This vignette focuses on using collapse with the popular data.table package by Matt Dowle and Arun Srinivasan. In contrast to dplyr and plm whose methods (‘grouped_df’, ‘pseries’, ‘pdata.frame’) collapse supports, the integration between collapse and data.table is hidden in the ‘data.frame’ methods and collapse’s C code.

From version 1.6.0 collapse seamlessly handles data.tables, permitting reference operations (set*, :=) on data tables created with collapse (qDT) or returned from collapse’s data manipulation functions (= all functions except print(.FAST_FUN), print(.OPERATOR_FUN), BY and TRA, see the NEWS of collapse 1.6.0 for details on the low-level integration). Apart from data.table reference semantics, both packages work similarly on the C/C++ side of things, and nicely complement each other in functionality.

Overview of Both Packages

Both data.table and collapse are high-performance packages that work well together. For effective co-use it is helpful to understand where each has its strengths, what one can do what the other cannot, and where they overlap. Therefore this small comparison:

  • data.table offers an enhanced data frame based class to contain data (including list columns). For this class it provides a concise data manipulation syntax which also includes fast aggregation / slit-apply-combine computing, (rolling, non-equi) joins, keying, reshaping, some time-series functionality like lagging and rolling statistics, set operations on tables and a number of very useful other functions like the fast csv reader, fast switches, list-transpose etc.. data.table makes data management, and computations on data very easy and salable, supporting huge datasets in a very memory efficient way. The package caters well to the end user by compressing an enormous amount of functionality into two square brackets []. Some of the exported functions are great for programming and also support other classes, but a lot of the functionality and optimization of data.table happens under the hood and can only be accessed through the non-standard evaluation table [i, j, by] syntax. This syntax has a cost of about 1-3 milliseconds for each call. Memory efficiency and thread-parallelization make data.table the star performer on huge data.

  • collapse is class-agnostic in nature, supporting vectors, matrices, data frames and non-destructively handling most R classes and objects. It focuses on advanced statistical computing, proving fast column-wise grouped and weighted statistical functions, fast and complex data aggregation and transformations, linear fitting, time series and panel data computations, advanced summary statistics, and recursive processing of lists of data objects. It also includes powerful functions for data manipulation, grouping / factor generation, recoding, handling outliers and missing values. The package default for missing values is na.rm = TRUE, which is implemented efficiently in C/C++ in all functions. collapse supports both tidyverse (piped) and base R / standard evaluation programming. It makes accessible most of it’s internal C/C++ based functionality (like grouping objects). collapse’s R functions are simple and strongly optimized, i.e. they access the serial C/C++ code quickly, resulting in baseline execution speeds of 10-50 microseconds. All of this makes collapse ideal for advanced statistical computing on matrices and larger datasets, and tasks requiring fast programs with repeated function executions.

Interoperating and some Do’s and Dont’s

Applying collapse functions to a data.table always gives a data.table back e.g. 

library(collapse)
library(magrittr)
library(data.table)

DT <- qDT(wlddev) # collapse::qDT converts objects to data.table using a shallow copy


DT %>% gby(country) %>% gv(9:13) %>% fmean
#                    country      PCGDP   LIFEEX     GINI        ODA         POP
#   1:           Afghanistan   483.8351 49.19717       NA 1487548499 18362258.22
#   2:               Albania  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:               Algeria  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4:        American Samoa 10071.0659       NA       NA         NA    43115.10
#   5:               Andorra 40083.0911       NA       NA         NA    51547.35
#  ---                                                                          
# 212: Virgin Islands (U.S.) 35629.7336 73.71292       NA         NA    92238.53
# 213:    West Bank and Gaza  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:           Yemen, Rep.  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:                Zambia  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:              Zimbabwe  1219.4360 54.53360 45.93333  397104997  9402160.33

# Same thing, but notice that fmean give's NA's for missing countries
DT[, lapply(.SD, mean, na.rm = TRUE), keyby = country, .SDcols = 9:13]
#                    country      PCGDP   LIFEEX     GINI        ODA         POP
#   1:           Afghanistan   483.8351 49.19717      NaN 1487548499 18362258.22
#   2:               Albania  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:               Algeria  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4:        American Samoa 10071.0659      NaN      NaN        NaN    43115.10
#   5:               Andorra 40083.0911      NaN      NaN        NaN    51547.35
#  ---                                                                          
# 212: Virgin Islands (U.S.) 35629.7336 73.71292      NaN        NaN    92238.53
# 213:    West Bank and Gaza  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:           Yemen, Rep.  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:                Zambia  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:              Zimbabwe  1219.4360 54.53360 45.93333  397104997  9402160.33

# This also works without magrittr pipes with the collap() function
collap(DT, ~ country, fmean, cols = 9:13)
#                    country      PCGDP   LIFEEX     GINI        ODA         POP
#   1:           Afghanistan   483.8351 49.19717       NA 1487548499 18362258.22
#   2:               Albania  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:               Algeria  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4:        American Samoa 10071.0659       NA       NA         NA    43115.10
#   5:               Andorra 40083.0911       NA       NA         NA    51547.35
#  ---                                                                          
# 212: Virgin Islands (U.S.) 35629.7336 73.71292       NA         NA    92238.53
# 213:    West Bank and Gaza  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:           Yemen, Rep.  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:                Zambia  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:              Zimbabwe  1219.4360 54.53360 45.93333  397104997  9402160.33

By default, collapse orders groups in aggregations, which is equivalent to using keyby with data.table. gby / fgroup_by has an argument sort = FALSE to yield an unordered grouping equivalent to data.table’s by on character data1.

At this data size collapse outperforms data.table (which might reverse as data size grows, depending in your computer, the number of data.table threads used, and the function in question):

library(microbenchmark)

microbenchmark(collapse = DT %>% gby(country) %>% get_vars(9:13) %>% fmean,
               data.table = DT[, lapply(.SD, mean, na.rm = TRUE), keyby = country, .SDcols = 9:13])
# Unit: microseconds
#        expr      min        lq     mean    median       uq       max neval cld
#    collapse  455.618  576.7745 1086.848  733.4075 1083.488  6707.986   100  a 
#  data.table 1875.577 2191.9655 3627.877 2986.9545 4226.184 12855.933   100   b

It is critical to never do something like this:

DT[, lapply(.SD, fmean), keyby = country, .SDcols = 9:13]
#                    country      PCGDP   LIFEEX     GINI        ODA         POP
#   1:           Afghanistan   483.8351 49.19717       NA 1487548499 18362258.22
#   2:               Albania  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:               Algeria  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4:        American Samoa 10071.0659       NA       NA         NA    43115.10
#   5:               Andorra 40083.0911       NA       NA         NA    51547.35
#  ---                                                                          
# 212: Virgin Islands (U.S.) 35629.7336 73.71292       NA         NA    92238.53
# 213:    West Bank and Gaza  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:           Yemen, Rep.  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:                Zambia  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:              Zimbabwe  1219.4360 54.53360 45.93333  397104997  9402160.33

The reason is that collapse functions are S3 generic with methods for vectors, matrices and data frames among others. So you incur a method-dispatch for every column and every group the function is applied to.

fmean
# function(x, ...) UseMethod("fmean")
# <bytecode: 0x0000000011e16ed0>
# <environment: namespace:collapse>
methods(fmean)
# [1] fmean.data.frame  fmean.default     fmean.grouped_df* fmean.list*       fmean.matrix     
# see '?methods' for accessing help and source code

You may now contend that base::mean is also S3 generic, but in this DT[, lapply(.SD, mean, na.rm = TRUE), by = country, .SDcols = 9:13] code data.table does not use base::mean, but data.table:::gmean, an internal optimized mean function which is efficiently applied over those groups (see ?data.table::GForce). fmean works similar, and includes this functionality explicitly.

args(fmean.data.frame)
# function (x, g = NULL, w = NULL, TRA = NULL, na.rm = TRUE, use.g.names = TRUE, 
#     drop = TRUE, ...) 
# NULL

