collapse and dplyr

Fast (Weighted) Aggregations and Transformations in a Piped Workflow

Sebastian Krantz

2020-09-13

collapse is a C/C++ based package for data transformation and statistical computing in R. It’s aims are:

  1. To facilitate complex data transformation, exploration and computing tasks in R.
  2. To help make R code fast, flexible, parsimonious and programmer friendly.

This vignette focuses on the integration of collapse and the popular dplyr package by Hadley Wickham. In particular it will demonstrate how using collapse’s fast functions and some fast alternatives for dplyr verbs can substantially facilitate and speed up basic data manipulation, grouped and weighted aggregations and transformations, and panel data computations (i.e. between- and within-transformations, panel-lags, differences and growth rates) in a dplyr (piped) workflow.


Notes:


1. Fast Aggregations

A key feature of collapse is it’s broad set of Fast Statistical Functions (fsum, fprod, fmean, fmedian, fmode, fvar, fsd, fmin, fmax, fnth, ffirst, flast, fNobs, fNdistinct) which are able to substantially speed-up column-wise, grouped and weighted computations on vectors, matrices or data frames. The functions are S3 generic, with a default (vector), matrix and data frame method, as well as a grouped_df method for grouped tibbles used by dplyr. The grouped tibble method has the following arguments:

FUN.grouped_df(x, [w = NULL,] TRA = NULL, [na.rm = TRUE,]
               use.g.names = FALSE, keep.group_vars = TRUE, [keep.w = TRUE,] ...)

where w is a weight variable, and TRA and can be used to transform x using the computed statistics and one of 10 available transformations ("replace_fill", "replace", "-", "-+", "/", "%", "+", "*", "%%", "-%%", discussed in section 2). na.rm efficiently removes missing values and is TRUE by default. use.g.names generates new row-names from the unique combinations of groups (default: disabled), whereas keep.group_vars (default: enabled) will keep the grouping columns as is custom in the native data %>% group_by(...) %>% summarize(...) workflow in dplyr. Finally, keep.w regulates whether a weighting variable used is also aggregated and saved in a column. For fsum, fmean, fmedian, fnth, fvar, fsd and fmode this will compute the sum of the weights in each group, whereas fprod returns the product of the weights.

With that in mind, let’s consider some straightforward applications.

1.1 Simple Aggregations

Consider the Groningen Growth and Development Center 10-Sector Database included in collapse and introduced in the main vignette:

library(collapse)
head(GGDC10S)
#   Country Regioncode             Region Variable Year      AGR      MIN       MAN        PU
# 1     BWA        SSA Sub-saharan Africa       VA 1960       NA       NA        NA        NA
# 2     BWA        SSA Sub-saharan Africa       VA 1961       NA       NA        NA        NA
# 3     BWA        SSA Sub-saharan Africa       VA 1962       NA       NA        NA        NA
# 4     BWA        SSA Sub-saharan Africa       VA 1963       NA       NA        NA        NA
# 5     BWA        SSA Sub-saharan Africa       VA 1964 16.30154 3.494075 0.7365696 0.1043936
# 6     BWA        SSA Sub-saharan Africa       VA 1965 15.72700 2.495768 1.0181992 0.1350976
#         CON      WRT      TRA     FIRE      GOV      OTH      SUM
# 1        NA       NA       NA       NA       NA       NA       NA
# 2        NA       NA       NA       NA       NA       NA       NA
# 3        NA       NA       NA       NA       NA       NA       NA
# 4        NA       NA       NA       NA       NA       NA       NA
# 5 0.6600454 6.243732 1.658928 1.119194 4.822485 2.341328 37.48229
# 6 1.3462312 7.064825 1.939007 1.246789 5.695848 2.678338 39.34710

# Summarize the Data: 
# descr(GGDC10S, cols = is.categorical)
# aperm(qsu(GGDC10S, ~Variable, cols = is.numeric))

Simple column-wise computations using the fast functions and pipe operators are performed as follows:

library(dplyr)

GGDC10S %>% fNobs                       # Number of Observations
#    Country Regioncode     Region   Variable       Year        AGR        MIN        MAN         PU 
#       5027       5027       5027       5027       5027       4364       4355       4355       4354 
#        CON        WRT        TRA       FIRE        GOV        OTH        SUM 
#       4355       4355       4355       4355       3482       4248       4364
GGDC10S %>% fNdistinct                  # Number of distinct values
#    Country Regioncode     Region   Variable       Year        AGR        MIN        MAN         PU 
#         43          6          6          2         67       4353       4224       4353       4237 
#        CON        WRT        TRA       FIRE        GOV        OTH        SUM 
#       4339       4344       4334       4349       3470       4238       4364
GGDC10S %>% select_at(6:16) %>% fmedian # Median
#        AGR        MIN        MAN         PU        CON        WRT        TRA       FIRE        GOV 
#  4394.5194   173.2234  3718.0981   167.9500  1473.4470  3773.6430  1174.8000   960.1251  3928.5127 
#        OTH        SUM 
#  1433.1722 23186.1936
GGDC10S %>% select_at(6:16) %>% fmean   # Mean
#        AGR        MIN        MAN         PU        CON        WRT        TRA       FIRE        GOV 
#  2526696.5  1867908.9  5538491.4   335679.5  1801597.6  3392909.5  1473269.7  1657114.8  1712300.3 
#        OTH        SUM 
#  1684527.3 21566436.8
GGDC10S %>% fmode                       # Mode
#            Country         Regioncode             Region           Variable               Year 
#              "USA"              "ASI"             "Asia"              "EMP"             "2010" 
#                AGR                MIN                MAN                 PU                CON 
# "171.315882316326"                "0" "4645.12507642586"                "0" "1.34623115930777" 
#                WRT                TRA               FIRE                GOV                OTH 
# "21.8380052682527" "8.97743416914571" "40.0701608636442"                "0" "3626.84423577048" 
#                SUM 
# "37.4822945751317"
GGDC10S %>% fmode(drop = FALSE)         # Keep data structure intact
#   Country Regioncode Region Variable Year      AGR MIN      MAN PU      CON      WRT      TRA
# 1     USA        ASI   Asia      EMP 2010 171.3159   0 4645.125  0 1.346231 21.83801 8.977434
#       FIRE GOV      OTH      SUM
# 1 40.07016   0 3626.844 37.48229

Moving on to grouped statistics, we can compute the average value added and employment by sector and country using:

GGDC10S %>% 
  group_by(Variable, Country) %>%
  select_at(6:16) %>% fmean
# # A tibble: 85 x 13
#    Variable Country     AGR     MIN     MAN     PU    CON    WRT    TRA   FIRE     GOV    OTH    SUM
#    <chr>    <chr>     <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
#  1 EMP      ARG       1420.   52.1   1932.  1.02e2 7.42e2 1.98e3 6.49e2  628.   2043.  9.92e2 1.05e4
#  2 EMP      BOL        964.   56.0    235.  5.35e0 1.23e2 2.82e2 1.15e2   44.6    NA   3.96e2 2.22e3
#  3 EMP      BRA      17191.  206.    6991.  3.65e2 3.52e3 8.51e3 2.05e3 4414.   5307.  5.71e3 5.43e4
#  4 EMP      BWA        188.   10.5     18.1 3.09e0 2.53e1 3.63e1 8.36e0   15.3    61.1 2.76e1 3.94e2
#  5 EMP      CHL        702.  101.     625.  2.94e1 2.96e2 6.95e2 2.58e2  272.     NA   1.00e3 3.98e3
#  6 EMP      CHN     287744. 7050.   67144.  1.61e3 2.09e4 2.89e4 1.39e4 4929.  22669.  3.10e4 4.86e5
#  7 EMP      COL       3091.  145.    1175.  3.39e1 5.24e2 2.07e3 4.70e2  649.     NA   1.73e3 9.89e3
#  8 EMP      CRI        231.    1.70   136.  1.43e1 5.76e1 1.57e2 4.24e1   54.9   128.  6.51e1 8.87e2
#  9 EMP      DEW       2490.  407.    8473.  2.26e2 2.09e3 4.44e3 1.48e3 1689.   3945.  9.99e2 2.62e4
# 10 EMP      DNK        236.    8.03   507.  1.38e1 1.71e2 4.55e2 1.61e2  181.    549.  1.11e2 2.39e3
# # ... with 75 more rows

Similarly we can aggregate using any other of the above functions.

It is important to not use dplyr’s summarize together with these functions since that would eliminate their speed gain. These functions are fast because they are executed only once and carry out the grouped computations in C++, whereas summarize will apply the function to each group in the grouped tibble.


Excursus: What is Happening Behind the Scenes?

To better explain this point it is perhaps good to shed some light on what is happening behind the scenes of dplyr and collapse. Fundamentally both packages follow different computing paradigms:

dplyr is an efficient implementation of the Split-Apply-Combine computing paradigm. Data is split into groups, these data-chunks are then passed to a function carrying out the computation, and finally recombined to produce the aggregated data.frame. This modus operandi is evident in the grouping mechanism of dplyr. When a data.frame is passed through group_by, a ‘groups’ attribute is attached:

GGDC10S %>% group_by(Variable, Country) %>% attr("groups")
# # A tibble: 85 x 3
#    Variable Country       .rows
#  * <chr>    <chr>   <list<int>>
#  1 EMP      ARG            [62]
#  2 EMP      BOL            [61]
#  3 EMP      BRA            [62]
#  4 EMP      BWA            [52]
#  5 EMP      CHL            [63]
#  6 EMP      CHN            [62]
#  7 EMP      COL            [61]
#  8 EMP      CRI            [62]
#  9 EMP      DEW            [61]
# 10 EMP      DNK            [64]
# # ... with 75 more rows

This object is a data.frame giving the unique groups and in the third (last) column vectors containing the indices of the rows belonging to that group. A command like summarize uses this information to split the data.frame into groups which are then passed sequentially to the function used and later recombined. These steps are also done in C++ which makes dplyr quite efficient.

Now collapse is based around one-pass grouped computations at the C++ level using its own grouped statistical functions. In other words the data is not split and recombined at all but the entire computation is performed in a single C++ loop running through that data and completing the computations for each group simultaneously. This modus operandi is also evident in collapse grouping objects. The method GRP.grouped_df takes a dplyr grouping object from a grouped tibble and efficiently converts it to a collapse grouping object:

GGDC10S %>% group_by(Variable, Country) %>% GRP %>% str
# List of 8
#  $ N.groups   : int 85
#  $ group.id   : int [1:5027] 46 46 46 46 46 46 46 46 46 46 ...
#  $ group.sizes: int [1:85] 62 61 62 52 63 62 61 62 61 64 ...
#  $ groups     :List of 2
#   ..$ Variable: chr [1:85] "EMP" "EMP" "EMP" "EMP" ...
#   .. ..- attr(*, "label")= chr "Variable"
#   .. ..- attr(*, "format.stata")= chr "%9s"
#   ..$ Country : chr [1:85] "ARG" "BOL" "BRA" "BWA" ...
#   .. ..- attr(*, "label")= chr "Country"
#   .. ..- attr(*, "format.stata")= chr "%9s"
#  $ group.vars : chr [1:2] "Variable" "Country"
#  $ ordered    : logi [1:2] TRUE TRUE
#  $ order      : NULL
#  $ call       : language GRP.grouped_df(X = .)
#  - attr(*, "class")= chr "GRP"

This object is a list where the first three elements give the number of groups, the group-id to which each row belongs and a vector of group-sizes. A function like fsum uses this information to (for each column) create a result vector of size ‘N.groups’ and the run through the column using the ‘group.id’ vector to add the i’th data point to the ’group.id[i]’th element of the result vector. When the loop is finished, the grouped computation is also finished.

It is obvious that collapse is faster than dplyr since it’s method of computing involves less steps, and it does not need to call statistical functions multiple times. See the benchmark section.


1.2 More Speed using collapse Verbs

collapse fast functions do not develop their maximal performance on a grouped tibble created with group_by because of the additional conversion cost of the grouping object incurred by GRP.grouped_df. This cost is already minimized through the use of C++, but we can do even better replacing group_by with collapse::fgroup_by. fgroup_by works like group_by but does the grouping with collapse::GRP (up to 10x faster than group_by) and simply attaches a collapse grouping object to the grouped_df. Thus the speed gain is 2-fold: Faster grouping and no conversion cost when calling collapse functions.

Another improvement comes from replacing the dplyr verb select with collapse::fselect, and, for selection using column names, indices or functions use collapse::get_vars instead of select_at or select_if. Next to get_vars, collapse also introduces the predicates num_vars, cat_vars, char_vars, fact_vars, logi_vars and Date_vars to efficiently select columns by type.

GGDC10S %>% fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fmedian
# # A tibble: 85 x 13
#    Variable Country     AGR     MIN     MAN     PU    CON    WRT    TRA   FIRE     GOV    OTH    SUM
#    <chr>    <chr>     <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
#  1 EMP      ARG       1325.   47.4   1988.  1.05e2 7.82e2 1.85e3 5.80e2  464.   1739.   866.  9.74e3
#  2 EMP      BOL        943.   53.5    167.  4.46e0 6.60e1 1.32e2 9.70e1   15.3    NA    384.  1.84e3
#  3 EMP      BRA      17481.  225.    7208.  3.76e2 4.05e3 6.45e3 1.58e3 4355.   4450.  4479.  5.19e4
#  4 EMP      BWA        175.   12.2     13.1 3.71e0 1.90e1 2.11e1 6.75e0   10.4    53.8   31.2 3.61e2
#  5 EMP      CHL        690.   93.9    607.  2.58e1 2.30e2 4.84e2 2.05e2  106.     NA    900.  3.31e3
#  6 EMP      CHN     293915  8150.   61761.  1.14e3 1.06e4 1.70e4 9.56e3 4328.  19468.  9954.  4.45e5
#  7 EMP      COL       3006.   84.0   1033.  3.71e1 4.19e2 1.55e3 3.91e2  655.     NA   1430.  8.63e3
#  8 EMP      CRI        216.    1.49   114.  7.92e0 5.50e1 8.98e1 2.55e1   19.6   122.    60.6 7.19e2
#  9 EMP      DEW       2178   320.    8459.  2.47e2 2.10e3 4.45e3 1.53e3 1656    3700    900   2.65e4
# 10 EMP      DNK        187.    3.75   508.  1.36e1 1.65e2 4.61e2 1.61e2  169.    642.   104.  2.42e3
# # ... with 75 more rows

microbenchmark(collapse = GGDC10S %>% fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fmedian,
               hybrid = GGDC10S %>% group_by(Variable, Country) %>% select_at(6:16) %>% fmedian,
               dplyr = GGDC10S %>% group_by(Variable, Country) %>% select_at(6:16) %>% summarise_all(median, na.rm = TRUE))
# Unit: microseconds
#      expr       min         lq      mean    median        uq      max neval cld
#  collapse   938.012   987.7685  1037.213  1031.501  1052.251  1262.88   100 a  
#    hybrid 11944.255 12572.3480 13045.616 12755.979 13085.533 31442.58   100  b 
#     dplyr 60455.797 61915.9180 64354.441 62579.488 66871.717 81970.72   100   c

