.Rhistory

header = TRUE,
na.strings = c("NA")
)
allSignupFreq = rbind(allSignupFreq, missingData)
allSignupFreq[allSignupFreq$Var1 == "2015-01-31",c("Freq")] = 30
allSignupFreq[allSignupFreq$Var1 == "2015-02-02",c("Freq")] = 35
allSignupFreq[allSignupFreq$Var1 == "2015-02-05",c("Freq")] = 20
View(allSignupFreq)
allSignupFreq = as.data.frame(table(newSignupsData$timestamp))
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = NA
missingData = read.table("~/code/athletigen/peacock/missing_data.csv",
sep=",",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NA")
)
allSignupFreq = rbind(allSignupFreq, missingData)
allSignupFreq[allSignupFreq$Var1 == "2015-01-31",c("Freq")] = 30
allSignupFreq[allSignupFreq$Var1 == "2015-02-02",c("Freq")] = 35
allSignupFreq[allSignupFreq$Var1 == "2015-02-05",c("Freq")] = 20
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = 30
allSignupFreq$Var1 = as.Date(as.character(allSignupFreq$Var1))
allSignupFreq = allSignupFreq[with(allSignupFreq, order(Var1)), ]
allSignupFreq$Freq = na.approx(allSignupFreq$Freq)
allSignupFreq$CumFreq = cumsum(allSignupFreq$Freq)
ggplot(allSignupFreq, aes(Var1, CumFreq)) + geom_line(color = "#56B4E9", size = 1) +
scale_y_continuous(breaks = round(seq(0, max(allSignupFreq$CumFreq)+5, by = 1000))) +
xlab("Month") + ylab("Number of Sign Ups") +
ggtitle("Total Sign Ups Trend")
rm(list=ls())
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(xlsx)
library(grid)
library(dplyr)
library(mailR)
library(DBI)
library(RSQLite)
library(zoo)
library(reshape2)
#sequenom has gwas_profile_id, 42
given_date <- Sys.Date()
today = format(given_date, format="%Y-%m-%d")
filename = paste("~/code/data_tmp/account_analytics_2015-07-02.tsv",sep="")
athletigenAnalyticsData = read.table(filename,
sep="\t",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NULL")
)
athletigenAnalyticsData$file_upload_date = as.Date(as.POSIXct(athletigenAnalyticsData$file_upload_date))
athletigenAnalyticsData$account.creation.date = as.Date(as.POSIXct(athletigenAnalyticsData$account.creation.date))
signupsData = read.xlsx2("~/code/athletigen/peacock/account_creation_dates_2.xlsx",
sheetIndex = 1, stringsAsFactors = FALSE)
signupsData$timestamp = as.Date(as.POSIXct(signupsData$timestamp))
# athletigenAnalyticsData = athletigenAnalyticsData[, c(2,3)]
names(athletigenAnalyticsData) = c("account_no", "timestamp")
newSignupsData = rbind(athletigenAnalyticsData, signupsData)
given_date <- Sys.Date()
today = format(given_date, format="%Y-%m-%d")
filename = paste("~/code/data_tmp/account_analytics_2015-07-02.tsv",sep="")
athletigenAnalyticsData = read.table(filename,
sep="\t",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NULL")
)
athletigenAnalyticsData$file_upload_date = as.Date(as.POSIXct(athletigenAnalyticsData$file_upload_date))
athletigenAnalyticsData$account.creation.date = as.Date(as.POSIXct(athletigenAnalyticsData$account.creation.date))
signupsData = read.xlsx2("~/code/athletigen/peacock/account_creation_dates_2.xlsx",
sheetIndex = 1, stringsAsFactors = FALSE)
signupsData$timestamp = as.Date(as.POSIXct(signupsData$timestamp))
View(signupsData)
View(athletigenAnalyticsData)
athletigenAnalyticsData = athletigenAnalyticsData[, c(2,3)]
names(athletigenAnalyticsData) = c("account_no", "timestamp")
newSignupsData = rbind(athletigenAnalyticsData, signupsData)
