mariposa (version 0.7.3)

describe: Get to Know Your Numeric Data

Description

describe() gives you a complete summary of numeric variables - like age, income, or satisfaction scores. It's your first step in any analysis, helping you understand what's typical, what's unusual, and how spread out your data is.

Think of it as a health check for your data that reveals:

• What's the average value?

• What's the middle value?

• How spread out are the responses?

• Are there unusual patterns or outliers?

Usage

describe(
  data,
  ...,
  weights = NULL,
  show = "short",
  probs = c(0.25, 0.5, 0.75),
  na.rm = TRUE,
  excess = TRUE
)

Value

A summary table with descriptive statistics for each variable

Arguments

data

Your survey data (a data frame or tibble)

...

The numeric variables you want to summarize. List them separated by commas, or use helpers like starts_with("trust")

weights

Optional survey weights for population-representative results. Without weights, you describe your sample. With weights, you describe the population.

show

Which statistics to display:

• "short" (default): Essential stats (mean, median, SD, range, IQR, skewness)

• "all": Everything including variance, kurtosis, mode, quantiles

• Custom list: Choose specific stats like c("mean", "sd", "range")

probs

For quantiles, which percentiles to show (default: 25th, 50th, 75th)

na.rm

Remove missing values before calculating? (Default: TRUE)

excess

For kurtosis, show excess kurtosis? (Default: TRUE, easier to interpret)

Details

Understanding the Results

Key statistics and what they tell you:

• n: How many valid responses (watch for too many missing)

• Mean: The average value

• Median: The middle value (half above, half below)

• SD: Standard deviation - how spread out values are

• Range: The minimum and maximum values

• IQR: Interquartile range - the middle 50% of values

• Skewness: Whether data leans left (negative) or right (positive)

• Kurtosis: Whether you have unusual outliers

When to Use This

Always start here! Use describe() to:

• Check data quality (impossible values?)

• Understand distributions before testing

• Spot outliers that might affect analyses

• Compare groups side by side

Interpreting Patterns

• Mean close to Median: Data is roughly symmetric

• Mean > Median: Right-skewed (tail extends right)

• Mean < Median: Left-skewed (tail extends left)

• Large SD: Responses vary widely

• Small SD: Responses are similar

See Also

summary for base R summary statistics.

frequency for categorical variable summaries.

w_mean, w_sd, w_median for individual weighted statistics.

t_test and oneway_anova for group comparisons.

Other descriptive: crosstab(), frequency(), multiple_response(), normality_test()

Examples

# Load required packages and data
library(dplyr)
data(survey_data)

# Basic unweighted analysis
survey_data %>% describe(age)

# Weighted analysis
survey_data %>% describe(age, weights = sampling_weight)

# Multiple variables with custom statistics
survey_data %>% describe(age, income, life_satisfaction, 
                        weights = sampling_weight, 
                        show = c("mean", "sd", "skew"))

# Grouped analysis
survey_data %>% 
  group_by(region) %>% 
  describe(age, weights = sampling_weight)