names(data_raw)[,30:length(data_raw)]
names(data_raw)[,30:ncol(data_raw)]
names(data_raw)
names(data_raw)[30:ncol(data_raw)]
organism_guild <- tibble(organism_ID = names(data_raw)[30:ncol(data_raw)], organism_guild = NA)
View(organism_guild)
write_csv(organism_guild, "organism_guild_ROMINA.csv")
write_csv(organism_guild, "organism_guild_ROMINA.csv")
library(tidyverse)
library("iNEXT")
library(openxlsx)
dir_ini <- getwd()
data_raw <- read.xlsx("master_nonbeemay24sep14.xlsx",
sheet = "master_nonbeemay2814", startRow = 1)
View(data_raw)
names_data_raw <- names(data_raw)
names_data_raw[duplicated(names_data_raw)]
#"Sphaerophoria_sp." "Syritta_pipiens"
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Sphaerophoria_sp.")
sum(!is.na(data_raw[,408]))
sum(!is.na(data_raw[,412]))
data_raw[,408] <- rowSums(data_raw[,c(408, 412)], na.rm=TRUE)
data_raw[,412] <- NULL
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Syritta_pipiens")
sum(!is.na(data_raw[,418]))==sum(!is.na(data_raw[,432]))
which(colnames(data_raw)=="Syritta_pipiens")
sum(!is.na(data_raw[,418]))==sum(!is.na(data_raw[,432]))
data_raw[,418] <- rowSums(data_raw[,c(418, 432)], na.rm=TRUE)
data_raw[,432] <- NULL
names(data_raw)[418]==names(data_raw)[432]
organism_guild <- tibble(organism_ID = names(data_raw)[30:ncol(data_raw)], organism_guild = NA)
View(organism_guild)
View(data_raw)
data_raw <- as_tibble(data_raw)
authors <- data_raw %>% group_by(author,crop,Year_of_study) %>% count()
View(authors)
resultados <- data_raw %>% group_by(author,crop,Year_of_study) %>%
summarise(number_points = length(latitude),lat_mean = sum(is.na(latitude)),
mean_Inflorescences_half_m2 = sum(is.na(Inflorescences_half_m2)),
mean_Flowers_per_Inflorescence = sum(is.na(Flowers_per_Inflorescence)),
mean_flowers_observed = sum(is.na(flowers_observed)),
mean_fruitset = sum(is.na(fruitset)),
mena_final_fruitset= sum(is.na(final_fruitset)))
View(resultados)
data_raw %>% filter(author=="Bartomeus")
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
View(bartomeus_data)
gild_list <- read_csv("Table_organism_guild_META.csv")
organism_guild <- tibble(organism_ID = names(data_raw)[30:ncol(data_raw)])
gild_list <- read_csv("Table_organism_guild_META.csv")
organism_guild <- tibble(Organism_ID = names(data_raw)[30:ncol(data_raw)])
gild_list <- read_csv("Table_organism_guild_META.csv")
organism_guild <- organism_guild %>% left_join(gild_list,by=c("Organism_ID"))
#Check NA's in guild
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count()
#Check NA's in guild
Guild_NA <- organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count()
View(Guild_NA)
View(gild_list)
organism_guild$Guild[organism_guild$Organism_ID=="Anasimyia_lineata"] <- "syrphids"
organism_guild$Guild[organism_guild$Organism_ID=="Bibo_hortulans"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Bibo_marci"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Bibo_varipes"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Chironomidae_sp."] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Culcidae.sp."] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Culicidae_sp1_hipolitodataset"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Culicidae_sp2_hiploitodataset"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Culicidae_sp3_hipolitodataset"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Culicidae_sp4_hipolitodataset"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Dilophilus_febrilis"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Dilophus_nigrostigma"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Elateratidae"] <- "beetles"
organism_guild$Guild[organism_guild$Organism_ID=="Eumeniinae_sp."] <- "non_bee_hymenoptera"
