procR

2026-07-02

library(procR)

# Read dataset
head(eusilc)
#>       hid        region hsize eqsize eqIncome pid      id age gender ecoStat
#> 39993   1 Upper Austria     2    1.5 11128.45   1 0000101  25   male       1
#> 39994   1 Upper Austria     2    1.5 11128.45   2 0000102  24 female       4
#> 31004   2        Styria     2    1.5 19694.85   1 0000201  57 female       1
#> 31005   2        Styria     2    1.5 19694.85   2 0000202  53   male       1
#> 29071   3        Styria     1    1.0  5066.24   1 0000301  30 female       6
#> 41322   4 Upper Austria     3    1.8 31480.01   1 0000401  32   male       1
#>       citizenship   py010n   py050n  py090n py100n py110n py120n py130n py140n
#> 39993       Other 16692.67     0.00    0.00      0      0      0      0      0
#> 39994          AT     0.00     0.00    0.00      0      0      0      0      0
#> 31004          AT     0.00 12564.59    0.00      0      0      0      0      0
#> 31005          AT 16884.06     0.00    0.00      0      0      0      0      0
#> 29071          AT     0.00     0.00 5066.24      0      0      0      0      0
#> 41322          AT 25047.39     0.00    0.00      0      0      0      0      0
#>       hy040n  hy050n hy070n hy080n hy090n  hy110n hy130n  hy145n  main
#> 39993      0    0.00      0      0   0.00    0.00   0.00    0.00  TRUE
#> 39994      0    0.00      0      0   0.00    0.00   0.00    0.00 FALSE
#> 31004      0    0.00      0      0   0.00    0.00  93.63 -187.26 FALSE
#> 31005      0    0.00      0      0   0.00    0.00  93.63 -187.26  TRUE
#> 29071      0    0.00      0      0   0.00    0.00   0.00    0.00  TRUE
#> 41322      0 7167.39      0      0  31.15 1349.91   0.00    0.00  TRUE

# Add weights
eusilc$weight <- rlnorm(nrow(eusilc), meanlog = 0, sdlog = 2)

# Normalise to total population size
eusilc$weight <- eusilc$weight / sum(eusilc$weight) * 8269

Overview

This vignette demonstrates the features included in procR by way of a small, reproducible example. The following steps are performed:

• Define variable-formats
• Perform aggregation
• Export to Excel

Define variable-formats

A variable in the dataset might contain many values. Which of these values should be shown in the aggregated table, how they should be grouped and in what order they should be displayed is determined using so-called ‘variable-formats’. The idea is identical to the PROC FORMAT command in SAS. Consider in particular the format age_f.

region_f <- new_format_asis(eusilc, var = region, name = "Region", add_total = TRUE)

citizenship_f <- new_format_asis(eusilc, var = citizenship, name = "Citizenship", add_total = TRUE, include_na = FALSE)

gender_f <- new_format_asis(eusilc, var = gender, name = "Gender", add_total = TRUE)

ecostat_f <- new_format(var = ecoStat, name = "Economic Status", add_total = TRUE,
                        "Working",
                        davon(1 ~ "Working full time",
                              2 ~ "Working part time"),
                        "Not working",
                        davon(3 ~ "Unemployed",
                              4 ~ "Pupil, student, further training or unpaid work experience or in compulsory military/community service",
                              5 ~ "In retirement/early retirement",
                              6 ~ "Permanently disabled/unfit to work/other inactive person",
                              7 ~ "Fulfilling domestic tasks"))

# Inspect some of the varformats
gender_f
#> Gender      code                                                                
#> ╲╴Total     gender %in% structure(1L, levels = c("male", "female"), class = ... 
#>    ├╴male   gender %in% structure(1L, levels = c("male", "female"), class = ... 
#>    └╴female gender %in% structure(2L, levels = c("male", "female"), class = ... 
#> 
ecostat_f
#> Economic Status                                      code                       
#> ╲╴Total                                              ecoStat %in% 1 | ecoSta... 
#>    ├╴Working                                         ecoStat %in% 1 | ecoSta... 
#>    │  ├╴Working full time                            ecoStat %in% 1             
#>    │  └╴Working part time                            ecoStat %in% 2             
#>    └╴Not working                                     ecoStat %in% 3 | ecoSta... 
#>       ├╴Unemployed                                   ecoStat %in% 3             
#>       ├╴Pupil, student, further training or unpaid work e... ecoStat %in% 4             
#>       ├╴In retirement/early retirement               ecoStat %in% 5             
#>       ├╴Permanently disabled/unfit to work/other inactive... ecoStat %in% 6             
#>       └╴Fulfilling domestic tasks                    ecoStat %in% 7             

