Solutions for Final Test of Learn R By Intensive Practice

final_test.R

## Solutions for Final Test of Learn R By Intensive Practice
Q1. 
```{r}
#1
sqrt (729)

#2
1203 %% 22

#3
d1 <- as.Date('2013-10-04')
d2 <- as.Date('2015-11-03')
d2 - d1
```


Q2. 
```{r}
b <- 1947.0
class(b)
object.size(b)
```


Q3.
```{r}
a <- as.character(b)
object.size(a)
```



Q4.
```{r}
getwd()
setwd ("path/to/my/desktop/work")
getwd()
```

Q5.
```{r}
# 5a. 
myEnv <- new.env()
# 5b.
assign('b', 3, envir = myEnv)
# 5c.
get('b', envir=myEnv)
# 5d.
rm(b, envir=myEnv)
```
Note: The ‘envir’ argument was common in ‘assign’, ‘get’ and ‘rm’ functions. Also note that the assign and get functions take in the variable name as a string while the rm function took the variable object itself.

Q6.
```{r}
# 6a. 
one_to_six <- c(1, 2, 3, 4, 5, 6)
class(one_to_six)
# 6b.
one_a <- c(1, "a", 2, "b")
class(one_a) # character
```


Q7.
```{r}
# 7a.
charHundred <- character(26)
charHundred
# 7b.
charHundred[1] <- "a"
```

Q8.
```{r}
# 8a.
myFriends <- c("alan", "bala", "amir", "tsong", "chan")

# 8b.
length(myFriends)

# 8c.
myFriends[1:2]

# 8d.
myFriends[c(2,3)]

# 8e.
# method 1
sort(myFriends)

# method 2
myFriends[order(myFriends)]

# 8f.
# method 1
sort(myFriends, decreasing=TRUE)

# method 2
myFriends[rev(order(myFriends))]
```



Q9.
```{r}
out <- c(rep('a', 2), seq(1, 5), seq(7, 11, by=2)) # because of the presence of 'a' character, the numbers are converted to characters as well.
```



Q10.
```{r}
myVec <- c(1, 2, NA, 4, 5, 6, 7, NA, 10)
myVec[!is.na(myVec)]
na.omit(myVec)
```


Q11.
```{r}
a <- 1:180
vec1 <-sample(a, 50, replace=T)
vec2 <-sample(a, length(a)*.25, replace=F)
```


Q12.
```{r}
vec1[duplicated(vec1)]
intersect(vec1, vec2)
```



Q13. 
```{r}
class (iris)  # get class
sapply (iris, class)  # get class of all columns
str (iris)  # structure
summary (iris)  # summary of airquality
head (iris)  # view the first 6 obs
fix (iris)  # view spreadsheet like grid
rownames (iris)  # row names
colnames (iris)  # columns names
nrow (iris)  # number of rows
ncol (iris)  # number of columns
```

Q14.
```{r}
# 14a.
numRows <- nrow(iris)
numCols <- ncol(iris)
iris[(numRows-1):numRows, (numCols-1):numCols]
# 14b.
iris[iris$Sepal.Width > 3, ]
iris[which(iris$Sepal.Width > 3), ]
# 14c.
subset(iris, Species == "versicolor")
```


Q15.
```{r}
set.seed(100)
Df1 <- iris[sample(1:nrow(iris), 10), c(1,2,3,5)]
Df2 <- iris[sample(1:nrow(iris), 10), c(1,2,4,5) ]

# 15.1
merge(Df1, Df2, by="Species", all=FALSE) # inner join
merge(Df1, Df2, by="Species", all=TRUE) # outer join
merge(Df1, Df2, by="Species", all.x=TRUE) # left join
merge(Df1, Df2, by="Species", all.y=TRUE) # right join
```



Q16.
```{r}
paste0 (c(rep("var", 3), rep("pred", 3)), 1:3)
```



Q17.
```{r}
table(iris$Species, iris$Sepal.Width)
aggregate(Petal.Width ~ Species, data=iris, FUN = mean)
```


Q18.
```{r}
output <- character(nrow(iris))
for(i in c(1:nrow(iris))){
  if (iris$Sepal.Length[i] > 5){
    output[i] <- "greater than 5"
  } else {
    output[i] <- "lesser than 5"
  }
}
```


Q19.
```{r}
output <- ifelse(iris$Sepal.Length > 5, "greater than 5", "lesser than 5") # works like the 'if' function in MS Excel, except that the condition is checked for every element of iris$Sepal.Length
```


Q20.
```{r}
# Define the function for apply() statement

myFunc <- function(x){
  if(x['Sepal.Length'] > 5){
    "greater than 5"
  } else {
    "lesser than 5"
  }
}

output <- apply(iris, 1, FUN=myFunc)
```