organism_guild$Guild[organism_guild$Organism_ID=="Hydrotaea_rostrata"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Lygaeidae_sp."] <- "other"
organism_guild$Guild[organism_guild$Organism_ID=="Melangyna_cincta"] <- "syrphids"
organism_guild$Guild[organism_guild$Organism_ID=="Melangyna_novaezelandiae"] <- "syrphids"
organism_guild$Guild[organism_guild$Organism_ID=="Micromus._tasmaniae"] <- "other"
organism_guild$Guild[organism_guild$Organism_ID=="Milichiidae"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Odontomyia_sp."] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Otitidae"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Phasmatodea_sp."] <- "other"
organism_guild$Guild[organism_guild$Organism_ID=="Protohystricia_alcis"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Salpinigaster_sp"] <- "syrphids"
organism_guild$Guild[organism_guild$Organism_ID=="Scaptia_sp."] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Scatophagidae"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Spilagona_melas"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Tanypezidae"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Tephritidae"] <- "other_flies"
organism_guild$Guild[organism_guild$Organism_ID=="Tineoidea_sp."] <- "lepidoptera"
organism_guild$Guild[organism_guild$Organism_ID=="Zygoptera_sp."] <- "other"
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count()
authors <- data_raw %>% group_by(author,crop,Year_of_study) %>% count()
View(authors)
write.csv(authors,"authors_list.csv")
library(tidyverse)
library("iNEXT")
library(openxlsx)
dir_ini <- getwd()
data_raw <- read.xlsx("master_nonbeemay24sep14.xlsx",
sheet = "master_nonbeemay2814", startRow = 1)
names_data_raw <- names(data_raw)
names_data_raw[duplicated(names_data_raw)]
#"Sphaerophoria_sp." "Syritta_pipiens"
names(data_raw)[408]==names(data_raw)[412]
names_data_raw <- names(data_raw)
names_data_raw <- names(data_raw)
names_data_raw[duplicated(names_data_raw)]
#"Sphaerophoria_sp." "Syritta_pipiens"
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Sphaerophoria_sp.")
sum(!is.na(data_raw[,408]))
sum(!is.na(data_raw[,412]))
data_raw[,408] <- rowSums(data_raw[,c(408, 412)], na.rm=TRUE)
data_raw[,412] <- NULL
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Syritta_pipiens")
sum(!is.na(data_raw[,418]))==sum(!is.na(data_raw[,432]))
data_raw[,418] <- rowSums(data_raw[,c(418, 432)], na.rm=TRUE)
data_raw[,432] <- NULL
names(data_raw)[418]==names(data_raw)[432]
#new column 418 is different from column 438
names(data_raw)[418]==names(data_raw)[432]
organism_guild <- tibble(Organism_ID = names(data_raw)[30:ncol(data_raw)])
gild_list <- read_csv("Table_organism_guild_META.csv")
organism_guild <- organism_guild %>% left_join(gild_list,by=c("Organism_ID"))
#Check NA's in guild
Guild_NA <- organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count()
View(Guild_NA)
#Check NA's in guild
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count()
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count()
data_raw <- as_tibble(data_raw)
View(data_raw)
data_filter <- data_raw %>% filter(!author %in% authors_in_Dainese)
authors_in_Dainese <- c("Anderson","Bartomeus","Carvalheiro",
"Chacoff","Freitas","Garratt_potts",
"garrett","Howlett","Stanley_stout",
"taki")
data_filter <- data_raw %>% filter(!author %in% authors_in_Dainese)
# We filter the works by Smitha
data_filter <- data_raw %>% filter(!(author=="smitha" & Year_of_study> 1900))
data_raw <- read.xlsx("master_nonbeemay24sep14.xlsx",
sheet = "master_nonbeemay2814", startRow = 1)
names_data_raw <- names(data_raw)
names_data_raw[duplicated(names_data_raw)]
#"Sphaerophoria_sp." "Syritta_pipiens"
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Sphaerophoria_sp.")
