A convenience sample consists of individuals who are easily accessible and are more likely to be included in the sample.
A confounding variable is an external variable that affects both the independent and dependent variables, potentially leading to a false assumption of causality between them. In the example, temperature is a confounding variable that affects both ice cream sales and the number of shark attacks.
R is a programming language and free software environment used for statistical computing and graphics.
Correlation is a statistical measure that expresses the extent to which two variables are linearly related. It ranges from -1 to 1, where 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship.
The 'caption' argument in the 'labs' function in ggplot2 is used to add a caption to the plot, typically to provide information about the data source.
The 'labs' function in ggplot2 is used to add labels to the plot, including the title, subtitle, and axis labels.
An explanatory variable is a variable that is suspected to causally affect another variable, which is labeled as the response variable.
The function aes() is used to create the mapping from dataset variables to the plot’s aesthetics in ggplot2.
The 'scale_colour_viridis_d()' function in ggplot2 is used to apply a discrete colour scale that is designed to be perceived by viewers with common colour blindness.
A function in ggplot2 used to create scatter plots. It adds points to a plot, with options to adjust size and transparency (alpha).
Treatment variables are conditions we can impose on the experimental units.
The `labs()` function is used to add a title to a plot in ggplot2. For example, `labs(title = "Bill depth and length")`.
The function used to initialize a plot in ggplot2 is ggplot().
A numerical variable can take a wide range of numerical values, and it is sensible to add, subtract, or take averages with those values.
When two variables show some connection with one another, they are called associated or dependent variables.
An observation is each row in a dataset.
The 'labs' function in ggplot2 is used to add labels to the plot, such as the title, subtitle, and axis labels.
'Mapping' in ggplot2 refers to the process of linking data variables to visual properties (aesthetics) of the plot, such as x and y coordinates, size, color, and alpha. This is done using the aes() function.
`geom_point()` is a function in ggplot2 that adds a layer of points to a plot, creating a scatter plot. Each point represents an observation in the dataset.
'facet_grid' is used in ggplot2 to create a grid of plots based on the values of two categorical variables, allowing for the comparison of data across these variables.
Faceting by species and sex in ggplot2 involves creating a grid of plots where each plot represents a subset of the data divided by the levels of two categorical variables, in this case, species and sex. This allows for the comparison of relationships within each subset.
Multistage sampling involves taking a simple random sample of clusters and then taking a simple random sample within each sampled cluster. It is more economical than other sampling techniques and is useful when there is large case-to-case variability within a cluster, but the clusters themselves do not look very different.
An observational study is a type of research where data is collected without directly interfering with how the data arise, meaning researchers merely observe. In this case, only a relationship between the explanatory and the response variables can be established.
ggplot() is the main function in ggplot2. It initializes the plot. The different layers of the plots are then added consecutively.
Exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics. Often, EDA is visual, but it might also involve calculating summary statistics and performing data transformation.
Commonly used aesthetics of a graphic are colour, shape, size, or alpha (transparency).
Anecdotal evidence is evidence based on a limited sample size that might not be representative of the population, often relying on personal stories or isolated examples.
In the provided ggplot2 code, the 'colour' aesthetic represents the 'species' of the penguins, which differentiates the points on the scatter plot by species.
The alpha aesthetic introduces different levels of transparency.
Extraneous variables that affect both the explanatory and the response variable and that make it seem like there is a relationship between the two.
Simple random sampling is a technique where cases are randomly selected from the population without any implied connection between the selected points. Each case in the population has an equal chance of being included in the final sample.
If there are variables that are known or suspected to affect the response variable, first group subjects into blocks based on these variables, and then randomize cases within each block to treatment groups.
The 'geom_point()' function in ggplot2 adds a layer of points to a plot, which is useful for creating scatter plots.
The values of shape can only be specified by a discrete variable. Using a continuous variable will lead to an error.
Random variation refers to the natural fluctuations that occur in any data-generating process. For example, when flipping a coin 100 times, while the chance of landing heads in any given flip is 50%, we probably won’t observe exactly 50 heads. This type of fluctuation is part of almost any type of data-generating process.
The 'aes' argument in ggplot is used to specify the aesthetic mappings, such as which variables to map to the x and y axes, and which variables to use for color, shape, and other visual properties.
The 'glimpse' function in R is used to provide a quick overview of a data frame, displaying the number of rows and columns, and a preview of the data in each column.
In addition to specifying colour with respect to species, we now define shape based on island.
The 'gg' in ggplot2 stands for Grammar of Graphics, which is a tool that enables us to concisely describe the components of a graphic.
facet_wrap() allows for specifying the number of columns (or rows) in the output when creating faceted plots in ggplot2.
A placebo is a fake treatment, often used as the control group for medical studies.
Faceting means creating smaller plots that display different subsets of the data. It is useful for exploring conditional relationships and large data.
Stratified sampling is especially useful when the cases in each stratum are very similar in terms of the outcome of interest.
