Date: 2025-10-15

# Examples

#### 2025-10-15

### Introduction

First, we apply labels on the well-known `mtcars` dataset:

    library(expss)
    data(mtcars)
    mtcars = apply_labels(mtcars,
                          mpg = "Miles/(US) gallon",
                          cyl = "Number of cylinders",
                          disp = "Displacement (cu.in.)",
                          hp = "Gross horsepower",
                          drat = "Rear axle ratio",
                          wt = "Weight (1000 lbs)",
                          qsec = "1/4 mile time",
                          vs = "Engine",
                          vs = c("V-engine" = 0,
                                 "Straight engine" = 1),
                          am = "Transmission",
                          am = c("Automatic" = 0,
                                 "Manual"=1),
                          gear = "Number of forward gears",
                          carb = "Number of carburetors"
    )

Table construction consists of at least of three functions chained with magrittr pipe operator: `%>%`. At first we need to specify variables for which statistics will be computed with `tab_cells`. Secondary, we calculate statistics with one of `tab_stat_*` functions. And last, we finalize table creation with `tab_pivot`: `dataset %>% tab_cells(variable) %>% tab_stat_cases() %>% tab_pivot()`. We can split our statistics by columns with `tab_cols` or by rows with `tab_rows`. After that we can sort table with `tab_sort_asc`, drop empty rows/columns with `drop_rc` and transpose with `tab_transpose`. Generally, resulting table is just a data.frame so we can use arbitrary operations on it. Statistic is always calculated on the last cells, column/row variables, weight, missing values and subgroup. To define new cell/column/row variables we can call appropriate function one more time. `tab_pivot` defines how we combine different statistics and where statistic labels will appear - inside/outside rows/columns.

### Simple column percent

    mtcars %>%
        tab_cells(cyl) %>%
        tab_cols(vs) %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        tab_caption("Simple column percent")

Simple column percent
| Engine
--- | ---
| V-engine | Straight engine
Number of cylinders
4 | 5.6 | 71.4
6 | 16.7 | 28.6
8 | 77.8 |
#Total cases | 18 | 14

### Split by columns and rows

    mtcars %>%
        tab_cells(cyl) %>%
        tab_cols(vs) %>%
        tab_rows(am) %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        tab_caption("Split by columns and rows")

Split by columns and rows
|  |  | Engine
--- | --- | --- | ---
|  |  |  | V-engine | Straight engine
Transmission
Automatic | Number of cylinders | 4 |  |  | 42.9
|  | 6 |  |  | 57.1
|  | 8 |  | 100.0 |
|  | #Total cases |  | 12 | 7
Manual | Number of cylinders | 4 |  | 16.7 | 100.0
|  | 6 |  | 50.0 |
|  | 8 |  | 33.3 |
|  | #Total cases |  | 6 | 7

### Multiple banners, table is sorted by total

    mtcars %>%
        tab_cells(cyl) %>%
        tab_cols(total(), vs, am) %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        tab_sort_desc() %>%
        tab_caption("Multiple banners, table is sorted by total")

Multiple banners, table is sorted by total
| #Total |  | Engine |  | Transmission
--- | --- | --- | --- | --- | ---
|  |  | V-engine | Straight engine |  | Automatic | Manual
Number of cylinders
8 | 43.8 |  | 77.8 |  |  | 63.2 | 15.4
4 | 34.4 |  | 5.6 | 71.4 |  | 15.8 | 61.5
6 | 21.9 |  | 16.7 | 28.6 |  | 21.1 | 23.1
#Total cases | 32 |  | 18 | 14 |  | 19 | 13

### Nested banners

    mtcars %>%
        tab_cells(cyl) %>%
        tab_cols(total(), vs %nest% am) %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        tab_caption("Nested banners")

Nested banners
| #Total |  | Engine
--- | --- | --- | ---
|  |  | V-engine |  | Straight engine
|  |  | Transmission |  | Transmission
|  |  | Automatic | Manual |  | Automatic | Manual
Number of cylinders
4 | 34.4 |  |  | 16.7 |  | 42.9 | 100
6 | 21.9 |  |  | 50.0 |  | 57.1 |
8 | 43.8 |  | 100 | 33.3 |  |  |
#Total cases | 32 |  | 12 | 6 |  | 7 | 7

