indexing: Fast Indexed Time Series and Panels
In collapse: Advanced and Fast Data Transformation
indexing R Documentation
Fast Indexed Time Series and Panels
Description
A fast and flexible indexed time series and panel data class that inherits from plm's 'pseries' and 'pdata.frame', but is more rigorous, natively handles irregularity, can be superimposed on any data.frame/list, matrix or vector, and supports ad-hoc computations inside data masking functions and model formulas.
Usage
## Create an 'indexed_frame' containing 'indexed_series'
findex_by(.X, ..., single = "auto", interact.ids = TRUE)
iby(.X, ..., single = "auto", interact.ids = TRUE) # Shorthand
## Retrieve the index ('index_df') from an 'indexed_frame' or 'indexed_series'
findex(x)
ix(x) # Shorthand
## Remove index from 'indexed_frame' or 'indexed_series' (i.e. get .X back)
unindex(x)
## Reindex 'indexed_frame' or 'indexed_series' (or index vectors / matrices)
reindex(x, index = findex(x), single = "auto")
## Check if 'indexed_frame', 'indexed_series', index or time vector is irregular
is_irregular(x, any_id = TRUE)
## Convert 'indexed_frame'/'indexed_series' to normal 'pdata.frame'/'pseries'
to_plm(x, row.names = FALSE)
# Subsetting & replacement methods: [(<-) methods call NextMethod().
# Also methods for fsubset, funique and roworder(v), na_omit (internal).
## S3 method for class 'indexed_series'
x[i, ..., drop.index.levels = "id"]
## S3 method for class 'indexed_frame'
x[i, ..., drop.index.levels = "id"]
## S3 replacement method for class 'indexed_frame'
x[i, j] <- value
## S3 method for class 'indexed_frame'
x$name
## S3 replacement method for class 'indexed_frame'
x$name <- value
## S3 method for class 'indexed_frame'
x[[i, ...]]
## S3 replacement method for class 'indexed_frame'
x[[i]] <- value
# Index subsetting and printing: optimized using ss()
## S3 method for class 'index_df'
x[i, j, drop = FALSE, drop.index.levels = "id"]
## S3 method for class 'index_df'
print(x, topn = 5, ...)
Arguments
.X
a data frame or list-like object of equal-length columns.
x
an 'indexed_frame' or 'indexed_series'. findex also works with 'pseries' and 'pdata.frame's created with plm. For is_irregular x can also be an index (inherits 'pindex') or a vector representing time.
...
for findex_by: variables identifying the individual (id) and/or time dimensions of the data. Passed either as unquoted comma-separated column names or (tagged) expressions involving columns, or as a vector of column names, indices, a logical vector or a selector function. The time variable must enter last. See Examples. Otherwise: further arguments passed to NextMethod().
single
character. If only one indexing variable is supplied, this can be declared as "id" or "time" variable. "auto" chooses "id" if the variable has anyDuplicated values.
interact.ids
logical. If n > 2 indexing variables are passed, TRUE calls finteraction on the first n-1 of them (n'th variable must be time). FALSE keeps all variables in the index. The latter slows down computations of lags / differences etc. because ad-hoc interactions need to be computed, but gives more flexibility for scaling / centering / summarising over different data dimensions.
index
and index (inherits 'pindex'), or an atomic vector or list of factors matching the data dimensions. Atomic vectors or lists with 1 factor will must be declared, see single. Atomic vectors will additionally be grouped / turned into time-factors. See Details.
drop.index.levels
character. Subset methods also subset the index (= a data.frame of factors), and this argument regulates which factor levels should be dropped: either "all", "id", "time" or "none". The default "id" only drops levels from id's. "all" or "time" should be used with caution because time-factors may contain levels for missing time periods (gaps in irregular sequences, or periods within a sequence removed through subsetting), and dropping those levels would create a variable that is ordinal but no longer represents time. The benefit of dropping levels is that it can speed-up subsequent computations by reducing the size of intermediate vectors created in C++.
any_id
logical. For panel series: FALSE returns the irregularity check performed for each id, TRUE calls any on those checks.
row.names
logical. TRUE creates descriptive row-names (or names for pseries) as in plm. This can be expensive and is usually not required for plm models to work.
topn
integer. The number of first and last rows to print.
i, j, name, drop, value
Arguments passed to NextMethod, or as in the data.frame methods. Note that for index subsetting to work, i needs to be integer or logical (or an expression evaluation to integer or logical if x is a data.table).
