#RCODE FOR LATINAMERICA MAP SERIES#

You can import data directly into R rather than downloading it using the read.csv() and/or read.table() functions. With that said read.csv() may work for some if not all computers now given upgrades to the base R code. Instead, you need to use functions in the RCurl package. Given the data are small and they could be moved over time, this is a good idea as now you have a backup of the data.Дata Tip: If you have a secure url (secure transfer protocols - i.e., https) you may not be able to use read.csv().

The download.file() function allows us to store a copy of the file on your computer. This is a format that is human readable and structured using a simple, non hierarchical (no nesting involved) format compared to JSON which can be hierarchical and thus efficiently support more complex data. The file that you downloaded above is stored using a. # read data into R boulder_precip <- read.csv ( 'data/week-13/boulder-precip-aug-oct-2013.csv' ) # fix date boulder_precip $ DATE <- as.Date ( boulder_precip $ DATE ) # plot data with ggplot ggplot ( boulder_precip, aes ( x = DATE, y = PRECIP )) + geom_point () + labs ( x = 'Date (2013)', y = 'Precipitation (inches)', title = 'Precipitation - Boulder, CO ', subtitle = 'August - October 2013' ) 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

#RCODE FOR LATINAMERICA MAP SERIES#

SECTION 2 INTRO TO R & WORK WITH TIME SERIES DATA.SECTION 1 DOCUMENT YOUR SCIENCE USING R MARKDOWN AND R.

Rcode for latinamerica map