### Multiple nested banners

    mtcars %>%
        tab_cells(carb) %>%
        tab_cols(total(), list(cyl, vs) %nest% am) %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        tab_caption("Multiple nested banners")

Multiple nested banners
| #Total |  | Number of cylinders |  | Engine
--- | --- | --- | --- | --- | ---
|  |  | 4 |  | 6 |  | 8 |  | V-engine |  | Straight engine
|  |  | Transmission |  | Transmission |  | Transmission |  | Transmission |  | Transmission
|  |  | Automatic | Manual |  | Automatic | Manual |  | Automatic | Manual |  | Automatic | Manual |  | Automatic | Manual
Number of carburetors
1 | 21.9 |  | 33.3 | 50 |  | 50 |  |  |  |  |  |  |  |  | 42.9 | 57.1
2 | 31.2 |  | 66.7 | 50 |  |  |  |  | 33.3 |  |  | 33.3 | 16.7 |  | 28.6 | 42.9
3 | 9.4 |  |  |  |  |  |  |  | 25.0 |  |  | 25.0 |  |  |  |
4 | 31.2 |  |  |  |  | 50 | 66.7 |  | 41.7 | 50 |  | 41.7 | 50.0 |  | 28.6 |
6 | 3.1 |  |  |  |  |  | 33.3 |  |  |  |  |  | 16.7 |  |  |
8 | 3.1 |  |  |  |  |  |  |  |  | 50 |  |  | 16.7 |  |  |
#Total cases | 32 |  | 3 | 8 |  | 4 | 3 |  | 12 | 2 |  | 12 | 6 |  | 7 | 7

### Multiple variable and multiple summary statistics

    mtcars %>%
        tab_cells(mpg, disp, hp, wt, qsec) %>%
        tab_cols(total(), am) %>%
        tab_stat_fun(Mean = w_mean, "Std. dev." = w_sd, "Valid N" = w_n) %>%
        tab_pivot() %>%
        tab_caption("Multiple variable and multiple summary statistics")

Multiple variable and multiple summary statistics
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic | Manual
Miles/(US) gallon
Mean | 20.1 |  | 17.1 | 24.4
Std. dev. | 6.0 |  | 3.8 | 6.2
Valid N | 32.0 |  | 19.0 | 13.0
Displacement (cu.in.)
Mean | 230.7 |  | 290.4 | 143.5
Std. dev. | 123.9 |  | 110.2 | 87.2
Valid N | 32.0 |  | 19.0 | 13.0
Gross horsepower
Mean | 146.7 |  | 160.3 | 126.8
Std. dev. | 68.6 |  | 53.9 | 84.1
Valid N | 32.0 |  | 19.0 | 13.0
Weight (1000 lbs)
Mean | 3.2 |  | 3.8 | 2.4
Std. dev. | 1.0 |  | 0.8 | 0.6
Valid N | 32.0 |  | 19.0 | 13.0
1/4 mile time
Mean | 17.8 |  | 18.2 | 17.4
Std. dev. | 1.8 |  | 1.8 | 1.8
Valid N | 32.0 |  | 19.0 | 13.0

### Multiple variable and multiple summary statistics - statistic lables in columns

    mtcars %>%
        tab_cells(mpg, disp, hp, wt, qsec) %>%
        tab_cols(total(), am) %>%
        tab_stat_fun(Mean = w_mean, "Valid N" = w_n, method = list) %>%
        tab_pivot() %>%
        tab_caption("Multiple variable and multiple summary statistics - statistic lables in columns")

Multiple variable and multiple summary statistics - statistic lables in columns
| #Total |  | Transmission
--- | --- | --- | ---
| Mean |  | Valid N |  | Automatic |  | Manual
|  |  |  |  | Mean | Valid N |  | Mean | Valid N
Miles/(US) gallon | 20.1 |  | 32 |  | 17.1 | 19 |  | 24.4 | 13
Displacement (cu.in.) | 230.7 |  | 32 |  | 290.4 | 19 |  | 143.5 | 13
Gross horsepower | 146.7 |  | 32 |  | 160.3 | 19 |  | 126.8 | 13
Weight (1000 lbs) | 3.2 |  | 32 |  | 3.8 | 19 |  | 2.4 | 13
1/4 mile time | 17.8 |  | 32 |  | 18.2 | 19 |  | 17.4 | 13

