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:

  • This vignette is targeted at dplyr / tidyverse users. collapse is a standalone package and can be programmed efficiently without pipes or dplyr verbs.

  • The ‘Introduction to collapse’ vignette provides a thorough introduction to the package and a built-in structured documentation is available under help("collapse-documentation") after installing the package. In addition help("collapse-package") provides a compact set of examples for quick-start.

  • Documentation and vignettes can also be viewed online.


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))

# Efficiently converting to tibble (no deep copy)
GGDC10S <- qTBL(GGDC10S)

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
# # A tibble: 1 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 USA     ASI        Asia   EMP       2010  171.     0 4645.     0  1.35  21.8  8.98  40.1     0
# # ... with 2 more variables: OTH <dbl>, SUM <dbl>

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   718.46   796.777   927.1658   823.9975   887.3645   8953.525   100 a  
#    hybrid 12246.39 12681.261 13564.9215 12951.6875 13480.7145  22790.793   100  b 
#     dplyr 56282.17 57989.072 62363.1482 59498.5065 64035.2900 135722.398   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. fgroup_by first assigns the class GDP_df which is for printing grouping information and subsetting, then the object classes (tbl_df, data.table or whatever else), followed by classes grouped_df and data.frame, and adds the grouping object in a ‘groups’ attribute. Since tbl_df is assigned before grouped_df, the object 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] "GRP_df"     "tbl_df"     "tbl"        "grouped_df" "data.frame"

The function fungroup removes classes ‘GDP_df’ and ‘grouped_df’ and the ‘groups’ attribute (and can thus also be used for grouped tibbles created with dplyr::group_by).

Note that any kind of data frame based class can be grouped with fgroup_by, and still retain full responsiveness to all methods defined for that class. Functions performing aggregation on the grouped data frame remove the grouping object and classes afterwards, yielding an object with the same class and attributes as the input.

The print method shown below reports the grouping variables, and then in square brackets the information [number of groups | average group size (standard-deviation of group sizes)]:

fgroup_by(GGDC10S, Variable, Country)
# # A tibble: 5,027 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  16.3  3.49  0.737  0.104  0.660  6.24  1.66  1.12  4.82
#  6 BWA     SSA        Sub-s~ VA        1965  15.7  2.50  1.02   0.135  1.35   7.06  1.94  1.25  5.70
#  7 BWA     SSA        Sub-s~ VA        1966  17.7  1.97  0.804  0.203  1.35   8.27  2.15  1.36  6.37
#  8 BWA     SSA        Sub-s~ VA        1967  19.1  2.30  0.938  0.203  0.897  4.31  1.72  1.54  7.04
#  9 BWA     SSA        Sub-s~ VA        1968  21.1  1.84  0.750  0.203  1.22   5.17  2.44  1.03  5.03
# 10 BWA     SSA        Sub-s~ VA        1969  21.9  5.24  2.14   0.578  3.47   5.75  2.72  1.23  5.59
# # ... with 5,017 more rows, and 2 more variables: OTH <dbl>, SUM <dbl>
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

Note further 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) %>% tail(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 EMP      EGY     5206.  29.0 2436.  307. 2733. 2977. 1992.  801. 5539.    NA 22020.
# 2 EMP      EGY     5186.  27.6 2374.  318. 2795. 3020. 2048.  815. 5636.    NA 22219.
# 3 EMP      EGY     5161.  24.8 2348.  325. 2931. 3110. 2065.  832. 5736.    NA 22533.
GGDC10S %>% group_by(Variable, Country) %>% get_vars(6:16) %>% tail(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 5206.  29.0 2436.  307. 2733. 2977. 1992.  801. 5539.    NA 22020.
# 2 5186.  27.6 2374.  318. 2795. 3020. 2048.  815. 5636.    NA 22219.
# 3 5161.  24.8 2348.  325. 2931. 3110. 2065.  832. 5736.    NA 22533.

