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2018_06_18_Data Cleaning and Feature Engineering.R
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setwd("D:/NA/spreedsheet")
install.packages('devtools')
# install.packages("dplyr")
library(tidyr)
library(dplyr)
library(tidyverse)
library(tidycensus)
library(sf)
library(imputeTS)
library(ggplot2)
library(devtools)
rawDC <- read.csv("OC Marine Debris Data for DC.csv")
locationDC <- separate(rawDC, GPS, c("lat","lon"), sep = ",", remove = FALSE)
locationDC$lat <- as.numeric(locationDC$lat)
locationDC$lon <- as.numeric(locationDC$lon)
write.csv(locationDC, file = "locationDC.csv")
setwd("D:/NA/WP")
DC <- st_read("DC.shp")
Delaware <- st_read("Delaware.shp")
Maryland <- st_read("Maryland.shp")
NC <- st_read("NC.shp")
Virginia <- st_read("Virginia.shp")
dataset <- rbind(Delaware, Maryland, NC, Virginia)
st_write(dataset, "comdata.shp", driver="ESRI Shapefile")
compdata <- st_read("comdata.shp")
# Data cleaning for National Aquarium
#---------------------------------Reading in the 08 data----------------------------------------------
# DC08 (6)
DC08 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2008 DC.xlsx", skip = 1) %>%
as.data.frame()
DC08 <- DC08[-seq(nrow(DC08),nrow(DC08)),]
# Delaware08 (32)
Delaware08 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2008 Delaware.xlsx", skip = 1) %>%
as.data.frame()
Delaware08 <- Delaware08[-seq(nrow(Delaware08),nrow(Delaware08)),]
# Maryland08 (15)
Maryland08 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2008 Maryland.xlsx", skip = 1) %>%
as.data.frame()
Maryland08 <- Maryland08[-seq(nrow(Maryland08),nrow(Maryland08)),]
# NorthCarolina08 (94)
NorthCarolina08 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2008 North Carolina.xlsx", skip = 1) %>%
as.data.frame()
NorthCarolina08 <- NorthCarolina08[-seq(nrow(NorthCarolina08),nrow(NorthCarolina08)),]
# Virginia08 (143)
Virginia08 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2008 Virginia.xlsx", skip = 1) %>%
as.data.frame()
Virginia08 <- Virginia08[-seq(nrow(Virginia08),nrow(Virginia08)),]
# comb08 (290)
comb08 <- rbind(DC08,Delaware08,Maryland08,NorthCarolina08,Virginia08)
# Replace the NAs and combine the data
comb08$`County/Zone`[is.na(comb08$`County/Zone`)] <- "DC"
na.replace(comb08, 0)
combdata08 <- comb08 %>%
dplyr::mutate(Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Adults = as.numeric(Adults),
Children = as.numeric(Children),
Adults__1 = as.numeric(Adults__1),
Children__1 = as.numeric(Children__1),
Adults__2 = as.numeric(Adults__2),
Children__2 = as.numeric(Children__2),
XofBags = as.numeric(`# of Bags`),
XofBags1 = as.numeric(`# of Bags__1`),
XofBags2 = as.numeric(`# of Bags__2`)
) %>%
na.replace(0) %>%
dplyr::mutate(Adults = Adults + Adults__1 + Adults__2,
Children = Children + Children__1 + Children__2,
People = People + People__1 + People__2,
Pounds = Pounds + Pounds__1 + Pounds__2,
Miles = Miles + Miles__1 + Miles__2,
XofBags = XofBags + XofBags1 + XofBags2) %>%
select(-Adults__1,-Children__1,-People__1,-Pounds__1,-Miles__1,`# of Bags__1`,
-Adults__2,-Children__2,-People__2,-Pounds__2,-Miles__2,`# of Bags__2`,
-XofBags1,-XofBags2,
-`# of Bags`,-`# of Bags__1`,-`# of Bags__2`)
#---------------------------------Reading in the 09 data----------------------------------------------
# DC09 (4)
DC09 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2009 DC.xlsx", skip = 1) %>%
as.data.frame()
DC09 <- DC09[-seq(nrow(DC09),nrow(DC09)),]
# Delaware09 (35)
Delaware09 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2009 Delaware.xlsx", skip = 1) %>%
as.data.frame()
Delaware09 <- Delaware09[-seq(nrow(Delaware09),nrow(Delaware09)),]
# Maryland09 (19)
Maryland09 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2009 Maryland.xlsx", skip = 1) %>%
as.data.frame()
Maryland09 <- Maryland09[-seq(nrow(Maryland09),nrow(Maryland09)),]
# NorthCarolina09 (90)
NorthCarolina09 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2009 North Carolina.xlsx", skip = 1) %>%
as.data.frame()
NorthCarolina09 <- NorthCarolina09[-seq(nrow(NorthCarolina09),nrow(NorthCarolina09)),]
# Virginia09 (128)
Virginia09 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2009 Virginia.xlsx", skip = 1) %>%
as.data.frame()
Virginia09 <- Virginia09[-seq(nrow(Virginia09),nrow(Virginia09)),]
# comb09 (276)
comb09 <- rbind(DC09,Delaware09,Maryland09,NorthCarolina09,Virginia09)
