DATA ANALYSIS REVISION 1
Leonessa T. Adobas
2023-11-16
Objective 1: EXPLORATORY DATA ANALYSIS
FIGURE 1
#Line graph: Figure 1
par(mfrow=c(1,2))
#par(mar = c(0.3)) # Reduce space around plots
#Cassava
Cassava <- c(10.5,7.2, 7.46, 15.76, 35.8)
P1 = plot(Cassava,type="o", col="blue", x = c(2018, 2019, 2020, 2021, 2022), ylim=c(0,40), pch=20, ylab = "percentage", xlab = "years")+title(main="Cassava Production", col.main="black", font.main=1)
# Swine Fattening
SwineF <- c(2.54,4.72,12.14,15.12,20.5)
P2 = plot(SwineF,type="o", col="red", x = c(2018, 2019, 2020, 2021, 2022), ylim=c(0,30), pch=20, ylab = "percentage", xlab = "years")
title(main="Swine Fattening", col.main="black", font.main=1)
# Rice Production
Rice <- c(11.52,26.28,31.13,37.66,43.3)
P3 = plot(Rice,type="o", col="black", x = c(2018, 2019, 2020, 2021, 2022), ylim=c(0,50), pch=20, ylab = "percentage", xlab = "years")
title(main="Rice Production", col.main="black", font.main=1)
# Corn Production
Corn <- c(18.8,17.24,8.12,5.20,15.77)
P4 = plot(Corn,type="o", col="brown", x = c(2018, 2019, 2020, 2021, 2022), ylim=c(0,20), pch=20, ylab = "percentage", xlab = "years")
title(main="Corn Production", col.main="black", font.main=1)
# Swine Production
P5= SwineP <- c(2.54,5.26,6.93,11.50,18.85)
plot(SwineP,type="o", col="orange", x = c(2018, 2019, 2020, 2021, 2022), ylim=c(0,20), pch=20, ylab = "percentage", xlab = "years")
title(main="Swine Production", col.main="black", font.main=1)
# Vegetable Production
Vegetable <- c(12.93,34.29,67.75)
P6 = plot(Vegetable,type="o", col="darkgreen", x = c(2020, 2021, 2022), ylim=c(0,80), pch=20, ylab = "percentage", xlab = "years")+title(main="Vegetable Production", col.main="black", font.main=1)
#GENERAL: LINE GRAPH AGRICULTURAL : FINAL
library(ggplot2)
library(tidyverse)
L = read.csv("LINE_GENERAL.csv")
head(L)## Year Intervention Percentage
## 1 2018 Cassava 10.50
## 2 2018 Swine Fattening 2.54
## 3 2018 Rice 11.52
## 4 2018 Corn 18.80
## 5 2018 Swine Production 2.54
## 6 2019 Cassava 7.20ggplot(L, aes(x=Year, y=Percentage, col= Intervention)) + geom_line() +geom_point()+ theme_gray()+
ggtitle("Figure 1:Percentage of Beneficiaries per Intervention(2018-2022)")+
theme(plot.title = element_text(hjust = 0.5))
#FOLLOW UP:
library(ggplot2)
library(tidyverse)
M1= read.csv("Agri_By_Municipality_Beneficiaries.csv")
M = cbind(M1$Cassava,M1$Corn, M1$Rice, M1$Swine.P, M1$Swine.F, M1$Vegetable)
barplot(M,xlab="Intervention",ylab="Count",ylim = c(0,200),
main="Figure 2: Agricultural Beneficiaries per Municipalities",
col=c("gray","red","yellow","pink","brown","orange","pink","green","blue"),
beside=TRUE, cex.names = .8,
names.arg=c("Cassava","Corn","Rice", "Swine P","Swine F",
"Vegetable"))
legend("topright",c("Bontoc","Malitbog","Sogod", "TO","Liloan",
"Pintuyan","San Francisco","St. Bernard","Libagon"),fill=c("gray","red","yellow","pink","brown","orange","pink","green","blue"),cex = .6)
#Figure 3
#GENERAL: LINE GRAPH FISHERY: FINAL
library(ggplot2)
library(tidyverse)
LF = read.csv("LINE_GRAPH_FISHERY.csv")
head(LF)## Year Intervention Percentage
## 1 2018 Motorized Boat 62.65
## 2 2019 Motorized Boat 61.47
## 3 2022 Bangus Production 44.84
## 4 2020 Motorized Boat 40.00
## 5 2021 Motorized Boat 34.55
## 6 2020 Lobster Production 34.42ggplot(LF, aes(x=Year, y=Percentage, col= Intervention)) + geom_line()+geom_point() + theme_gray()+
ggtitle("Figure 3: Percentage of Fishery Beneficiaries (2018-2022) ")+