Benchmarks on the different components of this code and with larger data are provided under ‘Benchmarks’. Note that a grouped tibble created with fgroup_by can no longer be used for grouped computations with dplyr verbs like mutate or summarize. To avoid errors with these functions and print.grouped_df, [.grouped_df etc., the classes assigned after fgroup_by are reshuffled, so that the data.frame is treated by the dplyr ecosystem like a normal tibble:

class(group_by(GGDC10S, Variable, Country))
# [1] "grouped_df" "tbl_df"     "tbl"        "data.frame"

class(fgroup_by(GGDC10S, Variable, Country))
# [1] "tbl_df"     "tbl"        "grouped_df" "data.frame"

Also note that fselect and get_vars are not full drop-in replacements for select because they do not have a grouped_df method:

GGDC10S %>% group_by(Variable, Country) %>% select_at(6:16) %>% head(3)
# # A tibble: 3 x 13
# # Groups:   Variable, Country [1]
#   Variable Country   AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH   SUM
#   <chr>    <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 VA       BWA        NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 2 VA       BWA        NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 3 VA       BWA        NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
GGDC10S %>% group_by(Variable, Country) %>% get_vars(6:16) %>% head(3)
# # A tibble: 3 x 11
#     AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH   SUM
#   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 2    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 3    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA

Since by default keep.group_vars = TRUE in the Fast Statistical Functions, the end result is nevertheless the same:

GGDC10S %>% group_by(Variable, Country) %>% select_at(6:16) %>% fmean %>% head(3)
# # A tibble: 3 x 13
#   Variable Country    AGR   MIN   MAN     PU   CON   WRT   TRA   FIRE   GOV   OTH    SUM
#   <chr>    <chr>    <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>
# 1 EMP      ARG      1420.  52.1 1932. 102.    742. 1982.  649.  628.  2043.  992. 10542.
# 2 EMP      BOL       964.  56.0  235.   5.35  123.  282.  115.   44.6   NA   396.  2221.
# 3 EMP      BRA     17191. 206.  6991. 365.   3525. 8509. 2054. 4414.  5307. 5710. 54273.
GGDC10S %>% group_by(Variable, Country) %>% get_vars(6:16) %>% fmean %>% head(3)
# # A tibble: 3 x 13
#   Variable Country    AGR   MIN   MAN     PU   CON   WRT   TRA   FIRE   GOV   OTH    SUM
#   <chr>    <chr>    <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>
# 1 EMP      ARG      1420.  52.1 1932. 102.    742. 1982.  649.  628.  2043.  992. 10542.
# 2 EMP      BOL       964.  56.0  235.   5.35  123.  282.  115.   44.6   NA   396.  2221.
# 3 EMP      BRA     17191. 206.  6991. 365.   3525. 8509. 2054. 4414.  5307. 5710. 54273.

Another useful verb introduced by collapse is fgroup_vars, which can be used to efficiently obtain the grouping columns or grouping variables from a grouped tibble:

# fgroup_by fully supports grouped tibbles created with group_by or fgroup_by: 
GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars %>% head(3)
# # A tibble: 3 x 2
#   Variable Country
#   <chr>    <chr>  
# 1 VA       BWA    
# 2 VA       BWA    
# 3 VA       BWA
GGDC10S %>% fgroup_by(Variable, Country) %>% fgroup_vars %>% head(3)
# # A tibble: 3 x 2
#   Variable Country
#   <chr>    <chr>  
# 1 VA       BWA    
# 2 VA       BWA    
# 3 VA       BWA

# The other possibilities:
GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("unique") %>% head(3)
# # A tibble: 3 x 2
#   Variable Country
#   <chr>    <chr>  
# 1 EMP      ARG    
# 2 EMP      BOL    
# 3 EMP      BRA
GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("names")
# [1] "Variable" "Country"
GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("indices")
# [1] 4 1
GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("named_indices")
# Variable  Country 
#        4        1
GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("logical")
#  [1]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("named_logical")
#    Country Regioncode     Region   Variable       Year        AGR        MIN        MAN         PU 
#       TRUE      FALSE      FALSE       TRUE      FALSE      FALSE      FALSE      FALSE      FALSE 
#        CON        WRT        TRA       FIRE        GOV        OTH        SUM 
#      FALSE      FALSE      FALSE      FALSE      FALSE      FALSE      FALSE

Another collapse verb to mention here is fsubset, a faster alternative to dplyr::filter which also provides an option to flexibly subset columns after the select argument:

# Two equivalent calls, the first is substantially faster
GGDC10S %>% fsubset(Variable == "VA" & Year > 1990, Country, Year, AGR:GOV) %>% head(3)
#   Country Year      AGR      MIN      MAN       PU      CON      WRT      TRA     FIRE      GOV
# 1     BWA 1991 303.1157 2646.950 472.6488 160.6079 580.0876 806.7509 232.7884 432.6965 1073.263
# 2     BWA 1992 333.4364 2690.939 537.4274 178.4532 678.7320 725.2577 285.1403 517.2141 1234.012
# 3     BWA 1993 404.5488 2624.928 567.3420 219.2183 634.2797 771.8253 349.7458 673.2540 1487.193

GGDC10S %>% filter(Variable == "VA" & Year > 1990) %>% select(Country, Year, AGR:GOV) %>% head(3)
#   Country Year      AGR      MIN      MAN       PU      CON      WRT      TRA     FIRE      GOV
# 1     BWA 1991 303.1157 2646.950 472.6488 160.6079 580.0876 806.7509 232.7884 432.6965 1073.263
# 2     BWA 1992 333.4364 2690.939 537.4274 178.4532 678.7320 725.2577 285.1403 517.2141 1234.012
# 3     BWA 1993 404.5488 2624.928 567.3420 219.2183 634.2797 771.8253 349.7458 673.2540 1487.193

collapse also offers roworder, frename, colorder, ftransform/TRA and fungroup as fast replacements for dplyr::arrange, dplyr::rename, dplyr::relocate, dplyr::mutate, and dplyr::ungroup.

1.3 Multi-Function Aggregations

One can also aggregate with multiple functions at the same time. For such operations it is often necessary to use curly braces { to prevent first argument injection so that %>% cbind(FUN1(.), FUN2(.)) does not evaluate as %>% cbind(., FUN1(.), FUN2(.)):

GGDC10S %>%
  fgroup_by(Variable, Country) %>%
  get_vars(6:16) %>% {
    cbind(fmedian(.),
          add_stub(fmean(., keep.group_vars = FALSE), "mean_"))
    } %>% head(3)
#   Variable Country        AGR       MIN       MAN         PU        CON      WRT        TRA
# 1      EMP     ARG  1324.5255  47.35255 1987.5912 104.738825  782.40283 1854.612  579.93982
# 2      EMP     BOL   943.1612  53.53538  167.1502   4.457895   65.97904  132.225   96.96828
# 3      EMP     BRA 17480.9810 225.43693 7207.7915 375.851832 4054.66103 6454.523 1580.81120
#         FIRE      GOV       OTH       SUM   mean_AGR  mean_MIN  mean_MAN    mean_PU  mean_CON
# 1  464.39920 1738.836  866.1119  9743.223  1419.8013  52.08903 1931.7602 101.720936  742.4044
# 2   15.34259       NA  384.0678  1842.055   964.2103  56.03295  235.0332   5.346433  122.7827
# 3 4354.86210 4449.942 4478.6927 51881.110 17191.3529 206.02389 6991.3710 364.573404 3524.7384
#    mean_WRT  mean_TRA  mean_FIRE mean_GOV  mean_OTH  mean_SUM
# 1 1982.1775  648.5119  627.79291 2043.471  992.4475 10542.177
# 2  281.5164  115.4728   44.56442       NA  395.5650  2220.524
# 3 8509.4612 2054.3731 4413.54448 5307.280 5710.2665 54272.985

The function add_stub used above is a collapse function adding a prefix (default) or suffix to variables names. The collapse predicate add_vars provides a more efficient alternative to cbind.data.frame. The idea here is ‘adding’ variables to the data.frame in the first argument i.e. the attributes of the first argument are preserved, so the expression below still gives a tibble instead of a data.frame:

GGDC10S %>%
  fgroup_by(Variable, Country) %>% {
   add_vars(get_vars(., "Reg", regex = TRUE) %>% ffirst, # Regular expression matching column names
            num_vars(.) %>% fmean(keep.group_vars = FALSE) %>% add_stub("mean_"), # num_vars selects all numeric variables
            fselect(., PU:TRA) %>% fmedian(keep.group_vars = FALSE) %>% add_stub("median_"), 
            fselect(., PU:CON) %>% fmin(keep.group_vars = FALSE) %>% add_stub("min_"))      
  } %>% head(3)
# # A tibble: 3 x 22
#   Variable Country Regioncode Region mean_Year mean_AGR mean_MIN mean_MAN mean_PU mean_CON mean_WRT
#   <chr>    <chr>   <chr>      <chr>      <dbl>    <dbl>    <dbl>    <dbl>   <dbl>    <dbl>    <dbl>
# 1 EMP      ARG     LAM        Latin~     1980.    1420.     52.1    1932.  102.       742.    1982.
# 2 EMP      BOL     LAM        Latin~     1980      964.     56.0     235.    5.35     123.     282.
# 3 EMP      BRA     LAM        Latin~     1980.   17191.    206.     6991.  365.      3525.    8509.
# # ... with 11 more variables: mean_TRA <dbl>, mean_FIRE <dbl>, mean_GOV <dbl>, mean_OTH <dbl>,
# #   mean_SUM <dbl>, median_PU <dbl>, median_CON <dbl>, median_WRT <dbl>, median_TRA <dbl>,
# #   min_PU <dbl>, min_CON <dbl>

Another nice feature of add_vars is that it can also very efficiently reorder columns i.e. bind columns in a different order than they are passed. This can be done by simply specifying the positions the added columns should have in the final data frame, and then add_vars shifts the first argument columns to the right to fill in the gaps.

GGDC10S %>%
  fsubset(Variable == "VA", Country, AGR, SUM) %>% 
  fgroup_by(Country) %>% {
   add_vars(fgroup_vars(.,"unique"),
            fmean(., keep.group_vars = FALSE) %>% add_stub("mean_"),
            fsd(., keep.group_vars = FALSE) %>% add_stub("sd_"), 
            pos = c(2,4,3,5))
  } %>% head(3)
#   Country  mean_AGR    sd_AGR   mean_SUM    sd_SUM
# 1     ARG 14951.292 33061.413  152533.84 301316.25
# 2     BOL  3299.718  4456.331   22619.18  33172.98
# 3     BRA 76870.146 59441.696 1200562.67 976963.14

A much more compact solution to multi-function and multi-type aggregation is offered by the function collapg:

# This aggregates numeric colums using the mean (fmean) and categorical columns with the mode (fmode)
GGDC10S %>% fgroup_by(Variable, Country) %>% collapg %>% head(3)
# # A tibble: 3 x 16
#   Variable Country Regioncode Region  Year    AGR   MIN   MAN     PU   CON   WRT   TRA   FIRE   GOV
#   <chr>    <chr>   <chr>      <chr>  <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>
# 1 EMP      ARG     LAM        Latin~ 1980.  1420.  52.1 1932. 102.    742. 1982.  649.  628.  2043.
# 2 EMP      BOL     LAM        Latin~ 1980    964.  56.0  235.   5.35  123.  282.  115.   44.6   NA 
# 3 EMP      BRA     LAM        Latin~ 1980. 17191. 206.  6991. 365.   3525. 8509. 2054. 4414.  5307.
# # ... with 2 more variables: OTH <dbl>, SUM <dbl>

By default it aggregates numeric columns using the fmean and categorical columns using fmode, and preserves the order of all columns. Changing these defaults is very easy:

# This aggregates numeric colums using the median and categorical columns using the first value
GGDC10S %>% fgroup_by(Variable, Country) %>% collapg(fmedian, flast) %>% head(3)
# # A tibble: 3 x 16
#   Variable Country Regioncode Region  Year    AGR   MIN   MAN     PU    CON   WRT    TRA   FIRE
#   <chr>    <chr>   <chr>      <chr>  <dbl>  <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl>
# 1 EMP      ARG     LAM        Latin~ 1980.  1325.  47.4 1988. 105.    782.  1855.  580.   464. 
# 2 EMP      BOL     LAM        Latin~ 1980    943.  53.5  167.   4.46   66.0  132.   97.0   15.3
# 3 EMP      BRA     LAM        Latin~ 1980. 17481. 225.  7208. 376.   4055.  6455. 1581.  4355. 
# # ... with 3 more variables: GOV <dbl>, OTH <dbl>, SUM <dbl>

One can apply multiple functions to both numeric and/or categorical data:

GGDC10S %>% fgroup_by(Variable, Country) %>%
  collapg(list(fmean, fmedian), list(first, fmode, flast)) %>% head(3)
# # A tibble: 3 x 32
#   Variable Country first.Regioncode fmode.Regioncode flast.Regioncode first.Region fmode.Region
#   <chr>    <chr>   <chr>            <chr>            <chr>            <chr>        <chr>       
# 1 EMP      ARG     LAM              LAM              LAM              Latin Ameri~ Latin Ameri~
# 2 EMP      BOL     LAM              LAM              LAM              Latin Ameri~ Latin Ameri~
# 3 EMP      BRA     LAM              LAM              LAM              Latin Ameri~ Latin Ameri~
# # ... with 25 more variables: flast.Region <chr>, fmean.Year <dbl>, fmedian.Year <dbl>,
# #   fmean.AGR <dbl>, fmedian.AGR <dbl>, fmean.MIN <dbl>, fmedian.MIN <dbl>, fmean.MAN <dbl>,
# #   fmedian.MAN <dbl>, fmean.PU <dbl>, fmedian.PU <dbl>, fmean.CON <dbl>, fmedian.CON <dbl>,
# #   fmean.WRT <dbl>, fmedian.WRT <dbl>, fmean.TRA <dbl>, fmedian.TRA <dbl>, fmean.FIRE <dbl>,
# #   fmedian.FIRE <dbl>, fmean.GOV <dbl>, fmedian.GOV <dbl>, fmean.OTH <dbl>, fmedian.OTH <dbl>,
# #   fmean.SUM <dbl>, fmedian.SUM <dbl>

Applying multiple functions to only numeric (or only categorical) data allows return in a long format:

GGDC10S %>% fgroup_by(Variable, Country) %>%
  collapg(list(fmean, fmedian), cols = is.numeric, return = "long") %>% head(3)
# # A tibble: 3 x 15
#   Function Variable Country  Year    AGR   MIN   MAN     PU   CON   WRT   TRA   FIRE   GOV   OTH
#   <chr>    <chr>    <chr>   <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
# 1 fmean    EMP      ARG     1980.  1420.  52.1 1932. 102.    742. 1982.  649.  628.  2043.  992.
# 2 fmean    EMP      BOL     1980    964.  56.0  235.   5.35  123.  282.  115.   44.6   NA   396.
# 3 fmean    EMP      BRA     1980. 17191. 206.  6991. 365.   3525. 8509. 2054. 4414.  5307. 5710.
# # ... with 1 more variable: SUM <dbl>

Finally, collapg also makes it very easy to apply aggregator functions to certain columns only:

GGDC10S %>% fgroup_by(Variable, Country) %>%
  collapg(custom = list(fmean = 6:8, fmedian = 10:12)) %>% head(3)
# # A tibble: 3 x 8
#   Variable Country fmean.AGR fmean.MIN fmean.MAN fmedian.CON fmedian.WRT fmedian.TRA
#   <chr>    <chr>       <dbl>     <dbl>     <dbl>       <dbl>       <dbl>       <dbl>
# 1 EMP      ARG         1420.      52.1     1932.       782.        1855.       580. 
# 2 EMP      BOL          964.      56.0      235.        66.0        132.        97.0
# 3 EMP      BRA        17191.     206.      6991.      4055.        6455.      1581.

