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.Rhistory[Conflit]
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ylab="Temperatures (°C)", pch=21,
ylim=round(range(clim_season_year[clim_season_year$Variable=="Temperature_C"&
clim_season_year$Season=="summer",
"Value"]), 0),
cex=2, xaxt='n', xlab='Year', col="blue", bg="blue", cex.axis=1.5, cex.lab=1.5,
main=paste("Nuuk year mean temp: F=", as.character(round(anova(mod_Ny_temp)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Ny_temp)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Ny_temp)$adj.r.squared, 3))))
abline(a=summary(mod_Ny_temp)$coefficients[1] , b=summary(mod_Ny_temp)$coefficients[2],
lwd=2, col="blue")
axis(side=1, at=seq(2007, 2019, by=5), cex.axis=1.5)
# Zack YEAR
mod_Zy_temp <- lm(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Zackenberg" &
clim_season_year$Variable=="Temperature_C"&
clim_season_year$Season=="summer", ])
summary(mod_Zy_temp)
anova(mod_Zy_temp)
plot(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Zackenberg" &
clim_season_year$Variable=="Temperature_C"&
clim_season_year$Season=="summer", ],
ylab=" ", pch=21, ylim=round(range(clim_season_year[clim_season_year$Variable=="Temperature_C"&
clim_season_year$Season=="summer",
"Value"]), 0),
cex=2, xaxt='n', xlab='Year', col="red", bg="red", cex.axis=1.5, cex.lab=1.5,
main=paste("Zackenberg year mean temp **: F=", as.character(round(anova(mod_Zy_temp)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Zy_temp)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Zy_temp)$adj.r.squared, 3))))
abline(a=summary(mod_Zy_temp)$coefficients[1] , b=summary(mod_Zy_temp)$coefficients[2],
lwd=2, col="red")
axis(side=1, at=seq(1995, 2019, by=5), cex.axis=1.5)
##END-----------------------------------------------------------------------------------
### PRECIPITATION
# YEARLY MEAN PRECIPITATION
# Nuuk YEAR
mod_Ny_prec <- lm(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Nuuk" &
clim_season_year$Variable=="Precipitation_mm"&
clim_season_year$Season=="summer", ])
summary(mod_Ny_prec)
anova(mod_Ny_prec)
plot(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Nuuk" &
clim_season_year$Variable=="Precipitation_mm"&
clim_season_year$Season=="summer", ],
ylab="Precipitation (mm)", pch=21,
ylim=round(range(clim_season_year[clim_season_year$Variable=="Precipitation_mm"&
clim_season_year$Season=="summer","Value"]), 3),
cex=2, xaxt='n', xlab='Year', col="blue", bg="blue", cex.axis=1.5, cex.lab=1.5,
main=paste("Nuuk year mean prec *: F=", as.character(round(anova(mod_Ny_prec)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Ny_prec)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Ny_prec)$adj.r.squared, 3))))
abline(a=summary(mod_Ny_prec)$coefficients[1] , b=summary(mod_Ny_prec)$coefficients[2],
lwd=2, col="blue")
axis(side=1, at=seq(2007, 2019, by=5), cex.axis=1.5)
# Zack YEAR
mod_Zy_prec <- lm(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Zackenberg" &
clim_season_year$Variable=="Precipitation_mm"&
clim_season_year$Season=="summer", ])
summary(mod_Zy_prec)
anova(mod_Zy_prec)
