vignettes/collapse_and_dplyr.Rmd
collapse_and_dplyr.RmdThis vignette focuses on the integration of collapse and the popular dplyr package by Hadley Wickham. In particular it will demonstrate how using collapse’s fast functions and some fast alternatives for dplyr verbs can substantially facilitate and speed up basic data manipulation, grouped and weighted aggregations and transformations, and panel data computations (i.e. between- and within-transformations, panel-lags, differences and growth rates) in a dplyr (piped) workflow.
Notes:
This vignette is targeted at dplyr / tidyverse users. collapse is a standalone package and can be programmed efficiently without pipes or dplyr verbs.
The ‘Introduction to collapse’ vignette provides a thorough introduction to the package and a built-in structured documentation is available under help("collapse-documentation") after installing the package. In addition help("collapse-package") provides a compact set of examples for quick-start.
Documentation and vignettes can also be viewed online.
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.
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.
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.
collapse fast functions do not develop their maximal performance on a grouped tibble created with group_by because of the additional conversion cost of the grouping object incurred by GRP.grouped_df. This cost is already minimized through the use of C++, but we can do even better replacing group_by with collapse::fgroup_by. fgroup_by works like group_by but does the grouping with collapse::GRP (up to 10x faster than group_by) and simply attaches a collapse grouping object to the grouped_df. Thus the speed gain is 2-fold: Faster grouping and no conversion cost when calling collapse functions.
Another improvement comes from replacing the dplyr verb select with collapse::fselect, and, for selection using column names, indices or functions use collapse::get_vars instead of select_at or select_if. Next to get_vars, collapse also introduces the predicates num_vars, cat_vars, char_vars, fact_vars, logi_vars and Date_vars to efficiently select columns by type.
GGDC10S %>% fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fmedian # # A tibble: 85 x 13 # Variable Country AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 EMP ARG 1325. 47.4 1988. 1.05e2 7.82e2 1.85e3 5.80e2 464. 1739. 866. 9.74e3 # 2 EMP BOL 943. 53.5 167. 4.46e0 6.60e1 1.32e2 9.70e1 15.3 NA 384. 1.84e3 # 3 EMP BRA 17481. 225. 7208. 3.76e2 4.05e3 6.45e3 1.58e3 4355. 4450. 4479. 5.19e4 # 4 EMP BWA 175. 12.2 13.1 3.71e0 1.90e1 2.11e1 6.75e0 10.4 53.8 31.2 3.61e2 # 5 EMP CHL 690. 93.9 607. 2.58e1 2.30e2 4.84e2 2.05e2 106. NA 900. 3.31e3 # 6 EMP CHN 293915 8150. 61761. 1.14e3 1.06e4 1.70e4 9.56e3 4328. 19468. 9954. 4.45e5 # 7 EMP COL 3006. 84.0 1033. 3.71e1 4.19e2 1.55e3 3.91e2 655. NA 1430. 8.63e3 # 8 EMP CRI 216. 1.49 114. 7.92e0 5.50e1 8.98e1 2.55e1 19.6 122. 60.6 7.19e2 # 9 EMP DEW 2178 320. 8459. 2.47e2 2.10e3 4.45e3 1.53e3 1656 3700 900 2.65e4 # 10 EMP DNK 187. 3.75 508. 1.36e1 1.65e2 4.61e2 1.61e2 169. 642. 104. 2.42e3 # # ... with 75 more rows microbenchmark(collapse = GGDC10S %>% fgroup_by(Variable, Country) %>% get_vars(6:16) %>% fmedian, hybrid = GGDC10S %>% group_by(Variable, Country) %>% select_at(6:16) %>% fmedian, dplyr = GGDC10S %>% group_by(Variable, Country) %>% select_at(6:16) %>% summarise_all(median, na.rm = TRUE)) # Unit: microseconds # expr min lq mean median uq max neval cld # collapse 718.46 796.777 927.1658 823.9975 887.3645 8953.525 100 a # hybrid 12246.39 12681.261 13564.9215 12951.6875 13480.7145 22790.793 100 b # dplyr 56282.17 57989.072 62363.1482 59498.5065 64035.2900 135722.398 100 c
Benchmarks on the different components of this code and with larger data are provided under ‘Benchmarks’. Note that a grouped tibble created with fgroup_by can no longer be used for grouped computations with dplyr verbs like mutate or summarize. fgroup_by first assigns the class GDP_df which is for printing grouping information and subsetting, then the object classes (tbl_df, data.table or whatever else), followed by classes grouped_df and data.frame, and adds the grouping object in a ‘groups’ attribute. Since tbl_df is assigned before grouped_df, the object is treated by the dplyr ecosystem like a normal tibble.
class(group_by(GGDC10S, Variable, Country)) # [1] "grouped_df" "tbl_df" "tbl" "data.frame" class(fgroup_by(GGDC10S, Variable, Country)) # [1] "GRP_df" "tbl_df" "tbl" "grouped_df" "data.frame"
The function fungroup removes classes ‘GDP_df’ and ‘grouped_df’ and the ‘groups’ attribute (and can thus also be used for grouped tibbles created with dplyr::group_by).
Note that any kind of data frame based class can be grouped with fgroup_by, and still retain full responsiveness to all methods defined for that class. Functions performing aggregation on the grouped data frame remove the grouping object and classes afterwards, yielding an object with the same class and attributes as the input.
The print method shown below reports the grouping variables, and then in square brackets the information [number of groups | average group size (standard-deviation of group sizes)]:
fgroup_by(GGDC10S, Variable, Country) # # A tibble: 5,027 x 16 # Country Regioncode Region Variable Year AGR MIN MAN PU CON WRT TRA FIRE GOV # <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA SSA Sub-s~ VA 1960 NA NA NA NA NA NA NA NA NA # 2 BWA SSA Sub-s~ VA 1961 NA NA NA NA NA NA NA NA NA # 3 BWA SSA Sub-s~ VA 1962 NA NA NA NA NA NA NA NA NA # 4 BWA SSA Sub-s~ VA 1963 NA NA NA NA NA NA NA NA NA # 5 BWA SSA Sub-s~ VA 1964 16.3 3.49 0.737 0.104 0.660 6.24 1.66 1.12 4.82 # 6 BWA SSA Sub-s~ VA 1965 15.7 2.50 1.02 0.135 1.35 7.06 1.94 1.25 5.70 # 7 BWA SSA Sub-s~ VA 1966 17.7 1.97 0.804 0.203 1.35 8.27 2.15 1.36 6.37 # 8 BWA SSA Sub-s~ VA 1967 19.1 2.30 0.938 0.203 0.897 4.31 1.72 1.54 7.04 # 9 BWA SSA Sub-s~ VA 1968 21.1 1.84 0.750 0.203 1.22 5.17 2.44 1.03 5.03 # 10 BWA SSA Sub-s~ VA 1969 21.9 5.24 2.14 0.578 3.47 5.75 2.72 1.23 5.59 # # ... with 5,017 more rows, and 2 more variables: OTH <dbl>, SUM <dbl> # # Grouped by: Variable, Country [85 | 59 (7.7)]
Note further that fselect and get_vars are not full drop-in replacements for select because they do not have a grouped_df method:
GGDC10S %>% group_by(Variable, Country) %>% select_at(6:16) %>% tail(3) # # A tibble: 3 x 13 # # Groups: Variable, Country [1] # Variable Country AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 EMP EGY 5206. 29.0 2436. 307. 2733. 2977. 1992. 801. 5539. NA 22020. # 2 EMP EGY 5186. 27.6 2374. 318. 2795. 3020. 2048. 815. 5636. NA 22219. # 3 EMP EGY 5161. 24.8 2348. 325. 2931. 3110. 2065. 832. 5736. NA 22533. GGDC10S %>% group_by(Variable, Country) %>% get_vars(6:16) %>% tail(3) # # A tibble: 3 x 11 # AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 5206. 29.0 2436. 307. 2733. 2977. 1992. 801. 5539. NA 22020. # 2 5186. 27.6 2374. 318. 2795. 3020. 2048. 815. 5636. NA 22219. # 3 5161. 24.8 2348. 325. 2931. 3110. 2065. 832. 5736. NA 22533.