### Filter dataset and exclude empty columns

    mtcars %>%
        tab_subgroup(am == 0) %>%
        tab_cells(cyl) %>%
        tab_cols(total(), vs %nest% am) %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        drop_empty_columns() %>%
        tab_caption("Filter dataset and exclude empty columns")

Filter dataset and exclude empty columns
| #Total |  | Engine
--- | --- | --- | ---
|  |  | V-engine |  | Straight engine
|  |  | Transmission |  | Transmission
|  |  | Automatic |  | Automatic
Number of cylinders
4 | 15.8 |  |  |  | 42.9
6 | 21.1 |  |  |  | 57.1
8 | 63.2 |  | 100 |  |
#Total cases | 19 |  | 12 |  | 7

### Total at the top of the table

    mtcars %>%
        tab_cells(cyl) %>%
        tab_cols(total(), vs) %>%
        tab_rows(am) %>%
        tab_stat_cpct(total_row_position = "above",
                      total_label = c("number of cases", "row %"),
                      total_statistic = c("u_cases", "u_rpct")) %>%
        tab_pivot() %>%
        tab_caption("Total at the top of the table")

Total at the top of the table
|  |  | #Total |  | Engine
--- | --- | --- | --- | --- | ---
|  |  |  |  |  | V-engine | Straight engine
Transmission
Automatic | Number of cylinders | #number of cases |  | 19 |  | 12 | 7
|  | #row % |  | 100 |  | 63.2 | 36.8
|  | 4 |  | 15.8 |  |  | 42.9
|  | 6 |  | 21.1 |  |  | 57.1
|  | 8 |  | 63.2 |  | 100.0 |
Manual | Number of cylinders | #number of cases |  | 13 |  | 6 | 7
|  | #row % |  | 100 |  | 46.2 | 53.8
|  | 4 |  | 61.5 |  | 16.7 | 100.0
|  | 6 |  | 23.1 |  | 50.0 |
|  | 8 |  | 15.4 |  | 33.3 |

### Three different statistics in each cell - stat. labels in rows

    mtcars %>%
        tab_cells(am) %>%
        tab_cols(total(), vs) %>%
        tab_total_row_position("none") %>%
        tab_stat_cpct(label = "col %") %>%
        tab_stat_rpct(label = "row %") %>%
        tab_stat_tpct(label = "table %") %>%
        tab_pivot(stat_position = "inside_rows") %>%
        tab_caption("Three different statistics in each cell - stat. labels in rows")

Three different statistics in each cell - stat. labels in rows
|  |  | #Total |  | Engine
--- | --- | --- | --- | --- | ---
|  |  |  |  | V-engine | Straight engine
Transmission
Automatic | col % |  | 59.4 |  | 66.7 | 50.0
| row % |  | 100.0 |  | 63.2 | 36.8
| table % |  | 59.4 |  | 37.5 | 21.9
Manual | col % |  | 40.6 |  | 33.3 | 50.0
| row % |  | 100.0 |  | 46.2 | 53.8
| table % |  | 40.6 |  | 18.8 | 21.9

### Three different statistics in each cell - stat. labels in columns

    mtcars %>%
        tab_cells(am) %>%
        tab_cols(total(), vs) %>%
        tab_total_row_position("none") %>%
        tab_stat_cpct(label = "col %") %>%
        tab_stat_rpct(label = "row %") %>%
        tab_stat_tpct(label = "table %") %>%
        tab_pivot(stat_position = "inside_columns") %>%
        tab_caption("Three different statistics in each cell - stat. labels in columns")

Three different statistics in each cell - stat. labels in columns
| #Total |  | Engine
--- | --- | --- | ---
| col % |  | row % |  | table % |  | V-engine |  | Straight engine
|  |  |  |  |  |  | col % | row % | table % |  | col % | row % | table %
Transmission
Automatic | 59.4 |  | 100 |  | 59.4 |  | 66.7 | 63.2 | 37.5 |  | 50 | 36.8 | 21.9
Manual | 40.6 |  | 100 |  | 40.6 |  | 33.3 | 46.2 | 18.8 |  | 50 | 53.8 | 21.9