sum(!is.na(data_raw[,408]))
sum(!is.na(data_raw[,412]))
data_raw[,408] <- rowSums(data_raw[,c(408, 412)], na.rm=TRUE)
data_raw[,412] <- NULL #remove column
#new column 412 is different from column 408
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Syritta_pipiens")
sum(!is.na(data_raw[,418]))==sum(!is.na(data_raw[,432]))
data_raw[,418] <- rowSums(data_raw[,c(418, 432)], na.rm=TRUE)
data_raw[,432] <- NULL
#new column 418 is different from column 438
names(data_raw)[418]==names(data_raw)[432]
organism_guild <- tibble(Organism_ID = names(data_raw)[30:ncol(data_raw)])
gild_list <- read_csv("Table_organism_guild_META.csv")
organism_guild <- organism_guild %>% left_join(gild_list,by=c("Organism_ID"))
#Check NA's in guild
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count()
data_raw <- as_tibble(data_raw)
authors_in_Dainese <- c("Anderson","Bartomeus","Carvalheiro",
"Chacoff","Freitas","Garratt_potts",
"garrett","Howlett","Stanley_stout",
"taki")
data_filter <- data_raw %>% filter(!author %in% authors_in_Dainese)
# We filter the works by Smitha
data_filter <- data_filter %>% filter(!(author=="smitha" & Year_of_study> 1900))
resultados <- data_raw %>% group_by(author,crop,Year_of_study) %>%
summarise(number_points = length(latitude),lat_mean = sum(is.na(latitude)),
mean_Inflorescences_half_m2 = sum(is.na(Inflorescences_half_m2)),
mean_Flowers_per_Inflorescence = sum(is.na(Flowers_per_Inflorescence)),
mean_flowers_observed = sum(is.na(flowers_observed)),
mean_fruitset = sum(is.na(fruitset)),
mena_final_fruitset= sum(is.na(final_fruitset)))
View(resultados)
resultados <- data_filter %>% group_by(author,crop,Year_of_study) %>%
summarise(number_points = length(latitude),lat_mean = sum(is.na(latitude)),
mean_Inflorescences_half_m2 = sum(is.na(Inflorescences_half_m2)),
mean_Flowers_per_Inflorescence = sum(is.na(Flowers_per_Inflorescence)),
mean_flowers_observed = sum(is.na(flowers_observed)),
mean_fruitset = sum(is.na(fruitset)),
mena_final_fruitset= sum(is.na(final_fruitset)))
View(resultados)
fruit_set <- resultados %>% select(-mean_Inflorescences_half_m2,
-mean_Flowers_per_Inflorescence,
-mean_flowers_observed)
View(fruit_set)
View(data_filter)
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
View(bartomeus_data)
240/15
library(tidyverse)
library("iNEXT")
library(openxlsx)
dir_ini <- getwd()
data_raw <- read.xlsx("master_nonbeemay24sep14.xlsx",
sheet = "master_nonbeemay2814", startRow = 1)
names_data_raw <- names(data_raw)
names_data_raw[duplicated(names_data_raw)]
#"Sphaerophoria_sp." "Syritta_pipiens"
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Sphaerophoria_sp.")
sum(!is.na(data_raw[,408]))
sum(!is.na(data_raw[,412]))
data_raw[,408] <- rowSums(data_raw[,c(408, 412)], na.rm=TRUE)
data_raw[,412] <- NULL #remove column
#new column 412 is different from column 408
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Syritta_pipiens")
sum(!is.na(data_raw[,418]))==sum(!is.na(data_raw[,432]))
data_raw[,418] <- rowSums(data_raw[,c(418, 432)], na.rm=TRUE)
data_raw <- read.xlsx("master_nonbeemay24sep14.xlsx",
sheet = "master_nonbeemay2814", startRow = 1)
names_data_raw <- names(data_raw)
names_data_raw[duplicated(names_data_raw)]
#"Sphaerophoria_sp." "Syritta_pipiens"
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Sphaerophoria_sp.")
