Exercise 2

#create a vector with fruit
fruit <- c("apple", "orange", "banana", "pear", "cherry", "plum", "grape")
# look for a fruit in the vector
"pear"%in% fruit

## [1] TRUE

#this will result in true
#check to see which position this is in
fruit=="pear"

## [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE

#this results in boolean vector of true and false - TRUE indicates which one was pear
#set a vector to this result
location<- fruit=="pear"
location

## [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE

x <- fruit[location]
x

## [1] "pear"

#let's find out which position it is located
# we will use a for loop
num<- length(fruit)
#first determine how many fruits there are
#first let's just print out the fruits
for (fruit.type in fruit) {  
  print(fruit.type)
}

## [1] "apple"
## [1] "orange"
## [1] "banana"
## [1] "pear"
## [1] "cherry"
## [1] "plum"
## [1] "grape"

#now let's look for the fruit pear
for (fruit.type in fruit) {  
  print(fruit.type)
  if (fruit.type == "pear")
  {print("found Fruit")}
}

## [1] "apple"
## [1] "orange"
## [1] "banana"
## [1] "pear"
## [1] "found Fruit"
## [1] "cherry"
## [1] "plum"
## [1] "grape"

#now comment out the print fruit.type
for (fruit.type in fruit) {  
  print(fruit.type)
  if (fruit.type == "pear")
  {print("found Fruit")}
}

## [1] "apple"
## [1] "orange"
## [1] "banana"
## [1] "pear"
## [1] "found Fruit"
## [1] "cherry"
## [1] "plum"
## [1] "grape"

# notice all we got was the statement "found fruit"
#but what about the location 
#let's try to keep track of the location 
#we will use a counter
countr <- 1 #set the counter to one

for (fruit.type in fruit) {  
  #  print(fruit.type)
  if (fruit.type == "pear")
  {  found.location <- countr # save the value of the counter
      print("found Fruit")}
countr<-countr+1 #increment the counter by 1 to keep counting
}

## [1] "found Fruit"

found.location #this will display the location

## [1] 4

#notice that we only set the value of found.location when we found the match.

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found Fruit

#we will now do this relying on the length - we set num to that value
countr <- 1 #set the counter to one

for (i in 1:num) {  
  # notice that we are referencing into the vector using [] 
  # i will have values from 1 to 7 since there are 7 fruits
  if (fruit[i] == "pear")
  {  found.location <- countr # save the value of the counter
  print("found Fruit")}
  #this point we no longer need countr since i will have the same value
  countr<-countr+1 #increment the counter by 1 to keep counting
}

## [1] "found Fruit"

found.location #this will display the location

## [1] 4

#the next code removes countr and replaces its use with i

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for (i in 1:num) {  
  # notice that we are referencing into the vector using [] 
  # i will have values from 1 to 7 since there are 7 fruits
  if (fruit[i] == "pear")
  {  found.location <- i # save the value of i
  print("found Fruit")}
  #this point we no longer need countr since i will have the same value
}

## [1] "found Fruit"

found.location #this will display the location

## [1] 4

#why is this of value?
#well sometimes we need to know where things are located
#for instances now we can reference the specific location in fruit
fruit[found.location]  # this of course displays pear

## [1] "pear"

#but look at what the following will do
fruit[c(-found.location)] #this displays everything but pear

## [1] "apple"  "orange" "banana" "cherry" "plum"   "grape"

#Something to note is that even though the location has been found the for loop will continue
#let's look at how we can use a different type of loop
#we will use the repeat - we will need to use a countr again

cnt <- 1 #i changed the name of the counter - too many letters to type
repeat { 

    if(fruit[cnt]=="pear") 
    { 
      found.location = cnt
      break } 
  cnt <- cnt+1  
    }
found.location #same value but we stop looking

## [1] 4

#we will now create a data frame that combines this information without using a loop
new.data<-cbind(fruit,location)  #remember location has the values of TRUE or FALSE where the match occurred
new.data

##      fruit    location
## [1,] "apple"  "FALSE" 
## [2,] "orange" "FALSE" 
## [3,] "banana" "FALSE" 
## [4,] "pear"   "TRUE"  
## [5,] "cherry" "FALSE" 
## [6,] "plum"   "FALSE" 
## [7,] "grape"  "FALSE"

newnew.data<-cbind(fruit, as.numeric(location)) #Hm.  I thought we just did this
newnew.data  #notice the difference  - this can happen because TRUE and FALSE can take on the values of 1 and 0

##      fruit       
## [1,] "apple"  "0"
## [2,] "orange" "0"
## [3,] "banana" "0"
## [4,] "pear"   "1"
## [5,] "cherry" "0"
## [6,] "plum"   "0"
## [7,] "grape"  "0"

class(newnew.data)  #notice that the class of this is a matrix

## [1] "matrix" "array"

str(newnew.data) # that means that the number we are seeing are treated as text - a matrix can only have one type

##  chr [1:7, 1:2] "apple" "orange" "banana" "pear" "cherry" "plum" "grape" ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : NULL
##   ..$ : chr [1:2] "fruit" ""

#let's use the data frame and see the difference
#we create a data frame using the same method for cbind but make sure that we don't have factors
fruit.df<-data.frame(fruit=fruit,found=as.numeric(location), stringsAsFactors = FALSE )
fruit.df

##    fruit found
## 1  apple     0
## 2 orange     0
## 3 banana     0
## 4   pear     1
## 5 cherry     0
## 6   plum     0
## 7  grape     0

class(fruit.df) #look at the difference

## [1] "data.frame"

str(fruit.df) #look at the structure

## 'data.frame':    7 obs. of  2 variables:
##  $ fruit: chr  "apple" "orange" "banana" "pear" ...
##  $ found: num  0 0 0 1 0 0 0

#####
#####now on your own add more fruits to the fruit vector
#be sure and duplicate some fruits
#now generate the matrix and the dataframe as shown above
## put your answer below

##in this section we will add a column to the original data frame it contains calories for the seven fruits
cal<-c(101, 90, 105, 96, 103, 91, 101)
##you will need to add calories for the fruits you added above
new.cal.df<-data.frame(fruit=fruit.df$fruit,cal= cal) 
#the following lines examine the data frame
new.cal.df

##    fruit cal
## 1  apple 101
## 2 orange  90
## 3 banana 105
## 4   pear  96
## 5 cherry 103
## 6   plum  91
## 7  grape 101

class(new.cal.df)

## [1] "data.frame"

str(new.cal.df)

## 'data.frame':    7 obs. of  2 variables:
##  $ fruit: chr  "apple" "orange" "banana" "pear" ...
##  $ cal  : num  101 90 105 96 103 91 101

new.cal.df$cal

## [1] 101  90 105  96 103  91 101

# let's look for fruits that have calories greater than or equal to 100
res.df<-data.frame(new.cal.df$fruit, new.cal.df$cal>=100)
res.df

##   new.cal.df.fruit new.cal.df.cal....100
## 1            apple                  TRUE
## 2           orange                 FALSE
## 3           banana                  TRUE
## 4             pear                 FALSE
## 5           cherry                  TRUE
## 6             plum                 FALSE
## 7            grape                  TRUE

# let's get a subset of this
redcd.df<-subset(new.cal.df, cal >= 100)  
redcd.df

##    fruit cal
## 1  apple 101
## 3 banana 105
## 5 cherry 103
## 7  grape 101

### do this for calories less than 100
###your answer below

## determine how many fruits have calories > 100

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Exercise 2

Johnatan Pulido

2024-07-31

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found Fruit

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