urban$X19.1Q <- mean(urban$X19.Jan + urban$X19.Feb)
View(urban)
apartment_price_urban <- read.csv("apartment_price_urban.csv",
stringsAsFactors = F,
fileEncoding = "euc-kr",
encoding="utf-8")
urban <- apartment_price_urban
View(urban)
urban <- t(urban)
urban <- data.frame(urban)
View(urban)
urban <- apartment_price_urban
View(urban)
urban <- apartment_price_urban
urban$X16.1Q <- urban$X16.Feb + urban$X16.Mar
urban$X16.2Q <- urban$X16.Apr + urban$X16.May + urban$X16.Jun
urban$X16.3Q <- urban$X16.Jul + urban$X16.Aug + urban$X16.Sep
urban$X16.4Q <- urban$X16.Oct + urban$X16.Nov + urban$X16.Dec
urban$X17.1Q <- urban$X17.Jan + urban$X17.Feb + urban$X17.Mar
urban$X17.2Q <- urban$X17.Apr + urban$X17.May + urban$X17.Jun
urban$X17.3Q <- urban$X17.Jul + urban$X17.Aug + urban$X17.Sep
urban$X17.4Q <- urban$X17.Oct + urban$X17.Nov + urban$X17.Dec
urban$X18.1Q <- urban$X18.Jan + urban$X18.Feb + urban$X18.Mar
urban$X18.2Q <- urban$X18.Apr + urban$X18.May + urban$X18.Jun
urban$X18.3Q <- urban$X18.Jul + urban$X18.Aug + urban$X18.Sep
urban$X18.4Q <- urban$X18.Oct + urban$X18.Nov + urban$X18.Dec
urban$X19.1Q <- urban$X19.Jan + urban$X19.Feb
View(urban)
urban$X16.Feb
urban$X16.Feb/2
urban$X16.1Q / 2
urban$X16.1Q <- urban$X16.1Q / 2
urban$X16.2Q <- urban$X16.2Q / 3
urban$X16.3Q <- urban$X16.3Q / 3
urban$X16.4Q <- urban$X16.4Q / 3
urban$X17.1Q <- urban$X17.1Q / 3
urban$X17.2Q <- urban$X17.2Q / 3
urban$X17.3Q <- urban$X17.3Q / 3
urban$X17.4Q <- urban$X17.4Q / 3
urban$X18.1Q <- urban$X18.1Q / 3
urban$X18.2Q <- urban$X18.2Q / 3
urban$X18.3Q <- urban$X18.3Q / 3
urban$X18.4Q <- urban$X18.4Q / 3
urban$X19.1Q <- urban$X19.1Q / 2
View(urban)
urban <- urban[, -c(1:38)]
View(urban)
colnames(urban) <- NULL
rownames(urban) <- NULL
urban <- t(urban)
urban <- data.frame(urban)
colnames(urban) <- c(size)
rownames(urban) <- timing
urban <- t(urban)
urban <- data.frame(urban)
average_urban <- c(mean(urban$X16.1Q), mean(urban$X16.2Q), mean(urban$X16.3Q), mean(urban$X16.4Q),
mean(urban$X17.1Q), mean(urban$X17.2Q), mean(urban$X17.3Q), mean(urban$X17.4Q),
mean(urban$X18.1Q), mean(urban$X18.2Q), mean(urban$X18.3Q), mean(urban$X18.4Q),
mean(urban$X19.1Q))
urban <- rbind(urban, average)
View(urban)
urban <- urban[-c(1:5),]
rownames(urban) <- "avg"
View(urban)
urban <- t(urban)
urban <- data.frame(urban)
View(urban)
rownames(urban) <- NULL
colnames(urban) <- NULL
urban <- cbind(timing, urban)
colnames(urban) <- c("timing", "avg")
rownames(urban) <- timing
View(urban)
urban_ = melt(id=1, data=urban)
head(urban_)
colnames(urban_) <- c("timing", "size_avg", "price")
head(urban_)
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
urban_price <- urban[-c(1),]
urban_price <- urban_price[-c(12),]
urban_price = melt(id=1, data=urban_price)
colnames(urban_price) <- c("timing", "size_avg", "price")
head(urban_price)
ggplot(data=urban_price, aes(x=timing, y=price, group=size_avg)) + geom_line()
apartment_price_rural <- read.csv("apartment_price_rural.csv",
stringsAsFactors = F,
fileEncoding = "euc-kr",
encoding = "utf-8")
rural <- apartment_price_rural
View(rural)
rural$X16.1Q <- rural$X16.Feb + rural$X16.Mar
rural$X16.2Q <- rural$X16.Apr + rural$X16.May + rural$X16.Jun
