In brouwern/compbio4all: Datasets and helpful functions for learning bioinformatics and computational biology

knitr::opts_chunk$set(echo = TRUE)

trillium <- read_excel("~/1_R/git/wildlifeR_home/trillium_surveys_resave.xlsx", 
     sheet = "for.calU")

summary(trillium$spp )
i.trill <- which(trillium$spp == "T")

trillium2 <- trillium[i.trill,]

trillium2$spp <- factor(trillium2$spp)
trillium2$status <- factor(trillium2$status)
trillium2$eaten <- factor(trillium2$eaten)

trillium2$sites <- factor(trillium2$sites)

grep("[a-zA-Z]",trillium2$length.stem.cm)

trillium2$length.leaf.cm <- as.numeric(trillium2$length.leaf.cm)

trillium2$length.stem.cm <- as.numeric(trillium2$length.stem.cm)



trillium2$site <- as.character(trillium2$sites)


trillium2$site[grep("BV",trillium2$sites)] <- "BV"
trillium2$site[grep("FH",trillium2$sites)] <- "FH"
trillium2$site[grep("DZ",trillium2$sites)] <- NA

head(trillium2)

summary(trillium2)

library(ggplot2)
library(ggpubr)

trillium2$site <- factor(trillium2$site)
ggboxplot(data = trillium2,
          y = "length.leaf.cm ",
          x = "site")



i.sites <- which(trillium2$site %in% c("ER","TW","WC","CN"))

trillium3 <- trillium2[i.sites,]


trillium4 <- trillium3[,c("site","spp",
                          "length.stem.cm",
                          "length.leaf.cm")]

ggboxplot(data = trillium4,
          y = "length.leaf.cm ",
          x = "site")

trillium <- trillium4


save(trillium, file = "./data/trillium.RData")

names(trillium)

brouwern/compbio4all documentation built on Dec. 19, 2021, 11:47 a.m.