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Call: | |
glm(formula = casino$Q6 == "City of Toronto" ~ GoBigorGoHome + | |
TechnicalDetails + Soc.Env.Issues, family = binomial(logit), | |
data = casino) | |
Deviance Residuals: | |
Min 1Q Median 3Q Max | |
-3.6426 -0.4745 -0.1156 0.4236 3.4835 | |
Coefficients: |
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library(ff) | |
library(ffbase) | |
library(stringr) | |
library(ggplot2) | |
library(ggthemes) | |
library(reshape2) | |
library(RgoogleMaps) | |
# Loading 2 copies of the same data set so that I can convert one and have the original for its text values | |
casino = read.csv("/home/inkhorn/Downloads/casino_survey_results20130325.csv") |
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# Here's where I extract the database IDs and repeat them 50 times to make the column long enough for | |
# my new long-form dataset (596,100 rows) | |
client.data.new = rep(client.data[,1],50) | |
for (i in 2:32){ | |
# for each column in the first 31 after the ID column, find the 49 matching columns | |
# to the right and stack them using melt | |
stacked.data = melt(client.data, id.vars="CnBio_ID", measure.vars=seq(i,(i+(31*49)),31), value.name=names(client.data)[i]) |
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penultimax = function(invector) { | |
# If the vector starts off as only having 1 or 0 numbers, return NA | |
if (length(invector) <= 1) { | |
return(NA) | |
} | |
first.max = safe.max(invector) | |
#Once we get the max, take it out of the vector and make newvector | |
newvector = invector[!invector == first.max] | |
#If newvector now has nothing in it, return NA | |
if (length(newvector) == 0) { |
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scents = read.table("clipboard",header=TRUE,sep="\t") | |
strial3.by.sex.wide = ddply(scents, 'Sex', function (x) quantile(x$S.Trial.3, c(0,.5,1), na.rm=TRUE)) | |
strial3.by.sex.smokers = melt(ddply(subset(scents,Smoker == "Y") , 'Sex', function (x) quantile(x$S.Trial.3, c(0,1), na.rm=TRUE)),variable.name="Percentile",value.name="Time") | |
ggplot() + geom_crossbar(data=strial3.by.sex.wide, aes(x=Sex, y=strial3.by.sex.wide$"50%", ymin=strial3.by.sex.wide$"0%", ymax=strial3.by.sex.wide$"100%"),fill="#bcc927",width=.75) + | |
geom_point(data=strial3.by.sex.smokers, aes(x=Sex, y=Time, stat="identity"), size=3) | |
+ opts(legend.title = theme_text(size=10, face="bold"), legend.text = theme_text(size=10), | |
axis.text.x=theme_text(size=10), axis.text.y=theme_text(size=10,hjust=1), axis.title.x=theme_text(size=12,face="bold"), axis.title.y=theme_text(size=12, angle=90, | |
face="bold")) + scale_y_continuous(name="Time to Completion") |
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# These column numbers represent fields with name/contact info that I've | |
# marked with 1s and 0s depending on whether or not there's anything in | |
# the field. | |
bio_cols = c(5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,23,24,25,26) | |
# Now we get the row numbers of all the records with duplicate IDs | |
dupe_id_rows = which(duplicated(big.dataset$ID) == TRUE) |
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testvars = c(6,7,9,10,11,12,13,14,16, 17,18,19,20,21,23,24,25,26,384,375,376,386,385,387,388) | |
resultlist = c() | |
for (i in testvars) { | |
xsq = chisq.test(big.dataset[,i], big.dataset$DV_3lvls)$statistic | |
varname = names(big.dataset)[i] | |
tab = xtabs(~DV_3lvls + big.dataset[,i], data=big.dataset) | |
resultlist = rbind(resultlist, list(chisq=xsq, testvar=varname, xtab=tab)) | |
} |
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safe.max = function(invector) { | |
na.pct = sum(is.na(invector))/length(invector) | |
if (na.pct == 1) { | |
return(NA) } | |
else { | |
return(max(invector,na.rm=TRUE)) | |
} | |
} |
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Call: | |
glm(formula = Probable.Match. ~ First.Name.Match + Spouse.First.Name.Match:Spouse.Last.Name.Match + | |
Parenthetical + Ampersand, family = binomial(logit), data = fuzzy.matching) | |
Deviance Residuals: | |
Min 1Q Median 3Q Max | |
-2.9371 -0.2437 -0.1136 -0.0462 3.3885 | |
Coefficients: | |
Estimate Std. Error z value Pr(>|z|) |
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# This function assumes that you're going to input ID1.name and ID2.name as strings. | |
df.sample.exIDs = function(main.df, sample1.df, n, ID1.name, ID2.name) { | |
main.ID1.notin.ID2 = main.df[!main.df[,ID1.name] %in% sample1.df[,ID2.name],] | |
sample2.df = main.ID1.notin.ID2[sample(nrow(main.ID1.notin.ID2), size=n),] | |
return(sample2.df) | |
} |