rural$X16.3Q <- rural$X16.Jul + rural$X16.Aug + rural$X16.Sep
rural$X16.4Q <- rural$X16.Oct + rural$X16.Nov + rural$X16.Dec
rural$X17.1Q <- rural$X17.Jan + rural$X17.Feb + rural$X17.Mar
rural$X17.2Q <- rural$X17.Apr + rural$X17.May + rural$X17.Jun
rural$X17.3Q <- rural$X17.Jul + rural$X17.Aug + rural$X17.Sep
rural$X17.4Q <- rural$X17.Oct + rural$X17.Nov + rural$X17.Dec
rural$X18.1Q <- rural$X18.Jan + rural$X18.Feb + rural$X18.Mar
rural$X18.2Q <- rural$X18.Apr + rural$X18.May + rural$X18.Jun
rural$X18.3Q <- rural$X18.Jul + rural$X18.Aug + rural$X18.Sep
rural$X18.4Q <- rural$X18.Oct + rural$X18.Nov + rural$X18.Dec
rural$X19.1Q <- rural$X19.Jan + rural$X19.Feb
rural$X16.1Q <- rural$X16.1Q / 2
rural$X16.2Q <- rural$X16.2Q / 3
rural$X16.3Q <- rural$X16.3Q / 3
rural$X16.4Q <- rural$X16.4Q / 3
rural$X17.1Q <- rural$X17.1Q / 3
rural$X17.2Q <- rural$X17.2Q / 3
rural$X17.3Q <- rural$X17.3Q / 3
rural$X17.4Q <- rural$X17.4Q / 3
rural$X18.1Q <- rural$X18.1Q / 3
rural$X18.2Q <- rural$X18.2Q / 3
rural$X18.3Q <- rural$X18.3Q / 3
rural$X18.4Q <- rural$X18.4Q / 3
rural$X19.1Q <- rural$X19.1Q / 2
View(rural)
rural <- rural[, -c(1:38)]
View(rural)
colnames(rural) <- NULL
rownames(rural) <- NULL
View(rural)
rural <- t(rural)
rural <- data.frame(rural)
View(rural)
colnames(rural) <- size
rownames(rural) <- timing
View(rural)
rural <- t(rural)
rural <- data.frame(rural)
View(rural)
average_rural <- c(mean(rural$X16.1Q), mean(rural$X16.2Q), mean(rural$X16.3Q), mean(rural$X16.4Q),
mean(rural$X17.1Q), mean(rural$X17.2Q), mean(rural$X17.3Q), mean(rural$X17.4Q),
mean(rural$X18.1Q), mean(rural$X18.2Q), mean(rural$X18.3Q), mean(rural$X18.4Q),
mean(rural$X19.1Q))
rural <- rbind(rural, average_rural)
View(rural)
rural <- rural[-c(1:5),]
rownames(rural) <- "avg"
View(rural)
rural <- t(rural)
rural <- data.frame(rural)
View(rural)
colnames(rural) <- NULL
rownames(rural) <- NULL
View(rural)
rural <- cbind(timing, rural)
colnames(rural) <- c("timing", "avg")
rownames(rural) <- timing
View(rural)
library(reshape2)
rural_ = melt(id=1, data=rural)
head(rural_)
colnames(rural_) <- c("timing", "size_avg", "price")
head(rural_)
library(ggplot2)
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
apartment_price_urban <- read.csv("apartment_price_urban.csv",
stringsAsFactors = F,
fileEncoding = "euc-kr",
encoding="utf-8")
urban <- apartment_price_urban
urban$X16.1Q <- urban$X16.Feb + urban$X16.Mar
urban$X16.2Q <- urban$X16.Apr + urban$X16.May + urban$X16.Jun
urban$X16.3Q <- urban$X16.Jul + urban$X16.Aug + urban$X16.Sep
urban$X16.4Q <- urban$X16.Oct + urban$X16.Nov + urban$X16.Dec
urban$X17.1Q <- urban$X17.Jan + urban$X17.Feb + urban$X17.Mar
urban$X17.2Q <- urban$X17.Apr + urban$X17.May + urban$X17.Jun
urban$X17.3Q <- urban$X17.Jul + urban$X17.Aug + urban$X17.Sep
urban$X17.4Q <- urban$X17.Oct + urban$X17.Nov + urban$X17.Dec
urban$X18.1Q <- urban$X18.Jan + urban$X18.Feb + urban$X18.Mar
urban$X18.2Q <- urban$X18.Apr + urban$X18.May + urban$X18.Jun
urban$X18.3Q <- urban$X18.Jul + urban$X18.Aug + urban$X18.Sep
urban$X18.4Q <- urban$X18.Oct + urban$X18.Nov + urban$X18.Dec
urban$X19.1Q <- urban$X19.Jan + urban$X19.Feb
urban$X16.1Q <- urban$X16.1Q / 2
urban$X16.2Q <- urban$X16.2Q / 3
