Descriptive Statistics and Frequencies

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

Overview

Before running any statistical tests, you should understand your data. Descriptive statistics summarize what is typical, what is unusual, and how responses are distributed.

mariposa provides four functions for data exploration:

Function	Best for
`describe()`	Numeric variables — means, medians, spread
`frequency()`	Categorical variables — counts and percentages
`crosstab()`	Relationships between two categorical variables
`codebook()`	Full data dictionary with types, labels, and values

All four support survey weights and grouped analysis.

Summary Statistics with describe()

Basic Usage

survey_data %>%
  describe(age)
#> 
#> Descriptive Statistics
#> ----------------------
#> 
#>   -------------------------------------------------------------------------
#>   Variable    Mean  Median      SD   Range     IQR  Skewness     N  Missing
#>   -------------------------------------------------------------------------
#>   age       50.550  50.000  16.976  77.000  24.000     0.172  2500        0
#>   -------------------------------------------------------------------------

Multiple Variables

survey_data %>%
  describe(age, income, life_satisfaction)
#> 
#> Descriptive Statistics
#> ----------------------
#> 
#>   --------------------------------------------------------------------------------------------
#>   Variable               Mean    Median        SD     Range       IQR  Skewness     N  Missing
#>   --------------------------------------------------------------------------------------------
#>   age                  50.550    50.000    16.976    77.000    24.000     0.172  2500        0
#>   income             3753.934  3500.000  1432.802  7200.000  1900.000     0.730  2186      314
#>   life_satisfaction     3.628     4.000     1.153     4.000     2.000    -0.501  2421       79
#>   --------------------------------------------------------------------------------------------

With Survey Weights

Add weights for population-representative statistics:

survey_data %>%
  describe(age, income, life_satisfaction, weights = sampling_weight)
#> 
#> Weighted Descriptive Statistics
#> -------------------------------
#> 
#>   --------------------------------------------------------------------------------------------
#>   Variable               Mean    Median        SD     Range       IQR  Skewness     N  Missing
#>   --------------------------------------------------------------------------------------------
#>   age                  50.514    50.000    17.084    77.000    25.000     0.159  2516        0
#>   income             3743.099  3500.000  1423.966  7200.000  1900.000     0.725  2201      315
#>   life_satisfaction     3.625     4.000     1.152     4.000     2.000    -0.499  2437       79
#>   --------------------------------------------------------------------------------------------

Grouped Analysis

Compare statistics across subgroups:

survey_data %>%
  group_by(region) %>%
  describe(income, life_satisfaction, weights = sampling_weight)
#> 
#> Weighted Descriptive Statistics
#> -------------------------------
#> 
#> Group: region = East
#> --------------------
#> 
#>   -------------------------------------------------------------------------------------------
#>   Variable               Mean    Median        SD     Range       IQR  Skewness    N  Missing
#>   -------------------------------------------------------------------------------------------
#>   income             3760.687  3600.000  1388.321  7200.000  1700.000     0.721  449       60
#>   life_satisfaction     3.623     4.000     1.203     4.000     2.000    -0.558  488       21
#>   -------------------------------------------------------------------------------------------
#> 
#> Group: region = West
#> --------------------
#> 
#>   --------------------------------------------------------------------------------------------
#>   Variable               Mean    Median        SD     Range       IQR  Skewness     N  Missing
#>   --------------------------------------------------------------------------------------------
#>   income             3738.586  3500.000  1433.325  7200.000  1900.000     0.727  1751      256
#>   life_satisfaction     3.625     4.000     1.139     4.000     2.000    -0.481  1949       58
#>   --------------------------------------------------------------------------------------------

Choosing Statistics

The show argument controls which statistics are displayed:

# Just the essentials
survey_data %>%
  describe(age, income, show = c("mean", "sd", "range"))
#> 
#> Descriptive Statistics
#> ----------------------
#> 
#>   -----------------------------------------------------
#>   Variable      Mean        SD     Range     N  Missing
#>   -----------------------------------------------------
#>   age         50.550    16.976    77.000  2500        0
#>   income    3753.934  1432.802  7200.000  2186      314
#>   -----------------------------------------------------