theme(plot.title = element_text(hjust = 0.5))
Figure 4
f4 = read.csv("Fishery_By_Municipality_Beneficiaries.csv")
f = cbind(f4$Motorized.Boat,f4$Bangus.P,f4$Lobster.P)
barplot(f,xlab="Intervention",ylab="Count",ylim = c(0,30),
main="Figure 4: Fishery Beneficiaries per Municipalities",
col=c("gray","blue","brown","red","yellow","pink","yellowgreen","orange","lightblue","violet","green"),
beside=TRUE, cex.names = .8,
names.arg=c("Motorized Boat","Bangus P.","Lobster P."))
legend("topright",c("Anahawan","Malitbog","Limasawa", "Bontoc","Libagon",
"Sogod","Hinunangan","Hinundayan","St. Bernard"),fill=c("gray","blue","brown","red","yellow","pink","yellowgreen","orange","lightblue","violet","green"),cex = .6)
#Data
Fig2 = data.frame(read.csv("Figure2.csv"))
head(Fig2)## Year Motorized.Boat Bangus.Production Lobster.Production
## 1 2018 62.65 19.62 17.72
## 2 2019 61.47 11.47 27.06
## 3 2020 40.00 25.58 34.42
## 4 2021 34.55 33.09 32.36
## 5 2022 31.61 44.84 23.55par(mfrow=c(2,3))
#2018
Fig2.slices <- as.numeric(Fig2[1,2:4])
Fig2.labels <- paste(Fig2.slices," %",sep="")
pie(Fig2.slices,Fig2.labels,
col= c("lightblue","yellowgreen","lightpink"),
main="2018")
legend("bottomleft", c("lightblue","yellowgreen","lightpink"),cex=0.6, fill= rainbow(length(Fig2.slices)))
#2019
Fig2.slicesb <- as.numeric(Fig2[2,2:4])
Fig2.labels <- paste(Fig2.slicesb," %",sep="")
pie(Fig2.slicesb,Fig2.labels,
col= c("lightblue","yellowgreen","lightpink"),
main="2019")
legend("bottomleft", c("lightblue","yellowgreen","lightpink"),cex=0.6, fill= rainbow(length(Fig2.slicesb)))
#2020
Fig2.slicesc <- as.numeric(Fig2[3,2:4])
Fig2.labels <- paste(Fig2.slicesc," %",sep="")
pie(Fig2.slicesc,Fig2.labels,
col= c("lightblue","yellowgreen","lightpink"),
main="2020")
legend("bottomleft", c("lightblue","yellowgreen","lightpink"),cex=0.6, fill= rainbow(length(Fig2.slicesc)))
#2021
Fig2.slicesd <- as.numeric(Fig2[4,2:4])
Fig2.labels <- paste(Fig2.slicesd," %",sep="")
pie(Fig2.slicesd,Fig2.labels,
col= c("lightblue","yellowgreen","lightpink"),
main="2021")
legend("bottomleft", c("lightblue","yellowgreen","lightpink"),cex=0.6, fill= rainbow(length(Fig2.slicesd)))
#2022
Fig2.slicese <- as.numeric(Fig2[4,2:4])
Fig2.labels <- paste(Fig2.slicese," %",sep="")
pie(Fig2.slicese,Fig2.labels,
col= c("lightblue","yellowgreen","lightpink"),
main="2022")
legend("bottomleft", c("lightblue","yellowgreen","lightpink"),cex=0.6, fill= rainbow(length(Fig2.slicese)))
#Figure 5
F3 = data.frame(read.csv("Figure3.csv"))
head(F3)## X Y2018 Y2019 Y2020 Y2021 Y2022
## 1 Swine Fattening 16.8 1.70 6.80 1.70 2.10
## 2 Swine P 5.6 2.10 6.80 7.40 0.55
## 3 Cassava 2.7 0.95 0.56 3.20 3.10
## 4 Rice 2.4 2.30 0.41 3.10 3.20
## 5 Corn 2.3 2.40 1.00 0.53 2.00F = cbind(F3$Y2018,F3$Y2019,F3$Y2020,F3$Y2021,F3$Y2022)
barplot(F,xlab="Years",ylab="Amount (in millions)",ylim = c(0,20),
main="Figure 5: Project Cost per Agricultural Intervention (2018-2022)",
col=c("gray","lightblue","yellowgreen","lightpink","brown"),
beside=TRUE, cex.names = .8,
names.arg=c("2018","2019","2020", "2021",
"2022"))
legend("topright",c("Swine Fattening","Swine Prduction","Cassava Production", "Rice Production",
"Corn Prduction"), fill=c("gray","lightblue","yellowgreen","lightpink","brown"),
cex = 0.8)
#Figure 6
F4 = data.frame(read.csv("Figure4.csv"))
Ff = cbind(F4$Y2018,F4$Y2019,F4$Y2020,F4$Y2021,F4$Y2022)