sum(!is.na(data_raw[,408]))
sum(!is.na(data_raw[,412]))
data_raw[,408] <- rowSums(data_raw[,c(408, 412)], na.rm=TRUE)
data_raw[,412] <- NULL #remove column
# Sanity check: new column 412 is different from column 408
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Syritta_pipiens")
sum(!is.na(data_raw[,418]))==sum(!is.na(data_raw[,432]))
data_raw[,418] <- rowSums(data_raw[,c(418, 432)], na.rm=TRUE)
data_raw[,432] <- NULL
# Sanity check: new column 418 is different from column 432
names(data_raw)[418]==names(data_raw)[432]
organism_guild <- tibble(Organism_ID = names(data_raw)[30:ncol(data_raw)])
gild_list <- read_csv("C:/Users/USUARIO/Desktop/OBservData/Thesaurus_Pollinators/Table_organism_guild_META.csv")
organism_guild <- organism_guild %>% left_join(gild_list,by=c("Organism_ID"))
#Check NA's in guild
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count()
View(organism_guild)
organism_guild <- tibble(Organism_ID = names(data_raw)[30:ncol(data_raw)])
gild_list <- read_csv("C:/Users/USUARIO/Desktop/OBservData/Thesaurus_Pollinators/Table_organism_guild_META.csv")
organism_guild <- organism_guild %>% left_join(gild_list,by=c("Organism_ID"))
#Check NA's in guild
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count() #No NA's
data_raw <- as_tibble(data_raw)
authors_in_Dainese <- c("Anderson","Bartomeus","Carvalheiro",
"Chacoff","Freitas","Garratt_potts",
"garrett","Howlett","Stanley_stout",
"taki")
data_filter <- data_raw %>% filter(!author %in% authors_in_Dainese)
# Filter the works by Smitha
data_filter <- data_filter %>% filter(!(author=="smitha" & Year_of_study> 1900))
View(data_filter)
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
View(bartomeus_data)
View(data_raw)
View(data_filter)
library(tidyverse)
library("iNEXT")
library(openxlsx)
dir_ini <- getwd()
data_raw <- read.xlsx("master_nonbeemay24sep14.xlsx",
sheet = "master_nonbeemay2814", startRow = 1)
names_data_raw <- names(data_raw)
names_data_raw[duplicated(names_data_raw)]
#"Sphaerophoria_sp." "Syritta_pipiens"
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Sphaerophoria_sp.")
sum(!is.na(data_raw[,408]))
sum(!is.na(data_raw[,412]))
data_raw[,408] <- rowSums(data_raw[,c(408, 412)], na.rm=TRUE)
data_raw[,412] <- NULL #remove column
# Sanity check: new column 412 is different from column 408
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Syritta_pipiens")
sum(!is.na(data_raw[,418]))==sum(!is.na(data_raw[,432]))
data_raw[,418] <- rowSums(data_raw[,c(418, 432)], na.rm=TRUE)
data_raw[,432] <- NULL
# Sanity check: new column 418 is different from column 432
names(data_raw)[418]==names(data_raw)[432]
organism_guild <- tibble(Organism_ID = names(data_raw)[30:ncol(data_raw)])
gild_list <- read_csv("C:/Users/USUARIO/Desktop/OBservData/Thesaurus_Pollinators/Table_organism_guild_META.csv")
organism_guild <- organism_guild %>% left_join(gild_list,by=c("Organism_ID"))
#Check NA's in guild
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count() #No NA's
data_raw <- as_tibble(data_raw)
authors_in_Dainese <- c("Anderson","Bartomeus","Carvalheiro",
"Chacoff","Freitas","Garratt_potts",
"garrett","Howlett","Stanley_stout",
"taki")
data_filter <- data_raw %>% filter(!author %in% authors_in_Dainese)
# Filter the works by Smitha
data_filter <- data_filter %>% filter(!(author=="smitha" & Year_of_study> 1900))
View(data_filter)