genoSignupData = read.xlsx2("~/coderepo/athletigen-analytics/account_creation_dates_2.xlsx", sheetIndex = 2, stringsAsFactors = FALSE)
genoSignupData = read.xlsx2("~/code/athletigen/peacock/account_creation_dates_2.xlsx", sheetIndex = 2, stringsAsFactors = FALSE)
genoSignupData$X. = NULL
genoSignupData$X..1 = NULL
genoSignupData$X..2 = NULL
genoSignupData = genoSignupData[!(genoSignupData$account_no == ""),]
mergedData = merge(x = signupsData, y = genoSignupData, by = "account_no", all.x = TRUE)
dataTransferSignupData = mergedData[!is.na(mergedData$genotype_value),]
demoAccountData = mergedData[is.na(mergedData$genotype_value),]
# 23andme signups
dataTransferSignupData = dataTransferSignupData[,c(2,3)]
names(dataTransferSignupData) = c( "timestamp","account_no")
str(dataTransferSignupData)
transferFreq = as.data.frame(table(dataTransferSignupData$timestamp))
transferAnalyticsData = athletigenAnalyticsData[, c(2,3,11)]
transferAnalyticsData = na.omit(transferAnalyticsData)
transferAnalyticsData = athletigenAnalyticsData[, c(2,3)]
names(transferAnalyticsData) = c("account_no", "timestamp")
transferAnalyticsData = na.omit(transferAnalyticsData)
allSignupFreq = as.data.frame(table(newSignupsData$timestamp))
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = NA
missingData = read.table("~/code/athletigen/peacock/missing_data.csv",
sep=",",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NA")
)
allSignupFreq = rbind(allSignupFreq, missingData)
allSignupFreq[allSignupFreq$Var1 == "2015-01-31",c("Freq")] = 30
allSignupFreq[allSignupFreq$Var1 == "2015-02-02",c("Freq")] = 35
allSignupFreq[allSignupFreq$Var1 == "2015-02-05",c("Freq")] = 20
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = 30
allSignupFreq$Var1 = as.Date(as.character(allSignupFreq$Var1))
allSignupFreq = allSignupFreq[with(allSignupFreq, order(Var1)), ]
allSignupFreq$Freq = na.approx(allSignupFreq$Freq)
allSignupFreq$CumFreq = cumsum(allSignupFreq$Freq)
write.csv(allSignupFreq, file="~/code/athletigen/peacock/all-signup-trend.csv")
aggregateData1 = read.csv("~/code/athletigen/peacock/all-signup-trend.csv")
names(aggregateData1) = c("row.names","Var1", "allFreq","allCumFreq")
p2 <-ggplot(aggregateData1, aes(Var1, TransferCumFreq)) + geom_line(color = "#CCFF01", size = 2) +
scale_y_continuous(breaks = round(seq(0, max(aggregateData1$allCumFreq)+5, by = 1000))) +
xlab("Month") + ylab("Number of Sign Ups") +
ggtitle("Daily total Sign Ups") +
theme(plot.title=element_text(family="Helvetica", face="bold", size=20, color="white"))  +
theme(axis.title=element_text(family="Helvetica", face="bold", size=15, color="white"))  +
theme(axis.text=element_text(family="Helvetica", face="bold", size=10, color="white"))  +
theme(panel.background = element_rect(fill = "black")) +
theme(plot.background = element_rect(fill = "black")) +
theme(panel.grid.minor = element_line(colour = "black")) +
theme(panel.grid.major = element_line(colour = "#4A4A4A"))
p2
View(aggregateData1)
p2 <-ggplot(aggregateData1, aes(Var1, allCumFreq)) + geom_line(color = "#CCFF01", size = 2) +
scale_y_continuous(breaks = round(seq(0, max(aggregateData1$allCumFreq)+5, by = 1000))) +
xlab("Month") + ylab("Number of Sign Ups") +
ggtitle("Daily total Sign Ups") +
theme(plot.title=element_text(family="Helvetica", face="bold", size=20, color="white"))  +
theme(axis.title=element_text(family="Helvetica", face="bold", size=15, color="white"))  +