barplot(Ff,xlab="Years",ylab="Amount (in millions)",ylim = c(0,5),
main="Figure 6: Project Cost per Fishery Intervention (2018-2022)",
col=c("gray","lightblue","yellowgreen"),
beside=TRUE, cex.names = 0.8,
names.arg=c("2018","2019","2020", "2021",
"2022"))
legend("topright",c("Motorized boat","Bangus Production","Lobster Production"), fill=c("gray","lightblue","yellowgreen"),
cex = 0.6)
Objective 2:
#PRE-POST:
P1 = read.csv("PDATA.csv")
head(P1)## Beneficiaries Before After Intervention Difference X X.1 X.2
## 1 4 2000 4900 T1 2900 NA NA X = 1052
## 2 128 2000 4900 T2 2900 NA NA Mean =
## 3 130 2000 4900 T2 2900 NA NA
## 4 132 2000 4900 T2 2900 NA NA 0
## 5 134 2000 4900 T2 2900 NA NA
## 6 136 2000 4900 T2 2900 NA NA Intervention
## X.3 X.4 X.5 X.6 X.7 X.8 X.9 X.10 X.11 X.12 X.13 X.14
## 1 NA NA NA
## 2 6600.570885 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 Mean Difference NA NA NAshapiro.test(P1$Before)##
## Shapiro-Wilk normality test
##
## data: P1$Before
## W = 0.8565, p-value < 2.2e-16shapiro.test(P1$After)##
## Shapiro-Wilk normality test
##
## data: P1$After
## W = 0.96532, p-value = 3.988e-15t.test(P1$After)##
## One Sample t-test
##
## data: P1$After
## t = 113.53, df = 1050, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 9706.718 10048.144
## sample estimates:
## mean of x
## 9877.431t.test(P1$Difference)##
## One Sample t-test
##
## data: P1$Difference
## t = 95.224, df = 1050, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 6464.557 6736.585
## sample estimates:
## mean of x
## 6600.571library(tidyverse)
library(ggplot2)
P1 = read.csv("PDATA.csv")
head(P1)## Beneficiaries Before After Intervention Difference X X.1 X.2
## 1 4 2000 4900 T1 2900 NA NA X = 1052
## 2 128 2000 4900 T2 2900 NA NA Mean =
## 3 130 2000 4900 T2 2900 NA NA
## 4 132 2000 4900 T2 2900 NA NA 0
## 5 134 2000 4900 T2 2900 NA NA
## 6 136 2000 4900 T2 2900 NA NA Intervention
## X.3 X.4 X.5 X.6 X.7 X.8 X.9 X.10 X.11 X.12 X.13 X.14
## 1 NA NA NA
## 2 6600.570885 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 Mean Difference NA NA NA#as.factor(P1$Beneficiaries)
par(mfrow=c(2,1))
T1 = P1 %>% select(c(Beneficiaries,Before, After, Intervention)) %>% filter(Intervention == "T1")
theme_set(theme_minimal())
Ta =T1 %>% ggplot(aes(Beneficiaries, Before)) +
geom_ribbon(aes(ymin = 500 , ymax = 16000),fill = "white") +
geom_line(aes(Beneficiaries, Before ), color = "black", size = 0.5) +
geom_line(aes(Beneficiaries, After ), color = "blue", size = 0.5)+
ggtitle("Cassava Income Before and After")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.T2 = P1 %>% select(c(Beneficiaries,Before, After, Intervention)) %>% filter(Intervention == "T2")
theme_set(theme_minimal())
Tb =T2 %>% ggplot(aes(Beneficiaries, Before)) +
geom_ribbon(aes(ymin = 500 , ymax = 16000),fill = "white") +
geom_line(aes(Beneficiaries, Before ), color = "black", size = 0.5) +
geom_line(aes(Beneficiaries, After ), color = "blue", size = 0.5)+
ggtitle("Corn Income Before and After")
T3 = P1 %>% select(c(Beneficiaries,Before, After, Intervention)) %>% filter(Intervention == "T3")
theme_set(theme_minimal())
Tc =T3 %>% ggplot(aes(Beneficiaries, Before)) +
geom_ribbon(aes(ymin = 500 , ymax = 16000),fill = "white") +
geom_line(aes(Beneficiaries, Before ), color = "black", size = 0.5) +
geom_line(aes(Beneficiaries, After ), color = "blue", size = 0.5)+
ggtitle("Rice Income Before and After")