# Replace the NAs and combine the data
comb09$`County/Zone`[is.na(comb09$`County/Zone`)] <- "DC"
na.replace(comb09, 0)
combdata09 <- comb09 %>%
dplyr::mutate(Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Adults = as.numeric(Adults),
Children = as.numeric(Children),
Adults__1 = as.numeric(Adults__1),
Children__1 = as.numeric(Children__1),
Adults__2 = as.numeric(Adults__2),
Children__2 = as.numeric(Children__2),
XofBags = as.numeric(`# of Bags`),
XofBags1 = as.numeric(`# of Bags__1`),
XofBags2 = as.numeric(`# of Bags__2`)
) %>%
na.replace(0) %>%
dplyr::mutate(Adults = Adults + Adults__1 + Adults__2,
Children = Children + Children__1 + Children__2,
People = People + People__1 + People__2,
Pounds = Pounds + Pounds__1 + Pounds__2,
Miles = Miles + Miles__1 + Miles__2,
XofBags = XofBags + XofBags1 + XofBags2) %>%
select(-Adults__1,-Children__1,-People__1,-Pounds__1,-Miles__1,`# of Bags__1`,
-Adults__2,-Children__2,-People__2,-Pounds__2,-Miles__2,`# of Bags__2`,
-XofBags1,-XofBags2,
-`# of Bags`,-`# of Bags__1`,-`# of Bags__2`)
#---------------------------------Reading in the 10 data----------------------------------------------
# DC10 (4)
DC10 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2010 DC.xlsx", skip = 1) %>%
as.data.frame()
DC10 <- DC10[-seq(nrow(DC10),nrow(DC10)),]
# Delaware10 (36)
Delaware10 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2010 Delaware.xlsx", skip = 1) %>%
as.data.frame()
Delaware10 <- Delaware10[-seq(nrow(Delaware10),nrow(Delaware10)),]
# Maryland10 (23)
Maryland10 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2010 Maryland.xlsx", skip = 1) %>%
as.data.frame()
Maryland10 <- Maryland10[-seq(nrow(Maryland10),nrow(Maryland10)),]
# NorthCarolina10 (79)
NorthCarolina10 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2010 North Carolina.xlsx", skip = 1) %>%
as.data.frame()
NorthCarolina10 <- NorthCarolina10[-seq(nrow(NorthCarolina10),nrow(NorthCarolina10)),]
# Virginia10 (150)
Virginia10 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2010 Virginia.xlsx", skip = 1) %>%
as.data.frame()
Virginia10 <- Virginia10[-seq(nrow(Virginia10),nrow(Virginia10)),]
# comb10 (292)
comb10 <- rbind(DC10,Delaware10,Maryland10,NorthCarolina10,Virginia10)
# Replace the NAs and combine the data
comb10$`County/Zone`[is.na(comb10$`County/Zone`)] <- "DC"
na.replace(comb10, 0)
combdata10 <- comb10 %>%
dplyr::mutate(Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Adults = as.numeric(Adults),
Children = as.numeric(Children),
Adults__1 = as.numeric(Adults__1),
Children__1 = as.numeric(Children__1),
Adults__2 = as.numeric(Adults__2),
Children__2 = as.numeric(Children__2),
XofBags = as.numeric(`# of Bags`),
XofBags1 = as.numeric(`# of Bags__1`),
XofBags2 = as.numeric(`# of Bags__2`)
) %>%
na.replace(0) %>%
dplyr::mutate(Adults = Adults + Adults__1 + Adults__2,
Children = Children + Children__1 + Children__2,
People = People + People__1 + People__2,
Pounds = Pounds + Pounds__1 + Pounds__2,
Miles = Miles + Miles__1 + Miles__2,
XofBags = XofBags + XofBags1 + XofBags2) %>%
select(-Adults__1,-Children__1,-People__1,-Pounds__1,-Miles__1,`# of Bags__1`,
-Adults__2,-Children__2,-People__2,-Pounds__2,-Miles__2,`# of Bags__2`,
-XofBags1,-XofBags2,
-`# of Bags`,-`# of Bags__1`,-`# of Bags__2`)
#---------------------------------Reading in the 11 data----------------------------------------------
# DC11 (2)
DC11 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2011 DC.xlsx", skip = 1) %>%
as.data.frame()
DC11 <- DC11[-seq(nrow(DC11),nrow(DC11)),]
# Delaware11 (38)
Delaware11 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2011 Delaware.xlsx", skip = 1) %>%
as.data.frame()
Delaware11 <- Delaware11[-seq(nrow(Delaware11),nrow(Delaware11)),]
# Maryland11 (30)
Maryland11 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2011 Maryland.xlsx", skip = 1) %>%
as.data.frame()
Maryland11 <- Maryland11[-seq(nrow(Maryland11),nrow(Maryland11)),]
# NorthCarolina11 (92)
NorthCarolina11 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2011 North Carolina.xlsx", skip = 1) %>%
as.data.frame()
NorthCarolina11 <- NorthCarolina11[-seq(nrow(NorthCarolina11),nrow(NorthCarolina11)),]
# Virginia11 (121)
Virginia11 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2011 Virginia.xlsx", skip = 1) %>%
as.data.frame()
Virginia11 <- Virginia11[-seq(nrow(Virginia11),nrow(Virginia11)),]
# comb11 (292)
comb11 <- rbind(DC11,Delaware11,Maryland11,NorthCarolina11,Virginia11)