urban$X16.3Q <- urban$X16.3Q / 3
urban$X16.4Q <- urban$X16.4Q / 3
urban$X17.1Q <- urban$X17.1Q / 3
urban$X17.2Q <- urban$X17.2Q / 3
urban$X17.3Q <- urban$X17.3Q / 3
urban$X17.4Q <- urban$X17.4Q / 3
urban$X18.1Q <- urban$X18.1Q / 3
urban$X18.2Q <- urban$X18.2Q / 3
urban$X18.3Q <- urban$X18.3Q / 3
urban$X18.4Q <- urban$X18.4Q / 3
urban$X19.1Q <- urban$X19.1Q / 2
urban <- urban[, -c(1:38)]
colnames(urban) <- NULL
rownames(urban) <- NULL
urban <- t(urban)
urban <- data.frame(urban)
colnames(urban) <- c(size)
rownames(urban) <- timing
urban <- t(urban)
urban <- data.frame(urban)
average_urban <- c(mean(urban$X16.1Q), mean(urban$X16.2Q), mean(urban$X16.3Q), mean(urban$X16.4Q),
mean(urban$X17.1Q), mean(urban$X17.2Q), mean(urban$X17.3Q), mean(urban$X17.4Q),
mean(urban$X18.1Q), mean(urban$X18.2Q), mean(urban$X18.3Q), mean(urban$X18.4Q),
mean(urban$X19.1Q))
urban <- rbind(urban, average_urban)
urban <- urban[-c(1:5),]
rownames(urban) <- "avg"
urban <- t(urban)
urban <- data.frame(urban)
rownames(urban) <- NULL
colnames(urban) <- NULL
urban <- cbind(timing, urban)
colnames(urban) <- c("timing", "avg")
rownames(urban) <- timing
urban_ = melt(id=1, data=urban)
head(urban_)
colnames(urban_) <- c("timing", "size_avg", "price")
head(urban_)
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
library(ggplot2)
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
View(rural)
head(rural_)
interest_rate <- read.csv("rate_for_balance.csv",
stringsAsFactors = F,
fileEncoding = "euc-kr",
encoding = "utf-8")
interest_rate <- read.csv("rate_for_balance.csv",
stringsAsFactors = F,
fileEncoding = "euc-kr",
encoding = "utf-8")
View(interest_rate)
rate_for_balance <- read.csv("rate_for_balance.csv",
stringsAsFactors = F,
fileEncoding = "euc-kr",
encoding = "utf-8")
interest_rate <- rate_for_balance
View(interest_rate)
interest_rate$X16.1Q <- (interest_rate$X16.Feb + interest_rate$X16.Mar) / 2
interest_rate$X16.2Q <- (interest_rate$X16.Apr + interest_rate$X16.May + interest_rate$X16.Jun) / 3
interest_rate$X16.3Q <- (interest_rate$X16.Jul + interest_rate$X16.Aug + interest_rate$X16.Sep) / 3
interest_rate$X16.4Q <- (interest_rate$X16.Oct + interest_rate$X16.Nov + interest_rate$X16.Dec) / 3
interest_rate$X17.1Q <- (interest_rate$X17.Jan + interest_rate$X17.Feb + interest_rate$X17.Mar) / 3
interest_rate$X17.2Q <- (interest_rate$X17.Apr + interest_rate$X17.May + interest_rate$X17.Jun) / 3
interest_rate$X17.3Q <- (interest_rate$X17.Jul + interest_rate$X17.Aug + interest_rate$X17.Sep) / 3
interest_rate$X17.4Q <- (interest_rate$X17.Oct + interest_rate$X17.Nov + interest_rate$X17.Dec) / 3
interest_rate$X18.1Q <- (interest_rate$X18.Jan + interest_rate$X18.Feb + interest_rate$X18.Mar) / 3
interest_rate$X18.2Q <- (interest_rate$X18.Apr + interest_rate$X18.May + interest_rate$X18.Jun) / 3
interest_rate$X18.3Q <- (interest_rate$X18.Jul + interest_rate$X18.Aug + interest_rate$X18.Sep) / 3
interest_rate$X18.4Q <- (interest_rate$X18.Oct + interest_rate$X18.Nov + interest_rate$X18.Dec) / 3
interest_rate$X19.1Q <- (interest_rate$X19.Jan + interest_rate$X19.Feb) / 2
View(interest_rate)
interest_rate <- interest_rate[, -c(1:38)]
View(interest_rate)
colnames(interest_rate) <- NULL