### Stacked statistics

    mtcars %>%
        tab_cells(cyl) %>%
        tab_cols(total(), am) %>%
        tab_stat_mean() %>%
        tab_stat_se() %>%
        tab_stat_valid_n() %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        tab_caption("Stacked statistics")

Stacked statistics
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic | Manual
Number of cylinders
Mean | 6.2 |  | 6.9 | 5.1
S. E. | 0.3 |  | 0.4 | 0.4
Valid N | 32.0 |  | 19.0 | 13.0
4 | 34.4 |  | 15.8 | 61.5
6 | 21.9 |  | 21.1 | 23.1
8 | 43.8 |  | 63.2 | 15.4
#Total cases | 32 |  | 19 | 13

### Stacked statistics with section headings

    mtcars %>%
        tab_cells(cyl) %>%
        tab_cols(total(), am) %>%
        tab_row_label("#Summary statistics") %>%
        tab_stat_mean() %>%
        tab_stat_se() %>%
        tab_stat_valid_n() %>%
        tab_row_label("#Column percent") %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        tab_caption("Stacked statistics with section headings")

Stacked statistics with section headings
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic | Manual
#Summary statistics
Number of cylinders
Mean | 6.2 |  | 6.9 | 5.1
S. E. | 0.3 |  | 0.4 | 0.4
Valid N | 32.0 |  | 19.0 | 13.0
#Column percent
Number of cylinders
4 | 34.4 |  | 15.8 | 61.5
6 | 21.9 |  | 21.1 | 23.1
8 | 43.8 |  | 63.2 | 15.4
#Total cases | 32 |  | 19 | 13

### Stacked statistics - different statistics for different variables

    mtcars %>%
        tab_cols(total(), am) %>%
        tab_cells(mpg, hp, qsec) %>%
        tab_stat_mean() %>%
        tab_cells(cyl, carb) %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        tab_caption("Stacked statistics - different statistics for different variables")

Stacked statistics - different statistics for different variables
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic | Manual
Miles/(US) gallon
Mean | 20.1 |  | 17.1 | 24.4
Gross horsepower
Mean | 146.7 |  | 160.3 | 126.8
1/4 mile time
Mean | 17.8 |  | 18.2 | 17.4
Number of cylinders
4 | 34.4 |  | 15.8 | 61.5
6 | 21.9 |  | 21.1 | 23.1
8 | 43.8 |  | 63.2 | 15.4
#Total cases | 32 |  | 19 | 13
Number of carburetors
1 | 21.9 |  | 15.8 | 30.8
2 | 31.2 |  | 31.6 | 30.8
3 | 9.4 |  | 15.8 |
4 | 31.2 |  | 36.8 | 23.1
6 | 3.1 |  |  | 7.7
8 | 3.1 |  |  | 7.7
#Total cases | 32 |  | 19 | 13

### Linear regression by groups

    mtcars %>%
        tab_cells(sheet(mpg, disp, hp, wt, qsec)) %>%
        tab_cols(total(), am) %>%
        tab_stat_fun_df(
            function(x){
                frm = reformulate(".", response = as.name(names(x)[1]))
                model = lm(frm, data = x)
                sheet('Coef.' = coef(model),
                      confint(model)
                )
            }
        ) %>%
        tab_pivot() %>%
        tab_caption("Linear regression by groups")

Linear regression by groups
| #Total |  | Transmission
--- | --- | --- | ---
| Coef. |  | 2.5 % |  | 97.5 % |  | Automatic |  | Manual
|  |  |  |  |  |  | Coef. | 2.5 % | 97.5 % |  | Coef. | 2.5 % | 97.5 %
(Intercept) | 27.3 |  | 9.6 |  | 45.1 |  | 21.8 | -1.9 | 45.5 |  | 13.3 | -21.9 | 48.4
`Displacement (cu.in.)` | 0.0 |  | 0.0 |  | 0.0 |  | 0.0 | 0.0 | 0.0 |  | 0.0 | -0.1 | 0.1
`Gross horsepower` | 0.0 |  | -0.1 |  | 0.0 |  | 0.0 | -0.1 | 0.0 |  | 0.0 | 0.0 | 0.1
`Weight (1000 lbs)` | -4.6 |  | -7.2 |  | -2.0 |  | -2.3 | -5.0 | 0.4 |  | -7.7 | -12.5 | -2.9
`1/4 mile time` | 0.5 |  | -0.4 |  | 1.5 |  | 0.4 | -0.7 | 1.6 |  | 1.6 | -0.2 | 3.4

