Joining several data frames with dplyr

Silvie Cinková

2025-08-09

Table of contents

• Join tables by a shared column
• Revision performed by a user
• gapminder
• geo
• Countries in geo vs. gapminder
• Little overlap in key column values?
• Control key selection
• gapminder Europe 2007
• European subset of geo
• Preferred gapminder, intersection
• Keep gapminder intact, no matter what.
• Which is missing?
• Focus on geo_europe
• Mismatches in geo_europe
• Get what you can
• anti_join detects mismatches instantly
• anti_join the other way round
• semi_join detects matches
• When your observations are not unique
• The two tibbles: math
• The two tibbles: social sciences
• Possible rescue: unique by several columns
• No chance to join
• dplyr::join help
• Data with typos in the key column(s)
• JRC Names
• JRC Person Names
• JRC each table
• Matching on Levenschtein Distance
• Matching on cosine distance between qgrams
• Matching on Jaccard distance

Join tables by a shared column

• Relational databases: tables connected by shared columns. Useful with big datasets evolving in time, in separate places, etc. Two main assets:

  1. A change propagates automatically to all places where relevant
  2. On-demand tables generated by queries (variability depends on how smart the connections between tables are).

• The figure models how Wikipedia administrators record the history of edits in a relational database. Each of the squares represents one table. The rows represents columns with their names. So, the top right table named page lists one Wikipedia page in each row. Its columns are called id, title, namespace, creation\_date, last\_edit\_date, and total\_edits. In the pagecategory table, each observation is a combination of the ID of a page and a Wikipedia category. These two tables are connected with a red arrow. It matters which table columns (which look as rows here in this scheme) these arrows exactly connect.

• The rows at the start and end of the arrows form a pair. They can have different names, but they describe the same thing in the data. Here these are page IDs. In the pages table, the id column is unique for each observation. It is the primary key of this table. Whenever I want to relate another table to this one, I must include a column with the page IDs. In each of these tables, the column of the related page IDs is called foreign key. The foreign key values need not bee unique. In our context, each page can be in multiple Wikipedia categories at the same time (e.g. “capitals”, “cities”, and “administration units”). If the page were about Prague, which is a capital and hence a city and administration unit, the ID of the Prague page would occur three times in the pageincategory table. The relation holds also the other way round: one category can describe many pages. In real database schemes, you would see different types of arrows that would tell you exactly how many source items can connect to how many target items. This figure does not quite follow this notation, so we can only tell by common sense.

When you want to get a table of titles of the pages and the names of the categories they belong to, you call a function that connects matching rows from the both tables by matching together the values of the primary key and the foreign key. By this way, you do not need to worry about how the rows are arranged in either table.

Designing the architecture of a relational database is a skill in its own right. You need not know exactly how to design a relational database, but you will often need to connect tables from sources that are formed so.

I have adopted this image from this academic paper: https://arxiv.org/pdf/1512.03523. I have corrected the yellow arrow from userincategory to category to point from category_id instead of from user_id.

Revision performed by a user

You can even look up things within one single table: look at the revision table: parentid: the id of the previous revision - to look up on a different row in the same table. I guess that a parent revision is simply the revision event immediately preceding in time.

• Revision table with columns id, page_id, user_id,timestamp, , parentid,text.
• primary key: unique id of each revision.
• page_id foreign key - turquoise arrow leads to a page table, to its primary key (probably called id but potentially anything else)
• user_id foreign key - purple arrow leads to a user table
• timestamp: when the revision took place

The scheme does not say whether the parent revision must be one on the same Wikipedia page or rather one performed by the same user. This would also be a design decision: whether you want to be able to track revision of pages or rather the activities of the users, or both. Anyway, from this table, you can connect to both users and pages, so you can generate a table with titles and names of both for each revision.

gapminder

# A tibble: 3 × 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.

You can join tables with dplyr. So far, we have worked quite a lot with diverse tables from Gapminder.org. You correctly anticipate that Gapminder.org stores its data in relational databases. Their tables are made for you to freely combine information across different tables.

In this session, we will enrich our familiar gapminder dataset with additional information from a table of unique countries. We will call that other table geo. This table contains many columns, so we will only select a few to keep this demonstration overseeable.