T4 = P1 %>% select(c(Beneficiaries,Before, After, Intervention)) %>% filter(Intervention == "T4")
theme_set(theme_minimal())
Td =T4 %>% ggplot(aes(Beneficiaries, Before)) +
geom_ribbon(aes(ymin = 500 , ymax = 16000),fill = "white") +
geom_line(aes(Beneficiaries, Before ), color = "black", size = 0.5) +
geom_line(aes(Beneficiaries, After ), color = "blue", size = 0.5)+
ggtitle("Swine Production Income Before and After")
Td
T5 = P1 %>% select(c(Beneficiaries,Before, After, Intervention)) %>% filter(Intervention == "T5")
theme_set(theme_minimal())
Te =T5 %>% ggplot(aes(Beneficiaries, Before)) +
geom_ribbon(aes(ymin = 500 , ymax = 16000),fill = "white") +
geom_line(aes(Beneficiaries, Before ), color = "black", size = 0.5) +
geom_line(aes(Beneficiaries, After ), color = "blue", size = 0.5)+
ggtitle("Swine Fattening Income Before and After")
library(ggplot2)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine# Combine the plots with gridExtra
grid.arrange(Ta, Tb) 
grid.arrange(Tc, Td) 
grid.arrange(Te,Td)
#ANOVA and Multiple Comparison Test
P1 = read.csv("PDATA.csv")
head(P1)## Beneficiaries Before After Intervention Difference X X.1 X.2
## 1 4 2000 4900 T1 2900 NA NA X = 1052
## 2 128 2000 4900 T2 2900 NA NA Mean =
## 3 130 2000 4900 T2 2900 NA NA
## 4 132 2000 4900 T2 2900 NA NA 0
## 5 134 2000 4900 T2 2900 NA NA
## 6 136 2000 4900 T2 2900 NA NA Intervention
## X.3 X.4 X.5 X.6 X.7 X.8 X.9 X.10 X.11 X.12 X.13 X.14
## 1 NA NA NA
## 2 6600.570885 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 Mean Difference NA NA NA#ANOVA TABLE
Intervention_aov <- anova(lm(Difference ~ Intervention, data = P1))
Intervention_aov## Analysis of Variance Table
##
## Response: Difference
## Df Sum Sq Mean Sq F value Pr(>F)
## Intervention 4 2582936238 645734059 248.38 < 2.2e-16 ***
## Residuals 1046 2719323420 2599736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aov1 <- with(P1, aov(Difference ~ Intervention))
anova(aov1)## Analysis of Variance Table
##
## Response: Difference
## Df Sum Sq Mean Sq F value Pr(>F)
## Intervention 4 2582936238 645734059 248.38 < 2.2e-16 ***
## Residuals 1046 2719323420 2599736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(agricolae)
LSD.test(y = aov1,
trt = "Intervention",
group = TRUE,
console = TRUE)##
## Study: aov1 ~ "Intervention"
##
## LSD t Test for Difference
##
## Mean Square Error: 2599736
##
## Intervention, means and individual ( 95 %) CI
##
## Difference std r LCL UCL Min Max
## T1 3507.937 593.3772 126 3226.079 3789.794 2900 5200
## T2 3531.034 950.3844 58 3115.601 3946.468 2900 5200
## T3 6673.438 1861.2399 320 6496.573 6850.302 2900 9400
## T4 7265.342 1549.2832 453 7116.692 7413.993 2900 9600
## T5 9188.298 2159.7917 94 8861.972 9514.624 4900 10500
##
## Alpha: 0.05 ; DF Error: 1046
## Critical Value of t: 1.962235
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## Difference groups
## T5 9188.298 a
## T4 7265.342 b
## T3 6673.438 c
## T2 3531.034 d
## T1 3507.937 dW = read.csv("PDATA.csv")
wilcox.test(W$Difference)##
## Wilcoxon signed rank test with continuity correction
##
## data: W$Difference
## V = 552826, p-value < 2.2e-16
## alternative hypothesis: true location is not equal to 0plot(W$Difference)
#DATA TRANSFORMATION
data_log <- log(W$Difference)
data_log## [1] 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466
