flanker | Python email address and Mime | Email library

 by   mailgun Python Version: v0.9.15 License: Apache-2.0

kandi X-RAY | flanker Summary

flanker is a Python library typically used in Messaging, Email applications. flanker has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.
Python email address and Mime parsing library
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                      kandi-support Support

                        summary
                        flanker has a medium active ecosystem.
                        summary
                        It has 1574 star(s) with 199 fork(s). There are 91 watchers for this library.
                        summary
                        There were 1 major release(s) in the last 6 months.
                        summary
                        There are 52 open issues and 36 have been closed. On average issues are closed in 129 days. There are 16 open pull requests and 0 closed requests.
                        summary
                        It has a neutral sentiment in the developer community.
                        summary
                        The latest version of flanker is v0.9.15
                        flanker Support
                          Best in #Email
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                                  kandi-Quality Quality

                                    summary
                                    flanker has 0 bugs and 0 code smells.
                                    flanker Quality
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                                        Average in #Email
                                        flanker Quality
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                                              kandi-Security Security

                                                summary
                                                flanker has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
                                                summary
                                                flanker code analysis shows 0 unresolved vulnerabilities.
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                                                There are 0 security hotspots that need review.
                                                flanker Security
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                                                          kandi-License License

                                                            summary
                                                            flanker is licensed under the Apache-2.0 License. This license is Permissive.
                                                            summary
                                                            Permissive licenses have the least restrictions, and you can use them in most projects.
                                                            flanker License
                                                              Best in #Email
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                                                                flanker License
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                                                                      kandi-Reuse Reuse

                                                                        summary
                                                                        flanker releases are available to install and integrate.
                                                                        summary
                                                                        Build file is available. You can build the component from source.
                                                                        summary
                                                                        flanker saves you 4291 person hours of effort in developing the same functionality from scratch.
                                                                        summary
                                                                        It has 9098 lines of code, 790 functions and 88 files.
                                                                        summary
                                                                        It has high code complexity. Code complexity directly impacts maintainability of the code.
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                                                                                  Top functions reviewed by kandi - BETA
                                                                                  kandi has reviewed flanker and discovered the below as its top functions. This is intended to give you an instant insight into flanker implemented functionality, and help decide if they suit your requirements.
                                                                                  • Parse an address
                                                                                    • Unquote a string
                                                                                    • Lift a parse result
                                                                                    • Convert val to text
                                                                                  • Validate a list
                                                                                    • Validate an address specification
                                                                                    • Lookup the mail exchange for a given domain
                                                                                    • Get the global cache
                                                                                  • Signs a message
                                                                                    • Parse an email address
                                                                                  • Return the size in bytes
                                                                                  • Match style parameters
                                                                                  • Match new style
                                                                                  • Get the content type
                                                                                  • Set the charset
                                                                                  • Create an instance from a stream
                                                                                  • Generate a new email address
                                                                                  • Return a list of MessageId
                                                                                  • Write headers to stream
                                                                                  • Sign a message
                                                                                  • Validate an email address
                                                                                  • Create an attachment
                                                                                  • Suggest an address for an address
                                                                                  • Apply a function to the headers
                                                                                  • The body
                                                                                  • Converts a message into a string
                                                                                  • Returns the filename of the MIME header
                                                                                  Get all kandi verified functions for this library.
                                                                                  Get all kandi verified functions for this library.

