README.md
In arg0naut91/chronshape: Tidy Up Date/Time Ranges
neatRanges
The aim of neatRanges is to provide tools for working with date &
timestamp ranges, namely:
- Collapsing,
- Partitioning,
- Combining,
- Expanding,
- Filling the gaps between ranges.
It primarily uses data.table in order to speed up the operations. One
of the functions - collapse_ranges - also uses Rcpp, thanks to the
idea from
Patrikios/customerRelationship.
You can install it from CRAN by install.packages('neatRanges').
Below is a quick overview of all functions, followed by a detailed
description of each one of them.
| Function | Description | Supports dates | Supports timestamps |
| ----------------- | ----------------------------------------------------- | ---------------- | ------------------- |
| collapse_ranges | Collapse consecutive ranges | Yes | Yes |
| partition_ranges | Split a range into multiple ranges (by month or year) | Months and years | No |
| expand_* | Expand a range | Yes | Yes |
| combine_ranges | Combine ranges from multiple tables | Yes | Yes |
| fill_ranges | Add missing ranges | Yes | Yes |
collapse_ranges
This function can be useful when we want to collapse several consecutive
date or timestamp ranges.
Let’s say we have a data frame of different financial actors and their
credit ratings for different time ranges.
Now you can see that some of them are actually consecutive. Instead of 6
rows, we’d therefore like to get only 4.
id rating start_date end_date marketCapStart marketCapEnd
1 1111 A+ 2014-01-01 2014-12-31 7.2 7.4
2 1111 AA 2015-01-01 2015-12-31 7.3 7.5
3 1111 AA 2016-01-01 2016-03-01 7.9 8.0
4 2222 B- 2017-01-01 2017-01-31 6.5 6.9
5 2222 B- 2018-01-01 2018-12-31 6.7 6.5
6 2222 B- 2019-01-01 2020-02-01 6.8 7.0
We can do this as below. A bit of explanation of some of the arguments:
-
dimension defines whether we are dealing with dates or timestamps;
defaults to dates;
-
max_gap defines what is considered as consecutive. By default, it
is set to 0, this means no gap whatsoever. If dimension is date,
this means 0 days; in case of timestamp, it refers to 0 seconds. A
gap of 1 day / 1 second would be expressed as max_gap = 1L;
-
fmt defines the format. By default, it uses %Y-%m-%d for dates,
and %Y-%m-%d %H:%M:%OS for timestamps. If your ranges are in
another format, you need to modify that accordingly;
-
groups argument is optional;
-
startAttr and endAttr are attributes of every date/timestamp
record (optional as arguments). Once the ranges are collapsed, each
record will keep the startAttr from the very beginning of the
range while the endAttr will contain the attributes from the very
end of the range. It’s possible to include multiple columns both in
startAttr as well as endAttr. Note that all of them will be -
for safety reasons - converted to character beforehand.
df_collapsed <- collapse_ranges(df,
groups = c("id", "rating"),
start_var = "start_date",
end_var = "end_date",
startAttr = "marketCapStart",
endAttr = "marketCapEnd",
max_gap = 0L,
fmt = "%Y-%m-%d",
dimension = "date"
)
df_collapsed
id rating start_date end_date marketCapStart marketCapEnd
1 1111 A+ 2014-01-01 2014-12-31 7.2 7.4
2 1111 AA 2015-01-01 2016-03-01 7.3 8
3 2222 B- 2017-01-01 2017-01-31 6.5 6.9
4 2222 B- 2018-01-01 2020-02-01 6.7 7
We can address timestamps in a similar way, only now we need to specify
the dimension as timestamp.
Note that here two additional arguments are important:
-
tz defines the time zone - by default, it is set to UTC;
-
origin specifies the origin for indexing/converting the dates; by
default, it is set to 1970-01-01.
Let’s say that now we’re dealing with individuals and the way they spend
their time:
id diary start_time end_time
1 1111 reading 2014-01-01 14:00:00 2014-01-01 14:59:59
2 1111 watching TV 2014-01-01 15:00:00 2014-01-01 16:29:59
3 1111 watching TV 2014-01-01 16:30:00 2014-01-01 19:00:00
4 2222 working 2015-01-01 15:00:00 2015-01-01 15:59:59
5 2222 working 2015-01-01 17:00:00 2015-01-01 18:59:59
6 2222 working 2015-01-01 19:00:00 2015-01-01 21:00:00
If we don’t specify the format, it will throw a warning:
df_collapsed <- collapse_ranges(df,
groups = c("id", "diary"),
start_var = "start_time",
end_var = "end_time",
dimension = "timestamp",
tz = 'UTC',
origin = '1970-01-01'
)
Warning in collapse_ranges(df, groups = c("id", "diary"), start_var =
"start_time", : Dimension 'timestamp' selected but format unchanged. Will try to
convert to '%Y-%m-%d %H:%M:%OS' ..
