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CIS-dump-CHR.R
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## R Script to process data exported from the CIS databases ##
# Author: B.S. Weir (2017)
#============Load and subset data================
CHR.dump <- read.csv("20170917 CHR Dump.csv")
# subset out "Deaccessioned=True", not implemented
# CHR.dump <- subset(noviruses,(Deaccessioned == "FALSE"))
#setting up per specimen type subsets, with summaries of each specimen type
CHR.alcohol <- subset(CHR.dump,(SpecimenType == "Alcohol"))
summary(CHR.alcohol, maxsum=40)
#============Quick data check================
#have a quick look at the data
head(CHR.dump)
# summary(CHR.dump.initial, maxsum=20) #data before subsetting, not implemented
summary(CHR.dump, maxsum=20) #data after subsetting
s <- summary(CHR.dump, maxsum=20)
capture.output(s, file = "CHR-summary.txt")
#============Type Specimens================
#ggplot code for type Specimens
d <- subset(CHR.dump,!(TypeStatus == ""))
attach(d) #this means we don't need the $ sign
require(ggplot2)
p <- ggplot(d, aes(TypeStatus)) + labs(title = "Types in the CHR") + labs(x = "'Kind' of type", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_types.png', width=10, height=10)
#another one showing just the number of types in each kind of culture?
#ggplot code for type Specimens factored by Specimen type
d <- subset(CHR.dump,!(TypeStatus == ""))
attach(d) #this means we don't need the $ sign
require(ggplot2)
p <- ggplot(d, aes(TypeStatus, fill=SpecimenType)) + labs(title = "Types in the CHR") + labs(x = "'Kind' of type", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR.types.by.kind.png', width=10, height=10)
#============Kingdom Level barcharts================
#plain code for a kingdom barchart
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(SpecimenType)) + labs(title = "Specimens in the CHR by Specimen type") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_kingdoms.png', width=7, height=7)
#kingdoms in GenBank
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(GenBank)) + labs(title = "Specimens in the CHR in GenBank") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
#ggsave(print_bars, file='CHR_kingdoms_genbank.png', width=7, height=7)
#kingdoms with literature
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(SpecimenType, fill=Literature)) + labs(title = "Specimens in the CHR in Literature") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_kingdoms_Literature.png', width=7, height=7)
#kingdoms with images
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(SpecimenType, fill=Images)) + labs(title = "Specimens in the CHR with images") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_kingdoms_images.png', width=7, height=7)
#could also do a stacked bar chart with images, genbank, literature all on one chart.
#kingdoms by Occurrence Description
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(SpecimenType, fill=OccurrenceDescription)) + labs(title = "Specimens in the CHR by occurrence in NZ") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_kingdoms_occurrence.png', width=7, height=7)
#CollectionEventMethod
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(CollectionEventMethod)) + labs(title = "Specimens in the CHR by Collection Event Method") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_CollectionEventMethod.png', width=7, height=7)
#kingdoms by Occurrence Description
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(OccurrenceDescription)) + labs(title = "Specimens in the CHR by occurrence in NZ") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_kingdoms_occurrence2.png', width=7, height=7)
#kingdoms by Order Status
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(SpecimenType, fill= LoanStatus)) + labs(title = "CHR Order Status") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_kingdoms_ LoanStatus.png', width=7, height=7)
#kingdoms by last updated by
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(SpecimenType, fill= UpdatedBy)) + labs(title = "CHR Last updated by") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_kingdoms_updated_by.png', width=7, height=7)
#need a kingdoms by NZ Specimens??