Here we can see the x argument for the data, the g argument for grouping vectors, a weight vector w, different options TRA to transform the original data using the computed means, and some functionality regarding missing values (default: removed / skipped), group names (which are added as row-names to a data frame, but not to a data.table) etc. So we can also do

fmean(gv(DT, 9:13), DT$country)
#           PCGDP   LIFEEX     GINI        ODA         POP
#   1:   483.8351 49.19717       NA 1487548499 18362258.22
#   2:  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4: 10071.0659       NA       NA         NA    43115.10
#   5: 40083.0911       NA       NA         NA    51547.35
#  ---                                                    
# 212: 35629.7336 73.71292       NA         NA    92238.53
# 213:  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:  1219.4360 54.53360 45.93333  397104997  9402160.33

# Or
g <- GRP(DT, "country")
add_vars(g[["groups"]], fmean(gv(DT, 9:13), g))
#                    country      PCGDP   LIFEEX     GINI        ODA         POP
#   1:           Afghanistan   483.8351 49.19717       NA 1487548499 18362258.22
#   2:               Albania  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:               Algeria  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4:        American Samoa 10071.0659       NA       NA         NA    43115.10
#   5:               Andorra 40083.0911       NA       NA         NA    51547.35
#  ---                                                                          
# 212: Virgin Islands (U.S.) 35629.7336 73.71292       NA         NA    92238.53
# 213:    West Bank and Gaza  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:           Yemen, Rep.  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:                Zambia  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:              Zimbabwe  1219.4360 54.53360 45.93333  397104997  9402160.33

To give us the same result obtained through the high-level functions gby / fgroup_by or collap. This is however not what data.table is doing in DT[, lapply(.SD, fmean), by = country, .SDcols = 9:13]. Since fmean is not a function it recognizes and is able to optimize, it does something like this,

BY(gv(DT, 9:13), g, fmean) # using collapse::BY
#           PCGDP   LIFEEX     GINI        ODA         POP
#   1:   483.8351 49.19717       NA 1487548499 18362258.22
#   2:  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4: 10071.0659       NA       NA         NA    43115.10
#   5: 40083.0911       NA       NA         NA    51547.35
#  ---                                                    
# 212: 35629.7336 73.71292       NA         NA    92238.53
# 213:  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:  1219.4360 54.53360 45.93333  397104997  9402160.33

which applies fmean to every group in every column of the data.

More generally, it is very important to understand that collapse is not based around applying functions to data by groups using some universal mechanism: The dplyr data %>% group_by(...) %>% summarize(...) / mutate(...) and data.table [i, j, by] syntax are essentially universal mechanisms to apply any function to data by groups. data.table additionally internally optimizes some functions (min, max, mean, median, var, sd, sum, prod, first, last, head, tail) which they called GForce, ?data.table::GForce.

collapse instead provides grouped statistical and transformation functions where all grouped computation is done efficiently in C++, and some supporting mechanisms (fgroup_by, collap) to operate them. In data.table words, everything2 in collapse, the Fast Statistical Functions, data transformations, time series etc. is GForce optimized.

The full set of optimized grouped statistical and transformation functions in collapse is:

.FAST_FUN
#  [1] "fmean"      "fmedian"    "fmode"      "fsum"       "fprod"      "fsd"        "fvar"      
#  [8] "fmin"       "fmax"       "fnth"       "ffirst"     "flast"      "fnobs"      "fndistinct"
# [15] "fcumsum"    "fscale"     "fbetween"   "fwithin"    "fhdbetween" "fhdwithin"  "flag"      
# [22] "fdiff"      "fgrowth"

Additional optimized grouped functions include TRA, qsu, varying, fFtest, psmat, psacf, pspacf, psccf.

The nice thing about those GForce (fast) functions provided by collapse is that they can be accessed explicitly and programmatically without any overhead as incurred through data.table, they cover a broader range of statistical operations (such as mode, distinct values, order statistics), support sampling weights, operate in a class-agnostic way on vectors, matrices, data.frame’s and many related classes, and cover transformations (replacing and sweeping, scaling, (higher order) centering, linear fitting) and time series functionality (lags, differences and growth rates, including irregular time series and unbalanced panels).

So if we would want to use fmean inside the data.table, we should do something like this:

# This does not save the grouping columns, we are simply passing a grouping vector to g
# and aggregating the subset of the data table (.SD).
DT[, fmean(.SD, country), .SDcols = 9:13]
#           PCGDP   LIFEEX     GINI        ODA         POP
#   1:   483.8351 49.19717       NA 1487548499 18362258.22
#   2:  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4: 10071.0659       NA       NA         NA    43115.10
#   5: 40083.0911       NA       NA         NA    51547.35
#  ---                                                    
# 212: 35629.7336 73.71292       NA         NA    92238.53
# 213:  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:  1219.4360 54.53360 45.93333  397104997  9402160.33

# If we want to keep the grouping columns, we need to group .SD first.
DT[, fmean(gby(.SD, country)), .SDcols = c(1L, 9:13)]
#                    country      PCGDP   LIFEEX     GINI        ODA         POP
#   1:           Afghanistan   483.8351 49.19717       NA 1487548499 18362258.22
#   2:               Albania  2819.2400 71.68027 31.41111  312928126  2708297.17
#   3:               Algeria  3532.2714 63.56290 34.36667  612238500 25305290.68
#   4:        American Samoa 10071.0659       NA       NA         NA    43115.10
#   5:               Andorra 40083.0911       NA       NA         NA    51547.35
#  ---                                                                          
# 212: Virgin Islands (U.S.) 35629.7336 73.71292       NA         NA    92238.53
# 213:    West Bank and Gaza  2388.4348 71.60780 34.52500 1638581462  3312289.13
# 214:           Yemen, Rep.  1069.6596 52.53707 35.46667  859950996 13741375.82
# 215:                Zambia  1318.8627 51.09263 52.68889  734624330  8614972.38
# 216:              Zimbabwe  1219.4360 54.53360 45.93333  397104997  9402160.33

Needless to say this kind of programming seems a bit arcane, so there is actually not that great of a scope to use collapse’s Fast Statistical Functions for aggregations inside data.table. I drive this point home with a benchmark:

microbenchmark(collapse = DT %>% gby(country) %>% get_vars(9:13) %>% fmean,
               data.table = DT[, lapply(.SD, mean, na.rm = TRUE), keyby = country, .SDcols = 9:13],
               data.table_base = DT[, lapply(.SD, base::mean, na.rm = TRUE), keyby = country, .SDcols = 9:13],
               hybrid_bad = DT[, lapply(.SD, fmean), keyby = country, .SDcols = 9:13],
               hybrid_ok = DT[, fmean(gby(.SD, country)), .SDcols = c(1L, 9:13)])
# Unit: microseconds
#             expr      min       lq       mean     median        uq       max neval   cld
#         collapse  398.499  503.813   607.2086   557.3625   670.487  1260.648   100 a    
#       data.table 1829.614 2036.895  2686.7335  2304.4205  2783.244 10714.839   100   c  
#  data.table_base 7843.685 8969.789 10086.5678  9729.3025 10836.888 17392.480   100    d 
#       hybrid_bad 7925.348 9343.745 11428.8924 10025.3875 11953.844 30408.615   100     e
#        hybrid_ok 1064.746 1245.699  1563.7750  1406.3480  1713.812  3482.958   100  b

It is evident that data.table has some overhead, so there is absolutely no need to do this kind of syntax manipulation.

There is more scope to use collapse transformation functions inside data.table.

