Throughout this book we work with “tibbles” instead of R’s traditional data.frame.Tibbles are data frames, but they tweak some older behaviours to make life a little easier. The first columns are the grouping variables, followed by a list column of tibbles with matching rows of the remaining columns. over-estimate and remove the extra columns in a subsequent step than to unnest_auto() inspects the inner names of the list-col: If all elements are unnamed, it uses unnest_longer(), If all elements are named, and there's at least one name in course, it has. But data frame are not limited to atomic vectors. hoist () allows you to selectively pull components of a list-column out in to their own top-level columns, using the same syntax as purrr::pluck () . json to a rectangling, collapsing deeply nested lists into regular columns. Finally, we can use a chain of Nesting creates a list-column of data frames; unnesting flattens it back out into regular columns.Nesting is a implicitly summarising operation: you get one row for each group defined by the non-nested columns.This is useful in conjunction with other summaries that work with whole datasets, most notably models. How can you tell if an object is a tibble? Getting the raw data in the format that data_sep is what I primarily Start Exercise a list column of length one. If a column evaluates to a data frame or tibble, it is nested or spliced. read_csv() and read_tsv() are special cases of the general read_delim(). We can get rid of the warning by providing an appropriate value for When plucking with a To have a nicer printed output in the console use the as_tibble() function and create a tibble object out of it. They can host general vectors, i.e. The variable "leagues.season.startDate" implicitly encodes three Grouped data frames The primary use case for group_nest() is with already grouped data frames, typically a result of group_by() . R Nested Data Frame Example. Exploring that question in Biontech/Pfizer’s vaccine trial, Deploying an R Shiny app on Heroku free tier, Forecasting Time Series ARIMA Models (10 Must-Know Tidyverse Functions #5), BlueSky Statistics Intro and User Guides Now Available, RObservations #4 Using Base R to Clean Data, What’s the most successful Dancing With the Stars “Profession”? To have a nicer printed output in the console use the as_tibble() function and create a tibble object out of it. assuming a nested tibble y y <- tibble(a=purrr::rerun(10,tibble(x=purrr::rerun(100,data.frame(xx=rnorm(10)))))) is there a way to pluck directly from depth d an element? Combining unlist() and tibble::enframe(), we are able to get a Visualizing with {gt}, Junior Data Scientist / Quantitative economist, Data Scientist – CGIAR Excellence in Agronomy (Ref No: DDG-R4D/DS/1/CG/EA/06/20), Data Analytics Auditor, Future of Audit Lead @ London or Newcastle, python-bloggers.com (python/data-science news), Boosting nonlinear penalized least squares, 13 Use Cases for Data-Driven Digital Transformation in Finance, MongoDB and Python – Simplifying Your Schema – ETL Part 2, MongoDB and Python – Inserting and Retrieving Data – ETL Part 1, Building a Data-Driven Culture at Bloomberg, Click here to close (This popup will not appear again). fromJSON() package only reduces the mess a bit. variables—"leagues", "season", and "startDate"—each deserving of under-estimate and lose data because there are not enough columns to put following solution to put me on the right path. output data frame: unnest_wider() preserves the rows, but changes the columns. These principles guide their behaviour when they are called with a In R, vectors are the most common data structure. You can pluck by name with a character To construct a list you use the function list(): R is an old language, and some things that were useful 10 or 20 years ago now get in your way. 2. matrix, poly,ts, table 3. the tibble that it creates from a list. We can do that by identifying the name with Or if you unnest_longer() a list of data (I only print out some of the top-level tibble() builds columns sequentially. 6.3 Nesting. so say you have a list column in a tibble which consists of tibbles. For unnamed vectors, the natural sequence is used as name column. A nested data frame is a data frame where one (or more) columns is a list of data frames. of columns to create with separate(). You can create simple nested data frames by hand: Given the format of the implicit variable sin the “name” column, We can Tibble is the central data structure for the set of packages known as the tidyverse, including dplyr, ggplot2, tidyr, and readr. Two of my students (who’ve learnt R in the tidyverse era) immediately suggested that I should be using first. Tidyr’s nest() offers help in more general group-wise operations. While this tibble is still not in a tidy format—there are variables variables suffixed with. See vctrs::vec_as_names() for more details on these terms and the hoist(), unnest_longer(), and unnest_wider() provide tools for data. Instead, you’ll create them from regular columns, using one of three methods: With tidyr::nest() to convert a grouped data frame into a nested data frame where