df_collapsed
id diary start_time end_time
1 1111 reading 2014-01-01 14:00:00 2014-01-01 14:59:59
2 1111 watching TV 2014-01-01 15:00:00 2014-01-01 19:00:00
3 2222 working 2015-01-01 15:00:00 2015-01-01 15:59:59
4 2222 working 2015-01-01 17:00:00 2015-01-01 21:00:00
partition_ranges
This function allows users to further split their ranges.
Currently, this allows only splitting of Date formats (partitioning by
either year or month).
Consider the following data frame:
group start end
1 a 2017-05-01 2018-09-01
2 a 2019-04-03 2020-04-03
3 b 2011-03-03 2012-05-03
4 b 2014-05-07 2016-04-02
5 c 2017-02-01 2017-04-05
Partitioning by year (default mode) would look like:
part_by_year <- partition_ranges(df,
start_var = "start",
end_var = "end",
partition_by = "year",
vars_to_keep = "group"
)
head(part_by_year)
group start end
1 a 2017-05-01 2017-12-31
2 a 2018-01-01 2018-09-01
3 a 2019-04-03 2019-12-31
4 a 2020-01-01 2020-04-03
5 b 2011-03-03 2011-12-31
6 b 2012-01-01 2012-05-03
On the other hand, partitioning by month would take the following
format:
part_by_month <- partition_ranges(df,
start_var = "start",
end_var = "end",
partition_by = "month",
vars_to_keep = "group"
)
head(part_by_month)
group start end
1 a 2017-05-01 2017-05-31
2 a 2017-06-01 2017-06-30
3 a 2017-07-01 2017-07-31
4 a 2017-08-01 2017-08-31
5 a 2017-09-01 2017-09-30
6 a 2017-10-01 2017-10-31
Note that the vars_to_keep argument is optional and basically
specifies which columns you’d like to keep.
There is also fmt argument that specifies the Date format (set to
%Y-%m-%d by default).
expand_* family
These are light-weight functions that allow the user to expand
date/timestamp range into a column of elements of the sequence.
Let’s say we have a data frame with start and end which we would
like to expand:
id gender start end
1 1111 M 2018-01-01 2018-01-05
2 2222 F 2019-01-01 2019-01-07
3 3333 F 2020-01-01 2020-01-08
This can be expanded with calling the expand_dates function with the
following arguments:
-
start_var and end_var as column names of our range;
-
name as the future name of our column. This is optional and
defaults to ‘Expanded’;
-
vars_to_keep as indicators of which columns we’d like to keep when
expanding (optional);
-
unit which defines the unit for expansion - defaults to each day
in the sequence.
Other optional argument is also fmt where you can set the format of
your dates - by default, it is %Y-%m-%d.
df_exp <- expand_dates(df,
start_var = "start",
end_var = "end",
name = "exp_seqs",
vars_to_keep = c("id", "gender"),
unit = "day"
)
head(df_exp)
id gender exp_seqs
1 1111 M 2018-01-01
2 1111 M 2018-01-02
3 1111 M 2018-01-03
4 1111 M 2018-01-04
5 1111 M 2018-01-05
6 2222 F 2019-01-01
You can also tackle timestamp formats in a similar way with
expand_times.
The arguments are pretty much the same, except that unit defaults to
hour, fmt to %Y-%m-%d %H:%M:%OS, and you can set an additional
argument tz (time zone; defaults to UTC).
id gender start end
1 1111 M 2018-01-01 15:00:00 2018-01-01 18:30:00
2 2222 F 2019-01-01 14:00:00 2019-01-01 17:30:00
3 3333 F 2020-01-01 19:00:00 2020-01-02 02:00:00
df_exp <- expand_times(df,
start_var = "start",
end_var = "end",
name = "exp_seqs",
vars_to_keep = c("id", "gender"),
unit = "hour"
)
head(df_exp)
id gender exp_seqs
1 1111 M 2018-01-01 15:00:00
2 1111 M 2018-01-01 16:00:00
3 1111 M 2018-01-01 17:00:00
4 1111 M 2018-01-01 18:00:00
5 2222 F 2019-01-01 14:00:00
6 2222 F 2019-01-01 15:00:00
combine_ranges
This function is essentially a wrapper around collapse_ranges that
allows you to combine ranges scattered around different tables into one
table with non-redundant splits & rows.
