c3-basic-commands.r

# rscript file.r to run from osx

c(42, 34, 4, 1) -> myFamilyAges
# this will assign the values to my newly named variable on the fly

myFamilyAges
# and this will print it, from this point of view, this looks even easier to learn than Python is for basic syntax. 

sum(myFamilyAges)
# 80

mean(myFamilyAges)
range(myFamilyAges)

c("dad","mom","colin","nolan") -> myFamilyNames

myFamilyNames

c("male","female","male","male") -> myFamilyGenders

c(159.7, 76, 18.1, 9.5) -> myFamilyWeights

myFamilyNames
myFamilyAges
myFamilyGenders
myFamilyWeights

mean(myFamilyWeights)
sum(myFamilyWeights)

myFamilyNames[2]

############

# lets make this into a datafram 
myFamily <- data.frame(myFamilyNames, myFamilyGenders, myFamilyAges, myFamilyWeights, stringsAsFactors = TRUE)

myFamily

#####
#str() allows us to understand what an object is at any time 

#  myFamilyNames myFamilyGenders myFamilyAges myFamilyWeights
#1           dad            male           42           159.7
#3         colin            male            4            18.1
#2           mom          female           34            76.0
#4         nolan            male            1             9.5

str(myFamily)

#'data.frame':   4 obs. of  4 variables:
# $ myFamilyNames  : Factor w/ 4 levels "colin","dad",..: 2 3 1 4
# $ myFamilyGenders: Factor w/ 2 levels "female","male": 2 1 2 2
# $ myFamilyAges   : num  42 34 4 1
# $ myFamilyWeights: num  159.7 76 18.1 9.5

# nb the Factor is for organising groups of labels essentially (in alphabetical order, ergo female = 1, male = 2)

str(myFamilyGenders)

# lets try the summary() function

summary(myFamily)

# myFamilyNames myFamilyGenders  myFamilyAges   myFamilyWeights 
# colin:1       female:1        Min.   : 1.00   Min.   :  9.50  
# dad  :1       male  :3        1st Qu.: 3.25   1st Qu.: 15.95  
# mom  :1                       Median :19.00   Median : 47.05  
# nolan:1                       Mean   :20.25   Mean   : 65.83  
#                               3rd Qu.:36.00   3rd Qu.: 96.92  
#                               Max.   :42.00   Max.   :159.70