you have list-column of data frames. read_delim.Rd. In tidyverse/tidyr: Tidy Messy Data. Use this argument if you want to check each elements to avoid cluttering the page.). with lots of NA values (corresponding to rows that don’t have the (I o… Of #>, Toothless dragon How to Train You… How to Train Your Dragon: …, #> character species color films Let us create our first list! There are two kinds of vectors: atomic vectors and lists. Let’s being with importing the package(s) that we’ll need. Used to check that output data frame has valid unnest_auto() picks between unnest_wider() or unnest_longer() The traditional R base functions read.table(), read.delim() and read.csv() import data into R as a data frame. As you’ll see, different kinds of vectors can hold different kinds of elements. tidy (nice!) filter and wrangle the data to generate a final, presentable format. And there we have it! Code review; Project management; Integrations; Actions; Packages; Security would-have-been-nested elements are joined by “.” in the “name” column, and the values associated with these elements are in the “value” column. (Hint: try printing mtcars, which is a regular data frame). Description. Also, I didn’t need the as_tibble, but I’ve been teaching my students to work with tibbles, so I wanted to do that here as well. Next, we’ll create a variable for the url from which we will get thedata. frames, the number of rows must be preserved, so each column is turned into With mutate() and vectorised functions that return a list. It is a bad idea to use bootstrapping as the outer resampling procedure (see the example below) implicitly stored in the “name” column rather than in their own The column names must be unique in a call to hoist(), although existing broad set of JSON-related “problems”. data.frame/tibble that is should be much easier to work with. For example, if you unnest_wider() a list of data The url here will request the scores for week 1 of the 2018 NFLseason from ESPN’s “secret”API. vector, by position with an integer vector, or with a combination of the Why GitHub? These libraries transform JSON data automatically into nested R lists or complex data frames. and the values associated with these elements are in the “value” column. Name of column to store vector values. Site built by pkgdown. 1, For this demonstration, I’ll start out by scraping National Football (e.g. Typically, you won’t create list-columns with tibble(). "unique": make sure names are unique and not empty. With these columns created, we can use tidyr::fill() and with _id suffix. based heuristics described below. In this book, we’ll often represent vectors like this: Each orange cell represents one element of the vector. from .col. Rectangling is the art and craft of taking a deeply nested list (often sourced from wild caught JSON or XML) and taming it into a tidy data set of rows and columns. As you’ll see, different kinds of vectors can hold different kinds of elements. Tibbles are a specific kind of list. useful for converting nested (nasty!) seeking to get the scores from the 16 games in week 1 of the NFL’s 2018 See purrr::pluck() for details. List-columns and the data frame that hosts them require some special handling. {dplyr} We get a warning indicating when using separate() because we have (Note that "warn" is the default value dplyr::filter() in a strategic manner to get rid of all the NAs Defaults to TRUE when col This isin contrast with tibble(), which builds a tibble from individual columns.as_tibble() is to tibble() as base::as.data.frame() is tobase::data.frame(). deframe () converts two-column data frames to a named vector or list, using the first column as name and the second column as value. Several libraries exist for working with JSON data in R, such as rjson, rjsonio and jsonlite. Could look at printing, e.g. However, after using another handy Otherwise, it falls back to unnest_longer(indices_include = TRUE). 2. #>, Dory blue tang blue Finding Nemo 4 transform tables the tidyverse cookbook 4 transform tables the tidyverse cookbook how to unlist a nested list in r data 4 transform tables the tidyverse cookbook. 2. separate()’s fill argument. The tbl_df class is a subclass of data.frame, created in order to have different default behaviour.The colloquial term "tibble" refers to a data frame that has the tbl_df class. The traditional R base functions read.table(), read.delim() and read.csv() import data into R as a data frame. element has the types you expect when simplifying. their own column.). Note that, If you want to take our free Intro to R course, here is the link. However, while this action gets rid of the warning, it does not actually Details. To customize the table font, you can set a font on the page, or on the table itself: The url here will request the scores for week 1 of the 2018 NFL Optionally, a named list of transformation functions lists as well. In particular, it is highly advantageous if the data frame is a tibble, which anticipates list-columns. tidyr is a part of the tidyverse, an ecosystem of packages designed with common APIs and a shared philosophy. How can we work with the NAs to get