Calculates Spearman's rank correlation coefficients (rho) between variables with support
for grouped data and multiple variable pairs. Provides significance testing and
SPSS-compatible output formatting. Survey weights only select cases (see
weights); the coefficient itself is unweighted.
Spearman's rho is a non-parametric measure of rank correlation that assesses monotonic relationships between variables. It is particularly suitable for ordinal data or when the assumptions of Pearson correlation are not met.
Arguments
- data
Your survey data (a data frame or tibble)
- ...
The variables you want to correlate. List two for a single correlation or more for a correlation matrix. You can use helpers like
starts_with("trust"). Numeric variables or ordered factors (ranked by their level order).- weights
Optional survey weights. They are used only to select cases (rows with a weight <= 0 or NA are left out); the rank correlation itself is computed unweighted on the remaining sample. For design-based weighted Spearman use
survey::svyolr()orwCorr::weightedCorr(method = "Spearman"). Give a column name (unquoted or as a string), an expression such assampling_weight * 2, or a numeric vector with one weight per row.- alternative
Direction of the test:
"two.sided"(default): Two-tailed test"less": One-tailed test (negative correlation)"greater": One-tailed test (positive correlation)
- use
How to handle missing values:
"pairwise"(default): Pairwise deletion - each correlation uses all available cases"listwise": Listwise deletion - only complete cases across all variables
- na.rm
Deprecated. Use
useinstead.
Value
Correlation results showing rank-based relationships between variables,
including the rho coefficient, p-value, t-statistic, and sample size for
each pair. For multiple variables, correlation, significance, and sample
size matrices are also provided.
Use summary() for the full SPSS-style output with toggleable sections.
Details
Understanding the Results
Spearman's rho measures the strength and direction of a monotonic relationship between two variables. Unlike Pearson correlation, it does not require a linear relationship – it just needs one variable to consistently increase (or decrease) as the other increases. Think of it as ranking your data first, then checking if the ranks tend to go together.
The rho value ranges from -1 to +1:
Strong positive (0.7 to 1.0): High ranks in one variable go with high ranks in the other
Moderate positive (0.3 to 0.7): Moderate tendency for ranks to increase together
Weak positive (0 to 0.3): Slight tendency for ranks to increase together
No correlation (near 0): No relationship between the variables
Negative values: As one variable's rank increases, the other's tends to decrease
The output also provides:
p-value: Probability of seeing this correlation by chance
n: Number of observation pairs used
significance stars: Visual indicator (*** very strong, ** strong, * moderate evidence)
When to Use This
Choose Spearman's rho when:
Your relationship is monotonic but not necessarily linear
Your data has outliers (they have less impact on ranks)
Your variables are ordinal (ordered categories)
You are not sure if your data meets Pearson correlation assumptions
You want to detect any monotonic trend, not just linear ones
References
Spearman, C. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15(1), 72–101.
Lehmann, E. L. (1975). Nonparametrics: Statistical Methods Based on Ranks. Holden-Day.
See also
cor with method = "spearman" for the base R
implementation.
kendall_tau for Kendall's rank correlation.
pearson_cor for Pearson correlation analysis.
summary.spearman_rho for detailed output with toggleable sections.
Other correlation:
kendall_tau(),
partial_cor(),
pearson_cor()
Examples
# Load required packages and data
library(dplyr)
data(survey_data)
# Basic correlation between two variables
survey_data %>%
spearman_rho(life_satisfaction, political_orientation)
#> Spearman Correlation: life_satisfaction x political_orientation
#> rho = -0.004, p = 0.833, N = 2228
#> Use summary() for detailed output.
# Correlation matrix for multiple variables
survey_data %>%
spearman_rho(life_satisfaction, political_orientation, trust_media)
#> Spearman Correlation: 3 variables
#> life_satisfaction x political_orientation: rho = -0.004, p = 0.833
#> life_satisfaction x trust_media: rho = 0.028, p = 0.181
#> political_orientation x trust_media: rho = 0.003, p = 0.885
#> 0/3 pairs significant (p < .05), N = 2177-2291
#> Use summary() for detailed output.
# Weights only select cases (weight <= 0 or NA left out); rho itself is
# unweighted
survey_data %>%
spearman_rho(age, income, weights = sampling_weight)
#> Spearman Correlation: age x income
#> rho = 0.003, p = 0.870, N = 2186
#> Use summary() for detailed output.
