#Timeslice in r how to
#Timeslice in r download

#Timeslice in r download

You can download the original complete data subset with additional documentation here.I've tried to use machine learning to make anycodings_time-series prediction based on time-series data.

#Timeslice in r how to

Specifically, you’ve aggregated the data to represent daily sum values and added some noData values to ensure you learn how to clean them! IMPORTANT: You have modified these data a bit for ease of teaching and learning. The metadata tell us that the noData value for these data is 999.99. JULIAN: The JULIAN DAY the data were collected.Īdditional information about the data, known as metadata, is available in the PRECIP_HLY_documentation.pdf.YEAR: The year the data were collected.Also important, hours with no precipitation are not recorded. Important: the metadata notes that the value 999.99 indicates missing data. DAILY_PRECIP: The total precipitation in inches.Notice that DATE is currently class chr, meaning the data is interpreted as a character class and not as a date. DATE: The date when the data were collected in the format: YYYYMMDD.ELEVATION, LATITUDE and LONGITUDE: The spatial location of the station.STATION and STATION_NAME: Identification of the COOP station.Viewing the structure of these data, you can see that different types of data are included in this file: # are there any unusual / No data values? summary ( boulder_daily_precip $ DAILY_PRECIP ) # Min. # $ STATION_NAME: chr 'BOULDER 2 CO US' 'BOULDER 2 CO US' 'BOULDER 2 CO US' 'BOULDER 2 CO US'. Finally, set stringsAsFactors to FALSE globally using options(stringsAsFactors = FALSE). To begin, load the ggplot2 and dplyr libraries. Note that the data directory is directly under the earth-analytics folder. Your `week_02` file directory should look like the one above. You may have to copy and paste your files to make this look right. They are not nested within another directory. When you do this, be sure that your directory looks like the image below: note that all of the data are within the week2 directory. lubridate: install.packages('lubridate')ĭownload Week 2 Data Important - Data Organizationīefore you begin this lesson, be sure that you’ve downloaded the dataset above.Also we recommend that you have an earth-analytics directory set up on your computer with a /data directory within it. You need R and RStudio to complete this tutorial. Describe what a pipe does and how it is used to manipulate data in R.Use dplyr pipes to manipulate data in R.Subset data using the dplyr filter() function.SECTION 15 LAST CLASS: FINAL PROJECT PRESENTATIONSĪfter completing this tutorial, you will be able to:.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