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padrWhen getting time series data ready for analysis, you might be confronted with the following two challenges:
padr aims to make light work of preparing time series
data by offering the two main functions thicken and
pad. A small example before we get into detail. Say I want
to make a line plot of my daily expenses at the coffee place. The data
for a few days might look like.
## time_stamp amount
## 1 2016-07-07 09:11:21 3.14
## 2 2016-07-07 09:46:48 2.98
## 3 2016-07-09 13:25:17 4.11
## 4 2016-07-10 10:45:11 3.14Using padr in combination with dplyr this
plot is made in the following way:
library(ggplot2); library(dplyr)
coffee %>%
thicken('day') %>%
group_by(time_stamp_day) %>%
summarise(day_amount = sum(amount)) %>%
pad() %>%
fill_by_value() %>%
ggplot(aes(time_stamp_day, day_amount)) + geom_line()
Quite some stuff going on here, let’s go through the functions one by one to see what they do.
thicken adds a column to a data frame that is of a
higher interval than that of the original datetime variable.
The interval in the padr context is the heartbeat of the
data, the recurrence of the observations.1 The original variable
“time_stamp” had the interval second, the added variable was of
interval day.
## [1] "sec"## [1] "day"thicken does figure out some stuff for you. First it
finds the datetime variable in your data frame (given there is only
one). Next it will determine the interval of this variable, which is one
of the following: year, quarter, month, week, day, hour, minute, or
second. Besides the interval, it also finds the interval unit (E.g. 5
minutes, 10 days, 2 months). Finally, it adds a variable to the data
frame that is of a higher interval than the interval of the original
datetime variable. The user can then use this variable to aggregate to
the higher level, for instance using dplyr’s
group_by and summarise. Besides the interval,
the user can also specify the units. When no unit is specified, a single
unit is applied.
to_thicken <- data.frame(day_var = as.Date(c('2016-08-12', '2016-08-13',
'2016-08-26', '2016-08-29')))
to_thicken %>% thicken(interval = "week")## day_var day_var_week
## 1 2016-08-12 2016-08-07
## 2 2016-08-13 2016-08-07
## 3 2016-08-26 2016-08-21
## 4 2016-08-29 2016-08-28## day_var day_var_4_day
## 1 2016-08-12 2016-08-11
## 2 2016-08-13 2016-08-11
## 3 2016-08-26 2016-08-23
## 4 2016-08-29 2016-08-27We see different default behavior for the different
intervals. Week intervals start on Sundays, day intervals start
on the first day found in the datetime variable. In many situations the
user will be content with thicken’s defaults. However, you
can specify the start_val as an offset if you would like to
start the returned interval on a different day or datetime.
We use the emergency data set for further illustration. It contains 120,450 emergency calls in Montgomery County, PA, between 2015-12-10 and 2016-10-17. It has four columns that contain information about the location of the emergency, a title field indicating the type of the emergency, and a time stamp. The data set was created from a Google Api, thanks to Mike Chirico for maintaining this set.
## # A tibble: 6 × 6
## lat lng zip title time_stamp twp
## <dbl> <dbl> <int> <chr> <dttm> <chr>
## 1 40.3 -75.6 19525 EMS: BACK PAINS/INJURY 2015-12-10 17:40:00 NEW HANOVER
## 2 40.3 -75.3 19446 EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00 HATFIELD TOWNSH…
## 3 40.1 -75.4 19401 Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00 NORRISTOWN
## 4 40.1 -75.3 19401 EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01 NORRISTOWN
## 5 40.3 -75.6 NA EMS: DIZZINESS 2015-12-10 17:40:01 LOWER POTTSGROVE
## 6 40.3 -75.3 19446 EMS: HEAD INJURY 2015-12-10 17:40:01 LANSDALESay we are interested in the number of overdoses that occurred daily.
However, we don’t want incidents during the same night to be split into
two days, what would have happened when we use the default behavior.
Rather, we reset the count at 8 am, grouping all nightly cases to the
same day. The interval is still day, but each new day starts at
8 am instead of midnight. The start_val serves as an
offset.
emergency %>% filter(title == 'EMS: OVERDOSE') %>%
thicken('day',
start_val = as.POSIXct('2015-12-11 08:00:00', tz = 'EST'),
colname = 'daystart') %>%
group_by(daystart) %>%
summarise(nr_od = n()) %>%
head()## # A tibble: 6 × 2
## daystart nr_od
## <dttm> <int>
## 1 2015-12-11 08:00:00 1
## 2 2015-12-12 08:00:00 6
## 3 2015-12-13 08:00:00 7
## 4 2015-12-14 08:00:00 8
## 5 2015-12-15 08:00:00 1
## 6 2015-12-16 08:00:00 4Note also that we specified the column name of the added column. If
we don’t, thicken takes the column name of the original
datetime variable and appends it with the interval of the thickened
variable, separated by an underscore.
Two final points on intervals before we are going to
pad:
The second workhorse of padr is pad. It
does date padding:
account <- data.frame(day = as.Date(c('2016-10-21', '2016-10-23', '2016-10-26')),
balance = c(304.46, 414.76, 378.98))
account %>% pad()## pad applied on the interval: day## day balance
## 1 2016-10-21 304.46
## 2 2016-10-22 NA
## 3 2016-10-23 414.76
## 4 2016-10-24 NA
## 5 2016-10-25 NA
## 6 2016-10-26 378.98The account dataframe has three observations on different days. Like
thicken, the pad function figures out what the
datetime variable in the data frame is, and then assesses its interval.
Next it notices that within the interval, day in this case,
rows are lacking between the first and last observation. It inserts a
row in the data frame for every time point that is lacking from the data
set. All non-datetime values will get missing values at the padded
rows.
