Categorical Data Visualization - Problem Set 6

Objectives

• The objective of this assignment is to conduct an exploratory data analysis of a data set that you are not familiar with. In this week’s lecture, we discussed a number of visualization approaches in order to explore a data set with categorical variables. This assignment will apply those tools and techniques. An important distinction between class examples and applied data science work is iterative and repetitive nature of exploring a data set. It takes time to understand what the data is and what is interesting about the data (patterns).

• For this week, we will be exploring the Copenhagen Housing Conditions Survey:

  • Sat - satisfaction of householders with their present housing circumstances
  • Infl - perceived degree of influence householders have on the management of the property
  • Type - type of rental accomodation
  • Cont - contact residents arre afforded with other residents
  • Freq - Frequencies: the numbers of residents in each class

• Your task for this assignment is to use ggplot and the facet_grid and facet_wrap functions to explore the Copenhagen Housing Conditions Survey. Your objective is to develop 5 report quality visualizations (at least 4 of the visualizations should use the facet_wrap or facet_grid functions) and identify interesting patterns and trends within the data that may be indicative of large scale trends. For each visualization you need to write a few sentences about the trends and patterns that you discover from the visualization.

To submit this homework you will create the document in Rstudio, using the knitr package (button included in Rstudio) and then submit the document to your Rpubs account. Once uploaded you will submit the link to that document on Canvas. Please make sure that this link is hyperlinked and that I can see the visualization and the code required to create it.

Look at the data

str(housing)

## 'data.frame':    72 obs. of  5 variables:
##  $ Sat : Ord.factor w/ 3 levels "Low"<"Medium"<..: 1 2 3 1 2 3 1 2 3 1 ...
##  $ Infl: Factor w/ 3 levels "Low","Medium",..: 1 1 1 2 2 2 3 3 3 1 ...
##  $ Type: Factor w/ 4 levels "Tower","Apartment",..: 1 1 1 1 1 1 1 1 1 2 ...
##  $ Cont: Factor w/ 2 levels "Low","High": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Freq: int  21 21 28 34 22 36 10 11 36 61 ...

housing

##       Sat   Infl      Type Cont Freq
## 1     Low    Low     Tower  Low   21
## 2  Medium    Low     Tower  Low   21
## 3    High    Low     Tower  Low   28
## 4     Low Medium     Tower  Low   34
## 5  Medium Medium     Tower  Low   22
## 6    High Medium     Tower  Low   36
## 7     Low   High     Tower  Low   10
## 8  Medium   High     Tower  Low   11
## 9    High   High     Tower  Low   36
## 10    Low    Low Apartment  Low   61
## 11 Medium    Low Apartment  Low   23
## 12   High    Low Apartment  Low   17
## 13    Low Medium Apartment  Low   43
## 14 Medium Medium Apartment  Low   35
## 15   High Medium Apartment  Low   40
## 16    Low   High Apartment  Low   26
## 17 Medium   High Apartment  Low   18
## 18   High   High Apartment  Low   54
## 19    Low    Low    Atrium  Low   13
## 20 Medium    Low    Atrium  Low    9
## 21   High    Low    Atrium  Low   10
## 22    Low Medium    Atrium  Low    8
## 23 Medium Medium    Atrium  Low    8
## 24   High Medium    Atrium  Low   12
## 25    Low   High    Atrium  Low    6
## 26 Medium   High    Atrium  Low    7
## 27   High   High    Atrium  Low    9
## 28    Low    Low   Terrace  Low   18
## 29 Medium    Low   Terrace  Low    6
## 30   High    Low   Terrace  Low    7
## 31    Low Medium   Terrace  Low   15
## 32 Medium Medium   Terrace  Low   13
## 33   High Medium   Terrace  Low   13
## 34    Low   High   Terrace  Low    7
## 35 Medium   High   Terrace  Low    5
## 36   High   High   Terrace  Low   11
## 37    Low    Low     Tower High   14
## 38 Medium    Low     Tower High   19
## 39   High    Low     Tower High   37
## 40    Low Medium     Tower High   17
## 41 Medium Medium     Tower High   23
## 42   High Medium     Tower High   40
## 43    Low   High     Tower High    3
## 44 Medium   High     Tower High    5
## 45   High   High     Tower High   23
## 46    Low    Low Apartment High   78
## 47 Medium    Low Apartment High   46
## 48   High    Low Apartment High   43
## 49    Low Medium Apartment High   48
## 50 Medium Medium Apartment High   45
## 51   High Medium Apartment High   86
## 52    Low   High Apartment High   15
## 53 Medium   High Apartment High   25
## 54   High   High Apartment High   62
## 55    Low    Low    Atrium High   20
## 56 Medium    Low    Atrium High   23
## 57   High    Low    Atrium High   20
## 58    Low Medium    Atrium High   10
## 59 Medium Medium    Atrium High   22
## 60   High Medium    Atrium High   24
## 61    Low   High    Atrium High    7
## 62 Medium   High    Atrium High   10
## 63   High   High    Atrium High   21
## 64    Low    Low   Terrace High   57
## 65 Medium    Low   Terrace High   23
## 66   High    Low   Terrace High   13
## 67    Low Medium   Terrace High   31
## 68 Medium Medium   Terrace High   21
## 69   High Medium   Terrace High   13
## 70    Low   High   Terrace High    5
## 71 Medium   High   Terrace High    6
## 72   High   High   Terrace High   13

