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74 01-Using_R/code/data/ufo/.svn/text-base/16154.yaml.svn-base
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----
-infochimps_schema:
- title: Infochimps Simple Schema (ICSS)
- version: 0.1.1
- generated_at: Wed Oct 20 14:34:20 +0000 2010
- url: "http://infochimps.com/datasets/d60000-documented-ufo-sightings-with-text-descriptions-and-metad.yaml"
- description: >-
- This is an Infochimps Simple Schema (ICSS).
-
- It describes data on Infochimps.org and is meant to be both human
- and machine readable.
-
- An ICSS file is distributed with every download on Infochimps and
- is also available by request to the above URL.
-
- ICSS files bundled with downloads are generated at the time the
- download was created so they can become out of date as changes are
- made to the data. If this ICSS came with data you downloaded then
- it is possible that it is already out of date. You can get the
- most recent version at the URL above.
-
-dataset:
- id: 16154
- title: 60,000+ Documented UFO Sightings With Text Descriptions And Metadata
- subtitle: ~
- main_link: ~
- url: "http://infochimps.com/datasets/d60000-documented-ufo-sightings-with-text-descriptions-and-metad"
- created_at: "Wed Oct 20 04:49:06 UTC 2010"
- updated_at: "Wed Oct 20 04:50:01 UTC 2010"
- tags: [ alien,aliens,awesome,database,ET,extra-terrestrial,flying,ship,sighting,space,tsv,ufo,unidentified-flying-objec ]
- categories: [ ~ ]
- created_by:
- id: 675
- username: Ganglion
- name: Jacob Perkins
- institution: ~
- main_link: ~
- url: "http://infochimps.com/users/Ganglion"
- description: >-
- h2. Description:
-
- A comprehensive listing of over 60,000 unidentified-flying-object (UFO) sightings in the United States (and some elsewhere). Fields of particular interest are the @shape@, which describes the shape of the UFO encountered as well as the @description@ which is free-form descriptive text about the event. Data available as both a tab-separated-values (.tsv) file and "Avro":http://avro.apache.org/docs/1.4.1/
-
- h2. Fields:
-
- |_.Short name|_.Type|_.Description|
- |sighted_at|Long| Date the event occurred (yyyymmdd) |
- |reported_at|Long| Date the event was reported |
- |location|String| City and State where event occurred |
- |shape|String|One word string description of the UFO shape|
- |duration|String|Event duration (raw text field)|
- |description|String|A long, ~20-30 line, raw text description|
-
- h2. Snippet:
-
- |19980710|19980721| Brick, NJ| sphere|5 minutes|On the evening of July 10, 1998, I was walking near my home ... |
- |19940815|19980625| Rancho Mirage, CA| rectangle|45 seconds|An extreemly close sighting of a ... |
- |19970527|19981207| Arlington, VA| disk|A few seconds|As I was on my way home, ... |
- |19980828|19980830| Mt. Vernon, OR| fireball|4-5 sec|Bright Blue (as that of an arc welder) light, that lit up ... |
- |19981106|19981108| Bend, OR| formation|15-20 seconds|5 objects in straight line following one another... |
- license:
-
- sources:
-
- packages:
-
- - id: 17001
- number_of_records: ~
- basename: icsdata-d60000-documented-ufo-sightings-with-text-descriptions-and-metad_20101020052908-avro.zip
- fmt: avro
- pkg_fmt: zip
-
- fields:
-
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26 ...ta_Exploration/code/drafts/.svn/text-base/01_generate_heights_weights_data_set.R.svn-base
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-# Generate enough data that marginals look Gaussian.
-n <- 10000
-
-# Assume that men's heights are normal: N(69.1, 2.9)
-# Assume that women's heights are normal: N(63.7, 2.7)
-# For now, assume that men and women are each exactly 50% of the population.
-heights.weights <- rbind(data.frame(Gender = rep('Male', n / 2),
- Height = rnorm(n / 2, 69.1, 2.9)),
- data.frame(Gender = rep('Female', n / 2),
- Height = rnorm(n / 2, 63.7, 2.7)))
-
-# Assume that weights, given heights, are generated by the following linear function.
-# This was derived using NHANES data.
-#Coefficients:
-# (Intercept) heightInches
-# -223.904 5.955
-heights.weights <- transform(heights.weights, Weight = -223.904 + 5.955 * Height)
-
-# These weights are too high for women. Let's drop 20 pounds.
-heights.weights <- transform(heights.weights, Weight = Weight + ifelse(Gender == 'Female', -20, 0))
-
-# Add some Gaussian noise.
