#
# Unit 8 - Data frames 
#
# Dimensions and "Slicing"  
#


# Loading data 
setwd("c:/R")
employees <- read.csv("employees.csv")
print(employees)


# Dimensions

print(dim(employees))
empDim <- dim(employees)
print(empDim[2])

print(nrow(employees))
print(ncol(employees))

# works, yet intended for vectors 
print(length(employees))


# "Slicing" 

# Our goal - "slicing" various 
#            shapes of slices 
#            Use this as a reference. 



# Slicing

# Not all possible patterns. 

# (1) Slicing a cell

# Back to the [ ] operator 
# index, index 
# to the right of the comma - row index 
# to its left - column index 
cell <- employees[10, 3]
print(cell)
print(class(cell))

cell <- employees[9, 4]
print(cell)
print(class(cell))


# index, name 

# to the right of the comma - row index (label) 
# to its left - column name
print(employees)
cell <- employees[10, "SALARY"]
print(cell)


# (2) Slicing a column
#     Several methods. 

# The $ operator 
salary <- employees$SALARY
print(salary)
print(class(salary))

# Also possible
salary <- employees[c("SALARY")]
print(salary)
# Notice this: 
print(class(salary))


# If you would like to column as 
# a vector 
salary <- employees[,c("SALARY")]
print(salary)
# Notice this: 
print(class(salary))

# Note that these are also impossible
# Yet for the sake of consistency we 
# will keep using the c() 
salary <- employees["SALARY"]
print(salary)
salary <- employees[,"SALARY"]
print(salary)

# And yet we can also use an index
# Yet this is not always practical. 
salary <- employees[c(6)]
print(salary)
salary <- employees[,c(6)]
print(salary)
# What does this code print? 
print(employees[6])



# (3) Slicing consecutive columns
partialDF <- employees[,2:4]
print(partialDF)


# (4) Slicing non-consecutive columns
partialDF <- employees[,c(1,4)]
print(partialDF)

partialDF <- employees[,c(1:2,4)]
print(partialDF)

partialDF <- employees[,c("ID","SALARY")]
print(partialDF)

partialDF <- employees[,c("SALARY", "ID")]
print(partialDF)


# with dplyr's select () 
library(dplyr)
partialDF <- select(.data = employees, 
                    SALARY, ID)
print(partialDF)

partialDF <- select(.data = employees, 
                    "ID", "SALARY")
print(partialDF)

partialDF <- select(.data = employees, 
                    SALARY, ID)
print(partialDF)

partialDF <- select(.data = employees, 
                    GENDER:SALARY)
print(partialDF)

partialDF <- select(.data = employees, 
                    ID, EDUC:SALARY)
print(partialDF)


# (5) Slicing a row 
oneRow <- employees[2,]
print(oneRow)
print(class(oneRow))


# (6) Slicing consecutive rows
#     Here: only using indices. 
partialDF <- employees[3:8,]
print(partialDF)
print(class(partialDF))

# from the head downwards 
partialDF <- head(employees, n = 3)
print(partialDF)

# from the tail upwards 
partialDF <- tail(employees, n = 4)
print(partialDF)

partialDF <- tail(employees, n = 15)
print(partialDF)

partialDF <- head(employees, n = 15)
print(partialDF)


# (6) Slicing nonconsecutive rows
#     Note the indices row. 
partialDF <- employees[c(1:5, 8),]
print(partialDF)

# Forthcoming - 
# Slicing nonconsecutive rows 
# according to a logical condition
# namely: filtering.