Details
The first thing to note about these new 'indexed_frame', 'indexed_series' and 'index_df' classes is that they inherit plm's 'pdata.frame', 'pseries' and 'pindex' classes, respectively. They add, improve, and, in some cases, remove functionality offered by plm, with the aim of striking an optimal balance of flexibility and performance. The inheritance means that all 'pseries' and 'pdata.frame' methods in collapse, and also some methods in plm, apply to them. Where compatibility or performance considerations allow for it, collapse will continue to create methods for plm's classes instead of the new classes.
The use of these classes does not require much knowledge of plm, but as a basic background: A 'pdata.frame' is a data.frame with an index attribute: a data.frame of 2 factors identifying the individual and time-dimension of the data. When pulling a variable out of the pdata.frame using a method like $.pdata.frame or [[.pdata.frame (defined in plm), a 'pseries' is created by transferring the index attribute to the vector. Methods defined for functions like lag / flag etc. use the index for correct computations on this panel data, also inside plm's estimation commands.
Main Features and Enhancements
The 'indexed_frame' and 'indexed_series' classes extend and enhance 'pdata.frame' and 'pseries' in a number of critical dimensions. Most notably they:
Support both time series and panel data, by allowing indexation of data with one, two or more variables.
Are class-agnostic: any data.frame/list (such as data.table, tibble, tsibble, sf etc.) can become an 'indexed_frame' and continue to function as usual for most use cases. Similarly, any vector or matrix (such as ts, mts, xts) can become an 'indexed_series'. This also allows for transient workflows e.g. some_df |> findex_by(...) |> 'do something using collapse functions' |> unindex() |> 'continue working with some_df'.
Have a comprehensive and efficient set of methods for subsetting and manipulation, including methods for fsubset, funique, roworder(v) (internal) and na_omit (internal, na.omit also works but is slower). It is also possible to group indexed data with fgroup_by for transformations e.g. using fmutate, but aggregation requires unindex()ing.
-
Natively handle irregularity: time objects (such as 'Date', 'POSIXct' etc.) are passed to timeid, which efficiently determines the temporal structure by finding the greatest common divisor (GCD), and creates a time-factor with levels corresponding to a complete time-sequence. The latter is also done with plain numeric vectors, which are assumed to represent unit time steps (GDC = 1) and coerced to integer (but can also be passed through timeid if non-unitary). Character time variables are converted to factor, which might also capture irregular gaps in panel series. Using this time-factor in the index, collapse's functions efficiently perform correct computations on irregular sequences and panels without the need to 'expand' the data / fill gaps. is_irregular can be used to check for irregularity in the entire sequence / panel or separately for each individual in panel data.
Support computations inside data-masking functions and formulas, by virtue of "deep indexation": Each variable inside an 'indexed_frame' is an 'indexed_series' which contains in its 'index_df' attribute an external pointer to the 'index_df' attribute of the frame. Functions operating on 'indexed_series' stored inside the frame (such as with(data, flag(column))) can fetch the index from this pointer. This allows worry-free application inside arbitrary data masking environments (with, %$%, attach, etc..) and estimation commands (glm, feols, lmrob etc..) without duplication of the index in memory. A limitation is that external pointers are only valid during the present R session, thus when saving an 'indexed_frame' and loading it again, you need to call data = reindex(data) before computing on it.
Indexed series also have simple Math and Ops methods, which apply the operation to the unindexed series and shallow copy the attributes of the original object to the result, unless the result it is a logical vector (from operations like !, == etc.). For Ops methods, if the LHS object is an 'indexed_series' its attributes are taken, otherwise the attributes of the RHS object are taken.
Limits to plm Compatibility
In contrast to 'pseries' and 'pdata.frame's, 'indexed_series' and 'indexed_frames' do not have descriptive "names" or "row.names" attributes attached to them, mainly for efficiency reasons.
Furthermore, the index is stored in an attribute named 'index_df' (same as the class name), not 'index' as in plm, mainly to make these classes work with data.table, tsibble and xts, which also utilize 'index' attributes. This for the most part poses no problem to plm compatibility because plm source code fetches the index using attr(x, "index"), and attr by default performs partial matching.
A much greater obstacle in working with plm is that some internal plm code is hinged on there being no [.pseries method, and the existence of [.indexed_series limits the use of these classes in most plm estimation commands. Therefore the to_plm function is provided to efficiently coerce the classes to ordinary plm objects before estimation. See Examples.
Overall these classes don't really benefit plm, especially given that collapse's plm methods also support native plm objects. However, they work very well inside other models and software, including stats models, fixest / lfe, and a whole bunch of time series and ML models. See Examples.
