Overview
Before analyzing survey data, you often need to transform variables — recode responses, create dummy variables, standardize scales, or compute row-wise indices. mariposa provides a set of functions that handle these tasks with a clean, survey-oriented syntax.
| Function | Purpose |
|---|---|
rec() |
Recode values using a string syntax |
to_dummy() |
Create dummy (0/1) variables from categorical columns |
std() |
Z-standardize variables (4 methods) |
center() |
Mean-center variables (grand-mean or group-mean) |
row_means() |
Compute row-wise means across items |
row_sums() |
Compute row-wise sums across items |
row_count() |
Count specific values per row |
pomps() |
Transform to Percent of Maximum Possible Scores (0–100) |
Recoding with rec()
rec() uses a concise string syntax to recode variables.
It works inside mutate() and supports ranges,
reverse-coding, and inline value labels.
Simple Recoding
Collapse categories by mapping old values to new values:
survey_data <- rec(survey_data, age,
rules = "18:29=1 [Young]; 30:49=2 [Middle]; 50:99=3 [Older]",
suffix = "_group", as_factor = TRUE
)
frequency(survey_data, age_group)
#>
#> Frequency Analysis Results
#> --------------------------
#>
#> age_group (Age in years (recoded))
#> # total N=2500 valid N=2500
#>
#> +--------+------+--------+---------+--------+
#> | Value | N | Raw % | Valid % | Cum. % |
#> +--------+------+--------+---------+--------+
#> | Young | 292 | 11.68 | 11.68 | 11.68 |
#> | Middle | 935 | 37.40 | 37.40 | 49.08 |
#> | Older | 1273 | 50.92 | 50.92 | 100.00 |
#> +--------+------+--------+---------+--------+
#> | Total | 2500 | 100.00 | 100.00 | |
#> +--------+------+--------+---------+--------+The bracket notation ([Young]) automatically creates
value labels on the recoded variable. The suffix argument
creates a new column (here age_group) instead of
overwriting the original.
Reverse-Coding
Reverse the direction of a Likert scale. Useful when some items in a
scale are negatively worded. Give the scale range explicitly with
"rev(min, max)": reversing is min + max - x on
the scale, which differs from the observed range whenever
nobody chose an end point (common in subgroups). With plain
"rev", the range comes from the value labels, or from the
observed values with a message saying so:
survey_data <- rec(survey_data, trust_government,
rules = "rev(1, 5)", suffix = "_rev"
)
# Original: 1=low trust ... 5=high trust
# Reversed: 1=high trust ... 5=low trust
head(data.frame(
original = survey_data$trust_government,
reversed = survey_data$trust_government_rev
))
#> original reversed
#> 1 3 3
#> 2 4 2
#> 3 1 5
#> 4 1 5
#> 5 2 4
#> 6 1 5Dichotomizing
Split a variable at its median into two groups:
survey_data <- rec(survey_data, income,
rules = "dicho", suffix = "_dicho"
)
frequency(survey_data, income_dicho)
#>
#> Frequency Analysis Results
#> --------------------------
#>
#> income_dicho (Monthly household income (EUR) (recoded))
#> # total N=2500 valid N=2186 mean=0.49 sd=0.50 skewness=0.03
#>
#> +-------------+------+--------+---------+--------+
#> | Value | N | Raw % | Valid % | Cum. % |
#> +-------------+------+--------+---------+--------+
#> | 0 | 1108 | 44.32 | 50.69 | 50.69 |
#> | 1 | 1078 | 43.12 | 49.31 | 100.00 |
#> +-------------+------+--------+---------+--------+
#> | Total valid | 2186 | 87.44 | 100.00 | |
#> +-------------+------+--------+---------+--------+
#> | System | 314 | 12.56 | | |
#> +-------------+------+--------+---------+--------+
#> | Total | 2500 | 100.00 | | |
#> +-------------+------+--------+---------+--------+Other split options:
Keeping and Copying Values
Use copy to keep values that are not explicitly recoded,
and else for a catch-all:
survey_data <- rec(survey_data, education,
rules = "1:2=1 [Lower]; else=2 [Higher]",
suffix = "_binary", as_factor = TRUE
)
frequency(survey_data, education_binary)
#>
#> Frequency Analysis Results
#> --------------------------
#>
#> education_binary (Highest educational attainment (recoded))
#> # total N=2500 valid N=2500
#>
#> +--------+------+--------+---------+--------+
#> | Value | N | Raw % | Valid % | Cum. % |
#> +--------+------+--------+---------+--------+
#> | Lower | 1470 | 58.80 | 58.80 | 58.80 |
#> | Higher | 1030 | 41.20 | 41.20 | 100.00 |
#> +--------+------+--------+---------+--------+
#> | Total | 2500 | 100.00 | 100.00 | |
#> +--------+------+--------+---------+--------+Dummy Coding with to_dummy()
to_dummy() creates binary (0/1) indicator variables from
categorical columns — also known as one-hot encoding.