To understand more about collapg, look it up in the documentation (?collapg).

1.4 Weighted Aggregations

Weighted aggregations are possible with the functions fsum, fprod, fmean, fmedian, fnth, fmode, fvar and fsd. The implementation is such that by default (option keep.w = TRUE) these functions also aggregate the weights, so that further weighted computations can be performed on the aggregated data. fprod saves the product of the weights, whereas the other functions save the sum of the weights in a column next to the grouping variables. If na.rm = TRUE (the default), rows with missing weights are omitted from the computation.

# This computes a frequency-weighted grouped standard-deviation, taking the total EMP / VA as weight
GGDC10S %>%
  fgroup_by(Variable, Country) %>%
  fselect(AGR:SUM) %>% fsd(SUM) %>% head(3)
# # A tibble: 3 x 13
#   Variable Country  sum.SUM    AGR   MIN   MAN    PU   CON   WRT    TRA   FIRE   GOV   OTH
#   <chr>    <chr>      <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>
# 1 EMP      ARG      653615.  225.   22.2  176. 20.5   285.  856.  195.   493.  1123.  506.
# 2 EMP      BOL      135452.   99.7  17.1  168.  4.87  123.  324.   98.1   69.8   NA   258.
# 3 EMP      BRA     3364925. 1587.   73.8 2952. 93.8  1861. 6285. 1306.  3003.  3621. 4257.

# This computes a weighted grouped mode, taking the total EMP / VA as weight
GGDC10S %>%
  fgroup_by(Variable, Country) %>%
  fselect(AGR:SUM) %>% fmode(SUM) %>% head(3)
# # A tibble: 3 x 13
#   Variable Country  sum.SUM    AGR   MIN    MAN    PU   CON    WRT   TRA   FIRE    GOV    OTH
#   <chr>    <chr>      <dbl>  <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>  <dbl>  <dbl>
# 1 EMP      ARG      653615.  1162. 127.   2164. 152.  1415.  3768. 1060.  1748.  4336.  1999.
# 2 EMP      BOL      135452.   819.  37.6   604.  10.8  433.   893.  333.   321.    NA   1057.
# 3 EMP      BRA     3364925. 16451. 313.  11841. 388.  8154. 21860. 5169. 12011. 12149. 14235.

The weighted variance / standard deviation is currently only implemented with frequency weights.

Weighted aggregations may also be performed with collapg. By default fsum is used to compute a sum of the weights, but it is also possible here to aggregate the weights with other functions:

# This aggregates numeric colums using the weighted mean (the default) and categorical columns using the weighted mode (the default).
# Weights (column SUM) are aggregated using both the sum and the maximum. 
GGDC10S %>% group_by(Variable, Country) %>% 
  collapg(w = SUM, wFUN = list(fsum, fmax)) %>% head(3)
# # A tibble: 3 x 17
#   Variable Country fsum.SUM fmax.SUM Regioncode Region  Year    AGR   MIN   MAN     PU   CON    WRT
#   <chr>    <chr>      <dbl>    <dbl> <chr>      <chr>  <dbl>  <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>
# 1 EMP      ARG      653615.   17929. LAM        Latin~ 1985.  1361.  56.5 1935. 105.    811.  2217.
# 2 EMP      BOL      135452.    4508. LAM        Latin~ 1987.   977.  57.9  296.   7.07  167.   400.
# 3 EMP      BRA     3364925.  102572. LAM        Latin~ 1989. 17746. 238.  8466. 389.   4436. 11376.
# # ... with 4 more variables: TRA <dbl>, FIRE <dbl>, GOV <dbl>, OTH <dbl>

2. Fast Transformations

collapse also provides some fast transformations that significantly extend the scope and speed of manipulations that can be performed with dplyr::mutate.

2.1 Fast Transform and Compute Variables

The function ftransform can be used to manipulate columns in the same ways as mutate:

GGDC10S %>% fsubset(Variable == "VA", Country, Year, AGR, SUM) %>%
  ftransform(AGR_perc = AGR / SUM * 100,  # Computing % of VA in Agriculture
             AGR_mean = fmean(AGR),       # Average Agricultural VA
             AGR = NULL, SUM = NULL) %>%  # Deleting columns AGR and SUM
             head
#   Country Year AGR_perc AGR_mean
# 1     BWA 1960       NA  5137561
# 2     BWA 1961       NA  5137561
# 3     BWA 1962       NA  5137561
# 4     BWA 1963       NA  5137561
# 5     BWA 1964 43.49132  5137561
# 6     BWA 1965 39.96990  5137561

The modification brought by ftransformv enables transformations of groups of columns like dplyr::mutate_at and dplyr::mutate_if:

# This replaces variables mpg, carb and wt by their log (.c turns expressions into character vectors)
mtcars %>% ftransformv(.c(mpg, carb, wt), log) %>% head
#                        mpg cyl disp  hp drat        wt  qsec vs am gear      carb
# Mazda RX4         3.044522   6  160 110 3.90 0.9631743 16.46  0  1    4 1.3862944
# Mazda RX4 Wag     3.044522   6  160 110 3.90 1.0560527 17.02  0  1    4 1.3862944
# Datsun 710        3.126761   4  108  93 3.85 0.8415672 18.61  1  1    4 0.0000000
# Hornet 4 Drive    3.063391   6  258 110 3.08 1.1678274 19.44  1  0    3 0.0000000
# Hornet Sportabout 2.928524   8  360 175 3.15 1.2354715 17.02  0  0    3 0.6931472
# Valiant           2.895912   6  225 105 2.76 1.2412686 20.22  1  0    3 0.0000000

# Logging numeric variables
iris %>% ftransformv(is.numeric, log) %>% head
#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1     1.629241    1.252763    0.3364722  -1.6094379  setosa
# 2     1.589235    1.098612    0.3364722  -1.6094379  setosa
# 3     1.547563    1.163151    0.2623643  -1.6094379  setosa
# 4     1.526056    1.131402    0.4054651  -1.6094379  setosa
# 5     1.609438    1.280934    0.3364722  -1.6094379  setosa
# 6     1.686399    1.360977    0.5306283  -0.9162907  setosa

Instead of column = value type arguments, it is also possible to pass a single list of transformed variables to ftransform, which will be regarded in the same way as an evaluated list of column = value arguments. It can be used for more complex transformations:

# Logging values and replacing generated Inf values
mtcars %>% ftransform(fselect(., mpg, cyl, vs:gear) %>% lapply(log) %>% replace_Inf) %>% head
#                        mpg      cyl disp  hp drat    wt  qsec vs am     gear carb
# Mazda RX4         3.044522 1.791759  160 110 3.90 2.620 16.46 NA  0 1.386294    4
# Mazda RX4 Wag     3.044522 1.791759  160 110 3.90 2.875 17.02 NA  0 1.386294    4
# Datsun 710        3.126761 1.386294  108  93 3.85 2.320 18.61  0  0 1.386294    1
# Hornet 4 Drive    3.063391 1.791759  258 110 3.08 3.215 19.44  0 NA 1.098612    1
# Hornet Sportabout 2.928524 2.079442  360 175 3.15 3.440 17.02 NA NA 1.098612    2
# Valiant           2.895912 1.791759  225 105 2.76 3.460 20.22  0 NA 1.098612    1

If only the computed columns need to be returned, fcompute provides an efficient alternative:

GGDC10S %>% fsubset(Variable == "VA", Country, Year, AGR, SUM) %>%
  fcompute(AGR_perc = AGR / SUM * 100,
           AGR_mean = fmean(AGR)) %>% head
#   AGR_perc AGR_mean
# 1       NA  5137561
# 2       NA  5137561
# 3       NA  5137561
# 4       NA  5137561
# 5 43.49132  5137561
# 6 39.96990  5137561

ftransform and fcompute are an order of magnitude faster than mutate, but they do not support grouped computations using arbitrary functions. We will see that this is hardly a limitation as collapse provides very efficient and elegant alternative programming mechanisms…

2.2 Replacing and Sweeping out Statistics

All statistical (scalar-valued) functions in the collapse package (fsum, fprod, fmean, fmedian, fmode, fvar, fsd, fmin, fmax, fnth, ffirst, flast, fNobs, fNdistinct) have a TRA argument which can be used to efficiently transforms data by either (column-wise) replacing data values with computed statistics or sweeping the statistics out of the data. Operations can be specified using either an integer or quoted operator / string. The 10 operations supported by TRA are:

Simple transformations are again straightforward to specify:

# This subtracts the median value from all data points i.e. centers on the median
GGDC10S %>% num_vars %>% fmedian(TRA = "-") %>% head
#   Year       AGR       MIN       MAN        PU       CON       WRT       TRA      FIRE       GOV
# 1  -22        NA        NA        NA        NA        NA        NA        NA        NA        NA
# 2  -21        NA        NA        NA        NA        NA        NA        NA        NA        NA
# 3  -20        NA        NA        NA        NA        NA        NA        NA        NA        NA
# 4  -19        NA        NA        NA        NA        NA        NA        NA        NA        NA
# 5  -18 -4378.218 -169.7294 -3717.362 -167.8456 -1472.787 -3767.399 -1173.141 -959.0059 -3923.690
# 6  -17 -4378.792 -170.7277 -3717.080 -167.8149 -1472.101 -3766.578 -1172.861 -958.8783 -3922.817
#         OTH       SUM
# 1        NA        NA
# 2        NA        NA
# 3        NA        NA
# 4        NA        NA
# 5 -1430.831 -23148.71
# 6 -1430.494 -23146.85

# This replaces all data points with the mode
GGDC10S %>% char_vars %>% fmode(TRA = "replace") %>% head
#   Country Regioncode Region Variable
# 1     USA        ASI   Asia      EMP
# 2     USA        ASI   Asia      EMP
# 3     USA        ASI   Asia      EMP
# 4     USA        ASI   Asia      EMP
# 5     USA        ASI   Asia      EMP
# 6     USA        ASI   Asia      EMP

Similarly for grouped transformations:

# Replacing data with the 2nd quartile (25%)
GGDC10S %>%
  fselect(Variable, Country, AGR:SUM) %>% 
   fgroup_by(Variable, Country) %>% fnth(0.25, TRA = "replace_fill") %>% head(3)
# # A tibble: 3 x 13
#   Variable Country   AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH   SUM
#   <chr>    <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 VA       BWA      61.3  21.7  23.1  6.31  23.2  26.7  8.98  11.3  27.0  10.1  220.
# 2 VA       BWA      61.3  21.7  23.1  6.31  23.2  26.7  8.98  11.3  27.0  10.1  220.
# 3 VA       BWA      61.3  21.7  23.1  6.31  23.2  26.7  8.98  11.3  27.0  10.1  220.

# Scaling sectoral data by Variable and Country
GGDC10S %>%
  fselect(Variable, Country, AGR:SUM) %>% 
   fgroup_by(Variable, Country) %>% fsd(TRA = "/") %>% head
# # A tibble: 6 x 13
#   Variable Country     AGR      MIN      MAN       PU      CON      WRT      TRA     FIRE      GOV
#   <chr>    <chr>     <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
# 1 VA       BWA     NA      NA       NA       NA       NA       NA       NA       NA       NA      
# 2 VA       BWA     NA      NA       NA       NA       NA       NA       NA       NA       NA      
# 3 VA       BWA     NA      NA       NA       NA       NA       NA       NA       NA       NA      
# 4 VA       BWA     NA      NA       NA       NA       NA       NA       NA       NA       NA      
# 5 VA       BWA      0.0270  5.56e-4  5.23e-4  3.88e-4  5.11e-4  0.00194  0.00154  5.23e-4  0.00134
# 6 VA       BWA      0.0260  3.97e-4  7.23e-4  5.03e-4  1.04e-3  0.00220  0.00180  5.83e-4  0.00158
# # ... with 2 more variables: OTH <dbl>, SUM <dbl>

The benchmarks below will demonstrate that these internal sweeping and replacement operations fully performed in C++ compute significantly faster than using dplyr::mutate, especially as the number of groups grows large. The S3 generic nature of the Fast Statistical Functions further allows us to perform grouped mutations on the fly (together with ftransform or fcompute), without the need of first creating a grouped tibble:

# AGR_gmed = TRUE if AGR is greater than it's median value, grouped by Variable and Country
# Note: This calls fmedian.default
settransform(GGDC10S, AGR_gmed = AGR > fmedian(AGR, list(Variable, Country), TRA = "replace"))
tail(GGDC10S, 3)
#      Country Regioncode                       Region Variable Year      AGR      MIN      MAN
# 5025     EGY       MENA Middle East and North Africa      EMP 2010 5205.529 28.99641 2435.549
# 5026     EGY       MENA Middle East and North Africa      EMP 2011 5185.919 27.56394 2373.814
# 5027     EGY       MENA Middle East and North Africa      EMP 2012 5160.590 24.78083 2348.434
#            PU      CON      WRT      TRA     FIRE      GOV OTH      SUM AGR_gmed
# 5025 307.2712 2732.953 2977.063 1992.274 801.2984 5538.946  NA 22019.88     TRUE
# 5026 317.9979 2795.264 3020.236 2048.335 814.7403 5635.522  NA 22219.39     TRUE
# 5027 324.9332 2931.196 3109.522 2065.004 832.4770 5735.623  NA 22532.56     TRUE