plot(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Zackenberg" &
clim_season_year$Variable=="Precipitation_mm"&
clim_season_year$Season=="summer", ],
ylab=" ", pch=21,
ylim=round(range(clim_season_year[clim_season_year$Site=="Zackenberg" &
clim_season_year$Variable=="Precipitation_mm"&
clim_season_year$Season=="summer", "Value"]), 3),
cex=2, xaxt='n', xlab='Year', col="red", bg="red", cex.axis=1.5, cex.lab=1.5,
main=paste("Zackenberg year mean prec **: F=" , as.character(round(anova(mod_Zy_prec)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Zy_prec)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Zy_prec)$adj.r.squared, 3))))
abline(a=summary(mod_Zy_prec)$coefficients[1] , b=summary(mod_Zy_prec)$coefficients[2],
lwd=2, col="red")
axis(side=1, at=seq(1995, 2019, by=5), cex.axis=1.5)
##END-----------------------------------------------------------------------------------
### HUMIDITY
# YEARLY MEAN HUMIDITY
# Nuuk YEAR
mod_Ny_hum <- lm(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Nuuk" &
clim_season_year$Variable=="Humidity_%"&
clim_season_year$Season=="summer", ])
summary(mod_Ny_hum)
anova(mod_Ny_hum)
plot(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Nuuk" &
clim_season_year$Variable=="Humidity_%"&
clim_season_year$Season=="summer", ],
ylab="Humidity (%)", pch=21,
ylim=round(range(clim_season_year[clim_season_year$Variable=="Humidity_%"&
clim_season_year$Season=="summer","Value"]), 0),
cex=2, xaxt='n', xlab='Year', col="blue", bg="blue", cex.axis=1.5, cex.lab=1.5,
main=paste("Nuuk year mean hum ***: F=" , as.character(round(anova(mod_Ny_hum)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Ny_hum)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Ny_hum)$adj.r.squared, 3))))
abline(a=summary(mod_Ny_hum)$coefficients[1] , b=summary(mod_Ny_hum)$coefficients[2],
lwd=2, col="blue")
axis(side=1, at=seq(2007, 2019, by=5), cex.axis=1.5)
# Zack YEAR
mod_Zy_hum <- lm(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Zackenberg" &
clim_season_year$Variable=="Humidity_%"&
clim_season_year$Season=="summer", ])
summary(mod_Zy_hum)
anova(mod_Zy_hum)
plot(Value ~ Year , data=clim_season_year[clim_season_year$Site=="Zackenberg" &
clim_season_year$Variable=="Humidity_%"&
clim_season_year$Season=="summer", ],
ylab=" ", pch=21,
ylim=round(range(clim_season_year[clim_season_year$Site=="Zackenberg" &
clim_season_year$Variable=="Humidity_%"&
clim_season_year$Season=="summer","Value"]), 0),
cex=2, xaxt='n', xlab='Year', col="red", bg="red", cex.axis=1.5, cex.lab=1.5,
main=paste("Zackenberg year mean hum **: F=" , as.character(round(anova(mod_Zy_hum)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Zy_hum)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Zy_hum)$adj.r.squared, 3))))
abline(a=summary(mod_Zy_hum)$coefficients[1] , b=summary(mod_Zy_hum)$coefficients[2],
lwd=2, col="red")
axis(side=1, at=seq(1995, 2019, by=5), cex.axis=1.5)
# END -------------------------------------------------------------------------------
### BASIC CLIMATIC EXPLORATION #----------------------------------------------------
# distribution of temperature along year
par(mfrow=c(1,2))
