-
Notifications
You must be signed in to change notification settings - Fork 0
/
codes_extra summary figure.R
196 lines (139 loc) · 7.93 KB
/
codes_extra summary figure.R
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
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
setwd("C:/Users/syedm/Desktop/Bioenergy")
data <- read.csv("gin.csv")
data$Jan <- rowMeans(data[,c("jan_peak", "jan_ubase", "jan_lbase")], na.rm=TRUE)
data$Feb <- rowMeans(data[,c("feb_peak", "feb_ubase", "feb_lbase")], na.rm=TRUE)
data$Mar <- rowMeans(data[,c("mar_peak", "mar_ubase", "mar_lbase")], na.rm=TRUE)
data$Apr <- rowMeans(data[,c("apr_ubase", "apr_lbase")], na.rm=TRUE)
data$May <- rowMeans(data[,c("may_ubase", "may_lbase")], na.rm=TRUE)
data$Jun <- rowMeans(data[,c("jun_peak", "jun_ubase", "jun_lbase")], na.rm=TRUE)
data$Jul <- rowMeans(data[,c("jul_peak", "jul_ubase", "jul_lbase")], na.rm=TRUE)
data$Aug <- rowMeans(data[,c("aug_peak", "aug_ubase", "aug_lbase")], na.rm=TRUE)
data$Sep <- rowMeans(data[,c("sep_peak", "sep_ubase", "sep_lbase")], na.rm=TRUE)
data$Jan <- data$jan_peak
data$Feb <- data$feb_peak
data$Mar <- data$mar_peak
data$Apr <- data$apr_ubase
data$May <- data$may_ubase
data$Jun <- data$jun_peak
data$Jul <- data$jul_peak
data$Aug <- data$aug_peak
data$Sep <- data$sep_peak
new_df = subset(data, select = c(Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep))
library(reshape)
library(tidyr)
library(dplyr)
meltData <- melt(new_df)
boxplot(data=meltData, value~variable)
means <- aggregate(value ~ variable, meltData, mean)
library(ggplot2)
p <- ggplot(meltData, aes(factor(variable), value))
p + geom_boxplot(outlier.shape = NA, coef = 0) +
scale_y_continuous(limits = quantile(meltData$value, c(0.1, 0.97))) +
geom_point(data=means, aes(y=value))
df <- data.frame (month = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep"),
prod = c(178.0283, 184.5256, 196.1748, 181.2413, 189.922,
196.3127, 202.3672, 192.5513, 187.5426),
shape = c("star", "dot", "dot", "dot", "dot", "dot", "dot", "dot", "dot"),
label = c("E[i]", "E[i+1]~'|'~mu[P~i+1]", "E[i+2]~'|'~mu[P~i+2]", "E[i+3]~'|'~mu[P~i+3]",
"E[i+4]~'|'~mu[P~i+4]", "E[i+5]~'|'~mu[P~i+5]", "E[i+6]~'|'~mu[P~i+6]",
"E[i+7]~'|'~mu[P~i+7]", "E[i+8]~'|'~mu[P~i+8]"))
df %>% mutate(month =ordered(month, levels = unique(month))) %>%
ggplot(aes(x=month, y=prod, color = factor(shape), shape = factor(shape))) + geom_point(size = 3) +
ylim(170, 210) +
scale_shape_manual(values = c(16, 1)) +
scale_color_manual(values = c("star" = "black",
"dot" = "black")) -> plot
plot + theme(legend.position = "none") + xlab("Month") + ylab("Electricity production (MWh)") +
geom_segment(aes(xend=month), yend=0, linetype="dotted") +
expand_limits(y=0) + theme(text = element_text(size=20)) +
geom_text(aes(label = label, hjust=0.50, vjust=-0.5), size=5, parse=TRUE) +
geom_point(aes(x="Jan", y=178.0283), colour="red", size=3, shape=16) +
scale_x_discrete(expand = c(0, 1)) + theme_classic() +
theme(legend.position = "none") +
theme(text = element_text(size=20))
plot + geom_text(x = "Jul", y = 225,
label = expression(Sigma[i == Jan]^Sept ~ X), size=5) + theme(legend.position = "none") +
theme(legend.position = "none") + xlab("Month") + ylab("Electricity production (MWh)") +
geom_segment(aes(xend=month), yend=0, linetype="dotted") +
expand_limits(y=0) + theme(text = element_text(size=20))
"C[t] ~ (Given ~ CGW-C[t-1])"
df2 <- data.frame (month = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep"),
prod = c(178.0283, 184.5256, 196.1748, 181.2413, 189.922,
196.3127, 202.3672, 192.5513, 187.5426),
shape = c("star", "star", "dot", "dot", "dot", "dot", "dot", "dot", "dot"),
label = c("E[i-1]", "E[i]", "E[i+1]~'|'~mu[P~i+1]", "E[i+2]~'|'~mu[P~i+2]", "E[i+3]~'|'~mu[P~i+3]",