# Dividing (scaling) the sectoral data (columns 6 through 16) by their grouped standard deviation
settransformv(GGDC10S, 6:16, fsd, list(Variable, Country), TRA = "/", apply = FALSE)
tail(GGDC10S, 3)
#      Country Regioncode                       Region Variable Year      AGR      MIN      MAN
# 5025     EGY       MENA Middle East and North Africa      EMP 2010 8.410070 2.279497 4.320400
# 5026     EGY       MENA Middle East and North Africa      EMP 2011 8.378388 2.166885 4.210888
# 5027     EGY       MENA Middle East and North Africa      EMP 2012 8.337467 1.948097 4.165868
#            PU      CON      WRT      TRA     FIRE      GOV OTH      SUM AGR_gmed
# 5025 3.556820 3.621614 3.754698 3.750569 3.138243 3.795534  NA 4.441450     TRUE
# 5026 3.680987 3.704186 3.809148 3.856107 3.190888 3.861712  NA 4.481691     TRUE
# 5027 3.761267 3.884319 3.921756 3.887489 3.260353 3.930305  NA 4.544858     TRUE

Weights are easily added to any grouped transformation:

# This subtracts weighted group means from the data, using SUM column as weights.. 
GGDC10S %>%
  fselect(Variable, Country, AGR:SUM) %>% 
   fgroup_by(Variable, Country) %>% fmean(SUM, "-") %>% head
# # A tibble: 6 x 13
#   Variable Country      SUM   AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH
#   <chr>    <chr>      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 VA       BWA     NA       NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   
# 2 VA       BWA     NA       NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   
# 3 VA       BWA     NA       NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   
# 4 VA       BWA     NA       NA    NA    NA    NA    NA    NA    NA    NA    NA    NA   
# 5 VA       BWA      0.00180 -2.15 -2.12 -2.11 -1.97 -2.12 -2.03 -2.00 -2.07 -2.09 -2.02
# 6 VA       BWA      0.00189 -2.16 -2.12 -2.11 -1.97 -2.12 -2.03 -2.00 -2.07 -2.09 -2.02

Sequential operations are also easily performed:

# This scales and then subtracts the median
GGDC10S %>%
  fselect(Variable, Country, AGR:SUM) %>% 
   fgroup_by(Variable, Country) %>% fsd(TRA = "/") %>% fmedian(TRA = "-")
# # A tibble: 5,027 x 13
#    Variable Country    AGR    MIN    MAN     PU    CON     WRT     TRA    FIRE    GOV     OTH    SUM
#  * <chr>    <chr>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>   <dbl>  <dbl>   <dbl>  <dbl>
#  1 VA       BWA     NA     NA     NA     NA     NA     NA      NA      NA      NA     NA      NA    
#  2 VA       BWA     NA     NA     NA     NA     NA     NA      NA      NA      NA     NA      NA    
#  3 VA       BWA     NA     NA     NA     NA     NA     NA      NA      NA      NA     NA      NA    
#  4 VA       BWA     NA     NA     NA     NA     NA     NA      NA      NA      NA     NA      NA    
#  5 VA       BWA     -0.182 -0.235 -0.183 -0.245 -0.118 -0.0820 -0.0724 -0.0661 -0.108 -0.0848 -0.146
#  6 VA       BWA     -0.183 -0.235 -0.183 -0.245 -0.117 -0.0817 -0.0722 -0.0660 -0.108 -0.0846 -0.146
#  7 VA       BWA     -0.180 -0.235 -0.183 -0.245 -0.117 -0.0813 -0.0720 -0.0659 -0.107 -0.0843 -0.145
#  8 VA       BWA     -0.177 -0.235 -0.183 -0.245 -0.117 -0.0826 -0.0724 -0.0659 -0.107 -0.0841 -0.146
#  9 VA       BWA     -0.174 -0.235 -0.183 -0.245 -0.117 -0.0823 -0.0717 -0.0661 -0.108 -0.0848 -0.146
# 10 VA       BWA     -0.173 -0.234 -0.182 -0.243 -0.115 -0.0821 -0.0715 -0.0660 -0.108 -0.0846 -0.145
# # ... with 5,017 more rows

Of course it is also possible to combine multiple functions as in the aggregation section, or to add variables to existing data:

# This adds a groupwise observation count next to each column
add_vars(GGDC10S, seq(7,27,2)) <- GGDC10S %>%
    fgroup_by(Variable, Country) %>% fselect(AGR:SUM) %>%
    fNobs("replace_fill") %>% add_stub("N_")

head(GGDC10S)
#   Country Regioncode             Region Variable Year        AGR N_AGR          MIN N_MIN
# 1     BWA        SSA Sub-saharan Africa       VA 1960         NA    47           NA    47
# 2     BWA        SSA Sub-saharan Africa       VA 1961         NA    47           NA    47
# 3     BWA        SSA Sub-saharan Africa       VA 1962         NA    47           NA    47
# 4     BWA        SSA Sub-saharan Africa       VA 1963         NA    47           NA    47
# 5     BWA        SSA Sub-saharan Africa       VA 1964 0.02699257    47 0.0005557549    47
# 6     BWA        SSA Sub-saharan Africa       VA 1965 0.02604122    47 0.0003969678    47
#            MAN N_MAN           PU N_PU          CON N_CON         WRT N_WRT         TRA N_TRA
# 1           NA    47           NA   47           NA    47          NA    47          NA    47
# 2           NA    47           NA   47           NA    47          NA    47          NA    47
# 3           NA    47           NA   47           NA    47          NA    47          NA    47
# 4           NA    47           NA   47           NA    47          NA    47          NA    47
# 5 0.0005231589    47 0.0003884954   47 0.0005107143    47 0.001939917    47 0.001541949    47
# 6 0.0007231902    47 0.0005027587   47 0.0010416550    47 0.002195030    47 0.001802278    47
#           FIRE N_FIRE         GOV N_GOV         OTH N_OTH         SUM N_SUM AGR_gmed
# 1           NA     47          NA    47          NA    47          NA    47       NA
# 2           NA     47          NA    47          NA    47          NA    47       NA
# 3           NA     47          NA    47          NA    47          NA    47       NA
# 4           NA     47          NA    47          NA    47          NA    47       NA
# 5 0.0005234982     47 0.001336269    47 0.001810803    47 0.001799950    47    FALSE
# 6 0.0005831804     47 0.001578270    47 0.002071448    47 0.001889501    47    FALSE
rm(GGDC10S)

There are lots of other examples one could construct using the 10 operations and 14 functions listed above, the examples provided just outline the suggested programming basics. Performance considerations make it very much worthwhile to spend some time and think how complex operations can be implemented in this programming framework, before defining some function in R and applying it to data using dplyr::mutate.

2.3 More Control using the TRA Function

Towards this end, calling TRA() directly also facilitates more complex and customized operations. Behind the scenes of the TRA = ... argument, the Fast Statistical Functions first compute the grouped statistics on all columns of the data, and these statistics are then directly fed into a C++ function that uses them to replace or sweep them out of data points in one of the 10 ways described above. This function can also be called directly by the name of TRA.

Fundamentally, TRA is a generalization of base::sweep for column-wise grouped operations1. Direct calls to TRA enable more control over inputs and outputs.

The two operations below are equivalent, although the first is slightly more efficient as it only requires one method dispatch and one check of the inputs:

# This divides by the product
GGDC10S %>%
  fgroup_by(Variable, Country) %>%
    get_vars(6:16) %>% fprod(TRA = "/") %>% head
# # A tibble: 6 x 11
#          AGR        MIN        MAN        PU        CON        WRT       TRA      FIRE        GOV
#        <dbl>      <dbl>      <dbl>     <dbl>      <dbl>      <dbl>     <dbl>     <dbl>      <dbl>
# 1 NA         NA         NA         NA        NA         NA         NA        NA        NA        
# 2 NA         NA         NA         NA        NA         NA         NA        NA        NA        
# 3 NA         NA         NA         NA        NA         NA         NA        NA        NA        
# 4 NA         NA         NA         NA        NA         NA         NA        NA        NA        
# 5  1.29e-105  2.81e-127  1.40e-101  4.44e-74  4.19e-102  3.97e-113  6.91e-92  1.01e-97  2.51e-117
# 6  1.24e-105  2.00e-127  1.94e-101  5.75e-74  8.55e-102  4.49e-113  8.08e-92  1.13e-97  2.96e-117
# # ... with 2 more variables: OTH <dbl>, SUM <dbl>

# Same thing
GGDC10S %>%
  fgroup_by(Variable, Country) %>%
    get_vars(6:16) %>% 
     TRA(fprod(., keep.group_vars = FALSE), "/") %>% head # [same as TRA(.,fprod(., keep.group_vars = FALSE),"/")]
# # A tibble: 6 x 11
#          AGR        MIN        MAN        PU        CON        WRT       TRA      FIRE        GOV
#        <dbl>      <dbl>      <dbl>     <dbl>      <dbl>      <dbl>     <dbl>     <dbl>      <dbl>
# 1 NA         NA         NA         NA        NA         NA         NA        NA        NA        
# 2 NA         NA         NA         NA        NA         NA         NA        NA        NA        
# 3 NA         NA         NA         NA        NA         NA         NA        NA        NA        
# 4 NA         NA         NA         NA        NA         NA         NA        NA        NA        
# 5  1.29e-105  2.81e-127  1.40e-101  4.44e-74  4.19e-102  3.97e-113  6.91e-92  1.01e-97  2.51e-117
# 6  1.24e-105  2.00e-127  1.94e-101  5.75e-74  8.55e-102  4.49e-113  8.08e-92  1.13e-97  2.96e-117
# # ... with 2 more variables: OTH <dbl>, SUM <dbl>

TRA.grouped_df was designed such that it matches the columns of the statistics (aggregated columns) to those of the original data, and only transforms matching columns while returning the whole data frame. Thus it is easily possible to only apply a transformation to the first two sectors:

# This only demeans Agriculture (AGR) and Mining (MIN)
GGDC10S %>%
  fgroup_by(Variable, Country) %>%
    TRA(fselect(., AGR, MIN) %>% fmean(keep.group_vars = FALSE), "-") %>% head
# # A tibble: 6 x 16
#   Country Regioncode Region Variable  Year   AGR    MIN    MAN     PU    CON   WRT   TRA  FIRE   GOV
#   <chr>   <chr>      <chr>  <chr>    <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 BWA     SSA        Sub-s~ VA        1960   NA     NA  NA     NA     NA     NA    NA    NA    NA   
# 2 BWA     SSA        Sub-s~ VA        1961   NA     NA  NA     NA     NA     NA    NA    NA    NA   
# 3 BWA     SSA        Sub-s~ VA        1962   NA     NA  NA     NA     NA     NA    NA    NA    NA   
# 4 BWA     SSA        Sub-s~ VA        1963   NA     NA  NA     NA     NA     NA    NA    NA    NA   
# 5 BWA     SSA        Sub-s~ VA        1964 -446. -4505.  0.737  0.104  0.660  6.24  1.66  1.12  4.82
# 6 BWA     SSA        Sub-s~ VA        1965 -446. -4506.  1.02   0.135  1.35   7.06  1.94  1.25  5.70
# # ... with 2 more variables: OTH <dbl>, SUM <dbl>

Since TRA is already built into all Fast Statistical Functions as an argument, it is best used in computations where grouped statistics are computed using some other function.

# Same as above, with one line of code using fmean.data.frame and ftransform...
GGDC10S %>% ftransform(fmean(list(AGR = AGR, MIN = MIN), list(Variable, Country), TRA = "-")) %>% head
#   Country Regioncode             Region Variable Year       AGR       MIN       MAN        PU
# 1     BWA        SSA Sub-saharan Africa       VA 1960        NA        NA        NA        NA
# 2     BWA        SSA Sub-saharan Africa       VA 1961        NA        NA        NA        NA
# 3     BWA        SSA Sub-saharan Africa       VA 1962        NA        NA        NA        NA
# 4     BWA        SSA Sub-saharan Africa       VA 1963        NA        NA        NA        NA
# 5     BWA        SSA Sub-saharan Africa       VA 1964 -445.8739 -4505.178 0.7365696 0.1043936
# 6     BWA        SSA Sub-saharan Africa       VA 1965 -446.4485 -4506.176 1.0181992 0.1350976
#         CON      WRT      TRA     FIRE      GOV      OTH      SUM
# 1        NA       NA       NA       NA       NA       NA       NA
# 2        NA       NA       NA       NA       NA       NA       NA
# 3        NA       NA       NA       NA       NA       NA       NA
# 4        NA       NA       NA       NA       NA       NA       NA
# 5 0.6600454 6.243732 1.658928 1.119194 4.822485 2.341328 37.48229
# 6 1.3462312 7.064825 1.939007 1.246789 5.695848 2.678338 39.34710

Another potential use of TRA is to do computations in two- or more steps, for example if both aggregated and transformed data are needed, or if computations are more complex and involve other manipulations in-between the aggregating and sweeping part:

# Get grouped tibble
gGGDC <- GGDC10S %>% fgroup_by(Variable, Country)

# Get aggregated data
gsumGGDC <- gGGDC %>% fselect(AGR:SUM) %>% fsum
head(gsumGGDC)
# # A tibble: 6 x 13
#   Variable Country     AGR     MIN     MAN     PU     CON    WRT    TRA   FIRE     GOV    OTH    SUM
#   <chr>    <chr>     <dbl>   <dbl>   <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
# 1 EMP      ARG      8.80e4   3230.  1.20e5  6307.  4.60e4 1.23e5 4.02e4 3.89e4  1.27e5 6.15e4 6.54e5
# 2 EMP      BOL      5.88e4   3418.  1.43e4   326.  7.49e3 1.72e4 7.04e3 2.72e3 NA      2.41e4 1.35e5
# 3 EMP      BRA      1.07e6  12773.  4.33e5 22604.  2.19e5 5.28e5 1.27e5 2.74e5  3.29e5 3.54e5 3.36e6
# 4 EMP      BWA      8.84e3    493.  8.49e2   145.  1.19e3 1.71e3 3.93e2 7.21e2  2.87e3 1.30e3 1.85e4
# 5 EMP      CHL      4.42e4   6389.  3.94e4  1850.  1.86e4 4.38e4 1.63e4 1.72e4 NA      6.32e4 2.51e5
# 6 EMP      CHN      1.73e7 422972.  4.03e6 96364.  1.25e6 1.73e6 8.36e5 2.96e5  1.36e6 1.86e6 2.91e7

# Get transformed (scaled) data
head(TRA(gGGDC, gsumGGDC, "/"))
# # A tibble: 6 x 16
#   Country Regioncode Region Variable  Year      AGR      MIN      MAN       PU      CON      WRT
#   <chr>   <chr>      <chr>  <chr>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
# 1 BWA     SSA        Sub-s~ VA        1960 NA       NA       NA       NA       NA       NA      
# 2 BWA     SSA        Sub-s~ VA        1961 NA       NA       NA       NA       NA       NA      
# 3 BWA     SSA        Sub-s~ VA        1962 NA       NA       NA       NA       NA       NA      
# 4 BWA     SSA        Sub-s~ VA        1963 NA       NA       NA       NA       NA       NA      
# 5 BWA     SSA        Sub-s~ VA        1964  7.50e-4  1.65e-5  1.66e-5  1.03e-5  1.57e-5  6.82e-5
# 6 BWA     SSA        Sub-s~ VA        1965  7.24e-4  1.18e-5  2.30e-5  1.33e-5  3.20e-5  7.72e-5
# # ... with 5 more variables: TRA <dbl>, FIRE <dbl>, GOV <dbl>, OTH <dbl>, SUM <dbl>

As discussed, whether using the argument to fast statistical functions or TRA directly, these data transformations are essentially a two-step process: Statistics are first computed and then used to transform the original data.