# NUUK TEMPERATURE
plot(clim[clim$Site=="Nuuk" & clim$Variable=="Temperature_C" , "Month"],
clim[clim$Site=="Nuuk" & clim$Variable=="Temperature_C" , "Value"],
ylab="Temperatures (°C)", xlab="Month",
ylim=range(clim[clim$Variable=="Temperature_C" , "Value"]),
main="Nuuk, distribution of temperature")
abline(0, 0, col="grey")
abline(v=c('05', "09"), col="red")
abline(v=c('04', "10"), col="orange")
# summer at Nuuk==05, 06, 07, 08, 09
# spring at Nuuk==04 ; fall=10
# ZACKENBERG TEMPERATURE
plot(clim[clim$Site=="Zackenberg" & clim$Variable=="Temperature_C" , "Month"],
clim[clim$Site=="Zackenberg" & clim$Variable=="Temperature_C" , "Value"],
xlab="Month",
ylim=range(clim[clim$Variable=="Temperature_C" , "Value"]),
main="Zackenberg, distribution of temperature")
abline(0, 0, col="grey")
abline(v=c('06', "08"), col="red")
abline(v=c('05', "09"), col="orange")
# summer at Zack==06, 07, 08
# spring at Nuuk==05 ; fall=09
### CLIMATIC ANALYSIS #----------------------------------------------------------------
### TEMPERATURE
par(mfrow=c(3,2))
# YEARLY MEAN TEMPERATURE
# Nuuk YEAR
mod_Ny_temp <- lm(Value ~ Year , data=clim_year[clim_year$Site=="Nuuk" &
clim_year$Variable=="Temperature_C", ])
summary(mod_Ny_temp)
anova(mod_Ny_temp)
plot(Value ~ Year , data=clim_year[clim_year$Site=="Nuuk" &
clim_year$Variable=="Temperature_C", ],
ylab="Temperatures (°C)", pch=21,
ylim=round(range(clim_year[clim_year$Variable=="Temperature_C",
"Value"]), 0),
cex=2, xaxt='n', xlab='Year', col="blue", bg="blue", cex.axis=1.5, cex.lab=1.5,
main=paste("Nuuk year mean temp: F=", as.character(round(anova(mod_Ny_temp)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Ny_temp)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Ny_temp)$adj.r.squared, 3))))
abline(a=summary(mod_Ny_temp)$coefficients[1] , b=summary(mod_Ny_temp)$coefficients[2],
lwd=2, col="blue")
axis(side=1, at=seq(2007, 2019, by=5), cex.axis=1.5)
# Zack YEAR
mod_Zy_temp <- lm(Value ~ Year , data=clim_year[clim_year$Site=="Zackenberg" &
clim_year$Variable=="Temperature_C", ])
summary(mod_Zy_temp)
anova(mod_Zy_temp)
plot(Value ~ Year , data=clim_year[clim_year$Site=="Zackenberg" &
clim_year$Variable=="Temperature_C", ],
ylab=" ", pch=21, ylim=round(range(clim_year[clim_year$Variable=="Temperature_C",
"Value"]), 0),
cex=2, xaxt='n', xlab='Year', col="red", bg="red", cex.axis=1.5, cex.lab=1.5,
main=paste("Zackenberg year mean temp **: F=", as.character(round(anova(mod_Zy_temp)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Zy_temp)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Zy_temp)$adj.r.squared, 3))))
abline(a=summary(mod_Zy_temp)$coefficients[1] , b=summary(mod_Zy_temp)$coefficients[2],
lwd=2, col="red")
axis(side=1, at=seq(1995, 2019, by=5), cex.axis=1.5)
##END---------------
### PRECIPITATION
# YEARLY MEAN PRECIPITATION
# Nuuk YEAR
mod_Ny_prec <- lm(Value ~ Year , data=clim_year[clim_year$Site=="Nuuk" &
clim_year$Variable=="Precipitation_mm", ])
summary(mod_Ny_prec)
anova(mod_Ny_prec)
plot(Value ~ Year , data=clim_year[clim_year$Site=="Nuuk" &