#============High Taxonomy================
#Phylum
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(Phylum)) + labs(title = "CHR by phylum") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_phylum.png', width=10, height=10)
#ggplot code for Class
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(Class)) + labs(title = "CHR by class") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_class.png', width=10, height=10)
#ggplot code for Order
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(Order)) + labs(title = "CHR by order") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_order.png', width=10, height=10)
#ggplot code for Order
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(Order, fill=SpecimenType)) + labs(title = "CHR by order") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_order-speciemtype.png', width=10, height=10)
#ggplot code for Family
attach(CHR.dump)
require(ggplot2)
p <- ggplot(CHR.dump, aes(Family)) + labs(title = "CHR by bacterial family") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
#ggsave(print_bars, file='CHR_family.png', width=20, height=10)
# ----- fungal taxon grouping -----
f <- subset(CHR.dump, SpecimenType == "Fungal Culture")
#ggplot code for fungal Phylum
attach(f)
require(ggplot2)
p <- ggplot(f, aes(Phylum)) + labs(title = "CHR by fungal phylum") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_fungal-phylum.png', width=10, height=10)
#ggplot code for fungal Class
attach(f)
require(ggplot2)
p <- ggplot(f, aes(Class)) + labs(title = "CHR by fungal class") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_fungal-class.png', width=10, height=10)
#ggplot code for fungal Order
attach(f)
require(ggplot2)
p <- ggplot(f, aes(Order)) + labs(title = "CHR by fungal order") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_fungal-order.png', width=10, height=10)
#ggplot code for fungal Family
attach(f)
require(ggplot2)
p <- ggplot(f, aes(Family)) + labs(title = "CHR by fungal family") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_fungal-family.png', width=20, height=10)
#============Other names================
# error Kingdom is missing
names.present.fungi <- subset(CHR.dump,(Kingdom == "Fungi" & OccurrenceDescription == "Present"))
summary(names.present.fungi, maxsum=40)
#ggplot code for fungal Phylum
require(ggplot2)
p <- ggplot(names, aes(names$Phlum)) + labs(title = "names by phylum") + labs(x = "Taxon", y = "number of names")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='names-phylum.png', width=10, height=10)
#ggplot code for fungal Phylum
require(ggplot2)
p <- ggplot(names, aes(names$Phlum, fill=OccurrenceDescription)) + labs(title = "names by phylum") + labs(x = "Taxon", y = "number of names")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='names-phylum-occurrence.png', width=10, height=10)
#ggplot code for Kingdom
require(ggplot2)
p <- ggplot(names, aes(names$Kingdom, fill=OccurrenceDescription)) + labs(title = "names by Kingdom") + labs(x = "Taxon", y = "number of names")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='names-Kindom-occurrence.png', width=10, height=10)
#ggplot code for Kingdom biostatus
require(ggplot2)
p <- ggplot(names, aes(names$Kingdom, fill=BioStatusDescription)) + labs(title = "names by Kingdom") + labs(x = "Taxon", y = "number of names")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='names-Kindom-biostatus.png', width=10, height=10)
#ggplot code for Fungal Family present in NZ
require(ggplot2)
p <- ggplot(names.present.fungi, aes(names.present.fungi$Family)) + labs(title = "names by family in NZ") + labs(x = "Taxon", y = "number of species")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='names-Family-occurrence-NZ.png', width=10, height=35)
#============Countries================
CHR.dump.NZ <- subset(CHR.dump,(Country == "New Zealand"))
attach(CHR.dump.NZ)
require(ggplot2)