                                                                                  flanker Key Features

                                                                                  Python email address and Mime parsing library

                                                                                  flanker Examples and Code Snippets

                                                                                  An example experiment
                                                                                  Jupyter Notebookdot imgLines of Code : 7dot imgLicense : Permissive (Apache-2.0)
                                                                                  copy iconCopy
                                                                                  
                                                                                                                      train_11-1-1-1-1_total_pool_contrast_areafactor_lr0.001.sh train_11-1-1-1-1_total_pool_contrast_areafactor_lr0.01.sh train_11-1-1-1-1_total_pool_contrast_areafactor_lr0.1.sh train_11-1-1-1-1_total_pool_contrast_None_lr0.001.sh train_11-1-1-1-1_total_pool_contrast_None_lr0.01.sh train_11-1-1-1-1_total_pool_contrast_None_lr0.1.sh train_all.sh
                                                                                  Community Discussions

                                                                                  Trending Discussions on flanker

                                                                                  How to calculate conditional means from .txt files
                                                                                  chevron right
                                                                                  x-axis labels ggplot2 in R
                                                                                  chevron right
                                                                                  Changing font size on collated plot
                                                                                  chevron right
                                                                                  JSON navigation
                                                                                  chevron right
                                                                                  GGPlot combining/overlaying column and line (Gantt) charts
                                                                                  chevron right

                                                                                  QUESTION

                                                                                  How to calculate conditional means from .txt files
                                                                                  Asked 2021-Dec-16 at 01:01

                                                                                  I'm fairly new to programming and am looking for some guidance. Any help is appreciated.

                                                                                  Here's what I'm trying to do: I have a large number of .txt files from a cognitive experiment (Flanker task, if curious) that I need to compute means for based on condition. The files have no headers and look like below:

                                                                                  XXXXX 1 1 675
                                                                                  XXYXX 0 1 844
                                                                                  YYYYY 1 1 599
                                                                                  YYXYY 0 1 902

                                                                                  I would like to compute means for miliseconds (rightmost column; c4) based on the experimental condition (0 or 1; c2). I would also need the file name of each .txt file (my participant ID) included in the output.

                                                                                  I'm most familiar with R but really just for data analysis. I also have a little experience with Python and Matlab if those (or something else) better suit my needs. Again, a point in any direction would be greatly appreciated.

                                                                                  Thanks

                                                                                  ANSWER

                                                                                  Answered 2021-Dec-16 at 00:46

                                                                                  The Tidyverse collection of packages specially the dplyr and readr can easy do this task for you on a grammar likely SQL.

                                                                                  Something like

                                                                                  #loading packages
                                                                                  library(tidyverse)
                                                                                  
                                                                                  #importing data
                                                                                  df <- read_delim("file.txt", delim="|", col_names=c("col1", "col2", "col3", "col4"))
                                                                                  
                                                                                  #dealing with data
                                                                                  #only mean for col2 == 1
                                                                                  df %>%
                                                                                  filter(col2 == 1) %>%
                                                                                  summarize(mean_exp = mean(col4))
                                                                                  
                                                                                  #mean considering grouping by col2
                                                                                  df %>%
                                                                                  group_by(col2) %>%
                                                                                  summarize(mean_exp = mean(col4))
                                                                                  

                                                                                  I may suggest you search for cheatsheets available on the links above. They are very easy to understand and reproduce the code.

                                                                                  Source https://stackoverflow.com/questions/70371957

                                                                                  QUESTION

                                                                                  x-axis labels ggplot2 in R
                                                                                  Asked 2021-Apr-21 at 21:09

                                                                                  I am trying to label my individual boxplots in this graph as "Cong." and "Incong." I am drawing from a df "flanker.Summary.ID.RT", and using the column in this df "Type" for the boxplot x-axis, and the column "Flanker.RT" for the boxplot y-axis. I am currently trying this code:

                                                                                  flanker.A.1 <- ggplot() + 
                                                                                    geom_line(data=flanker.Summary.ID.RT, aes(x=Type, y=Flanker.RT, group=ID),scale_x_discrete(labels=c("Cong.","Incong.")),
                                                                                              color="gray")  +
                                                                                    geom_point(data=flanker.Summary.ID.RT, aes(x=Type, y=Flanker.RT, group=ID), color="gray")  + 
                                                                                    geom_boxplot(data=flanker.Summary.ID.RT, aes(x=Type, y=Flanker.RT),
                                                                                                 width=0.4, alpha=0.6, fill="#CEB888", outlier.colour = "gray") +
                                                                                    theme_cowplot(font_size=24) +
                                                                                    scale_x_discrete(name="Trial type") +
                                                                                    scale_y_continuous(name="RT (ms)", limits=c(300, 630), breaks=c(300, 400, 500, 600)) +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  flanker.A.1
                                                                                  

                                                                                  As you can see, I am trying to use the scale_x_discrete to create labels, but the boxplot labels being produced from this code are drawing from the names in the "Type" and "Flanker.RT" columns in the original df instead. What am I doing wrong?