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 %>% tail(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       VEN      6.86e3  3.55e4  19553.   1064. 1.17e4 1.93e4 8.03e3 5.60e3 NA      19986. 1.28e5
# 2 VA       ZAF      1.64e4  4.29e4  87572.  13826. 1.64e4 6.83e4 4.53e4 6.64e4  7.58e4 30167. 4.63e5
# 3 VA       ZMB      1.27e6  1.01e6 899510. 219164. 8.66e5 2.10e6 7.05e5 9.10e5  1.10e6 81871. 9.16e6
GGDC10S %>% group_by(Variable, Country) %>% get_vars(6:16) %>% fmean %>% tail(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       VEN      6.86e3  3.55e4  19553.   1064. 1.17e4 1.93e4 8.03e3 5.60e3 NA      19986. 1.28e5
# 2 VA       ZAF      1.64e4  4.29e4  87572.  13826. 1.64e4 6.83e4 4.53e4 6.64e4  7.58e4 30167. 4.63e5
# 3 VA       ZMB      1.27e6  1.01e6 899510. 219164. 8.66e5 2.10e6 7.05e5 9.10e5  1.10e6 81871. 9.16e6

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)
# # A tibble: 3 x 11
#   Country  Year   AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV
#   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 BWA      1991  303. 2647.  473.  161.  580.  807.  233.  433. 1073.
# 2 BWA      1992  333. 2691.  537.  178.  679.  725.  285.  517. 1234.
# 3 BWA      1993  405. 2625.  567.  219.  634.  772.  350.  673. 1487.

GGDC10S %>% filter(Variable == "VA" & Year > 1990) %>% select(Country, Year, AGR:GOV) %>% head(3)
# # A tibble: 3 x 11
#   Country  Year   AGR   MIN   MAN    PU   CON   WRT   TRA  FIRE   GOV
#   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 BWA      1991  303. 2647.  473.  161.  580.  807.  233.  433. 1073.
# 2 BWA      1992  333. 2691.  537.  178.  679.  725.  285.  517. 1234.
# 3 BWA      1993  405. 2625.  567.  219.  634.  772.  350.  673. 1487.

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

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)
# # A tibble: 3 x 5
#   Country mean_AGR sd_AGR mean_SUM  sd_SUM
#   <chr>      <dbl>  <dbl>    <dbl>   <dbl>
# 1 ARG       14951. 33061.  152534. 301316.
# 2 BOL        3300.  4456.   22619.  33173.
# 3 BRA       76870. 59442. 1200563. 976963.

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    AGR   MIN   MAN    CON   WRT    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
# # A tibble: 6 x 4
#   Country  Year AGR_perc AGR_mean
#   <chr>   <dbl>    <dbl>    <dbl>
# 1 BWA      1960     NA   5137561.
# 2 BWA      1961     NA   5137561.
# 3 BWA      1962     NA   5137561.
# 4 BWA      1963     NA   5137561.
# 5 BWA      1964     43.5 5137561.
# 6 BWA      1965     40.0 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
# # A tibble: 6 x 2
#   AGR_perc AGR_mean
#      <dbl>    <dbl>
# 1     NA   5137561.
# 2     NA   5137561.
# 3     NA   5137561.
# 4     NA   5137561.
# 5     43.5 5137561.
# 6     40.0 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 transform 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:

  • 1 - “replace_fill” : replace and overwrite missing values (same as mutate)

  • 2 - “replace” : replace but preserve missing values

  • 3 - “-” : subtract (center)

  • 4 - “-+” : subtract group-statistics but add average of group statistics

  • 5 - “/” : divide (scale)

  • 6 - “%” : compute percentages (divide and multiply by 100)

  • 7 - “+” : add

  • 8 - "*" : multiply

  • 9 - “%%” : modulus

  • 10 - “-%%” : subtract modulus

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
# # A tibble: 6 x 12
#    Year    AGR   MIN    MAN    PU    CON    WRT    TRA  FIRE    GOV    OTH     SUM
#   <dbl>  <dbl> <dbl>  <dbl> <dbl>  <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl>   <dbl>
# 1   -22    NA    NA     NA    NA     NA     NA     NA    NA     NA     NA      NA 
# 2   -21    NA    NA     NA    NA     NA     NA     NA    NA     NA     NA      NA 
# 3   -20    NA    NA     NA    NA     NA     NA     NA    NA     NA     NA      NA 
# 4   -19    NA    NA     NA    NA     NA     NA     NA    NA     NA     NA      NA 
# 5   -18 -4378. -170. -3717. -168. -1473. -3767. -1173. -959. -3924. -1431. -23149.
# 6   -17 -4379. -171. -3717. -168. -1472. -3767. -1173. -959. -3923. -1430. -23147.