# Everything available
survey_data %>%
  describe(age, show = "all")
#> 
#> Descriptive Statistics
#> ----------------------
#> 
#>   --------------------------------------------------------------------------------------------------------------------------------------------
#>   Variable    Mean  Median      SD     SE     Min     Max   Range     IQR  Skewness  Kurtosis  Variance    Mode     Q25     Q75     N  Missing
#>   --------------------------------------------------------------------------------------------------------------------------------------------
#>   age       50.550  50.000  16.976  0.340  18.000  95.000  77.000  24.000     0.172    -0.364   288.185  18.000  38.000  62.000  2500        0
#>   --------------------------------------------------------------------------------------------------------------------------------------------

Understanding the Output

n: Number of valid (non-missing) cases
mean (\(\bar{x}\)): The arithmetic average
sd: Standard deviation — how spread out the values are
median: The middle value (50th percentile)
min / max: The range of observed values
skewness: Distribution symmetry. Values near 0 indicate symmetry; values beyond \(\pm 1\) indicate notable skew
kurtosis: Tail heaviness. Values near 0 indicate a normal-like shape

Frequency Tables with frequency()

Basic Frequency

survey_data %>%
  frequency(education)
#> 
#> Frequency Analysis Results
#> --------------------------
#> 
#> education (Highest educational attainment)
#> # total N=2500 valid N=2500
#> 
#> +------------------------+------+--------+---------+--------+
#> | Value                  |    N |  Raw % | Valid % | Cum. % |
#> +------------------------+------+--------+---------+--------+
#> | Basic Secondary        |  841 |  33.64 |   33.64 |  33.64 |
#> | Intermediate Secondary |  629 |  25.16 |   25.16 |  58.80 |
#> | Academic Secondary     |  631 |  25.24 |   25.24 |  84.04 |
#> | University             |  399 |  15.96 |   15.96 | 100.00 |
#> +------------------------+------+--------+---------+--------+
#> | Total                  | 2500 | 100.00 |  100.00 |        |
#> +------------------------+------+--------+---------+--------+

Understanding the Output

Frequency: Number of respondents in each category
Percent: Percentage of all cases, including missing
Valid Percent: Percentage of non-missing cases only
Cumulative Percent: Running total of valid percentages

Use Valid Percent when missing values are truly missing (e.g., skip patterns). Use Percent when non-response is itself meaningful.

Weighted Frequencies

survey_data %>%
  frequency(education, weights = sampling_weight)
#> 
#> Weighted Frequency Analysis Results
#> -----------------------------------
#> 
#> education (Highest educational attainment)
#> # total N=2516 valid N=2516
#> 
#> +------------------------+------+--------+---------+--------+
#> | Value                  |    N |  Raw % | Valid % | Cum. % |
#> +------------------------+------+--------+---------+--------+
#> | Basic Secondary        |  848 |  33.71 |   33.71 |  33.71 |
#> | Intermediate Secondary |  641 |  25.47 |   25.47 |  59.18 |
#> | Academic Secondary     |  642 |  25.51 |   25.51 |  84.69 |
#> | University             |  385 |  15.31 |   15.31 | 100.00 |
#> +------------------------+------+--------+---------+--------+
#> | Total                  | 2516 | 100.00 |  100.00 |        |
#> +------------------------+------+--------+---------+--------+