Performance Considerations
When indexing long time-series or panels with a single variable, setting single = "id" or "time" avoids a potentially expensive call to anyDuplicated. Note also that when panel-data are regular and sorted, omitting the time variable in the index can bring >= 2x performance improvements in operations like lagging and differencing (alternatively use shift = "row" argument to flag, fdiff etc.) .
When dealing with long Date or POSIXct time sequences, it may also be that the internal processing by timeid is slow simply because calling strftime on these sequences to create factor levels is slow. In this case you may choose to generate an index factor with integer levels by passing timeid(t) to findex_by or reindex (which by default generates a 'qG' object which is internally converted to factor using as_factor_qG. The lazy evaluation of expressions like as.character(seq_len(nlev)) in modern R makes this extremely efficient).
With multiple id variables e.g. findex_by(data, id1, id2, id3, time), the default call to finteraction() can be expensive because of pasting the levels together. In this case, users may gain performance by manually invoking finteraction() (or its shorthand itn()) with argument factor = FALSE e.g. findex_by(data, ids = itn(id1, id2, id3, factor = FALSE), time). This will generate a factor with integer levels instead.
Print Method
The print methods for 'indexed_frame' and 'indexed_series' first call print(unindex(x), ...), followed by the index variables with the number of categories (index factor levels) in square brackets. If the time factor contains unused levels (= irregularity in the sequence), the square brackets indicate the number of used levels (periods), followed by the total number of levels (periods in the sequence) in parentheses.
See Also
timeid,
Time Series and Panel Series, Collapse Overview
Examples
oldopts <- options(max.print = 70)
# Indexing panel data ----------------------------------------------------------
wldi <- findex_by(wlddev, iso3c, year)
wldi
wldi[1:100,1] # Works like a data frame
POP <- wldi$POP # indexed_series
qsu(POP) # Summary statistics
G(POP) # Population growth
STD(G(POP, c(1, 10))) # Within-standardized 1 and 10-year growth rates
psmat(POP) # Panel-Series Matrix
plot(psmat(log10(POP)))
POP[30:5000] # Subsetting indexed_series
Dlog(POP[30:5000]) # Log-difference of subset
psacf(identity(POP[30:5000])) # ACF of subset
L(Dlog(POP[30:5000], c(1, 10)), -1:1) # Multiple computations on subset
# Fast Statistical Functions don't have dedicated methods
# Thus for aggregation we need to unindex beforehand ...
fmean(unindex(POP))
wldi |> unindex() |>
fgroup_by(iso3c) |> num_vars() |> fmean()
library(magrittr)
# ... or unindex after taking group identifiers from the index
fmean(unindex(fgrowth(POP)), ix(POP)$iso3c)
wldi |> num_vars() %>%
fgroup_by(iso3c = ix(.)$iso3c) |>
unindex() |> fmean()
# With matrix methods it is easier as most attributes are dropped upon aggregation.
G(POP, c(1, 10)) %>% fmean(ix(.)$iso3c)
# Example of index with multiple ids
GGDC10S |> findex_by(Variable, Country, Year) |> head() # default is interact.ids = TRUE
GGDCi <- GGDC10S |> findex_by(Variable, Country, Year, interact.ids = FALSE)
head(GGDCi)
findex(GGDCi)
# The benefit is increased flexibility for summary statistics and data transformation
qsu(GGDCi, effect = "Country")
STD(GGDCi$SUM, effect = "Variable") # Standardizing by variable
STD(GGDCi$SUM, effect = c("Variable", "Year")) # ... by variable and year
# But time-based operations are a bit more expensive because of the necessary interactions
D(GGDCi$SUM)
# Panel-Data modelling ---------------------------------------------------------
# Linear model of 5-year annualized growth rates of GDP on Life Expactancy + 5y lag
lm(G(PCGDP, 5, p = 1/5) ~ L(G(LIFEEX, 5, p = 1/5), c(0, 5)), wldi) # p abbreviates "power"
# Same, adding time fixed effects via plm package: need to utilize to_plm function
plm::plm(G(PCGDP, 5, p = 1/5) ~ L(G(LIFEEX, 5, p = 1/5), c(0, 5)), to_plm(wldi), effect = "time")
# With country and time fixed effects via fixest
fixest::feols(G(PCGDP, 5, p=1/5) ~ L(G(LIFEEX, 5, p=1/5), c(0, 5)), wldi, fixef = .c(iso3c, year))
## Not run:
# Running a robust MM regression without fixed effects
robustbase::lmrob(G(PCGDP, 5, p = 1/5) ~ L(G(LIFEEX, 5, p = 1/5), c(0, 5)), wldi)
# Running a robust MM regression with country and time fixed effects
wldi |> fselect(PCGDP, LIFEEX) |>
fgrowth(5, power = 1/5) |> ftransform(LIFEEX_L5 = L(LIFEEX, 5)) |>
# drop abbreviates drop.index.levels (not strictly needed here but more consistent)
na_omit(drop = "all") |> fhdwithin(na.rm = FALSE) |> # For TFE use fwithin(effect = "year")
unindex() |> robustbase::lmrob(formula = PCGDP ~.) # using lm() gives same result as fixest