### Subtotals

    mtcars %>%
        tab_cells(mpg) %>%
        tab_cols(total(), vs) %>%
        tab_rows(subtotal(cyl, 1:2, 3:4, "5 and more" = 5 %thru% hi)) %>%
        tab_stat_mean() %>%
        tab_pivot() %>%
        tab_caption("Subtotals in rows")

Subtotals in rows
|  |  | #Total |  | Engine
--- | --- | --- | --- | --- | ---
|  |  |  |  |  | V-engine | Straight engine
Number of cylinders
1 | Miles/(US) gallon | Mean |  |  |  |  |
2 | Miles/(US) gallon | Mean |  |  |  |  |
TOTAL 1/2 | Miles/(US) gallon | Mean |  |  |  |  |
3 | Miles/(US) gallon | Mean |  |  |  |  |
4 | Miles/(US) gallon | Mean |  | 26.7 |  | 26.0 | 26.7
TOTAL 3/4 | Miles/(US) gallon | Mean |  | 26.7 |  | 26.0 | 26.7
6 | Miles/(US) gallon | Mean |  | 19.7 |  | 20.6 | 19.1
8 | Miles/(US) gallon | Mean |  | 15.1 |  | 15.1 |
5 and more | Miles/(US) gallon | Mean |  | 16.6 |  | 16.1 | 19.1

### Subtotals at the bottom of the table

    mtcars %>%
        tab_cells(mpg, qsec) %>%
        tab_cols(total(), vs) %>%
        tab_rows(subtotal(cyl, 1:2, 3:4, "TOTAL 5 and more" = 5 %thru% hi, position = "bottom")) %>%
        tab_stat_mean() %>%
        tab_pivot() %>%
        tab_caption("Subtotals at the bottom of the table")

Subtotals at the bottom of the table
|  |  | #Total |  | Engine
--- | --- | --- | --- | --- | ---
|  |  |  |  |  | V-engine | Straight engine
Number of cylinders
1 | Miles/(US) gallon | Mean |  |  |  |  |
| 1/4 mile time | Mean |  |  |  |  |
2 | Miles/(US) gallon | Mean |  |  |  |  |
| 1/4 mile time | Mean |  |  |  |  |
3 | Miles/(US) gallon | Mean |  |  |  |  |
| 1/4 mile time | Mean |  |  |  |  |
4 | Miles/(US) gallon | Mean |  | 26.7 |  | 26.0 | 26.7
| 1/4 mile time | Mean |  | 19.1 |  | 16.7 | 19.4
6 | Miles/(US) gallon | Mean |  | 19.7 |  | 20.6 | 19.1
| 1/4 mile time | Mean |  | 18.0 |  | 16.3 | 19.2
8 | Miles/(US) gallon | Mean |  | 15.1 |  | 15.1 |
| 1/4 mile time | Mean |  | 16.8 |  | 16.8 |
TOTAL 1/2 | Miles/(US) gallon | Mean |  |  |  |  |
| 1/4 mile time | Mean |  |  |  |  |
TOTAL 3/4 | Miles/(US) gallon | Mean |  | 26.7 |  | 26.0 | 26.7
| 1/4 mile time | Mean |  | 19.1 |  | 16.7 | 19.4
TOTAL 5 and more | Miles/(US) gallon | Mean |  | 16.6 |  | 16.1 | 19.1
| 1/4 mile time | Mean |  | 17.2 |  | 16.7 | 19.2

### Nets

Net, contrary to `subtotal`, remove original categories.

    mtcars %>%
        tab_cells(mpg) %>%
        tab_cols(total(), vs) %>%
        tab_rows(net(cyl, 1:2, 3:4, "NET 5 and more" = 5 %thru% hi, prefix = "NET ")) %>%
        tab_stat_mean() %>%
        tab_pivot() %>%
        tab_caption("Nets in rows, custom prefix")

