Codebook

knitr::opts_chunk$set(
  warning = FALSE, # show warnings during codebook generation
  message = FALSE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())

# set base directory
basedir = "/home/ecco_rais/data/clean/RAIS-homogenized/output/"
# adjust as necessary
startyear = 2003
endyear = 2004

# libraries
library(codebook)
library(rio)

# Start the codebook loop

#for ( year in startyear:endyear) {
year = "2011"
    # for CSV
    codebook_data <- fread(file.path(basedir, paste0("harmo_", year, ".csv")))

    # omit the following lines, if your missing values are already properly labelled
    codebook_data <- detect_missing(codebook_data,
        only_labelled = TRUE, # only labelled values are autodetected as
                              # missing
        negative_values_are_missing = FALSE, # negative values are missing values
        ninety_nine_problems = TRUE,   # 99/999 are missing values, if they
                                      # are more than 5 MAD from the median
    )

    # If you are not using formr, the codebook package needs to guess which items
    # form a scale. The following line finds item aggregates with names like this:
    # scale = scale_1 + scale_2R + scale_3R
    # identifying these aggregates allows the codebook function to
    # automatically compute reliabilities.
    # However, it will not reverse items automatically.
    codebook_data <- detect_scales(codebook_data)

## Warning in detect_scales(codebook_data): cbo items found, but no aggregate

## Warning in detect_scales(codebook_data): cnae items found, but no aggregate

## Warning in detect_scales(codebook_data): active items found, but no aggregate

    codebook_data <- as.data.table(codebook_data)
    codebook(codebook_data)

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning in max(f): no non-missing arguments to max; returning -Inf

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling
## back to `character`.

Metadata

Description

Dataset name: codebook_data

The dataset has N=70971125 rows and 47 columns. 0 rows have no missing values on any column.

Metadata for search engines

• Date published: 2024-06-02

x adm_date cbo94 cbo02 cei cnae20 cnae20sub cnae95 firmID cnpj_root cpf schooling age cei_avail pat disabled simples alvara_ind termination_month separation_cause muni nationality legal_form name ctps pis hired_hours race_color sex establishment_size tenure adm_type disability_type firmID_type wage_type contract_type active1231 earn_dec_mw earn_dec mean_earn_mw mean_earn hired_wage last_wage yr termination_year adm_day adm_month adm_year

#Variables

adm_date

Distribution

Distribution of values for adm_date

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
adm_date	character	0	1	6865	0	7	8	0	NA

cbo94

Distribution

## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed

## No non-missing values to show.

70971125 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	count	label
cbo94	logical	70971125	0	:	NA

cbo02

Distribution

Distribution of values for cbo02

14543 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
cbo02	numeric	14543	0.9997951	10105	514320	992225	519101.8	204596	▁▃▇▆▁	NA

cei

Distribution

## Error in `ggplot2::geom_histogram()`:
## ! Problem while computing position.
## ℹ Error occurred in the 1st layer.
## Caused by error in `if (...) NULL`:
## ! missing value where TRUE/FALSE needed

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
cei	character	0	1	51939	0	1	21	0	NA

cnae20

Distribution

Distribution of values for cnae20

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
cnae20	numeric	0	1	1113	47890	99008	54576.59	26117.32	▃▂▇▂▇	NA

cnae20sub

Distribution

Distribution of values for cnae20sub

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
cnae20sub	numeric	0	1	111301	4789099	9900800	5457667	2611730	▃▂▇▂▇	NA

cnae95

Distribution

Distribution of values for cnae95

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
cnae95	numeric	0	1	1112	52493	99007	55027.3	22805.71	▂▂▇▇▂	NA

firmID

Distribution

## Error in `ggplot2::geom_histogram()`:
## ! Problem while computing stat.
## ℹ Error occurred in the 1st layer.
## Caused by error in `seq_len()`:
## ! argument must be coercible to non-negative integer

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
firmID	character	0	1	3541200	0	15	21	0	NA

cnpj_root

Distribution

Distribution of values for cnpj_root

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
cnpj_root	numeric	0	1	0	1e+07	9.9e+07	23820961	27187509	▇▁▁▁▁	NA

cpf

Distribution

## Error in `ggplot2::geom_histogram()`:
## ! Problem while computing position.
## ℹ Error occurred in the 1st layer.
## Caused by error in `if (...) NULL`:
## ! missing value where TRUE/FALSE needed