Tip

Did you know that readr allows you to select columns before you even load the data? When you know their names, that is.

geo

geo <- read_csv(glue("https://raw.githubusercontent.com/open-numbers/",
                     "ddf--gapminder--fasttrack/master/",
                     "ddf--entities--geo--country.csv"))
geo <- geo %>% select(country, name, main_religion_2008, income_3groups, world_4region)
glimpse(geo)

Rows: 273
Columns: 5
$ country            <chr> "abkh", "abw", "afg", "ago", "aia", "akr_a_dhe", "a…
$ name               <chr> "Abkhazia", "Aruba", "Afghanistan", "Angola", "Angu…
$ main_religion_2008 <chr> NA, "christian", "muslim", "christian", "christian"…
$ income_3groups     <chr> NA, "high_income", "low_income", "middle_income", N…
$ world_4region      <chr> "europe", "americas", "asia", "africa", "americas",…

Now we have a data frame called geo. It lists 273 countries, each only once, and for each country it gives us its dominant religion (snapshot taken in 2008) and to which income group it belongs. The income groups are Gapminder’s own design and obviously refer to the time of creating this dataset. The countries are encoded by their names (column name) and by the abbreviations of these names (column country).

Important

The gapminder data frame does not use abbreviations for country names. But even more importantly, its country column does not correspond to country but to name in the geo data frame. This is something to keep in mind when we try to join these two tables!

Countries in geo vs. gapminder

unique(geo$name) %>% length()

[1] 273

unique(gapminder$country) %>% length()

[1] 142

When we want to join geo and gapminder, we can apparently use geo’s name and gapminder’s country as the primary key - foreign key pair.

The first question you must ask when you want to join two tables by a pair of keys is how much overlap they have at all. geo contains many more countries than gapminder, so much is clear. But it is not given that all of its countries are listed in geo! Therefore we do this quick check.

Note

You could as well call distinct and pull on each, but this base-R way is shorter. It accesses a data frame column as a vector and calls the unique function, which acts on vectors like dplyr::distinct on data frames.

Little overlap in key column values?

setdiff(gapminder$country, geo$name) 

[1] "Korea, Dem. Rep."   "Korea, Rep."        "Swaziland"         
[4] "United Kingdom"     "United States"      "West Bank and Gaza"
[7] "Yemen, Rep."       

setdiff(geo$name, gapminder$country) %>% length()

[1] 138

setdiff tells you which elements of the first vectors are not in the second vector. We see that the gapminder data frame is not a full subset of the geo data frame. When we enrich gapminder with the information from geo, some values will be missing. This has no programmatic solution. This is your design decision. Drop these seven countries from gapminder because you don’t get the additional information, or live with NAs in the new columns? Or you even find the geo table more important and only want to enrich with gapminder’s pop, lifeExp, and gdpPercap in a given year? For each of your possible decisions, dplyr comes with a dedicated _join function.

Control key selection

When blue and yellow mix, they give green. This scheme depicts the possible joining outcomes. The yellow area represents the gapminder data, the blue the geo data, and the green area the resulting new table.

1. With inner_join you join just the rows that match in both tables. So you will not have any new empty values.
2. The left_join and right_join functions are two different perspectives of the same thing, what exactly each does obviously depends on the order in which you feed in the tables. If we always feed gapminder as first (the x argument) and geo second (y), left join will keep all rows in gapminder and match them to geo. We know that seven countries will not be matched, which will result in 7 times 12 rows with NA values in the new columns containing the abbreviation, main religion in 2008, and the income group. The twelve comes from the number of observations (remember, 1952 to 2007, in five-year intervals).
3. With right_join, we will get a monstrous table of all geo's countries, and the ones that match gapminder, will be matched twelve times each, with a warning for “many-to-many” relations occurring. The new table is going to have four new columns: year, pop, lifeExp, and gdpPercap. They will be filled with NA except in the rows with countries whose names matched across both tables, and the seven countries from gapminder will not appear in the new table.
4. With full_join, no row will be deleted, but there will be NA in all mismatched rows.
5. All these tables are going to have the same columns.

gapminder Europe 2007

gapminder_europe <- gapminder %>% 
  filter(continent == "Europe", year == 2007) %>%
  select(!c(continent, year))
glimpse(gapminder_europe)

Rows: 30
Columns: 4
$ country   <fct> "Albania", "Austria", "Belgium", "Bosnia and Herzegovina", "…
$ lifeExp   <dbl> 76.423, 79.829, 79.441, 74.852, 73.005, 75.748, 76.486, 78.3…
$ pop       <int> 3600523, 8199783, 10392226, 4552198, 7322858, 4493312, 10228…
$ gdpPercap <dbl> 5937.030, 36126.493, 33692.605, 7446.299, 10680.793, 14619.2…

We make a subset of gapminder to keep the data small. Just European countries in 2007 and we drop the columns continent and year because they are all equal.