# Using a random forest model without fixed effects
# ranger does not support these kinds of formulas, thus we need some preprocessing...
wldi |> fselect(PCGDP, LIFEEX) |>
fgrowth(5, power = 1/5) |> ftransform(LIFEEX_L5 = L(LIFEEX, 5)) |>
unindex() |> na_omit() |> ranger::ranger(formula = PCGDP ~.)
## End(Not run)
# Indexing other data frame based classes --------------------------------------
library(tibble)
wlditbl <- qTBL(wlddev) |> findex_by(iso3c, year)
wlditbl[,2] # Works like a tibble...
wlditbl[[2]]
wlditbl[1:1000, 10]
head(wlditbl)
library(data.table)
wldidt <- qDT(wlddev) |> findex_by(iso3c, year)
wldidt[1:1000] # Works like a data.table...
wldidt[year > 2000]
wldidt[, .(sum_PCGDP = sum(PCGDP, na.rm = TRUE)), by = country] # Aggregation unindexes the result
wldidt[, lapply(.SD, sum, na.rm = TRUE), by = country, .SDcols = .c(PCGDP, LIFEEX)]
# This also works but is a bit inefficient since the index is subset and then dropped
# -> better unindex beforehand
wldidt[year > 2000, .(sum_PCGDP = sum(PCGDP, na.rm = TRUE)), by = country]
wldidt[, PCGDP_gr_5Y := G(PCGDP, 5, power = 1/5)] # Can add Variables by reference
# Note that .SD is a data.table of indexed_series, not an indexed_frame, so this is WRONG!
wldidt[, .c(PCGDP_gr_5Y, LIFEEX_gr_5Y) := G(slt(.SD, PCGDP, LIFEEX), 5, power = 1/5)]
# This gives the correct outcome
wldidt[, .c(PCGDP_gr_5Y, LIFEEX_gr_5Y) := lapply(slt(.SD, PCGDP, LIFEEX), G, 5, power = 1/5)]
## Not run:
library(sf)
nc <- st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
nci <- findex_by(nc, SID74)
nci[1:10, "AREA"]
st_centroid(nci) # The geometry column is never indexed, thus sf computations work normally
st_coordinates(nci)
fmean(st_area(nci))
library(tsibble)
pedi <- findex_by(pedestrian, Sensor, Date_Time)
pedi[1:5, ]
findex(pedi) # Time factor with 17k levels from POSIXct
# Now here is a case where integer levels in the index can really speed things up
ix(iby(pedestrian, Sensor, timeid(Date_Time)))
library(microbenchmark)
microbenchmark(descriptive_levels = findex_by(pedestrian, Sensor, Date_Time),
integer_levels = findex_by(pedestrian, Sensor, timeid(Date_Time)))
# Data has irregularity
is_irregular(pedi)
is_irregular(pedi, any_id = FALSE) # irregularity in all sequences
# Manipulation such as lagging with tsibble/dplyr requires expanding rows and grouping
# Collapse can just compute correct lag on indexed series or frames
library(dplyr)
microbenchmark(
dplyr = fill_gaps(pedestrian) |> group_by_key() |> mutate(Lag_Count = lag(Count)),
collapse = fmutate(pedi, Lag_Count = flag(Count)), times = 10)
## End(Not run)
# Indexing Atomic objects ---------------------------------------------------------
## ts
print(AirPassengers)
AirPassengers[-(20:30)] # Ts class does not support irregularity, subsetting drops class
G(AirPassengers[-(20:30)], 12) # Annual Growth Rate: Wrong!
# Now indexing AirPassengers (identity() is a trick so that the index is named time(AirPassengers))
iAP <- reindex(AirPassengers, identity(time(AirPassengers)))
iAP
findex(iAP) # See the index
iAP[-(20:30)] # Subsetting
G(iAP[-(20:30)], 12) # Annual Growth Rate: Correct!
L(G(iAP[-(20:30)], c(0,1,12)), 0:1) # Lagged level, period and annual growth rates...
