# 1.
airquality <- read.csv("airquality.csv")
head(airquality)
names(airquality)
# [1] "Ozone" "Solar.R" "Wind" "Temp" "Month" "Day"
dim(airquality)
# [1] 153 6
library(mice)
## a)
(md <- md.pattern(airquality, plot = TRUE, rotate.names = FALSE))
# Month Day Temp Solar.R Wind Ozone
# 96 1 1 1    1 1 1 0
# 31 1 1 1    1 1 0 1
# 11 1 1 1    1 0 1 1
# 3    1 1 1    1 0 0 2
# 5    1 1 1    0 1 1 1
# 2    1 1 1    0 1 0 2
# 2    1 1 0    1 1 1 1
# 1    1 1 0    1 1 0 2
# 2    1 1 0    1 0 1 2
#    0 0 5    7 16 37 65
### i.
# [1] 65

### ii)
# Apparently, `Ozone` with 37 missing.

### iii)
row.names(md)
# [1] "96" "31" "11" "3" "5" "2" "2" "1" "2"

## b)
colMeans(airquality, na.rm=TRUE)
# Ozone Solar.R    Wind    Temp    Month    Day
# 42.129310 185.931507 9.978832 77.912162 6.993464 15.803922

## c)
air.median <- airquality
air.median$Solar.R <- median(air.median$Solar.R, na.rm=TRUE)

air.median <- apply(air.median, 2, function(x) {
x[is.na(x)==TRUE] <- median(x, na.rm=TRUE)
x
})
head(air.median)

In the dataset you will find the data pertaining to mortgage defaul...