The first column contains the IDs for each individual, and each successive column describes their heights at time points 0, 10, and 20 in that order.Įxample <- ame(indiv = c( "A", "B", "C", "D", "E"),Īpply() lets you perform a function across a data frame’s rows or columns. This data set is in wide format* and describes the heights of five individuals (e.g., plants) in inches at three different time points (0, 10, and 20 days). These functions all end in apply() because you apply the function you want across all the specified elements. I’m going to discuss the functions apply(), lapply(), sapply(), and tapply() in this blog post (as well as using the dplyr library for similar tasks). For those of you familiar with ‘for’ loops, the apply() family often allows you to avoid constructing those and instead wrap the loop into one simple function. Note that %b represents the abbreviated month which will be plotted as labels on the x-axis.Today I’m going to talk about a useful family of functions that allows you to repetitively perform a specified function (e.g., sum(), mean()) across a vector, list, matrix, or data frame. If R reads the column as a date, you can then use the:įunction with ggplot(), to format the x axis as a date.
DPLYR SUMMARIZE SUM IF CODE
You can use the code above with the mutate() function to create a new month column that contains the month of the year as a class of type date. IMPORTANT: this will only work on data where you’ve already converted the date into a date class that R can read as a date.īelow, you extract just the date from the date field using the month() function. 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
DPLYR SUMMARIZE SUM IF DOWNLOAD
# 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.SECTION 8 QUANTIFY FIRE IMPACTS - REMOTE SENSING.SECTION 7 MULTISPECTRAL IMAGERY R - NAIP, LANDSAT, FIRE & REMOTE SENSING.Uncertainty in Scientific Data & Metadata SECTION 5 LIDAR DATA IN R - REMOTE SENSING UNCERTAINTY.Refine R Markdown Reports with Images and Basemaps
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