Since by default keep.group_vars = TRUE in the Fast Statistical Functions, the end result is nevertheless the same:
GGDC10S %>% group_by(Variable, Country) %>% select_at(6:16) %>% fmean %>% tail(3) # # A tibble: 3 x 13 # Variable Country AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 VA VEN 6.86e3 3.55e4 19553. 1064. 1.17e4 1.93e4 8.03e3 5.60e3 NA 19986. 1.28e5 # 2 VA ZAF 1.64e4 4.29e4 87572. 13826. 1.64e4 6.83e4 4.53e4 6.64e4 7.58e4 30167. 4.63e5 # 3 VA ZMB 1.27e6 1.01e6 899510. 219164. 8.66e5 2.10e6 7.05e5 9.10e5 1.10e6 81871. 9.16e6 GGDC10S %>% group_by(Variable, Country) %>% get_vars(6:16) %>% fmean %>% tail(3) # # A tibble: 3 x 13 # Variable Country AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 VA VEN 6.86e3 3.55e4 19553. 1064. 1.17e4 1.93e4 8.03e3 5.60e3 NA 19986. 1.28e5 # 2 VA ZAF 1.64e4 4.29e4 87572. 13826. 1.64e4 6.83e4 4.53e4 6.64e4 7.58e4 30167. 4.63e5 # 3 VA ZMB 1.27e6 1.01e6 899510. 219164. 8.66e5 2.10e6 7.05e5 9.10e5 1.10e6 81871. 9.16e6
Another useful verb introduced by collapse is fgroup_vars, which can be used to efficiently obtain the grouping columns or grouping variables from a grouped tibble:
# fgroup_by fully supports grouped tibbles created with group_by or fgroup_by: GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars %>% head(3) # # A tibble: 3 x 2 # Variable Country # <chr> <chr> # 1 VA BWA # 2 VA BWA # 3 VA BWA GGDC10S %>% fgroup_by(Variable, Country) %>% fgroup_vars %>% head(3) # # A tibble: 3 x 2 # Variable Country # <chr> <chr> # 1 VA BWA # 2 VA BWA # 3 VA BWA # The other possibilities: GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("unique") %>% head(3) # # A tibble: 3 x 2 # Variable Country # <chr> <chr> # 1 EMP ARG # 2 EMP BOL # 3 EMP BRA GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("names") # [1] "Variable" "Country" GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("indices") # [1] 4 1 GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("named_indices") # Variable Country # 4 1 GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("logical") # [1] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE GGDC10S %>% group_by(Variable, Country) %>% fgroup_vars("named_logical") # Country Regioncode Region Variable Year AGR MIN MAN PU # TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE # CON WRT TRA FIRE GOV OTH SUM # FALSE FALSE FALSE FALSE FALSE FALSE FALSE
Another collapse verb to mention here is fsubset, a faster alternative to dplyr::filter which also provides an option to flexibly subset columns after the select argument:
# Two equivalent calls, the first is substantially faster GGDC10S %>% fsubset(Variable == "VA" & Year > 1990, Country, Year, AGR:GOV) %>% head(3) # # A tibble: 3 x 11 # Country Year AGR MIN MAN PU CON WRT TRA FIRE GOV # <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA 1991 303. 2647. 473. 161. 580. 807. 233. 433. 1073. # 2 BWA 1992 333. 2691. 537. 178. 679. 725. 285. 517. 1234. # 3 BWA 1993 405. 2625. 567. 219. 634. 772. 350. 673. 1487. GGDC10S %>% filter(Variable == "VA" & Year > 1990) %>% select(Country, Year, AGR:GOV) %>% head(3) # # A tibble: 3 x 11 # Country Year AGR MIN MAN PU CON WRT TRA FIRE GOV # <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA 1991 303. 2647. 473. 161. 580. 807. 233. 433. 1073. # 2 BWA 1992 333. 2691. 537. 178. 679. 725. 285. 517. 1234. # 3 BWA 1993 405. 2625. 567. 219. 634. 772. 350. 673. 1487.
collapse also offers roworder, frename, colorder and ftransform/TRA as fast replacements for dplyr::arrange, dplyr::rename, dplyr::relocate and dplyr::mutate.
One can also aggregate with multiple functions at the same time. For such operations it is often necessary to use curly braces { to prevent first argument injection so that %>% cbind(FUN1(.), FUN2(.)) does not evaluate as %>% cbind(., FUN1(.), FUN2(.)):
GGDC10S %>% fgroup_by(Variable, Country) %>% get_vars(6:16) %>% { cbind(fmedian(.), add_stub(fmean(., keep.group_vars = FALSE), "mean_")) } %>% head(3) # Variable Country AGR MIN MAN PU CON WRT TRA # 1 EMP ARG 1324.5255 47.35255 1987.5912 104.738825 782.40283 1854.612 579.93982 # 2 EMP BOL 943.1612 53.53538 167.1502 4.457895 65.97904 132.225 96.96828 # 3 EMP BRA 17480.9810 225.43693 7207.7915 375.851832 4054.66103 6454.523 1580.81120 # FIRE GOV OTH SUM mean_AGR mean_MIN mean_MAN mean_PU mean_CON # 1 464.39920 1738.836 866.1119 9743.223 1419.8013 52.08903 1931.7602 101.720936 742.4044 # 2 15.34259 NA 384.0678 1842.055 964.2103 56.03295 235.0332 5.346433 122.7827 # 3 4354.86210 4449.942 4478.6927 51881.110 17191.3529 206.02389 6991.3710 364.573404 3524.7384 # mean_WRT mean_TRA mean_FIRE mean_GOV mean_OTH mean_SUM # 1 1982.1775 648.5119 627.79291 2043.471 992.4475 10542.177 # 2 281.5164 115.4728 44.56442 NA 395.5650 2220.524 # 3 8509.4612 2054.3731 4413.54448 5307.280 5710.2665 54272.985
The function add_stub used above is a collapse function adding a prefix (default) or suffix to variables names. The collapse predicate add_vars provides a more efficient alternative to cbind.data.frame. The idea here is ‘adding’ variables to the data.frame in the first argument i.e. the attributes of the first argument are preserved, so the expression below still gives a tibble instead of a data.frame:
GGDC10S %>% fgroup_by(Variable, Country) %>% { add_vars(get_vars(., "Reg", regex = TRUE) %>% ffirst, # Regular expression matching column names num_vars(.) %>% fmean(keep.group_vars = FALSE) %>% add_stub("mean_"), # num_vars selects all numeric variables fselect(., PU:TRA) %>% fmedian(keep.group_vars = FALSE) %>% add_stub("median_"), fselect(., PU:CON) %>% fmin(keep.group_vars = FALSE) %>% add_stub("min_")) } %>% head(3) # # A tibble: 3 x 22 # Variable Country Regioncode Region mean_Year mean_AGR mean_MIN mean_MAN mean_PU mean_CON mean_WRT # <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 EMP ARG LAM Latin~ 1980. 1420. 52.1 1932. 102. 742. 1982. # 2 EMP BOL LAM Latin~ 1980 964. 56.0 235. 5.35 123. 282. # 3 EMP BRA LAM Latin~ 1980. 17191. 206. 6991. 365. 3525. 8509. # # ... with 11 more variables: mean_TRA <dbl>, mean_FIRE <dbl>, mean_GOV <dbl>, mean_OTH <dbl>, # # mean_SUM <dbl>, median_PU <dbl>, median_CON <dbl>, median_WRT <dbl>, median_TRA <dbl>, # # min_PU <dbl>, min_CON <dbl>
Another nice feature of add_vars is that it can also very efficiently reorder columns i.e. bind columns in a different order than they are passed. This can be done by simply specifying the positions the added columns should have in the final data frame, and then add_vars shifts the first argument columns to the right to fill in the gaps.
GGDC10S %>% fsubset(Variable == "VA", Country, AGR, SUM) %>% fgroup_by(Country) %>% { add_vars(fgroup_vars(.,"unique"), fmean(., keep.group_vars = FALSE) %>% add_stub("mean_"), fsd(., keep.group_vars = FALSE) %>% add_stub("sd_"), pos = c(2,4,3,5)) } %>% head(3) # # A tibble: 3 x 5 # Country mean_AGR sd_AGR mean_SUM sd_SUM # <chr> <dbl> <dbl> <dbl> <dbl> # 1 ARG 14951. 33061. 152534. 301316. # 2 BOL 3300. 4456. 22619. 33173. # 3 BRA 76870. 59442. 1200563. 976963.