                                                                                  ANSWER

                                                                                  Answered 2021-Apr-21 at 21:09

                                                                                  To relabel values on the x axis with scale_x_discrete() you need to access the labels argument. Here's a demonstration:

                                                                                  set.seed(1234)
                                                                                  x <- c(sample(LETTERS[1:3], 100, replace=TRUE))
                                                                                  
                                                                                  p <- ggplot(as.data.frame(x), aes(x=x)) + geom_bar(aes(fill=x))
                                                                                  p
                                                                                  

                                                                                  If you want to relabel the bars on the x axis, you use scale_x_discrete() and pass a vector to the labels argument. The name argument is the title of the axis. If you pass a normal vector to the labels argument, the order of the vector will be mapped according to the order of the x axis items. You can specify the mapping if you pass a named vector, like I show here:

                                                                                  p + scale_x_discrete(
                                                                                    name="new axis name",
                                                                                    labels=c("B" = "BBB", 'A'= 'AAA', 'C'= 'CCC')
                                                                                  )
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/67203268

                                                                                  QUESTION

                                                                                  Changing font size on collated plot
                                                                                  Asked 2021-Apr-21 at 14:22

                                                                                  I want to make the font bigger on the axes on a collated plot. I'd like both the axes on the collated plot, as well as the axes on each individual plot to be bigger. Is there an easy way to do this without individually going into each of the plots I've collated together and changing the font size- for example, can I add anything to the plot_grid() function to do this? Code for context is included below.

                                                                                  # Make Figure 4.
                                                                                  # Flanker.
                                                                                  flanker.Training <- ggplot(data=correlations, aes(x=`Flanker.Con-Incon`, y=Training.ACC)) + 
                                                                                    geom_smooth(method = "lm", color="#CEB888") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Flanker") +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    labs(title="Training") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  flanker.Training
                                                                                  
                                                                                  flanker.Pretest <- ggplot(data=correlations, aes(x=`Flanker.Con-Incon`, y=`Pre-test.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#CEB888") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Flanker") +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    labs(title="Pre-test") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  flanker.Pretest
                                                                                  
                                                                                  flanker.Posttest <- ggplot(data=correlations, aes(x=`Flanker.Con-Incon`, y=`Post-test.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#CEB888") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Flanker") +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    labs(title="Post-test") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  flanker.Posttest
                                                                                  
                                                                                  flanker.PostPre <- ggplot(data=correlations, aes(x=`Flanker.Con-Incon`, y=`Post-Pre.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#CEB888") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Flanker") +
                                                                                    scale_y_continuous(name=expression(Delta~p(Correct))) +
                                                                                    theme(legend.position="none") +
                                                                                    labs(title="Learning") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  flanker.PostPre
                                                                                  
                                                                                  # Pitch.
                                                                                  pitch.Training <- ggplot(data=correlations, aes(x=Pitch.Dprime, y=Training.ACC)) + 
                                                                                    geom_smooth(method = "lm", color="#004369") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Pitch perception (d')", limits=c(-0.6, 4.2)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  pitch.Training
                                                                                  
                                                                                  pitch.Pretest <- ggplot(data=correlations, aes(x=Pitch.Dprime, y=`Pre-test.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#004369") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Pitch perception (d')", limits=c(-0.6, 4.2)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  pitch.Pretest
                                                                                  