# This replaces all data points with the mode
GGDC10S %>% char_vars %>% fmode(TRA = "replace") %>% head
# # A tibble: 6 x 4
#   Country Regioncode Region Variable
#   <chr>   <chr>      <chr>  <chr>   
# 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)
# # A tibble: 3 x 17
#   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 EGY     MENA       Middl~ EMP       2010 5206.  29.0 2436.  307. 2733. 2977. 1992.  801. 5539.
# 2 EGY     MENA       Middl~ EMP       2011 5186.  27.6 2374.  318. 2795. 3020. 2048.  815. 5636.
# 3 EGY     MENA       Middl~ EMP       2012 5161.  24.8 2348.  325. 2931. 3110. 2065.  832. 5736.
# # ... with 3 more variables: OTH <dbl>, SUM <dbl>, AGR_gmed <lgl>

# 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)
# # A tibble: 3 x 17
#   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 EGY     MENA       Middl~ EMP       2010  8.41  2.28  4.32  3.56  3.62  3.75  3.75  3.14  3.80
# 2 EGY     MENA       Middl~ EMP       2011  8.38  2.17  4.21  3.68  3.70  3.81  3.86  3.19  3.86
# 3 EGY     MENA       Middl~ EMP       2012  8.34  1.95  4.17  3.76  3.88  3.92  3.89  3.26  3.93
# # ... with 3 more variables: OTH <dbl>, SUM <dbl>, AGR_gmed <lgl>
rm(GGDC10S)

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      37.5 -1301. -13317. -2965. -529. -2746. -6540. -2157. -4431. -7551. -2613.
# 6 VA       BWA      39.3 -1302. -13318. -2964. -529. -2745. -6540. -2156. -4431. -7550. -2613.

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
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

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)
# # A tibble: 6 x 27
#   Country Regioncode Region Variable  Year   AGR N_AGR   MIN N_MIN    MAN N_MAN     PU  N_PU    CON
#   <chr>   <chr>      <chr>  <chr>    <dbl> <dbl> <int> <dbl> <int>  <dbl> <int>  <dbl> <int>  <dbl>
# 1 BWA     SSA        Sub-s~ VA        1960  NA      47 NA       47 NA        47 NA        47 NA    
# 2 BWA     SSA        Sub-s~ VA        1961  NA      47 NA       47 NA        47 NA        47 NA    
# 3 BWA     SSA        Sub-s~ VA        1962  NA      47 NA       47 NA        47 NA        47 NA    
# 4 BWA     SSA        Sub-s~ VA        1963  NA      47 NA       47 NA        47 NA        47 NA    
# 5 BWA     SSA        Sub-s~ VA        1964  16.3    47  3.49    47  0.737    47  0.104    47  0.660
# 6 BWA     SSA        Sub-s~ VA        1965  15.7    47  2.50    47  1.02     47  0.135    47  1.35 
# # ... with 13 more variables: N_CON <int>, WRT <dbl>, N_WRT <int>, TRA <dbl>, N_TRA <int>,
# #   FIRE <dbl>, N_FIRE <int>, GOV <dbl>, N_GOV <int>, OTH <dbl>, N_OTH <int>, SUM <dbl>,
# #   N_SUM <int>
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
# # 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>

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 %>% tail(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 4444.  34.9 1614.  131.  997. 1307.  799.  320. 2958.    NA 12605.
# 2 4444.  34.9 1614.  131.  997. 1307.  799.  320. 2958.    NA 12605.

GGDC10S %>% # Same as ... %>% fmean(TRA = "replace_fill")
  fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fbetween(fill = TRUE) %>% tail(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 4444.  34.9 1614.  131.  997. 1307.  799.  320. 2958.    NA 12605.
# 2 4444.  34.9 1614.  131.  997. 1307.  799.  320. 2958.    NA 12605.

GGDC10S %>% # Same as ... %>% fmean(TRA = "-")
  fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fwithin %>% tail(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  742.  -7.35  760.  187. 1798. 1713. 1249.  495. 2678.    NA 9614.
# 2  717. -10.1   734.  194. 1934. 1803. 1266.  512. 2778.    NA 9928.

