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spicy: frequency tables, cross-tabulations, and summary tables in R spicy website

CRAN status r-universe R-CMD-check R-hub Codecov Project Status: Active MIT License DOI CRAN downloads

spicy is an R package for frequency tables, cross-tabulations, association measures, summary tables, and labelled survey data workflows.

What is spicy?

spicy helps you explore categorical and labelled data in R with readable, console-first outputs. It is designed for survey research, descriptive statistics, and reporting workflows, with tools for frequency tables, cross-tabulations with chi-squared tests and effect sizes, categorical and continuous summary tables, variable inspection, and codebooks.

With spicy, you can:

Works with labelled, factor, ordered, Date, POSIXct, and other common variable types. For a full introduction, see Getting started with spicy.

Installation

Install the current CRAN release:

install.packages("spicy")

Install the latest build from r-universe:

install.packages("spicy", repos = c("https://amaltawfik.r-universe.dev", "https://cloud.r-project.org"))

This may be newer than the current CRAN release.

Install the development version from GitHub with pak:

# install.packages("pak")
pak::pak("amaltawfik/spicy")

Quick tour

The examples below use the bundled sochealth dataset.

Inspect variables

varlist demo with labelled data

varlist(sochealth, tbl = TRUE)
#> # A tibble: 24 × 7
#>    Variable          Label                 Values Class N_distinct N_valid   NAs
#>    <chr>             <chr>                 <chr>  <chr>      <int>   <int> <int>
#>  1 sex               Sex                   Femal… fact…          2    1200     0
#>  2 age               Age (years)           25, 2… nume…         51    1200     0
#>  3 age_group         Age group             25-34… orde…          4    1200     0
#>  4 education         Highest education le… Lower… orde…          3    1200     0
#>  5 social_class      Subjective social cl… Lower… orde…          5    1200     0
#>  6 region            Region of residence   Centr… fact…          6    1200     0
#>  7 employment_status Employment status     Emplo… fact…          4    1200     0
#>  8 income_group      Household income gro… Low, … orde…          4    1182    18
#>  9 income            Monthly household in… 1000,… nume…       1052    1200     0
#> 10 smoking           Current smoker        No, Y… fact…          2    1175    25
#> # ℹ 14 more rows

See Explore variables and build codebooks in R for more on varlist(), vl(), and code_book().

Frequency tables and cross-tabulations

freq(sochealth, income_group)
#> Frequency table: income_group
#> 
#>  Category │ Values        Freq.  Percent  Valid Percent 
#> ──────────┼─────────────────────────────────────────────
#>  Valid    │ Low             247     20.6           20.9 
#>           │ Lower middle    388     32.3           32.8 
#>           │ Upper middle    328     27.3           27.7 
#>           │ High            219     18.2           18.5 
#>  Missing  │ NA               18      1.5                
#> ──────────┼─────────────────────────────────────────────
#>  Total    │                1200    100.0          100.0 
#> 
#> Label: Household income group
#> Class: ordered, factor
#> Data: sochealth

cross_tab(sochealth, smoking, education, percent = "col")
#> Crosstable: smoking x education (Column %)
#> 
#>  Values      │      Lower secondary       Upper secondary       Tertiary 
#> ─────────────┼───────────────────────────────────────────────────────────
#>  No          │                 69.6                  78.7           84.9 
#>  Yes         │                 30.4                  21.3           15.1 
#> ─────────────┼───────────────────────────────────────────────────────────
#>  Total       │                100.0                 100.0          100.0 
#>  N           │                  257                   527            391 
#> 
#>  Values      │      Total 
#> ─────────────┼────────────
#>  No          │       78.8 
#>  Yes         │       21.2 
#> ─────────────┼────────────
#>  Total       │      100.0 
#>  N           │       1175 
#> 
#> Chi-2(2) = 21.6, p < 0.001
#> Cramer's V = 0.14

See Frequency tables and cross-tabulations in R for freq(), cross_tab(), percentages, weights, and tests.

Association measures

tbl <- xtabs(~ self_rated_health + education, data = sochealth)

# Quick scalar estimate
cramer_v(tbl)
#> [1] 0.1761697

# Detailed result with CI and p-value
cramer_v(tbl, detail = TRUE)
#> Estimate  CI lower  CI upper        p
#>    0.176     0.120     0.231  < 0.001

See Cramer’s V, Phi, and association measures in R for a guide on choosing the right measure.

