You use the lubridate package to quickly extract the month from an existing date formatted field. Note that you could do this for any particular time subset that you want. Next, you will create a month column in the data which will allow us to summarize the data by month. However, what if you don’t have these columns in your data? This column, however already existed in your data. In the example above, you plotted your data plot by day of the year. # subset 2 months around flood boulder_daily_precip %>% filter ( JULIAN > 230 & JULIAN % ggplot ( aes ( x = JULIAN, y = DAILY_PRECIP )) + geom_bar ( stat = "identity", fill = "darkorchid4" ) + facet_wrap ( ~ YEAR, ncol = 3 ) + labs ( title = "Daily Precipitation - Boulder, Colorado", subtitle = "Data plotted by year", y = "Daily precipitation (inches)", x = "Date" ) + theme_bw ( base_size = 15 ) Mutate(DATE = as.Date(DATE, format = "%m/%d/%y"))īecause you are using a pipe you need to reassign your ame output to the boulder_daily_precip object. In this case you will reassign the date column to the values populated by the as.Date() function with converts the class of the column to a date class. So if you want to create a new date column contain the information from the existing DATE column you’d write Mutate(column_name = what_you_want_to_store_in_this_column) The syntax for the mutate function is as follows: The mutate() function in dplyr is used to # download the data # download.file(url = "", # destfile = "data/week-02/805325-precip-dailysum_2003-2013.csv", # method = "libcurl") # import data boulder_daily_precip % ) to achieve the same thing. SECTION 15 LAST CLASS: FINAL PROJECT PRESENTATIONS.SECTION 14 FINAL PROJECTS & COURSE FEEDBACK DISCUSSION.SECTION 10 MIDTERM REVIEW / PRESENTATION BEST PRACTICES.SECTION 9 STUDY FIRE USING REMOTE SENSING DATA.8.1 Fire / spectral remote sensing data - in R.
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