Sara_data_box_whisker

Helpful Tips

Useful keyboard shortcuts these work for windows, they may be different for Macs

• Cntrl + Z functions like the Undo button in word or excel. Undoes whatever action you just did
• Cntrl + Alt + I inserts a new code chunk
• Cntrl + Enter runs the code that your courser is on (good for if you want to run small sections of code within a larger code chunk)
• Cntrl + Shift + M inserts the pipe functions %>%
• Cntrl + F allows you to find things within the document (can be useful if you want to replace the name of an object everywhere in the document at the same time)
• Cntrl + Shift + O opens a document outline that you can use to quickly navigate between titled sections of your document
• Cntrl + C to copy, Cntrl + V to paste
• Cntrl + S to save

Use rm(list = ls()) to Clear workspace (if desired/needed). If run correctly will clear everything from your environment tab. Typically it’s good to do this at the start of each of your sessions. Defiantly should do it if you open a new Rmd file.

Use thisgetwd() to check working directory. Will most likely be whatever folder your Rmd file is in. If you need to change it for some reason use setwd()

Downloading/loading packages

Use install.packages to download and install a new R package. You only need to do this once, then you can just use library() to load it and be able to call functions within it. and load the package libraries.

TIP: It’s generally considered best practice to keep the commands to call all the packages you need to use at the top of the document

tidyr, dplyr, and readr are probably the most commonly used packages for data wrangling/cleaning.ggplot2 is very common for creating publication quality figures.

Note: The order which you install packages is important. Functions with the same names will be masked by packages loaded in after the previous.
Ex: The select() function from MASS will be masked by select() from dplyar

library("tidyr")
library("dplyr")
library("readr")
library("ggplot2")
library("ggpubr")

Data Wrangling

Read in your data

data_orig <- read.csv("../Data/sara_raw_data.csv")

Look at the data to see what it contains
Make sure the column names in each data set match so they can be merged together

head(data_orig) #opens first 6 rows
tail(data_orig) #opens last 6 rows
summary(data_orig)
str(data_orig) #tells us what data types (numbers, factors, etc) are in the data frame

Remove any data that you will not need for the analysis

Check unique values

unique(data_orig$Site)

##  [1] "Bens Run"                 "Control"                 
##  [3] "Covered Bridge"           "Dead Run"                
##  [5] "East Beaverdam"           "Goodwin Run"             
##  [7] "Gwynn Falls Chartly"      "Kelly Branch"            
##  [9] "Minebank "                "Moores Branch"           
## [11] "Scotts Level McDonogh"    "Scotts Level Park"       
## [13] "Towson Run"               "Tributary to Gwynn Falls"
## [15] "Wellwood"

drop the controls

data_orig <- data_orig %>% filter(Site != "Control")

data_sum_by_plot <- (data_orig) %>% 
  group_by(Site, Mat.Number) %>% 
  summarise(sumBYmat = sum(Sum.of.total.germinants.by.pot.), n = n())

## `summarise()` has grouped output by 'Site'. You can override using the `.groups` argument.

rst <- (data_sum_by_plot) %>% 
  filter(Site == "Dead Run" | Site== "East Beaverdam" | Site == "Gwynn Falls Chartly" | Site == "Kelly Branch" | Site == "Scotts Level McDonogh" | Site =="Towson Run" | Site == "Wellwood")

urst <- (data_sum_by_plot) %>%
  filter(Site == "Bens Run" | Site== "Covered Bridge" | Site == "Goodwin Run" | Site == "Minebank " | Site == "Moores Branch" | Site =="Scotts Level Park" | Site == "Tributary to Gwynn Falls")

#unique(urst$Site)

#create vectors 
rest <- c("restored")
unrest <- c("unrestored")

#assign staus in new column 
rst["status"] <-rest
urst["status"] <-unrest


MyMerge       <- function(x, y){
  df            <- merge(x, y, all = TRUE)
  rownames(df)  <- df$Row.names
  df$Row.names  <- NULL
  return(df)
}
status_sum_by_plot           <- Reduce(MyMerge, list(rst, urst))

  BoxPlotall <- ggplot(status_sum_by_plot, aes(x= status, y=sumBYmat, fill = status))+
      geom_boxplot(position=position_dodge(.85))+
     theme_light()+
     theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
     theme(axis.title.y = element_text(face = "bold"))+
     theme(axis.title.x = element_text(face = "bold"))+
     theme(plot.title = element_text(hjust = .5))+
     theme(axis.text.x = element_text(color = "black", size = 8, angle = 45, hjust = 1))+
     theme(axis.text.y = element_text(color = "black", size = 8))+
     theme(aspect.ratio = 9/18.5)+
  labs(y = "Mean # of seeds", x = "Stream status")

print(BoxPlotall)

#ggsave("Figures/Sara_BOXPLOT.jpeg")