a final format League (NFL) 2018 regular season week 1 score Additionally, we can drop the dummy name View source: R/rectangle.R. hoist() allows you to selectively pull components of a list-column out in to their own top … And now, the actual HTTP GET request for the data (using the 1. Learn more at tidyverse.org. nest() creates a list of data frames containing all the nested variables: this seems to be the most useful form in practice. non-primary data type. Browse other questions tagged r tidyr nested-lists purrr tibble or ask your own question. (very) long data.frame without any nested elements! in this case i would want to get back a long data.frame of xx's with concatenated id's from each parent level. wanted to show. inner names or position (if not named) of the values. data from ESPN, which involves lots of nested unnest_longer() turns each element of a list-column into a row. (Reminder: We’re The results include a column for the outer data split objects, one or more id columns, and a column of nested tibbles called inner_resamples with the additional resamples.. of the fill argument.). Default: Other inputs are first coerced with base::as.d… unnest() can change both rows and columns. One could go on and try some other functions from the {jsonlite} "check_unique": (the default), no name repair, but check they are unique, "universal": make the names unique and syntactic. hoist() allows you to selectively pull components of a list-column out Hi community, I'd like to modify the first value (numeric) of a nested list in a tibble by adding another numeric variable. data.frame!). strategies used to enforce them. common acros all components, it uses unnest_wider(). (This is the crux of what I want to show.) Typically, you won’t create list-columns with tibble(). what the data set is that you are working with. If a string, the inner and outer names will be paste together using My investigations so far have led me to believe list_modify is the function that will get me there, but I can't figure out how to modify by list position rather than list name. believe that the techniques that I demonstrate are generalizable to a A nested data frame is a data frame where one (or more) columns is a list of data frames. assuming a nested tibble y y <- tibble(a=purrr::rerun(10,tibble(x=purrr::rerun(100,data.frame(xx=rnorm(10)))))) is there a way to pluck directly from depth d an element? If TRUE, will attempt to simplify lists of However, these final However, after using another handy{httr} function—content()—to extract the data, we see that the datais an nasty nested format! Well, after some struggling, I stumbled upon the Everything seems to be going well. with my specification of (dummy) column names with the into argument, Here is a simple tutorial on how to unlist a nested list with the help of R. Problems may appear when nested lists are a different length for each record. week of regular season games in the 2018 NFL regular season. frame, the number of columns must be preserved so it creates a packed {httr} function—content()—to extract the data, we see that the data In the vector functions unit, you learned that mutate() creates new columns by creating vectors that contain an element for each row in the tibble. they are theoretically pleasing. (These are the default column names that tibble::enframe() assigns to the tibble that it creates from a list.) The Overflow Blog Podcast 298: A Very Crypto Christmas I say that it’s a secret because it’s API documentation is out of date. data in its raw form. I guessed that there we would need 10 columns. regular season.) the “separated” data in. You saw that you can do any of the following to create this vector: Give mutate() a single value, which is then repeated for each row in the tibble. … Basics. hoist(), unnest_longer(), and unnest_wider() provide tools for rectangling, collapsing deeply nested lists into regular columns. Even if one does not care for sports and knows nothing about the NFL, I However, working with these complex objects can be difficult. The tidyjson package takes a different approach to structuring JSON data into tidy data frames. Grouped data frames The primary use case for group_nest() is with already grouped data frames, typically a result of group_by() . In this book, we’ll often represent vectors like this: Each orange cell represents one element of the vector. #>, Toothless dragon black How to Train Your Dragon 2 Add an index column? in to their own top-level columns, using the same syntax as purrr::pluck(). hoist(df, col, "x") For example, chat sessions and corresponding lists of conversations that differ in length. There are two kinds of vectors: atomic vectors and lists. columns that we created with the tidyr::separate() call before. Developed by Hadley Wickham. Defaults to col. A string giving the name of column which will contain the everything up to this point would have an analogous action no matter is short-hand for hoist(df, col, x = "x"). filtered for in the step above. I need to do this by position as the list elements have different names in different rows. #>, # unnest_longer() is useful when each component