European subset of geo

geo_europe <- geo %>% filter(world_4region == "europe") %>% 
  select(!world_4region)
glimpse(geo)

Rows: 273
Columns: 5
$ country            <chr> "abkh", "abw", "afg", "ago", "aia", "akr_a_dhe", "a…
$ name               <chr> "Abkhazia", "Aruba", "Afghanistan", "Angola", "Angu…
$ main_religion_2008 <chr> NA, "christian", "muslim", "christian", "christian"…
$ income_3groups     <chr> NA, "high_income", "low_income", "middle_income", N…
$ world_4region      <chr> "europe", "americas", "asia", "africa", "americas",…

Preferred gapminder, intersection

dplyr::inner_join(x = gapminder_europe, y = geo_europe, 
                  by = c("country" = "name")) 

# A tibble: 29 × 7
   country  lifeExp    pop gdpPercap country.y main_religion_2008 income_3groups
   <chr>      <dbl>  <int>     <dbl> <chr>     <chr>              <chr>         
 1 Albania     76.4 3.60e6     5937. alb       muslim             middle_income 
 2 Austria     79.8 8.20e6    36126. aut       christian          high_income   
 3 Belgium     79.4 1.04e7    33693. bel       christian          high_income   
 4 Bosnia …    74.9 4.55e6     7446. bih       <NA>               middle_income 
 5 Bulgaria    73.0 7.32e6    10681. bgr       christian          middle_income 
 6 Croatia     75.7 4.49e6    14619. hrv       christian          high_income   
 7 Czech R…    76.5 1.02e7    22833. cze       christian          high_income   
 8 Denmark     78.3 5.47e6    35278. dnk       christian          high_income   
 9 Finland     79.3 5.24e6    33207. fin       christian          high_income   
10 France      80.7 6.11e7    30470. fra       christian          high_income   
# ℹ 19 more rows

We would like to add information from geo to gapminder_europe.

The resulting data frame is going to inherit only the rows with matching country names from both.

The _join functions in dplyr always need a first data frame (x), a second data frame (y), and a vector of column names that provide the key pair(s) in the argument called by. The whole thing reads: “Join x with y by this vector of key pairs.” Each pair contains the name of the key column in x and the name of the corresponding column in y, in exactly this order. Relational databases are designed to answer most queries with just one pair, but sometimes you need several to uniquely identify observations in at least one of the data frames.

Mind the quotes in the column names in by. This time they are mandatory.

There were 30 countries in gapminder_europe, but the resulting data frame has only 29 rows. One country was missing in geo_europe, although it is so much longer than gapminder_europe. We will find out later which. You can see an NA in the fourth row, 6th column. This has nothing to do with joining; it was like this in the original geo_europe data frame.

Look closely at the names of the columns. Can you see country and country.y? The former is from gapminder, the latter from geo_europe. Column names must remain unique, therefore dplyr adds suffixes to duplicate names. Typically, the duplicate column name from the x data frame gets the suffix .x and the other one .y. You can replace them with your own suffixes in the suffix argument. In this case, only the one from the y data frame got the suffix. This is because it was used as a by column. By default, the key column from y disappears and only the x key column stays. You can also fiddle with this in a dedicated argument keep. So, in this case, the x country column stayed, the paired name column from y vanished, and then dplyr spotted another country column among the columns inherited from y. The function is just written in such a way that it does only put the suffix on one, when the other one was used as key. Do not ponder on it, just remember.

Keep gapminder intact, no matter what.

One country gets NA in geo_europe columns.

dplyr::left_join(x = gapminder_europe, y = geo_europe,
                 by = c("country" = "name")) 

# A tibble: 30 × 7
   country  lifeExp    pop gdpPercap country.y main_religion_2008 income_3groups
   <chr>      <dbl>  <int>     <dbl> <chr>     <chr>              <chr>         
 1 Albania     76.4 3.60e6     5937. alb       muslim             middle_income 
 2 Austria     79.8 8.20e6    36126. aut       christian          high_income   
 3 Belgium     79.4 1.04e7    33693. bel       christian          high_income   
 4 Bosnia …    74.9 4.55e6     7446. bih       <NA>               middle_income 
 5 Bulgaria    73.0 7.32e6    10681. bgr       christian          middle_income 
 6 Croatia     75.7 4.49e6    14619. hrv       christian          high_income   
 7 Czech R…    76.5 1.02e7    22833. cze       christian          high_income   
 8 Denmark     78.3 5.47e6    35278. dnk       christian          high_income   
 9 Finland     79.3 5.24e6    33207. fin       christian          high_income   
10 France      80.7 6.11e7    30470. fra       christian          high_income   
# ℹ 20 more rows

Which is missing?