Below some basic examples:

# Computing a column containing the sum of ODA received by country
DT[, sum_ODA := sum(ODA, na.rm = TRUE), by = country]
# Same using fsum; "replace_fill" overwrites missing values, "replace" keeps the
DT[, sum_ODA := fsum(ODA, country, TRA = "replace_fill")]
# Same: A native collapse solution using settransform (or its shortcut form)
settfm(DT, sum_ODA = fsum(ODA, country, TRA = "replace_fill"))

# settfm may be more convenient than `:=` for multiple column modifications,
# each involving a different grouping:
  # This computes the percentage of total ODA distributed received by 
  # each country both over time and within a given year
settfm(DT, perc_c_ODA = fsum(ODA, country, TRA = "%"),
           perc_y_ODA = fsum(ODA, year, TRA = "%"))

The TRA argument is available to all Fast Statistical Functions (see the macro .FAST_STAT_FUN) and offers 10 different replacing and sweeping operations. Note that TRA() can also be called directly to replace or sweep with a previously aggregated data.table. A set of operators %rr%, %r+%, %r-%, %r*%, %r/%, %cr%, %c+%, %c-%, %c*%, %c/% additionally facilitate row- or column-wise replacing or sweeping out vectors of statistics or other data.table’s.

Similarly, we can use the following vector valued functions

setdiff(.FAST_FUN, .FAST_STAT_FUN)
# [1] "fcumsum"    "fscale"     "fbetween"   "fwithin"    "fhdbetween" "fhdwithin"  "flag"      
# [8] "fdiff"      "fgrowth"

for very efficient data transformations:

# Centering GDP
DT[, demean_PCGDP := PCGDP - mean(PCGDP, na.rm = TRUE), by = country]
DT[, demean_PCGDP := fwithin(PCGDP, country)]

# Lagging GDP
DT[order(year), lag_PCGDP := shift(PCGDP, 1L), by = country]
DT[, lag_PCGDP := flag(PCGDP, 1L, country, year)]

# Computing a growth rate
DT[order(year), growth_PCGDP := (PCGDP / shift(PCGDP, 1L) - 1) * 100, by = country]
DT[, lag_PCGDP := fgrowth(PCGDP, 1L, 1L, country, year)] # 1 lag, 1 iteration

# Several Growth rates
DT[order(year), paste0("growth_", .c(PCGDP, LIFEEX, GINI, ODA)) := (.SD / shift(.SD, 1L) - 1) * 100,
   by = country, .SDcols = 9:13]

# Same thing using collapse
DT %<>% tfm(gv(., 9:13) %>% fgrowth(1L, 1L, country, year) %>% add_stub("growth_"))

# Or even simpler using settransform and the Growth operator
settfmv(DT, 9:13, G, 1L, 1L, country, year, apply = FALSE)

head(DT)
#        country iso3c       date year decade     region     income  OECD PCGDP LIFEEX GINI       ODA
# 1: Afghanistan   AFG 1961-01-01 1960   1960 South Asia Low income FALSE    NA 32.446   NA 116769997
# 2: Afghanistan   AFG 1962-01-01 1961   1960 South Asia Low income FALSE    NA 32.962   NA 232080002
# 3: Afghanistan   AFG 1963-01-01 1962   1960 South Asia Low income FALSE    NA 33.471   NA 112839996
# 4: Afghanistan   AFG 1964-01-01 1963   1960 South Asia Low income FALSE    NA 33.971   NA 237720001
# 5: Afghanistan   AFG 1965-01-01 1964   1960 South Asia Low income FALSE    NA 34.463   NA 295920013
# 6: Afghanistan   AFG 1966-01-01 1965   1960 South Asia Low income FALSE    NA 34.948   NA 341839996
#        POP     sum_ODA perc_c_ODA perc_y_ODA demean_PCGDP lag_PCGDP growth_PCGDP growth_LIFEEX
# 1: 8996973 89252909923  0.1308305  0.4441407           NA        NA           NA            NA
# 2: 9169410 89252909923  0.2600251  0.7356654           NA        NA           NA      1.590335
# 3: 9351441 89252909923  0.1264272  0.3494956           NA        NA           NA      1.544202
# 4: 9543205 89252909923  0.2663443  0.7003399           NA        NA           NA      1.493830
# 5: 9744781 89252909923  0.3315522  0.8570540           NA        NA           NA      1.448294
# 6: 9956320 89252909923  0.3830015  0.8992630           NA        NA           NA      1.407306
#    growth_GINI growth_ODA growth_POP G1.PCGDP G1.LIFEEX G1.GINI    G1.ODA   G1.POP
# 1:          NA         NA         NA       NA        NA      NA        NA       NA
# 2:          NA   98.74969   1.916611       NA  1.590335      NA  98.74969 1.916611
# 3:          NA  -51.37884   1.985199       NA  1.544202      NA -51.37884 1.985199
# 4:          NA  110.66998   2.050636       NA  1.493830      NA 110.66998 2.050636
# 5:          NA   24.48259   2.112246       NA  1.448294      NA  24.48259 2.112246
# 6:          NA   15.51770   2.170793       NA  1.407306      NA  15.51770 2.170793

Since transformations (:= operations) are not highly optimized in data.table, collapse will be faster in most circumstances. Also time series functionality in collapse is significantly faster as it does not require data to be ordered or balanced to compute. For example flag computes an ordered lag without sorting the entire data first.

# Lets generate a large dataset and benchmark this stuff
DT_large <- replicate(1000, qDT(wlddev), simplify = FALSE) %>%
    lapply(tfm, country = paste(country, rnorm(1))) %>%
    rbindlist

# 12.7 million Obs
fdim(DT_large)
# [1] 13176000       13

microbenchmark(
  S1 = DT_large[, sum_ODA := sum(ODA, na.rm = TRUE), by = country],
  S2 = DT_large[, sum_ODA := fsum(ODA, country, TRA = "replace_fill")],
  S3 = settfm(DT_large, sum_ODA = fsum(ODA, country, TRA = "replace_fill")),
  W1 = DT_large[, demean_PCGDP := PCGDP - mean(PCGDP, na.rm = TRUE), by = country],
  W2 = DT_large[, demean_PCGDP := fwithin(PCGDP, country)],
  L1 = DT_large[order(year), lag_PCGDP := shift(PCGDP, 1L), by = country],
  L2 = DT_large[, lag_PCGDP := flag(PCGDP, 1L, country, year)],
  L3 = DT_large[, lag_PCGDP := shift(PCGDP, 1L), by = country], # Not ordered
  L4 = DT_large[, lag_PCGDP := flag(PCGDP, 1L, country)], # Not ordered
  times = 5
)
# Unit: milliseconds
#  expr       min        lq      mean    median        uq       max neval  cld
#    S1  802.3076  889.1670 1061.8275  946.5860 1045.4601 1625.6170     5 a   
#    S2  653.5904  704.0244  740.0257  729.1744  764.1388  849.2006     5 a   
#    S3  667.5870  724.0720  740.0615  733.5593  754.0848  821.0045     5 a   
#    W1 2952.1137 3274.1354 4353.9091 4260.5248 5000.9496 6281.8220     5  b  
#    W2  513.2676  525.1489  642.4413  575.5682  664.4615  933.7604     5 a   
#    L1 7130.6986 7507.8328 8203.0861 8454.6597 8614.0338 9308.2055     5    d
#    L2  953.3716 1198.7955 1400.4773 1387.8155 1637.6697 1824.7342     5 a   
#    L3 5501.0065 5779.1144 6270.8155 5962.5675 6873.2074 7238.1817     5   c 
#    L4  617.3672  660.9214  708.6608  682.7210  699.0300  883.2644     5 a

rm(DT_large)
gc()
#            used  (Mb) gc trigger   (Mb)  max used   (Mb)
# Ncells  2272360 121.4    4502255  240.5   4502255  240.5
# Vcells 21279081 162.4  278033052 2121.3 345710161 2637.6

Further collapse features supporting data.table’s

As mentioned, qDT is a flexible and very fast function to create / column-wise convert R objects to data.table’s. You can also row-wise convert a matrix to data.table using mrtl:

# Creating a matrix from mtcars
m <- qM(mtcars)
str(m)
#  num [1:32, 1:11] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
#  - attr(*, "dimnames")=List of 2
#   ..$ : chr [1:32] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
#   ..$ : chr [1:11] "mpg" "cyl" "disp" "hp" ...