resultados <- data_filter %>% group_by(author,crop,Year_of_study) %>%
summarise(number_points = length(latitude),lat_mean = sum(is.na(latitude)),
mean_Inflorescences_half_m2 = sum(is.na(Inflorescences_half_m2)),
mean_Flowers_per_Inflorescence = sum(is.na(Flowers_per_Inflorescence)),
mean_flowers_observed = sum(is.na(flowers_observed)),
mean_fruitset = sum(is.na(fruitset)),
mena_final_fruitset= sum(is.na(final_fruitset)))
View(resultados)
View(resultados)
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
View(bartomeus_data)
View(bartomeus_data)
resultados <- data_filter %>% group_by(author,crop,Year_of_study) %>%
summarise(number_points = length(latitude),lat_mean = sum(is.na(latitude)),
mean_Inflorescences_half_m2 = sum(is.na(Inflorescences_half_m2)),
mean_Flowers_per_Inflorescence = sum(is.na(Flowers_per_Inflorescence)),
mean_flowers_observed = sum(is.na(flowers_observed)),
mean_fruitset = sum(is.na(fruitset)),
mena_final_fruitset= sum(is.na(final_fruitset)))
View(resultados)
View(data_filter)
View(bartomeus_data)
library(tidyverse)
library("iNEXT")
library(openxlsx)
dir_ini <- getwd()
data_raw <- read.xlsx("master_nonbeemay24sep14.xlsx",
sheet = "master_nonbeemay2814", startRow = 1)
names_data_raw <- names(data_raw)
names_data_raw[duplicated(names_data_raw)]
#"Sphaerophoria_sp." "Syritta_pipiens"
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Sphaerophoria_sp.")
sum(!is.na(data_raw[,408]))
sum(!is.na(data_raw[,412]))
data_raw[,408] <- rowSums(data_raw[,c(408, 412)], na.rm=TRUE)
data_raw[,412] <- NULL #remove column
# Sanity check: new column 412 is different from column 408
names(data_raw)[408]==names(data_raw)[412]
which(colnames(data_raw)=="Syritta_pipiens")
sum(!is.na(data_raw[,418]))==sum(!is.na(data_raw[,432]))
data_raw[,418] <- rowSums(data_raw[,c(418, 432)], na.rm=TRUE)
data_raw[,432] <- NULL
# Sanity check: new column 418 is different from column 432
names(data_raw)[418]==names(data_raw)[432]
organism_guild <- tibble(Organism_ID = names(data_raw)[30:ncol(data_raw)])
gild_list <- read_csv("C:/Users/USUARIO/Desktop/OBservData/Thesaurus_Pollinators/Table_organism_guild_META.csv")
organism_guild <- organism_guild %>% left_join(gild_list,by=c("Organism_ID"))
#Check NA's in guild
organism_guild %>% filter(is.na(Guild)) %>% group_by(Organism_ID,Family) %>% count() #No NA's
data_raw <- as_tibble(data_raw)
authors_in_Dainese <- c("Anderson","Bartomeus","Carvalheiro",
"Chacoff","Freitas","Garratt_potts",
"garrett","Howlett","Stanley_stout",
"taki")
data_filter <- data_raw %>% filter(!author %in% authors_in_Dainese)
# Filter the works by Smitha
data_filter <- data_filter %>% filter(!(author=="smitha" & Year_of_study> 1900))
resultados <- data_filter %>% group_by(author,crop,Year_of_study) %>%
summarise(number_points = length(latitude),lat_mean = sum(is.na(latitude)),
mean_Inflorescences_half_m2 = sum(is.na(Inflorescences_half_m2)),
mean_Flowers_per_Inflorescence = sum(is.na(Flowers_per_Inflorescence)),
mean_flowers_observed = sum(is.na(flowers_observed)),
mean_fruitset = sum(is.na(fruitset)),
mena_final_fruitset= sum(is.na(final_fruitset)))
fruit_set <- resultados %>% select(-mean_Inflorescences_half_m2,
-mean_Flowers_per_Inflorescence,
-mean_flowers_observed)
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
View(bartomeus_data)
# Remove columns full of NA's
df <- df[,colSums(is.na(df))<nrow(df)]