# Replace the NAs and combine the data
comb11$`County/Zone`[is.na(comb11$`County/Zone`)] <- "DC"
na.replace(comb11, 0)
combdata11 <- comb11 %>%
dplyr::mutate(Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Adults = as.numeric(Adults),
Children = as.numeric(Children),
Adults__1 = as.numeric(Adults__1),
Children__1 = as.numeric(Children__1),
Adults__2 = as.numeric(Adults__2),
Children__2 = as.numeric(Children__2),
XofBags = as.numeric(`# of Bags`),
XofBags1 = as.numeric(`# of Bags__1`),
XofBags2 = as.numeric(`# of Bags__2`)
) %>%
na.replace(0) %>%
dplyr::mutate(Adults = Adults + Adults__1 + Adults__2,
Children = Children + Children__1 + Children__2,
People = People + People__1 + People__2,
Pounds = Pounds + Pounds__1 + Pounds__2,
Miles = Miles + Miles__1 + Miles__2,
XofBags = XofBags + XofBags1 + XofBags2) %>%
select(-Adults__1,-Children__1,-People__1,-Pounds__1,-Miles__1,`# of Bags__1`,
-Adults__2,-Children__2,-People__2,-Pounds__2,-Miles__2,`# of Bags__2`,
-XofBags1,-XofBags2,
-`# of Bags`,-`# of Bags__1`,-`# of Bags__2`)
#---------------------------------Reading in the 12 data----------------------------------------------
# DC12 (3)
DC12 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2012 DC.xlsx", skip = 1) %>%
as.data.frame()
DC12 <- DC12[-seq(nrow(DC12),nrow(DC12)),]
# Delaware12 (38)
Delaware12 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2012 Delaware.xlsx", skip = 1) %>%
as.data.frame()
Delaware12 <- Delaware12[-seq(nrow(Delaware12),nrow(Delaware12)),]
# Maryland12 (26)
Maryland12 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2012 Maryland.xlsx", skip = 1) %>%
as.data.frame()
Maryland12 <- Maryland12[-seq(nrow(Maryland12),nrow(Maryland12)),]
# NorthCarolina12 (81)
NorthCarolina12 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2012 North Carolina.xlsx", skip = 1) %>%
as.data.frame()
NorthCarolina12 <- NorthCarolina12[-seq(nrow(NorthCarolina12),nrow(NorthCarolina12)),]
# Virginia12 (144)
Virginia12 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2012 Virginia.xlsx", skip = 1) %>%
as.data.frame()
Virginia12 <- Virginia12[-seq(nrow(Virginia12),nrow(Virginia12)),]
# comb12 (292)
comb12 <- rbind(DC12,Delaware12,Maryland12,NorthCarolina12,Virginia12)
# Replace the NAs and combine the data
comb12$`County/Zone`[is.na(comb12$`County/Zone`)] <- "DC"
na.replace(comb12, 0)
combdata12 <- comb12 %>%
dplyr::mutate(Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Adults = as.numeric(Adults),
Children = as.numeric(Children),
Adults__1 = as.numeric(Adults__1),
Children__1 = as.numeric(Children__1),
Adults__2 = as.numeric(Adults__2),
Children__2 = as.numeric(Children__2),
XofBags = as.numeric(`# of Bags`),
XofBags1 = as.numeric(`# of Bags__1`),
XofBags2 = as.numeric(`# of Bags__2`)
) %>%
na.replace(0) %>%
dplyr::mutate(Adults = Adults + Adults__1 + Adults__2,
Children = Children + Children__1 + Children__2,
People = People + People__1 + People__2,
Pounds = Pounds + Pounds__1 + Pounds__2,
Miles = Miles + Miles__1 + Miles__2,
XofBags = XofBags + XofBags1 + XofBags2) %>%
select(-Adults__1,-Children__1,-People__1,-Pounds__1,-Miles__1,`# of Bags__1`,
-Adults__2,-Children__2,-People__2,-Pounds__2,-Miles__2,`# of Bags__2`,
-XofBags1,-XofBags2,
-`# of Bags`,-`# of Bags__1`,-`# of Bags__2`)
#---------------------------------Reading in the 13 data----------------------------------------------
# DC13 (3)
# ******* There's no data for DC 2013
DC13 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2013 DC.xlsx", skip = 1) %>%
as.data.frame()
DC13 <- DC13[-seq(nrow(DC13),nrow(DC13)),]
# Delaware13 (42)
Delaware13 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2013 Delaware.xlsx", skip = 1) %>%
as.data.frame()
Delaware13 <- Delaware13[-seq(nrow(Delaware13),nrow(Delaware13)),]
# Maryland13 (36)
Maryland13 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2013 Maryland.xlsx", skip = 1) %>%
as.data.frame()
Maryland13 <- Maryland13[-seq(nrow(Maryland13),nrow(Maryland13)),]
# NorthCarolina13 (85)
NorthCarolina13 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2013 North Carolina.xlsx", skip = 1) %>%
as.data.frame()
NorthCarolina13 <- NorthCarolina13[-seq(nrow(NorthCarolina13),nrow(NorthCarolina13)),]
# Virginia13 (131)
Virginia13 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2013 Virginia.xlsx", skip = 1) %>%
as.data.frame()
Virginia13 <- Virginia13[-seq(nrow(Virginia13),nrow(Virginia13)),]