Although both steps are efficiently done in C++, it would be even more efficient to do them in a single step without materializing all the statistics before transforming the data. Such slightly more efficient functions are provided for the very commonly applied tasks of centering and averaging data by groups (widely known as ‘between’-group and ‘within’-group transformations), and scaling and centering data by groups (also known as ‘standardizing’ data).

2.4 Faster Centering, Averaging and Standardizing

The functions fbetween and fwithin are slightly more memory efficient implementations of fmean invoked with different TRA options:

GGDC10S %>% # Same as ... %>% fmean(TRA = "replace")
  fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fbetween %>% head(2)
# # A tibble: 2 x 11
#     AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH   SUM
#   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 2    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA

GGDC10S %>% # Same as ... %>% fmean(TRA = "replace_fill")
  fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fbetween(fill = TRUE) %>% head(2)
# # A tibble: 2 x 11
#     AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH    SUM
#   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
# 1  462. 4509.  942.  216.  895. 1948.  635. 1359. 2373.  773. 14112.
# 2  462. 4509.  942.  216.  895. 1948.  635. 1359. 2373.  773. 14112.

GGDC10S %>% # Same as ... %>% fmean(TRA = "-")
  fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fwithin %>% head(2)
# # A tibble: 2 x 11
#     AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH   SUM
#   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 2    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA

Apart from higher speed, fwithin has a mean argument to assign an arbitrary mean to centered data, the default being mean = 0. A very common choice for such an added mean is just the overall mean of the data, which can be added in by invoking mean = "overall.mean":

GGDC10S %>% 
  fgroup_by(Variable, Country) %>% 
    fselect(Country, Variable, AGR:SUM) %>% fwithin(mean = "overall.mean") %>% head(3)
# # A tibble: 3 x 13
#   Country Variable   AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH   SUM
#   <chr>   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 BWA     VA          NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 2 BWA     VA          NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 3 BWA     VA          NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA

This can also be done using weights. The code below uses the SUM column as weights, and then for each variable and each group subtracts out the weighted mean, and then adds the overall weighted column mean back to the centered columns. The SUM column is just kept as it is and added after the grouping columns.

GGDC10S %>% 
  fgroup_by(Variable, Country) %>% 
    fselect(Country, Variable, AGR:SUM) %>% fwithin(SUM, mean = "overall.mean") %>% head(3)
# # A tibble: 3 x 13
#   Country Variable   SUM   AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH
#   <chr>   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 BWA     VA          NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 2 BWA     VA          NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
# 3 BWA     VA          NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA

Another argument to fwithin is the theta parameter, allowing partial- or quasi-demeaning operations, e.g. fwithin(gdata, theta = theta) is equal to gdata - theta * fbetween(gdata). This is particularly useful to prepare data for variance components (also known as ‘random-effects’) estimation.

Apart from fbetween and fwithin, the function fscale exists to efficiently scale and center data, to avoid sequential calls such as ... %>% fsd(TRA = "/") %>% fmean(TRA = "-").

# This efficiently scales and centers (i.e. standardizes) the data
GGDC10S %>%
  fgroup_by(Variable, Country) %>%
    fselect(Country, Variable, AGR:SUM) %>% fscale
# # A tibble: 5,027 x 13
#    Country Variable    AGR    MIN    MAN     PU    CON    WRT    TRA   FIRE    GOV    OTH    SUM
#  * <chr>   <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#  1 BWA     VA       NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA    
#  2 BWA     VA       NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA    
#  3 BWA     VA       NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA    
#  4 BWA     VA       NA     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA    
#  5 BWA     VA       -0.738 -0.717 -0.668 -0.805 -0.692 -0.603 -0.589 -0.635 -0.656 -0.596 -0.676
#  6 BWA     VA       -0.739 -0.717 -0.668 -0.805 -0.692 -0.603 -0.589 -0.635 -0.656 -0.596 -0.676
#  7 BWA     VA       -0.736 -0.717 -0.668 -0.805 -0.692 -0.603 -0.589 -0.635 -0.656 -0.595 -0.676
#  8 BWA     VA       -0.734 -0.717 -0.668 -0.805 -0.692 -0.604 -0.589 -0.635 -0.655 -0.595 -0.676
#  9 BWA     VA       -0.730 -0.717 -0.668 -0.805 -0.692 -0.604 -0.588 -0.635 -0.656 -0.596 -0.676
# 10 BWA     VA       -0.729 -0.716 -0.667 -0.803 -0.690 -0.603 -0.588 -0.635 -0.656 -0.596 -0.675
# # ... with 5,017 more rows

fscale also has additional mean and sd arguments allowing the user to (group-) scale data to an arbitrary mean and standard deviation. Setting mean = FALSE just scales the data but preserves the means, and is thus different from fsd(..., TRA = "/") which simply divides all values by the standard deviation:

# Saving grouped tibble
gGGDC <- GGDC10S %>%
  fgroup_by(Variable, Country) %>%
    fselect(Country, Variable, AGR:SUM)

# Original means
head(fmean(gGGDC)) 
# # A tibble: 6 x 13
#   Variable Country     AGR    MIN     MAN      PU     CON    WRT    TRA   FIRE     GOV    OTH    SUM
#   <chr>    <chr>     <dbl>  <dbl>   <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
# 1 EMP      ARG       1420.   52.1  1932.   102.     742.  1.98e3 6.49e2  628.   2043.  9.92e2 1.05e4
# 2 EMP      BOL        964.   56.0   235.     5.35   123.  2.82e2 1.15e2   44.6    NA   3.96e2 2.22e3
# 3 EMP      BRA      17191.  206.   6991.   365.    3525.  8.51e3 2.05e3 4414.   5307.  5.71e3 5.43e4
# 4 EMP      BWA        188.   10.5    18.1    3.09    25.3 3.63e1 8.36e0   15.3    61.1 2.76e1 3.94e2
# 5 EMP      CHL        702.  101.    625.    29.4    296.  6.95e2 2.58e2  272.     NA   1.00e3 3.98e3
# 6 EMP      CHN     287744. 7050.  67144.  1606.   20852.  2.89e4 1.39e4 4929.  22669.  3.10e4 4.86e5

# Mean Preserving Scaling
head(fmean(fscale(gGGDC, mean = FALSE)))
# # A tibble: 6 x 13
#   Variable Country     AGR    MIN     MAN      PU     CON    WRT    TRA   FIRE     GOV    OTH    SUM
#   <chr>    <chr>     <dbl>  <dbl>   <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
# 1 EMP      ARG       1420.   52.1  1932.   102.     742.  1.98e3 6.49e2  628.   2043.  9.92e2 1.05e4
# 2 EMP      BOL        964.   56.0   235.     5.35   123.  2.82e2 1.15e2   44.6    NA   3.96e2 2.22e3
# 3 EMP      BRA      17191.  206.   6991.   365.    3525.  8.51e3 2.05e3 4414.   5307.  5.71e3 5.43e4
# 4 EMP      BWA        188.   10.5    18.1    3.09    25.3 3.63e1 8.36e0   15.3    61.1 2.76e1 3.94e2
# 5 EMP      CHL        702.  101.    625.    29.4    296.  6.95e2 2.58e2  272.     NA   1.00e3 3.98e3
# 6 EMP      CHN     287744. 7050.  67144.  1606.   20852.  2.89e4 1.39e4 4929.  22669.  3.10e4 4.86e5
head(fsd(fscale(gGGDC, mean = FALSE)))
# # A tibble: 6 x 13
#   Variable Country   AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV   OTH   SUM
#   <chr>    <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 EMP      ARG      1.    1.    1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.  
# 2 EMP      BOL      1.    1.00  1.    1.00  1.00  1.    1.    1.   NA     1.    1.  
# 3 EMP      BRA      1.    1.    1.    1.00  1.    1.00  1.00  1.00  1.    1.00  1.00
# 4 EMP      BWA      1.00  1.00  1.    1.    1.    1.00  1.    1.00  1.    1.00  1.00
# 5 EMP      CHL      1.    1.    1.00  1.    1.    1.    1.00  1.   NA     1.    1.00
# 6 EMP      CHN      1.    1.    1.    1.00  1.00  1.    1.    1.    1.00  1.00  1.

One can also set mean = "overall.mean", which group-centers columns on the overall mean as illustrated with fwithin. Another interesting option is setting sd = "within.sd". This group-scales data such that every group has a standard deviation equal to the within-standard deviation of the data:

# Just using VA data for this example
gGGDC <- GGDC10S %>%
  fsubset(Variable == "VA", Country, AGR:SUM) %>% 
      fgroup_by(Country)

# This calculates the within- standard deviation for all columns
fsd(num_vars(ungroup(fwithin(gGGDC))))
#       AGR       MIN       MAN        PU       CON       WRT       TRA      FIRE       GOV       OTH 
#  45046972  40122220  75608708   3062688  30811572  44125207  20676901  16030868  20358973  18780869 
#       SUM 
# 306429102

# This scales all groups to take on the within- standard deviation while preserving group means 
fsd(fscale(gGGDC, mean = FALSE, sd = "within.sd"))
# # A tibble: 43 x 12
#    Country      AGR      MIN      MAN     PU     CON     WRT     TRA    FIRE     GOV     OTH     SUM
#    <chr>      <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#  1 ARG       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7  2.04e7  1.88e7  3.06e8
#  2 BOL       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7 NA       1.88e7  3.06e8
#  3 BRA       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7  2.04e7  1.88e7  3.06e8
#  4 BWA       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7  2.04e7  1.88e7  3.06e8
#  5 CHL       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7 NA       1.88e7  3.06e8
#  6 CHN       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7  2.04e7  1.88e7  3.06e8
#  7 COL       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7 NA       1.88e7  3.06e8
#  8 CRI       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7  2.04e7  1.88e7  3.06e8
#  9 DEW       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7  2.04e7  1.88e7  3.06e8
# 10 DNK       4.50e7   4.01e7   7.56e7 3.06e6  3.08e7  4.41e7  2.07e7  1.60e7  2.04e7  1.88e7  3.06e8
# # ... with 33 more rows

A grouped scaling operation with both mean = "overall.mean" and sd = "within.sd" thus efficiently achieves a harmonization of all groups in the first two moments without changing the fundamental properties (in terms of level and scale) of the data.

2.5 Lags / Leads, Differences and Growth Rates

This section introduces 3 further powerful collapse functions: flag, fdiff and fgrowth. The first function, flag, efficiently computes sequences of fully identified lags and leads on time series and panel data. The following code computes 1 fully-identified panel-lag and 1 fully identified panel-lead of each variable in the data:

GGDC10S %>%
  fselect(-Region, -Regioncode) %>% 
    fgroup_by(Variable, Country) %>% flag(-1:1, Year)
# # A tibble: 5,027 x 36
#    Country Variable  Year F1.AGR   AGR L1.AGR F1.MIN   MIN L1.MIN F1.MAN    MAN L1.MAN  F1.PU     PU
#  * <chr>   <chr>    <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#  1 BWA     VA        1960   NA    NA     NA    NA    NA     NA    NA     NA     NA     NA     NA    
#  2 BWA     VA        1961   NA    NA     NA    NA    NA     NA    NA     NA     NA     NA     NA    
#  3 BWA     VA        1962   NA    NA     NA    NA    NA     NA    NA     NA     NA     NA     NA    
#  4 BWA     VA        1963   16.3  NA     NA     3.49 NA     NA     0.737 NA     NA      0.104 NA    
#  5 BWA     VA        1964   15.7  16.3   NA     2.50  3.49  NA     1.02   0.737 NA      0.135  0.104
#  6 BWA     VA        1965   17.7  15.7   16.3   1.97  2.50   3.49  0.804  1.02   0.737  0.203  0.135
#  7 BWA     VA        1966   19.1  17.7   15.7   2.30  1.97   2.50  0.938  0.804  1.02   0.203  0.203
#  8 BWA     VA        1967   21.1  19.1   17.7   1.84  2.30   1.97  0.750  0.938  0.804  0.203  0.203
#  9 BWA     VA        1968   21.9  21.1   19.1   5.24  1.84   2.30  2.14   0.750  0.938  0.578  0.203
# 10 BWA     VA        1969   23.1  21.9   21.1  10.2   5.24   1.84  4.15   2.14   0.750  1.12   0.578
# # ... with 5,017 more rows, and 22 more variables: L1.PU <dbl>, F1.CON <dbl>, CON <dbl>,
# #   L1.CON <dbl>, F1.WRT <dbl>, WRT <dbl>, L1.WRT <dbl>, F1.TRA <dbl>, TRA <dbl>, L1.TRA <dbl>,
# #   F1.FIRE <dbl>, FIRE <dbl>, L1.FIRE <dbl>, F1.GOV <dbl>, GOV <dbl>, L1.GOV <dbl>, F1.OTH <dbl>,
# #   OTH <dbl>, L1.OTH <dbl>, F1.SUM <dbl>, SUM <dbl>, L1.SUM <dbl>

If the time-variable passed does not exactly identify the data (i.e. because of gaps or repeated values in each group), all 3 functions will issue appropriate error messages. flag, fdiff and fgrowth support unbalanced panels with different start and end periods and duration of coverage for each individual, but not irregular panels. A workaround for such panels exists with the function seqid which generates a new panel-id identifying consecutive time-sequences at the sub-individual level, see ?seqid.

