Navigate
Home Compendium
Exercises
Tidyverse packages
dplyr ggplot2 tidyr stringr lubridate purrr forcats readr broom Tidyverse R for Data Science
Deep dives
dplyr Joins dplyr Window Functions dplyr group_by ggplot2 Themes ggplot2 Facets ggplot2 Color Scales ggplot2 Bar Charts ggplot2 Heatmaps tidyr Pivot tidyr Nest/Unnest Regex
Wrangling & EDA
Data Wrangling Data Cleaning EDA Data Visualization data.table dbplyr / SQL Web Scraping API Calls
Statistics
Hypothesis Testing Linear Regression Logistic Regression Correlation Probability Distributions
Machine Learning
Machine Learning tidymodels Random Forest XGBoost Clustering PCA Cross-Validation
Time Series
Time Series ARIMA
By Industry
Finance Marketing Analytics Healthcare Biostatistics Genomics Sports Analytics A/B Testing
Reporting & Apps
Shiny R Markdown
Levels
R for Beginners R Interview Questions
R r‑statistics.co

tidyr hoist() in R: Extract Specific List Column Elements

The hoist() function in tidyr extracts SPECIFIC named elements from a list column into new top-level columns. Unlike unnest_wider() which spreads ALL elements, hoist picks only what you ask for.

By Selva Prabhakaran  ·  Published May 11, 2026  ·  Last updated May 11, 2026
⚡ Quick Answer
df |> hoist(json_col, name = "name", age = "age")
df |> hoist(json_col, city = list("address", "city"))   # deep path
df |> hoist(json_col, n = "count", .remove = FALSE)
df |> unnest_wider(json_col)              # different: ALL elements

Need explanation? Read on for examples and pitfalls.

📊 Is hoist() the right tool?
STARTextract a few SPECIFIC fields from list columnhoist()extract ALL fieldsunnest_wider()deep nested pathhoist(col, x = list("a","b","c"))preserve original list column.remove = FALSEvectors -> rowsunnest_longer()

What hoist() does in one sentence

hoist(data, col, ...) extracts SPECIFIC named (or pathed) elements from a list column into new columns; the original list column is removed by default. Unlike unnest_wider, you specify exactly which fields to extract.

Syntax

hoist(data, col, ..., .remove = TRUE, .ptype = NULL). ... is new_col_name = "field_name" or new_col_name = list(path).

Run live
Run live, no install needed. Every R block on this page runs in your browser. Click Run, edit the code, re-run instantly. No setup.
RExtract specific fields
library(tidyr) library(dplyr) df <- tibble( user = c("a","b"), details = list( list(age = 30, city = "NYC", country = "US"), list(age = 25, city = "LA", country = "US") ) ) df |> hoist(details, age = "age", city = "city") #> user age city #> 1 a 30 NYC #> 2 b 25 LA

  

  





Tip
Use hoist when you only need a few fields from a deeply nested list column. unnest_wider extracts ALL fields, which is often more than you want for downstream analysis.



Five common patterns



1. Extract two specific fields




  

    
RJust age and city

    
df |> hoist(details, age = "age", city = "city")

    

      
      
    

    

  

  





2. Deep path via list




  

    
RNested object access

    
df <- tibble(
  user = "a",
  data = list(list(profile = list(address = list(city = "NYC"))))
)

df |>
  hoist(data, city = list("profile", "address", "city"))
#>   user city
#> 1    a  NYC

    

      
      
    

    

  

  





The list path navigates nested structures.



3. Keep the original list column




  

    
RDon't remove, keep for further extraction

    
df |> hoist(details, age = "age", .remove = FALSE)
#> Both age column AND details list column survive

    

      
      
    

    

  

  





4. Specify type




  

    
RForce the new column type

    
df |> hoist(details, age = "age", .ptype = list(age = integer()))

    

      
      
    

    

  

  





5. Combine with unnest_wider for hybrid




  

    
RHoist some, unnest the rest

    
df |>
  hoist(details, age = "age", .remove = FALSE) |>
  unnest_wider(details)

    

      
      
    

    

  

  





Key Insight
hoist is more efficient than unnest_wider when you only need a few fields. unnest_wider creates a column for every name in every list cell; hoist creates only the columns you ask for. For wide JSON with 50+ fields and you only need 3, hoist is much cleaner.



hoist() vs unnest_wider() vs purrr::map







Function
Extracts
Best for






hoist(col, x = "x")
Specific named fields
A few fields from many




unnest_wider(col)
ALL named fields
Most fields needed




purrr::map_chr(col, "x")
One field, vector output
Quick scalar extraction







When to use which:



    


  • hoist for selective extraction with deep paths.
    • 


  • unnest_wider when you want everything.
    • 


  • purrr::map_* for one-off vector extraction.
    • 





A practical workflow



Use hoist for selective JSON parsing in API response pipelines.




  

    
RInteractive R

    
library(jsonlite)

api_responses |>
  mutate(parsed = lapply(json_str, fromJSON)) |>
  hoist(parsed,
        user_id = "user_id",
        name    = list("user", "name"),
        email   = list("user", "contact", "email"))

    

      
      
    

    

  

  





Extract only the fields you need; ignore the rest of the JSON.



Common pitfalls



Pitfall 1: deep path syntax. Use list("a", "b", "c") for nested paths, not "a.b.c". Strings are field names; lists are path navigators.



Pitfall 2: missing fields. If a list cell lacks the requested field, the new column has NA for that row. Useful for sparse data.



Warning
hoist() with .remove = TRUE (default) removes the source list column. Pass .remove = FALSE if you want to do further extraction or keep the original.



Try it yourself





Try it: Extract only "age" from a list column. Save to ex_age.



  

    
RYour turn: hoist age

    
df <- tibble(
  user = c("a","b","c"),
  info = list(list(age = 30, city = "NYC"),
               list(age = 25, city = "LA"),
               list(age = 40, city = "SF"))
)

ex_age <- df |>
  # your code here

ex_age
#> Expected: user, age columns (no city, no info)

    

      
      
    

    

  

  





Click to reveal solution



  

    
RSolution

    
ex_age <- df |>
  hoist(info, age = "age")

ex_age
#>   user age
#> 1    a  30
#> 2    b  25
#> 3    c  40

    

      
      
    

    

  

  





Explanation: hoist extracts only the "age" field; the rest of info is discarded (because .remove = TRUE).










After mastering hoist, look at:



    


  • unnest_wider(): extract all fields
    • 


  • unnest_longer(): vectors to rows
    • 


  • purrr::map_chr() / map_dbl(): scalar extraction
    • 


  • jsonlite::fromJSON(): JSON to R objects
    • 


  • pluck(): deep list navigation
    • 





FAQ



What does hoist do in tidyr?



hoist(data, col, ...) extracts specific named elements from a list column into new columns. Unlike unnest_wider, it picks only what you specify.



What is the difference between hoist and unnest_wider?



hoist extracts SPECIFIC fields (you name them). unnest_wider extracts ALL fields. hoist is selective; unnest_wider is comprehensive.



How do I extract a deeply nested field with hoist?



Use list("path","to","field"): hoist(col, x = list("a", "b", "c")) navigates obj$a$b$c.



Does hoist remove the original list column?



By default yes (.remove = TRUE). Pass .remove = FALSE to keep it.



What happens if a field is missing in some cells?



That row's new column gets NA. hoist tolerates missing fields gracefully.



          

        


        
      


      

        

        
© 2016-2026 Selva Prabhakaran.
          This work is licensed under the Creative Commons License.
        

        

          About ·
          RSS