Examples

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

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