• country mismatch \(\rightarrow\) is.na(country.y)

dplyr::left_join(x = gapminder_europe, y = geo_europe,
                 by = c("country" = "name")) %>%
  filter(is.na(country.y))

# A tibble: 1 × 7
  country   lifeExp    pop gdpPercap country.y main_religion_2008 income_3groups
  <chr>       <dbl>  <int>     <dbl> <chr>     <chr>              <chr>         
1 United K…    79.4 6.08e7    33203. <NA>      <NA>               <NA>          

Focus on geo_europe

dplyr::right_join(x = gapminder_europe, y = geo_europe,
                 by = c("country" = "name")) 

# A tibble: 73 × 7
   country  lifeExp    pop gdpPercap country.y main_religion_2008 income_3groups
   <chr>      <dbl>  <int>     <dbl> <chr>     <chr>              <chr>         
 1 Albania     76.4 3.60e6     5937. alb       muslim             middle_income 
 2 Austria     79.8 8.20e6    36126. aut       christian          high_income   
 3 Belgium     79.4 1.04e7    33693. bel       christian          high_income   
 4 Bosnia …    74.9 4.55e6     7446. bih       <NA>               middle_income 
 5 Bulgaria    73.0 7.32e6    10681. bgr       christian          middle_income 
 6 Croatia     75.7 4.49e6    14619. hrv       christian          high_income   
 7 Czech R…    76.5 1.02e7    22833. cze       christian          high_income   
 8 Denmark     78.3 5.47e6    35278. dnk       christian          high_income   
 9 Finland     79.3 5.24e6    33207. fin       christian          high_income   
10 France      80.7 6.11e7    30470. fra       christian          high_income   
# ℹ 63 more rows

Mismatches in geo_europe

dplyr::right_join(x = gapminder_europe, y = geo_europe,
                 by = c("country" = "name")) %>% 
  filter(is.na(lifeExp) | is.na(pop) | is.na(gdpPercap))

# A tibble: 44 × 7
   country   lifeExp   pop gdpPercap country.y main_religion_2008 income_3groups
   <chr>       <dbl> <int>     <dbl> <chr>     <chr>              <chr>         
 1 Abkhazia       NA    NA        NA abkh      <NA>               <NA>          
 2 Akrotiri…      NA    NA        NA akr_a_dhe <NA>               <NA>          
 3 Åland          NA    NA        NA ala       <NA>               <NA>          
 4 Andorra        NA    NA        NA and       christian          high_income   
 5 Armenia        NA    NA        NA arm       christian          middle_income 
 6 Antarcti…      NA    NA        NA ata       <NA>               <NA>          
 7 Azerbaij…      NA    NA        NA aze       muslim             middle_income 
 8 Belarus        NA    NA        NA blr       christian          middle_income 
 9 Channel …      NA    NA        NA chanisl   christian          high_income   
10 Czechosl…      NA    NA        NA cheslo    <NA>               <NA>          
# ℹ 34 more rows

We must look for a column that was not in geo_europe. The country column now is the result of country and name, so it will contain all countries from geo_europe but will have dropped United Kingdom, which was only in gapminder_europe. Any of the numeric columns from gapminder will help us though.