library(ggplot2)

1. First plot

# place code for vis here
ggplot(housing, aes(x=Cont, y=Freq)) +
geom_point() +
facet_wrap(~Type) +
theme_light() +
labs(x = 'Afforded Conatct',
    y = 'Numbers of Residents',
    title = 'Afforded Contact VS Number of Residents')

## as we can see the graph above, apartment type has the highest number of residents in every different class. however, atrium type has the smallest number of residents among four different types.

2. Second plot

# place code for vis here
ggplot(housing, aes(x=Sat, y=Freq)) +
geom_point()+
facet_grid(Cont~.)+
 theme_light() +
  labs(x = 'Satisfaction',
       y = 'Numbers of Residents',
       title = 'Satisfaction Vs Numbers of Residents')

## We can see from the chart above,  Residents who have the Low afforded contact have low satisfaction. Residents who have the High afforded contact have the high satisfaction 

3. Third plot

# place code for vis here
graph_3 = housing %>%
  group_by(Cont, Type, Infl) %>%
  summarise(Freq = sum(Freq))

ggplot(graph_3, aes(x = Infl, y = Cont)) +
   geom_point(aes(col=Infl, size=Freq)) + 
  theme_light() +
  facet_grid(Type ~ .)+
  labs(x = 'Influence of Management',
       y = 'Contact',
       title = 'Contact Vs Influence on Management, Resident ')

## From the chart above, we can know that the higher the contact the higher the house hold and have the high influence on the Management. 

4. Fourth plot

# place code for vis here
graph_2 = housing %>%
group_by(Sat, Type, Infl) %>%
summarise(Freq = sum(Freq))

ggplot(graph_2, aes(x = Infl, y = Freq))+
  geom_point(aes(col=Infl)) +
  theme_light() +
  facet_wrap(~Type) +
  labs(x = 'Influence of Management',
       y = 'Numbers of Residents',
       title = 'Resident Vs Satisfaction, Rental Accomodation and Management Influence')

## Based on the chart we created, we can see that apartment residents have a high degree of influence on the Management versus the Atrium Residence.

5. Fifth plot

# place code for vis here
ggplot(housing) +
geom_bar(aes(x = Sat, y = Freq, fill = Cont), stat = 'identity', width =.4, position = "dodge") +
  labs(title="Number of Resident by Satisfaction with their Rental Type ")+ 
  xlab("Satisfaction")+ 
  ylab("Number of Resident") + 
  theme_light() +
  facet_wrap(~Type) 

## As we can see from the four charts above, apartment residents are more Satisfied the other type of accommodation.And atrium residents have the lowest satisfaction.