collapse and dplyr

Fast (Weighted) Aggregations and Transformations in a Piped Workflow

Sebastian Krantz

2020-11-10

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

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

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


Notes:


1. Fast Aggregations

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

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

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

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

1.1 Simple Aggregations

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

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

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

# 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   942.476  1033.066  1122.02  1059.394  1157.792  1947.874   100 a  
#    hybrid 12567.245 13084.000 14059.24 13524.893 14201.852 22570.792   100  b 
#     dplyr 59600.474 62732.691 66548.49 64481.761 69187.895 90369.739   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 fmean.AGR fmean.MIN fmean.MAN fmedian.CON fmedian.WRT fmedian.TRA
#   <chr>    <chr>       <dbl>     <dbl>     <dbl>       <dbl>       <dbl>       <dbl>
# 1 EMP      ARG         1420.      52.1     1932.       782.        1855.       580. 
# 2 EMP      BOL          964.      56.0      235.        66.0        132.        97.0
# 3 EMP      BRA        17191.     206.      6991.      4055.        6455.      1581.

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

1.4 Weighted Aggregations

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

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

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

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

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

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

2. Fast Transformations

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

2.1 Fast Transform and Compute Variables

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

GGDC10S %>% fsubset(Variable == "VA", Country, Year, AGR, SUM) %>%
  ftransform(AGR_perc = AGR / SUM * 100,  # Computing % of VA in Agriculture
             AGR_mean = fmean(AGR),       # Average Agricultural VA
             AGR = NULL, SUM = NULL) %>%  # Deleting columns AGR and SUM
             head
# # 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 transforms data by either (column-wise) replacing data values with computed statistics or sweeping the statistics out of the data. Operations can be specified using either an integer or quoted operator / string. The 10 operations supported by TRA are:

Simple transformations are again straightforward to specify:

# This subtracts the median value from all data points i.e. centers on the median
GGDC10S %>% num_vars %>% fmedian(TRA = "-") %>% head
# # 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 gaps or repeated values in each group), all 3 functions will issue appropriate error messages. flag, fdiff and fgrowth support unbalanced panels with different start and end periods and duration of coverage for each individual, but not irregular panels. A workaround for such panels exists with the function seqid which generates a new panel-id identifying consecutive time-sequences at the sub-individual level, see ?seqid.

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

GGDC10S %>%
  fselect(Variable, Country,AGR:SUM) %>% 
    fgroup_by(Variable, Country) %>% flag
# # A tibble: 5,027 x 13
#    Variable Country   AGR   MIN    MAN     PU    CON   WRT   TRA  FIRE   GOV   OTH   SUM
#  * <chr>    <chr>   <dbl> <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#  1 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  2 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  3 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  4 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  5 VA       BWA      NA   NA    NA     NA     NA     NA    NA    NA    NA    NA     NA  
#  6 VA       BWA      16.3  3.49  0.737  0.104  0.660  6.24  1.66  1.12  4.82  2.34  37.5
#  7 VA       BWA      15.7  2.50  1.02   0.135  1.35   7.06  1.94  1.25  5.70  2.68  39.3
#  8 VA       BWA      17.7  1.97  0.804  0.203  1.35   8.27  2.15  1.36  6.37  2.99  43.1
#  9 VA       BWA      19.1  2.30  0.938  0.203  0.897  4.31  1.72  1.54  7.04  3.31  41.4
# 10 VA       BWA      21.1  1.84  0.750  0.203  1.22   5.17  2.44  1.03  5.03  2.36  41.1
# # ... with 5,017 more rows
# 
# 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  1941713 103.7    3719735 198.7  3719735 198.7
# Vcells 19911201 152.0   28373960 216.5 23085355 176.2

