QualCoder | Qualitative data analysis for text , images , audio , video | Machine Learning library

by ccbogel Python Version: 3.2 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | QualCoder Summary

QualCoder is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. QualCoder has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However QualCoder has 7 bugs. You can download it from GitHub.

QualCoder is a qualitative data analysis application written in python3 (python 3.6 or newer versions) and pyqt5. Text files can be typed in manually or loaded from txt, odt, docx, html, htm, epub and pdf files. Images, video and audio can also be imported for coding. Codes can be assigned to text, images and a/v selections and grouped into categories in hierarchical fashion. Various types of reports can be produced including visual coding graphs, coder comparisons and coding frequencies. This project has been tested under Ubuntu 20.04 and Windows 10. It has been used on MacOS and various Linux distros. Instructions and other information are available here: and on the Github Wiki. NOTE Transcriptions created with QualCoder 2.8 and newer will have a .txt file ending. These transcription files will not be recoginsed in older QualCoder versions (2.7 and earlier). You will have to change the transcription name ending from '.txt' to '.transcribed' before opening any audio/video files to view or code if opening the project in older versions. In Sql Statements run this to have older versions recognise transcriptions (replace video.mp4 with the actual filename you have): update source set name='video.mp4.transcribed' where name='video.mp4.txt'.

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Support

Quality

Security

License

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Support

QualCoder has a low active ecosystem.

It has 208 star(s) with 46 fork(s). There are 16 watchers for this library.

There were 1 major release(s) in the last 6 months.

There are 9 open issues and 637 have been closed. On average issues are closed in 17 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of QualCoder is 3.2

QualCoder Support

Best in #Machine Learning

Average in #Machine Learning

QualCoder Support

Best in #Machine Learning

Average in #Machine Learning

Quality

QualCoder has 7 bugs (5 blocker, 0 critical, 2 major, 0 minor) and 1436 code smells.

QualCoder Quality

Best in #Machine Learning

Average in #Machine Learning

QualCoder Quality

Best in #Machine Learning

Average in #Machine Learning

Security

QualCoder has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

QualCoder code analysis shows 0 unresolved vulnerabilities.

There are 35 security hotspots that need review.

QualCoder Security

Best in #Machine Learning

Average in #Machine Learning

QualCoder Security

Best in #Machine Learning

Average in #Machine Learning

License

QualCoder is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

QualCoder License

Best in #Machine Learning

Average in #Machine Learning

QualCoder License

Best in #Machine Learning

Average in #Machine Learning

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QualCoder releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

QualCoder saves you 13007 person hours of effort in developing the same functionality from scratch.

It has 39553 lines of code, 1890 functions and 107 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

QualCoder Reuse

Best in #Machine Learning

Average in #Machine Learning

QualCoder Reuse

Best in #Machine Learning

Average in #Machine Learning

Top functions reviewed by kandi - BETA

kandi has reviewed QualCoder and discovered the below as its top functions. This is intended to give you an instant insight into QualCoder implemented functionality, and help decide if they suit your requirements.

Updates the positions of the text

Highlight the text
Removes formatting
Update annotation annotations

Import project data

Convert a datetime into a datetime object
Write INI file

Setup the UI

Translates the UI text

Update the positions of the text

Highlight the document

Creates the text edit menu for the given position

Loads text from file

Performs a search on a document

Creates a table

Creates a text edit menu

Generate a picture

Setup UI

Export the project

Populate the table menu

Updates the positions in the text editor

Save the current graph

Import project

Select attribute names

Select attributes

Overrides drop event

Make a heatmap

Get all kandi verified functions for this library.

QualCoder Key Features

Qualitative data analysis for text, images, audio, video. Cross platform. Python 3.7 or newer and PyQt6.

QualCoder Examples and Code Snippets

No Code Snippets are available at this moment for QualCoder.

Community Discussions

Trending Discussions on QualCoder

R: Output columns as text

Trending Discussions on QualCoder

R: Output columns as text

QUESTION

R: Output columns as text

Asked 2021-Sep-05 at 15:09

Background: I have survey results in an SPSS .sav file. Some of the survey questions were open, where respondents could type their own responses. In SPSS, I can just select one or more columns, and the responses will be output as a text file, with the responses grouped by each question (column contents), and each response separated by a blank line. This text file can then be used for thematic analysis by assigning code to phrases or sentences in the text.