It is also possible to omit the time-variable if one is certain that the data is sorted:

GGDC10S %>%
  fselect(Variable, Country,AGR:SUM) %>% 
    fgroup_by(Variable, Country) %>% flag
# # A tibble: 5,027 x 13
#    Variable Country   AGR   MIN    MAN     PU    CON   WRT   TRA  FIRE   GOV   OTH   SUM
#  * <chr>    <chr>   <dbl> <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#  1 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  2 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  3 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  4 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  5 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  6 VA       BWA      16.3  3.49  0.737  0.104  0.660  6.24  1.66  1.12  4.82  2.34  37.5
#  7 VA       BWA      15.7  2.50  1.02   0.135  1.35   7.06  1.94  1.25  5.70  2.68  39.3
#  8 VA       BWA      17.7  1.97  0.804  0.203  1.35   8.27  2.15  1.36  6.37  2.99  43.1
#  9 VA       BWA      19.1  2.30  0.938  0.203  0.897  4.31  1.72  1.54  7.04  3.31  41.4
# 10 VA       BWA      21.1  1.84  0.750  0.203  1.22   5.17  2.44  1.03  5.03  2.36  41.1
# # ... with 5,017 more rows

fdiff computes sequences of lagged-leaded and iterated differences as well as quasi-differences and log-differences on time series and panel data. The code below computes the 1 and 10 year first and second differences of each variable in the data:

GGDC10S %>%
  fselect(-Region, -Regioncode) %>% 
    fgroup_by(Variable, Country) %>% fdiff(c(1, 10), 1:2, Year)
# # A tibble: 5,027 x 47
#    Country Variable  Year D1.AGR D2.AGR L10D1.AGR L10D2.AGR D1.MIN D2.MIN L10D1.MIN L10D2.MIN D1.MAN
#  * <chr>   <chr>    <dbl>  <dbl>  <dbl>     <dbl>     <dbl>  <dbl>  <dbl>     <dbl>     <dbl>  <dbl>
#  1 BWA     VA        1960 NA     NA            NA        NA NA     NA            NA        NA NA    
#  2 BWA     VA        1961 NA     NA            NA        NA NA     NA            NA        NA NA    
#  3 BWA     VA        1962 NA     NA            NA        NA NA     NA            NA        NA NA    
#  4 BWA     VA        1963 NA     NA            NA        NA NA     NA            NA        NA NA    
#  5 BWA     VA        1964 NA     NA            NA        NA NA     NA            NA        NA NA    
#  6 BWA     VA        1965 -0.575 NA            NA        NA -0.998 NA            NA        NA  0.282
#  7 BWA     VA        1966  1.95   2.53         NA        NA -0.525  0.473        NA        NA -0.214
#  8 BWA     VA        1967  1.47  -0.488        NA        NA  0.328  0.854        NA        NA  0.134
#  9 BWA     VA        1968  1.95   0.488        NA        NA -0.460 -0.788        NA        NA -0.188
# 10 BWA     VA        1969  0.763 -1.19         NA        NA  3.41   3.87         NA        NA  1.39 
# # ... with 5,017 more rows, and 35 more variables: D2.MAN <dbl>, L10D1.MAN <dbl>, L10D2.MAN <dbl>,
# #   D1.PU <dbl>, D2.PU <dbl>, L10D1.PU <dbl>, L10D2.PU <dbl>, D1.CON <dbl>, D2.CON <dbl>,
# #   L10D1.CON <dbl>, L10D2.CON <dbl>, D1.WRT <dbl>, D2.WRT <dbl>, L10D1.WRT <dbl>, L10D2.WRT <dbl>,
# #   D1.TRA <dbl>, D2.TRA <dbl>, L10D1.TRA <dbl>, L10D2.TRA <dbl>, D1.FIRE <dbl>, D2.FIRE <dbl>,
# #   L10D1.FIRE <dbl>, L10D2.FIRE <dbl>, D1.GOV <dbl>, D2.GOV <dbl>, L10D1.GOV <dbl>,
# #   L10D2.GOV <dbl>, D1.OTH <dbl>, D2.OTH <dbl>, L10D1.OTH <dbl>, L10D2.OTH <dbl>, D1.SUM <dbl>,
# #   D2.SUM <dbl>, L10D1.SUM <dbl>, L10D2.SUM <dbl>

Log-differences of the form \(log(x_t) - log(x_{t-s})\) are also easily computed.

GGDC10S %>%
  fselect(-Region, -Regioncode) %>% 
    fgroup_by(Variable, Country) %>% fdiff(c(1, 10), 1, Year, log = TRUE)
# # A tibble: 5,027 x 25
#    Country Variable  Year Dlog1.AGR L10Dlog1.AGR Dlog1.MIN L10Dlog1.MIN Dlog1.MAN L10Dlog1.MAN
#  * <chr>   <chr>    <dbl>     <dbl>        <dbl>     <dbl>        <dbl>     <dbl>        <dbl>
#  1 BWA     VA        1960   NA                NA    NA               NA    NA               NA
#  2 BWA     VA        1961   NA                NA    NA               NA    NA               NA
#  3 BWA     VA        1962   NA                NA    NA               NA    NA               NA
#  4 BWA     VA        1963   NA                NA    NA               NA    NA               NA
#  5 BWA     VA        1964   NA                NA    NA               NA    NA               NA
#  6 BWA     VA        1965   -0.0359           NA    -0.336           NA     0.324           NA
#  7 BWA     VA        1966    0.117            NA    -0.236           NA    -0.236           NA
#  8 BWA     VA        1967    0.0796           NA     0.154           NA     0.154           NA
#  9 BWA     VA        1968    0.0972           NA    -0.223           NA    -0.223           NA
# 10 BWA     VA        1969    0.0355           NA     1.05            NA     1.05            NA
# # ... with 5,017 more rows, and 16 more variables: Dlog1.PU <dbl>, L10Dlog1.PU <dbl>,
# #   Dlog1.CON <dbl>, L10Dlog1.CON <dbl>, Dlog1.WRT <dbl>, L10Dlog1.WRT <dbl>, Dlog1.TRA <dbl>,
# #   L10Dlog1.TRA <dbl>, Dlog1.FIRE <dbl>, L10Dlog1.FIRE <dbl>, Dlog1.GOV <dbl>, L10Dlog1.GOV <dbl>,
# #   Dlog1.OTH <dbl>, L10Dlog1.OTH <dbl>, Dlog1.SUM <dbl>, L10Dlog1.SUM <dbl>

Finally, it is also possible to compute quasi-differences and quasi-log-differences of the form \(x_t - \rho x_{t-s}\) or \(log(x_t) - \rho log(x_{t-s})\):

GGDC10S %>%
  fselect(-Region, -Regioncode) %>% 
    fgroup_by(Variable, Country) %>% fdiff(t = Year, rho = 0.95)
# # A tibble: 5,027 x 14
#    Country Variable  Year    AGR    MIN    MAN      PU     CON    WRT    TRA   FIRE    GOV    OTH
#  * <chr>   <chr>    <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#  1 BWA     VA        1960 NA     NA     NA     NA      NA      NA     NA     NA     NA     NA    
#  2 BWA     VA        1961 NA     NA     NA     NA      NA      NA     NA     NA     NA     NA    
#  3 BWA     VA        1962 NA     NA     NA     NA      NA      NA     NA     NA     NA     NA    
#  4 BWA     VA        1963 NA     NA     NA     NA      NA      NA     NA     NA     NA     NA    
#  5 BWA     VA        1964 NA     NA     NA     NA      NA      NA     NA     NA     NA     NA    
#  6 BWA     VA        1965  0.241 -0.824  0.318  0.0359  0.719   1.13   0.363  0.184  1.11   0.454
#  7 BWA     VA        1966  2.74  -0.401 -0.163  0.0743  0.0673  1.56   0.312  0.174  0.955  0.449
#  8 BWA     VA        1967  2.35   0.427  0.174  0.0101 -0.381  -3.55  -0.323  0.246  0.988  0.465
#  9 BWA     VA        1968  2.91  -0.345 -0.141  0.0101  0.365   1.08   0.804 -0.427 -1.66  -0.780
# 10 BWA     VA        1969  1.82   3.50   1.43   0.385   2.32    0.841  0.397  0.252  0.818  0.385
# # ... with 5,017 more rows, and 1 more variable: SUM <dbl>

The quasi-differencing feature was added to fdiff to facilitate the preparation of time series and panel data for least-squares estimations suffering from serial correlation following Cochrane & Orcutt (1949).

Finally, fgrowth computes growth rates in the same way. By default exact growth rates are computed in percentage terms using \((x_t-x_{t-s}) / x_{t-s} \times 100\) (the default argument is scale = 100). The user can also request growth rates obtained by log-differencing using \(log(x_t/ x_{t-s}) \times 100\).

# Exact growth rates, computed as: (x/lag(x) - 1) * 100
GGDC10S %>%
  fselect(-Region, -Regioncode) %>% 
    fgroup_by(Variable, Country) %>% fgrowth(c(1, 10), 1, Year)
# # A tibble: 5,027 x 25
#    Country Variable  Year G1.AGR L10G1.AGR G1.MIN L10G1.MIN G1.MAN L10G1.MAN G1.PU L10G1.PU G1.CON
#  * <chr>   <chr>    <dbl>  <dbl>     <dbl>  <dbl>     <dbl>  <dbl>     <dbl> <dbl>    <dbl>  <dbl>
#  1 BWA     VA        1960  NA           NA   NA          NA   NA          NA  NA         NA   NA  
#  2 BWA     VA        1961  NA           NA   NA          NA   NA          NA  NA         NA   NA  
#  3 BWA     VA        1962  NA           NA   NA          NA   NA          NA  NA         NA   NA  
#  4 BWA     VA        1963  NA           NA   NA          NA   NA          NA  NA         NA   NA  
#  5 BWA     VA        1964  NA           NA   NA          NA   NA          NA  NA         NA   NA  
#  6 BWA     VA        1965  -3.52        NA  -28.6        NA   38.2        NA  29.4       NA  104. 
#  7 BWA     VA        1966  12.4         NA  -21.1        NA  -21.1        NA  50.0       NA    0  
#  8 BWA     VA        1967   8.29        NA   16.7        NA   16.7        NA   0         NA  -33.3
#  9 BWA     VA        1968  10.2         NA  -20          NA  -20          NA   0         NA   35.7
# 10 BWA     VA        1969   3.61        NA  185.         NA  185.         NA 185.        NA  185. 
# # ... with 5,017 more rows, and 13 more variables: L10G1.CON <dbl>, G1.WRT <dbl>, L10G1.WRT <dbl>,
# #   G1.TRA <dbl>, L10G1.TRA <dbl>, G1.FIRE <dbl>, L10G1.FIRE <dbl>, G1.GOV <dbl>, L10G1.GOV <dbl>,
# #   G1.OTH <dbl>, L10G1.OTH <dbl>, G1.SUM <dbl>, L10G1.SUM <dbl>

# Log-difference growth rates, computed as: log(x / lag(x)) * 100
GGDC10S %>%
  fselect(-Region, -Regioncode) %>% 
    fgroup_by(Variable, Country) %>% fgrowth(c(1, 10), 1, Year, logdiff = TRUE)
# # A tibble: 5,027 x 25
#    Country Variable  Year Dlog1.AGR L10Dlog1.AGR Dlog1.MIN L10Dlog1.MIN Dlog1.MAN L10Dlog1.MAN
#  * <chr>   <chr>    <dbl>     <dbl>        <dbl>     <dbl>        <dbl>     <dbl>        <dbl>
#  1 BWA     VA        1960     NA              NA      NA             NA      NA             NA
#  2 BWA     VA        1961     NA              NA      NA             NA      NA             NA
#  3 BWA     VA        1962     NA              NA      NA             NA      NA             NA
#  4 BWA     VA        1963     NA              NA      NA             NA      NA             NA
#  5 BWA     VA        1964     NA              NA      NA             NA      NA             NA
#  6 BWA     VA        1965     -3.59           NA     -33.6           NA      32.4           NA
#  7 BWA     VA        1966     11.7            NA     -23.6           NA     -23.6           NA
#  8 BWA     VA        1967      7.96           NA      15.4           NA      15.4           NA
#  9 BWA     VA        1968      9.72           NA     -22.3           NA     -22.3           NA
# 10 BWA     VA        1969      3.55           NA     105.            NA     105.            NA
# # ... with 5,017 more rows, and 16 more variables: Dlog1.PU <dbl>, L10Dlog1.PU <dbl>,
# #   Dlog1.CON <dbl>, L10Dlog1.CON <dbl>, Dlog1.WRT <dbl>, L10Dlog1.WRT <dbl>, Dlog1.TRA <dbl>,
# #   L10Dlog1.TRA <dbl>, Dlog1.FIRE <dbl>, L10Dlog1.FIRE <dbl>, Dlog1.GOV <dbl>, L10Dlog1.GOV <dbl>,
# #   Dlog1.OTH <dbl>, L10Dlog1.OTH <dbl>, Dlog1.SUM <dbl>, L10Dlog1.SUM <dbl>

fdiff and fgrowth can also perform leaded (forward) differences and growth rates (i.e. ... %>% fgrowth(-c(1, 10), 1:2, Year) would compute one and 10-year leaded first and second differences). Again it is possible to perform sequential operations:

# This computes the 1 and 10-year growth rates, for the current period and lagged by one period
GGDC10S %>%
  fselect(-Region, -Regioncode) %>% 
    fgroup_by(Variable, Country) %>% fgrowth(c(1, 10), 1, Year) %>% flag(0:1, Year)
# # A tibble: 5,027 x 47
#    Country Variable  Year G1.AGR L1.G1.AGR L10G1.AGR L1.L10G1.AGR G1.MIN L1.G1.MIN L10G1.MIN
#  * <chr>   <chr>    <dbl>  <dbl>     <dbl>     <dbl>        <dbl>  <dbl>     <dbl>     <dbl>
#  1 BWA     VA        1960  NA        NA           NA           NA   NA        NA          NA
#  2 BWA     VA        1961  NA        NA           NA           NA   NA        NA          NA
#  3 BWA     VA        1962  NA        NA           NA           NA   NA        NA          NA
#  4 BWA     VA        1963  NA        NA           NA           NA   NA        NA          NA
#  5 BWA     VA        1964  NA        NA           NA           NA   NA        NA          NA
#  6 BWA     VA        1965  -3.52     NA           NA           NA  -28.6      NA          NA
#  7 BWA     VA        1966  12.4      -3.52        NA           NA  -21.1     -28.6        NA
#  8 BWA     VA        1967   8.29     12.4         NA           NA   16.7     -21.1        NA
#  9 BWA     VA        1968  10.2       8.29        NA           NA  -20        16.7        NA
# 10 BWA     VA        1969   3.61     10.2         NA           NA  185.      -20          NA
# # ... with 5,017 more rows, and 37 more variables: L1.L10G1.MIN <dbl>, G1.MAN <dbl>,
# #   L1.G1.MAN <dbl>, L10G1.MAN <dbl>, L1.L10G1.MAN <dbl>, G1.PU <dbl>, L1.G1.PU <dbl>,
# #   L10G1.PU <dbl>, L1.L10G1.PU <dbl>, G1.CON <dbl>, L1.G1.CON <dbl>, L10G1.CON <dbl>,
# #   L1.L10G1.CON <dbl>, G1.WRT <dbl>, L1.G1.WRT <dbl>, L10G1.WRT <dbl>, L1.L10G1.WRT <dbl>,
# #   G1.TRA <dbl>, L1.G1.TRA <dbl>, L10G1.TRA <dbl>, L1.L10G1.TRA <dbl>, G1.FIRE <dbl>,
# #   L1.G1.FIRE <dbl>, L10G1.FIRE <dbl>, L1.L10G1.FIRE <dbl>, G1.GOV <dbl>, L1.G1.GOV <dbl>,
# #   L10G1.GOV <dbl>, L1.L10G1.GOV <dbl>, G1.OTH <dbl>, L1.G1.OTH <dbl>, L10G1.OTH <dbl>,
# #   L1.L10G1.OTH <dbl>, G1.SUM <dbl>, L1.G1.SUM <dbl>, L10G1.SUM <dbl>, L1.L10G1.SUM <dbl>

3. Benchmarks

This section seeks to demonstrate that the functionality introduced in the preceding 2 sections indeed produces code that evaluates substantially faster than native dplyr.