# Remove columns full of NA's
bartomeus_data <- bartomeus_data[,colSums(is.na(bartomeus_data))<nrow(bartomeus_data)]
View(bartomeus_data)
bartomeus_data_g <- bartomeus_data %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,observation_location) %>% summarise_each(funs(sum))
bartomeus_data_g <- bartomeus_data %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,observation_location) %>% summarise_all(funs(mean))
warnings()
View(bartomeus_data_g)
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
bartomeus_data_obs <- bartomeus_data %>%
select(site,round,row,observation_location, names(bartomeus_data[30:ncol(bartomeus_data)]))
View(bartomeus_data_obs)
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
# Remove columns full of NA's
bartomeus_data_obs <-
bartomeus_data_obs[,colSums(is.na(bartomeus_data_obs))<nrow(bartomeus_data_obs)]
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
bartomeus_data_obs <- bartomeus_data %>%
select(site,round,row,observation_location, names(bartomeus_data[30:ncol(bartomeus_data)]))
# Remove columns full of NA's
bartomeus_data_obs <-
bartomeus_data_obs[,colSums(is.na(bartomeus_data_obs))<nrow(bartomeus_data_obs)]
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
names(bartomeus_data)
View(bartomeus_data_obs)
names(bartomeus_data_obs)
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
bartomeus_data_obs <- bartomeus_data %>%
select(site,round,row,observation_location, names(bartomeus_data[30:ncol(bartomeus_data)]))
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
str(bartomeus_data_obs)
bartomeus_data[30:ncol(bartomeus_data)] <- as.numeric(bartomeus_data[30:ncol(bartomeus_data)])
bartomeus_data[,30:ncol(bartomeus_data)] <- as.numeric(bartomeus_data[,30:ncol(bartomeus_data)])
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
bartomeus_data[,30:ncol(bartomeus_data)]
as.numeric(bartomeus_data[,30:ncol(bartomeus_data)])
bartomeus_data[,30:ncol(bartomeus_data)] <- sapply(bartomeus_data[,30:ncol(bartomeus_data)], as.numeric)
bartomeus_data_obs <- bartomeus_data %>%
select(site,round,row,observation_location, names(bartomeus_data[30:ncol(bartomeus_data)]))
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
# Remove columns full of NA's
bartomeus_data_obs <-
bartomeus_data_obs[,colSums(is.na(bartomeus_data_obs))<nrow(bartomeus_data_obs)]
View(bartomeus_data_obs)
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
# Remove columns full of NA's
bartomeus_data_g <-
bartomeus_data_g[,colSums(is.na(bartomeus_data_g))<nrow(bartomeus_data_g)]
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
bartomeus_data[,30:ncol(bartomeus_data)] <- sapply(bartomeus_data[,30:ncol(bartomeus_data)], as.numeric)
bartomeus_data_obs <- bartomeus_data %>%
select(site,round,row,observation_location, names(bartomeus_data[30:ncol(bartomeus_data)]))
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
# Remove columns full of NA's
bartomeus_data_g <-
bartomeus_data_g[,colSums(is.na(bartomeus_data_g))<nrow(bartomeus_data_g)]
View(bartomeus_data)
bartomeus_data_g %>%  group_by(site) %>% summarise_all(funs(sum))
bartomeus_data_g %>% select(-round,-row,-observation_location) %>%
group_by(site) %>% summarise_all(funs(sum))
bartomeus_data_g %>% select(-round,-observation_location) %>%
group_by(site) %>% summarise_all(funs(sum))
# Remove columns full of NA's
bartomeus_data_g <-