Label-Based Column Names
Use suffix = "label" to name columns after the value
labels instead of numeric codes:
Reference Category (n-1 Coding)
For regression analysis, you typically need n-1 dummies (one category omitted as reference):
dummies <- to_dummy(survey_data, education, ref = 1, append = FALSE)
head(dummies)
#> # A tibble: 6 × 3
#> `education_Intermediate Secondary` education_Academic S…¹ education_University
#> <int> <int> <int>
#> 1 1 0 0
#> 2 0 1 0
#> 3 0 1 0
#> 4 0 0 0
#> 5 0 0 0
#> 6 1 0 0
#> # ℹ abbreviated name: ¹`education_Academic Secondary`Adding to Existing Data
By default, dummy columns are appended to the original data:
survey_data <- to_dummy(survey_data, gender, suffix = "label")
# Adds gender_Male, gender_Female to the data frameStandardization with std()
std() z-standardizes variables so they have mean 0 and
standard deviation 1. This is useful for comparing variables on
different scales.
Basic Standardization
survey_data <- survey_data %>%
std(age, income)
# Check: mean ≈ 0, sd ≈ 1
survey_data %>%
describe(age, income, show = c("mean", "sd"))
#>
#> Descriptive Statistics
#> ----------------------
#>
#> -------------------------------------
#> Variable Mean SD N Missing
#> -------------------------------------
#> age 0.000 1.000 2500 0
#> income 0.000 1.000 2186 314
#> -------------------------------------Standardization Methods
std() supports four methods:
# Default: divide by SD
survey_data_methods <- survey_data %>%
std(life_satisfaction, method = "sd", suffix = "_sd") %>%
std(life_satisfaction, method = "2sd", suffix = "_2sd") %>%
std(life_satisfaction, method = "mad", suffix = "_mad")
survey_data_methods %>%
describe(life_satisfaction_sd, life_satisfaction_2sd, life_satisfaction_mad,
show = c("mean", "sd"))
#>
#> Descriptive Statistics
#> ----------------------
#>
#> ---------------------------------------------------
#> Variable Mean SD N Missing
#> ---------------------------------------------------
#> life_satisfaction_sd 0.000 1.000 2421 79
#> life_satisfaction_2sd 0.000 0.500 2421 79
#> life_satisfaction_mad -0.251 0.778 2421 79
#> ----------------------------------------------------
"sd"(default): Classic z-standardization (\frac{x - \bar{x}}{SD}) -
"2sd": Gelman’s (2008) recommendation — divides by 2 SD, making coefficients comparable to untransformed binary predictors -
"mad": Robust standardization using median and MAD (resistant to outliers) -
"gmd": Standardization using the Gini Mean Difference
Weighted Standardization
survey_data <- survey_data %>%
std(income, weights = sampling_weight, suffix = "_wstd")
survey_data %>%
describe(income_wstd, show = c("mean", "sd"))
#>
#> Descriptive Statistics
#> ----------------------
#>
#> ----------------------------------------
#> Variable Mean SD N Missing
#> ----------------------------------------
#> income_wstd 0.008 1.006 2186 314
#> ----------------------------------------Centering with center()
center() subtracts the mean from each value, shifting
the distribution so the mean is zero while preserving the original
scale.