# Demonstrating another nice feature of qDT
qDT(m, row.names.col = "car") %>% head
#                  car  mpg cyl disp  hp drat    wt  qsec vs am gear carb
# 1:         Mazda RX4 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
# 2:     Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
# 3:        Datsun 710 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
# 4:    Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
# 5: Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
# 6:           Valiant 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

# Row-wise conversion to data.table
mrtl(m, names = TRUE, return = "data.table") %>% head(2)
#    Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant Duster 360 Merc 240D
# 1:        21            21       22.8           21.4              18.7    18.1       14.3      24.4
# 2:         6             6        4.0            6.0               8.0     6.0        8.0       4.0
#    Merc 230 Merc 280 Merc 280C Merc 450SE Merc 450SL Merc 450SLC Cadillac Fleetwood
# 1:     22.8     19.2      17.8       16.4       17.3        15.2               10.4
# 2:      4.0      6.0       6.0        8.0        8.0         8.0                8.0
#    Lincoln Continental Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla Toyota Corona
# 1:                10.4              14.7     32.4        30.4           33.9          21.5
# 2:                 8.0               8.0      4.0         4.0            4.0           4.0
#    Dodge Challenger AMC Javelin Camaro Z28 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
# 1:             15.5        15.2       13.3             19.2      27.3            26         30.4
# 2:              8.0         8.0        8.0              8.0       4.0             4          4.0
#    Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
# 1:           15.8         19.7            15       21.4
# 2:            8.0          6.0             8        4.0

The computational efficiency of these functions makes them very useful to use in data.table based workflows.

# Benchmark
microbenchmark(qDT(m, "car"), mrtl(m, TRUE, "data.table"))
# Unit: microseconds
#                         expr    min      lq     mean median     uq     max neval cld
#                qDT(m, "car") 18.296 19.8585 38.17215 31.907 39.716 327.991   100   b
#  mrtl(m, TRUE, "data.table") 11.602 13.6105 27.73449 21.866 24.321 292.739   100  a

For example we could regress the growth rate of GDP per capita on the Growth rate of life expectancy in each country and save results in a data.table:

library(lmtest)

wlddev %>% fselect(country, PCGDP, LIFEEX) %>%
  # This counts missing values on PCGDP and LIFEEX only
  na_omit(cols = -1L) %>%
  # This removes countries with less than 20 observations
  fsubset(fnobs(PCGDP, country, "replace_fill") > 20L) %>%
  qDT %>%
  # Run estimations by country using data.table
  .[, qDT(coeftest(lm(G(PCGDP) ~ G(LIFEEX))), "Coef"), keyby = country] %>% head
#    country        Coef   Estimate Std. Error    t value    Pr(>|t|)
# 1: Albania (Intercept) -3.6146411   2.371885 -1.5239527 0.136023086
# 2: Albania   G(LIFEEX) 22.1596308   7.288971  3.0401591 0.004325856
# 3: Algeria (Intercept)  0.5973329   1.740619  0.3431726 0.732731107
# 4: Algeria   G(LIFEEX)  0.8412547   1.689221  0.4980134 0.620390703
# 5:  Angola (Intercept) -3.3793976   1.540330 -2.1939445 0.034597175
# 6:  Angola   G(LIFEEX)  4.2362895   1.402380  3.0207852 0.004553260

If we only need the coefficients, not the standard errors, we can also use collapse::flm together with mrtl:

wlddev %>% fselect(country, PCGDP, LIFEEX) %>%
  na_omit(cols = -1L) %>%
  fsubset(fnobs(PCGDP, country, "replace_fill") > 20L) %>%
  qDT %>%
  .[, mrtl(flm(fgrowth(PCGDP)[-1L],
               cbind(Intercept = 1,
                     LIFEEX = fgrowth(LIFEEX)[-1L])), TRUE),
    keyby = country] %>% head
#                country   Intercept     LIFEEX
# 1:             Albania -3.61464113 22.1596308
# 2:             Algeria  0.59733291  0.8412547
# 3:              Angola -3.37939760  4.2362895
# 4: Antigua and Barbuda -3.11880717 18.8700870
# 5:           Argentina  1.14613567 -0.2896305
# 6:             Armenia  0.08178344 11.5523992

… which provides a significant speed gain here:


microbenchmark(

A = wlddev %>% fselect(country, PCGDP, LIFEEX) %>%
  na_omit(cols = -1L) %>%
  fsubset(fnobs(PCGDP, country, "replace_fill") > 20L) %>%
  qDT %>%
  .[, qDT(coeftest(lm(G(PCGDP) ~ G(LIFEEX))), "Coef"), keyby = country],

B = wlddev %>% fselect(country, PCGDP, LIFEEX) %>%
  na_omit(cols = -1L) %>%
  fsubset(fnobs(PCGDP, country, "replace_fill") > 20L) %>%
  qDT %>%
  .[, mrtl(flm(fgrowth(PCGDP)[-1L],
               cbind(Intercept = 1,
                     LIFEEX = fgrowth(LIFEEX)[-1L])), TRUE),
    keyby = country]
)
# Unit: milliseconds
#  expr       min        lq      mean    median        uq      max neval cld
#     A 285.21927 304.04621 328.20886 319.23490 341.46001 585.1312   100   b
#     B  10.68583  12.08281  13.33857  12.69528  13.59447  31.5162   100  a

Another feature to highlight at this point are collapse’s list processing functions, in particular rsplit, rapply2d, get_elem and unlist2d. rsplit is an efficient recursive generalization of split:

DT_list <- rsplit(DT, country + year + PCGDP + LIFEEX ~ region + income)

# Note: rsplit(DT, year + PCGDP + LIFEEX ~ region + income, flatten = TRUE) 
# would yield a simple list with interacted categories (like split) 