Perform aggregation

The desired table is specified as a formula, where the left-hand-side describes the row variables and the right-hand-side the column variables. Two operators can be used:

• + means that variable-formats should be displayed one after the other, without interaction
• * means that variable-formats should be cross-combined, such that each value in one format is combined with each value in the other format

The parameter where is a filter condition applied before performing the aggregation. w is a variable containing weights. Both parameters should be provided as unquoted expressions.

system.time({
  tab <- create_table_count(df = eusilc,
                            formula = citizenship_f * ecostat_f ~ region_f * gender_f,
                            where = main == TRUE,
                            w = weight) |>
    set_metadata(title = "Population in Austria by citizenship, economic status, region and gender",
                 subtitle = "Estimates (in thousands)",
                 footer = "Source: Austrian EU-SILC data from 2006")
})
#>    user  system elapsed 
#>   1.053   0.200   1.294

to_gt(tab)

Population in Austria by citizenship, economic status, region and gender
Estimates (in thousands)
Citizenship, Economic Status		Total			Burgenland			Lower Austria			Vienna			Carinthia			Styria			Upper Austria			Salzburg			Tyrol			Vorarlberg
		Total	male	female	Total	male	female	Total	male	female	Total	male	female	Total	male	female	Total	male	female	Total	male	female	Total	male	female	Total	male	female	Total	male	female
Total	Total	3 459	2 106	1 352	98	67	31	612	386	227	821	451	371	250	179	70	458	279	178	555	385	170	258	140	118	278	139	139	129	80	49
	Working	2 009	1 415	594	43	34	9	337	249	87	508	316	192	133	98	34	253	175	78	357	285	71	144	94	50	146	98	48	90	64	25
	Working full time	1 809	1 353	457	42	34	7	314	247	67	463	295	168	123	97	26	224	167	57	318	269	48	122	93	29	125	91	34	80	59	20
	Working part time	200	62	138	1	0	1	23	3	21	45	21	24	10	1	9	28	8	20	39	16	23	22	1	21	21	7	14	10	5	5
	Not working	1 450	691	758	55	33	22	275	136	139	313	134	179	117	81	36	205	105	100	199	100	99	113	45	68	133	42	91	39	16	24
	Unemployed	119	79	40	2	2	0	14	11	3	65	46	19	10	3	7	6	4	2	12	9	4	4	2	2	4	0	4	2	2	0
	Pupil, student, further training or unpaid work experience or in compulsory military/community service	44	13	31	0	0	0	0	0	0	32	5	26	2	1	0	5	3	2	2	1	1	3	2	2	0	0	0	0	0	0
	In retirement/early retirement	1 172	571	602	49	28	22	237	123	114	197	81	115	100	74	26	164	83	81	166	86	80	98	42	56	126	40	86	35	13	22
	Permanently disabled/unfit to work/other inactive person	52	27	25	3	3	0	13	1	11	5	1	4	2	2	0	15	13	2	9	4	5	3	0	3	1	1	0	1	1	0
	Fulfilling domestic tasks	63	2	61	0	0	0	11	0	11	15	1	15	3	0	3	15	1	14	10	0	10	5	0	5	1	0	1	2	0	2
AT	Total	3 257	1 990	1 267	95	65	31	597	373	224	723	403	320	236	167	69	439	267	172	537	372	165	242	132	109	262	132	129	126	78	48
	Working	1 893	1 334	559	42	34	9	324	239	85	457	282	175	131	98	34	236	164	73	346	276	70	137	88	48	131	91	40	88	63	25
	Working full time	1 709	1 277	433	41	34	7	302	237	66	419	263	155	122	97	25	212	156	56	308	260	48	115	87	28	112	85	27	78	58	20