rownames(interest_rate) <- NULL
View(interest_rate)
interest_rate <- t(interest_rate)
interest_rate <- data.frame(interest_rate)
View(interest_rate)
rownames(interest_rate) <- timing
View(interest_rate)
interest_rate <- cbind(timing, interest_rate)
View(interest_rate)
library(reshape2)
View(rural)
rate <- melt(id=1, data=interest_rate)
head(rate)
colnames(rate) <- c("timing", "default", "rate")
head(rate)
library(ggplot2)
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rural_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
plot(urban_, type="l")
View(urban_)
View(urban)
View(interest_rate)
View(urban)
interest_rate$interest_rate
cbind(urban, interest_rate$interest_rate)
result <- cbind(urban, interest_rate$interest_rate)
View(result)
library(dplyr)
result$`interest_rate$interest_rate` <- rename(result, interest_rate = interest_rate$interest_rate)
result <- rename(result, interest_rate = interest_rate$interest_rate)
result <- rename(result, interest_rate = `interest_rate$interest_rate`)
View(result)
result_graph <- ggplot(result, aes(x=timing))
result_graph <- result_graph + geom_line(aes(y=avg))
result_graph
result_graph <- result_graph + geom_line(aes(y=rate))
result_graph
result_graph <- ggplot(result, aes(x=timing))
result_graph <- result_graph + geom_line(aes(y=avg, colour="Average of Apartment Price"))
result_graph <- result_graph + geom_line(aes(y=rate, colour="Interest Rate"))
result_graph <- result_graph + scale_y_continuous(sec.axis = sec_axis(~.*5, name="Interest Rate"))
result_graph <- result_graph + scale_colour_manual(values = c("blue", "red"))
result_graph <- result_graph + labs(y = "Average of Apartment price",
x = "Timing",
colour = "Parameter")
result_graph <- result_graph + theme(legend.position = c(0.8, 0.9))
result_graph
plot(result$avg ~ result$timing, type="l")
plot(result$avg ~ result$timing)
plot(result$avg ~ result$timing)
lines(result$timing, result$avg)
lines(result$timing, result$avg, col="red")
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
lines(result$timing, result$avg, col="red")
lines(result$timing, result$avg, col="red")
lines(result$timing, result$avg, col="red")
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
result <- cbind(urban, interest_rate$interest_rate)
result <- rename(result, interest_rate = `interest_rate$interest_rate`)
View(result)
lines(result$timing, result$avg, col="red")
lines(result$timing, result$avg, col="red")
ggplot(data = urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
lines(result$timing, result$avg, col="red")
lines(result$timing, result$avg)
result <- cbind(urban, interest_rate$interest_rate)
result <- rename(result, interest_rate = `interest_rate$interest_rate`)
lines(result$timing, result$avg)
View(result)
result <- t(result)
result <- data.frame(result)
View(result)
result <- t(result)
result <- data.frame(result)
View(result)
install.packages("devtools")
devtools::install_github('trinker/plotflow')
library(plotflow)
devtools::install_github('trinker/plotflow')
library(plotflow)
plotflow::ggdual_axis(
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line(),
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