To do this properly, the different components of a typical piped call (selecting / subsetting, ordering, grouping, and performing some computation) are bechmarked separately on 2 different data sizes.

All benchmarks are run on a Windows 8.1 laptop with a 2x 2.2 GHZ Intel i5 processor, 8GB DDR3 RAM and a Samsung 850 EVO SSD hard drive.

3.1 Data

Bechmarks are run on the original GGDC10S data used throughout this vignette and a larger dataset with approx. 1 million observations, obtained by replicating and row-binding GGDC10S 200 times while maintaining unique groups.

# This shows the groups in GGDC10S
GRP(GGDC10S, ~ Variable + Country)
# collapse grouping object of length 5027 with 85 ordered groups
# 
# Call: GRP.default(X = GGDC10S, by = ~Variable + Country), X is unordered
# 
# Distribution of group sizes: 
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#    4.00   53.00   62.00   59.14   63.00   65.00 
# 
# Groups with sizes: 
# EMP.ARG EMP.BOL EMP.BRA EMP.BWA EMP.CHL EMP.CHN 
#      62      61      62      52      63      62 
#   ---
# VA.TWN VA.TZA VA.USA VA.VEN VA.ZAF VA.ZMB 
#     63     52     65     63     52     52

# This replicates the data 200 times 
data <- replicate(200, GGDC10S, simplify = FALSE) 
# This function adds a number i to the country and variable columns of each dataset
uniquify <- function(x, i) ftransform(x, lapply(unclass(x)[c(1,4)], paste0, i))
# Making datasets unique and row-binding them
data <- unlist2d(Map(uniquify, data, as.list(1:200)), idcols = FALSE)
fdim(data)
# [1] 1005400      16

# This shows the groups in the replicated data
GRP(data, ~ Variable + Country)
# collapse grouping object of length 1005400 with 17000 ordered groups
# 
# Call: GRP.default(X = data, by = ~Variable + Country), X is unordered
# 
# Distribution of group sizes: 
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#    4.00   53.00   62.00   59.14   63.00   65.00 
# 
# Groups with sizes: 
# EMP1.ARG1 EMP1.BOL1 EMP1.BRA1 EMP1.BWA1 EMP1.CHL1 EMP1.CHN1 
#        62        61        62        52        63        62 
#   ---
# VA99.TWN99 VA99.TZA99 VA99.USA99 VA99.VEN99 VA99.ZAF99 VA99.ZMB99 
#         63         52         65         63         52         52

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1925730 102.9    3670961 196.1  3670961 196.1
# Vcells 19873576 151.7   28319693 216.1 23047670 175.9

3.1 Selecting, Subsetting, Ordering and Grouping

## Selecting columns
# Small
microbenchmark(dplyr = select(GGDC10S, Country, Variable, AGR:SUM),
               collapse = fselect(GGDC10S, Country, Variable, AGR:SUM))
# Unit: microseconds
#      expr      min       lq       mean   median       uq       max neval cld
#     dplyr 2785.476 2990.972 3785.35528 3269.654 4031.399 19673.258   100   b
#  collapse   11.156   16.512   31.27768   29.676   38.601   189.655   100  a

# Large
microbenchmark(dplyr = select(data, Country, Variable, AGR:SUM),
               collapse = fselect(data, Country, Variable, AGR:SUM))
# Unit: microseconds
#      expr      min       lq       mean    median       uq      max neval cld
#     dplyr 2922.474 3169.471 3455.85976 3354.8875 3627.991 6057.808   100   b
#  collapse   11.157   16.065   29.23833   33.2455   37.485   85.679   100  a

## Subsetting columns 
# Small
microbenchmark(dplyr = filter(GGDC10S, Variable == "VA"),
               collapse = fsubset(GGDC10S, Variable == "VA"))
# Unit: microseconds
#      expr      min       lq      mean    median        uq      max neval cld
#     dplyr 1399.432 1577.485 1727.9992 1696.1860 1851.0340 2323.609   100   b
#  collapse  153.063  181.400  257.4582  226.6935  331.1155  477.038   100  a

# Large
microbenchmark(dplyr = filter(data, Variable == "VA"),
               collapse = fsubset(data, Variable == "VA"))
# Unit: milliseconds
#      expr       min        lq      mean    median        uq       max neval cld
#     dplyr 17.453178 17.687011 23.874368 17.869526 21.784454 190.28073   100   b
#  collapse  7.778537  8.092695  9.424132  8.168334  8.335453  29.07569   100  a

## Ordering rows
# Small
microbenchmark(dplyr = arrange(GGDC10S, desc(Country), Variable, Year),
               collapse = roworder(GGDC10S, -Country, Variable, Year))
# Unit: microseconds
#      expr      min        lq     mean    median       uq       max neval cld
#     dplyr 8184.175 8458.6175 8971.686 8711.6395 9334.601 13803.768   100   b
#  collapse  564.949  635.9025  762.329  693.9145  875.538  1238.783   100  a

# Large
microbenchmark(dplyr = arrange(data, desc(Country), Variable, Year),
               collapse = roworder(data, -Country, Variable, Year), times = 2)
# Unit: milliseconds
#      expr      min       lq      mean    median        uq       max neval cld
#     dplyr 2599.315 2599.315 2603.1014 2603.1014 2606.8883 2606.8883     2   b
#  collapse  352.841  352.841  369.0206  369.0206  385.2002  385.2002     2  a


## Grouping 
# Small
microbenchmark(dplyr = group_by(GGDC10S, Country, Variable),
               collapse = fgroup_by(GGDC10S, Country, Variable))
# Unit: microseconds
#      expr      min       lq      mean    median        uq      max neval cld
#     dplyr 3513.305 3729.735 4579.5328 4529.8565 5306.3265 7613.425   100   b
#  collapse  394.929  422.819  530.7712  471.4605  638.5805 1044.665   100  a

# Large
microbenchmark(dplyr = group_by(data, Country, Variable),
               collapse = fgroup_by(data, Country, Variable), times = 10)
# Unit: milliseconds
#      expr      min       lq      mean   median        uq      max neval cld
#     dplyr 76.81924 81.70832 103.52451 99.98751 118.78389 144.5373    10   a
#  collapse 70.20764 72.23360  89.79366 89.60311  97.21185 127.2961    10   a

## Computing a new column 
# Small
microbenchmark(dplyr = mutate(GGDC10S, NEW = AGR+1),
               collapse = ftransform(GGDC10S, NEW = AGR+1))
# Unit: microseconds
#      expr      min       lq      mean    median        uq      max neval cld
#     dplyr 2587.788 2719.208 2903.6197 2796.4085 3011.7230 5338.456   100   b
#  collapse   26.775   32.353   46.5795   39.9395   58.9045  107.993   100  a

# Large
microbenchmark(dplyr = mutate(data, NEW = AGR+1),
               collapse = ftransform(data, NEW = AGR+1))
# Unit: milliseconds
#      expr      min       lq     mean   median       uq      max neval cld
#     dplyr 6.727624 7.011661 9.542815 7.286995 8.217867 47.08444   100   b
#  collapse 2.625273 3.824563 4.872467 3.944603 4.124218 32.16015   100  a

## All combined with pipes 
# Small
microbenchmark(dplyr = filter(GGDC10S, Variable == "VA") %>% 
                       select(Country, Year, AGR:SUM) %>% 
                       arrange(desc(Country), Year) %>%
                       mutate(NEW = AGR+1) %>%
                       group_by(Country),
               collapse = fsubset(GGDC10S, Variable == "VA", Country, Year, AGR:SUM) %>% 
                       roworder(-Country, Year) %>%
                       ftransform(NEW = AGR+1) %>%
                       fgroup_by(Country))
# Unit: microseconds
#      expr       min         lq       mean   median        uq       max neval cld
#     dplyr 15580.278 15944.1925 17116.7876 16124.03 16560.014 31040.515   100   b
#  collapse   720.244   812.1705   893.6506   851.44   937.343  1629.695   100  a

# Large
microbenchmark(dplyr = filter(data, Variable == "VA") %>% 
                       select(Country, Year, AGR:SUM) %>% 
                       arrange(desc(Country), Year) %>%
                       mutate(NEW = AGR+1) %>%
                       group_by(Country),
               collapse = fsubset(data, Variable == "VA", Country, Year, AGR:SUM) %>% 
                       roworder(-Country, Year) %>%
                       ftransform(NEW = AGR+1) %>%
                       fgroup_by(Country), times = 10)
# Unit: milliseconds
#      expr      min        lq      mean    median        uq       max neval cld
#     dplyr 27.95115 30.444775 33.074153 30.625504 33.469439 51.665172    10   b
#  collapse  7.30953  8.459064  8.501323  8.662999  8.839713  9.357359    10  a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1931338 103.2    3670961 196.1  3670961 196.1
# Vcells 21389735 163.2   44616105 340.4 44616105 340.4

3.1 Aggregation

## Grouping the data
cgGGDC10S <- fgroup_by(GGDC10S, Variable, Country) %>% fselect(-Region, -Regioncode)
gGGDC10S <- group_by(GGDC10S, Variable, Country) %>% fselect(-Region, -Regioncode)
cgdata <- fgroup_by(data, Variable, Country) %>% fselect(-Region, -Regioncode)
gdata <- group_by(data, Variable, Country) %>% fselect(-Region, -Regioncode)
rm(data, GGDC10S) 
gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1948325 104.1    3670961 196.1  3670961 196.1
# Vcells 20490274 156.4   44616105 340.4 44616105 340.4

## Conversion of Grouping object: This time would be required extra in all hybrid calls 
## i.e. when calling collapse functions on data grouped with dplyr::group_by
# Small
microbenchmark(GRP(gGGDC10S))
# Unit: microseconds
#           expr    min      lq     mean  median       uq     max neval
#  GRP(gGGDC10S) 166.45 169.574 184.0549 171.806 183.4075 345.396   100

# Large
microbenchmark(GRP(gdata))
# Unit: milliseconds
#        expr      min       lq     mean   median       uq      max neval
#  GRP(gdata) 32.37256 33.89293 35.72334 35.43583 37.05905 47.21386   100


## Sum 
# Small
microbenchmark(dplyr = summarise_all(gGGDC10S, sum, na.rm = TRUE),
               collapse = fsum(cgGGDC10S))
# Unit: microseconds
#      expr      min       lq      mean   median       uq       max neval cld
#     dplyr 8878.982 9221.254 9601.6576 9399.976 9712.572 17043.076   100   b
#  collapse  236.065  253.469  353.7717  290.284  302.779  7129.693   100  a

# Large
microbenchmark(dplyr = summarise_all(gdata, sum, na.rm = TRUE),
               collapse = fsum(cgdata), times = 10)
# Unit: milliseconds
#      expr      min        lq      mean    median        uq       max neval cld
#     dplyr 596.3983 604.90515 626.68443 609.52493 613.27273 787.86207    10   b
#  collapse  41.0311  41.24083  42.76254  41.91913  42.83572  48.89041    10  a

## Mean
# Small
microbenchmark(dplyr = summarise_all(gGGDC10S, mean.default, na.rm = TRUE),
               collapse = fmean(cgGGDC10S))
# Unit: microseconds
#      expr       min         lq       mean    median        uq       max neval cld
#     dplyr 11904.539 12605.3705 13905.4984 13037.784 13326.506 34580.594   100   b
#  collapse   254.361   278.2355   314.8902   311.481   336.917   465.882   100  a

# Large
microbenchmark(dplyr = summarise_all(gdata, mean.default, na.rm = TRUE),
               collapse = fmean(cgdata), times = 10)
# Unit: milliseconds
#      expr        min         lq       mean     median         uq        max neval cld
#     dplyr 1443.86968 1589.43153 1735.38027 1705.82420 1913.18304 2079.88139    10   b
#  collapse   44.94335   45.02055   53.25979   45.67229   48.87479   84.43222    10  a

## Median
# Small
microbenchmark(dplyr = summarise_all(gGGDC10S, median, na.rm = TRUE),
               collapse = fmedian(cgGGDC10S))
# Unit: microseconds
#      expr       min         lq       mean     median         uq        max neval cld
#     dplyr 54052.595 55337.5640 62975.4834 57875.1495 63560.7860 140828.949   100   b
#  collapse   495.335   529.2495   577.9932   561.8255   599.5335    927.748   100  a

# Large
microbenchmark(dplyr = summarise_all(gdata, median, na.rm = TRUE),
               collapse = fmedian(cgdata), times = 2)
# Unit: milliseconds
#      expr         min          lq        mean      median          uq         max neval cld
#     dplyr 10023.36417 10023.36417 10179.66228 10179.66228 10335.96039 10335.96039     2   b
#  collapse    84.26711    84.26711    86.39191    86.39191    88.51672    88.51672     2  a

## Standard Deviation
# Small
microbenchmark(dplyr = summarise_all(gGGDC10S, sd, na.rm = TRUE),
               collapse = fsd(cgGGDC10S))
# Unit: microseconds
#      expr       min        lq       mean     median         uq      max neval cld
#     dplyr 24646.238 25476.704 26824.4733 25918.2655 26506.6425 34665.38   100   b
#  collapse   431.521   464.097   505.1789   487.5255   516.7545   790.75   100  a

# Large
microbenchmark(dplyr = summarise_all(gdata, sd, na.rm = TRUE),
               collapse = fsd(cgdata), times = 2)
# Unit: milliseconds
#      expr        min         lq       mean     median         uq        max neval cld
#     dplyr 4841.99901 4841.99901 4995.99783 4995.99783 5149.99665 5149.99665     2   b
#  collapse   85.60853   85.60853   86.28593   86.28593   86.96333   86.96333     2  a

## Maximum
# Small
microbenchmark(dplyr = summarise_all(gGGDC10S, max, na.rm = TRUE),
               collapse = fmax(cgGGDC10S))
# Unit: microseconds
#      expr       min        lq      mean    median       uq      max neval cld
#     dplyr 11642.145 12097.318 12557.020 12266.891 12554.94 19887.90   100   b
#  collapse   179.392   190.994   229.117   236.957   241.42   529.25   100  a

# Large
microbenchmark(dplyr = summarise_all(gdata, max, na.rm = TRUE),
               collapse = fmax(cgdata), times = 10)
# Unit: milliseconds
#      expr        min         lq       mean     median         uq        max neval cld
#     dplyr 1094.55534 1108.52109 1151.61069 1127.70035 1190.50073 1250.25771    10   b
#  collapse   25.58916   25.61192   27.19672   25.94169   27.21796   34.62477    10  a