theme(axis.text=element_text(family="Helvetica", face="bold", size=10, color="white"))  +
theme(panel.background = element_rect(fill = "black")) +
theme(plot.background = element_rect(fill = "black")) +
theme(panel.grid.minor = element_line(colour = "black")) +
theme(panel.grid.major = element_line(colour = "#4A4A4A"))
p2
View(aggregateData1)
ggplot(aggregateData1, aes(Var1, allCumFreq)) + geom_line() +
scale_y_continuous(breaks = round(seq(0, max(aggregateData$value)+5, by = 1000))) +
xlab("Month") + ylab("Number of Sign Ups") +
ggtitle("Total Sign Ups Trend")
ggplot(aggregateData1, aes(Var1, allCumFreq)) + geom_line() +
scale_y_continuous(breaks = round(seq(0, max(aggregateData1$allCumFreq)+5, by = 1000))) +
xlab("Month") + ylab("Number of Sign Ups") +
ggtitle("Total Sign Ups Trend")
aggregateData1 = read.csv("~/code/athletigen/peacock/all-signup-trend.csv")
View(allSignupFreq)
View(allSignupFreq)
View(allSignupFreq)
aggregateData1$Var1 = as.Date(as.character(aggregateData$Var1))
aggregateData1$Var1 = as.Date(as.character(aggregateData1$Var1))
p2 <-ggplot(aggregateData1, aes(Var1, allCumFreq)) + geom_line(color = "#CCFF01", size = 2) +
scale_y_continuous(breaks = round(seq(0, max(aggregateData1$allCumFreq)+5, by = 1000))) +
xlab("Month") + ylab("Number of Sign Ups") +
ggtitle("Daily total Sign Ups") +
theme(plot.title=element_text(family="Helvetica", face="bold", size=20, color="white"))  +
theme(axis.title=element_text(family="Helvetica", face="bold", size=15, color="white"))  +
theme(axis.text=element_text(family="Helvetica", face="bold", size=10, color="white"))  +
theme(panel.background = element_rect(fill = "black")) +
theme(plot.background = element_rect(fill = "black")) +
theme(panel.grid.minor = element_line(colour = "black")) +
theme(panel.grid.major = element_line(colour = "#4A4A4A"))
View(aggregateData1)
source('~/code/athletigen/peacock/Conversion_Trends.R', echo=TRUE)
p2 <-ggplot(aggregateData1, aes(Var1, allCumFreq)) + geom_line(color = "#CCFF01", size = 2) +
scale_y_continuous(breaks = round(seq(0, max(aggregateData1$allCumFreq, na.rm = T)+5, by = 1000))) +
xlab("Month") + ylab("Number of Sign Ups") +
ggtitle("Daily total Sign Ups") +
theme(plot.title=element_text(family="Helvetica", face="bold", size=20, color="white"))  +
theme(axis.title=element_text(family="Helvetica", face="bold", size=15, color="white"))  +
theme(axis.text=element_text(family="Helvetica", face="bold", size=10, color="white"))  +
theme(panel.background = element_rect(fill = "black")) +
theme(plot.background = element_rect(fill = "black")) +
theme(panel.grid.minor = element_line(colour = "black")) +
theme(panel.grid.major = element_line(colour = "#4A4A4A"))
given_date <- Sys.Date()
today = format(given_date, format="%Y-%m-%d")
filename = paste("~/code/data_tmp/account_analytics_2015-07-02.tsv",sep="")
athletigenAnalyticsData = read.table(filename,
sep="\t",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NULL")
)
athletigenAnalyticsData$file_upload_date = as.Date(as.POSIXct(athletigenAnalyticsData$file_upload_date))
athletigenAnalyticsData$account.creation.date = as.Date(as.POSIXct(athletigenAnalyticsData$account.creation.date))
signupsData = read.xlsx2("~/code/athletigen/peacock/account_creation_dates_2.xlsx",
sheetIndex = 1, stringsAsFactors = FALSE)
signupsData$timestamp = as.Date(as.POSIXct(signupsData$timestamp))