## xts
library(xts)
library(zoo) # Needed for as.yearmon() and index() functions
X <- wlddev |> fsubset(iso3c == "DEU", date, PCGDP:POP) %>% {
xts(num_vars(.), order.by = as.yearmon(.$date))
} |> ss(-(30:40)) %>% reindex(identity(index(.))) # Introducing a gap
# plot(G(unindex(X)))
diff(unindex(X)) # diff.xts gixes wrong result
fdiff(X) # fdiff gives right result
# But xts range-based subsets do not work...
## Not run:
X["1980/"]
## End(Not run)
# Thus a better way is not to index and perform ad-hoc omputations on the xts index
X <- unindex(X)
X["1980/"] %>% fdiff(t = index(.)) # xts index is internally processed by timeid()
## Of course you can also index plain vectors / matrices...
options(oldopts)
collapse documentation built on Nov. 3, 2024, 9:08 a.m.
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| indexing | R Documentation |
Fast Indexed Time Series and Panels
Description
A fast and flexible indexed time series and panel data class that inherits from plm's 'pseries' and 'pdata.frame', but is more rigorous, natively handles irregularity, can be superimposed on any data.frame/list, matrix or vector, and supports ad-hoc computations inside data masking functions and model formulas.
Usage
## Create an 'indexed_frame' containing 'indexed_series'
findex_by(.X, ..., single = "auto", interact.ids = TRUE)
iby(.X, ..., single = "auto", interact.ids = TRUE) # Shorthand
## Retrieve the index ('index_df') from an 'indexed_frame' or 'indexed_series'
findex(x)
ix(x) # Shorthand
## Remove index from 'indexed_frame' or 'indexed_series' (i.e. get .X back)
unindex(x)
## Reindex 'indexed_frame' or 'indexed_series' (or index vectors / matrices)
reindex(x, index = findex(x), single = "auto")
## Check if 'indexed_frame', 'indexed_series', index or time vector is irregular
is_irregular(x, any_id = TRUE)
## Convert 'indexed_frame'/'indexed_series' to normal 'pdata.frame'/'pseries'
to_plm(x, row.names = FALSE)
# Subsetting & replacement methods: [(<-) methods call NextMethod().
# Also methods for fsubset, funique and roworder(v), na_omit (internal).
## S3 method for class 'indexed_series'
x[i, ..., drop.index.levels = "id"]
## S3 method for class 'indexed_frame'
x[i, ..., drop.index.levels = "id"]
## S3 replacement method for class 'indexed_frame'
x[i, j] <- value
## S3 method for class 'indexed_frame'
x$name
## S3 replacement method for class 'indexed_frame'
x$name <- value
## S3 method for class 'indexed_frame'
x[[i, ...]]
## S3 replacement method for class 'indexed_frame'
x[[i]] <- value
# Index subsetting and printing: optimized using ss()
## S3 method for class 'index_df'
x[i, j, drop = FALSE, drop.index.levels = "id"]
## S3 method for class 'index_df'
print(x, topn = 5, ...)
Arguments
.X |
a data frame or list-like object of equal-length columns. |
x |
an 'indexed_frame' or 'indexed_series'. |
... |
for |
single |
character. If only one indexing variable is supplied, this can be declared as |
interact.ids |
logical. If |
index |
and index (inherits 'pindex'), or an atomic vector or list of factors matching the data dimensions. Atomic vectors or lists with 1 factor will must be declared, see |
drop.index.levels |
character. Subset methods also subset the index (= a data.frame of factors), and this argument regulates which factor levels should be dropped: either |
any_id |
logical. For panel series: |
row.names |
logical. |
topn |
integer. The number of first and last rows to print. |
i, j, name, drop, value |
Arguments passed to |
Details
The first thing to note about these new 'indexed_frame', 'indexed_series' and 'index_df' classes is that they inherit plm's 'pdata.frame', 'pseries' and 'pindex' classes, respectively. They add, improve, and, in some cases, remove functionality offered by plm, with the aim of striking an optimal balance of flexibility and performance. The inheritance means that all 'pseries' and 'pdata.frame' methods in collapse, and also some methods in plm, apply to them. Where compatibility or performance considerations allow for it, collapse will continue to create methods for plm's classes instead of the new classes.
The use of these classes does not require much knowledge of plm, but as a basic background: A 'pdata.frame' is a data.frame with an index attribute: a data.frame of 2 factors identifying the individual and time-dimension of the data. When pulling a variable out of the pdata.frame using a method like $.pdata.frame or [[.pdata.frame (defined in plm), a 'pseries' is created by transferring the index attribute to the vector. Methods defined for functions like lag / flag etc. use the index for correct computations on this panel data, also inside plm's estimation commands.