p <- ggplot(CHR.dump, aes(NZAreaCode)) + labs(title = "NZ Specimens in the CHR by Area Code") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_NZAreaCode.png', width=7, height=7)
#ggplot code for country
cy <- subset(CHR.dump,!(Country == ""))
require(ggplot2)
con <- ggplot(cy, aes(Country)) + labs(title = "Top 10 Countries") + labs(x = "Country", y = "number")
con <- con + theme(axis.text.x=element_text(angle=-90, hjust=0))
con + geom_bar()+ coord_flip()
print_bars <- con + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_country.png', width=10, height=10)
#ggplot code for top ten countries by specimen type
positions <- c("New Zealand", "United States", "Australia", "United Kingdom", "Brazil", "Japan", "India", "China", "Italy", "France")
c <- subset(CHR.dump, (Country == "New Zealand" | Country == "United States" | Country == "Australia" | Country == "United Kingdom" | Country == "Brazil" | Country == "Japan" | Country == "India" | Country == "France" | Country == "China" | Country == "Italy"))
attach(c) #this means we don't need the $ sign
require(ggplot2)
con <- ggplot(c, aes(Country, fill=SpecimenType)) + labs(title = "Top 10 Countries in the CHR") + labs(x = "Country", y = "number of specimens")
con <- con + theme(axis.text.x=element_text(angle=-90, hjust=0))
con + geom_bar()+ coord_flip() + scale_x_discrete(limits = positions)
print_bars <- con + geom_bar()+ coord_flip() + scale_x_discrete(limits = positions)
ggsave(print_bars, file='CHR_country_by_kind.png', width=6, height=5)
ggsave(print_bars, file='CHR_country_by_kind.svg', width=6, height=5)
ggsave(print_bars, file='CHR_country_by_kind.eps', width=6, height=5)
#ggplot code for pacific country
c <- subset(CHR.dump, (Country == "Fiji" | Country == "American Samoa" | Country == "Cook Islands" | Country == "Solomon Islands" | Country == "Micronesia" | Country == "New Caledonia" | Country == "Niue" | Country == "Norfolk Island" | Country == "Samoa" | Country == "Vanuatu"))
attach(c) #this means we don't need the $ sign
require(ggplot2)
con <- ggplot(c, aes(Country, fill=SpecimenType)) + labs(title = "Pacific Countries Specimens in the CHR") + labs(x = "Country", y = "number of specimens")
con <- con + theme(axis.text.x=element_text(angle=-90, hjust=0))
con + geom_bar()+ coord_flip()
print_bars <- con + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR-pacific-countries.png', width=10, height=10)
#ggplot code for country
positions <- c("United States", "Australia", "United Kingdom", "Brazil", "Japan", "India", "China", "France", "Italy", "Canada")
c <- subset(CHR.dump, (Country == "Canada" | Country == "United States" | Country == "Australia" | Country == "United Kingdom" | Country == "Brazil" | Country == "Japan" | Country == "India" | Country == "France" | Country == "China" | Country == "Italy"))
attach(c) #this means we don't need the $ sign
require(ggplot2)
con <- ggplot(c, aes(Country, fill=SpecimenType)) + labs(title = "Top 10 Countries in the CHR (not including NZ)") + labs(x = "Country", y = "number of specimens")
con <- con + theme(axis.text.x=element_text(angle=-90, hjust=0))
con + geom_bar()+ coord_flip() + scale_x_discrete(limits = positions)
print_bars <- con + geom_bar()+ coord_flip() + scale_x_discrete(limits = positions)
ggsave(print_bars, file='CHR_country_by_kind_not_nz.png', width=10, height=10)
## could make a pseudo dataset manually replaceing all non target countries with "other"
#============Over time================
# also do a trend line of growth. so do a scatterplot and fir a trend line to project growth.
# Do on recived date and check for any blanks
#can do a culmalative graph?
attach(CHR.dump)
require(ggplot2)
di <- ggplot(CHR.dump, aes(as.Date(CollectionDateISO))) + labs(title = "Isolation dates of CHR Specimens") + labs(x = "Date of isolation", y = "Number of Specimens" , fill = "")
di <- di + scale_x_date()
di + geom_histogram(binwidth=365.25) # this is a bin of two years binwidth=730