Multiple Variables

survey_data %>%
  frequency(education, employment, region, weights = sampling_weight)
#> 
#> Weighted Frequency Analysis Results
#> -----------------------------------
#> 
#> education (Highest educational attainment)
#> # total N=2516 valid N=2516
#> 
#> +------------------------+------+--------+---------+--------+
#> | Value                  |    N |  Raw % | Valid % | Cum. % |
#> +------------------------+------+--------+---------+--------+
#> | Basic Secondary        |  848 |  33.71 |   33.71 |  33.71 |
#> | Intermediate Secondary |  641 |  25.47 |   25.47 |  59.18 |
#> | Academic Secondary     |  642 |  25.51 |   25.51 |  84.69 |
#> | University             |  385 |  15.31 |   15.31 | 100.00 |
#> +------------------------+------+--------+---------+--------+
#> | Total                  | 2516 | 100.00 |  100.00 |        |
#> +------------------------+------+--------+---------+--------+
#> 
#> 
#> employment (Employment status)
#> # total N=2516 valid N=2516
#> 
#> +------------+------+--------+---------+--------+
#> | Value      |    N |  Raw % | Valid % | Cum. % |
#> +------------+------+--------+---------+--------+
#> | Student    |   80 |   3.18 |    3.18 |   3.18 |
#> | Employed   | 1603 |  63.71 |   63.71 |  66.89 |
#> | Unemployed |  184 |   7.32 |    7.32 |  74.21 |
#> | Retired    |  534 |  21.21 |   21.21 |  95.41 |
#> | Other      |  115 |   4.59 |    4.59 | 100.00 |
#> +------------+------+--------+---------+--------+
#> | Total      | 2516 | 100.00 |  100.00 |        |
#> +------------+------+--------+---------+--------+
#> 
#> 
#> region (Region (East/West))
#> # total N=2516 valid N=2516
#> 
#> +-------+------+--------+---------+--------+
#> | Value |    N |  Raw % | Valid % | Cum. % |
#> +-------+------+--------+---------+--------+
#> | East  |  509 |  20.23 |   20.23 |  20.23 |
#> | West  | 2007 |  79.77 |   79.77 | 100.00 |
#> +-------+------+--------+---------+--------+
#> | Total | 2516 | 100.00 |  100.00 |        |
#> +-------+------+--------+---------+--------+

Grouped Frequencies

survey_data %>%
  group_by(gender) %>%
  frequency(education, weights = sampling_weight)
#> 
#> Weighted Frequency Analysis Results
#> -----------------------------------
#> 
#> education (Highest educational attainment)
#> 
#> Group: gender = Male
#> --------------------
#> # total N=1195 valid N=1195
#> 
#> +------------------------+------+--------+---------+--------+
#> | Value                  |    N |  Raw % | Valid % | Cum. % |
#> +------------------------+------+--------+---------+--------+
#> | Basic Secondary        |  402 |  33.61 |   33.61 |  33.61 |
#> | Intermediate Secondary |  291 |  24.35 |   24.35 |  57.96 |
#> | Academic Secondary     |  326 |  27.31 |   27.31 |  85.27 |
#> | University             |  176 |  14.73 |   14.73 | 100.00 |
#> +------------------------+------+--------+---------+--------+
#> | Total                  | 1195 | 100.00 |  100.00 |        |
#> +------------------------+------+--------+---------+--------+
#> 
#> 
#> Group: gender = Female
#> ----------------------
#> # total N=1321 valid N=1321
#> 
#> +------------------------+------+--------+---------+--------+
#> | Value                  |    N |  Raw % | Valid % | Cum. % |
#> +------------------------+------+--------+---------+--------+
#> | Basic Secondary        |  447 |  33.79 |   33.79 |  33.79 |
#> | Intermediate Secondary |  350 |  26.49 |   26.49 |  60.28 |
#> | Academic Secondary     |  316 |  23.89 |   23.89 |  84.17 |
#> | University             |  209 |  15.83 |   15.83 | 100.00 |
#> +------------------------+------+--------+---------+--------+
#> | Total                  | 1321 | 100.00 |  100.00 |        |
#> +------------------------+------+--------+---------+--------+

Cross-Tabulation with crosstab()