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") %>% tail(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 EGY     EMP       2.53e6  1.87e6  5.54e6 335856.  1.80e6  3.39e6 1.47e6 1.66e6 1.71e6    NA 2.16e7
# 2 EGY     EMP       2.53e6  1.87e6  5.54e6 335867.  1.80e6  3.39e6 1.47e6 1.66e6 1.71e6    NA 2.16e7
# 3 EGY     EMP       2.53e6  1.87e6  5.54e6 335873.  1.80e6  3.39e6 1.47e6 1.66e6 1.72e6    NA 2.16e7

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") %>% tail(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 EGY     EMP      22020.  4.29e8  3.70e8  7.38e8  2.73e7  2.83e8  4.33e8 1.97e8 1.55e8 2.10e8    NA
# 2 EGY     EMP      22219.  4.29e8  3.70e8  7.38e8  2.73e7  2.83e8  4.33e8 1.97e8 1.55e8 2.10e8    NA
# 3 EGY     EMP      22533.  4.29e8  3.70e8  7.38e8  2.73e7  2.83e8  4.33e8 1.97e8 1.55e8 2.10e8    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
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

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>
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

If the time-variable passed does not exactly identify the data (i.e. because of repeated values in each group), all 3 functions will issue appropriate error messages. flag, fdiff and fgrowth support irregular time series and unbalanced panels.

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
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

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>
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

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>
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

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>
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

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>
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

# 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>
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

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>
# 
# Grouped by:  Variable, Country  [85 | 59 (7.7)]

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  2302151 123.0    4243897 226.7  4243897 226.7
# Vcells 20490773 156.4   33392469 254.8 23056172 176.0

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 3090.270 3248.465 3661.88243 3536.0715 3786.640 6404.556   100   b
#  collapse   11.157   16.958   29.17596   32.3535   38.378   73.185   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 2754.691 2889.2350 8368.50765 5763.966 9659.937 58699.945   100   b
#  collapse   11.603   25.6595   55.95086   36.147   66.937   475.701   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 2690.877 4656.6010 6438.2071 5563.824 7006.9905 21035.252   100   b
#  collapse  226.248  407.4245  609.9681  536.167  592.8415  2983.616   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 16.792323 17.895003 22.77324 18.831232 23.67950 120.14164   100   b
#  collapse  7.738392  8.147379 13.18525  8.839063 11.82893  64.30482   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 24976.517 26012.482 29693.9499 26860.8 28740.17 75848.820   100   b
#  collapse   641.259   821.097   985.8207   868.4   945.60  5745.893   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 19761.3839 19761.3839 20073.6533 20073.6533 20385.9227 20385.9227     2   b
#  collapse   219.3337   219.3337   250.1585   250.1585   280.9834   280.9834     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 2945.685 3023.555 3147.8758 3095.848 3188.445 4213.477   100   b
#  collapse  348.520  362.800  390.8334  392.475  409.433  649.291   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 195.11314 198.33728 203.22878 201.86710 206.23230 217.56790    10   b
#  collapse  67.23489  68.28447  70.03188  69.50495  70.84281  76.11835    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 3007.268 3155.868 3378.26033 3254.489 3391.0410 6666.057   100   b
#  collapse   26.775   34.808   50.87266   43.287   60.9135  199.473   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 4.037655 6.412142 9.527133 7.036666 9.000605 65.98272   100   b
#  collapse 1.270469 2.059881 4.785661 3.826580 4.584309 59.38627   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 22404.787 23159.3935 25073.1336 24354.446 25676.0105 37566.073   100   b
#  collapse   432.861   493.9975   541.1744   542.861   577.4455   838.055   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 22.216917 22.565437 26.111103 24.982094 29.705409 33.18168    10   b
#  collapse  6.839201  7.054293  8.346583  8.210076  9.081599 10.25345    10  a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  2307708 123.3    4243897 226.7  4243897 226.7
# Vcells 22006745 167.9   73138358 558.1 73138358 558.1

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  2324701 124.2    4243897 226.7  4243897 226.7
# Vcells 21107320 161.1   73138358 558.1 73138358 558.1