clim_year$Variable=="Precipitation_mm", ],
ylab="Precipitation (mm)", pch=21, ylim=round(range(clim_year[clim_year$Variable=="Precipitation_mm",
"Value"]), 3),
cex=2, xaxt='n', xlab='Year', col="blue", bg="blue", cex.axis=1.5, cex.lab=1.5,
main=paste("Nuuk year mean prec *: F=", as.character(round(anova(mod_Ny_prec)[1,4], 3)),
"P-value=" , as.character(round(anova(mod_Ny_prec)[1,5], 3)),
"R2adj=" , as.character(round(summary(mod_Ny_prec)$adj.r.squared, 3))))
abline(a=summary(mod_Ny_prec)$coefficients[1] , b=summary(mod_Ny_prec)$coefficients[2],
lwd=2, col="blue")
axis(side=1, at=seq(2007, 2019, by=5), cex.axis=1.5)
summary(mod_Ny_prec)
summary(mod_Zy_prec)
summary(mod_Ny_hum)
summary(mod_Zy_hum)
# clean R work-space
rm(list=ls())
# Load 02 - Creation database (load scripts 00 and 01)
source("Scripts/00_Load_libraries.r")
source("Scripts/01_Import_DB.r")
source("Scripts/02_Creation_DB.R")
source("Scripts/02_Climatic_covariates.R")
source("Scripts/C2_Climatic_covariates.R")
# TEMPORAL TRENDS AT PLOT LEVEL WITH LOG ON DENSITY ONLY
# 1 - Flower density per m2 ## --------------------------------------------
flow_den <- ggplot(flow, aes(Year, Flow_m2, group=Plot, color=Plot)) +
geom_point() +
geom_smooth(method='lm', se=TRUE) +
labs(y='Flower density / m2') +
ggtitle('A - Flower density') +
facet_grid(Site+Species~., scales="free_y") +
theme_linedraw() +
theme(legend.position = "none")
# 3 - Log Flower density !=0 ## --------------------------------------------
flow_log_nn <- ggplot(flow, aes(Year, log(trans_Flow_m2), group=Plot, color=Plot)) +
geom_point() +
geom_smooth(method='lm', se=TRUE) +
labs(y='log(Flower density / m2)') +
ggtitle('B - Log(Flower density)') +
facet_grid(Site+Species~., scales="free_y")+
theme_linedraw() +
theme(legend.position = "none")
# graph
gridExtra::grid.arrange(flow_den, flow_log_nn, ncol=2,
top = "Plot level temporal trends in flowering")
### END PLOT LEVEL ## ------------------------------------------------------
# TEMPORAL TRENDS AT SPECIE LEVEL DENSITY ONLY
# 4 - Flower density per m2 ## --------------------------------------------
flow_den2 <- ggplot(flow, aes(Year, Flow_m2, group=Species, color=Species)) +
geom_point() +
geom_smooth(method='lm', se=TRUE) +
labs(y='Flower density/m2') +
ggtitle('A - Flower density') +
facet_grid(Site+Species ~., scales="free_y") +
theme_linedraw()+
theme(legend.position = "none") +
scale_color_brewer(palette="Dark2")
# 6 - Total Flower per plot ## --------------------------------------------
flow_den2_log_nn <- ggplot(flow, aes(Year, log(trans_Flow_m2), group=Species, color=Species)) +
geom_point() +
geom_smooth(method='lm', se=TRUE) +
labs(y='log(Flower density/m2)') +
ggtitle('B - Log(Flower density)') +
facet_grid(Site+Species~., scales="free_y")+
theme_linedraw() +
theme(legend.position = "none") +
scale_color_brewer(palette="Dark2")
# graph
gridExtra::grid.arrange(flow_den2, flow_den2_log_nn, ncol=2,
top = "Temporal flowering trends at species level in Zackenberg")
### END SP LEVEL ## -----------------------------------------------------
# graph
gridExtra::grid.arrange(flow_den, flow_log_nn, ncol=2,