Basic Crosstab

Examine the relationship between two categorical variables:

survey_data %>%
  crosstab(education, employment)
#> 
#> Crosstabulation: Highest educational attainment x Employment status
#> -------------------------------------------------------------------
#> - Row variable: education
#> - Column variable: employment
#> - Percentages: Row percentages
#> - N (valid): 2500
#> 
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> |                                |                     Employment status                      |
#> | Highest educational attainment | Student | Employed | Unemployed | Retired | Other |  Total |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Basic Secondary                |       0 |      571 |         65 |     171 |    34 |    841 |
#> |   row %                        |    0.0% |    67.9% |       7.7% |   20.3% |  4.0% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Intermediate Secondary         |       0 |      412 |         51 |     137 |    29 |    629 |
#> |   row %                        |    0.0% |    65.5% |       8.1% |   21.8% |  4.6% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Academic Secondary             |      44 |      366 |         44 |     145 |    32 |    631 |
#> |   row %                        |    7.0% |    58.0% |       7.0% |   23.0% |  5.1% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | University                     |      34 |      251 |         22 |      72 |    20 |    399 |
#> |   row %                        |    8.5% |    62.9% |       5.5% |   18.0% |  5.0% | 100.0% |
#> +================================+=========+==========+============+=========+=======+========+
#> | Total                          |      78 |     1600 |        182 |     525 |   115 |   2500 |
#> |   row %                        |    3.1% |    64.0% |       7.3% |   21.0% |  4.6% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+

Understanding the Output

Count: Number of cases in each cell
Row %: Percentage within each row (sums to 100% across)
Column %: Percentage within each column (sums to 100% down)
Cell %: Percentage of the total sample

Row percentages answer: “Of people with this education level, what proportion has each employment status?”

Column percentages answer: “Of people with this employment status, what proportion has each education level?”

Weighted Crosstabs

survey_data %>%
  crosstab(education, employment, weights = sampling_weight)
#> 
#> Crosstabulation: Highest educational attainment x Employment status
#> -------------------------------------------------------------------
#> - Row variable: education
#> - Column variable: employment
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 2516 (weighted)
#> 
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> |                                |                     Employment status                      |
#> | Highest educational attainment | Student | Employed | Unemployed | Retired | Other |  Total |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Basic Secondary                |       0 |      573 |         66 |     175 |    34 |    848 |
#> |   row %                        |    0.0% |    67.6% |       7.8% |   20.6% |  4.0% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Intermediate Secondary         |       0 |      420 |         52 |     139 |    29 |    640 |
#> |   row %                        |    0.0% |    65.6% |       8.1% |   21.7% |  4.5% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Academic Secondary             |      46 |      370 |         45 |     149 |    33 |    643 |
#> |   row %                        |    7.2% |    57.5% |       7.0% |   23.2% |  5.1% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | University                     |      34 |      240 |         21 |      72 |    20 |    387 |
#> |   row %                        |    8.8% |    62.0% |       5.4% |   18.6% |  5.2% | 100.0% |
#> +================================+=========+==========+============+=========+=======+========+
#> | Total                          |      80 |     1603 |        184 |     535 |   116 |   2518 |
#> |   row %                        |    3.2% |    63.7% |       7.3% |   21.2% |  4.6% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+