## First Value
# Small
microbenchmark(dplyr = summarise_all(gGGDC10S, first),
               collapse = ffirst(cgGGDC10S, na.rm = FALSE))
# Unit: microseconds
#      expr       min         lq       mean    median         uq        max neval cld
#     dplyr 27781.571 29151.1040 37713.0008 32005.525 39130.5325 175534.493   100   b
#  collapse    58.459    68.2765   110.1163   116.917   124.7265    254.808   100  a

# Large
microbenchmark(dplyr = summarise_all(gdata, first),
               collapse = ffirst(cgdata, na.rm = FALSE), times = 10)
# Unit: milliseconds
#      expr        min          lq        mean      median         uq        max neval cld
#     dplyr 5287.80853 5536.760512 6052.829610 5843.829293 6487.43323 7594.13408    10   b
#  collapse    4.35939    4.782878    7.239961    5.775779   10.80186   11.97237    10  a

## Number of Distinct Values
# Small
microbenchmark(dplyr = summarise_all(gGGDC10S, n_distinct, na.rm = TRUE),
               collapse = fNdistinct(cgGGDC10S))
# Unit: milliseconds
#      expr        min         lq       mean     median         uq        max neval cld
#     dplyr 370.148289 389.433311 440.051493 404.357159 456.413466 911.286487   100   b
#  collapse   1.295457   1.392739   1.714009   1.488458   1.670304   5.470991   100  a

# Large
microbenchmark(dplyr = summarise_all(gdata, n_distinct, na.rm = TRUE),
               collapse = fNdistinct(cgdata), times = 5)
# Unit: milliseconds
#      expr        min         lq       mean   median         uq        max neval cld
#     dplyr 79475.8625 80011.9974 81132.3027 80408.21 81346.1701 84419.2694     5   b
#  collapse   329.4514   333.4636   364.8668   361.65   390.5476   409.2213     5  a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1951095 104.2    3670961 196.1  3670961 196.1
# Vcells 20496602 156.4   44616105 340.4 44616105 340.4

Below are some additional benchmarks for weighted aggregations and aggregations using the statistical mode, which cannot easily or efficiently be performed with dplyr.

## Weighted Mean
# Small
microbenchmark(fmean(cgGGDC10S, SUM)) 
# Unit: microseconds
#                   expr     min       lq     mean   median       uq     max neval
#  fmean(cgGGDC10S, SUM) 285.599 296.7545 310.1957 303.2255 307.9105 459.635   100

# Large 
microbenchmark(fmean(cgdata, SUM), times = 10) 
# Unit: milliseconds
#                expr      min       lq     mean   median     uq     max neval
#  fmean(cgdata, SUM) 49.36432 49.50623 53.60889 51.50207 55.776 63.3863    10

## Weighted Standard-Deviation
# Small
microbenchmark(fsd(cgGGDC10S, SUM)) 
# Unit: microseconds
#                 expr    min       lq     mean  median       uq     max neval
#  fsd(cgGGDC10S, SUM) 432.86 452.9415 490.4216 455.618 467.8905 929.533   100

# Large 
microbenchmark(fsd(cgdata, SUM), times = 10) 
# Unit: milliseconds
#              expr      min       lq     mean   median       uq      max neval
#  fsd(cgdata, SUM) 78.84118 79.66987 87.02884 81.03962 98.85895 105.6825    10

## Statistical Mode
# Small
microbenchmark(fmode(cgGGDC10S)) 
# Unit: milliseconds
#              expr      min       lq     mean   median       uq      max neval
#  fmode(cgGGDC10S) 1.574807 1.608945 1.704473 1.657586 1.810425 1.997403   100

# Large 
microbenchmark(fmode(cgdata), times = 10) 
# Unit: milliseconds
#           expr      min       lq     mean   median      uq      max neval
#  fmode(cgdata) 363.2765 364.2016 382.6044 379.5054 397.086 420.8072    10

## Weighted Statistical Mode
# Small
microbenchmark(fmode(cgGGDC10S, SUM)) 
# Unit: milliseconds
#                   expr      min       lq     mean   median       uq      max neval
#  fmode(cgGGDC10S, SUM) 1.762677 1.812434 1.912795 1.860182 1.937606 3.470911   100

# Large 
microbenchmark(fmode(cgdata, SUM), times = 10) 
# Unit: milliseconds
#                expr      min       lq     mean   median       uq      max neval
#  fmode(cgdata, SUM) 431.9522 462.3095 471.5851 466.6216 494.8882 499.4739    10

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1950542 104.2    3670961 196.1  3670961 196.1
# Vcells 20493197 156.4   44616105 340.4 44616105 340.4

3.2 Transformation


## Replacing with group sum
# Small
microbenchmark(dplyr = mutate_all(gGGDC10S, sum, na.rm = TRUE),
               collapse = fsum(cgGGDC10S, TRA = "replace_fill"))
# Unit: microseconds
#      expr      min        lq       mean    median        uq       max neval cld
#     dplyr 10398.45 11077.196 13284.4561 12050.685 14680.867 28479.948   100   b
#  collapse   337.81   387.789   436.1445   416.349   481.501   695.254   100  a

# Large
microbenchmark(dplyr = mutate_all(gdata, sum, na.rm = TRUE),
               collapse = fsum(cgdata, TRA = "replace_fill"), times = 10)
# Unit: milliseconds
#      expr       min         lq      mean     median         uq       max neval cld
#     dplyr 1123.4025 1196.13550 1244.5459 1227.63074 1268.49941 1411.6154    10   b
#  collapse   63.9044   71.43794  111.7684   80.88968   91.88053  394.9043    10  a

## Dividing by group sum
# Small
microbenchmark(dplyr = mutate_all(gGGDC10S, function(x) x/sum(x, na.rm = TRUE)),
               collapse = fsum(cgGGDC10S, TRA = "/"))
# Unit: microseconds
#      expr      min       lq       mean   median        uq       max neval cld
#     dplyr 9400.199 9757.197 10469.7067 9906.690 10207.683 21045.022   100   b
#  collapse  547.545  566.065   609.8953  600.426   607.789  1363.732   100  a

# Large
microbenchmark(dplyr = mutate_all(gdata, function(x) x/sum(x, na.rm = TRUE)),
               collapse = fsum(cgdata, TRA = "/"), times = 10)
# Unit: milliseconds
#      expr       min        lq      mean    median        uq      max neval cld
#     dplyr 1169.3321 1582.4174 1700.6890 1648.3897 2024.6757 2089.681    10   b
#  collapse  120.3382  123.1678  157.5447  138.8315  154.5461  340.741    10  a

## Centering
# Small
microbenchmark(dplyr = mutate_all(gGGDC10S, function(x) x-mean.default(x, na.rm = TRUE)),
               collapse = fwithin(cgGGDC10S))
# Unit: microseconds
#      expr      min        lq      mean    median        uq       max neval cld
#     dplyr 12377.56 13086.425 17582.817 14728.392 20003.258 63137.297   100   b
#  collapse   309.25   356.775   420.981   376.187   428.398  2113.874   100  a

# Large
microbenchmark(dplyr = mutate_all(gdata, function(x) x-mean.default(x, na.rm = TRUE)),
               collapse = fwithin(cgdata), times = 10)
# Unit: milliseconds
#      expr       min         lq      mean    median        uq       max neval cld
#     dplyr 2781.7480 3013.05630 3611.4475 3637.8264 4048.2085 4641.7812    10   b
#  collapse   78.7497   88.16463  171.8093  143.1789  179.3352  474.1199    10  a

## Centering and Scaling (Standardizing)
# Small
microbenchmark(dplyr = mutate_all(gGGDC10S, function(x) (x-mean.default(x, na.rm = TRUE))/sd(x, na.rm = TRUE)),
               collapse = fscale(cgGGDC10S))
# Unit: microseconds
#      expr       min         lq       mean    median        uq       max neval cld
#     dplyr 30876.743 31498.1420 34679.3403 31906.235 34905.463 63674.133   100   b
#  collapse   501.136   516.3085   567.0824   549.331   584.138   960.771   100  a

# Large
microbenchmark(dplyr = mutate_all(gdata, function(x) (x-mean.default(x, na.rm = TRUE))/sd(x, na.rm = TRUE)),
               collapse = fscale(cgdata), times = 2)
# Unit: milliseconds
#      expr       min        lq      mean    median        uq       max neval cld
#     dplyr 6815.0367 6815.0367 6898.6044 6898.6044 6982.1720 6982.1720     2   b
#  collapse  104.2023  104.2023  124.3979  124.3979  144.5935  144.5935     2  a

## Lag
# Small
microbenchmark(dplyr_unordered = mutate_all(gGGDC10S, dplyr::lag),
               collapse_unordered = flag(cgGGDC10S),
               dplyr_ordered = mutate_all(gGGDC10S, dplyr::lag, order_by = "Year"),
               collapse_ordered = flag(cgGGDC10S, t = Year))
# Unit: microseconds
#                expr        min          lq        mean     median          uq        max neval cld
#     dplyr_unordered  44826.879  46679.2510  50637.9331  47723.023  50856.1260  80238.831   100  b 
#  collapse_unordered    340.041    377.5255    449.8220    440.446    464.5440    874.198   100 a  
#       dplyr_ordered 124238.814 127791.8345 136933.8218 130585.120 136636.6790 191353.513   100   c
#    collapse_ordered    310.588    348.9655    406.0808    375.071    415.2335    844.300   100 a

# Large
microbenchmark(dplyr_unordered = mutate_all(gdata, dplyr::lag),
               collapse_unordered = flag(cgdata),
               dplyr_ordered = mutate_all(gdata, dplyr::lag, order_by = "Year"),
               collapse_ordered = flag(cgdata, t = Year), times = 2)
# Unit: milliseconds
#                expr         min          lq        mean      median          uq         max neval
#     dplyr_unordered  8807.75622  8807.75622  8956.36750  8956.36750  9104.97878  9104.97878     2
#  collapse_unordered    46.64087    46.64087    51.28162    51.28162    55.92237    55.92237     2
#       dplyr_ordered 27296.92937 27296.92937 28132.59569 28132.59569 28968.26200 28968.26200     2
#    collapse_ordered    83.39648    83.39648    94.62562    94.62562   105.85477   105.85477     2
#  cld
#   b 
#  a  
#    c
#  a

## First-Difference (unordered)
# Small
microbenchmark(dplyr_unordered = mutate_all(gGGDC10S, function(x) x - dplyr::lag(x)),
               collapse_unordered = fdiff(cgGGDC10S))
# Unit: microseconds
#                expr       min        lq       mean    median       uq        max neval cld
#     dplyr_unordered 59924.763 63774.092 71231.3617 66303.644 76502.85 109085.595   100   b
#  collapse_unordered   374.402   431.744   524.6622   477.039   546.43   1134.361   100  a

# Large
microbenchmark(dplyr_unordered = mutate_all(gdata, function(x) x - dplyr::lag(x)),
               collapse_unordered = fdiff(cgdata), times = 2)
# Unit: milliseconds
#                expr         min          lq        mean      median          uq         max neval
#     dplyr_unordered 13010.48101 13010.48101 13384.45341 13384.45341 13758.42582 13758.42582     2
#  collapse_unordered    48.63739    48.63739    55.65105    55.65105    62.66472    62.66472     2
#  cld
#    b
#   a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1952921 104.3    4741504 253.3  4741504 253.3
# Vcells 21542121 164.4   62556832 477.3 76753397 585.6

Below again some benchmarks for transformations not easily of efficiently performed with dplyr, such as centering on the overall mean, mean-preserving scaling, weighted scaling and centering, sequences of lags / leads, (iterated) panel-differences and growth rates.

# Centering on overall mean
microbenchmark(fwithin(cgdata, mean = "overall.mean"), times = 10)
# Unit: milliseconds
#                                    expr      min       lq     mean  median       uq     max neval
#  fwithin(cgdata, mean = "overall.mean") 66.96164 89.12183 97.07003 98.7485 104.3518 124.258    10

# Weighted Centering
microbenchmark(fwithin(cgdata, SUM), times = 10)
# Unit: milliseconds
#                  expr      min      lq    mean   median       uq    max neval
#  fwithin(cgdata, SUM) 68.66005 89.6551 102.258 103.4307 114.6298 139.72    10
microbenchmark(fwithin(cgdata, SUM, mean = "overall.mean"), times = 10)
# Unit: milliseconds
#                                         expr      min       lq     mean   median       uq     max
#  fwithin(cgdata, SUM, mean = "overall.mean") 73.04979 94.59996 101.1462 99.11777 114.8663 139.778
#  neval
#     10

# Weighted Scaling and Standardizing
microbenchmark(fsd(cgdata, SUM, TRA = "/"), times = 10)
# Unit: milliseconds
#                         expr      min       lq     mean   median      uq      max neval
#  fsd(cgdata, SUM, TRA = "/") 139.7937 171.8882 191.0733 178.0029 188.963 340.0654    10
microbenchmark(fscale(cgdata, SUM), times = 10)
# Unit: milliseconds
#                 expr      min       lq     mean   median       uq      max neval
#  fscale(cgdata, SUM) 95.23943 104.8632 132.5975 140.9041 146.9867 166.0495    10

# Sequence of lags and leads
microbenchmark(flag(cgdata, -1:1), times = 10)
# Unit: milliseconds
#                expr     min       lq     mean   median      uq      max neval
#  flag(cgdata, -1:1) 79.7667 108.5273 221.3109 238.5866 295.766 352.4519    10

# Iterated difference
microbenchmark(fdiff(cgdata, 1, 2), times = 10)
# Unit: milliseconds
#                 expr      min       lq     mean   median       uq      max neval
#  fdiff(cgdata, 1, 2) 68.04736 85.90751 95.14523 95.61562 111.1995 113.5704    10

# Growth Rate
microbenchmark(fgrowth(cgdata,1), times = 10)
# Unit: milliseconds
#                expr      min       lq     mean   median       uq      max neval
#  fgrowth(cgdata, 1) 76.58898 96.65314 97.30958 98.39663 100.0393 112.0112    10

References

Timmer, M. P., de Vries, G. J., & de Vries, K. (2015). “Patterns of Structural Change in Developing Countries.” . In J. Weiss, & M. Tribe (Eds.), Routledge Handbook of Industry and Development. (pp. 65-83). Routledge.

Cochrane, D. & Orcutt, G. H. (1949). “Application of Least Squares Regression to Relationships Containing Auto-Correlated Error Terms”. Journal of the American Statistical Association. 44 (245): 32–61.

Prais, S. J. & Winsten, C. B. (1954). “Trend Estimators and Serial Correlation”. Cowles Commission Discussion Paper No. 383. Chicago.


  1. Row-wise operations are not supported by TRA.↩︎