A much more compact solution to multi-function and multi-type aggregation is offered by the function collapg:
# This aggregates numeric colums using the mean (fmean) and categorical columns with the mode (fmode) GGDC10S %>% fgroup_by(Variable, Country) %>% collapg %>% head(3) # # A tibble: 3 x 16 # Variable Country Regioncode Region Year AGR MIN MAN PU CON WRT TRA FIRE GOV # <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 EMP ARG LAM Latin~ 1980. 1420. 52.1 1932. 102. 742. 1982. 649. 628. 2043. # 2 EMP BOL LAM Latin~ 1980 964. 56.0 235. 5.35 123. 282. 115. 44.6 NA # 3 EMP BRA LAM Latin~ 1980. 17191. 206. 6991. 365. 3525. 8509. 2054. 4414. 5307. # # ... with 2 more variables: OTH <dbl>, SUM <dbl>
By default it aggregates numeric columns using the fmean and categorical columns using fmode, and preserves the order of all columns. Changing these defaults is very easy:
# This aggregates numeric colums using the median and categorical columns using the first value GGDC10S %>% fgroup_by(Variable, Country) %>% collapg(fmedian, flast) %>% head(3) # # A tibble: 3 x 16 # Variable Country Regioncode Region Year AGR MIN MAN PU CON WRT TRA FIRE # <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 EMP ARG LAM Latin~ 1980. 1325. 47.4 1988. 105. 782. 1855. 580. 464. # 2 EMP BOL LAM Latin~ 1980 943. 53.5 167. 4.46 66.0 132. 97.0 15.3 # 3 EMP BRA LAM Latin~ 1980. 17481. 225. 7208. 376. 4055. 6455. 1581. 4355. # # ... with 3 more variables: GOV <dbl>, OTH <dbl>, SUM <dbl>
One can apply multiple functions to both numeric and/or categorical data:
GGDC10S %>% fgroup_by(Variable, Country) %>% collapg(list(fmean, fmedian), list(first, fmode, flast)) %>% head(3) # # A tibble: 3 x 32 # Variable Country first.Regioncode fmode.Regioncode flast.Regioncode first.Region fmode.Region # <chr> <chr> <chr> <chr> <chr> <chr> <chr> # 1 EMP ARG LAM LAM LAM Latin Ameri~ Latin Ameri~ # 2 EMP BOL LAM LAM LAM Latin Ameri~ Latin Ameri~ # 3 EMP BRA LAM LAM LAM Latin Ameri~ Latin Ameri~ # # ... with 25 more variables: flast.Region <chr>, fmean.Year <dbl>, fmedian.Year <dbl>, # # fmean.AGR <dbl>, fmedian.AGR <dbl>, fmean.MIN <dbl>, fmedian.MIN <dbl>, fmean.MAN <dbl>, # # fmedian.MAN <dbl>, fmean.PU <dbl>, fmedian.PU <dbl>, fmean.CON <dbl>, fmedian.CON <dbl>, # # fmean.WRT <dbl>, fmedian.WRT <dbl>, fmean.TRA <dbl>, fmedian.TRA <dbl>, fmean.FIRE <dbl>, # # fmedian.FIRE <dbl>, fmean.GOV <dbl>, fmedian.GOV <dbl>, fmean.OTH <dbl>, fmedian.OTH <dbl>, # # fmean.SUM <dbl>, fmedian.SUM <dbl>
Applying multiple functions to only numeric (or only categorical) data allows return in a long format:
GGDC10S %>% fgroup_by(Variable, Country) %>% collapg(list(fmean, fmedian), cols = is.numeric, return = "long") %>% head(3) # # A tibble: 3 x 15 # Function Variable Country Year AGR MIN MAN PU CON WRT TRA FIRE GOV OTH # <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 fmean EMP ARG 1980. 1420. 52.1 1932. 102. 742. 1982. 649. 628. 2043. 992. # 2 fmean EMP BOL 1980 964. 56.0 235. 5.35 123. 282. 115. 44.6 NA 396. # 3 fmean EMP BRA 1980. 17191. 206. 6991. 365. 3525. 8509. 2054. 4414. 5307. 5710. # # ... with 1 more variable: SUM <dbl>
Finally, collapg also makes it very easy to apply aggregator functions to certain columns only:
GGDC10S %>% fgroup_by(Variable, Country) %>% collapg(custom = list(fmean = 6:8, fmedian = 10:12)) %>% head(3) # # A tibble: 3 x 8 # Variable Country AGR MIN MAN CON WRT TRA # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 EMP ARG 1420. 52.1 1932. 782. 1855. 580. # 2 EMP BOL 964. 56.0 235. 66.0 132. 97.0 # 3 EMP BRA 17191. 206. 6991. 4055. 6455. 1581.
To understand more about collapg, look it up in the documentation (?collapg).
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>
collapse also provides some fast transformations that significantly extend the scope and speed of manipulations that can be performed with dplyr::mutate.
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…
All statistical (scalar-valued) functions in the collapse package (fsum, fprod, fmean, fmedian, fmode, fvar, fsd, fmin, fmax, fnth, ffirst, flast, fNobs, fNdistinct) have a TRA argument which can be used to efficiently transform data by either (column-wise) replacing data values with computed statistics or sweeping the statistics out of the data. Operations can be specified using either an integer or quoted operator / string. The 10 operations supported by TRA are:
1 - “replace_fill” : replace and overwrite missing values (same as mutate)
2 - “replace” : replace but preserve missing values
3 - “-” : subtract (center)
4 - “-+” : subtract group-statistics but add average of group statistics
5 - “/” : divide (scale)
6 - “%” : compute percentages (divide and multiply by 100)
7 - “+” : add
8 - "*" : multiply
9 - “%%” : modulus
10 - “-%%” : subtract modulus
Simple transformations are again straightforward to specify:
# This subtracts the median value from all data points i.e. centers on the median GGDC10S %>% num_vars %>% fmedian(TRA = "-") %>% head # # A tibble: 6 x 12 # Year AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 -22 NA NA NA NA NA NA NA NA NA NA NA # 2 -21 NA NA NA NA NA NA NA NA NA NA NA # 3 -20 NA NA NA NA NA NA NA NA NA NA NA # 4 -19 NA NA NA NA NA NA NA NA NA NA NA # 5 -18 -4378. -170. -3717. -168. -1473. -3767. -1173. -959. -3924. -1431. -23149. # 6 -17 -4379. -171. -3717. -168. -1472. -3767. -1173. -959. -3923. -1430. -23147. # This replaces all data points with the mode GGDC10S %>% char_vars %>% fmode(TRA = "replace") %>% head # # A tibble: 6 x 4 # Country Regioncode Region Variable # <chr> <chr> <chr> <chr> # 1 USA ASI Asia EMP # 2 USA ASI Asia EMP # 3 USA ASI Asia EMP # 4 USA ASI Asia EMP # 5 USA ASI Asia EMP # 6 USA ASI Asia EMP
Similarly for grouped transformations:
# Replacing data with the 2nd quartile (25%) GGDC10S %>% fselect(Variable, Country, AGR:SUM) %>% fgroup_by(Variable, Country) %>% fnth(0.25, TRA = "replace_fill") %>% head(3) # # A tibble: 3 x 13 # Variable Country AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 VA BWA 61.3 21.7 23.1 6.31 23.2 26.7 8.98 11.3 27.0 10.1 220. # 2 VA BWA 61.3 21.7 23.1 6.31 23.2 26.7 8.98 11.3 27.0 10.1 220. # 3 VA BWA 61.3 21.7 23.1 6.31 23.2 26.7 8.98 11.3 27.0 10.1 220. # Scaling sectoral data by Variable and Country GGDC10S %>% fselect(Variable, Country, AGR:SUM) %>% fgroup_by(Variable, Country) %>% fsd(TRA = "/") %>% head # # A tibble: 6 x 13 # Variable Country AGR MIN MAN PU CON WRT TRA FIRE GOV # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 VA BWA NA NA NA NA NA NA NA NA NA # 2 VA BWA NA NA NA NA NA NA NA NA NA # 3 VA BWA NA NA NA NA NA NA NA NA NA # 4 VA BWA NA NA NA NA NA NA NA NA NA # 5 VA BWA 0.0270 5.56e-4 5.23e-4 3.88e-4 5.11e-4 0.00194 0.00154 5.23e-4 0.00134 # 6 VA BWA 0.0260 3.97e-4 7.23e-4 5.03e-4 1.04e-3 0.00220 0.00180 5.83e-4 0.00158 # # ... with 2 more variables: OTH <dbl>, SUM <dbl>
The benchmarks below will demonstrate that these internal sweeping and replacement operations fully performed in C++ compute significantly faster than using dplyr::mutate, especially as the number of groups grows large. The S3 generic nature of the Fast Statistical Functions further allows us to perform grouped mutations on the fly (together with ftransform or fcompute), without the need of first creating a grouped tibble:
# AGR_gmed = TRUE if AGR is greater than it's median value, grouped by Variable and Country # Note: This calls fmedian.default settransform(GGDC10S, AGR_gmed = AGR > fmedian(AGR, list(Variable, Country), TRA = "replace")) tail(GGDC10S, 3) # # A tibble: 3 x 17 # Country Regioncode Region Variable Year AGR MIN MAN PU CON WRT TRA FIRE GOV # <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 EGY MENA Middl~ EMP 2010 5206. 29.0 2436. 307. 2733. 2977. 1992. 801. 5539. # 2 EGY MENA Middl~ EMP 2011 5186. 27.6 2374. 318. 2795. 3020. 2048. 815. 5636. # 3 EGY MENA Middl~ EMP 2012 5161. 24.8 2348. 325. 2931. 3110. 2065. 832. 5736. # # ... with 3 more variables: OTH <dbl>, SUM <dbl>, AGR_gmed <lgl> # Dividing (scaling) the sectoral data (columns 6 through 16) by their grouped standard deviation settransformv(GGDC10S, 6:16, fsd, list(Variable, Country), TRA = "/", apply = FALSE) tail(GGDC10S, 3) # # A tibble: 3 x 17 # Country Regioncode Region Variable Year AGR MIN MAN PU CON WRT TRA FIRE GOV # <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 EGY MENA Middl~ EMP 2010 8.41 2.28 4.32 3.56 3.62 3.75 3.75 3.14 3.80 # 2 EGY MENA Middl~ EMP 2011 8.38 2.17 4.21 3.68 3.70 3.81 3.86 3.19 3.86 # 3 EGY MENA Middl~ EMP 2012 8.34 1.95 4.17 3.76 3.88 3.92 3.89 3.26 3.93 # # ... with 3 more variables: OTH <dbl>, SUM <dbl>, AGR_gmed <lgl> rm(GGDC10S)
Weights are easily added to any grouped transformation:
# This subtracts weighted group means from the data, using SUM column as weights.. GGDC10S %>% fselect(Variable, Country, AGR:SUM) %>% fgroup_by(Variable, Country) %>% fmean(SUM, "-") %>% head # # A tibble: 6 x 13 # Variable Country SUM AGR MIN MAN PU CON WRT TRA FIRE GOV OTH # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 2 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 3 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 4 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 5 VA BWA 37.5 -1301. -13317. -2965. -529. -2746. -6540. -2157. -4431. -7551. -2613. # 6 VA BWA 39.3 -1302. -13318. -2964. -529. -2745. -6540. -2156. -4431. -7550. -2613.