# comb13 (294)
# *****This is important
# *****Remember to add DC 13 data after getting it
comb13 <- rbind(Delaware13,Maryland13,NorthCarolina13,Virginia13)
# Replace the NAs and combine the data
comb13$`County/Zone`[is.na(comb13$`County/Zone`)] <- "DC"
na.replace(comb13, 0)
combdata13 <- comb13 %>%
dplyr::mutate(Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Adults = as.numeric(Adults),
Children = as.numeric(Children),
Adults__1 = as.numeric(Adults__1),
Children__1 = as.numeric(Children__1),
Adults__2 = as.numeric(Adults__2),
Children__2 = as.numeric(Children__2),
XofBags = as.numeric(`# of Bags`),
XofBags1 = as.numeric(`# of Bags__1`),
XofBags2 = as.numeric(`# of Bags__2`)
) %>%
na.replace(0) %>%
dplyr::mutate(Adults = Adults + Adults__1 + Adults__2,
Children = Children + Children__1 + Children__2,
People = People + People__1 + People__2,
Pounds = Pounds + Pounds__1 + Pounds__2,
Miles = Miles + Miles__1 + Miles__2,
XofBags = XofBags + XofBags1 + XofBags2) %>%
select(-Adults__1,-Children__1,-People__1,-Pounds__1,-Miles__1,`# of Bags__1`,
-Adults__2,-Children__2,-People__2,-Pounds__2,-Miles__2,`# of Bags__2`,
-XofBags1,-XofBags2,
-`# of Bags`,-`# of Bags__1`,-`# of Bags__2`)
#---------------------------------Reading in the 14 data----------------------------------------------
# DC14 (5)
DC14 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2014 DC.xlsx", skip = 1) %>%
as.data.frame()
DC14 <- DC14[-seq(nrow(DC14),nrow(DC14)),]
# Delaware14 (46)
Delaware14 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2014 Delaware.xlsx", skip = 1) %>%
as.data.frame()
Delaware14 <- Delaware14[-seq(nrow(Delaware14),nrow(Delaware14)),]
# Maryland14 (36)
Maryland14 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2014 Maryland.xlsx", skip = 1) %>%
as.data.frame()
Maryland14 <- Maryland14[-seq(nrow(Maryland14),nrow(Maryland14)),]
# NorthCarolina14 (83)
NorthCarolina14 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2014 North Carolina.xlsx", skip = 1) %>%
as.data.frame()
NorthCarolina14 <- NorthCarolina14[-seq(nrow(NorthCarolina14),nrow(NorthCarolina14)),]
# Virginia14 (128)
Virginia14 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2014 Virginia.xlsx", skip = 1) %>%
as.data.frame()
Virginia14 <- Virginia14[-seq(nrow(Virginia14),nrow(Virginia14)),]
# comb14 (298)
comb14 <- rbind(DC14,Delaware14,Maryland14,NorthCarolina14,Virginia14)
# Replace the NAs and combine the data
comb14$`County/Zone`[is.na(comb14$`County/Zone`)] <- "DC"
na.replace(comb14, 0)
combdata14 <- comb14 %>%
dplyr::mutate(Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Adults = as.numeric(Adults),
Children = as.numeric(Children),
Adults__1 = as.numeric(Adults__1),
Children__1 = as.numeric(Children__1),
Adults__2 = as.numeric(Adults__2),
Children__2 = as.numeric(Children__2),
XofBags = as.numeric(`# of Bags`),
XofBags1 = as.numeric(`# of Bags__1`),
XofBags2 = as.numeric(`# of Bags__2`)
) %>%
na.replace(0) %>%
dplyr::mutate(Adults = Adults + Adults__1 + Adults__2,
Children = Children + Children__1 + Children__2,
People = People + People__1 + People__2,
Pounds = Pounds + Pounds__1 + Pounds__2,
Miles = Miles + Miles__1 + Miles__2,
XofBags = XofBags + XofBags1 + XofBags2) %>%
select(-Adults__1,-Children__1,-People__1,-Pounds__1,-Miles__1,`# of Bags__1`,
-Adults__2,-Children__2,-People__2,-Pounds__2,-Miles__2,`# of Bags__2`,
-XofBags1,-XofBags2,
-`# of Bags`,-`# of Bags__1`,-`# of Bags__2`)
#---------------------------------Reading in the 15 data----------------------------------------------
# DC15 (6)
DC15 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2015 DC.xlsx", skip = 1) %>%
as.data.frame()
DC15 <- DC15[-seq(nrow(DC15),nrow(DC15)),]
# Delaware15 (48)
Delaware15 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2015 Delaware.xlsx", skip = 1) %>%
as.data.frame()
Delaware15 <- Delaware15[-seq(nrow(Delaware15),nrow(Delaware15)),]
# Maryland15 (36)
Maryland15 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2015 Maryland.xlsx", skip = 1) %>%
as.data.frame()
Maryland15 <- Maryland15[-seq(nrow(Maryland15),nrow(Maryland15)),]
# NorthCarolina15 (92)
NorthCarolina15 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2015 North Carolina.xlsx", skip = 1) %>%
as.data.frame()
NorthCarolina15 <- NorthCarolina15[-seq(nrow(NorthCarolina15),nrow(NorthCarolina15)),]
# Virginia15 (120)
Virginia15 <- read_excel("D:/NA/spreedsheet/xls/OC Marine Debris Data for 2015 Virginia.xlsx", skip = 1) %>%