dip <- di + geom_histogram(binwidth=365.25)
ggsave(dip, file='CHR-isolation-dates.png', width=5, height=5)
ggsave(dip, file='CHR-isolation-dates.svg', width=5, height=5)
ggsave(dip, file='CHR-isolation-dates.eps', width=5, height=5)
attach(CHR.dump)
require(ggplot2)
dr <- ggplot(CHR.dump, aes(as.Date(ReceivedDateISO))) + labs(title = "REC dates of CHR Specimens") + labs(x = "Date of isolation", y = "Number of Specimens" , fill = "")
dr <- dr + scale_x_date()
dr + geom_histogram(binwidth=365.25) # this is a bin of two years binwidth=730
drp <- dr + geom_histogram(binwidth=365.25)
ggsave(dip, file='CHR-isolation-dates.png', width=5, height=5)
attach(CHR.dump)
require(ggplot2)
dr <- ggplot(CHR.dump, aes(as.Date(ReceivedDateISO))) + labs(title = "REC dates of CHR Specimens") + labs(x = "Date of isolation", y = "Number of Specimens" , fill = "")
dr <- dr + scale_x_date()
dr + geom_line() # this is a bin of two years binwidth=730
drp <- dr + geom_histogram(binwidth=365.25)
ggsave(dip, file='CHR-isolation-dates.png', width=5, height=5)
attach(CHR.dump)
require(ggplot2)
dr <- ggplot(CHR.dump, aes(as.Date(ReceivedDateISO))) + labs(title = "REC dates of CHR Specimens") + labs(x = "Date of isolation", y = "Number of Specimens" , fill = "")
dr <- dr + scale_x_date()
dr + geom_point
drp <- dr + geom_histogram(binwidth=365.25)
ggsave(dip, file='CHR-isolation-dates.png', width=5, height=5)
CHR.dump$topcontrib <- ifelse(CHR.dump$Contributor == "NZP", "NZP", "other")
CHR.dump$topcontrib
attach(CHR.dump)
require(ggplot2)
dr <- ggplot(CHR.dump, aes(as.Date(ReceivedDateISO),fill=topcontrib)) + labs(title = "Main Contributors to the CHR collection") + labs(x = "Date of Receipt", y = "Number of Specimens" , fill = "") #Alternatively, dates can be specified by a numeric value, representing the number of days since January 1, 1970. To input dates stored as the day of the year, the origin= argument can be used to interpret numeric dates relative to a different date.
dr <- dr + scale_x_date()
dr + geom_hline(yintercept=392, linetype=3) + geom_histogram(binwidth=365.25)
drp <- dr + geom_histogram(binwidth=365.25) + geom_hline(yintercept=392, linetype=2)
ggsave(drp, file='CHR-received-dates-contributor.png', width=15, height=10)
CHR.date <- read.csv("CHR.date.csv") #i removed all nulls
qplot(factor(as.Date(IsolationDateISO)), data=CHR.dump, geom="bar")
attach(CHR.dump) #this means we don't need the $ sign
require(ggplot2)
di <- ggplot(CHR.dump, aes(as.Date(IsolationDateISO))) + labs(title = "Isolation dates of CHR Specimens") + labs(x = "Date of isolation", y = "Number of Specimens" , fill = "")
di <- di + scale_x_date()
di + geom_histogram(binwidth=365.25) # this is a bin of two years binwidth=730
dip <- di + geom_histogram(binwidth=365.25)
ggsave(dip, file='CHR-isolation-dates2.png', width=4, height=3)
CHR.dump$topcontrib <- ifelse(CHR.dump$Contributor == "NZP", "NZP", "other")
CHR.dump$topcontrib
attach(CHR.dump) #this means we don't need the $ sign
require(ggplot2)
ditc <- ggplot(CHR.dump, aes(as.Date(IsolationDateISO, fill=Contributor))) + labs(title = "Isolation dates of CHR Specimens") + labs(x = "Date of isolation", y = "Number of Specimens" , fill = "")
ditc <- ditc + scale_x_date()
ditc + geom_histogram(binwidth=365.25) # this is a bin of two years binwidth=730
ditcp <- ditc + geom_histogram(binwidth=365.25)
ggsave(dip, file='CHR-isolation-dates2.png', width=4, height=3)
attach(CHR.dump) #this means we don't need the $ sign
require(ggplot2)
dr <- ggplot(CHR.dump, aes(as.Date(ReceivedDateISO))) + labs(title = "Received dates of CHR Specimens") + labs(x = "Date of Receipt", y = "Number of Specimens" , fill = "") #Alternatively, dates can be specified by a numeric value, representing the number of days since January 1, 1970. To input dates stored as the day of the year, the origin= argument can be used to interpret numeric dates relative to a different date.
