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Explore all Dataset open source software, libraries, packages, source code, cloud functions and APIs.

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2.1.0

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Release v4.0.0 Release Candidate 2

datasets

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gods

v1.18.1

doccano

v1.6.2

geolib

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h3

Release v4.0.0 Release Candidate 2

Popular Libraries in Dataset

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1

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3

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1

19 Libraries

670

2

18 Libraries

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3

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292

4

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480

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13 Libraries

1607

6

12 Libraries

2237

7

12 Libraries

150

8

11 Libraries

1120

9

11 Libraries

1044

10

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330

Trending Kits in Dataset

go-dataset

6 best Go Dataset

Go is a statically typed, compiled programming language designed at Google by Robert Griesemer, Rob Pike, and Ken Thompson. Go is a modern programming language which provides the perfect combination of simplicity and performance. It's often used to build scalable web apps and APIs. Golang is one of the fastest-growing programming languages in the software industry. It has a lot of advantages that make it stand out among other languages. Go is a general-purpose language designed with systems programming in mind. It is strongly typed and garbage-collected and has explicit support for concurrent programming. The go-dataset is a data access layer that provides a consistent API across different data stores ranging from SQL databases to NoSQL databases and also files. It also provides utilities to work with existing database. Popular Go Dataset open source libraries among developers include: DNSGrep - Quickly Search Large DNS Datasets; commonspeak2 - Leverages publicly available datasets from Google BigQuery; datashim - kubernetes based framework for hassle free handling.

java-dataset

8 best Java Dataset

Java is an object-oriented programming language for applications and websites that was first released by Oracle in 1995. Data is a very important part of business. If a business does not have data, it will not be able to grow its revenue. In the past, businesses used to collect data manually from their users. Nowadays, companies use computer programs to gather data from their clients. These programs are called "datasets". Datasets are a structured collection of data which can be used for storing tabular, non-tabular and hierarchical data. Java ecosystem has many libraries and frameworks to help developers to manage data at scale. Data are the foundation of all research. They play a pivotal role in various fields such as data science and machine learning. Developers tend to use some of the following Java Dataset open source libraries: hollow - java library and toolset for disseminating in memory datasets; MiDaS - Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular De; mongolastic - dataset migration tool.

Trending Discussions on Dataset

    How do I unpack tuple format in R?
    react-chartjs-2 with chartJs 3: Error "arc" is not a registered element
    TypeError: load() missing 1 required positional argument: 'Loader' in Google Colab
    AttributeError: Can't get attribute 'new_block' on <module 'pandas.core.internals.blocks'>
    Configuring compilers on Mac M1 (Big Sur, Monterey) for Rcpp and other tools
    Group and create three new columns by condition [Low, Hit, High]
    Create new column based on existing columns whose names are stored in another column (dplyr)
    Select previous and next N rows with the same value as a certain row
    Is it possible to combine a ggplot legend and table
    Merge separate divergent size and fill (or color) legends in ggplot showing absolute magnitude with the size scale

QUESTION

How do I unpack tuple format in R?

Asked 2022-Mar-12 at 08:23

Here is the dataset.

1library(data.table)
2
3x <- structure(list(id = c("A", "B" ),
4                    segment_stemming = c("[('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]", 
5                                          "[('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]" )), 
6               row.names = c(NA, -2L), 
7               class = c("data.table", "data.frame" ))
8
9x
10# id                                                     segment_stemming
11# 1:  A               [('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]
12# 2:  B [('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]
13
14

I would like to split the tuple into rows. Here is my expected outcome.

1library(data.table)
2
3x <- structure(list(id = c("A", "B" ),
4                    segment_stemming = c("[('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]", 
5                                          "[('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]" )), 
6               row.names = c(NA, -2L), 
7               class = c("data.table", "data.frame" ))
8
9x
10# id                                                     segment_stemming
11# 1:  A               [('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]
12# 2:  B [('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]
13
14id             segment_stemming
15A              ('Brownie', 'Noun')
16A              ('From', 'Josa')
17A              ('Pi', 'Noun')
18B              ('Dung-caroon-gye', 'Noun')
19B              ('in', 'Josa')
20B              ('innovation', 'Noun')
21

I've searched the tuple format using R but cannot find out any clue to make the outcome.