## [9] 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466
## [17] 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466
## [25] 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466
## [33] 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466 8.101678 7.972466
## [41] 8.496990 8.101678 8.101678 8.496990 8.496990 8.496990 8.496990 8.496990
## [49] 8.101678 8.496990 8.101678 8.496990 8.496990 8.496990 8.496990 8.101678
## [57] 8.496990 8.101678 8.496990 8.101678 7.972466 8.101678 7.972466 8.101678
## [65] 8.101678 8.101678 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466
## [73] 8.496990 8.101678 8.101678 8.496990 8.496990 8.101678 8.496990 8.496990
## [81] 8.101678 8.101678 8.101678 8.101678 8.496990 8.496990 8.496990 8.496990
## [89] 8.496990 8.496990 8.101678 8.496990 8.496990 8.101678 8.496990 8.496990
## [97] 8.101678 8.496990 8.496990 8.496990 8.496990 8.101678 8.496990 8.101678
## [105] 8.101678 8.496990 8.496990 8.496990 8.101678 8.496990 7.972466 7.972466
## [113] 8.101678 8.556414 8.648221 8.556414 8.496990 7.972466 7.972466 8.101678
## [121] 8.101678 8.101678 8.101678 7.972466 7.972466 7.972466 7.972466 8.496990
## [129] 8.101678 8.496990 8.496990 8.101678 8.101678 8.101678 8.101678 8.101678
## [137] 8.101678 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990
## [145] 8.101678 8.101678 8.101678 8.496990 8.496990 8.496990 8.101678 8.101678
## [153] 8.101678 8.101678 8.101678 8.101678 8.101678 8.101678 8.101678 8.496990
## [161] 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990
## [169] 8.496990 8.496990 8.101678 8.496990 8.101678 8.101678 8.496990 8.496990
## [177] 8.101678 8.101678 8.101678 8.101678 8.101678 8.101678 8.101678 8.496990
## [185] 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990 8.101678
## [193] 8.496990 8.101678 8.496990 8.101678 8.496990 8.101678 8.496990 8.496990
## [201] 8.101678 8.101678 8.496990 8.496990 8.496990 8.496990 8.101678 8.496990
## [209] 8.496990 8.496990 8.101678 8.101678 8.101678 8.101678 8.101678 8.101678
## [217] 8.101678 8.101678 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990
## [225] 8.496990 8.496990 8.101678 8.101678 8.101678 8.101678 8.556414 8.101678
## [233] 8.101678 8.101678 8.496990 8.594154 8.496990 8.496990 8.496990 8.496990
## [241] 8.496990 8.612503 8.612503 8.101678 8.101678 8.101678 8.648221 8.496990
## [249] 8.556414 7.972466 7.972466 7.972466 7.972466 8.496990 8.496990 8.496990
## [257] 8.496990 8.496990 7.972466 7.972466 7.972466 7.972466 7.972466 7.972466
## [265] 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990 8.496990
## [273] 8.496990 8.496990 8.496990 8.648221 8.648221 8.648221 8.648221 8.648221
## [281] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [289] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [297] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [305] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [313] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [321] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [329] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [337] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [345] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [353] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [361] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [369] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [377] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [385] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [393] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [401] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [409] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [417] 