str(DT_list, give.attr = FALSE)
# List of 7
#  $ East Asia & Pacific       :List of 3
#   ..$ High income        :Classes 'data.table' and 'data.frame':  793 obs. of  4 variables:
#   .. ..$ country: chr [1:793] "Australia" "Australia" "Australia" "Australia" ...
#   .. ..$ year   : int [1:793] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:793] 19378 19469 19246 20053 21036 ...
#   .. ..$ LIFEEX : num [1:793] 70.8 71 70.9 70.9 70.9 ...
#   ..$ Lower middle income:Classes 'data.table' and 'data.frame':  793 obs. of  4 variables:
#   .. ..$ country: chr [1:793] "Cambodia" "Cambodia" "Cambodia" "Cambodia" ...
#   .. ..$ year   : int [1:793] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:793] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:793] 41.2 41.4 41.5 41.7 41.9 ...
#   ..$ Upper middle income:Classes 'data.table' and 'data.frame':  610 obs. of  4 variables:
#   .. ..$ country: chr [1:610] "American Samoa" "American Samoa" "American Samoa" "American Samoa" ...
#   .. ..$ year   : int [1:610] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:610] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:610] NA NA NA NA NA NA NA NA NA NA ...
#  $ Europe & Central Asia     :List of 4
#   ..$ High income        :Classes 'data.table' and 'data.frame':  2257 obs. of  4 variables:
#   .. ..$ country: chr [1:2257] "Andorra" "Andorra" "Andorra" "Andorra" ...
#   .. ..$ year   : int [1:2257] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:2257] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:2257] NA NA NA NA NA NA NA NA NA NA ...
#   ..$ Low income         :Classes 'data.table' and 'data.frame':  61 obs. of  4 variables:
#   .. ..$ country: chr [1:61] "Tajikistan" "Tajikistan" "Tajikistan" "Tajikistan" ...
#   .. ..$ year   : int [1:61] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:61] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:61] 50.6 50.9 51.2 51.5 51.9 ...
#   ..$ Lower middle income:Classes 'data.table' and 'data.frame':  244 obs. of  4 variables:
#   .. ..$ country: chr [1:244] "Kyrgyz Republic" "Kyrgyz Republic" "Kyrgyz Republic" "Kyrgyz Republic" ...
#   .. ..$ year   : int [1:244] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:244] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:244] 56.1 56.6 57 57.4 57.9 ...
#   ..$ Upper middle income:Classes 'data.table' and 'data.frame':  976 obs. of  4 variables:
#   .. ..$ country: chr [1:976] "Albania" "Albania" "Albania" "Albania" ...
#   .. ..$ year   : int [1:976] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:976] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:976] 62.3 63.3 64.2 64.9 65.5 ...
#  $ Latin America & Caribbean :List of 4
#   ..$ High income        :Classes 'data.table' and 'data.frame':  1037 obs. of  4 variables:
#   .. ..$ country: chr [1:1037] "Antigua and Barbuda" "Antigua and Barbuda" "Antigua and Barbuda" "Antigua and Barbuda" ...
#   .. ..$ year   : int [1:1037] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:1037] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:1037] 62 62.5 63 63.5 64 ...
#   ..$ Low income         :Classes 'data.table' and 'data.frame':  61 obs. of  4 variables:
#   .. ..$ country: chr [1:61] "Haiti" "Haiti" "Haiti" "Haiti" ...
#   .. ..$ year   : int [1:61] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:61] 1512 1439 1523 1466 1414 ...
#   .. ..$ LIFEEX : num [1:61] 41.8 42.2 42.6 43 43.4 ...
#   ..$ Lower middle income:Classes 'data.table' and 'data.frame':  244 obs. of  4 variables:
#   .. ..$ country: chr [1:244] "Bolivia" "Bolivia" "Bolivia" "Bolivia" ...
#   .. ..$ year   : int [1:244] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:244] 1005 1007 1042 1091 1112 ...
#   .. ..$ LIFEEX : num [1:244] 41.8 42.1 42.5 42.8 43.2 ...
#   ..$ Upper middle income:Classes 'data.table' and 'data.frame':  1220 obs. of  4 variables:
#   .. ..$ country: chr [1:1220] "Argentina" "Argentina" "Argentina" "Argentina" ...
#   .. ..$ year   : int [1:1220] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:1220] 5643 5853 5711 5323 5773 ...
#   .. ..$ LIFEEX : num [1:1220] 65.1 65.2 65.3 65.3 65.4 ...
#  $ Middle East & North Africa:List of 4
#   ..$ High income        :Classes 'data.table' and 'data.frame':  488 obs. of  4 variables:
#   .. ..$ country: chr [1:488] "Bahrain" "Bahrain" "Bahrain" "Bahrain" ...
#   .. ..$ year   : int [1:488] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:488] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:488] 51.9 53.2 54.6 55.9 57.2 ...
#   ..$ Low income         :Classes 'data.table' and 'data.frame':  122 obs. of  4 variables:
#   .. ..$ country: chr [1:122] "Syrian Arab Republic" "Syrian Arab Republic" "Syrian Arab Republic" "Syrian Arab Republic" ...
#   .. ..$ year   : int [1:122] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:122] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:122] 52 52.6 53.2 53.8 54.4 ...
#   ..$ Lower middle income:Classes 'data.table' and 'data.frame':  305 obs. of  4 variables:
#   .. ..$ country: chr [1:305] "Djibouti" "Djibouti" "Djibouti" "Djibouti" ...
#   .. ..$ year   : int [1:305] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:305] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:305] 44 44.5 44.9 45.3 45.7 ...
#   ..$ Upper middle income:Classes 'data.table' and 'data.frame':  366 obs. of  4 variables:
#   .. ..$ country: chr [1:366] "Algeria" "Algeria" "Algeria" "Algeria" ...
#   .. ..$ year   : int [1:366] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:366] 2481 2091 1638 2146 2214 ...
#   .. ..$ LIFEEX : num [1:366] 46.1 46.6 47.1 47.5 48 ...
#  $ North America             :List of 1
#   ..$ High income:Classes 'data.table' and 'data.frame':  183 obs. of  4 variables:
#   .. ..$ country: chr [1:183] "Bermuda" "Bermuda" "Bermuda" "Bermuda" ...
#   .. ..$ year   : int [1:183] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:183] 33363 34080 34763 34324 37202 ...
#   .. ..$ LIFEEX : num [1:183] NA NA NA NA NA ...
#  $ South Asia                :List of 3
#   ..$ Low income         :Classes 'data.table' and 'data.frame':  122 obs. of  4 variables:
#   .. ..$ country: chr [1:122] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
#   .. ..$ year   : int [1:122] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:122] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:122] 32.4 33 33.5 34 34.5 ...
#   ..$ Lower middle income:Classes 'data.table' and 'data.frame':  244 obs. of  4 variables:
#   .. ..$ country: chr [1:244] "Bangladesh" "Bangladesh" "Bangladesh" "Bangladesh" ...
#   .. ..$ year   : int [1:244] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:244] 372 384 394 381 411 ...
#   .. ..$ LIFEEX : num [1:244] 45.4 46 46.6 47.1 47.6 ...
#   ..$ Upper middle income:Classes 'data.table' and 'data.frame':  122 obs. of  4 variables:
#   .. ..$ country: chr [1:122] "Maldives" "Maldives" "Maldives" "Maldives" ...
#   .. ..$ year   : int [1:122] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:122] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:122] 37.3 37.9 38.6 39.2 39.9 ...
#  $ Sub-Saharan Africa        :List of 4
#   ..$ High income        :Classes 'data.table' and 'data.frame':  61 obs. of  4 variables:
#   .. ..$ country: chr [1:61] "Seychelles" "Seychelles" "Seychelles" "Seychelles" ...
#   .. ..$ year   : int [1:61] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:61] 2830 2617 2763 2966 3064 ...
#   .. ..$ LIFEEX : num [1:61] NA NA NA NA NA NA NA NA NA NA ...
#   ..$ Low income         :Classes 'data.table' and 'data.frame':  1464 obs. of  4 variables:
#   .. ..$ country: chr [1:1464] "Benin" "Benin" "Benin" "Benin" ...
#   .. ..$ year   : int [1:1464] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:1464] 712 724 689 710 745 ...
#   .. ..$ LIFEEX : num [1:1464] 37.3 37.7 38.2 38.7 39.1 ...
#   ..$ Lower middle income:Classes 'data.table' and 'data.frame':  1037 obs. of  4 variables:
#   .. ..$ country: chr [1:1037] "Angola" "Angola" "Angola" "Angola" ...
#   .. ..$ year   : int [1:1037] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:1037] NA NA NA NA NA NA NA NA NA NA ...
#   .. ..$ LIFEEX : num [1:1037] 37.5 37.8 38.1 38.4 38.8 ...
#   ..$ Upper middle income:Classes 'data.table' and 'data.frame':  366 obs. of  4 variables:
#   .. ..$ country: chr [1:366] "Botswana" "Botswana" "Botswana" "Botswana" ...
#   .. ..$ year   : int [1:366] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 ...
#   .. ..$ PCGDP  : num [1:366] 408 425 444 460 480 ...
#   .. ..$ LIFEEX : num [1:366] 49.2 49.7 50.2 50.6 51.1 ...

We can use rapply2d to apply a function to each data frame / data.table in an arbitrary nested structure:

# This runs region-income level regressions, with country fixed effects
# following Mundlak (1978)
lm_summary_list <- DT_list %>%
  rapply2d(lm, formula = G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)) %>%
  # Summarizing the results
  rapply2d(summary, classes = "lm")

