---
title: 'R for Researchers: Data preparation solutions'
date: "April 2015"
---

This article contains solutions to exercises for an article in the
series R for Researchers.
For a list of topics covered by this series, see the 
[Introduction](RFR_Introduction.html) article. 
If you\'re new to R we highly recommend reading the articles in order.


There is often more than one approach to the exercises.
Do not be concerned if your approach is different than
the solution provided.


#### Exercise solutions 


Do the following exercise in the AlfAnalysis script.

    ```{r, echo=FALSE, results="hide", message=FALSE, warning=FALSE}
    #######################################################
    #######################################################
    ##
    ##   Environment Setup
    ##
    #######################################################
    #######################################################
    
    library(faraway)      # glm support
    library(MASS)         # negative binomial support
    library(car)          # regression functions
    library(lme4)         # random effects
    library(ggplot2)      # plotting commands
    library(reshape2)     # wide to tall reshaping
    library(xtable)       # nice table formatting
    library(knitr)        # kable table formatting
    library(grid)         # units function for ggplot
    
    
    saveDir <- getwd()    # get the current working directory
    saveDir               # show me the saved directory
    
    wd <- "u:/RFR"        # path to my project
    # setwd(wd)           # set this path as my work directory
    ```


1. Import the dataset in alfalfa.txt from the course Dataset folder.
    
    ```{r, comment=NA }
    #######################################################
    #######################################################
    ##
    ##   Import Data
    ##
    #######################################################
    #######################################################
    
    alfalfaIn <- read.table("Datasets/alfalfa.txt", header=TRUE)
    str(alfalfaIn)
    alfalfa <- alfalfaIn
    ```



3. Change the variable names to \"shade\",\"irrig\",
\"inoc\", and \"yield\"

    ```{r, comment=NA }
    colnames(alfalfa) <- c("shade","irrig","inoc","yield")
    ```


4. Create a new variable for shade level (shadeLev) from
the shade variable by 
setting 1 to \"full\", 5 to \"none\", and the rest to \"part\".

    ```{r, comment=NA }
    shadeLev <- cut( alfalfa$shade, 
                     c(0,1.5,4.5,6),
                     labels=c("full","part","none")
                     )
    str(shadeLev)
    ```

5. Change the type of shadeLev to factor.

    ```{r, comment=NA }
    shadeLev <- factor(shadeLev)
    ```


6. Include shadeLev in the alfalfa data.frame.

    ```{r, comment=NA }
    alfalfa <- data.frame(alfalfa,
                          shadeLev=shadeLev
                          )
    ```


7. Change inoculate level E to control.
This is a more challenging problem.

    ```{r, comment=NA }
    alfalfa$inoc <- ifelse(alfalfa$inoc=="E","cntrl",
                           as.character(alfalfa$inoc)
                          )
    alfalfa$inoc <- factor(alfalfa$inoc)
    str(alfalfa)
    ```


8. Commit your changes to AlfAnalysis.

    There is no code associated with the solution to this problem.



Return to the [Data presentation](RFR_DataPres.html)  article.


Last Revised: 2/9/2015