ANSWER

Answered 2022-Mar-11 at 11:17

Here's a way using separate_rows:

copy icondownload icon

1library(data.table)
2
3x <- structure(list(id = c("A", "B" ),
4                    segment_stemming = c("[('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]", 
5                                          "[('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]" )), 
6               row.names = c(NA, -2L), 
7               class = c("data.table", "data.frame" ))
8
9x
10# id                                                     segment_stemming
11# 1:  A               [('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]
12# 2:  B [('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]
13
14id             segment_stemming
15A              ('Brownie', 'Noun')
16A              ('From', 'Josa')
17A              ('Pi', 'Noun')
18B              ('Dung-caroon-gye', 'Noun')
19B              ('in', 'Josa')
20B              ('innovation', 'Noun')
21library(tidyverse)
22
23x %>% 
24  mutate(segment_stemming = gsub("\\[|\\]", "", segment_stemming)) %>% 
25  separate_rows(segment_stemming, sep = ",\\s*(?![^()]*\\))")
26
27# A tibble: 6 x 2
28  id    segment_stemming           
29  <chr> <chr>                      
301 A     ('Brownie', 'Noun')        
312 A     ('From', 'Josa')           
323 A     ('Pi', 'Noun')             
334 B     ('Dung-caroon-gye', 'Noun')
345 B     ('in', 'Josa')             
356 B     ('innovation', 'Noun') 
36

One way to get a better result, with some manipulation (unnest_wider is not necessary).

copy icondownload icon

1library(data.table)
2
3x <- structure(list(id = c("A", "B" ),
4                    segment_stemming = c("[('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]", 
5                                          "[('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]" )), 
6               row.names = c(NA, -2L), 
7               class = c("data.table", "data.frame" ))
8
9x
10# id                                                     segment_stemming
11# 1:  A               [('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]
12# 2:  B [('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]
13
14id             segment_stemming
15A              ('Brownie', 'Noun')
16A              ('From', 'Josa')
17A              ('Pi', 'Noun')
18B              ('Dung-caroon-gye', 'Noun')
19B              ('in', 'Josa')
20B              ('innovation', 'Noun')
21library(tidyverse)
22
23x %>% 
24  mutate(segment_stemming = gsub("\\[|\\]", "", segment_stemming)) %>% 
25  separate_rows(segment_stemming, sep = ",\\s*(?![^()]*\\))")
26
27# A tibble: 6 x 2
28  id    segment_stemming           
29  <chr> <chr>                      
301 A     ('Brownie', 'Noun')        
312 A     ('From', 'Josa')           
323 A     ('Pi', 'Noun')             
334 B     ('Dung-caroon-gye', 'Noun')
345 B     ('in', 'Josa')             
356 B     ('innovation', 'Noun') 
36x %>% 
37  mutate(segment_stemming = gsub("\\[|\\]", "", segment_stemming)) %>% 
38  separate_rows(segment_stemming, sep = ",\\s*(?![^()]*\\))") %>% 
39  mutate(segment_stemming = segment_stemming %>% 
40           str_remove_all("[()',]") %>% 
41           str_split(" ")) %>% 
42  unnest_wider(segment_stemming)
43
44# A tibble: 6 x 3
45  id    ...1            ...2 
46  <chr> <chr>           <chr>
471 A     Brownie         Noun 
482 A     From            Josa 
493 A     Pi              Noun 
504 B     Dung-caroon-gye Noun 
515 B     in              Josa 
526 B     innovation      Noun 
53

Source https://stackoverflow.com/questions/71437352

Community Discussions contain sources that include Stack Exchange Network

    How do I unpack tuple format in R?
    react-chartjs-2 with chartJs 3: Error "arc" is not a registered element
    TypeError: load() missing 1 required positional argument: 'Loader' in Google Colab
    AttributeError: Can't get attribute 'new_block' on <module 'pandas.core.internals.blocks'>
    Configuring compilers on Mac M1 (Big Sur, Monterey) for Rcpp and other tools
    Group and create three new columns by condition [Low, Hit, High]
    Create new column based on existing columns whose names are stored in another column (dplyr)
    Select previous and next N rows with the same value as a certain row
    Is it possible to combine a ggplot legend and table
    Merge separate divergent size and fill (or color) legends in ggplot showing absolute magnitude with the size scale

QUESTION

How do I unpack tuple format in R?

Asked 2022-Mar-12 at 08:23

Here is the dataset.

1library(data.table)
2
3x <- structure(list(id = c("A", "B" ),
4                    segment_stemming = c("[('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]", 
5                                          "[('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]" )), 
6               row.names = c(NA, -2L), 
7               class = c("data.table", "data.frame" ))
8
9x
10# id                                                     segment_stemming
11# 1:  A               [('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]
12# 2:  B [('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]
13
14

I would like to split the tuple into rows. Here is my expected outcome.