I can't seem to find an easy way of doing the same thing in R. All the usual export formats output as a table. Selecting the columns in RStudio gives text output where each the responses are grouped by respondent rather than by column.

Toy example:

library(labelled)
library(tidyverse)

comments<-tibble(
  shakey=as.character(c("To be or not to be", "", "Alas poor Yorick", "", "Is this a dagger that I see before me?", "A rose by any other name")), 
  versey=as.character(c("", "The boy stood on the burning deck", "", "Oft in the stilly night", "", "Lars Porsena of Clusium, by the nine gods he swore"))
  )

var_label(comments$shakey)<-"Can you quote some Shakespeare?"
var_label(comments$versey)<-"Can you quote some poetry?"

The output I want from this is:

*Can you quote some Shakespeare?*

To be or not to be

Alas poor Yorick

Is this a dagger that I see before me?

A rose by any other name

*Can you quote some poetry?*

The boy stood on the burning deck

Oft in the stilly night

Lars Porsena of Clusium, by the nine gods he swore

with the column label as the heading and each column's non-blank responses listed one after the other, separated by a blank line.

The closest I've come so far is:

comlong<-pivot_longer(comments, everything(),
                     names_to="question",
                     values_to="response") %>%
  arrange(question) %>% 
  filter(response!="")

but while that gets all the responses in one column, it needs a bit of editing to get it into the desired format above, which is non-trivial on more extensive data.

Final result:

Akrun's additional summarise line is, I think, the most elegant. Tweaking that gives output very similar to SPSS:

comments %>%
  summarise(across(everything(), ~ c(paste0(sprintf('**%s**', cur_column()), "\n\n", sprintf('*%s*', var_label(.))), .))) %>%
  pivot_longer(cols = everything(), 
               names_to = 'question', 
               values_to = 'response') %>% 
  arrange(question) %>% 
  filter(response != '') %>% 
  select(response) %>% 
  write.table("comments.md",quote=FALSE, eol="\n\n", row.names=FALSE, col.names=FALSE)

This inserts the column name as well as the label (because I found that sometimes the labels are not sufficiently descriptive), and outputs it as a markdown file, which can be ingested for thematic coding in (eg) Qualcoder. Running:

pandoc comments.md -o comments.odt

will also produce word-processor output if you need it.

ANSWER

Answered 2021-Sep-05 at 15:09

We could use summarise with across first to extract the var_label and append by concatenating, then use pivot_longer

library(labelled)
library(dplyr)
library(tidyr)
comments %>% 
    summarise(across(everything(), ~ c(var_label(.), .))) %>% 
    pivot_longer(cols = everything(), names_to = 'question', 
        values_to = 'response') %>% 
    arrange(question) %>% 
    filter(response != '')

-output

# A tibble: 9 x 2
  question response                                          
                                                   
1 shakey   Can you quote some Shakespeare?                   
2 shakey   To be or not to be                                
3 shakey   Alas poor Yorick                                  
4 shakey   Is this a dagger that I see before me?            
5 shakey   A rose by any other name                          
6 versey   Can you quote some poetry?                        
7 versey   The boy stood on the burning deck                 
8 versey   Oft in the stilly night                           
9 versey   Lars Porsena of Clusium, by the nine gods he swore

or if we are first reshapeing first, there is an option as well i.e. do a group by 'question' and subset the first value of 'question' to extract the var_label from the original data and concatenate

pivot_longer(comments, everything(),
                     names_to="question",
                     values_to="response") %>%
  arrange(question) %>% 
  filter(response!="") %>% 
  group_by(question) %>% 
  summarise(response = c(var_label(comments[[first(question)]]), 
     response), .groups = 'drop')

-output

# A tibble: 9 x 2
  question response                                          
                                                   
1 shakey   Can you quote some Shakespeare?                   
2 shakey   To be or not to be                                
3 shakey   Alas poor Yorick                                  
4 shakey   Is this a dagger that I see before me?            
5 shakey   A rose by any other name                          
6 versey   Can you quote some poetry?                        
7 versey   The boy stood on the burning deck                 
8 versey   Oft in the stilly night                           
9 versey   Lars Porsena of Clusium, by the nine gods he swore

Source https://stackoverflow.com/questions/69058628

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install QualCoder

You can download it from GitHub.
You can use QualCoder like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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