## 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) 348.074 370.609 505.3809 385.782 440.4475 3243.779   100

# Large
microbenchmark(GRP(gdata))
# Unit: milliseconds
#        expr      min       lq    mean   median       uq     max neval
#  GRP(gdata) 29.38188 31.45002 38.0206 33.77832 36.68629 117.656   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 8284.60 8980.523 10514.0961 9480.768 10619.147 25634.732   100   b
#  collapse  239.19  280.913   321.0804  302.556   340.711   590.833   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 573.20993 602.61769 803.86710 718.05677 970.63463 1268.85923    10   b
#  collapse  41.60016  42.38511  47.29339  48.05893  50.62196   54.85953    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 10903.630 11145.2740 12220.4826 11411.238 11940.93 33783.673   100   b
#  collapse   258.824   274.8895   303.3732   314.159   319.96   445.802   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 1454.07280 1490.12386 1617.22808 1622.44629 1705.83358 1849.6304    10   b
#  collapse   44.97067   45.07152   46.11673   45.24266   47.32374   48.8508    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 48462.563 49467.291 53665.0865 50538.0640 55707.404 87132.655   100   b
#  collapse   488.642   549.555   590.2751   584.3625   622.517   851.889   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 9288.16884 9288.16884 9355.06257 9355.06257 9421.95631 9421.95631     2   b
#  collapse   93.17307   93.17307   94.58567   94.58567   95.99827   95.99827     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 24275.46 24748.707 26379.4763 25031.851 26548.872 39034.229   100   b
#  collapse   427.06   459.859   496.7192   485.965   512.739   970.144   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 4155.64069 4155.64069 4328.76628 4328.76628 4501.89187 4501.89187     2   b
#  collapse   81.51707   81.51707   82.09094   82.09094   82.66482   82.66482     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 9887.077 10194.0955 10714.3232 10393.792 10629.411 19342.186   100   b
#  collapse  182.516   196.5725   243.1297   244.098   251.238   973.267   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 841.05663 867.9507 900.67790 883.18202 901.57102 1089.203    10   b
#  collapse  25.78913  25.8583  26.28742  26.07406  26.86772   27.131    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 10452.920 10847.627 11462.3425 11079.23 11362.15 20952.696   100   b
#  collapse    57.566    68.276   117.0155   117.14   124.95  1543.573   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 1186.05831 1228.879404 1365.678287 1409.77351 1427.809301 1515.277980    10   b
#  collapse    4.45088    4.475871    4.548431    4.48658    4.652139    4.712828    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 65.569491 67.342659 70.766485 68.975481 73.907195 82.63760   100   b
#  collapse  1.311078  1.358826  1.531721  1.391179  1.463248 10.16554   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 12894.1111 13107.1415 17884.8903 16243.7631 17612.7355 29566.7005     5   b
#  collapse   311.8544   321.0761   332.4503   334.1387   334.6594   360.5231     5  a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  2327929 124.4    4243897 226.7  4243897 226.7
# Vcells 21114893 161.1   73138358 558.1 73138358 558.1

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) 319.96 332.232 369.2037 341.826 349.1895 765.762   100

# Large 
microbenchmark(fmean(cgdata, SUM), times = 10) 
# Unit: milliseconds
#                expr      min       lq     mean   median       uq     max neval
#  fmean(cgdata, SUM) 51.70009 53.60424 62.06417 56.69406 72.08695 82.5479    10

## Weighted Standard-Deviation
# Small
microbenchmark(fsd(cgGGDC10S, SUM)) 
# Unit: microseconds
#                 expr     min      lq     mean   median       uq      max neval
#  fsd(cgGGDC10S, SUM) 440.001 558.034 1153.771 904.9915 1082.599 9133.364   100

# Large 
microbenchmark(fsd(cgdata, SUM), times = 10) 
# Unit: milliseconds
#              expr      min       lq     mean   median       uq      max neval
#  fsd(cgdata, SUM) 79.68343 90.06718 100.0621 99.64724 110.8909 127.8064    10

## Statistical Mode
# Small
microbenchmark(fmode(cgGGDC10S)) 
# Unit: milliseconds
#              expr      min       lq    mean   median       uq      max neval
#  fmode(cgGGDC10S) 1.571241 1.792579 2.05617 1.829842 2.013249 5.649057   100

# Large 
microbenchmark(fmode(cgdata), times = 10) 
# Unit: milliseconds
#           expr      min       lq     mean   median       uq      max neval
#  fmode(cgdata) 412.6703 509.7057 585.4809 583.4205 618.5563 821.8684    10

## Weighted Statistical Mode
# Small
microbenchmark(fmode(cgGGDC10S, SUM)) 
# Unit: milliseconds
#                   expr      min       lq     mean   median       uq   max neval
#  fmode(cgGGDC10S, SUM) 1.764466 1.995623 2.051703 2.031769 2.100714 3.271   100

# Large 
microbenchmark(fmode(cgdata, SUM), times = 10) 
# Unit: milliseconds
#                expr      min       lq     mean  median       uq      max neval
#  fmode(cgdata, SUM) 517.7212 553.4684 648.3746 656.759 740.7869 767.1949    10

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  2327383 124.3    4243897 226.7  4243897 226.7
# Vcells 21111519 161.1   73138358 558.1 73138358 558.1

