Directions

During ANLY 512 we will be studying the theory and practice of data visualization. We will be using R and the packages within R to assemble data and construct many different types of visualizations. We begin by studying some of the theoretical aspects of visualization. To do that we must appreciate the basic steps in the process of making a visualization.

The objective of this assignment is to complete and explain basic plots before moving on to more complicated ways to graph data.

Each question is worth 5 points.

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.

Questions

  1. Using data from the nasaweather package, create a scatterplot between wind and pressure, with color being used to distinguish the type of storm.

Summary: We can see from the plot that a storm’s wind speed and air pressure have a negative relationship - the higher the wind speed, the lower the air pressure of the storm will be. Besides, Hurricane has the highest wind speed among all the storm types.

# str(storms)
# head(storms)
# colnames(storms)
ggplot(storms, aes(wind, pressure, color = type)) +
  geom_point()+
  labs(title = 'The relationship between Wind vs Pressure by Storm Type',
    color = "Type",
    x = 'Wind',
    y = 'Pressure')

  1. Use the MLB_teams data in the mdsr package to create an informative data graphic that illustrates the relationship between winning percentage and payroll in context.

It seems that the higher the winnning percentage of a team, the higher total payroll a team would get. But the variation of the team payroll of a certain winning percentage is large. So the relationship is not strong.

# head(MLB_teams)
ggplot(MLB_teams, aes(x = WPct*100, y = payroll/1000000)) +
  geom_point() +
  geom_smooth() +
  # Add Dollar Sign for Axis Labels
  scale_y_continuous(labels=scales::dollar_format()) +
  labs(
    title = "The Relationship Between Team's Winning Percentage and Payroll",
    x = 'Winning Perecntage (%)',
    y = 'Team Payroll (Millions)',
  )

  1. The RailTrail data set from the mosaicData package describes the usage of a rail trail in Western Massachusetts. Use these data to answer the following questions.
  1. Create a scatterplot of the number of crossings per day volume against the high temperature that day
  2. Separate your plot into facets by weekday (an indicator of weekend/holiday vs. weekday)
  3. Add regression lines to the two facets

We find that on weekdays, there’s a stronger positive relationship between the number of trail users and the day’s high temperature. The warmer the weather is, the more users cross the trail. The trend applies to holidays/weekends, but it is not so much linear.

# head(RailTrail)
# unique(RailTrail$dayType)

RailTrail2 <- RailTrail %>%
 mutate(weekday = ifelse(weekday, "Weekday", "Holidays/Weekends"))

ggplot(RailTrail2, aes(x = hightemp, y = volume)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~ weekday, nrow = 1) +
  labs(
    title = "The relationship between Trail Volume vs High Temperature by Day Type",
    x = "Daily High Temperature (F)",
    y = "Trail Volume")

  1. Using data from the nasaweather package, use the geom_path function to plot the path of each tropical storm in the storms data table. Use color to distinguish the storms from one another, and use faceting to plot each year in its own panel.

There are least number of tropical storms in 1997. The path of the storms are generally more across different latitudes than longitudes.

# head(storms)
tropical_storms <- storms %>%
  filter(type=='Tropical Storm')
# head(tropical_storms)

ggplot(tropical_storms, aes(x = lat, y = long, color = name)) +
  geom_path() +
  facet_wrap(~year, ncol = 2) +
  labs(
    title = "The Path of Tropical Storms (1995 - 2000)",
    col = "Storm Name",
    x = "Latitude",
    y = "Longitude"
  )

  1. Using the penguins data set from the palmerpenguins package.
  1. Create a scatterplot of bill_length_mm against bill_depth_mm where individual species are colored and a regression line is added to each species. Add regression lines to all of your facets. What do you observe about the association of bill depth and bill length?

There’s a positive relationship between a penguin’s bill length and bill depth for each species. Generally, the longer the bill length, the higher the bill depth. Specifically, Gentoo has the lowest bill depths among the 3 while Adelie generally has the shortest bill length.

  1. Repeat the same scatterplot but now separate your plot into facets by species. How would you summarize the association between bill depth and bill length.

We can see the bill depth difference among the species even closer using facet that Gentoo’s bill depth is generally lower than the other two. And Adelie and Chinstrap share a similar range of bill depth. Setting the faceting column to be 1, we can better compare the bill length among the three species. And we find that besides Adelie generally has the shortest bill length, Gentoo and Chinstrap share a similar range of bill length.

# head(penguins)
# unique(penguins$species)

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point() +
  geom_smooth(method = "lm", aes(color = species)) +
  labs(
    color = 'Species', #legend label
    title = 'The Relationship Between Bill Length vs Depth By Penguin Species',
    x = 'Bill Length (mm)',
    y = 'Bill Depth (mm)') 

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point() +
  geom_smooth(method = "lm", aes(color = species)) +
  facet_wrap(~ species, nrow = 1) +
  labs(
    color = 'Species', #legend label
    title = 'The Relationship Between Bill Length vs Depth By Penguin Species',
    x = 'Bill Length (mm)',
    y = 'Bill Depth (mm)') 

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point() +
  geom_smooth(method = "lm", aes(color = species)) +
  facet_wrap(~ species, ncol = 1) +
  labs(
    color = 'Species', #legend label
    title = 'The Relationship Between Bill Length vs Depth By Penguin Species',
    x = 'Bill Length (mm)',
    y = 'Bill Depth (mm)')