#Linear model rstudio how to#
#Linear model rstudio install#

Move the 'read_csv('stations.csv')' command from the History over to the fifth line of the script.It will move this command to the first line. Click on line 1, then click on the 'library(readr)' command in the History, and then hit the 'To Source' button. In your script, move the 'setwd' command you added earlier to the second line by hitting 'enter' after placing the cursor just before the 's'.

You will notice that two commands were executed: 'library' and 'read_csv'. You should now see a Data object called stations in there, with 295 observations of 15 variables.

Switch to the Environment tab on the upper right.

Press the Import button on the lower right.

Check that the Delimiter is set to 'Comma', and the Comment and NA are set to Default. Check that 'First Row as Names' and 'Trim Spaces' are selected, and un-select 'Open Data Viewer'.

In the Import Options at the bottom, you can leave the default name 'stations'.

Quickly look at the rows and columns in the preview and make sure there aren't any obvious mistakes in loading the data. If you correctly set up your working directory, the browser should open in the folder where the starting data for this workshop lives.

In the upper right corner, select the Browse. Once packages finish installing, an Import Text Data window will appear.

#Linear model rstudio install#

If you receive an 'Install Required Packages' popup, click Yes to install the necessary packages.

Note: this step might require a couple of updated R packages.

On the Environment tab, choose the Import Dataset menu, and select 'From Text (readr).' A wizard will pop up.

On the History tab, select that last command ('setwd') and press the 'To Source' button to add it to your script.

This sets the working directory to the directory noted inside the function. You will notice that a command ('setwd') will go to the terminal.

Set the working directory to this folder in the Files tab by choosing the More menu and choosing 'Set As Working Directory'.

Using the file browser in the Files tab of RStudio, navigate to the folder containing the starting data for this workshop.

Start RStudio and create a new script.

Initial region assignment for the site in the regional frequency analysis Site elevation from PRISM elevation data, mįinal region assignment for the site in the regional frequency analysis Normal average wintertime temperature from PRISM, ☌ Normal total wintertime precipitation from PRISM, mm Mean annual maximum 3-day wintertime precipitation ( L-mean), inĬoefficient of L-variation for annual maximum 3-day wintertime precipitationĬoefficient of L-skewness for annual maximum 3-day wintertime precipitationĬoefficient of L-kurtosis for annual maximum 3-day wintertime precipitation For this workshop we will be working with two variables, which are in bold. The fifteen variables contained in this dataframe are shown below. Using PRISM as the basis for the regression means that the predicted variable can be estimated almost anywhere. PRISM climate normals provide a spatially-continuous map of long-term averages of meteorological variables such as temperature, precipitation, etc. This allows for estimation of the mean annual maximum precipitation anywhere the seasonal total precipitation can be estimated. In regional precipitation-frequency analysis, it is common to try to predict the mean annual maximum precipitation at the study sites using annual or seasonal total precipitation as a predictor. 295 stations were analyzed and properties of their annual maximum precipitation as well as characteristics of the location of the station were placed in a data file. The data you have been provided in 'stations.csv' are from a precipitation-frequency analysis for 3-day maximum wintertime precipitation in the Pacific Northwest. The data required to start this workshop can be downloaded here: stations.csv Background

#Linear model rstudio how to#

If at any time you need a refresher on working with R commands or on how to use RStudio, feel free to look back at the R introduction or the RStudio introduction sections. In this part of the workshop, you will load a data file into a data frame, perform a regression analysis, and look at some of the diagnostics that you can produce in R for linear regression. Do not copy, forward, or release the information without United States Army Corps of Engineers approval. Disclaimer: The United States Army Corps of Engineers has granted access to these data for instructional purposes only.