-heights.weights <- transform(heights.weights, Weight = Weight + rnorm(nrow(heights.weights), 0, 10))
-
-# Save to CSV
-write.csv(heights.weights, file = '01_heights_weights_genders.csv', row.names = FALSE)
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-library('ggplot2')
-
-heights.weights <- read.csv('01_heights_weights_genders.csv', header = TRUE, sep = ',')
-
-ggplot(heights.weights, aes(x = Height, fill = Gender)) + geom_density()
-ggplot(heights.weights, aes(x = Weight, fill = Gender)) + geom_density()
-ggplot(heights.weights, aes(x = Height, y = Weight)) + geom_point(aes(color = Gender, alpha=0.25)) +
- scale_alpha(legend=FALSE)+scale_color_manual(values=c("Male"="black", "Female"="gray"))+theme_bw()
-ggsave(filename='../../images/heights_weights_scatterplot_with_genders.pdf')
-
-heights.weights <- transform(heights.weights, Male = ifelse(Gender == 'Male', 1, 0))
-logit.model <- glm(Male ~ Weight + Height, data = heights.weights, family = binomial(link = 'logit'))
-
-invlogit <- function (z) {return(1 / (1 + exp(-z)))}
-heights.weights <- transform(heights.weights, Prediction = as.numeric(invlogit(predict(logit.model)) > 0.5))
-nrow(subset(heights.weights, Male == Prediction)) / nrow(heights.weights)
-
-# Find constant line so that invlogit == 0.5 everywhere.
-
-# -coef(logit.model)[1] == coef(logit.model)[2] * Height + coef(logit.model)[3] * Weight
-# Height = (-coef(logit.model)[1] - coef(logit.model)[3] * Weight) / coef(logit.model)[2]
-# Height = -coef(logit.model)[1] / coef(logit.model)[2] - coef(logit.model)[3] / coef(logit.model)[2] * Weight
-
-ggplot(heights.weights, aes(x = Height, y = Weight)) + geom_point(aes(color = Gender, alpha=0.25)) +
- scale_alpha(legend=FALSE)+scale_color_manual(values=c("Male"="black", "Female"="gray"))+theme_bw() +
- stat_abline(intercept = - coef(logit.model)[1] / coef(logit.model)[2],
- slope = - coef(logit.model)[3] / coef(logit.model)[2],
- geom = 'abline',
- color = 'black')
-ggsave(filename='../../images/logistic_plot.pdf')
View
36 02-Data_Exploration/code/drafts/.svn/text-base/03_generate_data_shapes.R.svn-base
@@ -1,36 +0,0 @@
-N <- 1000000
-
-normal.data <- rnorm(N, 0, 1)
-ggplot(data.frame(X = normal.data), aes(x = X, fill = 1)) + geom_density()
-
-cauchy.data <- rcauchy(N, 0, 1)
-ggplot(data.frame(X = cauchy.data), aes(x = X, fill = 1)) + geom_density()
-
-# Normal
-x <- seq(-3, 3, by = 0.01)
-ggplot(data.frame(X = x, Y = dnorm(x, 0, 1)), aes(x = X, y = Y)) + geom_line()
-
-# Cauchy
-x <- seq(-3, 3, by = 0.01)
-ggplot(data.frame(X = x, Y = dcauchy(x, 0, 1)), aes(x = X, y = Y)) + geom_line()
-
-# Both Normal and Cauchy at once.
-M <- length(x)
-
-comparison.data <- data.frame(X = rep(x, 2),
- Y = c(dnorm(x, 0, 1),
- dcauchy(x, 0, 1)),
- Shape = c(rep('Normal', M), rep('Cauchy', M)))
-ggplot(comparison.data, aes(x = X, y = Y, color = Shape)) + geom_line()
-
-# Gamma
-x <- seq(0, 10, by = 0.01)
-ggplot(data.frame(X = x, Y = dgamma(x, 2, 1)), aes(x = X, y = Y)) + geom_line()
-
-# Exponential
-x <- seq(0, 10, by = 0.01)
-ggplot(data.frame(X = x, Y = dexp(x, 1)), aes(x = X, y = Y)) + geom_line()
-
-# Normal Mixture Model
-x <- seq(0, 10, by = 0.01)
-ggplot(data.frame(X = x, Y = 0.3 * dnorm(x, 2, 1) + 0.7 * dnorm(x, 5, 1)), aes(x = X, y = Y)) + geom_line()
View
34 02-Data_Exploration/code/drafts/.svn/text-base/04_logistic_plot.R.svn-base
@@ -1,34 +0,0 @@
-# http://www.amstat.org/publications/jse/v11n2/datasets.heinz.html
-
-measurements <- read.csv('body.dat', header = FALSE, sep = '')
-
-measurements <- measurements[, 23:25]
-
-names(measurements) <- c('Weight', 'Height', 'Male')
-
-measurements$Gender <- ifelse(measurements$Male == 1, 'Male', 'Female')
-
-library('ggplot2')
-ggplot(measurements, aes(x = Weight, y = Height, color = Gender)) + geom_point()
-
-logit.model <- glm(Male ~ Height + Weight, data = measurements, family = binomial(link = 'logit'))
-
-invlogit <- function (x) {1 / (1 + exp(-x))}
-
-library('arm')
-measurements$Prediction <- as.numeric(invlogit(predict(logit.model)) > 0.5)
-nrow(subset(measurements, Male == Prediction)) / nrow(measurements)
-
-# Find constant line so that invlogit == 0.5 everywhere.