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 3131.771 3480.291 3652.60932 3664.8140 3817.43 4672.666   100   b
#  collapse   10.711   16.288   28.84573   27.8905   39.27   90.142   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 2795.300 2821.405 3039.02691 2948.5860 3174.387 3642.725   100   b
#  collapse   12.049   15.619   27.15895   29.8985   35.700  109.331   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 2039.354 2304.426 2502.3374 2420.0045 2618.585 4196.519   100   b
#  collapse  157.080  185.863  236.6948  210.8525  288.946  449.818   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 16.888713 17.218713 20.515368 17.792811 18.9207 57.01804   100   b
#  collapse  6.448288  7.821841  8.897339  8.044964  8.6889 42.06024   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 7357.296 7802.874 8374.6212 8376.0805 8695.8175 12976.454   100   b
#  collapse  572.090  634.342  738.3358  684.9915  841.4015  1246.372   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 2747.9010 2747.9010 2787.0271 2787.0271 2826.1533 2826.1533     2   b
#  collapse  210.9429  210.9429  218.9627  218.9627  226.9824  226.9824     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 2896.151 3045.645 3277.9081 3223.4745 3467.573 4052.827   100   b
#  collapse  340.488  357.222  396.2643  393.5915  405.194  640.813   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 70.91956 72.24626 78.50734 75.53265 80.80798 104.8246    10   b
#  collapse 65.07817 65.23480 67.36426 67.12243 68.44467  72.2061    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 3038.505 3206.0710 3471.43249 3373.414 3667.046 4836.439   100   b
#  collapse   26.775   33.2455   46.61981   40.386   58.459   91.034   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 3.936803 4.424775 6.327970 6.504293 6.945186 29.73799   100   b
#  collapse 1.250388 1.551605 3.521283 3.676417 3.941042 33.47308   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 16467.454 17059.849 17876.7955 17674.333 18234.37 23710.956   100   b
#  collapse   697.486   767.324   865.2265   832.253   896.29  1863.533   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 24.329902 25.566010 26.407991 25.744063 28.291248 29.071737    10   b
#  collapse  6.918634  7.983828  8.220518  8.270543  8.671497  8.700949    10  a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1947525 104.1    3719735 198.7  3719735 198.7
# Vcells 21427698 163.5   57613243 439.6 66848295 510.1

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  1964529 105.0    3719735 198.7  3719735 198.7
# Vcells 20528287 156.7   57613243 439.6 66848295 510.1

## 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) 166.897 170.467 187.0318 172.6985 180.5075 443.571   100

# Large
microbenchmark(GRP(gdata))
# Unit: milliseconds
#        expr      min       lq    mean   median       uq      max neval
#  GRP(gdata) 31.06691 32.30324 34.0717 33.65069 35.22238 53.05223   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 8360.015 8720.1375 9211.3590 8949.2865 9471.62 17006.522   100   b
#  collapse  238.297  251.4615  291.3512  296.7555  302.78   485.519   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 598.74203 603.34954 651.50459 629.12953 700.37954 764.99048    10   b
#  collapse  40.13869  41.61845  43.07965  42.87554  44.06523  48.48621    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 11301.238 11792.334 13179.5550 12139.738 12715.845 33178.56   100   b
#  collapse   257.485   278.905   317.9387   315.274   324.423   561.38   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 1359.61766 1398.07310 1543.02306 1558.3396 1615.48214 1748.97419    10   b
#  collapse   43.21156   43.83541   45.03422   45.0356   46.34869   47.20191    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 51165.489 52280.887 55266.225 53735.210 56140.7110 75017.013   100   b
#  collapse   490.427   501.583   562.581   559.596   598.6415   811.726   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 9785.20062 9785.20062 10103.21901 10103.21901 10421.23740 10421.23740     2   b
#  collapse   92.81429   92.81429    93.39285    93.39285    93.97141    93.97141     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 23908.64 24836.8405 26208.7552 25443.52 26458.06 34351.300   100   b
#  collapse   427.06   444.0175   492.6495   484.18   504.93   835.377   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 4195.77894 4195.77894 4259.9021 4259.9021 4324.02534 4324.02534     2   b
#  collapse   81.48717   81.48717   82.4263   82.4263   83.36543   83.36543     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 10840.263 11216.674 11663.0739 11411.4615 11730.08 18719.67   100   b
#  collapse   183.408   193.672   237.3552   240.7515   244.99   516.31   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 1035.82526 1050.31806 1105.68453 1103.71926 1122.64331 1274.45251    10   b
#  collapse   24.29153   24.84398   27.10525   25.21771   26.08901   43.95501    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 10386.876 10866.816 11811.7102 11306.146 11918.6225 19031.597   100   b
#  collapse    58.459    68.053   103.2532   119.818   127.6275   225.355   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 1153.185432 1326.261042 1445.383896 1471.123943 1596.382711 1633.31243    10   b
#  collapse    4.334856    4.570029    5.226371    4.774411    5.898064    6.83697    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 68.530348 71.181732 75.602871 73.562244 78.482354 100.837385   100   b
#  collapse  1.273592  1.339414  1.429775  1.375338  1.509435   2.041586   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 13443.0170 13681.7897 14076.26 13824.7034 14275.9742 15155.8116     5   b
#  collapse   312.2297   319.8538   329.89   331.7874   336.9509   348.6283     5  a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1967244 105.1    3719735 198.7  3719735 198.7
# Vcells 20534540 156.7   57613243 439.6 66848295 510.1