athletigenAnalyticsData = athletigenAnalyticsData[, c(2,3)]
names(athletigenAnalyticsData) = c("account_no", "timestamp")
newSignupsData = rbind(athletigenAnalyticsData, signupsData)
allSignupFreq = as.data.frame(table(newSignupsData$timestamp))
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = NA
missingData = read.table("~/code/athletigen/peacock/missing_data.csv",
sep=",",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NA")
)
allSignupFreq = rbind(allSignupFreq, missingData)
allSignupFreq[allSignupFreq$Var1 == "2015-01-31",c("Freq")] = 30
allSignupFreq[allSignupFreq$Var1 == "2015-02-02",c("Freq")] = 35
allSignupFreq[allSignupFreq$Var1 == "2015-02-05",c("Freq")] = 20
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = 30
allSignupFreq$Var1 = as.Date(as.character(allSignupFreq$Var1))
allSignupFreq = allSignupFreq[with(allSignupFreq, order(Var1)), ]
allSignupFreq$Freq = na.approx(allSignupFreq$Freq)
allSignupFreq$CumFreq = cumsum(allSignupFreq$Freq)
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(xlsx)
library(grid)
library(dplyr)
library(mailR)
library(DBI)
library(RSQLite)
library(reshape2)
given_date <- Sys.Date()
today = format(given_date, format="%Y-%m-%d")
filename = paste("~/code/data_tmp/account_analytics_2015-07-02.tsv",sep="")
athletigenAnalyticsData = read.table(filename,
sep="\t",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NULL")
)
athletigenAnalyticsData$file_upload_date = as.Date(as.POSIXct(athletigenAnalyticsData$file_upload_date))
athletigenAnalyticsData$account.creation.date = as.Date(as.POSIXct(athletigenAnalyticsData$account.creation.date))
signupsData = read.xlsx2("~/code/athletigen/peacock/account_creation_dates_2.xlsx",
sheetIndex = 1, stringsAsFactors = FALSE)
signupsData$timestamp = as.Date(as.POSIXct(signupsData$timestamp))
athletigenAnalyticsData = athletigenAnalyticsData[, c(2,3)]
names(athletigenAnalyticsData) = c("account_no", "timestamp")
newSignupsData = rbind(athletigenAnalyticsData, signupsData)
allSignupFreq = as.data.frame(table(newSignupsData$timestamp))
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = NA
missingData = read.table("~/code/athletigen/peacock/missing_data.csv",
sep=",",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NA")
)
allSignupFreq = rbind(allSignupFreq, missingData)
allSignupFreq[allSignupFreq$Var1 == "2015-01-31",c("Freq")] = 30
allSignupFreq[allSignupFreq$Var1 == "2015-02-02",c("Freq")] = 35
allSignupFreq[allSignupFreq$Var1 == "2015-02-05",c("Freq")] = 20
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = 30
allSignupFreq$Var1 = as.Date(as.character(allSignupFreq$Var1))
allSignupFreq = allSignupFreq[with(allSignupFreq, order(Var1)), ]
allSignupFreq$Freq = na.approx(allSignupFreq$Freq)
allSignupFreq$CumFreq = cumsum(allSignupFreq$Freq)
install.packages("zoo")
library(zoo)
allSignupFreq = as.data.frame(table(newSignupsData$timestamp))
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = NA
missingData = read.table("~/code/athletigen/peacock/missing_data.csv",
sep=",",
stringsAsFactors = FALSE,
header = TRUE,
na.strings = c("NA")
)
allSignupFreq = rbind(allSignupFreq, missingData)
allSignupFreq[allSignupFreq$Var1 == "2015-01-31",c("Freq")] = 30
allSignupFreq[allSignupFreq$Var1 == "2015-02-02",c("Freq")] = 35
allSignupFreq[allSignupFreq$Var1 == "2015-02-05",c("Freq")] = 20
