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extract_donations.R
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extract_donations.R
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source("variables.R")
library(RSQLite)
sqlitedb.filename <- file.path("db", "kdd_cup_data.sqlite3")
# Projects data
drv <- dbDriver("SQLite")
con <- dbConnect(drv, dbname=sqlitedb.filename)
projects.data <- dbGetQuery(
con,
"
select
T1.projectid as projectid,
T1.teacher_acctid as teacher_acctid,
T1.schoolid as schoolid,
T1.school_ncesid as school_ncesid,
T1.school_latitude as school_latitude,
T1.school_longitude as school_longitude,
T1.school_city as school_city,
T1.school_state as school_state,
T1.school_zip as school_zip,
T1.school_metro as school_metro,
T1.school_district as school_district,
T1.school_county as school_county,
T1.school_charter as school_charter,
T1.school_magnet as school_magnet,
T1.school_year_round as school_year_round,
T1.school_nlns as school_nlns,
T1.school_kipp as school_kipp,
T1.school_charter_ready_promise as school_charter_ready_promise,
T1.teacher_prefix as teacher_prefix,
T1.teacher_teach_for_america as teacher_teach_for_america,
T1.teacher_ny_teaching_fellow as teacher_ny_teaching_fellow,
T1.primary_focus_subject as primary_focus_subject,
T1.primary_focus_area as primary_focus_area,
T1.secondary_focus_subject as secondary_focus_subject,
T1.secondary_focus_area as secondary_focus_area,
T1.resource_type as resource_type,
T1.poverty_level as poverty_level,
T1.grade_level as grade_level,
-- T1.fulfillment_labor_materials as fulfillment_labor_materials,
T1.total_price_excluding_optional_support as total_price_excluding_optional_support,
T1.total_price_including_optional_support as total_price_including_optional_support,
T1.students_reached as students_reached,
T1.eligible_double_your_impact_match as eligible_double_your_impact_match,
T1.eligible_almost_home_match as eligible_almost_home_match,
T1.date_posted as date_posted,
T2.typedataset as typedataset
from projects T1 inner join project_dataset T2 on (T1.projectid=T2.projectid)
"
)
dbDisconnect(con)
# Donations data
drv <- dbDriver("SQLite")
con <- dbConnect(drv, dbname=sqlitedb.filename)
donations.data <- dbGetQuery(
con,
"
select
*
from donations
"
)
dbDisconnect(con)
donations.data <- donations.data[, colnames(donations.data) != "row_names"]
# selection
library(lubridate)
projects.data$date_posted <- ymd(projects.data$date_posted)
projects.data$days_since_posted <- (as.integer(ymd("2014-05-12") - projects.data$date_posted)/(3600*24))
# projects.data <- subset(projects.data, days_since_posted <= 350)
# projects.data <- subset(projects.data, days_since_posted <= 180)
projects.data <- subset(projects.data, days_since_posted <= nb.days)
donations.data$donation_date <- ymd(substr(donations.data$donation_timestamp,1,10))
donations.data$days_since_donation <- (as.integer(ymd("2014-05-12") - donations.data$donation_date))
# donations.data <- subset(donations.data, days_since_donation <= 350)
# donations.data <- subset(donations.data, days_since_donation <= 180)
donations.data <- subset(donations.data, days_since_donation <= (nb.days*2))
# agg
library(plyr)
donations.data <- donations.data[order(donations.data$donor_acctid, donations.data$days_since_donation),]
mean.diff.days <- function(vec) {
return(mean(diff(c(0, vec))))
}
median.diff.days <- function(vec) {
return(median(diff(c(0, vec))))
}
donations.by.person.agg <- ddply(
donations.data,
.(donor_acctid),
summarise,
total_donation_to_project=sum(donation_to_project),
max_donation_to_project=max(donation_to_project),
mean_donation_to_project=mean(donation_to_project),
median_donation_to_project=median(donation_to_project),
sd_donation_to_project=sd(donation_to_project),
total_donation_optional_support=sum(donation_optional_support),
max_donation_optional_support=max(donation_optional_support),
mean_donation_optional_support=mean(donation_optional_support),
median_donation_optional_support=median(donation_optional_support),
sd_donation_optional_support=sd(donation_optional_support),
total_donation_total=sum(donation_to_project+donation_optional_support),
max_donation_total=max(donation_to_project+donation_optional_support),
mean_donation_total=mean(donation_to_project+donation_optional_support),
median_donation_total=median(donation_to_project+donation_optional_support),
sd_donation_total=sd(donation_to_project+donation_optional_support),
min_days_since_donation=min(days_since_donation),
max_days_since_donation=max(days_since_donation),
mean_days_since_donation=mean(days_since_donation),
median_days_since_donation=mean(days_since_donation),
sd_days_since_donation=sd(days_since_donation),
mean_diff_days_since_donation=mean.diff.days(days_since_donation),
median_diff_days_since_donation=median.diff.days(days_since_donation),
nb_donation=length(donor_acctid)
)
donations.by.person.agg <- subset(donations.by.person.agg, mean_donation_optional_support < 100000)
donations.by.person.agg <- subset(donations.by.person.agg, nb_donation < 10000)
save(donations.by.person.agg, file=file.path("tmp","donations_by_person_agg.RData"))
# # semantic
# library(tm)
#
# tmp <- ddply(
# donations.data,
# .(donor_acctid),
# summarise,
# donation_message_list=paste(donation_message, collapse=" ")
# )
#
#
# docs <- tmp$item_list
# names(docs) <- as.character(tmp$projectid)
# ds <- VectorSource(docs)
#
#
# print("generation corpus")
# corpus <- VCorpus(ds)
#
# print("generatition dtm")
# corpus <- tm_map(corpus, removeNumbers)
# corpus <- tm_map(corpus, removePunctuation)
# # corpus <- tm_map(corpus, toupper)
# corpus <- tm_map(corpus, stripWhitespace)
# corpus <- tm_map(corpus, stemDocument)
# corpus <- tm_map(corpus, removeWords, stopwords("english"))
#
# dtm <- DocumentTermMatrix(corpus,
# control=list(
# weighting=weightTfIdf,
# stopwords=TRUE))
#
# sparsed.dtm <- removeSparseTerms(dtm, 0.9)
#
# sparsed.dtm.tmp <- inspect(sparsed.dtm)
# sparsed.dtm.tmp <- data.frame(sparsed.dtm.tmp)
# colnames(sparsed.dtm.tmp) <- paste("word", "item_name", colnames(sparsed.dtm.tmp), sep=".")
#
# # for(col in colnames(sparsed.dtm.tmp)) {
# # sparsed.dtm.tmp[, col] <- ifelse(sparsed.dtm.tmp[,col] > 0, 1, 0)
# # }
#
# sparsed.dtm.tmp$projectid <- tmp$projectid
#
# semantic.item_name.data <- sparsed.dtm.tmp
#
# save(semantic.item_name.data, file=file.path("tmp","semantic_item_name.RData"))
#