Start here with your data! This is your first question.

How do I know?

Inspect your data with str or summary. Are there several variables?
What does the data show? Does the underlying logic of the data suggest dependencies between the variables?

Example: Inspecting the swiss dataset

Univariate statistics

How do I know?

Check the entry on Data formats to understand the difference between categorical and numeric variables.
Tip: integer and numeric data is not categorical, while factorial and character data is.

My variables have clear dependencies!

Does you data contain at least one categorical variable?
Yes, my data contains at least one categorical variable?
No, my data contains only continuous variables?

No, my variables have clear dependencies!

R commands: str, summary, head, tail, ordered(dataset$variablename, c(levels = “level1”, level2”...))

Relevant figures: hist(), boxplot()

Yes, my data contains at least one categorical variable?

Categorical variables

Does you data contain only categorical variables?
Yes, my data contain only categorical variables?
No, my data does contains continuous and categorical variables?

Yes, my data contain only categorical variables?

Chi-Square test

R commands: table, chisq.test( x, correct=TRUE)
Relevant figures: (stacked) bar charts : barplot(), pie(), bipartite

No, my data does contains continuous and categorical variables?

Categorical and continuous data

R commands: quantile(), str, summary,

Does you categorical dependent variables have 1-2 factor levels?
Yes, my categorical dependent variables has 1-2 factor levels!
No, my categorical dependent variables more than 2 factor levels!

Yes, my categorical dependent variables has 1-2 factor levels!

t-test

R commands: t.test, t_test Relevant figures:

No, my categorical dependent variables more than 2 factor levels!

Analysis of Variance

R commands: aov, Anova, ezAnova, var.test(), lm
Relevant figures: boxplot()

Is your dependent variable normally distributed?

R commands: ks.test, shapiro.test, hist

Yes, my dependent variable is normally distributed!
No, my dependent variable is binomial distributed!
No, my dependent variable is Poisson distributed!

Yes, my dependent variable is normally distributed!

Gaussian Anova

R commands: aov, lm
Relevant figures: boxplot

No, my dependent variable is not normally distributed!

Is your dependent variable binomial or Poisson

No, my dependent variable is Poisson distributed!

Dependent variable is count data

R commands: glm,
Relevant figures: plot

No, my dependent variable is binomial distributed!

Dependent variable is 0/1 or proportions

R commands:
Relevant figures:

Type III Anova

R commands: Anova(car)
Relevant figures: boxplot

Dependent variable not normally distributed

Dependent variable is count data

R commands: glm
Relevant figures: plot

Dependent variable is 0/1 or proportions

R commands: glm
Relevant figures:

Are there random factor variables?

Random factors

R commands: glmer, glmmPQL
Relevant figures:

Continuous variables

Non dependent relations?

Correlations

Clear dependent relations

Regression models

Dependent variable normally distributed

Linear Regression

Dependent variable not normally distributed

Generalised linear model

Dependent variable is count data

Dependent variable is 0/1 or proportions

R commands:
Relevant figures:

Yes, I have several continuous variables without clear dependencies!

Multivariate statistics

Statistics Flowchart - Clustering, Networks, Ordination.png

How do I know?

In an Ordination, you arrange your data alongside underlying gradients in the variables to see which variables most strongly define the data points. Check the entry on Ordinations (to be added) to learn more.
In a Cluster Analysis, you group your data points according to how similar they are, resulting in a tree structure. Check the entry on Clustering Methods to learn more.
In a Network Analysis, you arrange your data in a network structure to understand their connections and the distance between individual data points. Check the entry on Social Network Analysis to learn more.

Data is classified into groups

R commands:
Relevant figures:

Cluster analysis

DIFFERENCE BETWEEN SUPERVISED AND UNSUPERVISED?? DISTINCTION NOT MADE IN THE CLUSTERING ENTRY

How do I know?

TAKE OUT LINK TO CLUSTERING METHODS ENTRY???

Supervised classification

R commands:
Relevant figures:

Unsupervised classification

R commands:
Relevant figures:

Network analysis

How do I know?

Check your data using the R code ADD CODE

ADD MORE BELOW

Bipartite

If your data has two different kinds of nodes, your network is called a "bipartite" network.

R commands:

is_bipartite(graph)
make_bipartite_graph(types, edges, directed = FALSE)

Tripartite

R commands:
Relevant figures:

Ordinations

How do I know?

Check the entry on Data formats to learn more about the different data formats, and use str() to learn more about your data. Abundance data is marked as FORMATNAME, and continuous data is marked as FORMATNAME.

MAKE THE STUFF BELOW CLEARER