Let’s say we have the following data frames df1, df2 and df3:
start end group infoScores
1 2010-01-01 2010-08-05 a 0
2 2012-06-01 2013-03-03 a 3
3 2014-10-15 2015-01-01 b 2
end group start score
1 2012-04-05 b 2009-01-15 8
2 2014-06-09 a 2012-07-08 2
3 2009-02-01 b 2008-01-01 3
end group start scoreInfo
1 2011-04-05 a 2010-02-03 1
2 2014-12-09 b 2014-07-08 2
3 2009-02-01 c 2008-01-01 3
Note the column names: the range names (start and end) as well as
the grouping variables (group) are the same across all data frames.
This is a requirement for the function to run properly. However - as you
can see from the example -, there is no need for them to be in the same
order.
The arguments are almost identical to those of collapse_ranges, except
that at the beginning it is possible to pass as many data frames as you
would like to combine. They need to be combined together in a list.
For instance, we can combine the above data frames as follows (we don’t
need to specify the dimension as it defaults to date):
df <- combine_ranges(dfs = list(df1, df2, df3),
start_var = "start",
end_var = "end",
groups = "group"
)
df
group start end
1 a 2010-01-01 2011-04-05
2 a 2012-06-01 2014-06-09
3 b 2008-01-01 2012-04-05
4 b 2014-07-08 2015-01-01
5 c 2008-01-01 2009-02-01
As you have probably noticed, the output contains only the relevant
columns: range variables & grouping variables. Note that the groups
argument is optional. On the other hand, you can also use the
startAttr and endAttr arguments, the same as in collapse_ranges.
fill_ranges
The function adds missing ranges to a table. It supports both Date and
POSIXct formats; by default, it assumes the range columns are dates.
group start end cost score
1 a 2007-01-01 2008-02-05 143 99
2 a 2010-06-02 2013-04-05 144 33
3 b 2009-04-05 2009-06-03 105 44
4 b 2012-08-01 2013-02-17 153 22
5 b 2019-03-19 2021-04-21 124 33
6 c 2020-01-05 2020-01-09 105 105
7 d 2014-01-01 2014-12-31 153 153
8 d 2015-01-01 2016-12-31 124 124
The arguments are almost identical to those of collapse_ranges.
By dimension argument you indicate whether your data frame contains
date or timestamp values (defaults to dates). groups argument is
optional.
The output based on the above data frame:
df <- fill_ranges(df,
start_var = "start",
end_var = "end",
groups = "group"
)
df
group start end cost score
1 a 2007-01-01 2008-02-05 143 99
2 a 2008-02-06 2010-06-01 NA NA
3 a 2010-06-02 2013-04-05 144 33
4 b 2009-04-05 2009-06-03 105 44
5 b 2009-06-04 2012-07-31 NA NA
6 b 2012-08-01 2013-02-17 153 22
7 b 2013-02-18 2019-03-18 NA NA
8 b 2019-03-19 2021-04-21 124 33
9 c 2020-01-05 2020-01-09 105 105
10 d 2014-01-01 2014-12-31 153 153
11 d 2015-01-01 2016-12-31 124 124
As you can see, all the original variables are returned.
The rows corresponding to added ranges will by default have NA in the
columns that are not ranges or grouping variables (in the above case
cost & score variables).
You can change this behaviour by adjusting the fill parameter, like
below:
df <- fill_ranges(df,
groups = "group",
start_var = "start",
end_var = "end",
fill = "cost = 0, score = Missing"
)
df
group start end cost score
1 a 2007-01-01 2008-02-05 143 99
2 a 2008-02-06 2010-06-01 0 Missing
3 a 2010-06-02 2013-04-05 144 33
4 b 2009-04-05 2009-06-03 105 44
5 b 2009-06-04 2012-07-31 0 Missing
6 b 2012-08-01 2013-02-17 153 22
7 b 2013-02-18 2019-03-18 0 Missing
8 b 2019-03-19 2021-04-21 124 33
9 c 2020-01-05 2020-01-09 105 105
10 d 2014-01-01 2014-12-31 153 153
11 d 2015-01-01 2016-12-31 124 124
Note that this feature is somewhat experimental & currently the columns
to be filled are - for safety reasons - automatically converted to
character. Additional checks & more flexibility will be added in the
future.