Grouped Crosstabs

survey_data %>%
  group_by(region) %>%
  crosstab(education, employment, weights = sampling_weight)
#> 
#> Weighted Grouped Crosstabulation
#> --------------------------------
#> 
#> 
#> Group: region = East
#> --------------------
#> 
#> Crosstabulation: Highest educational attainment x Employment status
#> -------------------------------------------------------------------
#> - Row variable: education
#> - Column variable: employment
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 509 (weighted)
#> 
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> |                                |                     Employment status                      |
#> | Highest educational attainment | Student | Employed | Unemployed | Retired | Other |  Total |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Basic Secondary                |       0 |      112 |         11 |      39 |    12 |    174 |
#> |   row %                        |    0.0% |    64.4% |       6.3% |   22.4% |  6.9% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Intermediate Secondary         |       0 |       83 |         13 |      29 |     4 |    129 |
#> |   row %                        |    0.0% |    64.3% |      10.1% |   22.5% |  3.1% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Academic Secondary             |       6 |       72 |          8 |      35 |     1 |    122 |
#> |   row %                        |    4.9% |    59.0% |       6.6% |   28.7% |  0.8% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | University                     |       5 |       53 |          1 |      19 |     4 |     82 |
#> |   row %                        |    6.1% |    64.6% |       1.2% |   23.2% |  4.9% | 100.0% |
#> +================================+=========+==========+============+=========+=======+========+
#> | Total                          |      11 |      320 |         33 |     122 |    21 |    507 |
#> |   row %                        |    2.2% |    63.1% |       6.5% |   24.1% |  4.1% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> 
#> 
#> Group: region = West
#> --------------------
#> 
#> Crosstabulation: Highest educational attainment x Employment status
#> -------------------------------------------------------------------
#> - Row variable: education
#> - Column variable: employment
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 2007 (weighted)
#> 
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> |                                |                     Employment status                      |
#> | Highest educational attainment | Student | Employed | Unemployed | Retired | Other |  Total |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Basic Secondary                |       0 |      461 |         55 |     136 |    21 |    673 |
#> |   row %                        |    0.0% |    68.5% |       8.2% |   20.2% |  3.1% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Intermediate Secondary         |       0 |      337 |         40 |     110 |    26 |    513 |
#> |   row %                        |    0.0% |    65.7% |       7.8% |   21.4% |  5.1% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Academic Secondary             |      40 |      298 |         37 |     113 |    31 |    519 |
#> |   row %                        |    7.7% |    57.4% |       7.1% |   21.8% |  6.0% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | University                     |      28 |      187 |         20 |      52 |    16 |    303 |
#> |   row %                        |    9.2% |    61.7% |       6.6% |   17.2% |  5.3% | 100.0% |
#> +================================+=========+==========+============+=========+=======+========+
#> | Total                          |      68 |     1283 |        152 |     411 |    94 |   2008 |
#> |   row %                        |    3.4% |    63.9% |       7.6% |   20.5% |  4.7% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+

Multiple Response Sets with multiple_response()

“Check all that apply” questions arrive as a series of 0/1 indicator variables. multiple_response() (SPSS MULT RESPONSE) analyzes them as one set, with the two percentage bases SPSS users expect: percent of responses (sums to 100%) and percent of cases (can sum above 100%, because respondents tick several boxes):

# Example set: institutions a respondent trusts highly (rating 4-5)
trust_set <- survey_data %>%
  mutate(
    gov     = as.integer(trust_government >= 4),
    media   = as.integer(trust_media >= 4),
    science = as.integer(trust_science >= 4)
  )

trust_set %>%
  multiple_response(gov, media, science, weights = sampling_weight)
#> 
#> Weighted Multiple Response Results
#> ----------------------------------
#> - Set: gov, media, science
#> - Counted value: 1
#> - Weights Variable: sampling_weight
#> 
#> Frequencies
#>   ---------------------------------------------
#>   Option   Responses n  Responses %  % of Cases
#>   ---------------------------------------------
#>   gov              586         22.9        23.3
#>   media            477         18.7        18.9
#>   science         1490         58.4        59.2
#>   ---------------------------------------------
#>   Total           2553        100.0       101.5
#>   ---------------------------------------------
#>   Valid cases: 2516 | Total responses: 2553 | Excluded (all missing): 0
#>   % of Cases can sum above 100% (multiple mentions per case).

Cross the set against a demographic with by — mentions and case-based column percentages per group:

