Summarize count data

Produces a numerical and graphical summary of a count response variable, with the plot automatically adapting to the number and type of predictors in the formula.

Usage

summarizeCountData(formula, data)

Arguments

formula

A formula of the form y ~ x1 + x2 + ... where y is a non-negative integer count response. Offsets via offset() are supported.

data

A data frame containing the variables in formula.

Value

A named list with three elements:

summary

A one-row data.frame containing: mean, var, var_mean_ratio, n_zero, and n_total.

counts

A data.frame with columns count and freq giving the frequency of each observed count value.

plot

A ggplot object. The plot type depends on the number and type of predictors — see Details.

Details

The graphical summary is chosen based on the predictors in formula:

No predictors

Histogram of the count response.

One continuous predictor

Scatter plot with a loess smooth.

One categorical predictor

Violin plot with jittered points.

Two continuous predictors

2D bin plot (geom_bin2d) with a viridis fill scale.

Two categorical predictors

Tile heatmap of mean counts.

One continuous, one categorical predictor

Scatter plot with loess smooths coloured by the categorical variable.

Three or more predictors

A warning is issued and only the first two predictors are used.

The var_mean_ratio in the summary table is the variance-to-mean ratio. A value close to 1 is consistent with a Poisson distribution; values substantially greater than 1 suggest overdispersion.

See also

plot_count_data

Examples

set.seed(1)
df <- data.frame(
  y  = rpois(100, lambda = 3),
  x1 = rnorm(100),
  x2 = sample(c("A", "B"), 100, replace = TRUE)
)

# No predictors
summarizeCountData(y ~ 1, data = df)
#> $summary
#>   mean     var var_mean_ratio n_zero n_total
#> 1 3.05 2.14899      0.7045869      2     100
#> 
#> $counts
#>   count freq
#> 1     0    2
#> 2     1   11
#> 3     2   28
#> 4     3   20
#> 5     4   23
#> 6     5   12
#> 7     6    3
#> 8     8    1
#> 
#> $plot

#> 

# One continuous predictor
summarizeCountData(y ~ x1, data = df)
#> $summary
#>   mean     var var_mean_ratio n_zero n_total
#> 1 3.05 2.14899      0.7045869      2     100
#> 
#> $counts
#>   count freq
#> 1     0    2
#> 2     1   11
#> 3     2   28
#> 4     3   20
#> 5     4   23
#> 6     5   12
#> 7     6    3
#> 8     8    1
#> 
#> $plot
#> `geom_smooth()` using formula = 'y ~ x'

#> 

# One categorical predictor
summarizeCountData(y ~ x2, data = df)
#> $summary
#>   mean     var var_mean_ratio n_zero n_total
#> 1 3.05 2.14899      0.7045869      2     100
#> 
#> $counts
#>   count freq
#> 1     0    2
#> 2     1   11
#> 3     2   28
#> 4     3   20
#> 5     4   23
#> 6     5   12
#> 7     6    3
#> 8     8    1
#> 
#> $plot

#> 

# Mixed predictors
summarizeCountData(y ~ x1 + x2, data = df)
#> $summary
#>   mean     var var_mean_ratio n_zero n_total
#> 1 3.05 2.14899      0.7045869      2     100
#> 
#> $counts
#>   count freq
#> 1     0    2
#> 2     1   11
#> 3     2   28
#> 4     3   20
#> 5     4   23
#> 6     5   12
#> 7     6    3
#> 8     8    1
#> 
#> $plot
#> `geom_smooth()` using formula = 'y ~ x'

#>