as.data.frame()
Virginia15 <- Virginia15[-seq(nrow(Virginia15),nrow(Virginia15)),]
# comb15 (302)
comb15 <- rbind(DC15,Delaware15,Maryland15,NorthCarolina15,Virginia15)
# Replace the NAs and combine the data
comb15$`County/Zone`[is.na(comb15$`County/Zone`)] <- "DC"
na.replace(comb15, 0)
combdata15 <- comb15 %>%
dplyr::mutate(Latitude = as.numeric(Latitude),
Longitude = as.numeric(Longitude),
Adults = as.numeric(Adults),
Children = as.numeric(Children),
Adults__1 = as.numeric(Adults__1),
Children__1 = as.numeric(Children__1),
Adults__2 = as.numeric(Adults__2),
Children__2 = as.numeric(Children__2),
XofBags = as.numeric(`# of Bags`),
XofBags1 = as.numeric(`# of Bags__1`),
XofBags2 = as.numeric(`# of Bags__2`)
) %>%
na.replace(0) %>%
dplyr::mutate(Adults = Adults + Adults__1 + Adults__2,
Children = Children + Children__1 + Children__2,
People = People + People__1 + People__2,
Pounds = Pounds + Pounds__1 + Pounds__2,
Miles = Miles + Miles__1 + Miles__2,
XofBags = XofBags + XofBags1 + XofBags2) %>%
select(-Adults__1,-Children__1,-People__1,-Pounds__1,-Miles__1,`# of Bags__1`,
-Adults__2,-Children__2,-People__2,-Pounds__2,-Miles__2,`# of Bags__2`,
-XofBags1,-XofBags2,
-`# of Bags`,-`# of Bags__1`,-`# of Bags__2`)
#---------------------------------Reading in the 15-18 data----------------------------------------------
# DC1518 (251)
DC1518 <- read.csv("D:/NA/spreedsheet/xls/OC Marine Debris Data for DC.csv") %>%
as.data.frame()
DC1518 <- DC1518[-seq(nrow(DC1518),nrow(DC1518)),]
# Delaware1518 (239)
Delaware1518 <- read.csv("D:/NA/spreedsheet/xls/OC Marine Debris Data for Delaware.csv") %>%
as.data.frame()
Delaware1518 <- Delaware1518[-seq(nrow(Delaware1518),nrow(Delaware1518)),]
# Maryland1518 (223)
Maryland1518 <- read.csv("D:/NA/spreedsheet/xls/OC Marine Debris Data for Maryland.csv") %>%
as.data.frame()
Maryland1518 <- Maryland1518[-seq(nrow(Maryland1518),nrow(Maryland1518)),]
# NorthCarolina (1323)
NorthCarolina1518 <- read.csv("D:/NA/spreedsheet/xls/OC Marine Debris Data for North Carolina.csv") %>%
as.data.frame()
NorthCarolina1518 <- NorthCarolina1518[-seq(nrow(NorthCarolina1518),nrow(NorthCarolina1518)),]
# Virginia1518 (651)
Virginia1518 <- read.csv("D:/NA/spreedsheet/xls/OC Marine Debris Data for Virginia.csv") %>%
as.data.frame()
Virginia1518 <- Virginia1518[-seq(nrow(Virginia1518),nrow(Virginia1518)),]
#Don't know y but the loop is not working yet
# data1518 <- list(DC1518,Delaware1518,Maryland1518,NorthCarolina1518,Virginia1518)
# for (i in data1518){
# colnames(data1518[i]) <- c(
# "CleanupID", "Zone",
# "State", "Country",
# "GPS", "CleanupType",
# "CleanupDate", "GroupName",
# "Adults", "Children",
# "People", "Pounds",
# "Miles", "Xofbags",
# "CigaretteButts", "FoodWrapperscandychipsetc",
# "TakeOutAwayContainersPlastic","TakeOutAwayContainersFoam",
# "BottleCapsPlastic", "BottleCapsMetal",
# "LidsPlastic", "StrawsStirrers",
# "ForksKnivesSpoons", "BeverageBottlesPlastic",
# "BeverageBottlesGlass", "BeverageCans",
# "GroceryBagsPlastic", "OtherPlasticBags",
# "PaperBags", "CupsPlatesPaper",
# "CupsPlatesPlastic.", "CupsPlatesFoam",
# "FishingBuoysPotsTraps", "FishingNetPieces",
# "FishingLine1yardmeter1piece", "Rope1yardmeter1piece",
# "FishingGearCleanSwell", "X6PackHolders",
# "OtherPlasticFoamPackaging", "OtherPlasticBottlesoilbleachetc",
# "StrappingBands", "TobaccoPackagingWrap",
# "OtherPackagingCleanSwell", "Appliancesrefrigeratorswashersetc",
# "Balloons", "CigarTips",
# "CigaretteLighters", "ConstructionMaterials",
# "Fireworks", "Tires",
# "Toys", "OtherTrashCleanSwell",
# "Condoms", "Diapers",
# "Syringes", "TamponsTamponApplicators",
# "PersonalHygieneCleanSwell", "FoamPieces",
# "GlassPieces", "PlasticPieces",
# "TotalItemsCollected")}
# Rename and combine the 1518 datasets
colnames(DC1518) <- c("CleanupID", "Zone",
"State", "Country",
"GPS", "CleanupType",
"CleanupDate", "GroupName",
"Adults", "Children",
"People", "Pounds",
"Miles", "Xofbags",
"CigaretteButts", "FoodWrapperscandychipsetc",
"TakeOutAwayContainersPlastic","TakeOutAwayContainersFoam",
"BottleCapsPlastic", "BottleCapsMetal",
"LidsPlastic", "StrawsStirrers",
"ForksKnivesSpoons", "BeverageBottlesPlastic",
"BeverageBottlesGlass", "BeverageCans",
"GroceryBagsPlastic", "OtherPlasticBags",
"PaperBags", "CupsPlatesPaper",
"CupsPlatesPlastic.", "CupsPlatesFoam",
"FishingBuoysPotsTraps", "FishingNetPieces",