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neatRanges
The aim of neatRanges is to provide tools for working with date &
timestamp ranges, namely:
- Collapsing,
- Partitioning,
- Combining,
- Expanding,
- Filling the gaps between ranges.
It primarily uses data.table in order to speed up the operations. One
of the functions - collapse_ranges - also uses Rcpp, thanks to the
idea from
Patrikios/customerRelationship.
You can install it from CRAN by install.packages('neatRanges').
Below is a quick overview of all functions, followed by a detailed description of each one of them.
| Function | Description | Supports dates | Supports timestamps | | ----------------- | ----------------------------------------------------- | ---------------- | ------------------- | | collapse_ranges | Collapse consecutive ranges | Yes | Yes | | partition_ranges | Split a range into multiple ranges (by month or year) | Months and years | No | | expand_* | Expand a range | Yes | Yes | | combine_ranges | Combine ranges from multiple tables | Yes | Yes | | fill_ranges | Add missing ranges | Yes | Yes |
collapse_ranges
This function can be useful when we want to collapse several consecutive date or timestamp ranges.
Let’s say we have a data frame of different financial actors and their credit ratings for different time ranges.
Now you can see that some of them are actually consecutive. Instead of 6 rows, we’d therefore like to get only 4.
id rating start_date end_date marketCapStart marketCapEnd
1 1111 A+ 2014-01-01 2014-12-31 7.2 7.4
2 1111 AA 2015-01-01 2015-12-31 7.3 7.5
3 1111 AA 2016-01-01 2016-03-01 7.9 8.0
4 2222 B- 2017-01-01 2017-01-31 6.5 6.9
5 2222 B- 2018-01-01 2018-12-31 6.7 6.5
6 2222 B- 2019-01-01 2020-02-01 6.8 7.0
We can do this as below. A bit of explanation of some of the arguments:
-
dimensiondefines whether we are dealing with dates or timestamps; defaults to dates; -
max_gapdefines what is considered as consecutive. By default, it is set to 0, this means no gap whatsoever. Ifdimensionisdate, this means 0 days; in case oftimestamp, it refers to 0 seconds. A gap of 1 day / 1 second would be expressed asmax_gap = 1L; -
fmtdefines the format. By default, it uses%Y-%m-%dfor dates, and%Y-%m-%d %H:%M:%OSfor timestamps. If your ranges are in another format, you need to modify that accordingly; -
groupsargument is optional; -
startAttrandendAttrare attributes of every date/timestamp record (optional as arguments). Once the ranges are collapsed, each record will keep thestartAttrfrom the very beginning of the range while theendAttrwill contain the attributes from the very end of the range. It’s possible to include multiple columns both instartAttras well asendAttr. Note that all of them will be - for safety reasons - converted to character beforehand.
df_collapsed <- collapse_ranges(df,
groups = c("id", "rating"),
start_var = "start_date",
end_var = "end_date",
startAttr = "marketCapStart",
endAttr = "marketCapEnd",
max_gap = 0L,
fmt = "%Y-%m-%d",
dimension = "date"
)
df_collapsed
id rating start_date end_date marketCapStart marketCapEnd
1 1111 A+ 2014-01-01 2014-12-31 7.2 7.4
2 1111 AA 2015-01-01 2016-03-01 7.3 8
3 2222 B- 2017-01-01 2017-01-31 6.5 6.9
4 2222 B- 2018-01-01 2020-02-01 6.7 7
We can address timestamps in a similar way, only now we need to specify
the dimension as timestamp.
Note that here two additional arguments are important:
-
tzdefines the time zone - by default, it is set toUTC; -
originspecifies the origin for indexing/converting the dates; by default, it is set to1970-01-01.
Let’s say that now we’re dealing with individuals and the way they spend their time:
id diary start_time end_time
1 1111 reading 2014-01-01 14:00:00 2014-01-01 14:59:59
2 1111 watching TV 2014-01-01 15:00:00 2014-01-01 16:29:59
3 1111 watching TV 2014-01-01 16:30:00 2014-01-01 19:00:00
4 2222 working 2015-01-01 15:00:00 2015-01-01 15:59:59
5 2222 working 2015-01-01 17:00:00 2015-01-01 18:59:59
6 2222 working 2015-01-01 19:00:00 2015-01-01 21:00:00
If we don’t specify the format, it will throw a warning:
df_collapsed <- collapse_ranges(df,
groups = c("id", "diary"),
start_var = "start_time",
end_var = "end_time",
dimension = "timestamp",
tz = 'UTC',
origin = '1970-01-01'
)
Warning in collapse_ranges(df, groups = c("id", "diary"), start_var =
"start_time", : Dimension 'timestamp' selected but format unchanged. Will try to
convert to '%Y-%m-%d %H:%M:%OS' ..