of the list should, # Automatically creates names if widening. is an nasty nested format! the most number of .s. “over-estimated” how many columns we will need to create. #>, Toothless dragon black How to Train Your Dragon: The Hidden World as_tibble()is an S3 generic, with methods for: 1. data.frame: Thin wrapper around the listmethodthat implements tibble's treatment of rownames. With mutate() and vectorised functions that return a list. To create nested tables, use reactable() ... library data <-as_tibble (MASS:: ... (This may explain why tables look different in R Markdown documents or Shiny apps vs. standalone pages). By my interpretation, this data_sep variable is in tidy format. cluttering our tibble. See examples. Everything seems to be going well. However, the most modern R package readr provides several functions (read_delim(), read_tsv() and read_csv()), which are faster than R base functions and import data into R as a tbl_df (pronounced as “tibble … Value. In R, vectors are the most common data structure. (These are the default column names that tibble::enframe() assigns to I'm not sure how if these behaviours are useful in practice, but could not figure out how to use it to get the result that I wanted.). tidyr_legacy: use the name repair from tidyr 0.8. a formula: a purrr-style anonymous function (see rlang::as_function()). has inner names. two with a list. was unable to figure out a nice way of getting a data.frame(). For a list, the result will be a nested tibble with a column of type list . Column names are not modified. in this case i would want to get back a long data.frame of xx's with concatenated id's from each parent level. After Jenny Bryan’s fantastic PlotCon presentation Data Rectangling, I started thinking what would a d3.js hierarchy look like as a nested tibble. columns—it’s in a much more user-friendly format (in my opinion). Copyright © 2020 | MH Corporate basic by MH Themes, http://www.espn.com/nfl/scoreboard/_/year/2018/seasontype/2/week/1, Click here if you're looking to post or find an R/data-science job, PCA vs Autoencoders for Dimensionality Reduction, The Mathematics and Statistics of Infectious Disease Outbreaks, R – Sorting a data frame by the contents of a column, the riddle(r) of the certain winner losing in the end, Basic Multipage Routing Tutorial for Shiny Apps: shiny.router, Reverse Engineering AstraZeneca’s Vaccine Trial Press Release, Visualizing geospatial data in R—Part 1: Finding, loading, and cleaning data, xkcd Comics as a Minimal Example for Calling APIs, Downloading Files and Displaying PNG Images with R, To peek or not to peek after 32 cases? "How to Train Your Dragon: The Hidden World", # Turn all components of metadata into columns, #> character species color films applied to each component. With this number (7) identified, we can now choose the “correct” number read_csv() and read_tsv() are special cases of the general read_delim(). Tibbles are a specific kind of list. API. #>, #> character species first_film third_film metadata Given the nature of the data, we might hope that the output type of each component. Read a delimited file (including csv & tsv) into a tibble Source: R/read_delim.R. Must be one of the following options: "minimal": no name repair or checks, beyond basic existence. Use this function if you want transform or Description. Nonetheless, there’s more to the story! actions to get a pretty output. An tibble with nested_cv class and any other classes that outer resampling process normally contains. #>, Toothless dragon black How to Train Your Dragon Exercise: Convert data frame to Tibble speed dist 1 4 2 2 4 10 3 7 4 [ reached 'max' / getOption("max.print") -- omitted 47 rows ] The data frame cars reports the speed of cars and distances taken to stop. This is what I call a list-column. A nested data frame is a data frame where one (or more) columns is a list of data frames. Creating a list. is not to say that there is something wrong with the package—I simply My investigations so far have led me to believe list_modify is the function that will get me there, but I can't figure out how to modify by list position rather than list name. This ensures that each value lives only in one place. Posted on October 19, 2018 by r on Tony ElHabr in R bloggers | 0 Comments, In this “how-to” post, I want to detail an approach that others may find You can create simple nested data frames by hand: df1 <- tibble ( g = c ( 1 , 2 , 3 ), data = list ( tibble ( x = 1 , y = 2 ), tibble ( x = 4 : 5 , y = 6 : 7 ), tibble ( x = 10 ) ) ) df1 #> # A tibble: 3 x 2 #> g data #> #> 1 1 #> 2 2 #> 3 3 {jsonlite} 10.5: Exercises. Here is all code altogether and additional explanations below. Optionally, a named list of prototypes declaring the desired Components of .col to turn into columns in the form that is actually presentable? col_name = "pluck_specification". hoist (), unnest_longer (), and unnest_wider () provide tools for rectangling, collapsing deeply nested lists into regular columns. package will save us here. {httr} names_sep as a separator. actions are unique to this specific data. (This Because I The equivalent code using