iandanforth

A Checklist for Reproducibility in Reinforcement Learning

From a slide in a NeurIPS 2018 keynote by Joelle Pineau

For all algorithms presented, check if you include:

• A clear description of the algorithm.
• An analysis of the complexity (time, space, sample size) of the algorithm.
• A link to downloadable source code, including all dependencies.

z3tt

dat <- structure(list(Move = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 

benmarwick

library(ggplot2)

# coords for vertices of polygons that make the zones
polydata <- rbind(
  data.frame(x = c(0, 0.01, 0.99, 1), 
             y = c(0, 0.25, 0.25, 0), 
             group = "zone 1", 
             fill = "grey80", xc = .5, yc = 0.125),
  data.frame(x = c(0, 0.025, 0.495, 0.495), 
             y = c(-0.01, -0.25, -0.25, -0.01), 

johnjosephhorton

library(XML)
library(ggplot2)

df <- readHTMLTable("http://projects.dailycal.org/paychecker/departments/")[[1]]

DeMoney <- function(x) as.numeric(gsub(",", "", gsub("\\$", "", as.character(x))))

money.columns <- c("All", "Professor", "Associate professor", "Assistant professor", 
                   "Lecturer")

Robinlovelace

# install.packages("vistime")
library(vistime)
library(tidyverse)
library(lubridate)
# ?vistime

# d = read_csv("potential-projects/ofo/gantt.csv")
d = read.csv(stringsAsFactors = FALSE,text = "event,start,duration,group
compile datasets,0,2,descriptive analysis
baseline data,1,2,descriptive analysis

benmarwick

# Adapted from https://stackoverflow.com/a/7267364/1036500 by Andrie de Vries

# This is it: theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

library(ggplot2)

td <- expand.grid(
  hjust=c(0, 0.5, 1),
  vjust=c(0, 0.5, 1),
  angle=c(0, 45, 90),

benmarwick

# function to do a dodged half-boxplot and jittered points next to each other
#
# data_in should be a data frame
# factor_col should be a bare column name (not a string)
#     although it will work if that column is factor or a character type
# numeric_col is the y axis continuous variable
# offset is the width of the boxplots and jittered point cloud
#
# the basic approach is to draw a boxplot without the tails
#   (e.g. only the interquartile range) and then use segments to add the

benmarwick

## GOAL:
## re-create a figure similar to Fig. 2 in Wilson et al. (2018), 
## Nature 554: 183-188. Available from:
## https://www.nature.com/articles/nature25479#s1
##
## combines a boxplot (or violin) with the raw data, by splitting each
## category location in two (box on left, raw data on right)


## call required packages

jbburant

## GOAL:
## re-create a figure similar to Fig. 2 in Wilson et al. (2018), 
## Nature 554: 183-188. Available from:
## https://www.nature.com/articles/nature25479#s1
##
## combines a boxplot (or violin) with the raw data, by splitting each
## category location in two (box on left, raw data on right)


# initial set-up ----------------------------------------------------------

naupaka

# function to do a dodged half-boxplot and jittered points next to each other
#
# data_in should be a data frame
# factor_col should be a bare column name (not a string)
#     although it will work if that column is factor or a character type
# numeric_col is the y axis continuous variable
# offset is the width of the boxplots and jittered point cloud
#
# the basic approach is to draw a boxplot without the tails
#   (e.g. only the interquartile range) and then use segments to add the