Main Features and Enhancements
The 'indexed_frame' and 'indexed_series' classes extend and enhance 'pdata.frame' and 'pseries' in a number of critical dimensions. Most notably they:
Support both time series and panel data, by allowing indexation of data with one, two or more variables.
Are class-agnostic: any data.frame/list (such as data.table, tibble, tsibble, sf etc.) can become an 'indexed_frame' and continue to function as usual for most use cases. Similarly, any vector or matrix (such as ts, mts, xts) can become an 'indexed_series'. This also allows for transient workflows e.g.
some_df |> findex_by(...) |> 'do something using collapse functions' |> unindex() |> 'continue working with some_df'.Have a comprehensive and efficient set of methods for subsetting and manipulation, including methods for
fsubset,funique,roworder(v)(internal) andna_omit(internal,na.omitalso works but is slower). It is also possible to group indexed data withfgroup_byfor transformations e.g. usingfmutate, but aggregation requiresunindex()ing.-
Natively handle irregularity: time objects (such as 'Date', 'POSIXct' etc.) are passed to
timeid, which efficiently determines the temporal structure by finding the greatest common divisor (GCD), and creates a time-factor with levels corresponding to a complete time-sequence. The latter is also done with plain numeric vectors, which are assumed to represent unit time steps (GDC = 1) and coerced to integer (but can also be passed throughtimeidif non-unitary). Character time variables are converted to factor, which might also capture irregular gaps in panel series. Using this time-factor in the index, collapse's functions efficiently perform correct computations on irregular sequences and panels without the need to 'expand' the data / fill gaps.is_irregularcan be used to check for irregularity in the entire sequence / panel or separately for each individual in panel data. Support computations inside data-masking functions and formulas, by virtue of "deep indexation": Each variable inside an 'indexed_frame' is an 'indexed_series' which contains in its 'index_df' attribute an external pointer to the 'index_df' attribute of the frame. Functions operating on 'indexed_series' stored inside the frame (such as
with(data, flag(column))) can fetch the index from this pointer. This allows worry-free application inside arbitrary data masking environments (with,%$%,attach, etc..) and estimation commands (glm,feols,lmrobetc..) without duplication of the index in memory. A limitation is that external pointers are only valid during the present R session, thus when saving an 'indexed_frame' and loading it again, you need to calldata = reindex(data)before computing on it.
Indexed series also have simple Math and Ops methods, which apply the operation to the unindexed series and shallow copy the attributes of the original object to the result, unless the result it is a logical vector (from operations like !, == etc.). For Ops methods, if the LHS object is an 'indexed_series' its attributes are taken, otherwise the attributes of the RHS object are taken.
Limits to plm Compatibility
In contrast to 'pseries' and 'pdata.frame's, 'indexed_series' and 'indexed_frames' do not have descriptive "names" or "row.names" attributes attached to them, mainly for efficiency reasons.
Furthermore, the index is stored in an attribute named 'index_df' (same as the class name), not 'index' as in plm, mainly to make these classes work with data.table, tsibble and xts, which also utilize 'index' attributes. This for the most part poses no problem to plm compatibility because plm source code fetches the index using attr(x, "index"), and attr by default performs partial matching.
A much greater obstacle in working with plm is that some internal plm code is hinged on there being no [.pseries method, and the existence of [.indexed_series limits the use of these classes in most plm estimation commands. Therefore the to_plm function is provided to efficiently coerce the classes to ordinary plm objects before estimation. See Examples.
Overall these classes don't really benefit plm, especially given that collapse's plm methods also support native plm objects. However, they work very well inside other models and software, including stats models, fixest / lfe, and a whole bunch of time series and ML models. See Examples.
Performance Considerations
When indexing long time-series or panels with a single variable, setting single = "id" or "time" avoids a potentially expensive call to anyDuplicated. Note also that when panel-data are regular and sorted, omitting the time variable in the index can bring >= 2x performance improvements in operations like lagging and differencing (alternatively use shift = "row" argument to flag, fdiff etc.) .
When dealing with long Date or POSIXct time sequences, it may also be that the internal processing by timeid is slow simply because calling strftime on these sequences to create factor levels is slow. In this case you may choose to generate an index factor with integer levels by passing timeid(t) to findex_by or reindex (which by default generates a 'qG' object which is internally converted to factor using as_factor_qG. The lazy evaluation of expressions like as.character(seq_len(nlev)) in modern R makes this extremely efficient).