"FishingLine1yardmeter1piece", "Rope1yardmeter1piece",
"FishingGearCleanSwell", "X6PackHolders",
"OtherPlasticFoamPackaging", "OtherPlasticBottlesoilbleachetc",
"StrappingBands", "TobaccoPackagingWrap",
"OtherPackagingCleanSwell", "Appliancesrefrigeratorswashersetc",
"Balloons", "CigarTips",
"CigaretteLighters", "ConstructionMaterials",
"Fireworks", "Tires",
"Toys", "OtherTrashCleanSwell",
"Condoms", "Diapers",
"Syringes", "TamponsTamponApplicators",
"PersonalHygieneCleanSwell", "FoamPieces",
"GlassPieces", "PlasticPieces",
"TotalItemsCollected")
colnames(Delaware1518) <- c("CleanupID", "Zone",
"State", "Country",
"GPS", "CleanupType",
"CleanupDate", "GroupName",
"Adults", "Children",
"People", "Pounds",
"Miles", "Xofbags",
"CigaretteButts", "FoodWrapperscandychipsetc",
"TakeOutAwayContainersPlastic","TakeOutAwayContainersFoam",
"BottleCapsPlastic", "BottleCapsMetal",
"LidsPlastic", "StrawsStirrers",
"ForksKnivesSpoons", "BeverageBottlesPlastic",
"BeverageBottlesGlass", "BeverageCans",
"GroceryBagsPlastic", "OtherPlasticBags",
"PaperBags", "CupsPlatesPaper",
"CupsPlatesPlastic.", "CupsPlatesFoam",
"FishingBuoysPotsTraps", "FishingNetPieces",
"FishingLine1yardmeter1piece", "Rope1yardmeter1piece",
"FishingGearCleanSwell", "X6PackHolders",
"OtherPlasticFoamPackaging", "OtherPlasticBottlesoilbleachetc",
"StrappingBands", "TobaccoPackagingWrap",
"OtherPackagingCleanSwell", "Appliancesrefrigeratorswashersetc",
"Balloons", "CigarTips",
"CigaretteLighters", "ConstructionMaterials",
"Fireworks", "Tires",
"Toys", "OtherTrashCleanSwell",
"Condoms", "Diapers",
"Syringes", "TamponsTamponApplicators",
"PersonalHygieneCleanSwell", "FoamPieces",
"GlassPieces", "PlasticPieces",
"TotalItemsCollected")
colnames(Maryland1518) <- c("CleanupID", "Zone",
"State", "Country",
"GPS", "CleanupType",
"CleanupDate", "GroupName",
"Adults", "Children",
"People", "Pounds",
"Miles", "Xofbags",
"CigaretteButts", "FoodWrapperscandychipsetc",
"TakeOutAwayContainersPlastic","TakeOutAwayContainersFoam",
"BottleCapsPlastic", "BottleCapsMetal",
"LidsPlastic", "StrawsStirrers",
"ForksKnivesSpoons", "BeverageBottlesPlastic",
"BeverageBottlesGlass", "BeverageCans",
"GroceryBagsPlastic", "OtherPlasticBags",
"PaperBags", "CupsPlatesPaper",
"CupsPlatesPlastic.", "CupsPlatesFoam",
"FishingBuoysPotsTraps", "FishingNetPieces",
"FishingLine1yardmeter1piece", "Rope1yardmeter1piece",
"FishingGearCleanSwell", "X6PackHolders",
"OtherPlasticFoamPackaging", "OtherPlasticBottlesoilbleachetc",
"StrappingBands", "TobaccoPackagingWrap",
"OtherPackagingCleanSwell", "Appliancesrefrigeratorswashersetc",
"Balloons", "CigarTips",
"CigaretteLighters", "ConstructionMaterials",
"Fireworks", "Tires",
"Toys", "OtherTrashCleanSwell",
"Condoms", "Diapers",
"Syringes", "TamponsTamponApplicators",
"PersonalHygieneCleanSwell", "FoamPieces",
"GlassPieces", "PlasticPieces",
"TotalItemsCollected")
colnames(NorthCarolina1518) <- c("CleanupID", "Zone",
"State", "Country",
"GPS", "CleanupType",
"CleanupDate", "GroupName",
"Adults", "Children",
"People", "Pounds",
"Miles", "Xofbags",
"CigaretteButts", "FoodWrapperscandychipsetc",
"TakeOutAwayContainersPlastic","TakeOutAwayContainersFoam",
"BottleCapsPlastic", "BottleCapsMetal",
"LidsPlastic", "StrawsStirrers",
"ForksKnivesSpoons", "BeverageBottlesPlastic",
"BeverageBottlesGlass", "BeverageCans",
"GroceryBagsPlastic", "OtherPlasticBags",
"PaperBags", "CupsPlatesPaper",
"CupsPlatesPlastic.", "CupsPlatesFoam",
"FishingBuoysPotsTraps", "FishingNetPieces",
"FishingLine1yardmeter1piece", "Rope1yardmeter1piece",
"FishingGearCleanSwell", "X6PackHolders",
"OtherPlasticFoamPackaging", "OtherPlasticBottlesoilbleachetc",
"StrappingBands", "TobaccoPackagingWrap",
"OtherPackagingCleanSwell", "Appliancesrefrigeratorswashersetc",
"Balloons", "CigarTips",
"CigaretteLighters", "ConstructionMaterials",
"Fireworks", "Tires",
"Toys", "OtherTrashCleanSwell",
"Condoms", "Diapers",
"Syringes", "TamponsTamponApplicators",
"PersonalHygieneCleanSwell", "FoamPieces",
"GlassPieces", "PlasticPieces",
"TotalItemsCollected")
colnames(Virginia1518) <- c("CleanupID", "Zone",
"State", "Country",
"GPS", "CleanupType",
"CleanupDate", "GroupName",
"Adults", "Children",
"People", "Pounds",
"Miles", "Xofbags",
"CigaretteButts", "FoodWrapperscandychipsetc",
"TakeOutAwayContainersPlastic","TakeOutAwayContainersFoam",
"BottleCapsPlastic", "BottleCapsMetal",
"LidsPlastic", "StrawsStirrers",
"ForksKnivesSpoons", "BeverageBottlesPlastic",