                                                                                  pitch.Posttest <- ggplot(data=correlations, aes(x=Pitch.Dprime, y=`Post-test.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#004369") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Pitch perception (d')", limits=c(-0.6, 4.2)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  pitch.Posttest
                                                                                  
                                                                                  pitch.PostPre <- ggplot(data=correlations, aes(x=Pitch.Dprime, y=`Post-Pre.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#004369") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Pitch perception (d')", limits=c(-0.6, 4.2)) +
                                                                                    scale_y_continuous(name=expression(Delta~p(Correct))) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  pitch.PostPre
                                                                                  
                                                                                  # Identification slope.
                                                                                  id.Training <- ggplot(data=correlations, aes(x=ID.Slope, y=Training.ACC)) + 
                                                                                    geom_smooth(method = "lm", color="#BAA892") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Identification slope", limits=c(0, 0.3)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  id.Training
                                                                                  
                                                                                  id.Pretest <- ggplot(data=correlations, aes(x=ID.Slope, y=`Pre-test.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#BAA892") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Identification slope", limits=c(0, 0.3)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  id.Pretest
                                                                                  
                                                                                  id.Posttest <- ggplot(data=correlations, aes(x=ID.Slope, y=`Post-test.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#BAA892") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Identification slope", limits=c(0, 0.3)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  id.Posttest
                                                                                  
                                                                                  id.PostPre <- ggplot(data=correlations, aes(x=ID.Slope, y=`Post-Pre.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#BAA892") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Identification slope", limits=c(0, 0.3)) +
                                                                                    scale_y_continuous(name=expression(Delta~p(Correct))) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  id.PostPre
                                                                                  
                                                                                  # Within-category discrimination.
                                                                                  discrimination.Training <- ggplot(data=correlations, aes(x=Discrimination.Dprime, y=Training.ACC)) + 
                                                                                    geom_smooth(method = "lm", color="#79AFBA") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Discrimination (d')", limits=c(-0.6, 4.2)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  discrimination.Training
                                                                                  
                                                                                  discrimination.Pretest <- ggplot(data=correlations, aes(x=Discrimination.Dprime, y=`Pre-test.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#79AFBA") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Discrimination (d')", limits=c(-0.6, 4.2)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  discrimination.Pretest
                                                                                  
                                                                                  discrimination.Posttest <- ggplot(data=correlations, aes(x=Discrimination.Dprime, y=`Post-test.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#79AFBA") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Discrimination (d')", limits=c(-0.6, 4.2)) +
                                                                                    scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  discrimination.Posttest
                                                                                  
                                                                                  discrimination.PostPre <- ggplot(data=correlations, aes(x=Discrimination.Dprime, y=`Post-Pre.ACC`)) + 
                                                                                    geom_smooth(method = "lm", color="#79AFBA") +
                                                                                    geom_point() +
                                                                                    theme_cowplot(font_size=16) +
                                                                                    scale_x_continuous(name="Discrimination (d')", limits=c(-0.6, 4.2)) +
                                                                                    scale_y_continuous(name=expression(Delta~p(Correct))) +
                                                                                    theme(legend.position="none") +
                                                                                    theme(panel.grid.major.y = element_line(colour="grey"))
                                                                                  discrimination.PostPre
                                                                                  
                                                                                  # Collate to columns.
                                                                                  F4.C1 <- plot_grid(flanker.Training, pitch.Training, id.Training, discrimination.Training,
                                                                                                     align="v", ncol=1,
                                                                                                     rel_heights = c(0.28, 0.24, 0.24, 0.24))
                                                                                  F4.C1
                                                                                  
                                                                                  F4.C2 <- plot_grid(flanker.Pretest, pitch.Pretest, id.Pretest, discrimination.Pretest,
                                                                                                     align="v", ncol=1,
                                                                                                     rel_heights = c(0.28, 0.24, 0.24, 0.24))
                                                                                  F4.C2
                                                                                  