With multiple id variables e.g. findex_by(data, id1, id2, id3, time), the default call to finteraction() can be expensive because of pasting the levels together. In this case, users may gain performance by manually invoking finteraction() (or its shorthand itn()) with argument factor = FALSE e.g. findex_by(data, ids = itn(id1, id2, id3, factor = FALSE), time). This will generate a factor with integer levels instead.
Print Method
The print methods for 'indexed_frame' and 'indexed_series' first call print(unindex(x), ...), followed by the index variables with the number of categories (index factor levels) in square brackets. If the time factor contains unused levels (= irregularity in the sequence), the square brackets indicate the number of used levels (periods), followed by the total number of levels (periods in the sequence) in parentheses.
See Also
timeid,
Time Series and Panel Series, Collapse Overview
Examples
oldopts <- options(max.print = 70)
# Indexing panel data ----------------------------------------------------------
wldi <- findex_by(wlddev, iso3c, year)
wldi
wldi[1:100,1] # Works like a data frame
POP <- wldi$POP # indexed_series
qsu(POP) # Summary statistics
G(POP) # Population growth
STD(G(POP, c(1, 10))) # Within-standardized 1 and 10-year growth rates
psmat(POP) # Panel-Series Matrix
plot(psmat(log10(POP)))
POP[30:5000] # Subsetting indexed_series
Dlog(POP[30:5000]) # Log-difference of subset
psacf(identity(POP[30:5000])) # ACF of subset
L(Dlog(POP[30:5000], c(1, 10)), -1:1) # Multiple computations on subset
# Fast Statistical Functions don't have dedicated methods
# Thus for aggregation we need to unindex beforehand ...
fmean(unindex(POP))
wldi |> unindex() |>
fgroup_by(iso3c) |> num_vars() |> fmean()
library(magrittr)
# ... or unindex after taking group identifiers from the index
fmean(unindex(fgrowth(POP)), ix(POP)$iso3c)
wldi |> num_vars() %>%
fgroup_by(iso3c = ix(.)$iso3c) |>
unindex() |> fmean()
# With matrix methods it is easier as most attributes are dropped upon aggregation.
G(POP, c(1, 10)) %>% fmean(ix(.)$iso3c)
# Example of index with multiple ids
GGDC10S |> findex_by(Variable, Country, Year) |> head() # default is interact.ids = TRUE
GGDCi <- GGDC10S |> findex_by(Variable, Country, Year, interact.ids = FALSE)
head(GGDCi)
findex(GGDCi)
# The benefit is increased flexibility for summary statistics and data transformation
qsu(GGDCi, effect = "Country")
STD(GGDCi$SUM, effect = "Variable") # Standardizing by variable
STD(GGDCi$SUM, effect = c("Variable", "Year")) # ... by variable and year
# But time-based operations are a bit more expensive because of the necessary interactions
D(GGDCi$SUM)
# Panel-Data modelling ---------------------------------------------------------
# Linear model of 5-year annualized growth rates of GDP on Life Expactancy + 5y lag
lm(G(PCGDP, 5, p = 1/5) ~ L(G(LIFEEX, 5, p = 1/5), c(0, 5)), wldi) # p abbreviates "power"
# Same, adding time fixed effects via plm package: need to utilize to_plm function
plm::plm(G(PCGDP, 5, p = 1/5) ~ L(G(LIFEEX, 5, p = 1/5), c(0, 5)), to_plm(wldi), effect = "time")
# With country and time fixed effects via fixest
fixest::feols(G(PCGDP, 5, p=1/5) ~ L(G(LIFEEX, 5, p=1/5), c(0, 5)), wldi, fixef = .c(iso3c, year))
## Not run:
# Running a robust MM regression without fixed effects
robustbase::lmrob(G(PCGDP, 5, p = 1/5) ~ L(G(LIFEEX, 5, p = 1/5), c(0, 5)), wldi)
# Running a robust MM regression with country and time fixed effects
wldi |> fselect(PCGDP, LIFEEX) |>
fgrowth(5, power = 1/5) |> ftransform(LIFEEX_L5 = L(LIFEEX, 5)) |>
# drop abbreviates drop.index.levels (not strictly needed here but more consistent)
na_omit(drop = "all") |> fhdwithin(na.rm = FALSE) |> # For TFE use fwithin(effect = "year")
unindex() |> robustbase::lmrob(formula = PCGDP ~.) # using lm() gives same result as fixest
# Using a random forest model without fixed effects
# ranger does not support these kinds of formulas, thus we need some preprocessing...
wldi |> fselect(PCGDP, LIFEEX) |>
fgrowth(5, power = 1/5) |> ftransform(LIFEEX_L5 = L(LIFEEX, 5)) |>
unindex() |> na_omit() |> ranger::ranger(formula = PCGDP ~.)
