6. Frequently asked questions

Nathan Brouwer |brouwern@gmail.com |@lowbrowR

2018-12-10

I still get turned around loading data into R

The mammalsmilks package has an overview of loading data. https://brouwern.github.io/mammalsmilk/articles/b1-data_access.html

Chapters 10 (Loading data from .csv files into RStudio) and 11 (Loading Excel spreadsheets into RStudio) of the class workshop also contains information.

https://brouwern.github.io/BOOK_R_Ecological_Data_Science/loading-data-from-csv-files-into-rstudio.html

https://brouwern.github.io/BOOK_R_Ecological_Data_Science/loading-excel-spreadsheets-into-rstudio.html

How do I build a correlation table?

Use the cor() function in base R or psych::corr.test().

cor(iris[,-5])
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length    1.0000000  -0.1175698    0.8717538   0.8179411
#> Sepal.Width    -0.1175698   1.0000000   -0.4284401  -0.3661259
#> Petal.Length    0.8717538  -0.4284401    1.0000000   0.9628654
#> Petal.Width     0.8179411  -0.3661259    0.9628654   1.0000000
psych::corr.test(iris[,-5])
#> Call:psych::corr.test(x = iris[, -5])
#> Correlation matrix 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length         1.00       -0.12         0.87        0.82
#> Sepal.Width         -0.12        1.00        -0.43       -0.37
#> Petal.Length         0.87       -0.43         1.00        0.96
#> Petal.Width          0.82       -0.37         0.96        1.00
#> Sample Size 
#> [1] 150
#> Probability values (Entries above the diagonal are adjusted for multiple tests.) 
#>              Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length         0.00        0.15            0           0
#> Sepal.Width          0.15        0.00            0           0
#> Petal.Length         0.00        0.00            0           0
#> Petal.Width          0.00        0.00            0           0
#> 
#>  To see confidence intervals of the correlations, print with the short=FALSE option

How do I know if my data are not independent?

Do any of the followings statements apply to your data? If true, you might have to deal with non-independence.

1. Repeated measures on the same individual (repeated measures / longitudinal data)
2. Measurements on things that are members of one or more natural grouping, and several of these groupings occur in the data. Examples include: students in classes, kids families, species within a genus, patients all treated by the same doctor, businesses all within the same state, states within a country, countries within a continent, analyses all conducted in the same lab, technical replicates of the same blood sample (natural clusters).
3. Measurements on things that are intentionally or necessarily group, and multiple groups occur. Examples include: benches in a greenhouse, incubators, 96-well plates, growth chambers, Next-Gen sequence runs (experimental clusters / blocks).
4. Measurements on things arranged spatially, such as trees in a forest (spatial autocorrelation).
5. Measurements on things over a long period of time, such as CO2 from Hawaii (time series; temporal autocorrelation).

Do any of the followings statements apply to your data? If true, you might not have.

1. Did you (randomly) assign treatments to pool of individuals?
2. Did you (randomly) select individuals from an effectively homogenous population?

How do I draw a study diagram?

Citations which contain pictures which illustrate the structure of various studies can be found in “Appendix 5: Study diagrams.” Zuur & Ieno 2016 contain one example (Fig 4).