-
-# -coef(logit.model)[1] == coef(logit.model)[2] * Height + coef(logit.model)[3] * Weight
-# Height = (-coef(logit.model)[1] - coef(logit.model)[3] * Weight) / coef(logit.model)[2]
-# Height = -coef(logit.model)[1] / coef(logit.model)[2] - coef(logit.model)[3] / coef(logit.model)[2] * Weight
-
-ggplot(measurements, aes(x = Weight, y = Height, color = Gender)) +
- geom_point() +
- stat_abline(intercept = - coef(logit.model)[1] / coef(logit.model)[2],
- slope = - coef(logit.model)[3] / coef(logit.model)[2],
- geom = 'abline',
- color = 'black')
-
View
507 02-Data_Exploration/code/drafts/.svn/text-base/body.dat.svn-base
@@ -1,507 +0,0 @@
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-38.0 30.4 32.9 17.0 27.1 12.9 10.4 19.5 14.4 108.4 91.8 69.9 90.4 101.0 60.6 30.3 25.4 37.7 37.9 22.4 15.4 29.0 71.8 176.5 0
-35.3 28.7 30.4 17.7 25.6 12.4 9.8 17.3 13.6 99.3 87.3 63.5 79.2 89.5 55.2 30.1 23.6 35.6 33.3 22.4 15.2 21.0 55.5 164.4 0
-34.7 24.9 24.7 17.3 24.2 12.0 10.2 18.0 13.6 91.9 78.1 57.9 75.1 86.9 51.8 27.4 24.0 34.4 34.1 21.2 15.5 33.0 48.6 160.7 0
-38.5 29.0 32.9 15.3 25.6 12.0 9.8 18.6 13.3 107.1 90.9 72.2 89.4 98.6 59.0 30.6 24.9 38.4 36.6 22.0 15.5 33.0 66.4 174.0 0
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15 02-Data_Exploration/code/drafts/.svn/text-base/cauchy_gaussian.R.svn-base
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-set.seed(1)
-
-x <- seq(-3, 3, 0.1)
-y <- dnorm(x, 0, 1)
-z <- dcauchy(x, 0, 1)
-
-df <- data.frame(X = c(x, x), Distribution = c(rep('Normal', length(x)), rep('Cauchy', length(x))), Density = c(y, z))
-ggplot(df, aes(x = X, y = Density, linetype = Distribution, group = Distribution)) +
- geom_line()
-ggsave('images/cauchy_gaussian.pdf')
-
-ggplot(df, aes(x = X, y = Density, linetype = Distribution, group = Distribution)) +
- geom_line() +
- facet_grid(Distribution ~ .)
-ggsave('images/cauchy_gaussian_faceted.pdf')
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26 02-Data_Exploration/code/drafts/.svn/text-base/incomes_in_britain.R.svn-base
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-p <- 0.05
-
-N <- 1000000
-
-incomes <- rep(NA, N)
-
-for (i in 1:N)
-{
- if (rbinom(1, 1, p) == 1)
- {
- income <- 20000
- }
- else
- {
- income <- rlnorm(1, meanlog = log(40000), sdlog = 0.25)
- }
- incomes[i] <- income
-}
-
-pdf('incomes_in_britain.pdf')
-ggplot(data.frame(Income = incomes), aes(x = Income, fill = 1)) +
- geom_density() +
- ylab('') +
- xlab('Income in Dollars per Year') +
- opts(title = 'Distribution of Incomes in Society')
-dev.off()
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15 02-Data_Exploration/code/drafts/.svn/text-base/max_figure.R.svn-base
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-library('ggplot2')
-
-heights.weights <- read.csv('01_heights_weights_genders.csv', header = TRUE, sep = ',')
-
-heights.weights <- transform(heights.weights, Male = ifelse(Gender == 'Male', 1, 0))
-
-logit.model <- glm(Male ~ Weight + Height, data = heights.weights, family = binomial(link = 'logit'))
-
-ggplot(heights.weights, aes(y = Weight, x = Height, color = Gender)) +
- geom_point() +
- stat_abline(intercept = - coef(logit.model)[1] / coef(logit.model)[2],
- slope = - coef(logit.model)[3] / coef(logit.model)[2],
- geom = 'abline',
- color = 'black')
-ggsave('../../images/logistic_plot.pdf')
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1  02-Data_Exploration/code/drafts/.svn/text-base/stock_returns.R.svn-base
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-# Use AAPL daily change in closing stock price.
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11 04-Priority_Inbox/code/data/.svn/all-wcprops
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-K 25
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-V 91
-/!svn/ver/332685/external/authors/books/9781449303716/current/Chapters/04-Ranking/code/data
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-V 104
-/!svn/ver/332685/external/authors/books/9781449303716/current/Chapters/04-Ranking/code/data/final_df.csv
-END
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-10
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-https://prod.oreilly.com/external/authors/books/9781449303716/current/Chapters/04-Ranking/code/data
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-2011-05-27T19:49:53.573376Z
-332685
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