It is up to the user what to do with the missing records. In the case
of the balance of an account we want to carry the last observation
forward. It needs tidyr::fill to arrive at the tidy data
set.
## pad applied on the interval: day## day balance
## 1 2016-10-21 304.46
## 2 2016-10-22 304.46
## 3 2016-10-23 414.76
## 4 2016-10-24 414.76
## 5 2016-10-25 414.76
## 6 2016-10-26 378.98Also pad allows for deviations from its default
behavior. By default it pads all observations between the first and the
last observation, but you can use start_val and
end_val to deviate from this. You can also specify a lower
interval than the one of the variable, using pad as the
inverse of thicken.
## day balance
## 1 2016-10-20 22:00:00 NA
## 2 2016-10-20 23:00:00 NA
## 3 2016-10-21 00:00:00 304.46
## 4 2016-10-21 01:00:00 NA
## 5 2016-10-21 02:00:00 NA
## 6 2016-10-21 03:00:00 NAWhen you want to thicken and pad within
groups there are two options. Either you group the data with
dplyr::group_by() before applying them, or you specify the
group argument in pad. Note that
thicken does not have a grouping argument, because
thickening with or without grouping would give the same result. However,
thicken does preserve dplyr grouping.
grouping_df <- data.frame(
group = rep(c("A", "B"), c(3, 3)),
date = as.Date(c("2017-10-02", "2017-10-04", "2017-10-06", "2017-10-01",
"2017-10-03", "2017-10-04")),
value = rep(2, 6)
)
grouping_df %>%
pad(group = "group")## pad applied on the interval: day## group date value
## 1 A 2017-10-02 2
## 2 A 2017-10-03 NA
## 3 A 2017-10-04 2
## 4 A 2017-10-05 NA
## 5 A 2017-10-06 2
## 6 B 2017-10-01 2
## 7 B 2017-10-02 NA
## 8 B 2017-10-03 2
## 9 B 2017-10-04 2Note in the above that each group is padded from its own start to its
end. If you want the starts and ends of each groups to be similar use
the start_val and end_val arguments. Note
further that the interval on which to pad is assessed over the groups.
It is assumed that the user wants to bring all observations to the same
interval. If you do want each group to have its own interval, use
dplyr::do in the following way.
## pad applied on the interval: 2 day## pad applied on the interval: day## # A tibble: 7 × 3
## # Groups: group [3]
## group date value
## <chr> <date> <dbl>
## 1 A 2017-10-02 2
## 2 A 2017-10-04 2
## 3 A 2017-10-06 2
## 4 B 2017-10-01 2
## 5 <NA> 2017-10-02 NA
## 6 B 2017-10-03 2
## 7 B 2017-10-04 2We already saw tidyr::fill coming in handy for the
filling of missing values after padding. padr comes with
three more fill functions: fill_by_value,
fill_by_function, and fill_by_prevalent. They
fill missing values by respectively a single value, a function of the
nonmissing values, and the most prevalent value among the nonmissing
values.
counts <- data.frame(x = as.Date(c('2016-11-21', '2016-11-23', '2016-11-24')),
y = c(2, 4, 4)) %>% pad## pad applied on the interval: day## x y
## 1 2016-11-21 2
## 2 2016-11-22 0
## 3 2016-11-23 4
## 4 2016-11-24 4## x y
## 1 2016-11-21 2
## 2 2016-11-22 42
## 3 2016-11-23 4
## 4 2016-11-24 4## x y
## 1 2016-11-21 2.000000
## 2 2016-11-22 3.333333
## 3 2016-11-23 4.000000
## 4 2016-11-24 4.000000## x y
## 1 2016-11-21 2
## 2 2016-11-22 4
## 3 2016-11-23 4
## 4 2016-11-24 4Note that in the first fill_by_value the columns to fill
are not specified. In this case the filling is applied on all the
columns. The other two functions also have this default behavior.
After aggregating the data to a higher interval, all the observations
in an interval are represented by a single point in time. This is either
the first (rounding down) or the last (rounding up) datetime point of
the interval. Two functions are offered to reformat the datetime
variable, so the data might be better represented a table or a graph.
First of all, center_interval will move the time point to
the center of the interval. This would give a better representation in
point, line and bar graphs.
emergency %>%
thicken("hour", "h") %>%
count(h) %>%
slice(1:24) %>%
mutate(h_center = center_interval(h)) %>%
ggplot(aes(h_center, n)) + geom_bar(stat = "identity")
The bars are now between the hours, rather than on the hours. More true to the nature of the interval.
Next, there is format_interval. This creates a
categorical variable that describes the start and end of the interval.
This works great with asymmetric data, as shown in the dedicated
vignette, but can also be informative with regular intervals. You can
specify the way you want the start and the end to be formatted just like
you would in strftime.
emergency %>%
filter(title == "EMS: HEAD INJURY") %>%
thicken("6 hour", "hour6") %>%
count(hour6) %>%
pad() %>%
fill_by_value() %>%
mutate(hour6_fmt =
format_interval(hour6, start_format = "%Hh", sep = "-")) %>%
ggplot(aes(hour6_fmt, n)) +
geom_boxplot()
There are two more vignettes. In padr_implementation you
can find more information about how padr handles daylight
savings time, what it does with different time zones and how
thicken exactly is implemented. padr_custom
shows you how you can thicken and pad with asymmetric intervals.
Found a bug? Ideas for improving or expanding padr. Your
input is much appreciated. The code is maintained at https://github.com/EdwinTh/padr and you are most welcome
to file an issue or do a pull request.
Many users who work with date and time variables will be
using the lubridate package. The definition of an interval
in lubridate is different from the definition in
padr. In lubridate an interval is a period
between two time points and has nothing to do with recurrence. Please
keep this in mind.↩︎
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.