df_collapsed
id diary start_time end_time
1 1111 reading 2014-01-01 14:00:00 2014-01-01 14:59:59
2 1111 watching TV 2014-01-01 15:00:00 2014-01-01 19:00:00
3 2222 working 2015-01-01 15:00:00 2015-01-01 15:59:59
4 2222 working 2015-01-01 17:00:00 2015-01-01 21:00:00
partition_ranges
This function allows users to further split their ranges.
Currently, this allows only splitting of Date formats (partitioning by
either year or month).
Consider the following data frame:
group start end
1 a 2017-05-01 2018-09-01
2 a 2019-04-03 2020-04-03
3 b 2011-03-03 2012-05-03
4 b 2014-05-07 2016-04-02
5 c 2017-02-01 2017-04-05
Partitioning by year (default mode) would look like:
part_by_year <- partition_ranges(df,
start_var = "start",
end_var = "end",
partition_by = "year",
vars_to_keep = "group"
)
head(part_by_year)
group start end
1 a 2017-05-01 2017-12-31
2 a 2018-01-01 2018-09-01
3 a 2019-04-03 2019-12-31
4 a 2020-01-01 2020-04-03
5 b 2011-03-03 2011-12-31
6 b 2012-01-01 2012-05-03
On the other hand, partitioning by month would take the following format:
part_by_month <- partition_ranges(df,
start_var = "start",
end_var = "end",
partition_by = "month",
vars_to_keep = "group"
)
head(part_by_month)
group start end
1 a 2017-05-01 2017-05-31
2 a 2017-06-01 2017-06-30
3 a 2017-07-01 2017-07-31
4 a 2017-08-01 2017-08-31
5 a 2017-09-01 2017-09-30
6 a 2017-10-01 2017-10-31
Note that the vars_to_keep argument is optional and basically
specifies which columns you’d like to keep.
There is also fmt argument that specifies the Date format (set to
%Y-%m-%d by default).
expand_* family
These are light-weight functions that allow the user to expand date/timestamp range into a column of elements of the sequence.
Let’s say we have a data frame with start and end which we would
like to expand:
id gender start end
1 1111 M 2018-01-01 2018-01-05
2 2222 F 2019-01-01 2019-01-07
3 3333 F 2020-01-01 2020-01-08
This can be expanded with calling the expand_dates function with the
following arguments:
-
start_varandend_varas column names of our range; -
nameas the future name of our column. This is optional and defaults to ‘Expanded’; -
vars_to_keepas indicators of which columns we’d like to keep when expanding (optional); -
unitwhich defines the unit for expansion - defaults to each day in the sequence.
Other optional argument is also fmt where you can set the format of
your dates - by default, it is %Y-%m-%d.
df_exp <- expand_dates(df,
start_var = "start",
end_var = "end",
name = "exp_seqs",
vars_to_keep = c("id", "gender"),
unit = "day"
)
head(df_exp)
id gender exp_seqs
1 1111 M 2018-01-01
2 1111 M 2018-01-02
3 1111 M 2018-01-03
4 1111 M 2018-01-04
5 1111 M 2018-01-05
6 2222 F 2019-01-01
You can also tackle timestamp formats in a similar way with
expand_times.
The arguments are pretty much the same, except that unit defaults to
hour, fmt to %Y-%m-%d %H:%M:%OS, and you can set an additional
argument tz (time zone; defaults to UTC).
id gender start end
1 1111 M 2018-01-01 15:00:00 2018-01-01 18:30:00
2 2222 F 2019-01-01 14:00:00 2019-01-01 17:30:00
3 3333 F 2020-01-01 19:00:00 2020-01-02 02:00:00
df_exp <- expand_times(df,
start_var = "start",
end_var = "end",
name = "exp_seqs",
vars_to_keep = c("id", "gender"),
unit = "hour"
)
head(df_exp)
id gender exp_seqs
1 1111 M 2018-01-01 15:00:00
2 1111 M 2018-01-01 16:00:00
3 1111 M 2018-01-01 17:00:00
4 1111 M 2018-01-01 18:00:00
5 2222 F 2019-01-01 14:00:00
6 2222 F 2019-01-01 15:00:00
combine_ranges
This function is essentially a wrapper around collapse_ranges that
allows you to combine ranges scattered around different tables into one
table with non-redundant splits & rows.