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221 8.648221
## [425] 8.648221 8.594154 8.612503 8.496990 8.496990 8.665613 8.665613 8.665613
## [433] 8.665613 8.496990 8.496990 8.682708 8.682708 8.682708 8.682708 7.972466
## [441] 7.972466 7.972466 7.972466 8.496990 8.496990 7.972466 8.496990 7.972466
## [449] 7.972466 8.496990 7.972466 8.101678 8.556414 8.556414 8.556414 8.556414
## [457] 8.556414 8.556414 8.496990 8.496990 8.496990 8.101678 8.101678 8.101678
## [465] 8.496990 8.496990 8.101678 8.101678 8.496990 7.972466 8.612503 8.496990
## [473] 8.732305 8.732305 8.732305 8.732305 8.732305 8.732305 8.732305 8.732305
## [481] 8.732305 8.732305 8.732305 8.732305 8.732305 8.732305 8.732305 8.732305
## [489] 8.732305 8.732305 8.732305 8.732305 8.496990 8.496990 8.496990 8.496990
## [497] 8.101678 8.101678 8.101678 8.496990 8.496990 8.496990 8.101678 8.496990
## [505] 8.496990 8.496990 8.496990 8.496990 8.101678 8.101678 8.496990 8.496990
## [513] 8.496990 8.101678 8.101678 8.101678 8.648221 8.648221 8.496990 8.895630
## [521] 8.895630 8.895630 8.895630 8.895630 8.895630 8.895630 8.895630 7.972466
## [529] 8.496990 8.496990 8.909235 8.909235 8.909235 8.909235 8.909235 8.909235
## [537] 8.909235 8.909235 8.909235 8.909235 8.909235 8.909235 8.909235 8.909235
## [545] 8.909235 8.909235 8.935904 8.935904 8.935904 8.935904 8.935904 8.935904
## [553] 8.935904 8.935904 8.935904 8.935904 8.935904 8.935904 8.935904 8.935904
## [561] 8.935904 8.935904 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976
## [569] 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976
## [577] 8.948976 8.948976 8.496990 8.961879 8.961879 8.961879 8.961879 8.961879
## [585] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [593] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [601] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [609] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [617] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [625] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [633] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [641] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [649] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [657] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [665] 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879 8.961879
## [673] 8.974618 8.974618 8.974618 8.974618 8.974618 8.974618 8.974618 8.974618
## [681] 8.974618 8.974618 8.974618 8.974618 8.974618 8.974618 8.974618 9.011889
## [689] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [697] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [705] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [713] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [721] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [729] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [737] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [745] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [753] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [761] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [769] 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889 9.011889
## [777] 9.011889 9.011889 9.011889 8.575462 8.575462 