Import two related datasets from TidyTuesday Project.
library(tidyverse)
companies <- read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-01-27/companies.csv"
)## Rows: 141332 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): company_name, legal_nature, owner_qualification, company_size
## dbl (2): company_id, capital_stock
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.legal_nature <- read_csv(
"https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-01-27/legal_nature.csv"
)## Rows: 91 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): legal_nature
## dbl (1): id
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Describe the two datasets:
Data1
Data 2
set.seed(1234)
companies_small <- companies %>% sample_n(5)
legal_nature_small <- legal_nature %>% sample_n(5)
companies_small## # A tibble: 5 × 6
## company_id company_name legal_nature owner_qualification capital_stock
## <dbl> <chr> <chr> <chr> <dbl>
## 1 59656511 CORACAO DO GIGANTE … Limited Lia… Administrator / Ma… 2000000
## 2 48688910 ONCA MINERACAO LTDA. Limited Lia… Administrator / Ma… 20000000
## 3 47271098 H E I COMERCIO DE … Limited Lia… Managing Partner /… 300000
## 4 63195305 SOBERANA SUPERMERCA… Limited Lia… Managing Partner /… 500000
## 5 62189811 AUTO POSTO XAVANTE … Limited Lia… Managing Partner /… 300000
## # ℹ 1 more variable: company_size <chr>legal_nature_small## # A tibble: 5 × 2
## id legal_nature
## <dbl> <chr>
## 1 3115 Mediation and Arbitration Entity
## 2 3271 Local Governing Body of a Political Party
## 3 3263 Regional Governing Body of a Political Party
## 4 1147 State or District Public Law Foundation
## 5 2267 Simple Limited Partnership (Comandita)Describe the resulting data:
How is it different from the original two datasets? It combinded the rows that have matching legal nature keys in both datasets so only matching records are included
companies_small %>%
inner_join(legal_nature_small, by = "legal_nature")## # A tibble: 0 × 7
## # ℹ 7 variables: company_id <dbl>, company_name <chr>, legal_nature <chr>,
## # owner_qualification <chr>, capital_stock <dbl>, company_size <chr>,
## # id <dbl>Describe the resulting data:
How is it different from the original two datasets? It keeps all rows from the companies dataset and adds matching information from the legal nature dataset
companies_small %>%
left_join(legal_nature_small, by = "legal_nature")## # A tibble: 5 × 7
## company_id company_name legal_nature owner_qualification capital_stock
## <dbl> <chr> <chr> <chr> <dbl>
## 1 59656511 CORACAO DO GIGANTE … Limited Lia… Administrator / Ma… 2000000
## 2 48688910 ONCA MINERACAO LTDA. Limited Lia… Administrator / Ma… 20000000
## 3 47271098 H E I COMERCIO DE … Limited Lia… Managing Partner /… 300000
## 4 63195305 SOBERANA SUPERMERCA… Limited Lia… Managing Partner /… 500000
## 5 62189811 AUTO POSTO XAVANTE … Limited Lia… Managing Partner /… 300000
## # ℹ 2 more variables: company_size <chr>, id <dbl>Describe the resulting data:
How is it different from the original two datasets? It keeps all rows from the legal_nature dataset and adds matching information from the companies dataset
companies_small %>%
right_join(legal_nature_small, by = "legal_nature")## # A tibble: 5 × 7
## company_id company_name legal_nature owner_qualification capital_stock
## <dbl> <chr> <chr> <chr> <dbl>
## 1 NA <NA> Mediation and Arbit… <NA> NA
## 2 NA <NA> Local Governing Bod… <NA> NA
## 3 NA <NA> Regional Governing … <NA> NA
## 4 NA <NA> State or District P… <NA> NA
## 5 NA <NA> Simple Limited Part… <NA> NA
## # ℹ 2 more variables: company_size <chr>, id <dbl>Describe the resulting data:
How is it different from the original two datasets? It combines all rows from both datasets
companies_small %>%
full_join(legal_nature_small, by = "legal_nature")## # A tibble: 10 × 7
## company_id company_name legal_nature owner_qualification capital_stock
## <dbl> <chr> <chr> <chr> <dbl>
## 1 59656511 CORACAO DO GIGANTE… Limited Lia… Administrator / Ma… 2000000
## 2 48688910 ONCA MINERACAO LTD… Limited Lia… Administrator / Ma… 20000000
## 3 47271098 H E I COMERCIO DE… Limited Lia… Managing Partner /… 300000
## 4 63195305 SOBERANA SUPERMERC… Limited Lia… Managing Partner /… 500000
## 5 62189811 AUTO POSTO XAVANTE… Limited Lia… Managing Partner /… 300000
## 6 NA <NA> Mediation a… <NA> NA
## 7 NA <NA> Local Gover… <NA> NA
## 8 NA <NA> Regional Go… <NA> NA
## 9 NA <NA> State or Di… <NA> NA
## 10 NA <NA> Simple Limi… <NA> NA
## # ℹ 2 more variables: company_size <chr>, id <dbl>Describe the resulting data:
How is it different from the original two datasets? It only keeps the company rows that have a matching legal nature key in the second dataset
companies_small %>%
semi_join(legal_nature_small, by = "legal_nature")## # A tibble: 0 × 6
## # ℹ 6 variables: company_id <dbl>, company_name <chr>, legal_nature <chr>,
## # owner_qualification <chr>, capital_stock <dbl>, company_size <chr>Describe the resulting data:
How is it different from the original two datasets? It keeps the company rows that dont have a matching legal nature key in the second dataset
companies_small %>%
anti_join(legal_nature_small, by = "legal_nature")## # A tibble: 5 × 6
## company_id company_name legal_nature owner_qualification capital_stock
## <dbl> <chr> <chr> <chr> <dbl>
## 1 59656511 CORACAO DO GIGANTE … Limited Lia… Administrator / Ma… 2000000
## 2 48688910 ONCA MINERACAO LTDA. Limited Lia… Administrator / Ma… 20000000
## 3 47271098 H E I COMERCIO DE … Limited Lia… Managing Partner /… 300000
## 4 63195305 SOBERANA SUPERMERCA… Limited Lia… Managing Partner /… 500000
## 5 62189811 AUTO POSTO XAVANTE … Limited Lia… Managing Partner /… 300000
## # ℹ 1 more variable: company_size <chr>