Get what you can

dplyr::full_join(x = gapminder_europe, y = geo_europe,
                 by = c("country" = "name"))

# A tibble: 74 × 7
   country  lifeExp    pop gdpPercap country.y main_religion_2008 income_3groups
   <chr>      <dbl>  <int>     <dbl> <chr>     <chr>              <chr>         
 1 Albania     76.4 3.60e6     5937. alb       muslim             middle_income 
 2 Austria     79.8 8.20e6    36126. aut       christian          high_income   
 3 Belgium     79.4 1.04e7    33693. bel       christian          high_income   
 4 Bosnia …    74.9 4.55e6     7446. bih       <NA>               middle_income 
 5 Bulgaria    73.0 7.32e6    10681. bgr       christian          middle_income 
 6 Croatia     75.7 4.49e6    14619. hrv       christian          high_income   
 7 Czech R…    76.5 1.02e7    22833. cze       christian          high_income   
 8 Denmark     78.3 5.47e6    35278. dnk       christian          high_income   
 9 Finland     79.3 5.24e6    33207. fin       christian          high_income   
10 France      80.7 6.11e7    30470. fra       christian          high_income   
# ℹ 64 more rows

The full join is going to contain all country names from both data frames, with NA where they mismatched.

anti_join detects mismatches instantly

• like setdiff in vectors

anti_join(x = gapminder_europe, y = geo_europe,
                 by = c("country" = "name"))

# A tibble: 1 × 4
  country        lifeExp      pop gdpPercap
  <fct>            <dbl>    <int>     <dbl>
1 United Kingdom    79.4 60776238    33203.

anti_join the other way round

anti_join( x = geo_europe, y = gapminder_europe,
                 by = c( "name" = "country"))

# A tibble: 44 × 4
   country   name                  main_religion_2008 income_3groups
   <chr>     <chr>                 <chr>              <chr>         
 1 abkh      Abkhazia              <NA>               <NA>          
 2 akr_a_dhe Akrotiri and Dhekelia <NA>               <NA>          
 3 ala       Åland                 <NA>               <NA>          
 4 and       Andorra               christian          high_income   
 5 arm       Armenia               christian          middle_income 
 6 ata       Antarctica            <NA>               <NA>          
 7 aze       Azerbaijan            muslim             middle_income 
 8 blr       Belarus               christian          middle_income 
 9 chanisl   Channel Islands       christian          high_income   
10 cheslo    Czechoslovakia        <NA>               <NA>          
# ℹ 34 more rows

anti_join: Filter rows of x that are not matched in y.

semi_join detects matches

semi_join( x = geo_europe, y = gapminder_europe,
                 by = c( "name" = "country"))

# A tibble: 29 × 4
   country name                   main_religion_2008 income_3groups
   <chr>   <chr>                  <chr>              <chr>         
 1 alb     Albania                muslim             middle_income 
 2 aut     Austria                christian          high_income   
 3 bel     Belgium                christian          high_income   
 4 bgr     Bulgaria               christian          middle_income 
 5 bih     Bosnia and Herzegovina <NA>               middle_income 
 6 che     Switzerland            christian          high_income   
 7 cze     Czech Republic         christian          high_income   
 8 deu     Germany                christian          high_income   
 9 dnk     Denmark                christian          high_income   
10 esp     Spain                  christian          high_income   
# ℹ 19 more rows

Filter rows of x that are matched by y. In this context, the countries are going to be the same, no matter in which order you name the data frames, but you will of course get the rows of the one you fed in as x. It can be helpful to look at both sides, especially when you know that you have a lot of NA across the columns of both. If you have to opt for either left or right join, you can at least choose that with less missing data.

When your observations are not unique

The two tibbles: math

maths

# A tibble: 4 × 3
  name         birthplace math_test
  <chr>        <chr>          <dbl>
1 John Smith   Honolulu          72
2 Mary Brown   Milan             40
3 John Smith   Prague            25
4 Helene Field Beijing           91

The two tibbles: social sciences

social_sciences

# A tibble: 4 × 3
  name         birthplace soc_test
  <chr>        <chr>         <dbl>
1 Mary Brown   Milan            12
2 John Smith   Honolulu          5
3 John Smith   Prague           76
4 Helene Field Beijing          49

Students’ grades in two courses. You would like to have both exams in the same table. Are the students uniquely identified?

Possible rescue: unique by several columns

left_join(maths, social_sciences, by = (c("name", "birthplace")))

# A tibble: 4 × 4
  name         birthplace math_test soc_test
  <chr>        <chr>          <dbl>    <dbl>
1 John Smith   Honolulu          72        5
2 Mary Brown   Milan             40       12
3 John Smith   Prague            25       76
4 Helene Field Beijing           91       49

No chance to join

If you cannot find anything that makes them unique.

maths2 <- select(maths, -birthplace)
social_sciences2 <- select(social_sciences, !birthplace)
left_join(maths2, social_sciences2, by = "name")

Warning in left_join(maths2, social_sciences2, by = "name"): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 2 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