trust_set %>%
  multiple_response(gov, media, science, by = gender,
                    weights = sampling_weight)
#> 
#> Weighted Multiple Response Results
#> ----------------------------------
#> - Set: gov, media, science
#> - Counted value: 1
#> - Weights Variable: sampling_weight
#> - By: gender
#> 
#> Frequencies
#>   ---------------------------------------------
#>   Option   Responses n  Responses %  % of Cases
#>   ---------------------------------------------
#>   gov              586         22.9        23.3
#>   media            477         18.7        18.9
#>   science         1490         58.4        59.2
#>   ---------------------------------------------
#>   Total           2553        100.0       101.5
#>   ---------------------------------------------
#>   Valid cases: 2516 | Total responses: 2553 | Excluded (all missing): 0
#>   % of Cases can sum above 100% (multiple mentions per case).
#> 
#> Crosstab: set BY gender (% of cases per column)
#>   -----------------------------------------------------
#>   Option                Male       Female         Total
#>   -----------------------------------------------------
#>   gov            280 (23.4%)  306 (23.2%)   586 (23.3%)
#>   media          214 (17.9%)  262 (19.8%)   477 (18.9%)
#>   science        694 (58.1%)  797 (60.3%)  1490 (59.2%)
#>   -----------------------------------------------------
#>   Total (cases)         1195         1321          2516
#>   -----------------------------------------------------
#>   Percentages and totals are based on cases (respondents).

Data Dictionary with codebook()

For a comprehensive overview of your entire dataset, use codebook():

codebook(survey_data)

This opens an interactive HTML view in the RStudio Viewer showing variable names, types, labels, value distributions, and missing data patterns. It is the fastest way to orient yourself in a new dataset.

Complete Example

A typical descriptive analysis workflow:

# 1. Explore the dataset
find_var(survey_data, "trust|satisfaction")
#>   col              name                                           label
#> 1  10 life_satisfaction Life satisfaction (1=dissatisfied, 5=satisfied)
#> 2  11  trust_government        Trust in government (1=none, 5=complete)
#> 3  12       trust_media             Trust in media (1=none, 5=complete)
#> 4  13     trust_science           Trust in science (1=none, 5=complete)

# 2. Summarize numeric variables
survey_data %>%
  describe(age, income, life_satisfaction,
           weights = sampling_weight)
#> 
#> Weighted Descriptive Statistics
#> -------------------------------
#> 
#>   --------------------------------------------------------------------------------------------
#>   Variable               Mean    Median        SD     Range       IQR  Skewness     N  Missing
#>   --------------------------------------------------------------------------------------------
#>   age                  50.514    50.000    17.084    77.000    25.000     0.159  2516        0
#>   income             3743.099  3500.000  1423.966  7200.000  1900.000     0.725  2201      315
#>   life_satisfaction     3.625     4.000     1.152     4.000     2.000    -0.499  2437       79
#>   --------------------------------------------------------------------------------------------

# 3. Check categorical distributions
survey_data %>%
  frequency(education, employment,
            weights = sampling_weight)
#> 
#> Weighted Frequency Analysis Results
#> -----------------------------------
#> 
#> education (Highest educational attainment)
#> # total N=2516 valid N=2516
#> 
#> +------------------------+------+--------+---------+--------+
#> | Value                  |    N |  Raw % | Valid % | Cum. % |
#> +------------------------+------+--------+---------+--------+
#> | Basic Secondary        |  848 |  33.71 |   33.71 |  33.71 |
#> | Intermediate Secondary |  641 |  25.47 |   25.47 |  59.18 |
#> | Academic Secondary     |  642 |  25.51 |   25.51 |  84.69 |
#> | University             |  385 |  15.31 |   15.31 | 100.00 |
#> +------------------------+------+--------+---------+--------+
#> | Total                  | 2516 | 100.00 |  100.00 |        |
#> +------------------------+------+--------+---------+--------+
#> 
#> 
#> employment (Employment status)
#> # total N=2516 valid N=2516
#> 
#> +------------+------+--------+---------+--------+
#> | Value      |    N |  Raw % | Valid % | Cum. % |
#> +------------+------+--------+---------+--------+
#> | Student    |   80 |   3.18 |    3.18 |   3.18 |
#> | Employed   | 1603 |  63.71 |   63.71 |  66.89 |
#> | Unemployed |  184 |   7.32 |    7.32 |  74.21 |
#> | Retired    |  534 |  21.21 |   21.21 |  95.41 |
#> | Other      |  115 |   4.59 |    4.59 | 100.00 |
#> +------------+------+--------+---------+--------+
#> | Total      | 2516 | 100.00 |  100.00 |        |
#> +------------+------+--------+---------+--------+

