delay

rainfall data1_delay (d_delay)

1. 데이터 불러오기

d_delay <- read.csv("rainfall_data1_delay.csv")
d_delay3 <- read.csv("rainfall_data1_delay_정제.csv")

2. 데이터 시각화-전체 3x3

par(mfrow=c(3,3))
ts.plot(d_delay[2]) # 73
ts.plot(d_delay[3]) # 40
ts.plot(d_delay[4]) # 60
ts.plot(d_delay[5]) # 22
ts.plot(d_delay[6]) # 63
ts.plot(d_delay[7]) 
ts.plot(d_delay[8]) # 38
ts.plot(d_delay[9]) 
ts.plot(d_delay[10]) # 62

ts.plot(d_delay[11]) # 45
ts.plot(d_delay[12]) # 40
ts.plot(d_delay[13])
ts.plot(d_delay[14])
ts.plot(d_delay[15])
ts.plot(d_delay[16])
ts.plot(d_delay[17]) # 40
ts.plot(d_delay[18]) # 43
ts.plot(d_delay[19])

ts.plot(d_delay[20]) # 68
ts.plot(d_delay[21])
ts.plot(d_delay[22]) # 60
ts.plot(d_delay[23]) # 65

데이터 시각화 - 결측치 제외

par(mfrow=c(1,1))
ts.plot(d_delay[c(2,3,4,5,6,8,10,11,12,17,18,20,22,23)]) # 결측치 제외

ts.plot(d_delay[2:23])  # 전체

ts.plot(d_delay[c(2,4,6,10,20,22,23)])  # 60~70

ts.plot(d_delay[c(3,11,12,17,18)]) # 40

데이터 시각화 - 결측치 제외, 첫 피크

# 데이터 전체 2,4,5,18,23
# 첫번째 피크 ~ 0이하 3,6,8,10,11,12,17,20,22
# 나머지 제외
ts.plot(d_delay3[c(2,3,4,5,6,8,10,11,12,17,18,20,22)])

rainfall inflow delay (i_delay)

1. 데이터 불러오기

i_delay <- read.csv("rainfall_inflow_delay.csv")
i_delay3 <- read.csv("rainfall_inflow_delay정제.csv")

2. 데이터 시각화-전체 3x3

par(mfrow=c(3,3))
ts.plot(i_delay[2])
ts.plot(i_delay[3]) 
ts.plot(i_delay[4]) 
ts.plot(i_delay[5]) 
ts.plot(i_delay[6]) 
ts.plot(i_delay[7]) 
ts.plot(i_delay[8]) 
ts.plot(i_delay[9]) 
ts.plot(i_delay[10])

ts.plot(i_delay[11])
ts.plot(i_delay[12]) 
ts.plot(i_delay[13])
ts.plot(i_delay[14])
ts.plot(i_delay[15])
ts.plot(i_delay[16])
ts.plot(i_delay[17]) 
ts.plot(i_delay[18]) 
ts.plot(i_delay[19])

ts.plot(i_delay[20])
ts.plot(i_delay[21])
ts.plot(i_delay[22])
ts.plot(i_delay[23])

데이터 시각화 - 결측치 제외, 첫 피크

# 전체 3,8,15,16,19,21,22
# 첫 피크 6,9,10,11,17,18,20,23
# 나머지 제외
ts.plot(i_delay3[,c(3,6,8,9,10,11,15,16,17,18,19,20,21,22,23)])

data1 inflow delay

1. 데이터 불러오기

data_i_delay <- read.csv("data1_inflow_delay.csv")

2. 데이터 시각화-전체 3x3

par(mfrow=c(3,3))
ts.plot(data_i_delay[2])
ts.plot(data_i_delay[3]) 
ts.plot(data_i_delay[4])
ts.plot(data_i_delay[5]) 
ts.plot(data_i_delay[6]) 
ts.plot(data_i_delay[7]) 
ts.plot(data_i_delay[8])
ts.plot(data_i_delay[9]) 
ts.plot(data_i_delay[10]) 

ts.plot(data_i_delay[11])
ts.plot(data_i_delay[12])
ts.plot(data_i_delay[13])
ts.plot(data_i_delay[14])
ts.plot(data_i_delay[15])
ts.plot(data_i_delay[16])
ts.plot(data_i_delay[17])
ts.plot(data_i_delay[18])
ts.plot(data_i_delay[19])

ts.plot(data_i_delay[20])
ts.plot(data_i_delay[21])
ts.plot(data_i_delay[22])
ts.plot(data_i_delay[23])

데이터 시각화-전체

ts.plot(data_i_delay[,seq(2,23)])

데이터 시각화-유형별

# 22만 다르게 생겨서 제외
par(mfrow=c(1,1))
ts.plot(data_i_delay[,c(2,6,7,10,20,23)])

ts.plot(data_i_delay[,c(3,4,5,8,11,12,13)])

ts.plot(data_i_delay[,c(14,15,16,19,21)])

ts.plot(data_i_delay[,c(9,17,18)])