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
cpf	character	0	1	56458498	0	1	21	0	NA

schooling

Distribution

Distribution of values for schooling

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
schooling	numeric	0	1	1	7	11	6.342047	1.831692	▁▃▇▃▁	NA

age

Distribution

Distribution of values for age

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
age	numeric	0	1	0	32	100	34.4915	11.38974	▁▇▃▁▁	NA

cei_avail

Distribution

Distribution of values for cei_avail

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
cei_avail	numeric	0	1	0	0	1	0.0244207	0.1543513	▇▁▁▁▁	NA

pat

Distribution

Distribution of values for pat

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
pat	numeric	0	1	0	0	1	0.303396	0.4597248	▇▁▁▁▃	NA

disabled

Distribution

Distribution of values for disabled

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
disabled	numeric	0	1	0	0	1	0.0061065	0.0779049	▇▁▁▁▁	NA

simples

Distribution

Distribution of values for simples

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
simples	numeric	0	1	0	0	1	0.231537	0.4218147	▇▁▁▁▂	NA

alvara_ind

Distribution

Distribution of values for alvara_ind

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
alvara_ind	numeric	0	1	0	0	1	0.0001311	0.0114489	▇▁▁▁▁	NA

termination_month

Distribution

Distribution of values for termination_month

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
termination_month	numeric	0	1	0	0	12	2.315529	3.784521	▇▁▁▁▁	NA

separation_cause

Distribution

Distribution of values for separation_cause

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
separation_cause	numeric	0	1	0	0	80	5.412577	8.777336	▇▁▁▁▁	NA

muni

Distribution

Distribution of values for muni

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
muni	numeric	0	1	110001	351880	530010	345232.3	85890.2	▁▂▇▂▁	NA

nationality

Distribution

Distribution of values for nationality

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
nationality	numeric	0	1	10	10	70	10.03344	0.98813	▇▁▁▁▁	NA

legal_form

Distribution

Distribution of values for legal_form

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
legal_form	numeric	0	1	1015	2062	5037	2048.847	648.1891	▂▇▁▁▁	NA

name

Distribution

Distribution of values for name

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
name	character	0	1	40690346	0	1	52	0	NA

ctps

Distribution

Distribution of values for ctps

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
ctps	numeric	0	1	0	59378	1e+08	974151.7	3795814	▇▁▁▁▁	NA

pis

Distribution

## Error in `ggplot2::geom_histogram()`:
## ! Problem while computing position.
## ℹ Error occurred in the 1st layer.
## Caused by error in `if (...) NULL`:
## ! missing value where TRUE/FALSE needed

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace	label
pis	character	0	1	56641169	0	1	21	0	NA

hired_hours

Distribution

Distribution of values for hired_hours

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
hired_hours	numeric	0	1	1	44	44	41.36414	6.224379	▁▁▁▁▇	NA

race_color

Distribution

Distribution of values for race_color

9790114 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
race_color	numeric	9790114	0.862055	1	2	9	4.38991	2.931505	▇▁▁▁▅	NA

sex

Distribution

Distribution of values for sex

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
sex	numeric	0	1	0	0	1	0.4031834	0.490537	▇▁▁▁▆	NA

establishment_size

Distribution

Distribution of values for establishment_size

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
establishment_size	numeric	0	1	1	6	10	6.362215	2.805947	▃▅▆▅▇	NA

tenure

Distribution

Distribution of values for tenure

25 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
tenure	numeric	25	0.9999996	-7.1	15	600	44.22445	72.65064	▇▁▁▁▁	NA

adm_type

Distribution

Distribution of values for adm_type

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
adm_type	numeric	0	1	0	0	14	0.763386	1.04411	▇▁▁▁▁	NA

disability_type

Distribution

Distribution of values for disability_type

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
disability_type	numeric	0	1	0	0	6	0.0126126	0.2026474	▇▁▁▁▁	NA

firmID_type

Distribution

Distribution of values for firmID_type

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
firmID_type	numeric	0	1	0	1	1	0.9688325	0.1737702	▁▁▁▁▇	NA

wage_type

Distribution

Distribution of values for wage_type

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
wage_type	numeric	0	1	1	1	7	1.394726	1.220484	▇▁▁▁▁	NA