"BeverageBottlesGlass", "BeverageCans",
"GroceryBagsPlastic", "OtherPlasticBags",
"PaperBags", "CupsPlatesPaper",
"CupsPlatesPlastic.", "CupsPlatesFoam",
"FishingBuoysPotsTraps", "FishingNetPieces",
"FishingLine1yardmeter1piece", "Rope1yardmeter1piece",
"FishingGearCleanSwell", "X6PackHolders",
"OtherPlasticFoamPackaging", "OtherPlasticBottlesoilbleachetc",
"StrappingBands", "TobaccoPackagingWrap",
"OtherPackagingCleanSwell", "Appliancesrefrigeratorswashersetc",
"Balloons", "CigarTips",
"CigaretteLighters", "ConstructionMaterials",
"Fireworks", "Tires",
"Toys", "OtherTrashCleanSwell",
"Condoms", "Diapers",
"Syringes", "TamponsTamponApplicators",
"PersonalHygieneCleanSwell", "FoamPieces",
"GlassPieces", "PlasticPieces",
"TotalItemsCollected")
# Comb1518
comb1518 <- rbind(DC1518,Delaware1518,Maryland1518,NorthCarolina1518,Virginia1518)
#---------------------------------Combine everything into one----------------------------------------------
# combine 08 to 15
comb0812 <- rbind(combdata08,combdata09,combdata10,
combdata11,combdata12)
names0812 <- names(comb0812)
comb1315 <- rbind(combdata13,combdata14,combdata15)
names1315 <- names(comb1315)
names1518 <- names(comb1518)
write.csv(names0812,"names0812.csv")
write.csv(names1315,"names1315.csv")
write.csv(names1518,"names1518.csv")
#****problem here: data structurt difference
# clean the comb0812 for join
comb0812forjoin <- comb0812 %>%
mutate(TotalItemsCollected = `Paper Bags` + `Bags(Plastic)`+
`Balloons` + `Beverage Bottles (Plastic)` +
`Beverage Bottles (Glass)` + `Beverage Cans` +
`Caps, Lids` + `Clothing, Shoes` +
`Cups, Plates, Forks, Knives, Spoons` + `Food Wrappers/Containers` +
`Pull Tabs` + `6-Pack Holders` +
`Shotgun Shells/Wadding` + `Straws, Stirrers` +
`Toys` + `Bait Containers/Packaging` +
`Bleach/Cleaner Bottles` + `Buoys/Floats` +
`Crab/Lobster/Fish Traps` + `Crates` +
`Fishing Line` + `Fishing Lures/Light Sticks` +
`Fishing Net & Pieces` + `Light Bulbs/Tubes` +
`Oil/Lube Bottles` + `Pallets` +
`Plastic Sheeting/Tarps` + `Rope` +
`Strapping Bands` + `Cigarette Butts` +
`Cigarette Lighters` + `Cigar Tips` +
`Tobacco Packaging/Wrap` + `Appliances (refigerators, washers, etc.)` +
`Batteries` + `Construction Materials` +
`Cars/Car Parts` + `55-Gallon Drums` +
`Tires` + `Condoms` +
`Diapers` + `Syringes` +
`Tampons/Tampon Applicators`
) %>%
dplyr::select( Country,State,`County/Zone`,
# `Site Name`,`Site Type`,
`Cleanup Date`,`Cleanup Type`,`Latitude`,
`Longitude`, Adults, Children,
People, Pounds, Miles,
XofBags,TotalItemsCollected
) %>%
rename(Zone = `County/Zone`,
CleanupDate = `Cleanup Date`,
CleanupType = `Cleanup Type`,
Xofbags = XofBags)
# clean the comb1315 for join
comb1315forjoin <- comb1315 %>%
mutate(TotalItemsCollected = `Cigarette Butts`+`Food Wrappers (candy, chips, etc.)`+
`Take Out/Away Containers (Plastic)` + `Take Out/Away Containers (Foam)` +
`Bottle Caps (Plastic)` + `Take Out/Away Containers (Foam)` +
`Bottle Caps (Plastic)` + `Bottle Caps (Metal)` +
`Lids (Plastic)` + `Straws, Stirrers` +
`Forks, Knives, Spoons` + `Beverage Bottles (Plastic)` +
`Beverage Bottles (Glass)` + `Beverage Cans` +
`Grocery Bags (Plastic)` + `Other Plastic Bags` +
`Paper Bags` + `Cups & Plates (Paper)` +
`Cups & Plates (Plastic)` + `Cups & Plates (Foam)` +
`Fishing Buoys, Pots & Traps` + `Fishing Net & Pieces` +
`Fishing Line (1 yard/meter = 1 piece)` + `Rope (1 yard/meter = 1 piece)`+
`6-Pack Holders` + `Other Plastic/Foam Packaging` +
`Other Plastic Bottles (oil, bleach, etc.)` + `Strapping Bands` +
`Tobacco Packaging/Wrap` + `Appliances (refigerators, washers, etc.)` +
`Balloons` + `Cigar Tips` +
`Cigarette Lighters` + `Construction Materials` +
`Fireworks` + `Tires` +
`Condoms` + `Diapers` +
`Syringes` + `Tampons/Tampon Applicators` +
`Foam Pieces` + `Glass Pieces` +
`Plastic Pieces`) %>%
dplyr::select(Country,State,`County/Zone`,
# `Site Name`,`Site Type`,
`Cleanup Date`,`Cleanup Type`,`Latitude`,
`Longitude`, Adults, Children,
People, Pounds, Miles,
XofBags,TotalItemsCollected) %>%
rename(Zone = `County/Zone`,
CleanupDate = `Cleanup Date`,
CleanupType = `Cleanup Type`,
Xofbags = XofBags)
# clean the comb1518 for join
comb1518forjoin <- dplyr::select(comb1518,
Country,State,Zone,
# `Site Name`,`Site Type`,
CleanupDate, CleanupType,GPS,
Adults, Children,
People, Pounds, Miles,
Xofbags,TotalItemsCollected) %>%
separate(GPS, c("Latitude","Longitude"), sep = ",", remove = TRUE)
