Lab 4 - Data Visualization

Learning Goals

  • Read in and prepare the meteorological dataset
  • Create several graphs with different geoms() in ggplot2
  • Create a facet graph
  • Customize your plots
  • Create a detailed map

Lab Description

We will again work with the meteorological data presented in lecture.

The objective of the lab is to examine the association between weekly average dew point and wind speed in four regions of the US and by elevation.

Per Wikipedia: “The dew point of a given body of air is the temperature to which it must be cooled to become saturated with water vapor. This temperature depends on the pressure and water content of the air.”

Again, feel free to supplement your knowledge of this dataset by checking out the data dictionary.

Steps

1. Read in the data

First download and then read in with read.csv()

if (!file.exists("met_all.gz"))
  download.file(
    url = "https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz",
    destfile = "met_all.gz",
    method   = "libcurl",
    timeout  = 60
    )
met <- read.csv("met_all.gz")

2. Prepare the data

  • Remove temperatures less than -17C
  • Make sure there is no missing data in the key variables coded as 9999, 999, etc.
  • Generate a date variable using the functions as.Date() (hint: You will need the following to create a date paste(year, month, day, sep = "-")).
  • Subset the data to keep only the observations from the first week (ie. first 7 days) of the month.
  • Compute the mean by station of the variables temp, rh, wind.sp, vis.dist, dew.point, lat, lon, and elev.
  • Create a region variable for NW, SW, NE, SE based on lon = \(-98.00\) and lat = \(39.71\) degrees
  • Create a categorical variable for elevation as in the lecture slides

3. Use geom_violin to examine the relative humidity and dew point by region

You saw how to use geom_boxplot in class. Try using geom_violin instead (take a look at the help). (hint: you will need to set the x aesthetic to 1)

  • Use facets
  • Make sure to deal with NAs
  • Describe what you observe in the graph

4. Use geom_point with stat_smooth to examine the association between dew point and relative humidity by region

  • Color points by region
  • Make sure to deal with NAs
  • Fit a linear regression line by region
  • Describe what you observe in the graph

5. Use geom_bar to create barplots of the weather stations by elevation category colored by region

  • Bars by elevation category using position="dodge"
  • Change colors from the default. Color by region using scale_fill_brewer see this
  • Create nice labels on the axes and add a title
  • Describe what you observe in the graph
  • Make sure to deal with NA values

6. Use stat_summary to examine mean dew point and wind speed by region with standard deviation error bars

  • Make sure to remove NAs
  • Use fun.data="mean_sdl" in stat_summary
  • Add another layer of stats_summary but change the geom to "errorbar" (see the help).
  • Describe the graph and what you observe
  • Dew point is…
  • Wind speed is…

7. Make a map showing the spatial trend in relative humidity in the US

  • Make sure to remove NAs
  • Use leaflet() or ggplot2
  • Make a color palette with custom colors
  • Use addMarkers or geom_point to include the top 10 places in relative humidity (hint: this will be useful rank(-rh) <= 10)
  • Add a legend
  • Describe the trend in RH across the US

8. Use a ggplot extension

  • Pick an extension (except cowplot) from here and make a plot of your choice using the met data (or met_avg)
  • You might want to try examples that come with the extension first (e.g. ggtech, gganimate, ggforce)