top = "Plot level temporal trends in flowering")
# graph
gridExtra::grid.arrange(flow_den, flow_log_nn, ncol=2,
top = "Plot level temporal trends in flowering")
# DISTRIBUTION OF FLOWERING VARIABLES
# DENSITY
distdens <- ggplot(flow, aes(x=Flow_m2, fill=Species)) +
geom_density(alpha=.3) +
labs(y='Density', x='Flowering density/m2') +
ggtitle('A - Distribution of Flowering density') +
facet_wrap(.~Site+Species, scales="free") +
theme_linedraw() +
theme(legend.position = "none")
# log(density) !=0 (Flow_m2 + 0.0001 in 02_Creation_DB, line 131)
dist_logdens_nn <- ggplot(flow, aes(x=log(trans_Flow_m2), fill=Species)) +
geom_density(alpha=.3) +
labs(y='Density', x='log(Flowering density/m2)') +
ggtitle('B - Distribution of log(Flowering density) !=0') +
facet_wrap(Site+Species~ ., scales="free") +
theme_linedraw() +
theme(legend.position = "none")
# graph
gridExtra::grid.arrange(distdens, dist_logdens_nn, ncol=2,
top = "Distribution of flowering variable per species")
### END DISTRI PLOTS ## -------------------------------------------------
# CLIMATIC TRENDS
# Plot Climatic trends ## ---------------------------------------------
# ALL MONTHS
ggplot(clim_month, aes(Year, Value, group=Site, color=Site)) +
geom_point() +
geom_smooth(method='lm', se=TRUE) +
labs(y='Value') +
ggtitle('Climatic trends all months Nuuk & Zackenberg') +
facet_grid(Variable~., scales="free_y") +
theme_linedraw()
library(dplyr)
library(stringr)
library(tidyverse)
library(lubridate) ##for dealing with dates
# Snow cover (remarks on original fields only)
# load file
Zsnow <- read.csv("data/datasets/View_BioBasis_Zackenberg_Data_Abiotics_Snow_and_ice_cover02122020113324881.csv",
stringsAsFactors=FALSE, header=TRUE, sep="\t",
strip.white = T,na.strings = c("","NA"))
Zsnow<- Zsnow %>% dplyr::rename(Date=ï..Date) ##for some reason this changed when importing the file
# create a new col==Variable
Zsnow$Variable <- "Snow_cover"
# create new col==Site
Zsnow$Site <- "Zackenberg"
# remove field and general remarks
Zsnow <- as.data.frame(Zsnow %>%
dplyr::select(Date, Plot, Section, SnowCoverFraction,
Variable, Site)) %>%
#remove plots==ERI, ART, VEG
filter(!Plot %in% c("Art1","Art2","Art3","Art4","Art5","Art6","Art7",
"Eri1","Eri2","Eri3","Eri4", "Veg1"))
# devided the date into year, month, day
Zsnow <- cbind(as.data.frame(str_split(Zsnow$Date, "-", simplify=TRUE)), Zsnow)
colnames(Zsnow) <- c("Year", "Month", "Day", 'Date', "Plot", "Section",
"Value", "Variable", "Site")
##renaming plots and sections
sort(unique(Zsnow$Plot))
Zsnow$Plot[Zsnow$Plot== "Sax1Si1"] <- "Sax1Sil1"
Zsnow$Plot[Zsnow$Plot== "Sax2Si2"] <- "Sax2Sil2"
Zsnow$Plot[Zsnow$Plot== "Sax3Si3"] <- "Sax3Sil3"
Zsnow$Plot[Zsnow$Plot== "Si4"] <- "Sil4"
Zsnow$Section[Zsnow$Section== "A-D"] <- "A"
# Remove -9999, NA
Zsnow[Zsnow$Value==-9999, 'Value'] <- NA
Zsnow <- Zsnow[complete.cases(Zsnow), ]
# Snow cover (remarks on original fields only)
# load file
Zsnow <- read.csv("data/datasets/View_BioBasis_Zackenberg_Data_Abiotics_Snow_and_ice_cover02122020113324881.csv",
stringsAsFactors=FALSE, header=TRUE, sep="\t",