                                                                                  F4.C3 <- plot_grid(flanker.Posttest, pitch.Posttest, id.Posttest, discrimination.Posttest,
                                                                                                     align="v", ncol=1,
                                                                                                     rel_heights = c(0.28, 0.24, 0.24, 0.24))
                                                                                  F4.C3
                                                                                  
                                                                                  F4.C4 <- plot_grid(flanker.PostPre, pitch.PostPre, id.PostPre, discrimination.PostPre,
                                                                                                     align="v", ncol=1,
                                                                                                     rel_heights = c(0.28, 0.24, 0.24, 0.24))
                                                                                  F4.C4
                                                                                  
                                                                                  # Collate/print Figure 3.
                                                                                  Figure4 <- plot_grid(F4.C1, F4.C2, F4.C3, F4.C4,
                                                                                                       align="h", nrow=1)  
                                                                                  Figure4
                                                                                  pdf("Figure4.pdf", 16, 16, bg="transparent")
                                                                                  plot(Figure4)
                                                                                  dev.off()
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2021-Apr-21 at 14:22

                                                                                  If you're willing to switch to the patchwork package for plot composition, you can easily set global theme elements with the & theme(...) operation. Simplified example below.

                                                                                  library(patchwork)
                                                                                  library(ggplot2)
                                                                                  
                                                                                  p <- ggplot(mpg, aes(displ, hwy)) +
                                                                                    geom_point()
                                                                                  
                                                                                  p + p + p + p + plot_layout(ncol = 2, nrow = 2) &
                                                                                    theme(axis.text = element_text(size = rel(2)))
                                                                                  

                                                                                  Created on 2021-04-21 by the reprex package (v1.0.0)

                                                                                  I didn't understand what you meant with 'making axes bigger', so I've ignored that bit of the question.

                                                                                  Source https://stackoverflow.com/questions/67197510

                                                                                  QUESTION

                                                                                  JSON navigation
                                                                                  Asked 2020-Nov-03 at 00:27

                                                                                  I am trying to implement the Websters Dictionary into this python code so that I can look up the definition of a word.

                                                                                  As Trigonom pointed out I can search for "shortdef" in the JSON

                                                                                  @bot.command()
                                                                                  async def define(ctx, *, search):
                                                                                      with urllib.request.urlopen('https://dictionaryapi.com/api/v3/references/collegiate/json/' + search + '?key=632c5b56-d2ec-4c66-a432-93c5a5994748') as url:
                                                                                          lk = url.read()
                                                                                          word_dict = json.loads(lk)
                                                                                          defin = word_dict[0]['shortdef']
                                                                                          print (defin)
                                                                                  

                                                                                  I get this output when I search for "shotgun":

                                                                                  ['a usually smoothbore shoulder weapon capable of firing shot at short ranges', 'an offensive football formation in which the quarterback plays a few yards behind the line of scrimmage and the other backs are scattered as flankers or slotbacks']
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2020-Nov-03 at 00:23

                                                                                  As the error indicates, the JSON result is a list. In particular, a list of objects.

                                                                                  There are multiple shortdefs, and you need to parse each object out

                                                                                  results = [x['shortdef'] for x in json.loads(lk)]
                                                                                  

                                                                                  example output

                                                                                  [['a small vessel for travel on water',
                                                                                    'ship',
                                                                                    'a boat-shaped container, utensil, or device'],
                                                                                   ['to place in or bring into a boat', 'to go by boat'],
                                                                                   ['a pole-handled hook with a point or knob on the back used especially to pull or push a boat, raft, or log into place'],
                                                                                   ['refugees fleeing by boat'],
                                                                                   ['a low-cut shoe with a slip-resistant sole'],
                                                                                   ['an express train for transporting passengers between a port and a city'],
                                                                                   ['a small portable boat used in an amphibious military attack or in land warfare for crossing rivers or lakes'],
                                                                                   ['a seaplane with a hull designed for floating'],
                                                                                   ["a ship's boat of medium size used for general-purpose work"],
                                                                                   ['pt boat']]
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/64617996

                                                                                  QUESTION

                                                                                  GGPlot combining/overlaying column and line (Gantt) charts
                                                                                  Asked 2020-Jan-22 at 08:51

                                                                                  I would like to overlay rainfall data (column) over a Gantt chart that contains 'suggested sowing windows' and actual sowing dates. From the dataset, I can create both separately but not on one chart. Any pointers greatly appreciated.