Sequential operations are also easily performed:
# This scales and then subtracts the median GGDC10S %>% fselect(Variable, Country, AGR:SUM) %>% fgroup_by(Variable, Country) %>% fsd(TRA = "/") %>% fmedian(TRA = "-") # # A tibble: 5,027 x 13 # Variable Country AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 2 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 3 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 4 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 5 VA BWA -0.182 -0.235 -0.183 -0.245 -0.118 -0.0820 -0.0724 -0.0661 -0.108 -0.0848 -0.146 # 6 VA BWA -0.183 -0.235 -0.183 -0.245 -0.117 -0.0817 -0.0722 -0.0660 -0.108 -0.0846 -0.146 # 7 VA BWA -0.180 -0.235 -0.183 -0.245 -0.117 -0.0813 -0.0720 -0.0659 -0.107 -0.0843 -0.145 # 8 VA BWA -0.177 -0.235 -0.183 -0.245 -0.117 -0.0826 -0.0724 -0.0659 -0.107 -0.0841 -0.146 # 9 VA BWA -0.174 -0.235 -0.183 -0.245 -0.117 -0.0823 -0.0717 -0.0661 -0.108 -0.0848 -0.146 # 10 VA BWA -0.173 -0.234 -0.182 -0.243 -0.115 -0.0821 -0.0715 -0.0660 -0.108 -0.0846 -0.145 # # ... with 5,017 more rows # # Grouped by: Variable, Country [85 | 59 (7.7)]
Of course it is also possible to combine multiple functions as in the aggregation section, or to add variables to existing data:
# This adds a groupwise observation count next to each column add_vars(GGDC10S, seq(7,27,2)) <- GGDC10S %>% fgroup_by(Variable, Country) %>% fselect(AGR:SUM) %>% fNobs("replace_fill") %>% add_stub("N_") head(GGDC10S) # # A tibble: 6 x 27 # Country Regioncode Region Variable Year AGR N_AGR MIN N_MIN MAN N_MAN PU N_PU CON # <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <dbl> <int> <dbl> <int> <dbl> <int> <dbl> # 1 BWA SSA Sub-s~ VA 1960 NA 47 NA 47 NA 47 NA 47 NA # 2 BWA SSA Sub-s~ VA 1961 NA 47 NA 47 NA 47 NA 47 NA # 3 BWA SSA Sub-s~ VA 1962 NA 47 NA 47 NA 47 NA 47 NA # 4 BWA SSA Sub-s~ VA 1963 NA 47 NA 47 NA 47 NA 47 NA # 5 BWA SSA Sub-s~ VA 1964 16.3 47 3.49 47 0.737 47 0.104 47 0.660 # 6 BWA SSA Sub-s~ VA 1965 15.7 47 2.50 47 1.02 47 0.135 47 1.35 # # ... with 13 more variables: N_CON <int>, WRT <dbl>, N_WRT <int>, TRA <dbl>, N_TRA <int>, # # FIRE <dbl>, N_FIRE <int>, GOV <dbl>, N_GOV <int>, OTH <dbl>, N_OTH <int>, SUM <dbl>, # # N_SUM <int> rm(GGDC10S)
There are lots of other examples one could construct using the 10 operations and 14 functions listed above, the examples provided just outline the suggested programming basics. Performance considerations make it very much worthwhile to spend some time and think how complex operations can be implemented in this programming framework, before defining some function in R and applying it to data using dplyr::mutate.
TRA FunctionTowards 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).
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.
This section introduces 3 further powerful collapse functions: flag, fdiff and fgrowth. The first function, flag, efficiently computes sequences of fully identified lags and leads on time series and panel data. The following code computes 1 fully-identified panel-lag and 1 fully identified panel-lead of each variable in the data:
GGDC10S %>% fselect(-Region, -Regioncode) %>% fgroup_by(Variable, Country) %>% flag(-1:1, Year) # # A tibble: 5,027 x 36 # Country Variable Year F1.AGR AGR L1.AGR F1.MIN MIN L1.MIN F1.MAN MAN L1.MAN F1.PU PU # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA VA 1960 NA NA NA NA NA NA NA NA NA NA NA # 2 BWA VA 1961 NA NA NA NA NA NA NA NA NA NA NA # 3 BWA VA 1962 NA NA NA NA NA NA NA NA NA NA NA # 4 BWA VA 1963 16.3 NA NA 3.49 NA NA 0.737 NA NA 0.104 NA # 5 BWA VA 1964 15.7 16.3 NA 2.50 3.49 NA 1.02 0.737 NA 0.135 0.104 # 6 BWA VA 1965 17.7 15.7 16.3 1.97 2.50 3.49 0.804 1.02 0.737 0.203 0.135 # 7 BWA VA 1966 19.1 17.7 15.7 2.30 1.97 2.50 0.938 0.804 1.02 0.203 0.203 # 8 BWA VA 1967 21.1 19.1 17.7 1.84 2.30 1.97 0.750 0.938 0.804 0.203 0.203 # 9 BWA VA 1968 21.9 21.1 19.1 5.24 1.84 2.30 2.14 0.750 0.938 0.578 0.203 # 10 BWA VA 1969 23.1 21.9 21.1 10.2 5.24 1.84 4.15 2.14 0.750 1.12 0.578 # # ... with 5,017 more rows, and 22 more variables: L1.PU <dbl>, F1.CON <dbl>, CON <dbl>, # # L1.CON <dbl>, F1.WRT <dbl>, WRT <dbl>, L1.WRT <dbl>, F1.TRA <dbl>, TRA <dbl>, L1.TRA <dbl>, # # F1.FIRE <dbl>, FIRE <dbl>, L1.FIRE <dbl>, F1.GOV <dbl>, GOV <dbl>, L1.GOV <dbl>, F1.OTH <dbl>, # # OTH <dbl>, L1.OTH <dbl>, F1.SUM <dbl>, SUM <dbl>, L1.SUM <dbl> # # Grouped by: Variable, Country [85 | 59 (7.7)]
If the time-variable passed does not exactly identify the data (i.e. because of repeated values in each group), all 3 functions will issue appropriate error messages. flag, fdiff and fgrowth support irregular time series and unbalanced panels.