first would be Why 10? If TRUE, the default, will remove extracted components Next, we’ll create appropriately named columns for the values that we Only columns of length one are recycled. So, what to do now? Features →. package (or another JSON-related package), but, in my own attempts, I However, straightforward usage of it’s names. as is. Defaults to col Hi community, I'd like to modify the first value (numeric) of a nested list in a tibble by adding another numeric variable. Then, I’ll work towards getting the data in a workable format (a As a note to the reader, I don’t recommend suffixing variable names with numbers as I do in the next couple of step (i.e. You can create simple nested data frames by hand: df1 <- tibble( g = c(1, 2, 3), data = list( tibble(x = 1, y = 2), tibble(x = 4:5, y = 6:7), tibble(x = 10) ) ) df1 #> # A tibble: 3 x 2 #> g data #> #> 1 1 #> 2 2 #> 3 3 . 3 And now, the actual HTTP GET request for the data (using the{httr}package’s appropriately named GET()function). When defining a column, you can refer to columns created earlier in the call. package’s appropriately named GET() function). The first columns are the grouping variables, followed by a list column of tibbles with matching rows of the remaining columns. List-columns are expressly anticipated and do not require special tricks. Finally, I’ll Let’s being with importing the package(s) that we’ll need. Hopefully someone out there will find the technique(s) shown in this ), While this tibble is still not in a tidy format—there are variables parse individual elements as they are hoisted. maturing as_tibble() turns an existing object, such as a data frame ormatrix, into a so-called tibble, a data frame with class tbl_df. Next, we’ll create a variable for the url from which we will get the 3. enframe () converts named atomic vectors or lists to one- or two-column data frames. If NULL, the default, the names will be left resolve the underlying issue—specifying the correct number of columns to Exercise: Convert data frame to Tibble speed dist 1 4 2 2 4 10 3 7 4 [ reached 'max' / getOption("max.print") -- omitted 47 rows ] The data frame cars reports the speed of cars and distances taken to stop. Note that the only prints first 15 rows and enough variables where you can see them all, or by checking explicitly the class function 23. would-have-been-nested elements are joined by “.” in the “name” column, A nice, tidy tibble with the scores of the first unnest_longer() preserves the columns, but changes the rows. I need to do this by position as the list elements have different names in different rows. unnest_wider () turns each element of a list-column into a column, and unnest_longer () turns each element of a list-column … Note that we’ll still be left Instead, you’ll create them from regular columns, using one of three methods: With tidyr::nest() to convert a grouped data frame into a nested data frame where you have list-column of data frames. 10.1 Introduction. The three unnest() functions differ in how they change the shape of the be the last time I write about something of this nature. unnest_wider() turns each element of a list-column into a column, and read_csv2() uses ; for the field separator and , for the decimal point. I should note that it is likely that This is expected. columns with the same name will be overwritten. # But you'll usually want to provide names_sep. expected that 10 would be more than I needed, and it’s better to After Jenny Bryan’s fantastic PlotCon presentation Data Rectangling, I started thinking what would a d3.js hierarchy look like as a nested tibble. They're useful for reading the most common types of flat file data, comma separated values and tab separated values, respectively. They're useful for reading the most common types of flat file data, comma separated values and tab separated values, respectively. View source: R/rectangle.R. Description Usage Arguments Unnest variants unnest_auto() heuristics Examples. single string you can choose to omit the name, i.e. use tidyr::separate() to create columns for each. Creating a List in R. Practice Lists in R by using course material from DataCamp's Intro to R course. create with separate(). masuzi December 16, 2020 Uncategorized 0. maximum number of variables). We continue by filter the tibble for only the rows that we will need. column. Rectangle a nested list into a tidy tibble. 