STAT 615 Homework

Requirements

This is an individual assignment.

You will need to use ggplot2 for this exam.

(10 points) Create a missing value map. If there is any missing value, use the mice package to impute missing values. (5 points) Make a bar graph for IFC. How many levels does IFC have? Any possible problem? If any, correct it.

(10 points) Make a graph of IFC vs OT, colored by OLT.

(10 points) Make a graph of IV, for each combination of IFC and OT.

(5 points) All graph should have an appropriate title.

Part A

(10 points) Create a missing value map. If there is any missing value, use the mice package to impute missing values.

missmap(Mart)

## Warning: Unknown or uninitialised column: `arguments`.
## Unknown or uninitialised column: `arguments`.

## Warning: Unknown or uninitialised column: `imputations`.

Mart$Outlet_Size=recode(Mart$Outlet_Size,Small=1,Medium=2,High=3)
Mart_Imputed=Mart%>%mice()%>%complete()

## 
##  iter imp variable
##   1   1  Item_Weight  Outlet_Size
##   1   2  Item_Weight  Outlet_Size
##   1   3  Item_Weight  Outlet_Size
##   1   4  Item_Weight  Outlet_Size
##   1   5  Item_Weight  Outlet_Size
##   2   1  Item_Weight  Outlet_Size
##   2   2  Item_Weight  Outlet_Size
##   2   3  Item_Weight  Outlet_Size
##   2   4  Item_Weight  Outlet_Size
##   2   5  Item_Weight  Outlet_Size
##   3   1  Item_Weight  Outlet_Size
##   3   2  Item_Weight  Outlet_Size
##   3   3  Item_Weight  Outlet_Size
##   3   4  Item_Weight  Outlet_Size
##   3   5  Item_Weight  Outlet_Size
##   4   1  Item_Weight  Outlet_Size
##   4   2  Item_Weight  Outlet_Size
##   4   3  Item_Weight  Outlet_Size
##   4   4  Item_Weight  Outlet_Size
##   4   5  Item_Weight  Outlet_Size
##   5   1  Item_Weight  Outlet_Size
##   5   2  Item_Weight  Outlet_Size
##   5   3  Item_Weight  Outlet_Size
##   5   4  Item_Weight  Outlet_Size
##   5   5  Item_Weight  Outlet_Size

## Warning: Number of logged events: 6

missmap(Mart_Imputed)

Part B

(5 points) Make a bar graph for IFC. How many levels does IFC have? Any possible problem? If any, correct it.

ggplot(Mart, aes(x=Item_Fat_Content,y=Outlet_Type)) +
  geom_bar(stat="identity") +
  labs(x="Item Fat Content",
       y="Outlet Type",
       title="Cleaned")+
  theme(plot.title = element_text(hjust = 0.5))

Part C

(10 points) Make a graph of IFC vs OT, colored by OLT.

ggplot(Mart, aes(x=Item_Fat_Content,
                 fill=factor(Outlet_Location_Type))) +
  geom_bar() +
  facet_grid(.~Outlet_Type)+
  labs(x="Item Fat Content",
       y="Amount",
       title="Types Of Markets",
       fill="Outlet Location Type")+
  theme(plot.title = element_text(hjust = 0.5))

Part D

(10 points) Make a graph of IV, for each combination of IFC and OT.

ggplot(Mart, aes(x=Item_Visibility))+
  geom_histogram() +
  facet_grid(Item_Fat_Content~Outlet_Type)+
labs(x="Item Visibility",
     y="Amount",
     title="Item Visibility With Relation To Market Size and Fat Content")+
 theme(plot.title = element_text(hjust = 0.5))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.