                                                                                  ## plot Gantt chart with suggested sowing dates and actual sowing dates
                                                                                  sowdate.df$Element <- factor(sowdate.df$Element,levels=c("SOWING DATE","Dart","Spitfire","Suntop","Beckom","Flanker","Lancer","Sunmax","Kittyhawk"))
                                                                                  ggplot(sowdate.df, aes(Date1, Element, Color=Category, group=Item)) +
                                                                                    geom_line(size = 10) 
                                                                                  
                                                                                  ## plot rainfall
                                                                                  ggplot(sowdate.df, aes(Date1, rain)) + geom_col()
                                                                                  
                                                                                  
                                                                                  ## combine Gantt and rainfall
                                                                                  ggplot(sowdate.df) + 
                                                                                    geom_col(aes(Date1, rain), size = 1, color = "darkblue", fill = "white") +
                                                                                    geom_line(aes(Date1, Element, Color=Category, group=Item), size = 1.5, color="red", group = 1)
                                                                                  
                                                                                  
                                                                                  
                                                                                        Item     Element    Category Start-End      Date1 rain
                                                                                  1     1      Beckom     Variety     Start 2018-05-07   NA
                                                                                  2     2        Dart     Variety     Start 2018-06-01   NA
                                                                                  3     3     Flanker     Variety     Start 2018-05-01   NA
                                                                                  4     4   Kittyhawk     Variety     Start 2018-04-01   NA
                                                                                  5     5      Lancer     Variety     Start 2018-05-01   NA
                                                                                  6     6 SOWING DATE Sowing date     Start 2018-06-06   NA
                                                                                  7     7 SOWING DATE Sowing date     Start 2018-06-26   NA
                                                                                  8     8 SOWING DATE Sowing date     Start 2018-07-03   NA
                                                                                  9     9 SOWING DATE Sowing date     Start 2018-07-12   NA
                                                                                  10   10    Spitfire     Variety     Start 2018-05-21   NA
                                                                                  11   11      Sunmax     Variety     Start 2018-04-15   NA
                                                                                  12   12      Suntop     Variety     Start 2018-05-07   NA
                                                                                  13    1      Beckom     Variety       End 2018-05-31   NA
                                                                                  14    2        Dart     Variety       End 2018-06-30   NA
                                                                                  15    3     Flanker     Variety       End 2018-05-21   NA
                                                                                  16    4   Kittyhawk     Variety       End 2018-05-07   NA
                                                                                  17    5      Lancer     Variety       End 2018-05-21   NA
                                                                                  18    6 SOWING DATE Sowing date       End 2018-06-07   NA
                                                                                  19    7 SOWING DATE Sowing date       End 2018-06-27   NA
                                                                                  20    8 SOWING DATE Sowing date       End 2018-07-04   NA
                                                                                  21    9 SOWING DATE Sowing date       End 2018-07-13   NA
                                                                                  22   10    Spitfire     Variety       End 2018-06-21   NA
                                                                                  23   11      Sunmax     Variety       End 2018-05-07   NA
                                                                                  24   12      Suntop     Variety       End 2018-06-07   NA
                                                                                  25   13            Rainfall       2018-04-14  3.0
                                                                                  26   14            Rainfall       2018-03-30  7.0
                                                                                  27   15            Rainfall       2018-06-10  3.5
                                                                                  28   16            Rainfall       2018-06-18  4.0
                                                                                  29   17            Rainfall       2018-06-28 13.5
                                                                                  30   18            Rainfall       2018-07-23  3.0
                                                                                  31   19            Rainfall       2018-08-05  6.0
                                                                                  32   20            Rainfall       2018-08-25 23.0
                                                                                  33   21            Rainfall       2018-09-10  5.0
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2020-Jan-22 at 08:51

                                                                                  As you can see on the image that you have posted - the plot you are shown just overlays two plots. Although this is also possible to do with ggplot2, I don't find this very elegant, and can be very tricky, because you need to find the exact positions of both plots so that it looks neat.