## End(Not run)
# Indexing other data frame based classes --------------------------------------
library(tibble)
wlditbl <- qTBL(wlddev) |> findex_by(iso3c, year)
wlditbl[,2] # Works like a tibble...
wlditbl[[2]]
wlditbl[1:1000, 10]
head(wlditbl)
library(data.table)
wldidt <- qDT(wlddev) |> findex_by(iso3c, year)
wldidt[1:1000] # Works like a data.table...
wldidt[year > 2000]
wldidt[, .(sum_PCGDP = sum(PCGDP, na.rm = TRUE)), by = country] # Aggregation unindexes the result
wldidt[, lapply(.SD, sum, na.rm = TRUE), by = country, .SDcols = .c(PCGDP, LIFEEX)]
# This also works but is a bit inefficient since the index is subset and then dropped
# -> better unindex beforehand
wldidt[year > 2000, .(sum_PCGDP = sum(PCGDP, na.rm = TRUE)), by = country]
wldidt[, PCGDP_gr_5Y := G(PCGDP, 5, power = 1/5)] # Can add Variables by reference
# Note that .SD is a data.table of indexed_series, not an indexed_frame, so this is WRONG!
wldidt[, .c(PCGDP_gr_5Y, LIFEEX_gr_5Y) := G(slt(.SD, PCGDP, LIFEEX), 5, power = 1/5)]
# This gives the correct outcome
wldidt[, .c(PCGDP_gr_5Y, LIFEEX_gr_5Y) := lapply(slt(.SD, PCGDP, LIFEEX), G, 5, power = 1/5)]
## Not run:
library(sf)
nc <- st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
nci <- findex_by(nc, SID74)
nci[1:10, "AREA"]
st_centroid(nci) # The geometry column is never indexed, thus sf computations work normally
st_coordinates(nci)
fmean(st_area(nci))
library(tsibble)
pedi <- findex_by(pedestrian, Sensor, Date_Time)
pedi[1:5, ]
findex(pedi) # Time factor with 17k levels from POSIXct
# Now here is a case where integer levels in the index can really speed things up
ix(iby(pedestrian, Sensor, timeid(Date_Time)))
library(microbenchmark)
microbenchmark(descriptive_levels = findex_by(pedestrian, Sensor, Date_Time),
integer_levels = findex_by(pedestrian, Sensor, timeid(Date_Time)))
# Data has irregularity
is_irregular(pedi)
is_irregular(pedi, any_id = FALSE) # irregularity in all sequences
# Manipulation such as lagging with tsibble/dplyr requires expanding rows and grouping
# Collapse can just compute correct lag on indexed series or frames
library(dplyr)
microbenchmark(
dplyr = fill_gaps(pedestrian) |> group_by_key() |> mutate(Lag_Count = lag(Count)),
collapse = fmutate(pedi, Lag_Count = flag(Count)), times = 10)
## End(Not run)
# Indexing Atomic objects ---------------------------------------------------------
## ts
print(AirPassengers)
AirPassengers[-(20:30)] # Ts class does not support irregularity, subsetting drops class
G(AirPassengers[-(20:30)], 12) # Annual Growth Rate: Wrong!
# Now indexing AirPassengers (identity() is a trick so that the index is named time(AirPassengers))
iAP <- reindex(AirPassengers, identity(time(AirPassengers)))
iAP
findex(iAP) # See the index
iAP[-(20:30)] # Subsetting
G(iAP[-(20:30)], 12) # Annual Growth Rate: Correct!
L(G(iAP[-(20:30)], c(0,1,12)), 0:1) # Lagged level, period and annual growth rates...
## xts
library(xts)
library(zoo) # Needed for as.yearmon() and index() functions
X <- wlddev |> fsubset(iso3c == "DEU", date, PCGDP:POP) %>% {
xts(num_vars(.), order.by = as.yearmon(.$date))
} |> ss(-(30:40)) %>% reindex(identity(index(.))) # Introducing a gap
# plot(G(unindex(X)))
diff(unindex(X)) # diff.xts gixes wrong result
fdiff(X) # fdiff gives right result
# But xts range-based subsets do not work...
## Not run:
X["1980/"]
## End(Not run)
# Thus a better way is not to index and perform ad-hoc omputations on the xts index
X <- unindex(X)
X["1980/"] %>% fdiff(t = index(.)) # xts index is internally processed by timeid()
## Of course you can also index plain vectors / matrices...
options(oldopts)
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