Data Transformation

library(mariposa)
library(dplyr)
data(survey_data)

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        5

Dichotomizing

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:

# Split at the mean
survey_data <- rec(survey_data, income,
  rules = "mean", suffix = "_mean_split"
)

# Split at a fixed cut-point
survey_data <- rec(survey_data, income,
  rules = "dicho(3000)", suffix = "_custom"
)

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.

Basic Usage

# Create dummy variables for region
dummies <- to_dummy(survey_data, region, append = FALSE)
head(dummies)
#> # A tibble: 6 × 2
#>   region_East region_West
#>         <int>       <int>
#> 1           1           0
#> 2           0           1
#> 3           0           1
#> 4           0           1
#> 5           0           1
#> 6           1           0

Label-Based Column Names

Use suffix = "label" to name columns after the value labels instead of numeric codes:

dummies <- to_dummy(survey_data, gender, suffix = "label", append = FALSE)
head(dummies)
#> # A tibble: 6 × 2
#>   gender_Male gender_Female
#>         <int>         <int>
#> 1           0             1
#> 2           1             0
#> 3           1             0
#> 4           0             1
#> 5           1             0
#> 6           0             1

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 frame

Standardization 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
#>   ----------------------------------------

Group-Wise Standardization

Standardize within subgroups:

survey_data <- survey_data %>%
  group_by(region) %>%
  std(income, suffix = "_gstd") %>%
  ungroup()

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
#>   --------------------------------------

Weighted Centering

survey_data <- survey_data %>%
  center(age, weights = sampling_weight, suffix = "_wc")

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 |>:

survey_data <- survey_data %>%
  mutate(m_trust3 = row_means(
    pick(trust_government, trust_media, trust_science)
  ))

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.

# Transform multiple variables at once
survey_data <- survey_data %>%
  mutate(across(
    c(trust_government, trust_media, trust_science),
    ~ pomps(.x, scale_min = 1, scale_max = 5),
    .names = "{.col}_pomps"
  ))

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 nested ifelse() or case_when() for typical survey transformations.
Always specify min_valid for row_means(). Without it, a respondent who answered only 1 out of 10 items gets a scale score based on a single response. Setting min_valid to 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")