bartomeus_data_g[,colSums(is.na(bartomeus_data_g))<nrow(bartomeus_data_g)]
bartomeus_data_g %>% select(-round,-observation_location) %>%
group_by(site) %>% summarise_all(funs(sum))
x <- bartomeus_data_g %>% select(-round,-observation_location) %>%
group_by(site) %>% summarise_all(funs(sum))
View(x)
bart_raw <- read.xlsx("C:\Users\USUARIO\Desktop\OBservData\Datasets_Processing\POLLINATION DATABASE - DAINESE-20200218T092444Z-001\DATASETS1\Bart01_DataCollection_Pollination.xlsx",
sheet = "SpeciesData", startRow = 2)
bart_raw <- read.xlsx("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS1/Bart01_DataCollection_Pollination.xlsx",
sheet = "SpeciesData", startRow = 2)
bart_raw <- read.xlsx("../POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS1/Bart01_DataCollection_Pollination.xlsx",
sheet = "SpeciesData", startRow = 2)
dir_ini <- getwd()
dir_ini <- getwd()
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS1/")
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS1")
setwd("C:\Users\USUARIO\Desktop\OBservData\Datasets_Processing\POLLINATION DATABASE - DAINESE-20200218T092444Z-001\DATASETS")
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS1/")
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/")
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/")
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS1/")
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS/")
bart_raw <- read.xlsx("Bart01_DataCollection_Pollination.xlsx",
sheet = "SpeciesData", startRow = 2)
View(bart_raw)
bart_raw %>% group_by(SiteID,OrganismID) %>% summarise(Abundance=sum(Abundance))
bartomeus_data <- data_raw %>% filter(author=="Bartomeus")
bartomeus_data[,30:ncol(bartomeus_data)] <- sapply(bartomeus_data[,30:ncol(bartomeus_data)], as.numeric)
bartomeus_data_obs <- bartomeus_data %>%
select(site,round,row,observation_location, names(bartomeus_data[30:ncol(bartomeus_data)]))
bartomeus_data_g <- bartomeus_data_obs %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
# Remove columns full of NA's
bartomeus_data_g <-
bartomeus_data_g[,colSums(is.na(bartomeus_data_g))<nrow(bartomeus_data_g)]
x <- bartomeus_data_g %>% select(-round,-observation_location) %>%
group_by(site) %>% summarise_all(funs(sum))
dir_ini <- getwd()
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS/")
bart_raw <- read.xlsx("Bart01_DataCollection_Pollination.xlsx",
sheet = "SpeciesData", startRow = 2)
bart_raw %>% group_by(SiteID,OrganismID) %>% summarise(Abundance=sum(Abundance))
bartomeus_data_g <- bartomeus_data_obs %>%
filter(!observation_location %in% c("Control_Edge","Control_Crop")) %>%
group_by(site,round,row,observation_location) %>% summarise_all(funs(sum))
# Remove columns full of NA's
bartomeus_data_g <-
bartomeus_data_g[,colSums(is.na(bartomeus_data_g))<nrow(bartomeus_data_g)]
x <- bartomeus_data_g %>% select(-round,-observation_location) %>%
group_by(site) %>% summarise_all(funs(sum))
dir_ini <- getwd()
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_Processing/POLLINATION DATABASE - DAINESE-20200218T092444Z-001/DATASETS/")
bart_raw <- read.xlsx("Bart01_DataCollection_Pollination.xlsx",
sheet = "SpeciesData", startRow = 2)
bart_raw %>% group_by(SiteID,OrganismID) %>% summarise(Abundance=sum(Abundance))
bart_raw %>% group_by(SiteID,OrganismID) %>% summarise(Abundance=sum(Abundance)) %>% head(15)