strip.white = T,na.strings = c("","NA"))
Zsnow<- Zsnow %>% dplyr::rename(Date=ï..Date) ##for some reason this changed when importing the file
# Snow cover (remarks on original fields only)
# load file
Zsnow <- read.csv("data/datasets/View_BioBasis_Zackenberg_Data_Abiotics_Snow_and_ice_cover02122020113324881.csv",
stringsAsFactors=FALSE, header=TRUE, sep="\t",
strip.white = T,na.strings = c("","NA"))
# create a new col==Variable
Zsnow$Variable <- "Snow_cover"
# create new col==Site
Zsnow$Site <- "Zackenberg"
# remove field and general remarks
Zsnow <- as.data.frame(Zsnow %>%
dplyr::select(Date, Plot, Section, SnowCoverFraction,
Variable, Site)) %>%
#remove plots==ERI, ART, VEG
filter(!Plot %in% c("Art1","Art2","Art3","Art4","Art5","Art6","Art7",
"Eri1","Eri2","Eri3","Eri4", "Veg1"))
# devided the date into year, month, day
Zsnow <- cbind(as.data.frame(str_split(Zsnow$Date, "-", simplify=TRUE)), Zsnow)
colnames(Zsnow) <- c("Year", "Month", "Day", 'Date', "Plot", "Section",
"Value", "Variable", "Site")
##renaming plots and sections
sort(unique(Zsnow$Plot))
Zsnow$Plot[Zsnow$Plot== "Sax1Si1"] <- "Sax1Sil1"
Zsnow$Plot[Zsnow$Plot== "Sax2Si2"] <- "Sax2Sil2"
Zsnow$Plot[Zsnow$Plot== "Sax3Si3"] <- "Sax3Sil3"
Zsnow$Plot[Zsnow$Plot== "Si4"] <- "Sil4"
Zsnow$Section[Zsnow$Section== "A-D"] <- "A"
# Remove -9999, NA
Zsnow[Zsnow$Value==-9999, 'Value'] <- NA
Zsnow <- Zsnow[complete.cases(Zsnow), ]
Zsnow <- Zsnow %>%
# Create a col DOY=day of the year based on original date
mutate(DOY=yday(Date)) %>%
mutate(Year_Plot = as.factor(paste(Year, Plot, sep = "_"))) ##adding concatenation of Year and plot (as these are the units to look at)
Zsnow
Zsnow <- Zsnow %>%
# Create a col DOY=day of the year based on original date
mutate(DOY=yday(Date)) %>%
mutate(Year_Plot = as.factor(paste(Year, Plot, sep = "_"))) ##adding concatenation of Year and plot (as these are the units to look at)
library(lubridate) ##for dealing with dates
Zsnow <- Zsnow %>%
# Create a col DOY=day of the year based on original date
mutate(DOY=yday(Date)) %>%
mutate(Year_Plot = as.factor(paste(Year, Plot, sep = "_"))) ##adding concatenation of Year and plot (as these are the units to look at)
Zsnow
##some checks
#how many plots
n_distinct(Zsnow$Plot) #28
n_distinct(Zsnow$Year_Plot) #537
needtohave<- Zsnow %>%
select(Year_Plot) %>%
distinct()
View(needtohave)
needtohave
Zsnow_meltday50 <- Zsnow %>%
# Select the DOY with 50 of snow_cover (this will be the date of snow melt)
group_by(Year, Plot, Section) %>%
filter(Value==50) %>%
mutate(Year_Plot = as.factor(paste(Year, Plot, sep = "_"))) %>% ##adding concatenation of Year and plot (as these are the units to look at)
ungroup()
Zsnow_meltday50
n_distinct(Zsnow_meltday50$Plot) #25
n_distinct(Zsnow_meltday50$Year_Plot) #152
Zsnow_meltday50 %>%
group_by(Year, Plot) %>%
summarise(n= n_distinct(DOY)) %>%
arrange(., Year, Plot)
#1.1. how many DOY per plot and year -- to see if there is differences between sections
check1<- Zsnow_meltday50 %>%
group_by(Year, Plot) %>%
summarise(n= n_distinct(DOY)) %>%
arrange(., Year, Plot)
View(check1)
