oxbow | Swing UI Enhacements | Widget library

by eugener Java Version: Current License: BSD-3-Clause

kandi X-RAY | oxbow Summary

oxbow is a Java library typically used in User Interface, Widget, Spring Boot applications. oxbow has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However oxbow build file is not available. You can download it from GitHub.

SwingBits is a collection of useful components and utilities for Java Swing Toolkit. The documentation can be found in the Project Wiki.

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Quality

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oxbow has a low active ecosystem.
It has 136 star(s) with 61 fork(s). There are 19 watchers for this library.
It had no major release in the last 6 months.
There are 21 open issues and 25 have been closed. On average issues are closed in 37 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of oxbow is current.

oxbow Support

Best in #Widget

Average in #Widget

oxbow Support

Best in #Widget

Average in #Widget

quality kandi Quality

oxbow has 0 bugs and 0 code smells.

oxbow Quality

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Average in #Widget

oxbow Quality

Best in #Widget

Average in #Widget

security Security

oxbow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
oxbow code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.

oxbow Security

Best in #Widget

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oxbow Security

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license License

oxbow is licensed under the BSD-3-Clause License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.

oxbow License

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oxbow License

Best in #Widget

Average in #Widget

build Reuse

oxbow releases are not available. You will need to build from source code and install.
oxbow has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
oxbow saves you 2337 person hours of effort in developing the same functionality from scratch.
It has 5102 lines of code, 653 functions and 80 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.

oxbow Reuse

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oxbow Reuse

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Top functions reviewed by kandi - BETA

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

Build the content pane .

Build the dialog .

Overrides the default implementation to get the text for this cell .

Paint an icon .

Handles a mouse click event .

Updates the size of the popup .

Build the text .

Setup the header renderers .

Sets the distinct column filter .

Draws the resizer .

Get all kandi verified functions for this library.

oxbow Key Features

oxbow Examples and Code Snippets

Community Discussions

Trending Discussions on oxbow

Adding error bars to a clustered bar graph in R with ggplot

Trending Discussions on oxbow

QUESTION

Adding error bars to a clustered bar graph in R with ggplot

Asked 2020-Jan-13 at 22:55

I am trying to create a clustered bar graph with error bars for my independent variables (y axis) for each of my dependent variables (metrics on the x axis).

My data:

df <- data.frame (Parameter_Estimate=c('Burnham','Calumet','Northerly','Orland','Hickory Hammock','Lake O','MacArthur','Site E','Corrales','Oxbow','Tingley','Galilee','Jacobs Point','Prudence Island','Town Pond','Trial 1','Trial 2','2017','2018','Spring','Summer','0.5m','1m','Chicago','Florida','New Mexico','Rhode Island'),
     Species.Richness=c(79.4, -20.6, -12.6, -4, -63.15, -66.4, -69.15, -70.65, -52.07, -36.07, -67.23, -67.98, -69.9, -70.53, -74.73, 30.877, -2.743, 29.346, 0.9053, 29.721, -0.266, 28.898, 2.697, 70.1, -58.04, -42.49, -61.51),
     FQI=c(29.272,-0.271,-1.095,-2.652,-20.24,-19.66,-21.2,-22.56,-10.36,-5.158,-18.66,-8.802,-8.431,-7.372,-15.25,18.474,-0.14,17.869,2.1378,17.809,0.9189,18.155,1.004,28.268,-19.91,-10.39,-9.196),
     MeanC=c(3.294,0.4964,0.2252,-0.212,-0.631,-0.299,-0.339,-0.758,0.3185,0.3831,-0.135,2.7739,3.5218,4.089,3.1736,4.0308,0.1061,4.003,0.3103,4.0638,0.0266,4.0943,-0.052,3.4215,-0.634,0.0615,3.1985),
     Per.Non.Nat=c(11.912,-3.756,7.666,10.192,14.888,71.874,-4.33,-3.784,-6.288,9.517,3.31,-10.91,-11.65,-11.88,-6.388,16.086,-1.423,14.33,4.372,12.659,4.25,15.582,-0.702,15.437,16.136,-1.346,13.58),
     Shannon= c(3.6465,-0.449,-0.216,-0.4,-2.219,-2.897,-2.712,-2.504,-1.507,-0.798,-2.428,-2.066,-2.233,-2.528,-3.289,1.9313,-0.052,1.8939,0.268,1.8433,0.0975,1.8852,0.086,3.3804,-2.317,-1.311,-2.263),
     Weight.MeanC=c(3.3643,1.0304,0.4379,-0.428,-1.906,-2.679,-1.352,-1.106,1.8142,0.7625,0.1685,4.6151,5.1463,4.3978,3.6472,4.3178,0.0385,4.4046,-0.273,4.3543,-0.028,4.3357,-0.011,3.6243,-2.021,0.6551,4.1965),
     SE1=c(2.697,3.814,3.814,3.814,4.045,4.045,4.045,4.045,3.21,3.21,3.21,3.21,3.21,3.438,3.21,12.194,1.89,12.185,2.2041,12.299,2.0314,12.394,2.271,2.249,3.374,2.805,2.713))

Where SE1 is the standard error for each of my independent variables' data points for Species Richness (I haven't had the chance to add the other SE values for my other metrics, I'm just trying to experiment with the one for now).

I've created a figure using this code:

mm <- melt (df, id.vars='Parameter_Estimate')

ggplot (mm, aes (x=Parameter_Estimate, y=value) + geom_bar (stat='identity') + coord_flip()+facet_grid (.~variable) + labs (x=',y=')+scale_x_discrete (limits=df$Parameter_Estimate) +
  geom_errorbarh (data=df, aes (y=Species.Richness, xmin=SE1, xmax=SE1), size=5, color="blue",  inherit.aes = FALSE)

The barplot is not correct (SE1 is now one of the metrics along the x axis), and the error bars are not following my data for Species Richness.

Figure:

I'm unsure of what I'm doing wrong with the error bars and how to fix it!

ANSWER

Answered 2020-Jan-13 at 22:55

You can remove the extra SE1 column by excluding it from melt:

mm <- melt (df[,-ncol(df)], id.vars='Parameter_Estimate')

Then for the error bar to appear in the Species Richness facet, you need to specify variable for it, with same levels as mm above:

se_df = df[,c("Parameter_Estimate","Species.Richness","SE1")]
se_df$variable="Species.Richness"
se_df$variable=factor(se_df$variable,levels=levels(mm$variable))

Now we plot:

ggplot (mm, aes (x=Parameter_Estimate, y=value)) + 
geom_bar (stat='identity') + 
facet_grid (~variable) + labs (x='',y='')+
geom_errorbar(data=se_df, inherit.aes=FALSE,aes (x=Parameter_Estimate,
ymin=Species.Richness-SE1, ymax=Species.Richness+SE1),
size=1, color="blue")+
scale_x_discrete(limits=df$Parameter_Estimate)+
coord_flip()

Source https://stackoverflow.com/questions/59725015

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

Vulnerabilities

No vulnerabilities reported

Install oxbow

You can download it from GitHub.
You can use oxbow like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the oxbow component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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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