# Everything combined together
# It might make more sense to predict the bag/mile or totalitem/mile
# Is it better to remove the NAs?
finalcombdata <- rbind(comb0812forjoin, comb1315forjoin, comb1518forjoin) %>%
mutate(bagpermile = Xofbags/Miles) %>%
mutate(bagpermile = ifelse(bagpermile == Inf, Xofbags, bagpermile)) %>%
mutate(bagpermile = ifelse(is.na(bagpermile), Xofbags, bagpermile)) %>%
mutate(itemspermile = TotalItemsCollected/Miles) %>%
mutate(itemspermile = ifelse(itemspermile == Inf, TotalItemsCollected, itemspermile)) %>%
mutate(itemspermile = ifelse(is.na(itemspermile), TotalItemsCollected, itemspermile)) %>%
mutate(poundspermile = Pounds/Miles) %>%
mutate(poundspermile = ifelse(poundspermile == Inf, Pounds, poundspermile)) %>%
mutate(poundspermile = ifelse(is.na(poundspermile), Pounds, poundspermile))
# Deciding the cell size
# Median 0.8973 Mean 3.7856
summary(finalcombdata$Miles)
summary(finalcombdata$Xofbags)
summary(finalcombdata$TotalItemsCollected)
# Write final data for display into one csv
write.csv(finalcombdata,"finalcombdata.csv")
#---------------------------------Display the data----------------------------------------------
finalpoints <- st_read("D:/NA/WP/06_17_allpoints_dis2water.shp") %>%
st_transform(3857)
states <- st_read("D:/NA/WP/basemap/selectedstates.shp") %>%
st_transform(3857)
waterbody <- st_read("D:/NA/WP/basemap/slipwater_dissolve1.shp") %>%
st_transform(3857)
# !!!! don't plot this data
# waterclip <- st_read("D:/NA/WP/waterclip_proj.shp")
# Define plot theme
plotTheme <- function(base_size = 24) {
theme(
text = element_text( color = "black"),
plot.title = element_text(size = 18,colour = "black"),
plot.subtitle = element_text(size = 12,face="italic", colour = "#477371", hjust = 0),
plot.caption = element_text(hjust=0),
axis.ticks = element_blank(),
panel.background = element_blank(),
panel.grid.major.y = element_line("#477371", size = 0.1),
panel.border = element_rect(colour = "#477371", fill=NA, size=1),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text = element_text(size=10),
axis.title = element_text(size=8),
axis.text = element_text(size=8),
axis.title.y = element_text(size=12),
plot.background = element_blank(),
legend.background = element_blank(),
legend.title = element_text(colour = "black", face = "italic"),
legend.text = element_text(colour = "black", face = "italic"),
axis.ticks.y = element_line(color="grey70"),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank())
}
# basemap
baseMap <- ggplot() +
geom_sf(data=states, aes(), fill = "gray70", colour="darkgray", size = 0.5, alpha = 0.2) +
# geom_sf(data=streets, aes(), fill=NA, colour='white', size = 0.3,alpha=0.8) +
geom_sf(data=waterbody, aes(), fill="white", colour = NA) +
# geom_sf(data=roads, aes(), fill=NA, colour='gray90', alpha = 0.8) +
# geom_sf(data=parks, aes(), fill="white", colour = NA, alpha = 0.9) +
# geom_sf(data=minneapolis, aes(), fill=NA, colour="darkgray", size = 0.3) +
# geom_sf(data=boundary, aes(), fill = NA, colour="darkgray", size = 0.5, alpha = 0.2) +
plotTheme()
#---------------------------------Feature Engineering----------------------------------------------
library(AppliedPredictiveModeling)
library(randomForest)
#library(popbio) #
library(caret)
library(pscl)
library(plotROC)
library(ggmap)
library(pROC)
library(grid)
library(gridExtra)
library(ggplot2)
library(tidycensus)
#Install api key
#census_api_key("7ec628d12f68cb9fce1d74c77ababf2078dbc766", overwrite = FALSE, install = TRUE)
readRenviron("~/.Renviron")
us <- unique(fips_codes$state)[1:51]
# for variables
# 2016
v16 <- load_variables(2016, "acs5", cache = TRUE)
v16 <- load_variables(2016, "acs5", cache = TRUE)