Zsnow_meltday50 %>%
group_by(Year, Plot) %>%
count(DOY)) %>%
arrange(., Year, Plot)
Zsnow_meltday50 %>%
group_by(Year, Plot) %>%
count(DOY) %>%
arrange(., Year, Plot)
View(Zsnow_meltday50 %>%
group_by(Year, Plot) %>%
count(DOY) %>%
arrange(., Year, Plot))
check1
##for the ones with only one DOY - use that value
doy_ok1<- check1 %>%
filter(n==1) %>%
mutate(Year_Plot = as.factor(paste(Year, Plot, sep = "_")))
doy_ok1
Zsnow_meltday50
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(Year_Plot %in% doy_ok1$Year_Plot), ##this has 114 Year_Plot!! (152-114=38 in check2)
by="Year_Plot")
dim(needtohave[is.na(needtohave$DOY),])
dim(doy_ok1)
needtohave
##so just joining the DOY info
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(Year_Plot %in% doy_ok1$Year_Plot, by="Year_Plot",)
is.na(DOY))
##so just joining the DOY info
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(Year_Plot %in% doy_ok1$Year_Plot, by="Year_Plot",
is.na(DOY))) #
##so just joining the DOY info
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(Year_Plot %in% doy_ok1$Year_Plot), ##this has 114 Year_Plot!! (152-114=38 in check2)
by="Year_Plot")
##so just joining the DOY info
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(Year_Plot %in% doy_ok1$Year_Plot), ##this has 114 Year_Plot!! (152-114=38 in check2)
by="Year_Plot")
dim(needtohave[is.na(needtohave$DOY),])
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(Year_Plot %in% doy_ok1$Year_Plot), ##this has 114 Year_Plot!! (152-114=38 in check2)
by="Year_Plot") %>%
drop_na()
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(DOY!=NA,
Year_Plot %in% doy_ok1$Year_Plot), ##this has 114 Year_Plot!! (152-114=38 in check2)
by="Year_Plot")
dim(needtohave)
dim(needtohave[is.na(needtohave$DOY),])
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(Year_Plot %in% doy_ok1$Year_Plot), ##this has 114 Year_Plot!! (152-114=38 in check2)
by="Year_Plot")
dim(needtohave[is.na(needtohave$DOY),])
dim(needtohave)
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(DOY!=NA,
Year_Plot %in% doy_ok1$Year_Plot), ##this has 114 Year_Plot!! (152-114=38 in check2)
by="Year_Plot")
dim(needtohave)
head(needtohave)
##so just joining the DOY info
needtohave<- needtohave %>% left_join(., Zsnow_meltday50 %>%
select(Year_Plot, DOY) %>%
distinct(Year_Plot, .keep_all = T) %>%
filter(Year_Plot %in% doy_ok1$Year_Plot), ##this has 114 Year_Plot!! (152-114=38 in check2)
by="Year_Plot")
dim(needtohave[is.na(needtohave$DOY),])
check2 <- check1 %>%
##remove the ones that have only a single DOY==all good
filter(n!=1) %>%
mutate(Year_Plot = as.factor(paste(Year, Plot, sep = "_")))
unique(check2$Year_Plot)
dim(unique(check2$Year_Plot))
length(unique(check2$Year_Plot))
auxdata1 <- Zsnow_meltday50 %>%
filter(Year_Plot %in% check2$Year_Plot) ##get the records that match the problematic Year_Plot
auxdata1
ggplot(auxdata1) +
geom_point(aes(x=Year_Plot, y=DOY, colour=Section)) +
theme(axis.text.x = element_text(angle = 90))
library(ggplot2)
ggplot(auxdata1) +
geom_point(aes(x=Year_Plot, y=DOY, colour=Section)) +
theme(axis.text.x = element_text(angle = 90))