It is also possible to omit the time-variable if one is certain that the data is sorted:
GGDC10S %>% fselect(Variable, Country,AGR:SUM) %>% fgroup_by(Variable, Country) %>% flag # # A tibble: 5,027 x 13 # Variable Country AGR MIN MAN PU CON WRT TRA FIRE GOV OTH SUM # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 2 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 3 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 4 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 5 VA BWA NA NA NA NA NA NA NA NA NA NA NA # 6 VA BWA 16.3 3.49 0.737 0.104 0.660 6.24 1.66 1.12 4.82 2.34 37.5 # 7 VA BWA 15.7 2.50 1.02 0.135 1.35 7.06 1.94 1.25 5.70 2.68 39.3 # 8 VA BWA 17.7 1.97 0.804 0.203 1.35 8.27 2.15 1.36 6.37 2.99 43.1 # 9 VA BWA 19.1 2.30 0.938 0.203 0.897 4.31 1.72 1.54 7.04 3.31 41.4 # 10 VA BWA 21.1 1.84 0.750 0.203 1.22 5.17 2.44 1.03 5.03 2.36 41.1 # # ... with 5,017 more rows # # Grouped by: Variable, Country [85 | 59 (7.7)]
fdiff computes sequences of lagged-leaded and iterated differences as well as quasi-differences and log-differences on time series and panel data. The code below computes the 1 and 10 year first and second differences of each variable in the data:
GGDC10S %>% fselect(-Region, -Regioncode) %>% fgroup_by(Variable, Country) %>% fdiff(c(1, 10), 1:2, Year) # # A tibble: 5,027 x 47 # Country Variable Year D1.AGR D2.AGR L10D1.AGR L10D2.AGR D1.MIN D2.MIN L10D1.MIN L10D2.MIN D1.MAN # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA VA 1960 NA NA NA NA NA NA NA NA NA # 2 BWA VA 1961 NA NA NA NA NA NA NA NA NA # 3 BWA VA 1962 NA NA NA NA NA NA NA NA NA # 4 BWA VA 1963 NA NA NA NA NA NA NA NA NA # 5 BWA VA 1964 NA NA NA NA NA NA NA NA NA # 6 BWA VA 1965 -0.575 NA NA NA -0.998 NA NA NA 0.282 # 7 BWA VA 1966 1.95 2.53 NA NA -0.525 0.473 NA NA -0.214 # 8 BWA VA 1967 1.47 -0.488 NA NA 0.328 0.854 NA NA 0.134 # 9 BWA VA 1968 1.95 0.488 NA NA -0.460 -0.788 NA NA -0.188 # 10 BWA VA 1969 0.763 -1.19 NA NA 3.41 3.87 NA NA 1.39 # # ... with 5,017 more rows, and 35 more variables: D2.MAN <dbl>, L10D1.MAN <dbl>, L10D2.MAN <dbl>, # # D1.PU <dbl>, D2.PU <dbl>, L10D1.PU <dbl>, L10D2.PU <dbl>, D1.CON <dbl>, D2.CON <dbl>, # # L10D1.CON <dbl>, L10D2.CON <dbl>, D1.WRT <dbl>, D2.WRT <dbl>, L10D1.WRT <dbl>, L10D2.WRT <dbl>, # # D1.TRA <dbl>, D2.TRA <dbl>, L10D1.TRA <dbl>, L10D2.TRA <dbl>, D1.FIRE <dbl>, D2.FIRE <dbl>, # # L10D1.FIRE <dbl>, L10D2.FIRE <dbl>, D1.GOV <dbl>, D2.GOV <dbl>, L10D1.GOV <dbl>, # # L10D2.GOV <dbl>, D1.OTH <dbl>, D2.OTH <dbl>, L10D1.OTH <dbl>, L10D2.OTH <dbl>, D1.SUM <dbl>, # # D2.SUM <dbl>, L10D1.SUM <dbl>, L10D2.SUM <dbl> # # Grouped by: Variable, Country [85 | 59 (7.7)]
Log-differences of the form \(log(x_t) - log(x_{t-s})\) are also easily computed.
GGDC10S %>% fselect(-Region, -Regioncode) %>% fgroup_by(Variable, Country) %>% fdiff(c(1, 10), 1, Year, log = TRUE) # # A tibble: 5,027 x 25 # Country Variable Year Dlog1.AGR L10Dlog1.AGR Dlog1.MIN L10Dlog1.MIN Dlog1.MAN L10Dlog1.MAN # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA VA 1960 NA NA NA NA NA NA # 2 BWA VA 1961 NA NA NA NA NA NA # 3 BWA VA 1962 NA NA NA NA NA NA # 4 BWA VA 1963 NA NA NA NA NA NA # 5 BWA VA 1964 NA NA NA NA NA NA # 6 BWA VA 1965 -0.0359 NA -0.336 NA 0.324 NA # 7 BWA VA 1966 0.117 NA -0.236 NA -0.236 NA # 8 BWA VA 1967 0.0796 NA 0.154 NA 0.154 NA # 9 BWA VA 1968 0.0972 NA -0.223 NA -0.223 NA # 10 BWA VA 1969 0.0355 NA 1.05 NA 1.05 NA # # ... with 5,017 more rows, and 16 more variables: Dlog1.PU <dbl>, L10Dlog1.PU <dbl>, # # Dlog1.CON <dbl>, L10Dlog1.CON <dbl>, Dlog1.WRT <dbl>, L10Dlog1.WRT <dbl>, Dlog1.TRA <dbl>, # # L10Dlog1.TRA <dbl>, Dlog1.FIRE <dbl>, L10Dlog1.FIRE <dbl>, Dlog1.GOV <dbl>, L10Dlog1.GOV <dbl>, # # Dlog1.OTH <dbl>, L10Dlog1.OTH <dbl>, Dlog1.SUM <dbl>, L10Dlog1.SUM <dbl> # # Grouped by: Variable, Country [85 | 59 (7.7)]
Finally, it is also possible to compute quasi-differences and quasi-log-differences of the form \(x_t - \rho x_{t-s}\) or \(log(x_t) - \rho log(x_{t-s})\):
GGDC10S %>% fselect(-Region, -Regioncode) %>% fgroup_by(Variable, Country) %>% fdiff(t = Year, rho = 0.95) # # A tibble: 5,027 x 14 # Country Variable Year AGR MIN MAN PU CON WRT TRA FIRE GOV OTH # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA VA 1960 NA NA NA NA NA NA NA NA NA NA # 2 BWA VA 1961 NA NA NA NA NA NA NA NA NA NA # 3 BWA VA 1962 NA NA NA NA NA NA NA NA NA NA # 4 BWA VA 1963 NA NA NA NA NA NA NA NA NA NA # 5 BWA VA 1964 NA NA NA NA NA NA NA NA NA NA # 6 BWA VA 1965 0.241 -0.824 0.318 0.0359 0.719 1.13 0.363 0.184 1.11 0.454 # 7 BWA VA 1966 2.74 -0.401 -0.163 0.0743 0.0673 1.56 0.312 0.174 0.955 0.449 # 8 BWA VA 1967 2.35 0.427 0.174 0.0101 -0.381 -3.55 -0.323 0.246 0.988 0.465 # 9 BWA VA 1968 2.91 -0.345 -0.141 0.0101 0.365 1.08 0.804 -0.427 -1.66 -0.780 # 10 BWA VA 1969 1.82 3.50 1.43 0.385 2.32 0.841 0.397 0.252 0.818 0.385 # # ... with 5,017 more rows, and 1 more variable: SUM <dbl> # # Grouped by: Variable, Country [85 | 59 (7.7)]
The quasi-differencing feature was added to fdiff to facilitate the preparation of time series and panel data for least-squares estimations suffering from serial correlation following Cochrane & Orcutt (1949).
Finally, fgrowth computes growth rates in the same way. By default exact growth rates are computed in percentage terms using \((x_t-x_{t-s}) / x_{t-s} \times 100\) (the default argument is scale = 100). The user can also request growth rates obtained by log-differencing using \(log(x_t/ x_{t-s}) \times 100\).