Let’s say we have the following data frames df1, df2 and df3:
start end group infoScores
1 2010-01-01 2010-08-05 a 0
2 2012-06-01 2013-03-03 a 3
3 2014-10-15 2015-01-01 b 2
end group start score
1 2012-04-05 b 2009-01-15 8
2 2014-06-09 a 2012-07-08 2
3 2009-02-01 b 2008-01-01 3
end group start scoreInfo
1 2011-04-05 a 2010-02-03 1
2 2014-12-09 b 2014-07-08 2
3 2009-02-01 c 2008-01-01 3
Note the column names: the range names (start and end) as well as
the grouping variables (group) are the same across all data frames.
This is a requirement for the function to run properly. However - as you
can see from the example -, there is no need for them to be in the same
order.
The arguments are almost identical to those of collapse_ranges, except
that at the beginning it is possible to pass as many data frames as you
would like to combine. They need to be combined together in a list.
For instance, we can combine the above data frames as follows (we don’t
need to specify the dimension as it defaults to date):
df <- combine_ranges(dfs = list(df1, df2, df3),
start_var = "start",
end_var = "end",
groups = "group"
)
df
group start end
1 a 2010-01-01 2011-04-05
2 a 2012-06-01 2014-06-09
3 b 2008-01-01 2012-04-05
4 b 2014-07-08 2015-01-01
5 c 2008-01-01 2009-02-01
As you have probably noticed, the output contains only the relevant
columns: range variables & grouping variables. Note that the groups
argument is optional. On the other hand, you can also use the
startAttr and endAttr arguments, the same as in collapse_ranges.
fill_ranges
The function adds missing ranges to a table. It supports both Date and
POSIXct formats; by default, it assumes the range columns are dates.
group start end cost score
1 a 2007-01-01 2008-02-05 143 99
2 a 2010-06-02 2013-04-05 144 33
3 b 2009-04-05 2009-06-03 105 44
4 b 2012-08-01 2013-02-17 153 22
5 b 2019-03-19 2021-04-21 124 33
6 c 2020-01-05 2020-01-09 105 105
7 d 2014-01-01 2014-12-31 153 153
8 d 2015-01-01 2016-12-31 124 124
The arguments are almost identical to those of collapse_ranges.
By dimension argument you indicate whether your data frame contains
date or timestamp values (defaults to dates). groups argument is
optional.
The output based on the above data frame:
df <- fill_ranges(df,
start_var = "start",
end_var = "end",
groups = "group"
)
df
group start end cost score
1 a 2007-01-01 2008-02-05 143 99
2 a 2008-02-06 2010-06-01 NA NA
3 a 2010-06-02 2013-04-05 144 33
4 b 2009-04-05 2009-06-03 105 44
5 b 2009-06-04 2012-07-31 NA NA
6 b 2012-08-01 2013-02-17 153 22
7 b 2013-02-18 2019-03-18 NA NA
8 b 2019-03-19 2021-04-21 124 33
9 c 2020-01-05 2020-01-09 105 105
10 d 2014-01-01 2014-12-31 153 153
11 d 2015-01-01 2016-12-31 124 124
As you can see, all the original variables are returned.
The rows corresponding to added ranges will by default have NA in the
columns that are not ranges or grouping variables (in the above case
cost & score variables).
You can change this behaviour by adjusting the fill parameter, like
below:
df <- fill_ranges(df,
groups = "group",
start_var = "start",
end_var = "end",
fill = "cost = 0, score = Missing"
)
df
group start end cost score
1 a 2007-01-01 2008-02-05 143 99
2 a 2008-02-06 2010-06-01 0 Missing
3 a 2010-06-02 2013-04-05 144 33
4 b 2009-04-05 2009-06-03 105 44
5 b 2009-06-04 2012-07-31 0 Missing
6 b 2012-08-01 2013-02-17 153 22
7 b 2013-02-18 2019-03-18 0 Missing
8 b 2019-03-19 2021-04-21 124 33
9 c 2020-01-05 2020-01-09 105 105
10 d 2014-01-01 2014-12-31 153 153
11 d 2015-01-01 2016-12-31 124 124
Note that this feature is somewhat experimental & currently the columns to be filled are - for safety reasons - automatically converted to character. Additional checks & more flexibility will be added in the future.
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