# Grouped correlations
survey_data %>%
group_by(region) %>%
spearman_rho(age, income, life_satisfaction)
#> [region = East]
#> Spearman Correlation: 3 variables
#> age x income: rho = 0.058, p = 0.234
#> age x life_satisfaction: rho = -0.040, p = 0.391
#> income x life_satisfaction: rho = 0.440, p < 0.001 ***
#> 1/3 pairs significant (p < .05), N = 410-465
#> [region = West]
#> Spearman Correlation: 3 variables
#> age x income: rho = -0.008, p = 0.725
#> age x life_satisfaction: rho = -0.020, p = 0.382
#> income x life_satisfaction: rho = 0.470, p < 0.001 ***
#> 1/3 pairs significant (p < .05), N = 1705-1956
#> Use summary() for detailed output.
# Using tidyselect helpers
survey_data %>%
spearman_rho(starts_with("trust"), weights = sampling_weight)
#> Spearman Correlation: 3 variables
#> trust_government x trust_media: rho = 0.008, p = 0.723
#> trust_government x trust_science: rho = 0.027, p = 0.207
#> trust_media x trust_science: rho = 0.016, p = 0.453
#> 0/3 pairs significant (p < .05), N = 2227-2272
#> Use summary() for detailed output.
# Listwise deletion for missing data
survey_data %>%
spearman_rho(age, income, use = "listwise")
#> Spearman Correlation: age x income
#> rho = 0.003, p = 0.870, N = 2186
#> Use summary() for detailed output.
# One-tailed test
survey_data %>%
spearman_rho(age, income, alternative = "greater")
#> Spearman Correlation: age x income [one-sided: greater]
#> rho = 0.003, p = 0.435, N = 2186
#> Use summary() for detailed output.
# --- Three-layer output ---
result <- spearman_rho(survey_data, age, income, life_satisfaction)
result # compact one-line overview
#> Spearman Correlation: 3 variables
#> age x income: rho = 0.003, p = 0.870
#> age x life_satisfaction: rho = -0.024, p = 0.238
#> income x life_satisfaction: rho = 0.464, p < 0.001 ***
#> 1/3 pairs significant (p < .05), N = 2115-2421
#> Use summary() for detailed output.
summary(result) # full correlation, p-value, and N matrices
#>
#> Spearman's Rank Correlation Analysis
#> ------------------------------------
#>
#> - Method: Spearman's rho (rank correlation)
#> - Variables: age, income, life_satisfaction
#> - Missing data handling: pairwise deletion
#> - Alternative hypothesis: two.sided
#>
#>
#> Spearman's Rho Matrix:
#> ----------------------
#> age income life_satisfaction
#> age 1 0.003 -0.024
#> income 0.003 1 0.464***
#> life_satisfaction -0.024 0.464*** 1
#> ----------------------
#>
#> Significance Matrix (p-values, 2-tailed):
#> -----------------------------------------
#> age income life_satisfaction
#> age .870 .238
#> income .870 <.001
#> life_satisfaction .238 <.001
#> -----------------------------------------
#>
#> Sample Size Matrix:
#> -------------------
#> age income life_satisfaction
#> age 2500 2186 2421
#> income 2186 2186 2115
#> life_satisfaction 2421 2115 2421
#> -------------------
#>
#> Pairwise Results:
#> ------------------------------------------------------------
#> Pair rho t p N
#> ------------------------------------------------------------
#> age x income 0.003 0.163 .870 2186
#> age x life_satisfaction -0.024 -1.180 .238 2421
#> income x life_satisfaction 0.464 24.073 <.001 2115 ***
#> ------------------------------------------------------------
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05
summary(result, pvalue_matrix = FALSE) # hide p-values
#>
#> Spearman's Rank Correlation Analysis
#> ------------------------------------
#>
#> - Method: Spearman's rho (rank correlation)
#> - Variables: age, income, life_satisfaction
#> - Missing data handling: pairwise deletion
#> - Alternative hypothesis: two.sided
#>
#>
#> Spearman's Rho Matrix:
#> ----------------------
#> age income life_satisfaction
#> age 1 0.003 -0.024
#> income 0.003 1 0.464***
#> life_satisfaction -0.024 0.464*** 1
#> ----------------------
#>
#> Sample Size Matrix:
#> -------------------
#> age income life_satisfaction
#> age 2500 2186 2421
#> income 2186 2186 2115
#> life_satisfaction 2421 2115 2421
#> -------------------
#>
#> Pairwise Results:
#> ------------------------------------------------------------
#> Pair rho t p N
#> ------------------------------------------------------------
#> age x income 0.003 0.163 .870 2186
#> age x life_satisfaction -0.024 -1.180 .238 2421
#> income x life_satisfaction 0.464 24.073 <.001 2115 ***
#> ------------------------------------------------------------
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05