)
plotflow::ggdual_axis(
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg), colour="red") + geom_line(),
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
)
plotflow::ggdual_axis(
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg), colours="red") + geom_line(),
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
)
plotflow::ggdual_axis(
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line(color = 'red'),
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
)
plotflow::ggdual_axis(
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line(color = 'red'),
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line(color = 'blue')
)
plotflow::ggdual_axis(
ggplot(data=urban, aes(x=timing, y=price, group=size_avg)) + geom_line(color = 'red'),
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line(color = 'blue')
)
plotflow::ggdual_axis(
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line(color = 'red'),
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line(color = 'blue')
)
View(apartment_price_urban)
urban <- apartment_price_urban
urban$X16.1Q <- urban$X16.Feb + urban$X16.Mar
urban$X16.2Q <- urban$X16.Apr + urban$X16.May + urban$X16.Jun
urban$X16.3Q <- urban$X16.Jul + urban$X16.Aug + urban$X16.Sep
urban$X16.4Q <- urban$X16.Oct + urban$X16.Nov + urban$X16.Dec
urban$X17.1Q <- urban$X17.Jan + urban$X17.Feb + urban$X17.Mar
urban$X17.2Q <- urban$X17.Apr + urban$X17.May + urban$X17.Jun
urban$X17.3Q <- urban$X17.Jul + urban$X17.Aug + urban$X17.Sep
urban$X17.4Q <- urban$X17.Oct + urban$X17.Nov + urban$X17.Dec
urban$X18.1Q <- urban$X18.Jan + urban$X18.Feb + urban$X18.Mar
urban$X18.2Q <- urban$X18.Apr + urban$X18.May + urban$X18.Jun
urban$X18.3Q <- urban$X18.Jul + urban$X18.Aug + urban$X18.Sep
urban$X18.4Q <- urban$X18.Oct + urban$X18.Nov + urban$X18.Dec
urban$X19.1Q <- urban$X19.Jan + urban$X19.Feb
urban$X16.1Q <- urban$X16.1Q / 2
urban$X16.2Q <- urban$X16.2Q / 3
urban$X16.3Q <- urban$X16.3Q / 3
urban$X16.4Q <- urban$X16.4Q / 3
urban$X17.1Q <- urban$X17.1Q / 3
urban$X17.2Q <- urban$X17.2Q / 3
urban$X17.3Q <- urban$X17.3Q / 3
urban$X17.4Q <- urban$X17.4Q / 3
urban$X18.1Q <- urban$X18.1Q / 3
urban$X18.2Q <- urban$X18.2Q / 3
urban$X18.3Q <- urban$X18.3Q / 3
urban$X18.4Q <- urban$X18.4Q / 3
urban$X19.1Q <- urban$X19.1Q / 2
View(urban)
urban <- urban[, -c(1:38)]
View(urban)
urban <- apartment_price_urban
urban$X16.1Q <- urban$X16.Feb + urban$X16.Mar
urban$X16.2Q <- urban$X16.Apr + urban$X16.May + urban$X16.Jun
urban$X16.3Q <- urban$X16.Jul + urban$X16.Aug + urban$X16.Sep
urban$X16.4Q <- urban$X16.Oct + urban$X16.Nov + urban$X16.Dec
urban$X17.1Q <- urban$X17.Jan + urban$X17.Feb + urban$X17.Mar
urban$X17.2Q <- urban$X17.Apr + urban$X17.May + urban$X17.Jun
urban$X17.3Q <- urban$X17.Jul + urban$X17.Aug + urban$X17.Sep
urban$X17.4Q <- urban$X17.Oct + urban$X17.Nov + urban$X17.Dec
urban$X18.1Q <- urban$X18.Jan + urban$X18.Feb + urban$X18.Mar
urban$X18.2Q <- urban$X18.Apr + urban$X18.May + urban$X18.Jun
urban$X18.3Q <- urban$X18.Jul + urban$X18.Aug + urban$X18.Sep
urban$X18.4Q <- urban$X18.Oct + urban$X18.Nov + urban$X18.Dec
urban$X19.1Q <- urban$X19.Jan + urban$X19.Feb
urban$X16.1Q <- urban$X16.1Q / 2