Nets in rows, custom prefix
|  |  | #Total |  | Engine
--- | --- | --- | --- | --- | ---
|  |  |  |  |  | V-engine | Straight engine
Number of cylinders
NET 1/2 | Miles/(US) gallon | Mean |  |  |  |  |
NET 3/4 | Miles/(US) gallon | Mean |  | 26.7 |  | 26.0 | 26.7
NET 5 and more | Miles/(US) gallon | Mean |  | 16.6 |  | 16.1 | 19.1

### Nets with complex grouping

    mtcars %>%
        tab_cells(net(mpg, "Low mpg" = less(mean(mpg)), "High mpg" = greater_or_equal(mean(mpg)))) %>%
        tab_cols(total(), am) %>%
        tab_stat_cases() %>%
        tab_pivot() %>%
        tab_caption("Nets with complex grouping")

Nets with complex grouping
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic | Manual
Miles/(US) gallon
Low mpg | 18 |  | 15 | 3
High mpg | 14 |  | 4 | 10
#Total cases | 32 |  | 19 | 13

### Significance testing on column percent

Letters marks cells which are significantly greater than cells in the appropriate columns. `-` and `+` marks values which are lower/greater than values in the first column. Significance testing on column percent should be applied on the result of `tab_stat_cpct` with total row.

    mtcars %>%
        tab_cells(cyl) %>%
        tab_cols(total(), vs) %>%
        tab_stat_cpct() %>%
        tab_pivot() %>%
        significance_cpct(compare_type = c("first_column", "subtable"), sig_level = 0.05) %>%
        tab_caption("Significance testing on column percent")

Significance testing on column percent
| #Total |  | Engine
--- | --- | --- | ---
|  |  | V-engine |  | Straight engine
|  |  | A |  | B
Number of cylinders
4 | 34.4 |  | 5.6 - |  | 71.4 + A
6 | 21.9 |  | 16.7 |  | 28.6
8 | 43.8 |  | 77.8 + |  |
#Total cases | 32 |  | 18 |  | 14

### Significance testing on means

Significance testing on means should be applied on the result of `tab_stat_mean_sd_n`.

    mtcars %>%
        tab_cells(mpg, disp, hp, wt, qsec) %>%
        tab_cols(total(), am) %>%
        tab_stat_mean_sd_n() %>%
        tab_pivot() %>%
        significance_means(compare_type = c("first_column", "subtable")) %>%
        tab_caption("Significance testing on means")

Significance testing on means
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Miles/(US) gallon
Mean | 20.1 |  | 17.1 - |  | 24.4 + A
Std. dev. | 6.0 |  | 3.8 |  | 6.2
Unw. valid N | 32.0 |  | 19.0 |  | 13.0
Displacement (cu.in.)
Mean | 230.7 |  | 290.4 B |  | 143.5 -
Std. dev. | 123.9 |  | 110.2 |  | 87.2
Unw. valid N | 32.0 |  | 19.0 |  | 13.0
Gross horsepower
Mean | 146.7 |  | 160.3 |  | 126.8
Std. dev. | 68.6 |  | 53.9 |  | 84.1
Unw. valid N | 32.0 |  | 19.0 |  | 13.0
Weight (1000 lbs)
Mean | 3.2 |  | 3.8 + B |  | 2.4 -
Std. dev. | 1.0 |  | 0.8 |  | 0.6
Unw. valid N | 32.0 |  | 19.0 |  | 13.0
1/4 mile time
Mean | 17.8 |  | 18.2 |  | 17.4
Std. dev. | 1.8 |  | 1.8 |  | 1.8
Unw. valid N | 32.0 |  | 19.0 |  | 13.0

### Multiple-response variables with weighting

Here we load data with multiple-responce questions. `mrset` means that we treat set of variables as multiple response varibale with category encoding. For dichotomy encoding use `mdset`.