"E[i+4]~'|'~mu[P~i+4]", "E[i+5]~'|'~mu[P~i+5]", "E[i+6]~'|'~mu[P~i+6]",
"E[i+7]~'|'~mu[P~i+7]"))
df2 %>% mutate(month =ordered(month, levels = unique(month))) %>%
ggplot(aes(x=month, y=prod, shape = factor(shape))) + geom_point(size = 3) + ylim(170, 210) +
scale_shape_manual(values = c(16, 1)) -> plot2
plot2 + theme(legend.position = "none") + xlab("Month") + ylab("Electricity production (MWh)") +
geom_segment(aes(xend=month), yend=0, linetype="dotted") +
expand_limits(y=0) +
geom_text(aes(label = label, hjust=0.5, vjust=-0.50), size=5, parse=TRUE) +
geom_point(aes(x="Jan", y=178.0283), colour="red", size=3, shape=16) +
geom_point(aes(x="Feb", y=184.5256), colour="red", size=3, shape=16) +
scale_x_discrete(expand = c(0, 1)) + theme_classic() +
theme(legend.position = "none") +
theme(text = element_text(size=20))
plot2 + geom_text(x = "Jul", y = 225,
label = expression(Sigma[i == Feb]^Sept ~ X - Jan[CGW]), size=5) + theme(legend.position = "none") +
xlab("Month") + ylab("Electricity production (MWh)") +
geom_segment(aes(xend=month), yend=0, linetype="dotted") +
expand_limits(y=0) + theme(text = element_text(size=20))
q = c(.25, .5, .75)
meltData %>%
group_by(variable)%>%
summarise(Mean=mean(value), Median=median(value), q1 = quantile(value, probs = q[1]),
q3 = quantile(value, probs = q[3]), sd=sd(value)) -> df
means <- aggregate(value ~ variable, meltData, mean)
means$value[1] <- 61.25
means$value[8] <- 102.25
means$value[9] <- 88.36
ggplot(df, aes(factor(variable), Median)) + # ggplot2 plot with confidence intervals
geom_point(size=3) +
geom_errorbar(aes(ymin = q1, ymax = q3), width = 0.75, size = 1) +
geom_point(data=means, shape=4, size=5, aes(y=value)) +
xlab("Month") + ylab("Electricity price ($/MWh)") +
theme(text = element_text(size=20)) +
geom_point(aes(x="Jan", y=50), colour="red", size=3) +
geom_text(aes(x = "Jan", y = 50,
label = "Obs.P[i]", hjust=-0.05, vjust=0.50, size=8), parse=TRUE) + theme(legend.position = "none")
q = c(.25, .5, .75)
meltData %>%
group_by(variable)%>%
summarise(Mean=mean(value), Median=median(value), q1 = quantile(value, probs = q[1]),
q3 = quantile(value, probs = q[3]), sd=sd(value)) -> df
df$sd[8] <- 566
df$sd[9] <- 764
df$sd[4] <- 128.4
df$sd[5] <- 118.3
df$upper <- df$Mean+df$sd
df$lower <- df$Mean-df$sd
df$label <- c("mu[P~i]", "mu[P~i+1]", "mu[P~i+2]", "mu[P~i+3]",
"mu[P~i+4]", "mu[P~i+5]", "mu[P~i+6]",
"mu[P~i+7]", "mu[P~i+8]")
ggplot(df, aes(factor(variable), Mean)) + # ggplot2 plot with confidence intervals
geom_point(size=3) +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.75, size = 1) +
#geom_point(data=means, shape=16, size=3, aes(y=value)) +
xlab("Month") + ylab("Electricity price ($/MWh)") +
geom_point(aes(x="Jan", y=150), colour="red", size=3) +
geom_text(aes(x = "Jan", y = 150,
label = "Obs.P[i]", hjust=-0.15, vjust=0.50, size=6), parse=TRUE) +
theme_classic() +
theme(legend.position = "none") +
theme(text = element_text(size=20)) +
geom_text(aes(label = label, hjust=-0.15, vjust=1.00), size=5, parse=TRUE) +
scale_x_discrete(expand = c(0, 1))
#gin trash distribution
gin1 <- read.csv("Ropes Coop Gin Trash 2004-2018.csv")
gin2 <- read.csv("gin_quant.csv")
hist(gin1$gin, breaks=10, probability=TRUE, col="gray", border="white", main=NULL, xlab="Cotton Gin Waste (tons)", xlim=c(0, max(gin2$gin)+4000),
ylim=c(0,0.000275), cex.lab=1.5)
d <- density(gin2$gin)
lines(d, col="red", lwd = 3)
#abline(v = median(gin1$gin), col = "black", lwd = 3)
#text(median(gin1$gin), max(density(gin1$gin)[[2]])+0.00003, paste("Observed median =", 8740), pos = 4, srt = 0, cex = 1.50, col = "black")
#abline(v = median(gin2$gin), col = "black", lwd = 3, lty = 2)
#text(median(gin2$gin), max(density(gin2$gin)[[2]]), paste("Simulated median =", 9763), pos = 4, srt = 0, cex = 1.50, col = "black")