urban$X16.2Q <- urban$X16.2Q / 3
urban$X16.3Q <- urban$X16.3Q / 3
urban$X16.4Q <- urban$X16.4Q / 3
urban$X17.1Q <- urban$X17.1Q / 3
urban$X17.2Q <- urban$X17.2Q / 3
urban$X17.3Q <- urban$X17.3Q / 3
urban$X17.4Q <- urban$X17.4Q / 3
urban$X18.1Q <- urban$X18.1Q / 3
urban$X18.2Q <- urban$X18.2Q / 3
urban$X18.3Q <- urban$X18.3Q / 3
urban$X18.4Q <- urban$X18.4Q / 3
urban$X19.1Q <- urban$X19.1Q / 2
View(urban)
urban <- urban[, -c(1:38)]
View(urban)
colnames(urban) <- NULL
rownames(urban) <- NULL
urban <- t(urban)
urban <- data.frame(urban)
View(urban)
colnames(urban) <- size
rownames(urban) <- timing
urban <- t(urban)
urban <- data.frame(urban)
urban <- rbind(urban, average_urban)
View(urban)
urban <- urban[-c(1:5),]
rownames(urban) <- "avg"
urban <- t(urban)
urban <- data.frame(urban)
rownames(urban) <- NULL
colnames(urban) <- NULL
urban <- cbind(timing, urban)
colnames(urban) <- c("timing", "avg")
rownames(urban) <- timing
library(reshape2)
urban_ = melt(id=1, data=urban)
head(urban_)
colnames(urban_) <- c("timing", "size_avg", "price")
head(urban_)
library(ggplot2)
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line()
rate_for_balance <- read.csv("rate_for_balance.csv",
stringsAsFactors = F,
fileEncoding = "euc-kr",
encoding = "utf-8")
interest_rate <- rate_for_balance
View(interest_rate)
interest_rate$X16.1Q <- (interest_rate$X16.Feb + interest_rate$X16.Mar) / 2
interest_rate$X16.2Q <- (interest_rate$X16.Apr + interest_rate$X16.May + interest_rate$X16.Jun) / 3
interest_rate$X16.3Q <- (interest_rate$X16.Jul + interest_rate$X16.Aug + interest_rate$X16.Sep) / 3
interest_rate$X16.4Q <- (interest_rate$X16.Oct + interest_rate$X16.Nov + interest_rate$X16.Dec) / 3
interest_rate$X17.1Q <- (interest_rate$X17.Jan + interest_rate$X17.Feb + interest_rate$X17.Mar) / 3
interest_rate$X17.2Q <- (interest_rate$X17.Apr + interest_rate$X17.May + interest_rate$X17.Jun) / 3
interest_rate$X17.3Q <- (interest_rate$X17.Jul + interest_rate$X17.Aug + interest_rate$X17.Sep) / 3
interest_rate$X17.4Q <- (interest_rate$X17.Oct + interest_rate$X17.Nov + interest_rate$X17.Dec) / 3
interest_rate$X18.1Q <- (interest_rate$X18.Jan + interest_rate$X18.Feb + interest_rate$X18.Mar) / 3
interest_rate$X18.2Q <- (interest_rate$X18.Apr + interest_rate$X18.May + interest_rate$X18.Jun) / 3
interest_rate$X18.3Q <- (interest_rate$X18.Jul + interest_rate$X18.Aug + interest_rate$X18.Sep) / 3
interest_rate$X18.4Q <- (interest_rate$X18.Oct + interest_rate$X18.Nov + interest_rate$X18.Dec) / 3
interest_rate$X19.1Q <- (interest_rate$X19.Jan + interest_rate$X19.Feb) / 2
interest_rate <- interest_rate[, -c(1:38)]
colnames(interest_rate) <- NULL
rownames(interest_rate) <- NULL
interest_rate <- t(interest_rate)
interest_rate <- data.frame(interest_rate)
rownames(interest_rate) <- timing
interest_rate <- cbind(timing, interest_rate)
library(reshape2)
rate <- melt(id=1, data=interest_rate)
colnames(rate) <- c("timing", "default", "rate")
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line()
plotflow::ggdual_axis(
ggplot(data=urban_, aes(x=timing, y=price, group=size_avg)) + geom_line(color = 'red'),
ggplot(data=rate, aes(x=timing, y=rate, group=default)) + geom_line(color = 'blue')
)