                                                                                  Your workaround using geom_line with your factor levels as y values is interesting, but I am not sure if so desirable.

                                                                                  In any case - this is probably the core of your problem. You are mixing different y measures - and they are of different classes. Factor levels for one plot, numeric / integer for the other. This is problematic. I would not try hard and force those into one y-axis, but I would rather create two plots and combine them with one of the plot combining packages such as patchwork. Like so

                                                                                  I have renamed your columns, am using a package from GitHub user @alisdaire47 for reading your data and also change some columns in order to achieve the plot. Key is using the right classes: Dates as dates, numerics as numerics.

                                                                                  First read your data:

                                                                                  sowdate.df <- read.so::read_so('Item     Element    Category Start_End      Date1 rain
                                                                                  1     1      Beckom     Variety     Start 2018-05-07   NA
                                                                                  2     2        Dart     Variety     Start 2018-06-01   NA
                                                                                  3     3     Flanker     Variety     Start 2018-05-01   NA
                                                                                  4     4   Kittyhawk     Variety     Start 2018-04-01   NA
                                                                                  5     5      Lancer     Variety     Start 2018-05-01   NA
                                                                                  6     6 SOWING DATE Sowing date     Start 2018-06-06   NA
                                                                                  7     7 SOWING DATE Sowing date     Start 2018-06-26   NA
                                                                                  8     8 SOWING DATE Sowing date     Start 2018-07-03   NA
                                                                                  9     9 SOWING DATE Sowing date     Start 2018-07-12   NA
                                                                                  10   10    Spitfire     Variety     Start 2018-05-21   NA
                                                                                  11   11      Sunmax     Variety     Start 2018-04-15   NA
                                                                                  12   12      Suntop     Variety     Start 2018-05-07   NA
                                                                                  13    1      Beckom     Variety       End 2018-05-31   NA
                                                                                  14    2        Dart     Variety       End 2018-06-30   NA
                                                                                  15    3     Flanker     Variety       End 2018-05-21   NA
                                                                                  16    4   Kittyhawk     Variety       End 2018-05-07   NA
                                                                                  17    5      Lancer     Variety       End 2018-05-21   NA
                                                                                  18    6 SOWING DATE Sowing date       End 2018-06-07   NA
                                                                                  19    7 SOWING DATE Sowing date       End 2018-06-27   NA
                                                                                  20    8 SOWING DATE Sowing date       End 2018-07-04   NA
                                                                                  21    9 SOWING DATE Sowing date       End 2018-07-13   NA
                                                                                  22   10    Spitfire     Variety       End 2018-06-21   NA
                                                                                  23   11      Sunmax     Variety       End 2018-05-07   NA
                                                                                  24   12      Suntop     Variety       End 2018-06-07   NA
                                                                                  25   13            Rainfall       2018-04-14  3.0
                                                                                  26   14            Rainfall       2018-03-30  7.0
                                                                                  27   15            Rainfall       2018-06-10  3.5
                                                                                  28   16            Rainfall       2018-06-18  4.0
                                                                                  29   17            Rainfall       2018-06-28 13.5
                                                                                  30   18            Rainfall       2018-07-23  3.0
                                                                                  31   19            Rainfall       2018-08-05  6.0
                                                                                  32   20            Rainfall       2018-08-25 23.0
                                                                                  33   21            Rainfall       2018-09-10  5.0')
                                                                                  #> Warning: 8 parsing failures.
                                                                                  #> row col  expected    actual         file
                                                                                  #>   6  -- 6 columns 8 columns literal data
                                                                                  #>   7  -- 6 columns 8 columns literal data
                                                                                  #>   8  -- 6 columns 8 columns literal data
                                                                                  #>   9  -- 6 columns 8 columns literal data
                                                                                  #>  18  -- 6 columns 8 columns literal data
                                                                                  #> ... ... ......... ......... ............
                                                                                  #> See problems(...) for more details.
                                                                                  