dr <- dr + scale_x_date()
dr + geom_histogram(binwidth=365.25) + geom_hline(yintercept=392, linetype=2) + scale_x_continuous(breaks = scales::pretty_breaks(n = 10))
drp <- dr + geom_histogram(binwidth=365.25) + geom_hline(yintercept=392, linetype=2)
ggsave(drp, file='CHR-received-dates.png', width=10, height=10)
## CAN we do this by organism too?
attach(CHR.dump) #this means we don't need the $ sign
require(ggplot2)
dr <- ggplot(CHR.dump, aes(as.Date(ReceivedDateISO),fill=SpecimenType)) + labs(title = "Received dates of CHR Specimens") + labs(x = "Date of Receipt", y = "Number of Specimens" , fill = "") #Alternatively, dates can be specified by a numeric value, representing the number of days since January 1, 1970. To input dates stored as the day of the year, the origin= argument can be used to interpret numeric dates relative to a different date.
dr <- dr + scale_x_date()
dr + geom_hline(yintercept=392, linetype=3) + geom_histogram(binwidth=365.25)
drp <- dr + geom_histogram(binwidth=365.25) + geom_hline(yintercept=392, linetype=2)
ggsave(drp, file='CHR-received-dates-organism.png', width=15, height=10)
sum2 <- ggplot_build(drp) #this extracts the values from the histogram
sum2
attach(CHR.dump) #this means we don't need the $ sign
require(ggplot2)
dr <- ggplot(CHR.dump, aes(as.Date(ReceivedDateISO),fill=topcontrib)) + labs(title = "Main Contributors to the CHR collection") + labs(x = "Date of Receipt", y = "Number of Specimens" , fill = "") #Alternatively, dates can be specified by a numeric value, representing the number of days since January 1, 1970. To input dates stored as the day of the year, the origin= argument can be used to interpret numeric dates relative to a different date.
dr <- dr + scale_x_date()
dr + geom_hline(yintercept=392, linetype=3) + geom_histogram(binwidth=365.25)
drp <- dr + geom_histogram(binwidth=365.25) + geom_hline(yintercept=392, linetype=2)
ggsave(drp, file='CHR-received-dates-contributor.png', width=15, height=10)
CHR.dump$topcontrib <- ifelse(CHR.dump$Contributor == "NZP", "NZP", "other")
CHR.dump$topcontrib
#ggplot code for collections over the years
attach(CHR.dump) #this means we don't need the $ sign
require(ggplot2)
con <- ggplot(c, aes(Country, fill=SpecimenType)) + labs(title = "Pacific Countries Specimens in the CHR") + labs(x = "Country", y = "number of specimens")
con <- con + theme(axis.text.x=element_text(angle=-90, hjust=0))
con + geom_histogram()+ coord_flip()
print_bars <- con + geom_histogram()+ coord_flip()
ggsave(print_bars, file='CHR-pacific-countries.png', width=10, height=10)
#ggplot code for collections over the years in NZ
c <- subset(CHR.dump, (Country == "New Zealand"))
#also need something that plots monthly e.g. fungi versus collection month.
#======MAPS========
#New Zealand Area codes
CHR.nz <- subset(CHR.dump,(Country == "New Zealand"))
attach(CHR.nz)
require(ggplot2)
p <- ggplot(CHR.nz, aes(NZAreaCode)) + labs(title = "CHR specimens by NZ region") + labs(x = "Crosby Region", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_NZAreaCode.png', width=5, height=5)
#Using GGPLOT, plot the Base World Map
mp <- NULL
mapWorld <- borders("world", colour="gray50", fill="gray50") # create a layer of borders
mp <- ggplot() + mapWorld
#Now Layer the cities on top
mp <- mp+ geom_point(aes(x=visit.x, y=visit.y) ,color="blue", size=3)
mp
#======On Hosts========
# subset out kiwifruit
CHR.dump.kiwifruit <- subset(CHR.dump,(TaxonName_C2 == "Actinidia deliciosa"))
#ggplot code for bacterial Class
attach(b)
require(ggplot2)
p <- ggplot(CHR.dump.kiwifruit, aes(Family)) + labs(title = "Family of microbes on kiwifruit in the CHR") + labs(x = "Taxon", y = "number of specimens")
p <- p + theme(axis.text.x=element_text(angle=-90, hjust=0))
p + geom_bar()+ coord_flip()
print_bars <- p + geom_bar()+ coord_flip()
ggsave(print_bars, file='CHR_kiwifruit-family.png', width=10, height=10)