	Working part time	183	57	126	1	0	1	22	2	20	38	18	20	9	1	8	24	7	17	38	16	22	22	1	20	19	6	13	10	5	5
	Not working	1 364	657	708	53	31	22	272	134	138	266	121	145	105	70	35	203	104	99	191	96	95	105	44	61	131	41	89	38	15	23
	Unemployed	96	64	32	2	2	0	13	10	3	47	35	13	10	3	6	5	4	2	10	7	4	3	1	2	4	0	4	2	2	0
	Pupil, student, further training or unpaid work experience or in compulsory military/community service	33	12	20	0	0	0	0	0	0	21	5	16	2	1	0	5	3	2	2	1	1	3	2	2	0	0	0	0	0	0
	In retirement/early retirement	1 139	553	587	48	26	22	236	122	114	188	81	107	89	63	26	162	82	80	163	85	78	96	41	55	125	40	84	34	13	21
	Permanently disabled/unfit to work/other inactive person	44	26	18	3	3	0	13	1	11	1	1	0	2	2	0	15	13	2	7	3	4	1	0	0	1	1	0	1	1	0
	Fulfilling domestic tasks	52	2	51	0	0	0	11	0	10	10	1	9	3	0	3	15	1	14	9	0	9	2	0	2	1	0	1	1	0	1
EU	Total	88	41	47	2	2	0	2	2	1	47	13	34	12	11	1	8	5	2	5	2	3	3	1	2	8	4	4	1	1	0
	Working	39	24	15	0	0	0	2	1	1	20	11	10	0	0	0	6	5	1	1	1	0	2	1	1	6	4	2	0	0	0
	Working full time	32	21	11	0	0	0	1	1	0	17	9	8	0	0	0	6	5	1	1	1	0	2	1	1	5	4	1	0	0	0
	Working part time	6	3	4	0	0	0	1	0	0	4	2	2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Not working	49	18	32	2	2	0	1	0	0	27	3	24	12	11	1	2	1	1	4	1	3	1	0	1	2	0	1	0	0	0
	Unemployed	4	3	2	0	0	0	0	0	0	3	2	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	Pupil, student, further training or unpaid work experience or in compulsory military/community service	10	0	10	0	0	0	0	0	0	10	0	10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	In retirement/early retirement	24	15	10	2	2	0	0	0	0	4	0	4	11	11	0	2	0	1	2	1	2	1	0	1	2	0	1	0	0	0
	Permanently disabled/unfit to work/other inactive person	5	0	4	0	0	0	0	0	0	4	0	4	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0
	Fulfilling domestic tasks	6	0	6	0	0	0	0	0	0	5	0	5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Other	Total	113	74	39	1	1	0	13	11	2	51	34	17	1	0	1	11	7	4	13	12	1	13	6	7	9	3	6	2	1	1
	Working	78	57	20	1	1	0	11	9	1	31	24	7	1	0	1	10	6	4	9	9	1	5	4	1	8	3	5	1	1	0
	Working full time	67	55	12	1	1	0	10	9	1	28	23	5	1	0	0	6	6	1	9	9	0	5	4	0	7	2	5	1	1	0
	Working part time	10	2	8	0	0	0	1	0	1	3	1	2	0	0	0	4	1	3	0	0	0	1	0	1	1	1	0	0	0	0
	Not working	36	17	19	0	0	0	2	2	1	20	10	10	0	0	0	1	0	0	4	3	1	7	1	6	0	0	0	1	0	0
	Unemployed	18	12	6	0	0	0	1	1	0	15	9	5	0	0	0	0	0	0	2	2	0	1	0	0	0	0	0	0	0	0
	Pupil, student, further training or unpaid work experience or in compulsory military/community service	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	In retirement/early retirement	8	3	5	0	0	0	1	1	0	5	1	4	0	0	0	0	0	0	1	1	0	1	1	0	0	0	0	1	0	0
	Permanently disabled/unfit to work/other inactive person	4	1	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	2	0	2	0	0	0	0	0	0
	Fulfilling domestic tasks	4	0	4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3	0	3	0	0	0	0	0	0
Source: Austrian EU-SILC data from 2006

Perform export

# Create empty workbook
wb <- openxlsx2::wb_workbook()

# Write data
wb <- wb_add_procr_table(wb, tab)

# Export
wb$save(file.path(tempdir(), "procR_demo.xlsx"))