allSignupFreq[allSignupFreq$Var1 == "2015-02-09",c("Freq")] = 30
allSignupFreq$Var1 = as.Date(as.character(allSignupFreq$Var1))
allSignupFreq = allSignupFreq[with(allSignupFreq, order(Var1)), ]
allSignupFreq$Freq = na.approx(allSignupFreq$Freq)
allSignupFreq$CumFreq = cumsum(allSignupFreq$Freq)
aggregateData1 = read.csv("~/code/athletigen/peacock/all-signup-trend.csv")
names(aggregateData1) = c("row.names","Var1", "allFreq","allCumFreq")
aggregateData1$Var1 = as.Date(as.character(aggregateData$Var1))
aggregateData1$Var1 = as.Date(as.character(aggregateData1$Var1))
p2 <-ggplot(aggregateData1, aes(Var1, allCumFreq)) + geom_line(color = "#CCFF01", size = 2) +
scale_y_continuous(breaks = round(seq(0, max(aggregateData1$allCumFreq, na.rm = T)+5, by = 1000))) +
xlab("Month") + ylab("Number of Sign Ups") +
ggtitle("Daily total Sign Ups") +
theme(plot.title=element_text(family="Helvetica", face="bold", size=20, color="white"))  +
theme(axis.title=element_text(family="Helvetica", face="bold", size=15, color="white"))  +
theme(axis.text=element_text(family="Helvetica", face="bold", size=10, color="white"))  +
theme(panel.background = element_rect(fill = "black")) +
theme(plot.background = element_rect(fill = "black")) +
theme(panel.grid.minor = element_line(colour = "black")) +
theme(panel.grid.major = element_line(colour = "#4A4A4A"))
p2
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
rm(list=ls())
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(xlsx)
library(grid)
library(dplyr)
library(mailR)
library(DBI)
library(zoo)
library(RSQLite)
library(reshape2)
aggregateData1 = read.csv("~/code/athletigen/peacock/all-signup-trend.csv")
View(aggregateData1)
db_name = "~/code/athletigen/peacock/conversion_metrics.db"
aggregateData1 = read.csv("~/code/athletigen/peacock/all-signup-trend.csv")
aggregateData1$X=NULL
aggregateData1$CumFreq=NULL
db_connection <- dbConnect(RSQLite::SQLite(), db_name)
metric_data <- dbGetQuery(db_connection , paste('SELECT * FROM ', 'daily_accounts_metrics', sep=''))
metric_data$date = as.Date(as.POSIXct(metric_data$date))
View(metric_data)
View(aggregateData1)
View(metric_data)
View(aggregateData1)
View(metric_data)
View(aggregateData1)
View(metric_data)
aggregateData1$date = as.Date(as.POSIXct(aggregateData1$Var1))
View(aggregateData1)
db_name = "~/code/athletigen/peacock/conversion_metrics.db"
aggregateData1 = read.csv("~/code/athletigen/peacock/all-signup-trend.csv")
aggregateData1$date = as.Date(as.POSIXct(aggregateData1$Var1)) + 1
aggregateData1$X=NULL
aggregateData1$CumFreq=NULL
db_connection <- dbConnect(RSQLite::SQLite(), db_name)
metric_data <- dbGetQuery(db_connection , paste('SELECT * FROM ', 'daily_accounts_metrics', sep=''))
metric_data$date = as.Date(as.POSIXct(metric_data$date))
merged_data = merge(aggregateData1, metric_data, by="date")
View(merged_data)
merged_data = merge(aggregateData1, metric_data, by="date", all=T)
View(merged_data)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
library("devtools")
install_github("ropensci/plotly")
set_credentials_file("NehilJain", "lrvk8oiwim")
library(plotly)
library(plotly)
set_credentials_file("NehilJain", "lrvk8oiwim")
library(plotly)
help(signup, package="plotly")
plotly(username = "NehilJain", "lrvk8oiwim")
Sys.setenv("plotly_username"="NehilJain")
Sys.setenv("plotly_api_key"="lrvk8oiwim")
library(plotly)
py <- plotly()
py <- plot_ly()