# This is a nested list of linear model summaries
str(lm_summary_list, give.attr = FALSE)
# List of 7
#  $ East Asia & Pacific       :List of 3
#   ..$ High income        :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:441] -1.64 -2.59 2.75 3.45 2.48 ...
#   .. ..$ coefficients : num [1:3, 1:4] 0.531 2.494 3.83 0.706 0.759 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 4.59
#   .. ..$ df           : int [1:3] 3 438 3
#   .. ..$ r.squared    : num 0.0525
#   .. ..$ adj.r.squared: num 0.0481
#   .. ..$ fstatistic   : Named num [1:3] 12.1 2 438
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.02361 -0.00158 -0.04895 -0.00158 0.02728 ...
#   .. ..$ na.action    : 'omit' Named int [1:352] 1 61 62 63 64 65 66 67 68 69 ...
#   ..$ Lower middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:549] -39.6968 3.6618 -0.0944 -1.8261 -1.0491 ...
#   .. ..$ coefficients : num [1:3, 1:4] 1.348 0.524 0.949 0.701 0.757 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 5.4
#   .. ..$ df           : int [1:3] 3 546 3
#   .. ..$ r.squared    : num 0.00471
#   .. ..$ adj.r.squared: num 0.00106
#   .. ..$ fstatistic   : Named num [1:3] 1.29 2 546
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.016821 0.000511 -0.022767 0.000511 0.01965 ...
#   .. ..$ na.action    : 'omit' Named int [1:244] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Upper middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:312] -32.29 -11.61 2.91 11.23 10.28 ...
#   .. ..$ coefficients : num [1:3, 1:4] 1.507 -0.547 4.816 0.428 0.478 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 4.39
#   .. ..$ df           : int [1:3] 3 309 3
#   .. ..$ r.squared    : num 0.103
#   .. ..$ adj.r.squared: num 0.0976
#   .. ..$ fstatistic   : Named num [1:3] 17.8 2 309
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.009471 0.000492 -0.013551 0.000492 0.011842 ...
#   .. ..$ na.action    : 'omit' Named int [1:298] 1 2 3 4 5 6 7 8 9 10 ...
#  $ Europe & Central Asia     :List of 4
#   ..$ High income        :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:1355] 2.706 -0.548 1.001 3.034 0.257 ...
#   .. ..$ coefficients : num [1:3, 1:4] 3.254 -0.172 -2.506 0.407 0.227 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 3.3
#   .. ..$ df           : int [1:3] 3 1352 3
#   .. ..$ r.squared    : num 0.00257
#   .. ..$ adj.r.squared: num 0.00109
#   .. ..$ fstatistic   : Named num [1:3] 1.74 2 1352
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.015254 -0.000863 -0.05461 -0.000863 0.004722 ...
#   .. ..$ na.action    : 'omit' Named int [1:902] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Low income         :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:34] 0.166 -1.804 15.949 -0.778 7.165 ...
#   .. ..$ coefficients : num [1:2, 1:4] -5.31 9.36 2.03 2.56 -2.61 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE TRUE
#   .. ..$ sigma        : num 8.43
#   .. ..$ df           : int [1:3] 2 32 3
#   .. ..$ r.squared    : num 0.295
#   .. ..$ adj.r.squared: num 0.273
#   .. ..$ fstatistic   : Named num [1:3] 13.4 1 32
#   .. ..$ cov.unscaled : num [1:2, 1:2] 0.0582 -0.0514 -0.0514 0.092
#   .. ..$ na.action    : 'omit' Named int [1:27] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Lower middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:121] -1.626 8.745 -14.47 0.298 -11.886 ...
#   .. ..$ coefficients : num [1:3, 1:4] 0.106 4.631 1.499 1.315 0.938 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 6.02
#   .. ..$ df           : int [1:3] 3 118 3
#   .. ..$ r.squared    : num 0.178
#   .. ..$ adj.r.squared: num 0.164
#   .. ..$ fstatistic   : Named num [1:3] 12.7 2 118
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.047775 -0.000927 -0.142782 -0.000927 0.024298 ...
#   .. ..$ na.action    : 'omit' Named int [1:123] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Upper middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:511] 0.761 -2.153 -4.091 -6.476 -3.43 ...
#   .. ..$ coefficients : num [1:3, 1:4] 2.983 4.147 -3.351 0.698 0.779 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 8.28
#   .. ..$ df           : int [1:3] 3 508 3
#   .. ..$ r.squared    : num 0.0531
#   .. ..$ adj.r.squared: num 0.0493
#   .. ..$ fstatistic   : Named num [1:3] 14.2 2 508
#   .. ..$ cov.unscaled : num [1:3, 1:3] 7.11e-03 4.52e-05 -1.45e-02 4.52e-05 8.85e-03 ...
#   .. ..$ na.action    : 'omit' Named int [1:465] 1 2 3 4 5 6 7 8 9 10 ...
#  $ Latin America & Caribbean :List of 4
#   ..$ High income        :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:487] 2.39 6.02 6.1 1.71 -2.27 ...
#   .. ..$ coefficients : num [1:3, 1:4] 1.015 0.483 2.613 0.677 0.952 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 4.71
#   .. ..$ df           : int [1:3] 3 484 3
#   .. ..$ r.squared    : num 0.00592
#   .. ..$ adj.r.squared: num 0.00181
#   .. ..$ fstatistic   : Named num [1:3] 1.44 2 484
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.02062 0.00155 -0.05714 0.00155 0.04082 ...
#   .. ..$ na.action    : 'omit' Named int [1:550] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Low income         :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:59] -5.667 5.091 -4.46 -4.224 -0.526 ...
#   .. ..$ coefficients : num [1:2, 1:4] -3.18 4.02 1.73 2.28 -1.83 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE TRUE
#   .. ..$ sigma        : num 3.79
#   .. ..$ df           : int [1:3] 2 57 3
#   .. ..$ r.squared    : num 0.0516
#   .. ..$ adj.r.squared: num 0.0349
#   .. ..$ fstatistic   : Named num [1:3] 3.1 1 57
#   .. ..$ cov.unscaled : num [1:2, 1:2] 0.209 -0.265 -0.265 0.364
#   .. ..$ na.action    : 'omit' Named int [1:2] 1 61
#   ..$ Lower middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:231] -1.386 2.029 3.213 0.413 1.334 ...
#   .. ..$ coefficients : num [1:3, 1:4] -1.678 -0.479 3.896 2.26 0.709 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 3.96
#   .. ..$ df           : int [1:3] 3 228 3
#   .. ..$ r.squared    : num 0.0081
#   .. ..$ adj.r.squared: num -0.000602
#   .. ..$ fstatistic   : Named num [1:3] 0.931 2 228
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.3264 0.005 -0.4084 0.005 0.0321 ...
#   .. ..$ na.action    : 'omit' Named int [1:13] 1 61 62 63 64 65 66 67 122 123 ...
#   ..$ Upper middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:1065] 1.97 -4.16 -8.5 6.72 7.17 ...
#   .. ..$ coefficients : num [1:3, 1:4] 1.681 0.583 -0.124 0.353 0.512 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 4.22
#   .. ..$ df           : int [1:3] 3 1062 3
#   .. ..$ r.squared    : num 0.0016
#   .. ..$ adj.r.squared: num -0.000283
#   .. ..$ fstatistic   : Named num [1:3] 0.85 2 1062
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.006982 0.000348 -0.013936 0.000348 0.014734 ...
#   .. ..$ na.action    : 'omit' Named int [1:155] 1 61 62 122 123 183 184 244 245 305 ...
#  $ Middle East & North Africa:List of 4
#   ..$ High income        :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:334] -10.728 -11.988 2.151 0.985 -8.618 ...
#   .. ..$ coefficients : num [1:3, 1:4] 1.929 3.963 -3.533 1.102 0.996 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 8.36
#   .. ..$ df           : int [1:3] 3 331 3
#   .. ..$ r.squared    : num 0.0456
#   .. ..$ adj.r.squared: num 0.0399
#   .. ..$ fstatistic   : Named num [1:3] 7.91 2 331
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.01738 0.00101 -0.02441 0.00101 0.01419 ...
#   .. ..$ na.action    : 'omit' Named int [1:154] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Low income         :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:29] 0.468 3.424 0.415 3.842 3.342 ...
#   .. ..$ coefficients : num [1:2, 1:4] -6.91 11.38 2.11 3.64 -3.27 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE TRUE
#   .. ..$ sigma        : num 6.05
#   .. ..$ df           : int [1:3] 2 27 3
#   .. ..$ r.squared    : num 0.266
#   .. ..$ adj.r.squared: num 0.239
#   .. ..$ fstatistic   : Named num [1:3] 9.81 1 27
#   .. ..$ cov.unscaled : num [1:2, 1:2] 0.122 -0.178 -0.178 0.361
#   .. ..$ na.action    : 'omit' Named int [1:93] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Lower middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:191] -0.95 -2.047 4.541 5.594 -0.723 ...
#   .. ..$ coefficients : num [1:3, 1:4] 2.238 1.271 -0.647 1.002 0.599 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 3.94
#   .. ..$ df           : int [1:3] 3 188 3
#   .. ..$ r.squared    : num 0.0244
#   .. ..$ adj.r.squared: num 0.014
#   .. ..$ fstatistic   : Named num [1:3] 2.35 2 188
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.06471 -0.00043 -0.07801 -0.00043 0.02309 ...
#   .. ..$ na.action    : 'omit' Named int [1:114] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Upper middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:263] -18.068 -23.976 28.692 0.858 1.141 ...
#   .. ..$ coefficients : num [1:3, 1:4] 2.663 0.718 -1.19 3.538 1.318 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 13.8
#   .. ..$ df           : int [1:3] 3 260 3
#   .. ..$ r.squared    : num 0.00119
#   .. ..$ adj.r.squared: num -0.00649
#   .. ..$ fstatistic   : Named num [1:3] 0.155 2 260
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.065741 0.000795 -0.084456 0.000795 0.009122 ...
#   .. ..$ na.action    : 'omit' Named int [1:103] 1 61 62 122 123 124 125 126 127 128 ...
#  $ North America             :List of 1
#   ..$ High income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:137] 4.6986 -3.1098 1.8243 0.5643 0.0176 ...
#   .. ..$ coefficients : num [1:3, 1:4] 6.542 -1.461 -19.53 2.272 0.662 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 2.49
#   .. ..$ df           : int [1:3] 3 134 3
#   .. ..$ r.squared    : num 0.0657
#   .. ..$ adj.r.squared: num 0.0518
#   .. ..$ fstatistic   : Named num [1:3] 4.71 2 134
#   .. ..$ cov.unscaled : num [1:3, 1:3] 8.36e-01 3.82e-17 -3.60 3.82e-17 7.10e-02 ...
#   .. ..$ na.action    : 'omit' Named int [1:46] 1 2 3 4 5 6 7 8 9 10 ...
#  $ South Asia                :List of 3
#   ..$ Low income         :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:76] 0.544 -6.17 3.951 -0.964 7.829 ...
#   .. ..$ coefficients : num [1:3, 1:4] -108.62 -1.72 96.06 174.19 1.25 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 3.7
#   .. ..$ df           : int [1:3] 3 73 3
#   .. ..$ r.squared    : num 0.0494
#   .. ..$ adj.r.squared: num 0.0233
#   .. ..$ fstatistic   : Named num [1:3] 1.9 2 73
#   .. ..$ cov.unscaled : num [1:3, 1:3] 2210.639 -6.979 -1875.261 -6.979 0.114 ...
#   .. ..$ na.action    : 'omit' Named int [1:46] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Lower middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:216] 0.294 -0.293 -6.067 4.954 -4.164 ...
#   .. ..$ coefficients : num [1:3, 1:4] -2.232 0.238 5.972 1.074 0.493 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 3.44
#   .. ..$ df           : int [1:3] 3 213 3
#   .. ..$ r.squared    : num 0.111
#   .. ..$ adj.r.squared: num 0.103
#   .. ..$ fstatistic   : Named num [1:3] 13.3 2 213
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.09757 -0.00201 -0.10483 -0.00201 0.02054 ...
#   .. ..$ na.action    : 'omit' Named int [1:28] 1 61 62 63 64 65 66 67 68 69 ...
#   ..$ Upper middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:82] 3.262 3.976 3.128 1.67 -0.901 ...
#   .. ..$ coefficients : num [1:3, 1:4] 3.859 -0.577 -0.476 1.036 1.365 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 4.25
#   .. ..$ df           : int [1:3] 3 79 3
#   .. ..$ r.squared    : num 0.00622
#   .. ..$ adj.r.squared: num -0.0189
#   .. ..$ fstatistic   : Named num [1:3] 0.247 2 79
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.0595 -0.028 -0.0473 -0.028 0.1034 ...
#   .. ..$ na.action    : 'omit' Named int [1:40] 1 2 3 4 5 6 7 8 9 10 ...
#  $ Sub-Saharan Africa        :List of 4
#   ..$ High income        :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:39] -11.33 -5.041 -3.158 0.585 7.81 ...
#   .. ..$ coefficients : num [1:2, 1:4] 2.551 -0.644 0.775 0.55 3.293 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE TRUE
#   .. ..$ sigma        : num 4.8
#   .. ..$ df           : int [1:3] 2 37 3
#   .. ..$ r.squared    : num 0.0357
#   .. ..$ adj.r.squared: num 0.00959
#   .. ..$ fstatistic   : Named num [1:3] 1.37 1 37
#   .. ..$ cov.unscaled : num [1:2, 1:2] 0.026 -0.00217 -0.00217 0.01312
#   .. ..$ na.action    : 'omit' Named int [1:22] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Low income         :List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:1085] 0.694 -5.869 2.069 3.855 2.415 ...
#   .. ..$ coefficients : num [1:3, 1:4] -0.0756 0.5308 0.5124 0.8887 0.137 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 5.88
#   .. ..$ df           : int [1:3] 3 1082 3
#   .. ..$ r.squared    : num 0.0146
#   .. ..$ adj.r.squared: num 0.0128
#   .. ..$ fstatistic   : Named num [1:3] 8.01 2 1082
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.022858 -0.000025 -0.025534 -0.000025 0.000543 ...
#   .. ..$ na.action    : 'omit' Named int [1:379] 1 61 62 122 123 183 184 244 245 305 ...
#   ..$ Lower middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:891] -8.2839 -4.0289 0.0449 1.8231 -0.5267 ...
#   .. ..$ coefficients : num [1:3, 1:4] 2.352 0.782 -2.616 0.608 0.169 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 5.27
#   .. ..$ df           : int [1:3] 3 888 3
#   .. ..$ r.squared    : num 0.0277
#   .. ..$ adj.r.squared: num 0.0255
#   .. ..$ fstatistic   : Named num [1:3] 12.7 2 888
#   .. ..$ cov.unscaled : num [1:3, 1:3] 1.33e-02 -1.13e-05 -2.00e-02 -1.13e-05 1.02e-03 ...
#   .. ..$ na.action    : 'omit' Named int [1:146] 1 2 3 4 5 6 7 8 9 10 ...
#   ..$ Upper middle income:List of 12
#   .. ..$ call         : language FUN(formula = ..1, data = y)
#   .. ..$ terms        :Classes 'terms', 'formula'  language G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country)
#   .. ..$ residuals    : Named num [1:298] 0.7659 0.9133 0.0921 0.996 0.0765 ...
#   .. ..$ coefficients : num [1:3, 1:4] 0.584 0.456 4.112 2.472 0.652 ...
#   .. ..$ aliased      : Named logi [1:3] FALSE FALSE FALSE
#   .. ..$ sigma        : num 11.4
#   .. ..$ df           : int [1:3] 3 295 3
#   .. ..$ r.squared    : num 0.00658
#   .. ..$ adj.r.squared: num -0.000152
#   .. ..$ fstatistic   : Named num [1:3] 0.977 2 295
#   .. ..$ cov.unscaled : num [1:3, 1:3] 0.047213 0.000438 -0.070778 0.000438 0.003285 ...
#   .. ..$ na.action    : 'omit' Named int [1:68] 1 61 62 63 64 65 66 67 68 69 ...