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 9952.676 14189.356 18875.7491 16204.61 21243.20 63585.917   100   b
#  collapse  326.654   387.567   529.1079   434.20   547.77  1965.278   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 931.38933 1062.2262 1257.1730 1245.4816 1373.6432 1783.5825    10   b
#  collapse  60.55188   71.0048  172.5488  121.8175  300.3608  372.9703    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 9148.536 9726.2050 11421.2653 10392.8995 11773.592 23255.783   100   b
#  collapse  548.440  565.8435   633.4541   613.8145   678.744  1003.166   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 1203.2906 1580.4784 1837.4117 1698.4015 2089.7419 2804.4906    10   b
#  collapse  109.6271  115.0937  164.5631  128.1049  153.5871  463.0924    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 11884.930 12263.349 13834.5448 12822.0520 13502.135 60464.857   100   b
#  collapse   306.572   323.084   361.8897   359.6765   380.204   689.454   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 1920.4562 2827.10869 2798.1697 2872.0838 3033.6529 3205.626    10   b
#  collapse   65.4856   76.23839  114.8372  100.1533  162.7168  173.388    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 29655.429 32402.757 37043.271 34055.4375 39418.450 66339.269   100   b
#  collapse   496.675   541.969   584.706   560.7115   605.113  1238.785   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 6128.4079 6128.4079 6364.0317 6364.0317 6599.655 6599.655     2   b
#  collapse  103.6416  103.6416  114.0488  114.0488  124.456  124.456     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  44760.042  47003.5545  58805.844  50658.1050  60146.2355 183804.312   100  b 
#  collapse_unordered    404.747    466.1060    570.011    489.9805    518.3175   4665.080   100 a  
#       dplyr_ordered 105484.168 114729.3165 138474.255 121829.7970 144656.9585 347953.171   100   c
#    collapse_ordered    348.520    419.2505    553.308    445.5795    469.4535   5874.859   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 cld
#     dplyr_unordered  9322.60181  9322.60181  9512.82317  9512.82317  9703.0445  9703.0445     2  b 
#  collapse_unordered    43.45878    43.45878    53.30324    53.30324    63.1477    63.1477     2 a  
#       dplyr_ordered 24916.39806 24916.39806 24983.25163 24983.25163 25050.1052 25050.1052     2   c
#    collapse_ordered   100.26351   100.26351   118.91758   118.91758   137.5717   137.5717     2 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 60398.812 66697.384 82056.0223 75039.549 88612.19 199408.721   100   b
#  collapse_unordered   447.141   506.939   621.4501   538.176   654.20   2415.988   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 cld
#     dplyr_unordered 12687.34061 12687.34061 13168.5915 13168.5915 13649.8424 13649.8424     2   b
#  collapse_unordered    63.99022    63.99022   391.2529   391.2529   718.5155   718.5155     2  a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  2329725 124.5    5577265 297.9  5577265 297.9
# Vcells 22160371 169.1   73138358 558.1 73138358 558.1

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") 72.63048 91.55587 123.9617 116.3387 147.5185 206.2484    10

# Weighted Centering
microbenchmark(fwithin(cgdata, SUM), times = 10)
# Unit: milliseconds
#                  expr      min       lq    mean   median       uq     max neval
#  fwithin(cgdata, SUM) 73.07449 80.44161 92.4393 85.42308 103.6144 126.838    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") 63.72292 74.96703 86.8185 89.93889 96.73592 102.1511
#  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 = "/") 148.6587 165.4211 174.4325 173.45 180.4467 203.8864    10
microbenchmark(fscale(cgdata, SUM), times = 10)
# Unit: milliseconds
#                 expr      min       lq     mean   median       uq      max neval
#  fscale(cgdata, SUM) 103.4488 109.1581 150.8876 138.2665 163.0167 286.4303    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) 45.29777 94.41766 119.5111 112.0534 132.4804 245.7396    10

# Iterated difference
microbenchmark(fdiff(cgdata, 1, 2), times = 10)
# Unit: milliseconds
#                 expr      min      lq     mean   median       uq      max neval
#  fdiff(cgdata, 1, 2) 58.64149 61.9705 81.35423 80.24704 94.65238 107.5079    10

# Growth Rate
microbenchmark(fgrowth(cgdata,1), times = 10)
# Unit: milliseconds
#                expr      min       lq     mean   median       uq      max neval
#  fgrowth(cgdata, 1) 64.37712 69.60224 87.20517 86.34525 105.3159 116.9287    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.↩︎