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) 325.761 338.4795 412.8468 352.7595 489.9815 1034.85   100

# Large 
microbenchmark(fmean(cgdata, SUM), times = 10) 
# Unit: milliseconds
#                expr      min       lq    mean   median       uq      max neval
#  fmean(cgdata, SUM) 50.16411 55.41065 58.5007 56.05949 62.00754 71.00167    10

## Weighted Standard-Deviation
# Small
microbenchmark(fsd(cgGGDC10S, SUM)) 
# Unit: microseconds
#                 expr     min       lq     mean  median      uq     max neval
#  fsd(cgGGDC10S, SUM) 438.662 445.3565 472.0684 459.636 462.537 776.472   100

# Large 
microbenchmark(fsd(cgdata, SUM), times = 10) 
# Unit: milliseconds
#              expr      min       lq     mean   median      uq      max neval
#  fsd(cgdata, SUM) 78.89134 79.89674 81.15476 81.07149 82.1851 84.39447    10

## Statistical Mode
# Small
microbenchmark(fmode(cgGGDC10S)) 
# Unit: milliseconds
#              expr      min      lq     mean   median       uq      max neval
#  fmode(cgGGDC10S) 1.579719 1.60694 1.726731 1.669638 1.818462 2.583332   100

# Large 
microbenchmark(fmode(cgdata), times = 10) 
# Unit: milliseconds
#           expr      min       lq     mean   median       uq      max neval
#  fmode(cgdata) 365.3667 366.6117 392.7847 367.7157 409.1356 478.1885    10

## Weighted Statistical Mode
# Small
microbenchmark(fmode(cgGGDC10S, SUM)) 
# Unit: milliseconds
#                   expr      min       lq    mean   median      uq      max neval
#  fmode(cgGGDC10S, SUM) 1.773391 1.786555 1.88805 1.796819 2.01615 2.640898   100

# Large 
microbenchmark(fmode(cgdata, SUM), times = 10) 
# Unit: milliseconds
#                expr      min       lq     mean   median       uq      max neval
#  fmode(cgdata, SUM) 462.4471 464.0897 490.9401 473.4545 493.0575 582.1331    10

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1966698 105.1    3719735 198.7  3719735 198.7
# Vcells 20531165 156.7   72305010 551.7 72305007 551.7

