EconomiCurtis

install.packages('nycflights13')
library(nycflights13)
library(dplyr)
dim(flights)
flights
df = flights

# Curtis Kephart 
# [email protected]
# Questions and solution for Prof Morton's Experimental PoliSci R assignment. 

EconomiCurtis

Redwood.controller("SubjectCtrl", ["$rootScope", "$scope", "RedwoodSubject", 'SynchronizedStopWatch', function($rootScope, $scope, rs, SynchronizedStopWatch) {
    
    //Controls tick frequency for refreshing of flow chart
    var CLOCK_FREQUENCY = 7;
    var LOG_FREQUENCY = 7;

    //Controls how often the slider is allowed
    // to update the user's value. In ms.
    var SLIDER_REFRESH_TIME = 60;

EconomiCurtis

# Instructions
# `d` is the example datastructure
# `n` in kde2d is the granularity of the heatmap. It's how many pixels appears on the vertical and horizontal axes (total pixels is n^2)
# `n` in colours / rainbow controls the colors of the heatmap rainbow. 
# `cuts` in levelplot also controls the smoothness of the color bleeding between pixels. 
# - I don't think `cuts` can be set to more than the integer in `col.regions=rgb.palette(150)`. 

#  package
require(lattice)
require(MASS)

EconomiCurtis

#' I had a quick chat with Nikos earlier today, 
#' and I need to generate the same analysis Nikos described for three separate data sets 
#' (they're all attached and include variables labelled task2score and task3score respectively).
#' In the end what I'm trying to calculate is the probability a subject's task 3 score is 
#' strictly lower than a randomly selected task 2 score from the same experiment. 
#' Each file contains only the observations I need to include in the analysis.

EconomiCurtis

# fun with the gt package. 
# replicating the "Parts of a gt Table" at 
# https://blog.rstudio.com/2020/04/08/great-looking-tables-gt-0-2/


library(dplyr)
library(gt)

data.frame(
  row_label = c("ROW LABEL 1", "ROW LABEL 2"),