# A tibble: 6 × 3
  name         math_test soc_test
  <chr>            <dbl>    <dbl>
1 John Smith          72        5
2 John Smith          72       76
3 Mary Brown          40       12
4 John Smith          25        5
5 John Smith          25       76
6 Helene Field        91       49

Note John Smith occurring four times - all possible combinations get generated.

dplyr::join help

explore the arguments

• relationship

• multiple

• unmatched

Data with typos in the key column(s)

• libraries fuzzyjoin along with stringdist (used by fuzzyjoin)

• DataCamp course Intermediate Regular Expressions in R > Similarities Between Strings

You should only know that there are ways to cope with strings in columns that do not match completely. Only read further if you are particularly interested.

JRC Names

Steinberger Ralf, Bruno Pouliquen, Mijail Kabadjov, Jenya Belyaeva & Erik van der Goot (2011).JRC-Names: A freely available, highly multilingual named entity resource. Proceedings of the 8th International Conference Recent Advances in Natural Language Processing (RANLP). Hissar, Bulgaria, 12-14 September 2011.

jrc_1 <- read_tsv("datasets_ATRIUM/JRC_Names/jrc_1.tsv")
jrc_2 <- read_tsv("datasets_ATRIUM/JRC_Names/jrc_2.tsv")
jrc_3 <- read_tsv("datasets_ATRIUM/JRC_Names/jrc_3.tsv")
jrc_4 <- read_tsv("datasets_ATRIUM/JRC_Names/jrc_4.tsv")

JRC Person Names

• persons with 4 spellings of their names
• 2 tables, 20 rows,
  • equal pers IDs, different spelling
• test different fuzzy join metrics

Watch the number of rows in inner join. Sensitivity (recall): how many it catches of those to catch: the longer table the better Specificity (precision): how many of those caught were correct (how much noise): check that their ids match.

JRC each table

jrc_1 %>% slice_head(n = 5) %>% select(!c( n, index_id, starts_with("PersOrg")))

# A tibble: 5 × 2
     id name           
  <dbl> <chr>          
1    41 John+Ashcroft  
2    46 Richard+Boucher
3    56 Adam+Ereli     
4    92 Chris+Patten   
5   123 Dan+Senor      

jrc_2 %>% slice_head(n = 5) %>% select(!c(n, index_id, starts_with("PersOrg")))

# A tibble: 5 × 2
     id name        
  <dbl> <chr>       
1    41 John+Ascroft
2    46 Rick+Boucher
3    56 Adam+J+Ereli
4    92 CHRIS+PATTEN
5   123 Daniel+Senor

Matching on Levenschtein Distance

joinJRC_lv <- fuzzyjoin::stringdist_inner_join(x = jrc_1, y = jrc_2, 
distance_col = "distance", by = "name", ignore_case = TRUE,
method = "lv",  max_dist = 2) %>% 
  relocate(name.x, name.y, distance) %>% select(!c(n.x, n.y, index_id.x, starts_with("PersOrg")))
joinJRC_lv

# A tibble: 17 × 6
   name.x                  name.y                distance  id.x  id.y index_id.y
   <chr>                   <chr>                    <dbl> <dbl> <dbl>      <dbl>
 1 John+Ashcroft           John+Ascroft                 1    41    41          2
 2 Adam+Ereli              Adam+J+Ereli                 2    56    56          2
 3 Chris+Patten            CHRIS+PATTEN                 0    92    92          2
 4 Peter+Hain              PETER+HAIN                   0   159   159          2
 5 Roberto+Castelli        Robero+Castelli              1   173   173          2
 6 Shaukat+Sultan          Shaukat+Sultán               1   174   174          2
 7 Gerhard+Mayer+Vorfelder Gerhard+Mayer-Vorfel…        1   196   196          2
 8 Johannes+Rau            JOHANNES+RAU                 0   202   202          2
 9 Roland+Koch             ROLAND+KOCH                  0   215   215          2
10 Jesus+Caldera           Jesús+Caldera                1   231   231          2
11 Martin+Bartenstein      Martin+Barteinstein          1   241   241          2
12 Klaus+Zumwinkel         Klaus+Zumwinckel             1   252   252          2
13 Philip+Green            Phillip+Green                1   253   253          2
14 Umberto+Agnelli         UMBERTO+AGNELLI              0   259   259          2
15 Marco+Follini           MARCO+FOLLINI                0   284   284          2
16 Bernard+Thibault        BERNARD+THIBAULT             0   292   292          2
17 Seamus+Brennan          Séamus+Brennan               1   339   339          2