    data(product_test)
    codeframe_likes = num_lab("
                              1 Liked everything
                              2 Disliked everything
                              3 Chocolate
                              4 Appearance
                              5 Taste
                              6 Stuffing
                              7 Nuts
                              8 Consistency
                              98 Other
                              99 Hard to answer
                              ")

    set.seed(1)
    product_test = product_test %>%
        let(
            # recode age by groups
            age_cat = recode(s2a, lo %thru% 25 ~ 1, lo %thru% hi ~ 2),
            wgt = runif(.N, 0.25, 4),
            wgt = wgt/sum(wgt)*.N
        )  %>%
        apply_labels(
            age_cat = "Age",
            age_cat = c("18 - 25" = 1, "26 - 35" = 2),
            a1_1 = "Likes. VSX123",
            b1_1 = "Likes. SDF456",
            a1_1 = codeframe_likes,
            b1_1 = codeframe_likes
        )

    product_test %>%
        tab_cells(mrset(a1_1 %to% a1_6), mrset(b1_1 %to% b1_6)) %>%
        tab_cols(total(), age_cat) %>%
        tab_weight(wgt) %>%
        tab_stat_cpct() %>%
        tab_sort_desc() %>%
        tab_pivot() %>%
        tab_caption("Multiple-response variables with weighting")

Multiple-response variables with weighting
| #Total |  | Age
--- | --- | --- | ---
|  |  | 18 - 25 | 26 - 35
Likes. VSX123
Nuts | 68.0 |  | 82.5 | 53.7
Taste | 39.3 |  | 48.4 | 30.3
Chocolate | 34.5 |  | 31.9 | 37.0
Appearance | 33.6 |  | 26.7 | 40.4
Stuffing | 31.5 |  | 23.6 | 39.3
Consistency | 12.9 |  | 7.7 | 18.0
Disliked everything | 0.7 |  |  | 1.4
Liked everything |  |  |  |
Other |  |  |  |
Hard to answer |  |  |  |
#Total cases | 150 |  | 70 | 80
Likes. SDF456
Nuts | 63.3 |  | 62.1 | 64.4
Taste | 31.6 |  | 35.2 | 28.0
Appearance | 30.3 |  | 28.8 | 31.8
Chocolate | 27.2 |  | 27.9 | 26.4
Stuffing | 21.5 |  | 14.3 | 28.7
Consistency | 8.2 |  | 1.7 | 14.7
Disliked everything | 2.9 |  | 3.3 | 2.5
Other | 1.0 |  |  | 1.9
Liked everything |  |  |  |
Hard to answer |  |  |  |
#Total cases | 150 |  | 70 | 80

### Side-by-side variables comparison

To make side-by-side comparison we use “|” to suppress variable labels and put these labels to the statistic labels. Statistics labels we place in columns with `tab_pivot`.

    product_test %>%
        tab_cols(total(), age_cat) %>%
        tab_weight(wgt) %>%
        # '|' is needed to prevent automatic labels creation from argument
        tab_cells("|" = unvr(mrset(a1_1 %to% a1_6))) %>%
        tab_stat_cpct(label = var_lab(a1_1)) %>%
        tab_cells("|" = unvr(mrset(b1_1 %to% b1_6))) %>%
        tab_stat_cpct(label = var_lab(b1_1)) %>%
        tab_pivot(stat_position = "inside_columns") %>%
        tab_caption("Side-by-side variables comparison")

Side-by-side variables comparison
| #Total |  | Age
--- | --- | --- | ---
| Likes. VSX123 |  | Likes. SDF456 |  | 18 - 25 |  | 26 - 35
|  |  |  |  | Likes. VSX123 | Likes. SDF456 |  | Likes. VSX123 | Likes. SDF456
Liked everything |  |  |  |  |  |  |  |  |
Disliked everything | 0.7 |  | 2.9 |  |  | 3.3 |  | 1.4 | 2.5
Chocolate | 34.5 |  | 27.2 |  | 31.9 | 27.9 |  | 37.0 | 26.4
Appearance | 33.6 |  | 30.3 |  | 26.7 | 28.8 |  | 40.4 | 31.8
Taste | 39.3 |  | 31.6 |  | 48.4 | 35.2 |  | 30.3 | 28.0
Stuffing | 31.5 |  | 21.5 |  | 23.6 | 14.3 |  | 39.3 | 28.7
Nuts | 68.0 |  | 63.3 |  | 82.5 | 62.1 |  | 53.7 | 64.4
Consistency | 12.9 |  | 8.2 |  | 7.7 | 1.7 |  | 18.0 | 14.7
Other |  |  | 1.0 |  |  |  |  |  | 1.9
Hard to answer |  |  |  |  |  |  |  |  |
#Total cases | 150 |  | 150 |  | 70 | 70 |  | 80 | 80

### Multiple tables in the loop with knitr

To make the task more practical we will create table with means for variables which have more than 6 unique elements. For other variables we will calculate column percent table. Note that you need to set `results='asis'` in the chunk options.