Summary tables

table_categorical(
  sochealth,
  select = c(smoking, physical_activity),
  labels = c("Current smoker", "Physical activity")
)
#> Categorical table
#> 
#>  Variable               │        n          % 
#> ────────────────────────┼─────────────────────
#>  Current smoker         │                     
#>    No                   │      926       78.8 
#>    Yes                  │      249       21.2 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Physical activity      │                     
#>    No                   │      650       54.2 
#>    Yes                  │      550       45.8
table_categorical(
  sochealth,
  select = c(smoking, physical_activity),
  by = education,
  labels = c("Current smoker", "Physical activity")
)
#> Categorical table by education
#> 
#>  Variable          │ Lower secondary n  Lower secondary %  Upper secondary n 
#> ───────────────────┼─────────────────────────────────────────────────────────
#>  Current smoker    │                                                         
#>    No              │               179               69.6                415 
#>    Yes             │                78               30.4                112 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Physical activity │                                                         
#>    No              │               177               67.8                310 
#>    Yes             │                84               32.2                229 
#> 
#>  Variable          │ Upper secondary %  Tertiary n  Tertiary %  Total n 
#> ───────────────────┼────────────────────────────────────────────────────
#>  Current smoker    │                                                    
#>    No              │              78.7         332        84.9      926 
#>    Yes             │              21.3          59        15.1      249 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Physical activity │                                                    
#>    No              │              57.5         163        40.8      650 
#>    Yes             │              42.5         237        59.2      550 
#> 
#>  Variable          │ Total %       p  Cramer's V 
#> ───────────────────┼─────────────────────────────
#>  Current smoker    │          < .001         .14 
#>    No              │    78.8                     
#>    Yes             │    21.2                     
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Physical activity │          < .001         .21 
#>    No              │    54.2                     
#>    Yes             │    45.8
table_continuous(
  sochealth,
  select = c(bmi, life_sat_health)
)
#> Descriptive statistics
#> 
#>  Variable                       │   M     SD    Min    Max   95% CI LL 
#> ────────────────────────────────┼──────────────────────────────────────
#>  Body mass index                │ 25.93  3.72  16.00  38.90    25.72   
#>  Satisfaction with health (1-5) │ 3.55   1.25  1.00   5.00     3.48    
#> 
#>  Variable                       │ 95% CI UL     n 
#> ────────────────────────────────┼─────────────────
#>  Body mass index                │   26.14    1188 
#>  Satisfaction with health (1-5) │   3.62     1192
table_continuous(
  sochealth,
  select = c(bmi, life_sat_health),
  by = education
)
#> Descriptive statistics
#> 
#>  Variable                       │ Group              M     SD    Min    Max  
#> ────────────────────────────────┼────────────────────────────────────────────
#>  Body mass index                │ Lower secondary  28.09  3.47  18.20  38.90 
#>                                 │ Upper secondary  26.02  3.43  16.00  37.10 
#>                                 │ Tertiary         24.39  3.52  16.00  33.00 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Satisfaction with health (1-5) │ Lower secondary  2.71   1.20  1.00   5.00  
#>                                 │ Upper secondary  3.53   1.19  1.00   5.00  
#>                                 │ Tertiary         4.11   1.04  1.00   5.00  
#> 
#>  Variable                       │ Group            95% CI LL  95% CI UL    n 
#> ────────────────────────────────┼────────────────────────────────────────────
#>  Body mass index                │ Lower secondary    27.66      28.51    260 
#>                                 │ Upper secondary    25.73      26.31    534 
#>                                 │ Tertiary           24.04      24.74    394 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Satisfaction with health (1-5) │ Lower secondary    2.57       2.86     259 
#>                                 │ Upper secondary    3.43       3.63     534 
#>                                 │ Tertiary           4.01       4.21     399

See Categorical summary tables in R for categorical summaries, Continuous summary tables in R for continuous summaries and group comparisons, and Summary tables for APA-style reporting for an overview of summary tables.

Row-wise summaries

df <- data.frame(
  x1 = c(10, NA, 30, 40, 50),
  x2 = c(5, NA, 15, NA, 25),
  x3 = c(NA, 30, 20, 50, 10)
)

mean_n(df)
#> [1]       NA       NA 21.66667       NA 28.33333
sum_n(df, min_valid = 2)
#> [1] 15 NA 65 90 85
count_n(df, special = "NA")
#> [1] 1 2 0 1 0

See Getting started with spicy for a longer workflow using mean_n(), sum_n(), and count_n().

Label extraction

# LimeSurvey-style headers: "code. label"
df <- tibble::tibble(
  "age. Age of respondent" = c(25, 30),
  "score. Total score" = c(12, 14)
)
out <- label_from_names(df)
labelled::var_label(out)
#> $age
#> [1] "Age of respondent"
#> 
#> $score
#> [1] "Total score"

See Explore variables and build codebooks in R for more on label_from_names(), varlist(), and code_book().

Learn by task

If you are looking for a specific workflow, start with these vignettes:

Key reference pages:

Citation

To cite spicy in a publication or teaching material:

License

MIT. See LICENSE for details.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.