Grand-Mean Centering
survey_data <- survey_data %>%
center(age, income, suffix = "_c")
survey_data %>%
describe(age_c, income_c, show = c("mean", "sd", "min", "max"))
#>
#> Descriptive Statistics
#> ----------------------
#>
#> ----------------------------------------------------
#> Variable Mean SD Min Max N Missing
#> ----------------------------------------------------
#> age_c 0.000 1.000 -1.917 2.618 2500 0
#> income_c 0.000 1.000 -2.062 2.963 2186 314
#> ----------------------------------------------------Group-Mean Centering
Center within groups — each observation is expressed as a deviation from its group mean:
survey_data <- survey_data %>%
group_by(region) %>%
center(income, suffix = "_gc") %>%
ungroup()
# Group means are now zero within each region
survey_data %>%
group_by(region) %>%
describe(income_gc, show = c("mean", "sd"))
#>
#> Descriptive Statistics
#> ----------------------
#>
#> Group: region = East
#> --------------------
#>
#> -------------------------------------
#> Variable Mean SD N Missing
#> -------------------------------------
#> income_gc 0.000 0.968 429 56
#> -------------------------------------
#>
#> Group: region = West
#> --------------------
#>
#> --------------------------------------
#> Variable Mean SD N Missing
#> --------------------------------------
#> income_gc 0.000 1.008 1757 258
#> --------------------------------------Row Operations
Row operations compute values across columns for each respondent — essential for creating scale scores from multiple survey items.
Row Means
row_means() computes the arithmetic mean across selected
variables for each row:
survey_data <- survey_data %>%
mutate(m_trust = row_means(., trust_government, trust_media, trust_science))
survey_data %>%
describe(m_trust)
#>
#> Descriptive Statistics
#> ----------------------
#>
#> ---------------------------------------------------------------------
#> Variable Mean Median SD Range IQR Skewness N Missing
#> ---------------------------------------------------------------------
#> m_trust 2.915 3.000 0.700 4.000 1.000 0.015 2500 0
#> ---------------------------------------------------------------------Using tidyselect
survey_data <- survey_data %>%
mutate(m_trust2 = row_means(., starts_with("trust")))Using pick()
The pick() function works with both %>%
and |>:
Minimum Valid Items
Require a minimum number of non-missing items per row. This matches
SPSS MEAN.2() syntax:
survey_data <- survey_data %>%
mutate(m_trust_strict = row_means(
., trust_government, trust_media, trust_science,
min_valid = 2
))If a respondent answered fewer than 2 of the 3 items, they receive
NA instead of a potentially unreliable score.
Row Sums
row_sums() works like row_means() but
returns the total:
survey_data <- survey_data %>%
mutate(trust_total = row_sums(., trust_government, trust_media, trust_science))
survey_data %>%
describe(trust_total)
#>
#> Descriptive Statistics
#> ----------------------
#>
#> -------------------------------------------------------------------------
#> Variable Mean Median SD Range IQR Skewness N Missing
#> -------------------------------------------------------------------------
#> trust_total 8.282 8.000 2.170 13.000 3.000 -0.164 2500 0
#> -------------------------------------------------------------------------Row Count
row_count() counts how many times a specific value
appears in each row:
