1. Read in the data

First download and then read in with data.table:fread()

2. Prepare the data

• Remove temperatures less than -17C
• Make sure there are 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 = "-")).
• Using the data.table::week function, keep the observations of the first week 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 categorical variable for elevation (break point at 252 m)
• Create a region variable for NW, SW, NE, SE based on lon = -98.00 and lat = 39.71 degrees

3. Use geom_violin to examine the wind speed and dew point temperature 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 NA category
• Describe what you observe in the graph

4. Use geom_jitter with stat_smooth to examine the association between dew point temperature and wind speed by region

• Color points by region
• Make sure to deal with NA category
• 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 coloured 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 axes and add a title
• Try a different theme
• Describe what you observe in the graph
• Make sure to deal with NA

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

• Make sure to remove NA

• 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 temperature is…

• Wind speed is…

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

• Make sure to remove NA
• Use leaflet()
• Make a colour palette with custom colours
• Use addMarkers to include the top 10 places in relative h (hint: this will be useful rank(-rh) <= 10)
• Add a legend
• Describe trend in RH across the US

8. Use a ggplot extension

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