Description Usage Arguments Value Note Author(s) References See Also Examples
The brewdata method queries the GradCafe Results Search page for application decision data. It then calls the parseResults function to breakdown the text from "Decision & Date" into values useful for exploring admissions decisions.
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years |
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term |
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degree |
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focus |
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resolution |
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map |
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brewdata returns a data frame of the parsed Grade Cafe Results data. The data frame includes the following attributes:
school_name |
is the closest standardized name matching the name entered at the Grad Cafe. brewdata normalizes the names reported on the website to enable aggregate analysis. See the parse_names parameter description above for more details on the parsing methods. |
original_name |
is the original name of the university reported to the Grad Cafe. If map=TRUE, then brewdata includes a column showing the names reported on the website alongside the normalized names assigned by brewdata. This column is excluded by default. |
decision |
denotes a university's decision on an application. Possible decisions are accepted ('A'), wait listed ('W'), rejected ('R'), interview ('I') or other ('O'). |
status |
denotes an applicant's immigration status. This field is reported directly from the Grad Cafe. Per the website definitions, possible status values are American ('A'), International with a US degree ('U'), International without US degree ('I'), other ('O'), or unknown ('?'). |
gpa |
is the self-reported grade point average. |
gre_v |
is the self-reported GRE verbal section score |
gre_q |
is the self-reported GRE quantitative section score |
gre_aw |
is the self-reported GRE analytical writing score |
v_pct |
is the percent of verbal section scores below an applicant's self-reported score. Weighted numeric scores are converted to percentile scores using tables 1A and 1B on page 22 of the GRE score guide. Source: https://www.ets.org/s/gre/pdf/gre_guide.pdf. |
q_pct |
is the percent of quantitative section scores below an applicant's self-reported score. Weighted numeric scores are converted to percentile scores using tables 1A and 1B on page 22 of the GRE score guide. Source: https://www.ets.org/s/gre/pdf/gre_guide.pdf. |
aw_pct |
is the percent of analytical writing section scores below an applicant's self-reported score. Weighted numeric scores are converted to percentile scores using tables 1A and 1B on page 22 of the GRE score guide. Source: https://www.ets.org/s/gre/pdf/gre_guide.pdf. |
month |
is the month of the date that an admission decision was made–not the date an applicant uploaded the result to the Grad Cafe. |
day |
is the day of the date that an admission decision was made–not the date an applicant uploaded the result to the Grad Cafe. |
year |
is the day of the date that an admission decision was made–not the date an applicant uploaded the result to the Grad Cafe. |
Several specialty university departments are mapped to their parent institutions. For example, Booth, Wharton, and Teachers College are mapped to the University of Chicago, University of Pennsylvania, and Columbia University, respectively. If you are interested in results for such schools, set map=TRUE and use grep() on the original_name column to locate rows of data with the desired department. See below for an example.
Nathan Welch <nathan.welch@me.com>
Grad Cafe: http://www.thegradcafe.com GRE Score Guide: https://www.ets.org/s/gre/pdf/gre_guide.pdf
findScorePercentile
, parseResults
,
parseSchools
, translateScore
,
getGradCafeData
, getMaxPages
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | #Get data for fall 2015 PhD statistics admission decisions
one_yr_data = brewdata( years=2014 )
head( one_yr_data )
### Remaining examples commented out to satisfy CRAN policies ###
#Get several years of data
#yrs=2014:2015
#multi_yr_data = brewdata( years=yrs ); head( multi_yr_data )
#results_by_school = split(multi_yr_data[,-1],multi_yr_data$school_name)
#Find 2014 results for Chicago Booth
#f14 = brewdata( years=2014, map=TRUE )
#booth = f14[ grepl( "booth", tolower( f14$original_name ) ), ]
#booth
#Continuing with the f15 & school data, let's analyze results from a particular
#school, e.g. University of Washington
#uw = f15_by_school$'univ washington'; uw #show all UW decisions
#uw_stats = uw[ uw$gre_v!=0 & uw$gre_q!=0, ] #UW decisions with GRE stats
#plot( uw_stats$gpa, uw_stats$gre_q, xlab="Undergrad GPA", ylab="GRE Quant Score",
# main="University of Washington GPA vs GRE Quant", pch=NA )
#col_key = c('darkgreen','gold','red','black','darkgrey')
#lab = factor( uw_stats$decision, levels=c('A','W','R','I','N') )
#text( uw_stats$gpa, uw_stats$gre_q, label=lab, col=col_key[lab], cex=0.85 )
#Plot the last two years of Berkeley's GPA/GRE Quant decision trends
#yrs=2013:2014
#data = brewdata( years=yrs ); head( data )
#berk = split(data[,-1],data$school_name)$'univ california berkeley'
#berk_stats = berk[ berk$gre_v!=0 & berk$gre_q!=0, ]
#plot( berk_stats$gpa, berk_stats$gre_q, xlab="Undergrad GPA", ylab="GRE Quant Score",
# main="Berkeley GPA vs GRE Quant Fall 2010-2015", pch=NA )
#col_key = c('darkgreen','gold','red','black','darkgrey')
#lab = factor( berk_stats$decision, levels=c('A','W','R','I','N') )
#points( jitter( berk_stats$gpa ), jitter( berk_stats$gre_q ),
# col=col_key[lab], pch=20)
#lgd=c("Accepted", "Wait listed", "Rejected", "Interview", "Not Reported" )
#legend( "bottomleft", legend=lgd, col=col_key, pch=20, bty="n", cex=0.75 )
#Plot several years of results of Duke results using the same data from the
#Berkeley download.
#library( scatterplot3d )
#library( rgl )
#duke = split(data[,-1],data$school_name)$'duke univ'
#duke_stats = duke[ duke$gre_v!=0 & duke$gre_q!=0, ]
#col_key = c('darkgreen','gold','red','black','darkgrey')
#lab = factor( duke_stats$decision, levels=c('A','W','R','I','N') )
#scatterplot3d( duke_stats$gpa, duke_stats$gre_q, duke_stats$gre_v,
# xlab="Undergrad GPA", ylab="GRE Quant Score", zlab="GRE Verbal Score",
# main="Duke GPA vs GRE Quant vs GRE Verbal Fall 2010-2015", pch=20,
# color=col_key[lab] )
#plot3d( duke_stats$gpa, duke_stats$gre_q, duke_stats$gre_v,
# xlab="Undergrad GPA", ylab="GRE Quant Score", zlab="GRE Verbal Score",
# main="Duke GPA vs GRE Quant vs GRE Verbal Fall 2010-2015", pch=20,
# col=col_key[lab] )
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