1library(data.table)
2
3x <- structure(list(id = c("A", "B" ),
4                    segment_stemming = c("[('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]", 
5                                          "[('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]" )), 
6               row.names = c(NA, -2L), 
7               class = c("data.table", "data.frame" ))
8
9x
10# id                                                     segment_stemming
11# 1:  A               [('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]
12# 2:  B [('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]
13
14id             segment_stemming
15A              ('Brownie', 'Noun')
16A              ('From', 'Josa')
17A              ('Pi', 'Noun')
18B              ('Dung-caroon-gye', 'Noun')
19B              ('in', 'Josa')
20B              ('innovation', 'Noun')
21

I've searched the tuple format using R but cannot find out any clue to make the outcome.

ANSWER

Answered 2022-Mar-11 at 11:17

Here's a way using separate_rows:

copy icondownload icon

1library(data.table)
2
3x <- structure(list(id = c("A", "B" ),
4                    segment_stemming = c("[('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]", 
5                                          "[('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]" )), 
6               row.names = c(NA, -2L), 
7               class = c("data.table", "data.frame" ))
8
9x
10# id                                                     segment_stemming
11# 1:  A               [('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]
12# 2:  B [('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]
13
14id             segment_stemming
15A              ('Brownie', 'Noun')
16A              ('From', 'Josa')
17A              ('Pi', 'Noun')
18B              ('Dung-caroon-gye', 'Noun')
19B              ('in', 'Josa')
20B              ('innovation', 'Noun')
21library(tidyverse)
22
23x %>% 
24  mutate(segment_stemming = gsub("\\[|\\]", "", segment_stemming)) %>% 
25  separate_rows(segment_stemming, sep = ",\\s*(?![^()]*\\))")
26
27# A tibble: 6 x 2
28  id    segment_stemming           
29  <chr> <chr>                      
301 A     ('Brownie', 'Noun')        
312 A     ('From', 'Josa')           
323 A     ('Pi', 'Noun')             
334 B     ('Dung-caroon-gye', 'Noun')
345 B     ('in', 'Josa')             
356 B     ('innovation', 'Noun') 
36

One way to get a better result, with some manipulation (unnest_wider is not necessary).

copy icondownload icon

1library(data.table)
2
3x <- structure(list(id = c("A", "B" ),
4                    segment_stemming = c("[('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]", 
5                                          "[('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]" )), 
6               row.names = c(NA, -2L), 
7               class = c("data.table", "data.frame" ))
8
9x
10# id                                                     segment_stemming
11# 1:  A               [('Brownie', 'Noun'), ('From', 'Josa'), ('Pi', 'Noun')]
12# 2:  B [('Dung-caroon-gye', 'Noun'), ('in', 'Josa'), ('innovation', 'Noun')]
13
14id             segment_stemming
15A              ('Brownie', 'Noun')
16A              ('From', 'Josa')
17A              ('Pi', 'Noun')
18B              ('Dung-caroon-gye', 'Noun')
19B              ('in', 'Josa')
20B              ('innovation', 'Noun')
21library(tidyverse)
22
23x %>% 
24  mutate(segment_stemming = gsub("\\[|\\]", "", segment_stemming)) %>% 
25  separate_rows(segment_stemming, sep = ",\\s*(?![^()]*\\))")
26
27# A tibble: 6 x 2
28  id    segment_stemming           
29  <chr> <chr>                      
301 A     ('Brownie', 'Noun')        
312 A     ('From', 'Josa')           
323 A     ('Pi', 'Noun')             
334 B     ('Dung-caroon-gye', 'Noun')
345 B     ('in', 'Josa')             
356 B     ('innovation', 'Noun') 
36x %>% 
37  mutate(segment_stemming = gsub("\\[|\\]", "", segment_stemming)) %>% 
38  separate_rows(segment_stemming, sep = ",\\s*(?![^()]*\\))") %>% 
39  mutate(segment_stemming = segment_stemming %>% 
40           str_remove_all("[()',]") %>% 
41           str_split(" ")) %>% 
42  unnest_wider(segment_stemming)
43
44# A tibble: 6 x 3
45  id    ...1            ...2 
46  <chr> <chr>           <chr>
471 A     Brownie         Noun 
482 A     From            Josa 
493 A     Pi              Noun 
504 B     Dung-caroon-gye Noun 
515 B     in              Josa 
526 B     innovation      Noun 
53

Source https://stackoverflow.com/questions/71437352