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
rm(list=ls())
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(xlsx)
library(grid)
library(dplyr)
library(mailR)
library(DBI)
library(zoo)
library(RSQLite)
library(reshape2)
db_name = "~/code/athletigen/peacock/conversion_metrics.db"
aggregateData1 = read.csv("~/code/athletigen/peacock/all-signup-trend.csv")
aggregateData1$date = as.Date(as.POSIXct(aggregateData1$Var1)) + 1
aggregateData1$X=NULL
aggregateData1$CumFreq=NULL
db_connection <- dbConnect(RSQLite::SQLite(), db_name)
metric_data <- dbGetQuery(db_connection , paste('SELECT * FROM ', 'daily_accounts_metrics', sep=''))
metric_data$date = as.Date(as.POSIXct(metric_data$date))
merged_data = merge(aggregateData1, metric_data, by="date")
View(merged_data)
View(metric_data)
metric_data$cumfreq_tot = cumsum(metric_data$total)
View(metric_data)
rm(list=ls())
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(xlsx)
library(grid)
library(dplyr)
library(mailR)
library(DBI)
library(zoo)
library(RSQLite)
library(reshape2)
db_name = "~/code/athletigen/peacock/conversion_metrics.db"
aggregateData1 = read.csv("~/code/athletigen/peacock/all-signup-trend.csv")
aggregateData1$date = as.Date(as.POSIXct(aggregateData1$Var1)) + 1
aggregateData1$X=NULL
aggregateData1$CumFreq=NULL
db_connection <- dbConnect(RSQLite::SQLite(), db_name)
metric_data <- dbGetQuery(db_connection , paste('SELECT * FROM ', 'aggregate_accounts_metrics', sep=''))
metric_data$date = as.Date(as.POSIXct(metric_data$date))
merged_data = merge(aggregateData1, metric_data, by="date")
View(aggregateData1)
metric_data <- dbGetQuery(db_connection , paste('SELECT * FROM ', 'aggregate_accounts_metrics', sep=''))
db_name = "~/code/athletigen/peacock/conversion_metrics.db"
metric_data <- dbGetQuery(db_connection , paste('SELECT * FROM ', 'aggregate_accounts_metrics', sep=''))
9832 - 9832
9832 - 7225
2607 + 5310
-401+2607
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
source('~/code/athletigen/peacock/code/weekly-growth-trends.R', echo=TRUE)
df <- read.table('~/Downloads/ICPSR_34933/DS0001/34933-0001-Data.tsv', sep="\t", nrows = 10)
df
df.head()
hed(df)
head(df)
View(df)
df <- read.table('~/Downloads/ICPSR_34933/DS0001/34933-0001-Data.tsv', sep="\t", nrows = 10, header=T)
df <- read.table('~/Downloads/FIX_TRANSLATED_VIRUSES_COLLAPSED_2010_06_28_md.txt', sep="\t", nrows = 10, header=T)
df
View(df)
df <- read.table('~/Downloads/SRS011061.genels.txt', sep="\t", nrows = 10, header=T)
View(df)
df <- read.table('~/Downloads/FIX_TRANSLATED_VIRUSES_COLLAPSED_2010_06_28_md.txt', sep="\t", nrows = 10, header=T)
View(df)
install.packages("agridat")
library(agridat)
load(baker.barley.uniformity)
data("batchelor.apple.uniformity")
data()
baker.barley.uniformity
df <- baker.barley.uniformity
View(batchelor.apple.uniformity)
View(df)
library(ggplot2)
qplot(df$yield)
qplot(df, x=yield, y=year)
qplot(df, y=df$yield, x=df$year)
clear
library(slidify)
library(devtools)
install_github("slidify", 'ramnathv', ref="dev")
install_github("ramnathv/slidifyLibraries", 'ramnathv', ref="dev")
install_github("ramnathv/rCharts", 'ramnathv', ref="dev")
library(slidify)
library(slidifyLibraries)
getwd()
setwd("~/code/personal/beikome-r-ppt/")
slidify('index.Rmd')
slidify('index.Rmd')
install_version('stringr', version = 0.6.2)
install_version('stringr', version = '0.6.2')
library(stringr)