ReLiS allows you to export different information from the project.
To export the information, open the Classification phase of your project. Under the Result menu, select Export.
Click on corresponding Update file
to generate the latest version of the file you wish to export.
Classification
This is a CSV file of the classification table with all the data extracted. Each row correponds to a reference. The columns correspond to the categories listed in the classification form. Categories, for which multiple values are specified, are separated with a |
.
Papers
This is a CSV file of all the references imported in the project. The columns are: the reference ID, the BibTeX key, the title, the URL of the reference, the preview of the reference in HTML format, the abstract, the year published, and the search strategy.
Papers (BibTeX)
This is a BibTeX file of all the references imported in the project.
Papers included (BibTeX)
This is a BibTeX file of all the references included after the last screening phase.
Papers excluded (BibTeX)
This is a BibTeX file of all the references excluded after the last screening phase.
Papers Excluded Screening(CSV)
This is a CSV file of all the references excluded after the last screening phase. The columns are: the reference ID, the BibTeX key, the title, the preview of the reference in HTML format, the search strategy, the user who excluded the reference, the exclusion criteria selected, and the note left by the user as a comment.
Papers Excluded Classification (CSV)
This is a CSV file of all the references excluded only during the classification phase. The columns are: the reference ID, the BibTeX key, the title, the preview of the reference in HTML format, the user who excluded the reference, the exclusion criteria selected, the note left by the user as a comment, and the search strategy.
R configuration script
This redirects you to the R export configuration page. See the Export to R section below for more information on that page. Clicking on Generate R redirects you to the Exports page. From there, you can download the R scripts which you can run using R to compute different statistics on the results of the classification.
This page allows you to specify the type of variable scale for each category. We support the following scales:
Continuous
: For variables that are numeric and continuous in nature. This is typically forint
andreal
simple categories.Nominal
: For categorical variables. This is typically forlists
anddynamic lists
, including if they have multiple values. But it can also be used for other categories, such assimple bool
.Text
: For textual fields. If this scale type is assigned to a category, it will be excluded from all of the statistical tests and plots.
R Library
This is an R script file that is required for the R configuration script.
Export to R
You can export the results of the classification to R and run statistical analysis on them. To this end, you must download the following files exported from your project: Classification, R configuration script, and R Library.
The file to execute in R is the R configuration script. It contains a list of commented lines which consist of all possible statistical tests and plots available in the R library. Uncomment the lines plots/tests you wish to consider then run the script.
Note: You may need to change the path to the R library file based on your download locations. On line 1 of the R configuration script, make sure it states the proper name name and path to the R Library file you downloaded:
source("relis_r_lib_demo1_relis.R") # Replace this with the name of your imported library file
On line 7 of the R Library file, make sure it states the proper name and path to the classification file you downloaded:
relis_data <- read.csv("relis_classification_demo1_relis.csv", header = TRUE) # Replace this with the name of your imported data file
The R library supports the following plots and statistical tests. They are accessible by using the correpsonding variable. For example, statistics_vector[['Temperature']]
outputs the descriptive statistics of the Temperature
variable (this variable corresponds to a numerical category in the data extraction form.
Descriptive statistics
- Descriptive frequency tables (for all nominal variables): use the
desc_distr_vector
variable - Bar plots (for all nominal variables): use the
bar_plot_vector
variable - Statistics (for all continuous variables): use the
statistics_vector
variable - Box plots (for all continuous variables): use the
box_plot_vector
variable - Violin plots (for all continuous variables): use the
violin_plot_vector
variable
Evolution statistics
- Evolution frequency tables (for each nominal variables, with respect to the publication years of the references): use the
evo_distr_vector
variable - Evolution plots (for all nominal variables, with respect to the publication years of the references): use the
evolution_plot_vector
variable
Comparative statistics
- Comparative frequency tables (for all nominal variables): use the
comp_distr_vector
variable - Stacked bar plots (for all nominal variables): use the
stacked_bar_plot_vector
variable - Grouped bar plots (for all nominal variables): use the
grouped_bar_plot_vector
variable - Bubble charts (for all nominal variables): use the
bubble_chart_vector
variable - Fisher's exact test (for all nominal variables): use the
fisher_exact_test_vector
variable - Shapiro Wilk's test (for all continuous variables): use the
shapiro_wilk_test_vector
variable - Pearson's correlation test (for all continuous variables): use the
pearson_cor_test_vector
variable - Spearman's correlation test (for all continuous variables): use the
spearman_cor_test_vector
variable
Export to Python
You can export the results of the classification to Python and run statistical analysis on them. When you generate (button "update file") the environment, you will then be able to download the files required to run the statistical analysis on your local machine.
The file to execute in R is the R configuration script. It contains a list of commented lines which consist of all possible statistical tests and plots available in the R library. Uncomment the lines plots/tests you wish to consider then run the script.
Note: Every file you need will be downloaded inside the ZIP file. You should have a .csv, containing all your data, and 2 python scripts, the kernel and the playground, that are needed to generate a proper statistical analysis environment. Make sure every file are inside the same folder when the scripts are executed.
The Python statistical environment supports the following plots and statistical tests. They are accessible by using the correpsonding variable. For example, evo_plots([Variables.cocoa_origin], True)
outputs the evolutive statistics of the cocoa_origin
variable (this variable corresponds to a nominal category in the data extraction form).
Descriptive statistics
- Descriptive frequency tables (for all nominal variables): use the
desc_frequency_table
variable - Bar plots (for all nominal variables): use the
desc_bar_plot
variable - Statistics (for all continuous variables): use the
desc_statistics
variable - Box plots (for all continuous variables): use the
desc_box_plot
variable - Violin plots (for all continuous variables): use the
desc_violin_plot
variable
Evolution statistics
- Evolution frequency tables (for each nominal variables, with respect to the publication years of the references): use the
evo_frequency_table
variable - Evolution plots (for all nominal variables, with respect to the publication years of the references): use the
evo_plot
variable
Comparative statistics
- Comparative frequency tables (for all nominal variables): use the
comp_frequency_table
variable - Stacked bar plots (for all nominal variables): use the
comp_stacked_bar_plot
variable - Grouped bar plots (for all nominal variables): use the
comp_grouped_bar_plot
variable - Bubble charts (for all nominal variables): use the
comp_bubble_chart
variable - Chi Squared test (for all nominal variables): use the
comp_chi_squared_test
variable - Shapiro Wilk's test (for all continuous variables): use the
comp_shapiro_wilk_test
variable - Pearson's correlation test (for all continuous variables): use the
comp_pearson_cor_test
variable - Spearman's correlation test (for all continuous variables): use the
comp_spearman_cor_test
variable
Note: Originally this was "Fisher's exact test", but this test had some problems, so we replaced it with chi_squared for an alternative solution.