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 8766.994 9157.4615 9959.0079 9663.9530 10041.256 23563.248   100   b
#  collapse  292.739  308.3575  350.5506  344.2805   355.437   529.697   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 894.91344 906.01208 977.1793 931.67627 1050.9276 1203.0555    10   b
#  collapse  53.80996  73.11912 143.7252  98.83864  247.6076  286.7542    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 9078.475 9545.6970 10122.8167 9842.4525 10250.993 21063.811   100   b
#  collapse  550.671  567.8515   616.4342  604.8895   648.399   800.124   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 1167.2784 1535.0749 1500.3219 1560.0606 1586.4171 1623.8426    10   b
#  collapse  108.0041  116.8019  133.9792  134.9087  144.0409  167.1521    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 11938.481 12548.055 13547.9553 12717.630 13057.225 48706.66   100   b
#  collapse   307.466   324.869   358.0074   362.131   367.485   539.96   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 1999.81570 2716.63145 2655.66077 2749.2832 2777.8018 2875.7636    10   b
#  collapse   64.89476   72.39709   91.01573   85.0933  104.5368  137.5435    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 30282.855 31640.57 33557.79 32090.383 33465.497 50177.049   100   b
#  collapse   494.444   504.93   556.57   544.423   563.389   969.251   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 5954.8437 5954.8437 6147.7327 6147.7327 6340.6218 6340.6218     2   b
#  collapse  106.3566  106.3566  117.1928  117.1928  128.0291  128.0291     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  45658.786  47217.5305  49220.6008  48137.9180  49601.1660  79676.738   100  b 
#  collapse_unordered    340.488    402.9625    441.5808    444.2405    463.6520    664.018   100 a  
#       dplyr_ordered 110042.594 112531.3210 115768.3795 115080.0675 118492.5285 125884.854   100   c
#    collapse_ordered    313.266    348.2975    382.0200    377.9730    388.6825    614.037   100 a

# Large
microbenchmark(dplyr_unordered = mutate_all(gdata, dplyr::lag),
               collapse_unordered = flag(cgdata),
               dplyr_ordered = mutate_all(gdata, dplyr::lag, order_by = "Year"),
               collapse_ordered = flag(cgdata, t = Year), times = 2)
# Unit: milliseconds
#                expr         min          lq        mean      median          uq         max neval
#     dplyr_unordered  8805.61827  8805.61827  9302.45437  9302.45437  9799.29047  9799.29047     2
#  collapse_unordered    50.92095    50.92095    60.38029    60.38029    69.83964    69.83964     2
#       dplyr_ordered 23585.60849 23585.60849 27063.77245 27063.77245 30541.93641 30541.93641     2
#    collapse_ordered    87.31785    87.31785   106.08147   106.08147   124.84510   124.84510     2
#  cld
#   a 
#   a 
#    b
#   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 59850.819 61307.820 66010.7816 63691.2320 68270.6320 109735.129   100   b
#  collapse_unordered   369.493   422.374   485.4648   473.0235   496.8975   1885.399   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 13885.45342 13885.45342 13896.288 13896.288 13907.12190 13907.12190     2   b
#  collapse_unordered    41.58275    41.58275    50.468    50.468    59.35325    59.35325     2  a

gc()
#            used  (Mb) gc trigger  (Mb) max used  (Mb)
# Ncells  1969041 105.2    4900332 261.8  4900332 261.8
# Vcells 21580031 164.7   72305010 551.7 72305010 551.7

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") 64.03127 89.97258 100.3236 96.14308 104.1361 163.0997    10

# Weighted Centering
microbenchmark(fwithin(cgdata, SUM), times = 10)
# Unit: milliseconds
#                  expr      min       lq     mean   median      uq      max neval
#  fwithin(cgdata, SUM) 60.98429 66.16523 82.76161 86.65271 97.8734 102.3403    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") 64.31062 71.64204 86.91975 86.67837 102.1663 107.515
#  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 = "/") 155.4559 170.5115 187.0186 174.3258 218.5894 237.1676    10
microbenchmark(fscale(cgdata, SUM), times = 10)
# Unit: milliseconds
#                 expr      min       lq     mean   median       uq      max neval
#  fscale(cgdata, SUM) 98.63426 104.9522 150.7063 129.0923 158.1513 289.3277    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) 53.99247 117.4003 175.377 205.8782 236.3777 249.3649    10

# Iterated difference
microbenchmark(fdiff(cgdata, 1, 2), times = 10)
# Unit: milliseconds
#                 expr      min       lq     mean median       uq      max neval
#  fdiff(cgdata, 1, 2) 59.00473 64.41371 87.73701 93.595 101.7076 110.1796    10

# Growth Rate
microbenchmark(fgrowth(cgdata,1), times = 10)
# Unit: milliseconds
#                expr     min       lq    mean   median       uq     max neval
#  fgrowth(cgdata, 1) 62.5118 71.22524 89.1899 95.71914 100.5219 106.943    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.↩︎