    # here we specify dataset and banner
    banner = mtcars %>%
        tab_cols(total(), am)

    for(each in colnames(mtcars)){
        # note ..$ which is used for indirect reference to variable
        # specify variable
        curr_table = banner %>%
            tab_cells(..$each)
        # calculate statistics
        if(length(unique(mtcars[[each]]))>6){
            curr_table = curr_table %>%
                tab_stat_mean_sd_n() %>%
                tab_pivot() %>%
                significance_means()
        } else {
            curr_table = curr_table %>%
                tab_stat_cpct() %>%
                tab_pivot() %>%
                significance_cpct()
        }
        # finalize table
        curr_table %>%
            tab_caption("Variable name: ", each) %>%
            htmlTable() %>%
            print()
    }

Variable name: mpg
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Miles/(US) gallon
Mean | 20.1 |  | 17.1 |  | 24.4 A
Std. dev. | 6.0 |  | 3.8 |  | 6.2
Unw. valid N | 32.0 |  | 19.0 |  | 13.0

Variable name: cyl
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Number of cylinders
4 | 34.4 |  | 15.8 |  | 61.5 A
6 | 21.9 |  | 21.1 |  | 23.1
8 | 43.8 |  | 63.2 B |  | 15.4
#Total cases | 32 |  | 19 |  | 13

Variable name: disp
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Displacement (cu.in.)
Mean | 230.7 |  | 290.4 B |  | 143.5
Std. dev. | 123.9 |  | 110.2 |  | 87.2
Unw. valid N | 32.0 |  | 19.0 |  | 13.0

Variable name: hp
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Gross horsepower
Mean | 146.7 |  | 160.3 |  | 126.8
Std. dev. | 68.6 |  | 53.9 |  | 84.1
Unw. valid N | 32.0 |  | 19.0 |  | 13.0

Variable name: drat
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Rear axle ratio
Mean | 3.6 |  | 3.3 |  | 4.0 A
Std. dev. | 0.5 |  | 0.4 |  | 0.4
Unw. valid N | 32.0 |  | 19.0 |  | 13.0

Variable name: wt
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Weight (1000 lbs)
Mean | 3.2 |  | 3.8 B |  | 2.4
Std. dev. | 1.0 |  | 0.8 |  | 0.6
Unw. valid N | 32.0 |  | 19.0 |  | 13.0

Variable name: qsec
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
1/4 mile time
Mean | 17.8 |  | 18.2 |  | 17.4
Std. dev. | 1.8 |  | 1.8 |  | 1.8
Unw. valid N | 32.0 |  | 19.0 |  | 13.0

Variable name: vs
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Engine
V-engine | 56.2 |  | 63.2 |  | 46.2
Straight engine | 43.8 |  | 36.8 |  | 53.8
#Total cases | 32 |  | 19 |  | 13

Variable name: am
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Transmission
Automatic | 59.4 |  | 100.0 |  |
Manual | 40.6 |  |  |  | 100.0
#Total cases | 32 |  | 19 |  | 13

Variable name: gear
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Number of forward gears
3 | 46.9 |  | 78.9 |  |
4 | 37.5 |  | 21.1 |  | 61.5 A
5 | 15.6 |  |  |  | 38.5
#Total cases | 32 |  | 19 |  | 13

Variable name: carb
| #Total |  | Transmission
--- | --- | --- | ---
|  |  | Automatic |  | Manual
|  |  | A |  | B
Number of carburetors
1 | 21.9 |  | 15.8 |  | 30.8
2 | 31.2 |  | 31.6 |  | 30.8
3 | 9.4 |  | 15.8 |  |
4 | 31.2 |  | 36.8 |  | 23.1
6 | 3.1 |  |  |  | 7.7
8 | 3.1 |  |  |  | 7.7
#Total cases | 32 |  | 19 |  | 13