# Exact growth rates, computed as: (x/lag(x) - 1) * 100 GGDC10S %>% fselect(-Region, -Regioncode) %>% fgroup_by(Variable, Country) %>% fgrowth(c(1, 10), 1, Year) # # A tibble: 5,027 x 25 # Country Variable Year G1.AGR L10G1.AGR G1.MIN L10G1.MIN G1.MAN L10G1.MAN G1.PU L10G1.PU G1.CON # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA VA 1960 NA NA NA NA NA NA NA NA NA # 2 BWA VA 1961 NA NA NA NA NA NA NA NA NA # 3 BWA VA 1962 NA NA NA NA NA NA NA NA NA # 4 BWA VA 1963 NA NA NA NA NA NA NA NA NA # 5 BWA VA 1964 NA NA NA NA NA NA NA NA NA # 6 BWA VA 1965 -3.52 NA -28.6 NA 38.2 NA 29.4 NA 104. # 7 BWA VA 1966 12.4 NA -21.1 NA -21.1 NA 50.0 NA 0 # 8 BWA VA 1967 8.29 NA 16.7 NA 16.7 NA 0 NA -33.3 # 9 BWA VA 1968 10.2 NA -20 NA -20 NA 0 NA 35.7 # 10 BWA VA 1969 3.61 NA 185. NA 185. NA 185. NA 185. # # ... with 5,017 more rows, and 13 more variables: L10G1.CON <dbl>, G1.WRT <dbl>, L10G1.WRT <dbl>, # # G1.TRA <dbl>, L10G1.TRA <dbl>, G1.FIRE <dbl>, L10G1.FIRE <dbl>, G1.GOV <dbl>, L10G1.GOV <dbl>, # # G1.OTH <dbl>, L10G1.OTH <dbl>, G1.SUM <dbl>, L10G1.SUM <dbl> # # Grouped by: Variable, Country [85 | 59 (7.7)] # Log-difference growth rates, computed as: log(x / lag(x)) * 100 GGDC10S %>% fselect(-Region, -Regioncode) %>% fgroup_by(Variable, Country) %>% fgrowth(c(1, 10), 1, Year, logdiff = TRUE) # # A tibble: 5,027 x 25 # Country Variable Year Dlog1.AGR L10Dlog1.AGR Dlog1.MIN L10Dlog1.MIN Dlog1.MAN L10Dlog1.MAN # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA VA 1960 NA NA NA NA NA NA # 2 BWA VA 1961 NA NA NA NA NA NA # 3 BWA VA 1962 NA NA NA NA NA NA # 4 BWA VA 1963 NA NA NA NA NA NA # 5 BWA VA 1964 NA NA NA NA NA NA # 6 BWA VA 1965 -3.59 NA -33.6 NA 32.4 NA # 7 BWA VA 1966 11.7 NA -23.6 NA -23.6 NA # 8 BWA VA 1967 7.96 NA 15.4 NA 15.4 NA # 9 BWA VA 1968 9.72 NA -22.3 NA -22.3 NA # 10 BWA VA 1969 3.55 NA 105. NA 105. NA # # ... with 5,017 more rows, and 16 more variables: Dlog1.PU <dbl>, L10Dlog1.PU <dbl>, # # Dlog1.CON <dbl>, L10Dlog1.CON <dbl>, Dlog1.WRT <dbl>, L10Dlog1.WRT <dbl>, Dlog1.TRA <dbl>, # # L10Dlog1.TRA <dbl>, Dlog1.FIRE <dbl>, L10Dlog1.FIRE <dbl>, Dlog1.GOV <dbl>, L10Dlog1.GOV <dbl>, # # Dlog1.OTH <dbl>, L10Dlog1.OTH <dbl>, Dlog1.SUM <dbl>, L10Dlog1.SUM <dbl> # # Grouped by: Variable, Country [85 | 59 (7.7)]
fdiff and fgrowth can also perform leaded (forward) differences and growth rates (i.e. ... %>% fgrowth(-c(1, 10), 1:2, Year) would compute one and 10-year leaded first and second differences). Again it is possible to perform sequential operations:
# This computes the 1 and 10-year growth rates, for the current period and lagged by one period GGDC10S %>% fselect(-Region, -Regioncode) %>% fgroup_by(Variable, Country) %>% fgrowth(c(1, 10), 1, Year) %>% flag(0:1, Year) # # A tibble: 5,027 x 47 # Country Variable Year G1.AGR L1.G1.AGR L10G1.AGR L1.L10G1.AGR G1.MIN L1.G1.MIN L10G1.MIN # * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 BWA VA 1960 NA NA NA NA NA NA NA # 2 BWA VA 1961 NA NA NA NA NA NA NA # 3 BWA VA 1962 NA NA NA NA NA NA NA # 4 BWA VA 1963 NA NA NA NA NA NA NA # 5 BWA VA 1964 NA NA NA NA NA NA NA # 6 BWA VA 1965 -3.52 NA NA NA -28.6 NA NA # 7 BWA VA 1966 12.4 -3.52 NA NA -21.1 -28.6 NA # 8 BWA VA 1967 8.29 12.4 NA NA 16.7 -21.1 NA # 9 BWA VA 1968 10.2 8.29 NA NA -20 16.7 NA # 10 BWA VA 1969 3.61 10.2 NA NA 185. -20 NA # # ... with 5,017 more rows, and 37 more variables: L1.L10G1.MIN <dbl>, G1.MAN <dbl>, # # L1.G1.MAN <dbl>, L10G1.MAN <dbl>, L1.L10G1.MAN <dbl>, G1.PU <dbl>, L1.G1.PU <dbl>, # # L10G1.PU <dbl>, L1.L10G1.PU <dbl>, G1.CON <dbl>, L1.G1.CON <dbl>, L10G1.CON <dbl>, # # L1.L10G1.CON <dbl>, G1.WRT <dbl>, L1.G1.WRT <dbl>, L10G1.WRT <dbl>, L1.L10G1.WRT <dbl>, # # G1.TRA <dbl>, L1.G1.TRA <dbl>, L10G1.TRA <dbl>, L1.L10G1.TRA <dbl>, G1.FIRE <dbl>, # # L1.G1.FIRE <dbl>, L10G1.FIRE <dbl>, L1.L10G1.FIRE <dbl>, G1.GOV <dbl>, L1.G1.GOV <dbl>, # # L10G1.GOV <dbl>, L1.L10G1.GOV <dbl>, G1.OTH <dbl>, L1.G1.OTH <dbl>, L10G1.OTH <dbl>, # # L1.L10G1.OTH <dbl>, G1.SUM <dbl>, L1.G1.SUM <dbl>, L10G1.SUM <dbl>, L1.L10G1.SUM <dbl> # # Grouped by: Variable, Country [85 | 59 (7.7)]
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.
Bechmarks are run on the original GGDC10S data used throughout this vignette and a larger dataset with approx. 1 million observations, obtained by replicating and row-binding GGDC10S 200 times while maintaining unique groups.
# This shows the groups in GGDC10S GRP(GGDC10S, ~ Variable + Country) # collapse grouping object of length 5027 with 85 ordered groups # # Call: GRP.default(X = GGDC10S, by = ~Variable + Country), X is unordered # # Distribution of group sizes: # Min. 1st Qu. Median Mean 3rd Qu. Max. # 4.00 53.00 62.00 59.14 63.00 65.00 # # Groups with sizes: # EMP.ARG EMP.BOL EMP.BRA EMP.BWA EMP.CHL EMP.CHN # 62 61 62 52 63 62 # --- # VA.TWN VA.TZA VA.USA VA.VEN VA.ZAF VA.ZMB # 63 52 65 63 52 52 # This replicates the data 200 times data <- replicate(200, GGDC10S, simplify = FALSE) # This function adds a number i to the country and variable columns of each dataset uniquify <- function(x, i) ftransform(x, lapply(unclass(x)[c(1,4)], paste0, i)) # Making datasets unique and row-binding them data <- unlist2d(Map(uniquify, data, as.list(1:200)), idcols = FALSE) fdim(data) # [1] 1005400 16 # This shows the groups in the replicated data GRP(data, ~ Variable + Country) # collapse grouping object of length 1005400 with 17000 ordered groups # # Call: GRP.default(X = data, by = ~Variable + Country), X is unordered # # Distribution of group sizes: # Min. 1st Qu. Median Mean 3rd Qu. Max. # 4.00 53.00 62.00 59.14 63.00 65.00 # # Groups with sizes: # EMP1.ARG1 EMP1.BOL1 EMP1.BRA1 EMP1.BWA1 EMP1.CHL1 EMP1.CHN1 # 62 61 62 52 63 62 # --- # VA99.TWN99 VA99.TZA99 VA99.USA99 VA99.VEN99 VA99.ZAF99 VA99.ZMB99 # 63 52 65 63 52 52 gc() # used (Mb) gc trigger (Mb) max used (Mb) # Ncells 2302151 123.0 4243897 226.7 4243897 226.7 # Vcells 20490773 156.4 33392469 254.8 23056172 176.0
## Selecting columns # Small microbenchmark(dplyr = select(GGDC10S, Country, Variable, AGR:SUM), collapse = fselect(GGDC10S, Country, Variable, AGR:SUM)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 3090.270 3248.465 3661.88243 3536.0715 3786.640 6404.556 100 b # collapse 11.157 16.958 29.17596 32.3535 38.378 73.185 100 a # Large microbenchmark(dplyr = select(data, Country, Variable, AGR:SUM), collapse = fselect(data, Country, Variable, AGR:SUM)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 2754.691 2889.2350 8368.50765 5763.966 9659.937 58699.945 100 b # collapse 11.603 25.6595 55.95086 36.147 66.937 475.701 100 a ## Subsetting columns # Small microbenchmark(dplyr = filter(GGDC10S, Variable == "VA"), collapse = fsubset(GGDC10S, Variable == "VA")) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 2690.877 4656.6010 6438.2071 5563.824 7006.9905 21035.252 100 b # collapse 226.248 407.4245 609.9681 536.167 592.8415 2983.616 100 a # Large microbenchmark(dplyr = filter(data, Variable == "VA"), collapse = fsubset(data, Variable == "VA")) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 16.792323 17.895003 22.77324 18.831232 23.67950 120.14164 100 b # collapse 7.738392 8.147379 13.18525 8.839063 11.82893 64.30482 100 a ## Ordering rows # Small microbenchmark(dplyr = arrange(GGDC10S, desc(Country), Variable, Year), collapse = roworder(GGDC10S, -Country, Variable, Year)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 24976.517 26012.482 29693.9499 26860.8 28740.17 75848.820 100 b # collapse 641.259 821.097 985.8207 868.4 945.60 5745.893 100 a # Large microbenchmark(dplyr = arrange(data, desc(Country), Variable, Year), collapse = roworder(data, -Country, Variable, Year), times = 2) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 19761.3839 19761.3839 20073.6533 20073.6533 20385.9227 20385.9227 2 b # collapse 219.3337 219.3337 250.1585 250.1585 280.9834 280.9834 2 a ## Grouping # Small microbenchmark(dplyr = group_by(GGDC10S, Country, Variable), collapse = fgroup_by(GGDC10S, Country, Variable)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 2945.685 3023.555 3147.8758 3095.848 3188.445 4213.477 100 b # collapse 348.520 362.800 390.8334 392.475 409.433 649.291 100 a # Large microbenchmark(dplyr = group_by(data, Country, Variable), collapse = fgroup_by(data, Country, Variable), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 195.11314 198.33728 203.22878 201.86710 206.23230 217.56790 10 b # collapse 67.23489 68.28447 70.03188 69.50495 70.84281 76.11835 10 a ## Computing a new column # Small microbenchmark(dplyr = mutate(GGDC10S, NEW = AGR+1), collapse = ftransform(GGDC10S, NEW = AGR+1)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 3007.268 3155.868 3378.26033 3254.489 3391.0410 6666.057 100 b # collapse 26.775 34.808 50.87266 43.287 60.9135 199.473 100 a # Large microbenchmark(dplyr = mutate(data, NEW = AGR+1), collapse = ftransform(data, NEW = AGR+1)) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 4.037655 6.412142 9.527133 7.036666 9.000605 65.98272 100 b # collapse 1.270469 2.059881 4.785661 3.826580 4.584309 59.38627 100 a ## All combined with pipes # Small microbenchmark(dplyr = filter(GGDC10S, Variable == "VA") %>% select(Country, Year, AGR:SUM) %>% arrange(desc(Country), Year) %>% mutate(NEW = AGR+1) %>% group_by(Country), collapse = fsubset(GGDC10S, Variable == "VA", Country, Year, AGR:SUM) %>% roworder(-Country, Year) %>% ftransform(NEW = AGR+1) %>% fgroup_by(Country)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 22404.787 23159.3935 25073.1336 24354.446 25676.0105 37566.073 100 b # collapse 432.861 493.9975 541.1744 542.861 577.4455 838.055 100 a # Large microbenchmark(dplyr = filter(data, Variable == "VA") %>% select(Country, Year, AGR:SUM) %>% arrange(desc(Country), Year) %>% mutate(NEW = AGR+1) %>% group_by(Country), collapse = fsubset(data, Variable == "VA", Country, Year, AGR:SUM) %>% roworder(-Country, Year) %>% ftransform(NEW = AGR+1) %>% fgroup_by(Country), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 22.216917 22.565437 26.111103 24.982094 29.705409 33.18168 10 b # collapse 6.839201 7.054293 8.346583 8.210076 9.081599 10.25345 10 a gc() # used (Mb) gc trigger (Mb) max used (Mb) # Ncells 2307708 123.3 4243897 226.7 4243897 226.7 # Vcells 22006745 167.9 73138358 558.1 73138358 558.1
## Grouping the data cgGGDC10S <- fgroup_by(GGDC10S, Variable, Country) %>% fselect(-Region, -Regioncode) gGGDC10S <- group_by(GGDC10S, Variable, Country) %>% fselect(-Region, -Regioncode) cgdata <- fgroup_by(data, Variable, Country) %>% fselect(-Region, -Regioncode) gdata <- group_by(data, Variable, Country) %>% fselect(-Region, -Regioncode) rm(data, GGDC10S) gc() # used (Mb) gc trigger (Mb) max used (Mb) # Ncells 2324701 124.2 4243897 226.7 4243897 226.7 # Vcells 21107320 161.1 73138358 558.1 73138358 558.1 ## Conversion of Grouping object: This time would be required extra in all hybrid calls ## i.e. when calling collapse functions on data grouped with dplyr::group_by # Small microbenchmark(GRP(gGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval # GRP(gGGDC10S) 348.074 370.609 505.3809 385.782 440.4475 3243.779 100 # Large microbenchmark(GRP(gdata)) # Unit: milliseconds # expr min lq mean median uq max neval # GRP(gdata) 29.38188 31.45002 38.0206 33.77832 36.68629 117.656 100 ## Sum # Small microbenchmark(dplyr = summarise_all(gGGDC10S, sum, na.rm = TRUE), collapse = fsum(cgGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 8284.60 8980.523 10514.0961 9480.768 10619.147 25634.732 100 b # collapse 239.19 280.913 321.0804 302.556 340.711 590.833 100 a # Large microbenchmark(dplyr = summarise_all(gdata, sum, na.rm = TRUE), collapse = fsum(cgdata), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 573.20993 602.61769 803.86710 718.05677 970.63463 1268.85923 10 b # collapse 41.60016 42.38511 47.29339 48.05893 50.62196 54.85953 10 a ## Mean # Small microbenchmark(dplyr = summarise_all(gGGDC10S, mean.default, na.rm = TRUE), collapse = fmean(cgGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 10903.630 11145.2740 12220.4826 11411.238 11940.93 33783.673 100 b # collapse 258.824 274.8895 303.3732 314.159 319.96 445.802 100 a # Large microbenchmark(dplyr = summarise_all(gdata, mean.default, na.rm = TRUE), collapse = fmean(cgdata), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 1454.07280 1490.12386 1617.22808 1622.44629 1705.83358 1849.6304 10 b # collapse 44.97067 45.07152 46.11673 45.24266 47.32374 48.8508 10 a ## Median # Small microbenchmark(dplyr = summarise_all(gGGDC10S, median, na.rm = TRUE), collapse = fmedian(cgGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 48462.563 49467.291 53665.0865 50538.0640 55707.404 87132.655 100 b # collapse 488.642 549.555 590.2751 584.3625 622.517 851.889 100 a # Large microbenchmark(dplyr = summarise_all(gdata, median, na.rm = TRUE), collapse = fmedian(cgdata), times = 2) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 9288.16884 9288.16884 9355.06257 9355.06257 9421.95631 9421.95631 2 b # collapse 93.17307 93.17307 94.58567 94.58567 95.99827 95.99827 2 a ## Standard Deviation # Small microbenchmark(dplyr = summarise_all(gGGDC10S, sd, na.rm = TRUE), collapse = fsd(cgGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 24275.46 24748.707 26379.4763 25031.851 26548.872 39034.229 100 b # collapse 427.06 459.859 496.7192 485.965 512.739 970.144 100 a # Large microbenchmark(dplyr = summarise_all(gdata, sd, na.rm = TRUE), collapse = fsd(cgdata), times = 2) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 4155.64069 4155.64069 4328.76628 4328.76628 4501.89187 4501.89187 2 b # collapse 81.51707 81.51707 82.09094 82.09094 82.66482 82.66482 2 a ## Maximum # Small microbenchmark(dplyr = summarise_all(gGGDC10S, max, na.rm = TRUE), collapse = fmax(cgGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 9887.077 10194.0955 10714.3232 10393.792 10629.411 19342.186 100 b # collapse 182.516 196.5725 243.1297 244.098 251.238 973.267 100 a # Large microbenchmark(dplyr = summarise_all(gdata, max, na.rm = TRUE), collapse = fmax(cgdata), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 841.05663 867.9507 900.67790 883.18202 901.57102 1089.203 10 b # collapse 25.78913 25.8583 26.28742 26.07406 26.86772 27.131 10 a ## First Value # Small microbenchmark(dplyr = summarise_all(gGGDC10S, first), collapse = ffirst(cgGGDC10S, na.rm = FALSE)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 10452.920 10847.627 11462.3425 11079.23 11362.15 20952.696 100 b # collapse 57.566 68.276 117.0155 117.14 124.95 1543.573 100 a # Large microbenchmark(dplyr = summarise_all(gdata, first), collapse = ffirst(cgdata, na.rm = FALSE), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 1186.05831 1228.879404 1365.678287 1409.77351 1427.809301 1515.277980 10 b # collapse 4.45088 4.475871 4.548431 4.48658 4.652139 4.712828 10 a ## Number of Distinct Values # Small microbenchmark(dplyr = summarise_all(gGGDC10S, n_distinct, na.rm = TRUE), collapse = fNdistinct(cgGGDC10S)) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 65.569491 67.342659 70.766485 68.975481 73.907195 82.63760 100 b # collapse 1.311078 1.358826 1.531721 1.391179 1.463248 10.16554 100 a # Large microbenchmark(dplyr = summarise_all(gdata, n_distinct, na.rm = TRUE), collapse = fNdistinct(cgdata), times = 5) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 12894.1111 13107.1415 17884.8903 16243.7631 17612.7355 29566.7005 5 b # collapse 311.8544 321.0761 332.4503 334.1387 334.6594 360.5231 5 a gc() # used (Mb) gc trigger (Mb) max used (Mb) # Ncells 2327929 124.4 4243897 226.7 4243897 226.7 # Vcells 21114893 161.1 73138358 558.1 73138358 558.1
Below are some additional benchmarks for weighted aggregations and aggregations using the statistical mode, which cannot easily or efficiently be performed with dplyr.