Matching on cosine distance between qgrams

joinJRC12_cosine <- fuzzyjoin::stringdist_inner_join(x = jrc_1, y = jrc_2, distance_col = "distance",
          by = "name", ignore_case = TRUE, 
           method = "cosine",
          q = 1,
          max_dist = 0.15,
          ) %>% relocate(name.x, name.y, distance) %>% select(!c(n.x, n.y, index_id.x, starts_with("PersOrg")))
joinJRC12_cosine

# A tibble: 20 × 6
   name.x                  name.y                distance  id.x  id.y index_id.y
   <chr>                   <chr>                    <dbl> <dbl> <dbl>      <dbl>
 1 John+Ashcroft           John+Ascroft            0.0277    41    41          2
 2 Richard+Boucher         Rick+Boucher            0.1       46    46          2
 3 Adam+Ereli              Adam+J+Ereli            0.0551    56    56          2
 4 Chris+Patten            CHRIS+PATTEN            0         92    92          2
 5 Dan+Senor               Daniel+Senor            0.0955   123   123          2
 6 Peter+Hain              PETER+HAIN              0        159   159          2
 7 Roberto+Castelli        Robero+Castelli         0.0186   173   173          2
 8 Shaukat+Sultan          Shaukat+Sultán          0.0383   174   174          2
 9 Gerhard+Mayer+Vorfelder Gerhard+Mayer-Vorfel…   0.0165   196   196          2
10 Johannes+Rau            JOHANNES+RAU            0        202   202          2
11 Roland+Koch             ROLAND+KOCH             0        215   215          2
12 Jesus+Caldera           Jesús+Caldera           0.0526   231   231          2
13 Martin+Bartenstein      Martin+Barteinstein     0.0105   241   241          2
14 Klaus+Zumwinkel         Klaus+Zumwinckel        0.0230   252   252          2
15 Philip+Green            Phillip+Green           0.0227   253   253          2
16 Umberto+Agnelli         UMBERTO+AGNELLI         0        259   259          2
17 Thomas+Kean             Tom+Kean                0.117    274   274          2
18 Marco+Follini           MARCO+FOLLINI           0        284   284          2
19 Bernard+Thibault        BERNARD+THIBAULT        0        292   292          2
20 Seamus+Brennan          Séamus+Brennan          0.0392   339   339          2

Matching on Jaccard distance

joinJRC12_jaccard <- fuzzyjoin::stringdist_inner_join(x = jrc_1, y = jrc_2,
distance_col = "distance",  by = "name", ignore_case = TRUE, method = "jaccard",
          q = 1, max_dist = 0.18) %>% 
  relocate(name.x, name.y, distance) %>% select(!c(n.x, n.y, index_id.x, starts_with("PersOrg")))
joinJRC12_jaccard

# A tibble: 16 × 6
   name.x                  name.y                distance  id.x  id.y index_id.y
   <chr>                   <chr>                    <dbl> <dbl> <dbl>      <dbl>
 1 John+Ashcroft           John+Ascroft            0         41    41          2
 2 Adam+Ereli              Adam+J+Ereli            0.111     56    56          2
 3 Chris+Patten            CHRIS+PATTEN            0         92    92          2
 4 Peter+Hain              PETER+HAIN              0        159   159          2
 5 Roberto+Castelli        Robero+Castelli         0        173   173          2
 6 Shaukat+Sultan          Shaukat+Sultán          0.1      174   174          2
 7 Gerhard+Mayer+Vorfelder Gerhard+Mayer-Vorfel…   0.0714   196   196          2
 8 Johannes+Rau            JOHANNES+RAU            0        202   202          2
 9 Roland+Koch             ROLAND+KOCH             0        215   215          2
10 Martin+Bartenstein      Martin+Barteinstein     0        241   241          2
11 Klaus+Zumwinkel         Klaus+Zumwinckel        0.0769   252   252          2
12 Philip+Green            Phillip+Green           0        253   253          2
13 Umberto+Agnelli         UMBERTO+AGNELLI         0        259   259          2
14 Marco+Follini           MARCO+FOLLINI           0        284   284          2
15 Bernard+Thibault        BERNARD+THIBAULT        0        292   292          2
16 Seamus+Brennan          Séamus+Brennan          0.1      339   339          2