8.809863 8.809863 8.809863
## [785] 8.809863 8.809863 8.809863 8.809863 8.809863 8.809863 8.809863 8.809863
## [793] 8.809863 8.809863 8.809863 8.809863 8.809863 8.809863 8.809863 8.809863
## [801] 8.809863 8.809863 9.071078 9.071078 9.071078 9.071078 9.071078 9.071078
## [809] 9.071078 9.071078 9.071078 9.071078 9.071078 9.071078 9.071078 9.071078
## [817] 9.071078 9.071078 9.071078 9.071078 9.071078 9.047821 9.047821 9.047821
## [825] 9.047821 9.047821 9.047821 9.047821 9.047821 9.047821 9.047821 9.047821
## [833] 9.047821 9.047821 9.047821 9.047821 9.047821 9.047821 9.047821 9.047821
## [841] 9.047821 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976
## [849] 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976 8.948976
## [857] 8.948976 9.082507 9.082507 9.082507 9.082507 9.082507 9.082507 9.082507
## [865] 9.082507 9.082507 9.082507 9.082507 9.082507 9.082507 9.082507 9.082507
## [873] 9.082507 9.082507 9.082507 9.082507 9.148465 9.148465 9.148465 9.148465
## [881] 9.148465 9.148465 9.148465 9.148465 9.148465 9.148465 9.148465 9.148465
## [889] 9.148465 9.148465 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [897] 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [905] 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [913] 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [921] 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [929] 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [937] 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [945] 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [953] 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518 9.169518
## [961] 9.169518 9.169518 9.148465 9.148465 9.148465 9.148465 9.148465 9.148465
## [969] 9.148465 9.148465 9.148465 9.148465 9.148465 9.148465 9.148465 9.148465
## [977] 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143
## [985] 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143
## [993] 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143 9.230143
## [1001] 9.230143 9.259131 9.259131 9.259131 9.259131 9.259131 9.259131 9.259131
## [1009] 9.259131 9.259131 9.259131 9.259131 9.259131 9.259131 9.259131 9.259131
## [1017] 9.259131 9.259131 9.259131 9.259131 9.259131 9.259131 9.259131 9.259131
## [1025] 9.259131 9.259131 9.220291 9.220291 9.220291 9.220291 9.220291 9.220291
## [1033] 9.220291 9.220291 9.220291 9.220291 9.220291 9.220291 9.220291 9.220291
## [1041] 9.220291 9.220291 9.220291 9.220291 9.220291 9.220291 9.220291 9.220291
## [1049] 9.220291 9.220291 9.220291par(mfrow=c(1,2))
hist(W$Difference)
hist(data_log)
shapiro.test(data_log)##
## Shapiro-Wilk normality test
##
## data: data_log
## W = 0.90775, p-value < 2.2e-16P = read.csv("PDATA.csv")
#DATA TRANSFORMATION
#data_log1 <- log(P$Diffrence)
#data_log1
#par(mfrow=c(1,2))
#plot(P$Difference, col="blue")
#hist(P$Difference)
#hist(data_log1)
#shapiro.test(data_log1)
#shapiro.test(P$Difference)TRIAL AND ERROR 1
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectlibrary(boot)
library(rcompanion)#Code sa Morag Time Series
# figure 11
#library(nutshell)
#data(turkey.price.ts)
#plot(turkey.price.ts)#MAP CREATING
library(ggplot2)
library(maps)##
## Attaching package: 'maps'## The following object is masked from 'package:purrr':
##
## maplibrary(mapdata)
library(dplyr)
#install.packages("devtools")
#install.packages("stringr")
#install.packages(c("maps", "mapdata"))