each group to it
)
# A tibble: 2 x 2
bin MavValue
<dbl> <dbl>
1 2 0.669
2 5 0.664
Obviously, you can perform many operations (summaries of your bins/groups).
Note: You can create bins on multiple variables. Read up on group_by() and summarise(..., .groups = "drop"), if you want to use this interim tibble for further calculations.
Hope this gets you started.
clarification on grouping by 7 days
If you have a sequence of (integer) numbers, there is a neat way to group this into n-element bins, i.e. using integer division.
In your case the data comes already with a date-of-year DOY variable. For completeness: with lubridate you can pull the DOY from a timestamp with the function yday(), i.e. (df %>% mutate(DOY = yday(timestamp)).
# let's use integer division to group our DOYs into group of 7s
##--------- does not look at date or day
##--------- group 1-7 := 0, group 8-14 := 1, .... group 29-35 := 5
df <- df %>%
mutate(bin = DOY %/% 7)
This yields:
# A tibble: 6 x 6
Date time DOY Value timestamp bin
<chr> <chr> <dbl> <dbl> <dttm> <dbl>
1 2015-01-08 12:30:00 8 0.664 2015-01-08 12:30:00 1
2 2015-01-08 13:00:00 8 0.647 2015-01-08 13:00:00 1
3 2015-01-11 14:00:00 11 0.669 2015-01-11 14:00:00 1
4 2015-01-11 15:00:00 11 0.644 2015-01-11 15:00:00 1
5 2015-02-04 12:30:00 35 0.664 2015-02-04 12:30:00 5
6 2015-02-04 13:00:00 35 0.647 2015-02-04 13:00:00 5
And then build your max summary as before on the (new) grouping variable:
df %>%
group_by(bin) %>%
summarise(MaxValue = max(Value)
# A tibble: 2 x 2
bin MaxValue
<dbl> <dbl>
1 1 0.669
2 5 0.664
For the example data given the result is identical. However, with your full dataset and the offset between "weeks" (with their defined start date) vs cutting your DOYs into bins of 7 consecutive days, you will get a different summary (unless, the first day of the week (*) coincides with DOY 1).
(*): in lubridate you can set weeks to start Monday or Sunday as a parameter (in case you ever need this).