contract_type

Distribution

Distribution of values for contract_type

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
contract_type	numeric	0	1	10	10	97	15.95322	13.5378	▇▂▁▁▁	NA

active1231

Distribution

Distribution of values for active1231

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
active1231	numeric	0	1	0	1	1	0.6525278	0.4761673	▅▁▁▁▇	NA

earn_dec_mw

Distribution

Distribution of values for earn_dec_mw

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
earn_dec_mw	numeric	0	1	0	1.4	150	2.285725	4.52264	▇▁▁▁▁	NA

earn_dec

Distribution

Distribution of values for earn_dec

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
earn_dec	numeric	0	1	0	736	81750	1247.412	2465.332	▇▁▁▁▁	NA

mean_earn_mw

Distribution

Distribution of values for mean_earn_mw

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
mean_earn_mw	numeric	0	1	0	1.7	150	2.842161	4.206529	▇▁▁▁▁	NA

mean_earn

Distribution

Distribution of values for mean_earn

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
mean_earn	numeric	0	1	0	919	81750	1550.744	2288.076	▇▁▁▁▁	NA

hired_wage

Distribution

Distribution of values for hired_wage

171 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
hired_wage	numeric	171	0.9999976	0	756	992152	1173.692	2086.208	▇▁▁▁▁	NA

last_wage

Distribution

Distribution of values for last_wage

168 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
last_wage	numeric	168	0.9999976	0	706	993859	1243.547	2938.869	▇▁▁▁▁	NA

yr

Distribution

Distribution of values for yr

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
yr	numeric	0	1	2011	2011	2011	2011	0	▁▁▇▁▁	NA

termination_year

Distribution

Distribution of values for termination_year

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
termination_year	numeric	0	1	2011	2011	2011	2011	0	▁▁▇▁▁	NA

adm_day

Distribution

Distribution of values for adm_day

6905068 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
adm_day	numeric	6905068	0.902706	1	4	31	8.248127	8.370776	▇▂▂▂▁	NA

adm_month

Distribution

Distribution of values for adm_month

6905068 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
adm_month	numeric	6905068	0.902706	0	6	12	6.340709	3.417429	▆▆▇▆▇	NA

adm_year

Distribution

Distribution of values for adm_year

6905068 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
adm_year	numeric	6905068	0.902706	104	2010	2201	2009.348	2.363842	▁▁▁▁▇	NA

