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 are 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 ...

View(housing)

1. First plot

ggplot(housing, aes(Freq, Cont)) +
  geom_bar(aes(fill=Infl),stat='identity', position=position_dodge(0.9))+
  facet_grid(Infl ~ ., scales = 'fixed', space = 'fixed') +
  theme(strip.text.y = element_text(angle = 0),
        legend.position = 'none') +
  labs(y = 'Contact with other residents',
       x = 'Numbers of Residents',
       title = 'Number of Resident by contact and influence of Mgmt')

In the above plot, we tried to find the number of residents who have contact with other residents and their influence of management.There are maximum residents with medium influence of management also both highest and lowest contact with residents

2. Second plot

levels(housing$Infl) = list('Influence-High' = 'High', 'Influence-Medium' = 'Medium','Influence-Low' = 'Low')


housing %>%   
  mutate(influence = fct_reorder(Infl,Freq)) %>%
  mutate(Satisfaction = fct_reorder(Sat, -Freq)) %>% 
  ggplot(aes(Freq, influence)) +
   geom_point(aes(color = Satisfaction)) +
   facet_grid(Satisfaction ~ ., scales = "free", space = "free") +
   theme_light() +
   theme(strip.text.y = element_text(angle = 0),
        legend.position = "none") +
   labs(y = "Influence of Management",
       x = "Number of residents",
       title = "Number of residents with Influence and satisfaction")

From the above plot we can understand that the Maximum number of residents have lowest satisfaction level and htey had lowest interaction with management.If the residents have medium influence they have good satisfaction level. ## 3. Third plot

ggplot(housing, aes(x = Cont, y = Freq))+
  geom_bar(
    aes(fill = Sat), stat = "identity", color = "white",
    position = position_dodge(0.9)
    )+
  facet_wrap(~Type) +
    labs(x="Contact with residents",
              y="Number of Residents",
              title="Satisfaction and Number of householders with Type of Accomodation  ")+
  scale_fill_discrete('Satisfaction of Householders')

From the above plot we can conclude that ,when residents have high Contact with other residents then they have high satisfaction level.Maority of residents live in Apartments.People living in Atrium and Tower have Higher level of satisfaction compared ot other ## 4. Fourth plot

# place code for vis here

levels(housing$Sat) = list('Sat-Low' = 'Low', 'Sat-Medium' = 'Medium', 'Sat-High' = 'High')
levels(housing$Cont) = list('Contact-Low' = 'Low', 'Contact-High' = 'High')
ggplot(housing, aes(Freq, Sat)) +
  geom_point(aes(color=Type))+
  facet_grid(Type ~ Cont, space="fixed", scales="fixed") +
  theme(strip.text.y = element_text(angle = 0),
        legend.position = 'none') +
  labs(y = 'Satisfaction',
       x = 'Numbers of Residents',
       title = 'Number of Resident by Satisfaction,Contact with residents, and Type of Accomodation')

From the above plot we can conclude that ,when residents have high Contact with other residents then they have high satisfaction level.Maority of residents live in Apartments and most of them are either satisfied or not at all satisfied.People living in Atrium and Tower have Higher level of satisfaction compared to Terrace ## 5. Fifth plot

library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.0.2

ggballoonplot(housing,x="Cont",y="Sat", size="Freq", facet.by="Type", fill = "Freq")+ scale_fill_viridis_c(option = "B")

From the above plot we can conclude that ,when residents have high Contact with other residents then they have high satisfaction level.Maority of residents live in Apartments and most of them are either satisfied or not at all satisfied.People living in Atrium and Tower have Higher level of satisfaction compared to Terrace