####Slide 6 - Commonly used oparators 

DS <- c(5,6,3,6) 
DS.1 <- c('A','B','C') #Both DS and DS.1 are now objects that contain the data to the right of the <-

# This allows me to write comments in my syntax 

2 > 1 #This will return a logical value -- True 

2 == 2

(2 > 3 | 3 > 2) #This statement is asking R "Is 2 greater than 3 or is 3 greater than 2?"
		#If either one of those statements is true then R will return TRUE 

(2 > 3 | 3 < 2) #Because neither statement is true, R will return FALSE 

2 + 2  #Addition
2 - 2 #Subtraction
2^2  #Exponentiation 
2*2 #Multiplication 


####Slide 8 - Formatting your data for R: Three easy steps 

Dataset <- read.csv('Intro R Data.csv') #This reads in your dataset read.table() will read in text files 

Dataset #This will return your entire dataset 


####Slide 9 - Formatting your data for R: Common Mistakes 

Dataset <- read.csv('C:\Intro R Data.csv') #See R won't read \ 

Dataset <- read.csv('C:\\intro to R Data.csv')  #To R case matters. This is a pain 

Dataset <- read.csv('C:\\Intro R Data.txt')  #Can't locate your data because it's a csv file not txt file


####Slide 10 - Working with data in R: Things to check 

dim(Dataset)  #This returns the dimensions of your dataframe; rows x columns 

names(Dataset) #This returns the column (variable) names of your dataset 

colMeans(Dataset[,1:10], na.rm=T)  #This returns the means for columns 1 to 10 

sapply(Dataset[,1:10], na.rm=T)  #This returns the standard deviations of columns 1 to 10 


####Slide 11 - Working with data in R: Subsetting your dataset 

Dataset[5,1]  #Returns the observation that resides in row 5 column 1 

Dataset[,1]  #Returns every observation that resides in column 1

Dataset[2, 1:5]  #Returns every observation that resides in row 2 columns 1 through 5 



####Slide 12 - Working with Data in R: Subsetting your data 

Dataset$Var1  #This does the same thing that Dataset[,1] does
              #Returns every observation that resides in column 1 


ds.Female <- Dataset[Dataset$Var11 == 'Female',]   #This will create an R dataframe named ds.Female that only contains observations if Var = Female 




####Slide 13 - Working with data in R: Reverse Coding 

Dataset$Var12 <- 8 - Dataset$Var10  #Creates another column for Dataset named Var12 and sets it equal to 8 - Var10 
			            #I used 8 because my hypothetical scale ranges from 1 to 7 -- if it ranged from 1 to 5 I would have used 6 instead of 8 

cor(Dataset$Var10, Dataset$Var12, use='complete.obs') 



####Slide 14 - Working with Data in R: Internal R Functions 

mean(Dataset$Var1, na.rm=T) #Provides the mean for Var1 

sd(Dataset$Var1, na.rm=T)  #Provides the sd for Var1 

min(Dataset$Var5, na.rm=T)  #Provides the minimum value in Var1 

max(Dataset$Var5, na.rm=T)  #Returns the maximum value in Var5 

cor(Dataset, use='complete.obs') #Correlates every variable in your dataset


####Slide 15 - Working with data in R: Internal R Functions 

modlm <- lm(Var2 ~ Var3, data=Dataset)  #OLS Regression -- Variable 2 onto variable 3
summary(modlm)  #Provides the results of the OLS regression 

modanova <- lm(Var4 ~ as.factor(Var11), data=Dataset)   #OLS Regression -- Var2 on gender; as.factor() tells R the variable is a factor 
							#This is an ANOVA! 
summary(modanova) 

modanova1 <- aov(Var4 ~ as.factor(Var11), data=Dataset)  #aov is R's built in ANOVA function -- look at similarities between modanova and modanova1 -- IDENTICAL! 
summary(modanova1)


dif <- TukeyHSD(modanova1)  #Tukey's Honestly Significant Difference Test; Compare it to the modanova B1 -- The same 


####Slide 14 - Exporting data from R 

write.csv(Dataset, 'Exported Dataset.csv')  #Writes your R data to a csv file 

write.table(Dataset, 'Exported Dataset.txt')  #Writes your R data to a txt file