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "codebook_data",
  "datePublished": "2024-06-02",
  "description": "The dataset has N=70971125 rows and 47 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name               |label | n_missing|\n|:------------------|:-----|---------:|\n|adm_date           |NA    |         0|\n|cbo94              |NA    |  70971125|\n|cbo02              |NA    |     14543|\n|cei                |NA    |         0|\n|cnae20             |NA    |         0|\n|cnae20sub          |NA    |         0|\n|cnae95             |NA    |         0|\n|firmID             |NA    |         0|\n|cnpj_root          |NA    |         0|\n|cpf                |NA    |         0|\n|schooling          |NA    |         0|\n|age                |NA    |         0|\n|cei_avail          |NA    |         0|\n|pat                |NA    |         0|\n|disabled           |NA    |         0|\n|simples            |NA    |         0|\n|alvara_ind         |NA    |         0|\n|termination_month  |NA    |         0|\n|separation_cause   |NA    |         0|\n|muni               |NA    |         0|\n|nationality        |NA    |         0|\n|legal_form         |NA    |         0|\n|name               |NA    |         0|\n|ctps               |NA    |         0|\n|pis                |NA    |         0|\n|hired_hours        |NA    |         0|\n|race_color         |NA    |   9790114|\n|sex                |NA    |         0|\n|establishment_size |NA    |         0|\n|tenure             |NA    |        25|\n|adm_type           |NA    |         0|\n|disability_type    |NA    |         0|\n|firmID_type        |NA    |         0|\n|wage_type          |NA    |         0|\n|contract_type      |NA    |         0|\n|active1231         |NA    |         0|\n|earn_dec_mw        |NA    |         0|\n|earn_dec           |NA    |         0|\n|mean_earn_mw       |NA    |         0|\n|mean_earn          |NA    |         0|\n|hired_wage         |NA    |       171|\n|last_wage          |NA    |       168|\n|yr                 |NA    |         0|\n|termination_year   |NA    |         0|\n|adm_day            |NA    |   6905068|\n|adm_month          |NA    |   6905068|\n|adm_year           |NA    |   6905068|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["adm_date", "cbo94", "cbo02", "cei", "cnae20", "cnae20sub", "cnae95", "firmID", "cnpj_root", "cpf", "schooling", "age", "cei_avail", "pat", "disabled", "simples", "alvara_ind", "termination_month", "separation_cause", "muni", "nationality", "legal_form", "name", "ctps", "pis", "hired_hours", "race_color", "sex", "establishment_size", "tenure", "adm_type", "disability_type", "firmID_type", "wage_type", "contract_type", "active1231", "earn_dec_mw", "earn_dec", "mean_earn_mw", "mean_earn", "hired_wage", "last_wage", "yr", "termination_year", "adm_day", "adm_month", "adm_year"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "adm_date",
      "@type": "propertyValue"
    },
    {
      "name": "cbo94",
      "@type": "propertyValue"
    },
    {
      "name": "cbo02",
      "@type": "propertyValue"
    },
    {
      "name": "cei",
      "@type": "propertyValue"
    },
    {
      "name": "cnae20",
      "@type": "propertyValue"
    },
    {
      "name": "cnae20sub",
      "@type": "propertyValue"
    },
    {
      "name": "cnae95",
      "@type": "propertyValue"
    },
    {
      "name": "firmID",
      "@type": "propertyValue"
    },
    {
      "name": "cnpj_root",
      "@type": "propertyValue"
    },
    {
      "name": "cpf",
      "@type": "propertyValue"
    },
    {
      "name": "schooling",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "@type": "propertyValue"
    },
    {
      "name": "cei_avail",
      "@type": "propertyValue"
    },
    {
      "name": "pat",
      "@type": "propertyValue"
    },
    {
      "name": "disabled",
      "@type": "propertyValue"
    },
    {
      "name": "simples",
      "@type": "propertyValue"
    },
    {
      "name": "alvara_ind",
      "@type": "propertyValue"
    },
    {
      "name": "termination_month",
      "@type": "propertyValue"
    },
    {
      "name": "separation_cause",
      "@type": "propertyValue"
    },
    {
      "name": "muni",
      "@type": "propertyValue"
    },
    {
      "name": "nationality",
      "@type": "propertyValue"
    },
    {
      "name": "legal_form",
      "@type": "propertyValue"
    },
    {
      "name": "name",
      "@type": "propertyValue"
    },
    {
      "name": "ctps",
      "@type": "propertyValue"
    },
    {
      "name": "pis",
      "@type": "propertyValue"
    },
    {
      "name": "hired_hours",
      "@type": "propertyValue"
    },
    {
      "name": "race_color",
      "@type": "propertyValue"
    },
    {
      "name": "sex",
      "@type": "propertyValue"
    },
    {
      "name": "establishment_size",
      "@type": "propertyValue"
    },
    {
      "name": "tenure",
      "@type": "propertyValue"
    },
    {
      "name": "adm_type",
      "@type": "propertyValue"
    },
    {
      "name": "disability_type",
      "@type": "propertyValue"
    },
    {
      "name": "firmID_type",
      "@type": "propertyValue"
    },
    {
      "name": "wage_type",
      "@type": "propertyValue"
    },
    {
      "name": "contract_type",
      "@type": "propertyValue"
    },
    {
      "name": "active1231",
      "@type": "propertyValue"
    },
    {
      "name": "earn_dec_mw",
      "@type": "propertyValue"
    },
    {
      "name": "earn_dec",
      "@type": "propertyValue"
    },
    {
      "name": "mean_earn_mw",
      "@type": "propertyValue"
    },
    {
      "name": "mean_earn",
      "@type": "propertyValue"
    },
    {
      "name": "hired_wage",
      "@type": "propertyValue"
    },
    {
      "name": "last_wage",
      "@type": "propertyValue"
    },
    {
      "name": "yr",
      "@type": "propertyValue"
    },
    {
      "name": "termination_year",
      "@type": "propertyValue"
    },
    {
      "name": "adm_day",
      "@type": "propertyValue"
    },
    {
      "name": "adm_month",
      "@type": "propertyValue"
    },
    {
      "name": "adm_year",
      "@type": "propertyValue"
    }
  ]
}`

 # } # end year loop