We can turn this list into a data.table again by calling first get_elem to recursively extract the coefficient matrices and then unlist2d to recursively bind them to a new data.table:

lm_summary_list %>%
  get_elem("coefficients") %>%
  unlist2d(idcols = .c(Region, Income),
           row.names = "Coef",
           DT = TRUE) %>% head
#                 Region              Income                  Coef  Estimate Std. Error   t value
# 1: East Asia & Pacific         High income           (Intercept) 0.5313479  0.7058550 0.7527720
# 2: East Asia & Pacific         High income             G(LIFEEX) 2.4935584  0.7586943 3.2866443
# 3: East Asia & Pacific         High income B(G(LIFEEX), country) 3.8297123  1.6916770 2.2638554
# 4: East Asia & Pacific Lower middle income           (Intercept) 1.3476602  0.7008556 1.9228785
# 5: East Asia & Pacific Lower middle income             G(LIFEEX) 0.5238856  0.7574904 0.6916069
# 6: East Asia & Pacific Lower middle income B(G(LIFEEX), country) 0.9494439  1.2031228 0.7891496
#       Pr(>|t|)
# 1: 0.451991327
# 2: 0.001095466
# 3: 0.024071386
# 4: 0.055015131
# 5: 0.489478164
# 6: 0.430367103

The fact that this is a nested list of matrices, and that we can save both the names of the lists at each level of nesting and the row- and column- names of the matrices make unlist2d a significant generalization of rbindlist3.