# 4. Cross-tabulate key relationships
survey_data %>%
  crosstab(education, employment,
           weights = sampling_weight)
#> 
#> Crosstabulation: Highest educational attainment x Employment status
#> -------------------------------------------------------------------
#> - Row variable: education
#> - Column variable: employment
#> - Percentages: Row percentages
#> - Weights variable: sampling_weight
#> - N (valid): 2516 (weighted)
#> 
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> |                                |                     Employment status                      |
#> | Highest educational attainment | Student | Employed | Unemployed | Retired | Other |  Total |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Basic Secondary                |       0 |      573 |         66 |     175 |    34 |    848 |
#> |   row %                        |    0.0% |    67.6% |       7.8% |   20.6% |  4.0% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Intermediate Secondary         |       0 |      420 |         52 |     139 |    29 |    640 |
#> |   row %                        |    0.0% |    65.6% |       8.1% |   21.7% |  4.5% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | Academic Secondary             |      46 |      370 |         45 |     149 |    33 |    643 |
#> |   row %                        |    7.2% |    57.5% |       7.0% |   23.2% |  5.1% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+
#> | University                     |      34 |      240 |         21 |      72 |    20 |    387 |
#> |   row %                        |    8.8% |    62.0% |       5.4% |   18.6% |  5.2% | 100.0% |
#> +================================+=========+==========+============+=========+=======+========+
#> | Total                          |      80 |     1603 |        184 |     535 |   116 |   2518 |
#> |   row %                        |    3.2% |    63.7% |       7.3% |   21.2% |  4.6% | 100.0% |
#> +--------------------------------+---------+----------+------------+---------+-------+--------+

# 5. Compare across regions
survey_data %>%
  group_by(region) %>%
  describe(income, life_satisfaction,
           weights = sampling_weight)
#> 
#> Weighted Descriptive Statistics
#> -------------------------------
#> 
#> Group: region = East
#> --------------------
#> 
#>   -------------------------------------------------------------------------------------------
#>   Variable               Mean    Median        SD     Range       IQR  Skewness    N  Missing
#>   -------------------------------------------------------------------------------------------
#>   income             3760.687  3600.000  1388.321  7200.000  1700.000     0.721  449       60
#>   life_satisfaction     3.623     4.000     1.203     4.000     2.000    -0.558  488       21
#>   -------------------------------------------------------------------------------------------
#> 
#> Group: region = West
#> --------------------
#> 
#>   --------------------------------------------------------------------------------------------
#>   Variable               Mean    Median        SD     Range       IQR  Skewness     N  Missing
#>   --------------------------------------------------------------------------------------------
#>   income             3738.586  3500.000  1433.325  7200.000  1900.000     0.727  1751      256
#>   life_satisfaction     3.625     4.000     1.139     4.000     2.000    -0.481  1949       58
#>   --------------------------------------------------------------------------------------------

Practical Tips

Start with descriptives before testing. Understanding distributions, ranges, and missing data patterns prevents surprises in later analyses.
Check for impossible values. Negative ages, incomes above plausible limits, or out-of-range Likert responses indicate data quality issues.
Look at skewness. Values beyond \(\pm 1\) suggest the distribution departs notably from normality. This affects the choice between parametric and non-parametric tests.
Always use weights when available. Unweighted statistics describe your sample; weighted statistics estimate the population.
Consider your audience. For technical readers, include SD, skewness, and kurtosis. For general audiences, focus on means, medians, and percentages.

Summary

describe() summarizes numeric variables (mean, SD, median, range, skewness)
frequency() counts categories with percent, valid percent, and cumulative percent
crosstab() shows the joint distribution of two categorical variables
codebook() provides an interactive HTML data dictionary
All four functions support survey weights and group_by()

Next Steps

Test for significant differences — see vignette("hypothesis-testing")
Measure relationships between variables — see vignette("correlation-analysis")
Learn about survey weights — see vignette("survey-weights")