# How many trust items did each person rate as 5 (highest)?
survey_data <- survey_data %>%
mutate(n_high_trust = row_count(
., trust_government, trust_media, trust_science,
count = 5
))
frequency(survey_data, n_high_trust)
#>
#> Frequency Analysis Results
#> --------------------------
#>
#> n_high_trust
#> # total N=2500 valid N=2500 mean=0.29 sd=0.49 skewness=1.39
#>
#> +-------+------+--------+---------+--------+
#> | Value | N | Raw % | Valid % | Cum. % |
#> +-------+------+--------+---------+--------+
#> | 0 | 1829 | 73.16 | 73.16 | 73.16 |
#> | 1 | 628 | 25.12 | 25.12 | 98.28 |
#> | 2 | 43 | 1.72 | 1.72 | 100.00 |
#> +-------+------+--------+---------+--------+
#> | Total | 2500 | 100.00 | 100.00 | |
#> +-------+------+--------+---------+--------+POMPS Transformation
pomps() transforms scores to a Percent of Maximum
Possible Scores scale (0–100). This makes scores from different scales
directly comparable:
survey_data <- survey_data %>%
mutate(trust_pomps = pomps(m_trust, scale_min = 1, scale_max = 5))
survey_data %>%
describe(trust_pomps)
#>
#> Descriptive Statistics
#> ----------------------
#>
#> -----------------------------------------------------------------------------
#> Variable Mean Median SD Range IQR Skewness N Missing
#> -----------------------------------------------------------------------------
#> trust_pomps 47.885 50.000 17.500 100.000 25.000 0.015 2500 0
#> -----------------------------------------------------------------------------A score of 0 means the respondent chose the minimum on every item; 100 means the maximum on every item.
Always specify scale_min and scale_max
based on the theoretical scale range, not the observed range. This
ensures scores are comparable across samples.
Complete Example
A typical data transformation workflow before analysis:
data(survey_data) # fresh copy
# 1. Recode: create age groups and reverse-code an item
survey_data <- rec(survey_data, age,
rules = "18:29=1 [Young]; 30:49=2 [Middle]; 50:99=3 [Older]",
suffix = "_group", as_factor = TRUE)
survey_data <- rec(survey_data, trust_government,
rules = "rev(1, 5)", suffix = "_rev")
# 2. Create scale score
survey_data <- survey_data %>%
mutate(m_trust = row_means(., trust_government, trust_media, trust_science,
min_valid = 2))
# 3. Standardize for regression
survey_data <- survey_data %>%
std(age, income, suffix = "_z")
# 4. Use in analysis
survey_data %>%
t_test(m_trust, group = gender, weights = sampling_weight)
#> t-Test: m_trust by gender [Weighted]
#> t(2457.6) = -2.362, p = 0.018 *, g = -0.095 (negligible), N = 2499
#> Use summary() for detailed output.
survey_data %>%
linear_regression(life_satisfaction ~ age_z + income_z + m_trust,
weights = sampling_weight)
#> Linear Regression: life_satisfaction ~ age_z + income_z + m_trust [Weighted]
#> R2 = 0.201, adj.R2 = 0.200, F(3, 2109) = 177.02, p < 0.001 ***, N = 2113
#> Use summary() for detailed output.Practical Tips
Use
rec()for survey-specific recoding. The string syntax is more readable than nestedifelse()orcase_when()for typical survey transformations.Always specify
min_validforrow_means(). Without it, a respondent who answered only 1 out of 10 items gets a scale score based on a single response. Settingmin_validto half the number of items is a common rule of thumb.Center variables before regression. Centering makes the intercept interpretable as the expected value at the mean of all predictors. Group-mean centering separates within-group and between-group effects.
Use
std(method = "2sd")for mixed models. Gelman (2008) recommends dividing by 2 SD so that standardized coefficients for continuous predictors are comparable to those for binary predictors.Always use theoretical min/max in
pomps(). Using observed min/max makes scores sample-dependent and non-comparable.
Summary
-
rec()recodes variables with a concise string syntax (ranges, reverse, dichotomize, inline labels) -
to_dummy()creates binary indicator variables for regression -
std()z-standardizes with four methods (sd, 2sd, mad, gmd) and weight/group support -
center()mean-centers variables (grand-mean or group-mean) -
row_means(),row_sums(),row_count()compute values across columns per row -
pomps()transforms scores to a comparable 0–100 scale
Next Steps
- Explore your transformed data — see
vignette("descriptive-statistics") - Build and validate scales — see
vignette("scale-analysis") - Test group differences — see
vignette("hypothesis-testing") - Predict outcomes with regression — see
vignette("regression-analysis")