Intro to R

# Packages ####
# once you install packages to your computer, you can "check them out" of your packages library each time you need them
# make sure you check the "mask" messages that appear - sometimes packages have functions with the same names!

library(tidyverse)# really neat collection of packages! https://www.tidyverse.org/
library(lubridate)
library(readxl)
library(viridis)
library(plotly)
library(ggmap)


# Working Directory ####

setwd('C:/Users/ct991305/Documents/Workshop Material/2021-03-30-glatos-workshop/data/') #set folder you're going to work in
getwd() #check working directory

#you can also change it in the RStudio interface by navigating in the file browser where your working directory should be
#(if you can't see the folder you want, choose the three horizonal dots on the right side of the Home bar),
#and clicking on the blue gear icon "More", and select "Set As Working Directory".

3 + 5 #maths! including - , *, /

weight_kg <- 55 #assignment operator! for objects/variables. shortcut: alt + -
weight_kg

weight_lb <- 2.2 * weight_kg #can assign output to an object. can use objects to do calculations

#functions take "arguments": you have to tell them what to run their script against

ten <- sqrt(weight_kg) #contain calculations wrapped into one command to type.

round(3.14159) #don't have to assign

args(round) #the args() function will show you the required arguments of another function

?round #will show you the full help page for a function, so you can see what it does

weight_g <- c(21, 34, 39, 54, 55) #use the combine function to join values into a vector object

length(weight_g) #explore vector
class(weight_g) #a vector can only contain one data type
str(weight_g) #find the structure of your object.

#our vector is numeric.
#other options include: character (words), logical (TRUE or FALSE), integer etc.

animals <- c("mouse", "rat", "dog") #to create a character vector, use quotes

# Note:
#R will convert (force) all values in a vector to the same data type.
#for this reason: try to keep one data type in each vector
#a data table / data frame is just multiple vectors (columns)
#this is helpful to remember when setting up your field sheets!

animals #calling your object will print it out
animals[2] #square brackets = indexing. selects the 2nd value in your vector

weight_g > 50 #conditional indexing: selects based on criteria
weight_g[weight_g <=30 | weight_g == 55] #many new operators here!  
                                         #<= less than or equal to, | "or", == equal to
weight_g[weight_g >= 30 & weight_g == 21] #  >=  greater than or equal to, & "and"
                                          # this particular example give 0 results - why?

heights <- c(2, 4, 4, NA, 6)
mean(heights) #some functions cant handle NAs
mean(heights, na.rm = TRUE) #remove the NAs before calculating

#other ways to get a dataset without NAs:

heights[!is.na(heights)] #select for values where its NOT NA
#[] square brackets are the base R way to select a subset of data --> called indexing
#! is an operator that reverses the function

na.omit(heights) #omit the NAs

heights[complete.cases(heights)] #select only complete cases