## Weighted Mean # Small microbenchmark(fmean(cgGGDC10S, SUM)) # Unit: microseconds # expr min lq mean median uq max neval # fmean(cgGGDC10S, SUM) 319.96 332.232 369.2037 341.826 349.1895 765.762 100 # Large microbenchmark(fmean(cgdata, SUM), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fmean(cgdata, SUM) 51.70009 53.60424 62.06417 56.69406 72.08695 82.5479 10 ## Weighted Standard-Deviation # Small microbenchmark(fsd(cgGGDC10S, SUM)) # Unit: microseconds # expr min lq mean median uq max neval # fsd(cgGGDC10S, SUM) 440.001 558.034 1153.771 904.9915 1082.599 9133.364 100 # Large microbenchmark(fsd(cgdata, SUM), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fsd(cgdata, SUM) 79.68343 90.06718 100.0621 99.64724 110.8909 127.8064 10 ## Statistical Mode # Small microbenchmark(fmode(cgGGDC10S)) # Unit: milliseconds # expr min lq mean median uq max neval # fmode(cgGGDC10S) 1.571241 1.792579 2.05617 1.829842 2.013249 5.649057 100 # Large microbenchmark(fmode(cgdata), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fmode(cgdata) 412.6703 509.7057 585.4809 583.4205 618.5563 821.8684 10 ## Weighted Statistical Mode # Small microbenchmark(fmode(cgGGDC10S, SUM)) # Unit: milliseconds # expr min lq mean median uq max neval # fmode(cgGGDC10S, SUM) 1.764466 1.995623 2.051703 2.031769 2.100714 3.271 100 # Large microbenchmark(fmode(cgdata, SUM), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fmode(cgdata, SUM) 517.7212 553.4684 648.3746 656.759 740.7869 767.1949 10 gc() # used (Mb) gc trigger (Mb) max used (Mb) # Ncells 2327383 124.3 4243897 226.7 4243897 226.7 # Vcells 21111519 161.1 73138358 558.1 73138358 558.1
## Replacing with group sum # Small microbenchmark(dplyr = mutate_all(gGGDC10S, sum, na.rm = TRUE), collapse = fsum(cgGGDC10S, TRA = "replace_fill")) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 9952.676 14189.356 18875.7491 16204.61 21243.20 63585.917 100 b # collapse 326.654 387.567 529.1079 434.20 547.77 1965.278 100 a # Large microbenchmark(dplyr = mutate_all(gdata, sum, na.rm = TRUE), collapse = fsum(cgdata, TRA = "replace_fill"), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 931.38933 1062.2262 1257.1730 1245.4816 1373.6432 1783.5825 10 b # collapse 60.55188 71.0048 172.5488 121.8175 300.3608 372.9703 10 a ## Dividing by group sum # Small microbenchmark(dplyr = mutate_all(gGGDC10S, function(x) x/sum(x, na.rm = TRUE)), collapse = fsum(cgGGDC10S, TRA = "/")) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 9148.536 9726.2050 11421.2653 10392.8995 11773.592 23255.783 100 b # collapse 548.440 565.8435 633.4541 613.8145 678.744 1003.166 100 a # Large microbenchmark(dplyr = mutate_all(gdata, function(x) x/sum(x, na.rm = TRUE)), collapse = fsum(cgdata, TRA = "/"), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 1203.2906 1580.4784 1837.4117 1698.4015 2089.7419 2804.4906 10 b # collapse 109.6271 115.0937 164.5631 128.1049 153.5871 463.0924 10 a ## Centering # Small microbenchmark(dplyr = mutate_all(gGGDC10S, function(x) x-mean.default(x, na.rm = TRUE)), collapse = fwithin(cgGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 11884.930 12263.349 13834.5448 12822.0520 13502.135 60464.857 100 b # collapse 306.572 323.084 361.8897 359.6765 380.204 689.454 100 a # Large microbenchmark(dplyr = mutate_all(gdata, function(x) x-mean.default(x, na.rm = TRUE)), collapse = fwithin(cgdata), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 1920.4562 2827.10869 2798.1697 2872.0838 3033.6529 3205.626 10 b # collapse 65.4856 76.23839 114.8372 100.1533 162.7168 173.388 10 a ## Centering and Scaling (Standardizing) # Small microbenchmark(dplyr = mutate_all(gGGDC10S, function(x) (x-mean.default(x, na.rm = TRUE))/sd(x, na.rm = TRUE)), collapse = fscale(cgGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr 29655.429 32402.757 37043.271 34055.4375 39418.450 66339.269 100 b # collapse 496.675 541.969 584.706 560.7115 605.113 1238.785 100 a # Large microbenchmark(dplyr = mutate_all(gdata, function(x) (x-mean.default(x, na.rm = TRUE))/sd(x, na.rm = TRUE)), collapse = fscale(cgdata), times = 2) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr 6128.4079 6128.4079 6364.0317 6364.0317 6599.655 6599.655 2 b # collapse 103.6416 103.6416 114.0488 114.0488 124.456 124.456 2 a ## Lag # Small microbenchmark(dplyr_unordered = mutate_all(gGGDC10S, dplyr::lag), collapse_unordered = flag(cgGGDC10S), dplyr_ordered = mutate_all(gGGDC10S, dplyr::lag, order_by = "Year"), collapse_ordered = flag(cgGGDC10S, t = Year)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr_unordered 44760.042 47003.5545 58805.844 50658.1050 60146.2355 183804.312 100 b # collapse_unordered 404.747 466.1060 570.011 489.9805 518.3175 4665.080 100 a # dplyr_ordered 105484.168 114729.3165 138474.255 121829.7970 144656.9585 347953.171 100 c # collapse_ordered 348.520 419.2505 553.308 445.5795 469.4535 5874.859 100 a # Large microbenchmark(dplyr_unordered = mutate_all(gdata, dplyr::lag), collapse_unordered = flag(cgdata), dplyr_ordered = mutate_all(gdata, dplyr::lag, order_by = "Year"), collapse_ordered = flag(cgdata, t = Year), times = 2) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr_unordered 9322.60181 9322.60181 9512.82317 9512.82317 9703.0445 9703.0445 2 b # collapse_unordered 43.45878 43.45878 53.30324 53.30324 63.1477 63.1477 2 a # dplyr_ordered 24916.39806 24916.39806 24983.25163 24983.25163 25050.1052 25050.1052 2 c # collapse_ordered 100.26351 100.26351 118.91758 118.91758 137.5717 137.5717 2 a ## First-Difference (unordered) # Small microbenchmark(dplyr_unordered = mutate_all(gGGDC10S, function(x) x - dplyr::lag(x)), collapse_unordered = fdiff(cgGGDC10S)) # Unit: microseconds # expr min lq mean median uq max neval cld # dplyr_unordered 60398.812 66697.384 82056.0223 75039.549 88612.19 199408.721 100 b # collapse_unordered 447.141 506.939 621.4501 538.176 654.20 2415.988 100 a # Large microbenchmark(dplyr_unordered = mutate_all(gdata, function(x) x - dplyr::lag(x)), collapse_unordered = fdiff(cgdata), times = 2) # Unit: milliseconds # expr min lq mean median uq max neval cld # dplyr_unordered 12687.34061 12687.34061 13168.5915 13168.5915 13649.8424 13649.8424 2 b # collapse_unordered 63.99022 63.99022 391.2529 391.2529 718.5155 718.5155 2 a gc() # used (Mb) gc trigger (Mb) max used (Mb) # Ncells 2329725 124.5 5577265 297.9 5577265 297.9 # Vcells 22160371 169.1 73138358 558.1 73138358 558.1
Below again some benchmarks for transformations not easily of efficiently performed with dplyr, such as centering on the overall mean, mean-preserving scaling, weighted scaling and centering, sequences of lags / leads, (iterated) panel-differences and growth rates.
# Centering on overall mean microbenchmark(fwithin(cgdata, mean = "overall.mean"), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fwithin(cgdata, mean = "overall.mean") 72.63048 91.55587 123.9617 116.3387 147.5185 206.2484 10 # Weighted Centering microbenchmark(fwithin(cgdata, SUM), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fwithin(cgdata, SUM) 73.07449 80.44161 92.4393 85.42308 103.6144 126.838 10 microbenchmark(fwithin(cgdata, SUM, mean = "overall.mean"), times = 10) # Unit: milliseconds # expr min lq mean median uq max # fwithin(cgdata, SUM, mean = "overall.mean") 63.72292 74.96703 86.8185 89.93889 96.73592 102.1511 # neval # 10 # Weighted Scaling and Standardizing microbenchmark(fsd(cgdata, SUM, TRA = "/"), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fsd(cgdata, SUM, TRA = "/") 148.6587 165.4211 174.4325 173.45 180.4467 203.8864 10 microbenchmark(fscale(cgdata, SUM), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fscale(cgdata, SUM) 103.4488 109.1581 150.8876 138.2665 163.0167 286.4303 10 # Sequence of lags and leads microbenchmark(flag(cgdata, -1:1), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # flag(cgdata, -1:1) 45.29777 94.41766 119.5111 112.0534 132.4804 245.7396 10 # Iterated difference microbenchmark(fdiff(cgdata, 1, 2), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fdiff(cgdata, 1, 2) 58.64149 61.9705 81.35423 80.24704 94.65238 107.5079 10 # Growth Rate microbenchmark(fgrowth(cgdata,1), times = 10) # Unit: milliseconds # expr min lq mean median uq max neval # fgrowth(cgdata, 1) 64.37712 69.60224 87.20517 86.34525 105.3159 116.9287 10
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.
Row-wise operations are not supported by TRA.↩︎