                                                                                  now the plots

                                                                                  library(tidyverse)
                                                                                  library(patchwork)
                                                                                  

                                                                                  Prepare the data (the messiness is due to value scaling to your factor levels)

                                                                                  sowdate <- sowdate.df %>% mutate(element_f = factor(Element,levels=c("SOWING DATE","Dart","Spitfire","Suntop","Beckom","Flanker","Lancer","Sunmax","Kittyhawk")),
                                                                                                                   date = as.Date(Date1),
                                                                                                                   rain = as.numeric(rain),
                                                                                                                   rain_scaled = rain*max(length(levels(element_f))/max(rain, na.rm = TRUE)))
                                                                                  #> Warning: NAs introduced by coercion
                                                                                  

                                                                                  Method 1 - combine plots using patchwork. I recommend this, in order not to mix different classes into one y.

                                                                                  p1 <- ggplot(sowdate, aes(date, element_f, Color = Category, group = Item)) +
                                                                                    geom_line(size = 10) +
                                                                                    theme(axis.title.x = element_blank(),
                                                                                          axis.text.x = element_blank(),
                                                                                          axis.ticks.x = element_blank(),
                                                                                          plot.margin = margin(b = 0))
                                                                                  p2 <- ggplot(sowdate) +
                                                                                    geom_col(aes(date, rain)) +
                                                                                    theme(plot.margin = margin(t = 0))
                                                                                  p1 + p2 + plot_layout(nrow = 2, )
                                                                                  #> Warning: Removed 8 rows containing missing values (geom_path).
                                                                                  #> Warning: Removed 24 rows containing missing values (position_stack).
                                                                                  

                                                                                  I removed the axis text and title and ticks from the first plot and lower and upper plot margins to bring them closer together

                                                                                  Method 2 Combine different variable classes (I don't recommend that. This gets quite messy as you can see above and below). You'll need to scale your rain values to your factor levels, so that the columns overlap and don't get too long. Now this then requires a second y axis. For this you have to make your factor levels numeric, than create breaks and labels for the left y-axis and then re-transform the rain values to their real values, and hope that the breaks kind of works. I don't think a second y-axis really helps to read the graph.

                                                                                  
                                                                                  max_rain <- max(sowdate$rain,na.rm = TRUE)
                                                                                  breaks_ax <- 1:length(levels(sowdate$element_f)) - sum(is.na(levels(sowdate$element_f)))
                                                                                  labels_ax <- as.character(levels(sowdate$element_f)[which(!is.na(levels(sowdate$element_f)))])
                                                                                  
                                                                                  ggplot(sowdate, aes(date, as.numeric(element_f), Color = Category, group=Item)) +
                                                                                    geom_line(size = 10) +
                                                                                    geom_col(aes(date, rain_scaled)) +
                                                                                    scale_y_continuous(breaks = breaks_ax, labels = labels_ax, 
                                                                                                       sec.axis = sec_axis(~ .*max_rain/ max(length(levels(sowdate$element_f))))) +
                                                                                    labs(y = 'Element')
                                                                                  #> Warning: Removed 24 rows containing missing values (position_stack).
                                                                                  #> Warning: Removed 17 rows containing missing values (geom_path).
                                                                                  

                                                                                  Created on 2020-01-22 by the reprex package (v0.3.0)

                                                                                  Source https://stackoverflow.com/questions/59834007

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

                                                                                  Vulnerabilities

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