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Jodie Burchell t-redactyl

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t-redactyl / diamonds_sample_weighted.csv
Created October 2, 2019 17:59
carat cut color clarity depth table price x y z nclarity
0.42 Ideal G VS1 61.4 56.0 921 4.82 4.85 2.97 5
0.3 Ideal D VS1 62.2 56.0 835 4.31 4.27 2.67 5
0.31 Ideal G IF 61.5 54.0 871 4.4 4.41 2.71 1
1.79 Premium H VS1 62.6 56.0 14904 7.81 7.76 4.87 5
0.28 Very Good F VS1 62.1 59.0 487 4.16 4.21 2.6 5
1.2 Premium E VS1 60.7 57.0 10053 6.89 6.81 4.16 5
1.52 Fair J VS1 65.4 58.0 7186 7.22 7.17 4.7 5
0.31 Ideal E VS1 61.8 55.0 692 4.36 4.38 2.7 5
0.52 Ideal G VS1 60.9 55.0 1815 5.22 5.16 3.16 5
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t-redactyl / copper_data.csv
Created October 2, 2019 17:44
product year export percentage sum
copper 2006 4176 79 5255
copper 2007 8560 81 10505
copper 2008 6473 76 8519
copper 2009 10465 80 13027
copper 2010 14977 86 17325
copper 2011 15421 83 18629
copper 2012 14805 82 18079
copper 2013 15183 80 19088
copper 2014 14012 76 18437
@t-redactyl
t-redactyl / diamonds_sample_weighted.csv
Created June 16, 2019 17:59
0.42 Ideal G VS1 61.4 56.0 921 4.82 4.85 2.97 5
0.3 Ideal D VS1 62.2 56.0 835 4.31 4.27 2.67 5
0.31 Ideal G IF 61.5 54.0 871 4.4 4.41 2.71 1
1.79 Premium H VS1 62.6 56.0 14904 7.81 7.76 4.87 5
0.28 Very Good F VS1 62.1 59.0 487 4.16 4.21 2.6 5
1.2 Premium E VS1 60.7 57.0 10053 6.89 6.81 4.16 5
1.52 Fair J VS1 65.4 58.0 7186 7.22 7.17 4.7 5
0.31 Ideal E VS1 61.8 55.0 692 4.36 4.38 2.7 5
0.52 Ideal G VS1 60.9 55.0 1815 5.22 5.16 3.16 5
@t-redactyl
t-redactyl / diamonds_sample.csv
Created June 16, 2019 17:45
carat cut color clarity depth table price x y z
0.32 Ideal G VVS1 61.2 55.0 814 4.41 4.44 2.71
0.7 Fair I SI1 62.0 67.0 1848 5.54 5.5 3.42
1.46 Premium J SI2 60.1 58.0 6387 7.43 7.34 4.44
0.38 Premium G VS2 60.4 57.0 983 4.7 4.67 2.83
0.7 Very Good F VS2 62.9 56.0 2400 5.66 5.73 3.58
0.32 Ideal E SI2 62.7 55.0 576 4.42 4.39 2.76
0.71 Ideal F VS1 62.1 57.0 3066 5.73 5.76 3.57
0.3 Ideal E VS2 61.5 55.0 844 4.31 4.28 2.64
0.36 Ideal E VVS2 61.8 54.0 928 4.6 4.62 2.85
@t-redactyl
t-redactyl / contraction cleaner.py
Last active November 21, 2018 18:24
# This code is not mine! Copied from https://gist.github.com/nealrs/96342d8231b75cf4bb82, but with suggested alteration to include text.lower() in the function.
import re
cList = {
"ain't": "am not",
"aren't": "are not",
"can't": "cannot",
"can't've": "cannot have",
"'cause": "because",
"could've": "could have",
@t-redactyl
t-redactyl / Resolutions matching.py
Created April 15, 2017 07:58
# Import the relevant packages
import numpy as np
import re
# Create 6 new dummy variables which indicate whether one of the words associated with a resolution is present in the tweet.
twitter_df['Physical Health'] = np.where(twitter_df['Tweet'].str.contains('(?:^|\W)(weight|fit|exercise|gym|muscle|health|water|smoking|alcohol|drinking|walk|run|swim)(?:$|\W)',
flags = re.IGNORECASE), 1, 0)
twitter_df['Learning and Career'] = np.where(twitter_df['Tweet'].str.contains('(?:^|\W)(business|job|career|professional|study|learn|develop|advance|grades|school|university| read|study|skill|education)(?:$|\W)',
flags = re.IGNORECASE), 1, 0)
@t-redactyl
t-redactyl / christmas_tree.R
Last active December 19, 2017 07:16
# Load in the packages
library(ggplot2)
library(extrafont)
font_import()
loadfonts()
# Read in the base Christmas tree data
ChristmasTree <- read.csv("https://raw.githubusercontent.com/t-redactyl/Blog-posts/master/Christmas%20tree%20base%20data.csv")
# Generate the "lights"
@t-redactyl
t-redactyl / cleaning_data.R
Created November 4, 2015 05:52
Code associate with blog post
mtcars$am.f <- as.factor(mtcars$am); levels(mtcars$am.f) <- c("Automatic", "Manual")
mtcars$cyl.f <- as.factor(mtcars$cyl); levels(mtcars$cyl.f) <- c("4 cyl", "6 cyl", "8 cyl")
mtcars$vs.f <- as.factor(mtcars$vs); levels(mtcars$vs.f) <- c("V engine", "Straight engine")
mtcars$gear.f <- as.factor(mtcars$gear); levels(mtcars$gear.f) <- c("3 gears", "4 gears", "5 gears")
mtcars$carb.f <- as.factor(mtcars$carb)
@t-redactyl
t-redactyl / centred_chart.R
Created October 29, 2015 00:23
Code associated with blog post
library(ggplot2); library(gridExtra)
g1 <- ggplot(data=mtcars, aes(x=wt, y=mpg)) +
geom_point(alpha = 0.7, colour = "#0971B2") +
ylab("Miles per gallon") +
ylim(10, 35) +
xlab("Weight (`000 lbs)") +
ggtitle("Untransformed Weight") +
geom_vline(xintercept = 0) +
theme_bw()
@t-redactyl
t-redactyl / av_peds_2.R
Last active September 27, 2017 14:05
Code associated with blog post: http://t-redactyl.github.io/blog/2015/10/avoiding-the-crowds-in-melbourne-cbd-using-data.html
av_peds_2 <- ddply(p.subset, c("date", "collapsed_sensors_2"), summarise,
n_peds = sum(Hourly_Counts))
# Extract weekday versus weekend
av_peds_2$day <- weekdays(av_peds_2$date, abbreviate = FALSE)
av_peds_2$weekend <- ifelse((av_peds_2$day == "Saturday" | av_peds_2$day == "Sunday"),
"Weekend", "Weekday")
av_peds_2$weekend <- as.factor(av_peds_2$weekend)
# Extract time of day