But why do all this fuzz if we could have simply done:?

DT[, qDT(coeftest(lm(G(PCGDP) ~ G(LIFEEX) + B(G(LIFEEX), country))), "Coef"),
   keyby = .(region, income)] %>% head
#                 region              income                  Coef  Estimate Std. Error   t value
# 1: East Asia & Pacific         High income           (Intercept) 0.5313479  0.7058550 0.7527720
# 2: East Asia & Pacific         High income             G(LIFEEX) 2.4935584  0.7586943 3.2866443
# 3: East Asia & Pacific         High income B(G(LIFEEX), country) 3.8297123  1.6916770 2.2638554
# 4: East Asia & Pacific Lower middle income           (Intercept) 1.3476602  0.7008556 1.9228785
# 5: East Asia & Pacific Lower middle income             G(LIFEEX) 0.5238856  0.7574904 0.6916069
# 6: East Asia & Pacific Lower middle income B(G(LIFEEX), country) 0.9494439  1.2031228 0.7891496
#       Pr(>|t|)
# 1: 0.451991327
# 2: 0.001095466
# 3: 0.024071386
# 4: 0.055015131
# 5: 0.489478164
# 6: 0.430367103

Well we might want to do more things with that list of linear models first before tidying it, so this is a more general workflow. We might also be interested in additional statistics like the R-squared or the F-statistic:

DT_sum <- lm_summary_list %>%
get_elem("coef|r.sq|fstat", regex = TRUE) %>%
  unlist2d(idcols = .c(Region, Income, Statistic),
           row.names = "Coef",
           DT = TRUE)

head(DT_sum)
#                 Region      Income     Statistic                  Coef  Estimate Std. Error
# 1: East Asia & Pacific High income  coefficients           (Intercept) 0.5313479  0.7058550
# 2: East Asia & Pacific High income  coefficients             G(LIFEEX) 2.4935584  0.7586943
# 3: East Asia & Pacific High income  coefficients B(G(LIFEEX), country) 3.8297123  1.6916770
# 4: East Asia & Pacific High income     r.squared                  <NA>        NA         NA
# 5: East Asia & Pacific High income adj.r.squared                  <NA>        NA         NA
# 6: East Asia & Pacific High income    fstatistic                  <NA>        NA         NA
#     t value    Pr(>|t|)         V1    value numdf dendf
# 1: 0.752772 0.451991327         NA       NA    NA    NA
# 2: 3.286644 0.001095466         NA       NA    NA    NA
# 3: 2.263855 0.024071386         NA       NA    NA    NA
# 4:       NA          NA 0.05245359       NA    NA    NA
# 5:       NA          NA 0.04812690       NA    NA    NA
# 6:       NA          NA         NA 12.12325     2   438

# Reshaping to long form: 
DT_sum %>%
  melt(1:4, na.rm = TRUE) %>%
  roworderv(1:2) %>% head(20)
#                  Region              Income     Statistic                  Coef   variable
#  1: East Asia & Pacific         High income  coefficients           (Intercept)   Estimate
#  2: East Asia & Pacific         High income  coefficients             G(LIFEEX)   Estimate
#  3: East Asia & Pacific         High income  coefficients B(G(LIFEEX), country)   Estimate
#  4: East Asia & Pacific         High income  coefficients           (Intercept) Std. Error
#  5: East Asia & Pacific         High income  coefficients             G(LIFEEX) Std. Error
#  6: East Asia & Pacific         High income  coefficients B(G(LIFEEX), country) Std. Error
#  7: East Asia & Pacific         High income  coefficients           (Intercept)    t value
#  8: East Asia & Pacific         High income  coefficients             G(LIFEEX)    t value
#  9: East Asia & Pacific         High income  coefficients B(G(LIFEEX), country)    t value
# 10: East Asia & Pacific         High income  coefficients           (Intercept)   Pr(>|t|)
# 11: East Asia & Pacific         High income  coefficients             G(LIFEEX)   Pr(>|t|)
# 12: East Asia & Pacific         High income  coefficients B(G(LIFEEX), country)   Pr(>|t|)
# 13: East Asia & Pacific         High income     r.squared                  <NA>         V1
# 14: East Asia & Pacific         High income adj.r.squared                  <NA>         V1
# 15: East Asia & Pacific         High income    fstatistic                  <NA>      value
# 16: East Asia & Pacific         High income    fstatistic                  <NA>      numdf
# 17: East Asia & Pacific         High income    fstatistic                  <NA>      dendf
# 18: East Asia & Pacific Lower middle income  coefficients           (Intercept)   Estimate
# 19: East Asia & Pacific Lower middle income  coefficients             G(LIFEEX)   Estimate
# 20: East Asia & Pacific Lower middle income  coefficients B(G(LIFEEX), country)   Estimate
#            value
#  1: 5.313479e-01
#  2: 2.493558e+00
#  3: 3.829712e+00
#  4: 7.058550e-01
#  5: 7.586943e-01
#  6: 1.691677e+00
#  7: 7.527720e-01
#  8: 3.286644e+00
#  9: 2.263855e+00
# 10: 4.519913e-01
# 11: 1.095466e-03
# 12: 2.407139e-02
# 13: 5.245359e-02
# 14: 4.812690e-02
# 15: 1.212325e+01
# 16: 2.000000e+00
# 17: 4.380000e+02
# 18: 1.347660e+00
# 19: 5.238856e-01
# 20: 9.494439e-01

As a final example of this kind, lets suppose we are interested in the within-country correlations of all these variables by region and income group:

DT[, qDT(pwcor(W(.SD, country)), "Variable"),
   keyby = .(region, income), .SDcols = PCGDP:ODA] %>% head
#                 region              income Variable    W.PCGDP   W.LIFEEX    W.GINI       W.ODA
# 1: East Asia & Pacific         High income  W.PCGDP  1.0000000  0.7562668 0.6253844 -0.25258496
# 2: East Asia & Pacific         High income W.LIFEEX  0.7562668  1.0000000 0.3191255 -0.33611662
# 3: East Asia & Pacific         High income   W.GINI  0.6253844  0.3191255 1.0000000          NA
# 4: East Asia & Pacific         High income    W.ODA -0.2525850 -0.3361166        NA  1.00000000
# 5: East Asia & Pacific Lower middle income  W.PCGDP  1.0000000  0.4685618 0.4428879 -0.02508852
# 6: East Asia & Pacific Lower middle income W.LIFEEX  0.4685618  1.0000000 0.3231520  0.09356733

In summary: The list processing features, statistical capabilities and efficient converters of collapse and the flexibility of data.table work well together, facilitating more complex workflows.

Additional Benchmarks

See here or here.

These are all run on a 2 core laptop, so I honestly don’t know how collapse scales on powerful multi-core machines. My own limited computational resources are part of the reason I did not opt for a thread-parallel package from the start. But a multi-core version of collapse will eventually be released, maybe by end of 2021.

References

Mundlak, Yair. 1978. “On the Pooling of Time Series and Cross Section Data.” Econometrica 46 (1): 69–85.


  1. Grouping on numeric variables in collapse is always ordered.↩︎

  2. Apart from collapse::BY which is only an auxiliary function written in base R to perform flexible split-apply combine computing on vectors, matrices and data frames.↩︎

  3. unlist2d can similarly bind nested lists of arrays, data frames or data.table’s↩︎