4. Start Exercise Basics. Personally, I find web scraping to be fascinating, so I doubt this will length-1 vectors to an atomic vector. However, the most modern R package readr provides several functions (read_delim(), read_tsv() and read_csv()), which are faster than R base functions and import data into R as a tbl_df (pronounced as “tibble … ; Explicitly give mutate() a vector with an element for each row in the tibble. season from ESPN’s “secret” post to be useful for an endeavor of their own. Printed output in the console use the as_tibble ( ) ’ s being with importing the package s. Tab separated values, respectively frame that hosts them require some special handling atomic vector things that were useful or! And read.csv ( ) function and create a variable for the url from which we will need to.! Or by checking Explicitly the class function 23, read.delim ( ) ’ s API documentation out... Ll often represent vectors like this: each orange cell represents one element of the argument. Has valid names choose to omit the name, i.e there will find the technique s! ) import data into tidy data frames by hand: in tidyverse/tidyr: tidy Messy data ) special! To each component create list-columns with tibble ( ) package only reduces mess. If you want to get back a long data.frame of xx 's with concatenated id from... Tibble::enframe ( ) to an atomic vector ’ t create list-columns with tibble ( ) read.csv! Integrations ; actions ; Packages ; Security value rows and columns of.s formula: a Very Christmas... These terms and the data frame where one ( or more ) columns is part! Help in more general group-wise operations from each parent level that we’ll need ago now in... Other questions tagged R tidyr nested-lists purrr tibble or ask your own question you want get! Using first, it falls back to unnest_longer ( ) assigns to the tibble how many columns we will.. Ll often represent vectors like this: each orange cell represents one of... These complex objects can be difficult inner and outer names will be paste together using names_sep a! Only in one place common types of flat file data, comma separated and... Some of the vector for in the tibble that it ’ s a secret because it ’ s fromJSON )! This argument if you want transform or parse individual elements as they are..: R/read_delim.R unnamed vectors r nested list to tibble the default, the default column names must be unique in a tibble which of! Data.Frame! ) when simplifying to show. ) and some things that were 10! Pretty output together using names_sep as a separator appropriately named columns for decimal... Source: R/read_delim.R, presentable format names in different rows you can create simple nested data frame is list. ; actions ; Packages ; Security value data.frame of xx 's with id. A different approach to structuring JSON data automatically into nested R lists or complex frames. We ’ ll often represent vectors like this: each orange cell represents one of! Podcast 298: a Very Crypto Christmas Basics ): R nested frame. And enough variables where you can see them all, or by checking Explicitly the class function 23 be. Libraries exist for working with these complex objects can be difficult and jsonlite filter. Be one of the vector Hint: try printing mtcars, which is a regular frame. Me on the right path 2018 NFLseason from ESPN’s “secret”API rectangling, collapsing deeply nested lists regular... By hand: in tidyverse/tidyr: tidy Messy data and vectorised functions return! Natural sequence is used as name column the dummy name columns that we will to. Games in the tidyverse era ) immediately suggested that I should be using first lives only in one.! Is nested or spliced lists of length-1 vectors to an atomic vector common types of flat data. Of it ’ s fromJSON ( ) and vectorised functions that return a list. ) an endeavor of own! That differ in length they 're useful for an endeavor of their own ) a vector with element. Week 1 of the 2018 NFLseason from ESPN’s “secret”API element of the general (. Elements to avoid cluttering the page. ) is the default column names that tibble:enframe! Get in your way transform JSON data automatically into nested R lists or complex data frames is should be easier. Changes the rows that we created with the same name will be overwritten or parse individual elements as are. Tibble which consists of tibbles with matching rows of the 2018 NFL from! S fill argument. ) data automatically into nested R lists or complex data.. The link Blog Podcast 298: a purrr-style anonymous function ( see rlang::as_function ( ) provide for! 10 or 20 years ago now get in your way ll need deeply... Creates from a list of r nested list to tibble frames value of the following solution put! Do that by identifying the name, i.e same name will be a nested data.... List, the natural sequence is used as name column data frame or tibble, which is data! Raw data in R, vectors are the default column names that tibble::enframe ( import... Basic existence do not require special tricks repair or checks, beyond r nested list to tibble..., comma separated values, respectively browse other questions tagged R tidyr nested-lists purrr tibble or ask own... Page. ) values that we will get the data in R, such as,! See, different kinds of vectors: atomic vectors and lists ’ t create list-columns with (! Type list. ) get in your way appropriately named columns for the field separator and, the. & tsv ) into a tibble object out of it, there s. Of transformation functions applied to each component remove extracted components from.col be useful for reading the most common structure... `` warn '' is the link the as_tibble ( ) offers help more. Is highly advantageous if the data to generate a final format that data_sep is what I primarily to! Elements have different names in different rows parse individual elements as they are called with a string!
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