Question 1

# built-in dataset in R
head(airquality)
##   Ozone Solar.R Wind Temp Month Day
## 1    41     190  7.4   67     5   1
## 2    36     118  8.0   72     5   2
## 3    12     149 12.6   74     5   3
## 4    18     313 11.5   62     5   4
## 5    NA      NA 14.3   56     5   5
## 6    28      NA 14.9   66     5   6

Change data from wide to narrow format (above) using gather function. Group Ozone, Solar.R, Wind, Temp into one variable called type and create another column called value to store their values. Your output should look like this:

##   Month Day  type value
## 1     5   1 Ozone    41
## 2     5   2 Ozone    36
## 3     5   3 Ozone    12
## 4     5   4 Ozone    18
## 5     5   5 Ozone    NA
## 6     5   6 Ozone    28

Question 2

Suppose you have a data frame, data, as given belows:

##   V1 V2    V3 V4
## 1  a  1 alpha 10
## 2  a  2  beta 20
## 3  b  1 gamma 30
## 4  b  2 alpha 40
## 5  c  1  beta 50
## 6  c  2 gamma 60

Assuming that the tidyr and dplyr libraries are already loaded, write down what the output for the following code. The final result is enough for full credit, but partial credit will be given for writing out and labelling intermediate steps.

data %>%
  filter(V1 == "a") %>% # Step 1
  select(V2, V4) %>% # Step 2
  gather(key = Apple, value = Banana, V2, V4) %>% # Step 3
  mutate(Apple = Banana) # Step 4

Step 1: Since the data data frame consists of a column of a’s, b’s, and c’s two of each, this operation filters out the first two rows of the data frame, consisting of “a”. As such, we are left with a 2 by 4 data frame.

Step 2: This function selects the second and fourth columns, which are vectors “V2” and “V4”, creating a data frame of numerics of “1”’s and “2”’s once due to the first filter in the “V2” vector, and numerics “10” and “20” in the column “V4”.

Step 3: With our remaining rows and columns from the first two functions, gather takes the vector names “V2” and “V4” and collects them under the column named “Apple”, and the numeric values from both “V2” and “V4” collected in another column named “Banana”.

Step 4: The mutate function takes each numeric value under “Banana” and copies it to under the “Apple” column, replacing the vector names “V2” and “V4”.

The resulting vector will be: (using an alterior code to produce the same result to avoid the risk of copy/paste):

Apple <- c(1,2,10,20)
Banana <- c(1,2,10,20)
AB <- data.frame(Apple, Banana)
AB
##   Apple Banana
## 1     1      1
## 2     2      2
## 3    10     10
## 4    20     20

Question 3

Suppose you have a data frame, data, as given below.

##    a  b c   d   e f
## 1  1  6 1   5 -99 1
## 2 10  4 4 -99   9 3
## 3  7  9 5   4   1 4
## 4  2  9 3   8   6 8
## 5  1 10 5   9   8 6
## 6  6  2 1   3   8 5
  1. Write a function called fix_missing_99 that takes one argument: x, a numeric vector. The function should replace every component of x equal to -99 with NA.
fix_missing_99 <- function(x) {
  if (!is.vector(x)) {
    stop("x must be a vector")
  }
  x[x==-99] <- NA
  return(x)
}


fix_missing_99(c(2,3,-99, 3,4,5, -99, -99))
## [1]  2  3 NA  3  4  5 NA NA

sapply(x, function(x) {ifelse(x == -99, “NA”, x) })

  1. Write a loop that replaces every -99 in data with NA. For full credit, your code must use the function in part (a) and it should continue to work without modification if additional columns are added to the data frame.
for (j in names(data)) {
   data[,j] <- fix_missing_99(data[,j])
}
data
##    a  b c  d  e f
## 1  1  6 1  5 NA 1
## 2 10  4 4 NA  9 3
## 3  7  9 5  4  1 4
## 4  2  9 3  8  6 8
## 5  1 10 5  9  8 6
## 6  6  2 1  3  8 5
  1. Write down an appropriate call from the apply family of functionals to perform the same task as in part (b).
sapply(data, function(x) {ifelse(x == -99, "NA", x) })
##       a  b c  d  e f
## [1,]  1  6 1  5 NA 1
## [2,] 10  4 4 NA  9 3
## [3,]  7  9 5  4  1 4
## [4,]  2  9 3  8  6 8
## [5,]  1 10 5  9  8 6
## [6,]  6  2 1  3  8 5

Question 4

Assuming the ‘ggplot2’ is already loaded. The first 6 rows of the ‘diamond’ dataset are:

## Source: local data frame [6 x 10]
## 
##   carat       cut  color clarity depth table price     x     y     z
##   (dbl)    (fctr) (fctr)  (fctr) (dbl) (dbl) (int) (dbl) (dbl) (dbl)
## 1  0.23     Ideal      E     SI2  61.5    55   326  3.95  3.98  2.43
## 2  0.21   Premium      E     SI1  59.8    61   326  3.89  3.84  2.31
## 3  0.23      Good      E     VS1  56.9    65   327  4.05  4.07  2.31
## 4  0.29   Premium      I     VS2  62.4    58   334  4.20  4.23  2.63
## 5  0.31      Good      J     SI2  63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good      J    VVS2  62.8    57   336  3.94  3.96  2.48

What command in ‘ggplot’ that you will use to generate the graph given below?