We may want to use the information from those rows later on, however, and therefore this method is preferable to deleting the rows from the raw data file. This is necessary for this particular example so that instances of multiple resistance are not counted multiple times. The second line of the code above ( na.omit()) removes all lines in the data set that contain NA (which is the default value in R for missing data). In the data file, there are instances of multiple resistance (resistance to more than one mode of action). # 8 Alberta 6 Sinapis arvensis 1993 ALS inhibitors (B/2) # 7 Alberta 5 Avena fatua 1991 ACCase inhibitors (A/1) # 6 Alberta 4 Setaria viridis 1989 Microtubule inhibitors (K1/3) # 3 Alberta 3 Avena fatua 1989 Multiple Resistance: 2 Sites of Action # 2 Alberta 2 Kochia scoparia 1989 ALS inhibitors (B/2) # 1 Alberta 1 Stellaria media 1988 ALS inhibitors (B/2) Head(resistance) # Province ID SciName Year MOA The example below will calculate mean yield for each irrigation block (Full irrigation or Limited irrigation). The function you want to calculate (mean, standard deviation, maximum, etc.).The data column you wish to group the data by (treatment or grouping variable).The data column you want to summarize (response variable).The tapply() function requires three arguments: The tapply() function allows us to calculate similar descriptive statistics for groups of data, most commonly for treatments, but also for other logical groups, such as by experimental sites, or years. Summary statistics for the corn irrigation data set were calculated previously using the summary() function. The tapply() function can be used for this purpose. However these functions were used in the context of an entire data set or column from a data set in most cases it will be more informative to calculate these statistics for groups of data, such as experimental treatments. A previous section has already demonstrated how to obtain many of these statistics from a data set, using the summary(), mean(), and sd() functions. One of the most basic exploratory tasks with any data set involves computing the mean, variance, and other descriptive statistics. 12.4 Vertical and Horizontal Assessment.12 Nonlinear Regression - Selectivity of Herbicides.11.7.1 Example 1: Herbicide susceptibility.11.5 When Upper and Lower Limits are not similar.10 Logistic Regression (Binary Response).9.1 Regression Models with Mixed Effects.8.1.1 Model Comparison and Obtaining P-values.8.1 Mixed Effects Model using the lme4 Package.5.1.3 Mean Separation - multcomp package.5.1.2 Mean Separation - agricolae package. 2.2 Calculating other group statistics with tapply.Statistical Analysis of Agricultural Experiments using R.
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