Tables with labels in R

Introduction

expss package provides tabulation functions with support for ‘SPSS’-style labels, multiple / nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in ‘knitr’, R notebooks, ‘Shiny’ and ‘Jupyter’ notebooks. Proper methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package offers useful functions for data processing in marketing research / social surveys - popular data transformation functions from ‘SPSS’ Statistics (‘RECODE’, ‘COUNT’, ‘COMPUTE’, ‘DO IF’, etc.) and ‘Excel’ (‘COUNTIF’, ‘VLOOKUP’, etc.). Package is intended to help people to move data processing from ‘Excel’/‘SPSS’ to R. See examples below. You can get help about any function by typing ?function_name in the R console.

Links

Installation

expss is on CRAN, so for installation you can print in the console install.packages("expss").

Cross-tablulation examples

We will use for demonstartion well-known mtcars dataset. Let’s start with adding labels to the dataset. Then we can continue with tables creation.

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"
)

For quick cross-tabulation there are fre and cro family of function. For simplicity we demonstrate here only cro_cpct which caluclates column percent. Documentation for other functions, such as cro_cases for counts, cro_rpct for row percent, cro_tpct for table percent and cro_fun for custom summary functions can be seen by typing ?cro and ?cro_fun in the console.

# 'cro' examples
# just simple crosstabulation, similar to base R 'table' function
cro(mtcars$am, mtcars$vs)

	Engine
	V-engine	Straight engine
Transmission
Automatic	12	7
Manual	6	7
#Total cases	18	14

# Table column % with multiple banners
cro_cpct(mtcars$cyl, list(total(), mtcars$am, mtcars$vs))

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

# or, the same result with another notation
mtcars %>% calc_cro_cpct(cyl, list(total(), am, vs))

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

# Table with nested banners (column %).          
mtcars %>% calc_cro_cpct(cyl, list(total(), am %nest% vs))

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

We have more sophisticated interface for table construction with magrittr piping. Table construction consists of at least of three functions chained with 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 the tab_stat_* functions. And last, we finalize table creation with tab_pivot, e. g.: dataset %>% tab_cells(variable) %>% tab_stat_cases() %>% tab_pivot(). After that we can optionally sort table with tab_sort_asc, drop empty rows/columns with drop_rc and transpose with tab_transpose. Resulting table is just a data.frame so we can use usual R operations on it. Detailed documentation for table creation can be seen via ?tables. For significance testing see ?significance. Generally, tables automatically translated to HTML for output in knitr or Jupyter notebooks. However, if we want HTML output in the R notebooks or in the RStudio viewer we need to set options for that: expss_output_rnotebook() or expss_output_viewer().

# simple example
mtcars %>% 
    tab_cells(cyl) %>% 
    tab_cols(total(), am) %>% 
    tab_stat_cpct() %>% 
    tab_pivot()

	#Total	Transmission
		Automatic	Manual
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

# if we need caption then we use 'htmlTable'
mtcars %>% 
    tab_cells(mpg, disp, hp, wt, qsec) %>%
    tab_cols(total(), am) %>% 
    tab_stat_mean_sd_n() %>%
    tab_last_sig_means(subtable_marks = "both") %>% 
    tab_pivot() %>% 
    htmlTable(caption = "Table with summary statistics and significance marks.")

	#Total	Transmission
Table with summary statistics and significance marks.
		Automatic	Manual
		A	B
Miles/(US) gallon
Mean	20.1	17.1 < B	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 < A
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 < A
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

# Table with the same summary statistics. Statistics labels in columns.
mtcars %>% 
    tab_cells(mpg, disp, hp, wt, qsec) %>%
    tab_cols(total(label = "#Total| |"), am) %>% 
    tab_stat_fun(Mean = w_mean, "Std. dev." = w_sd, "Valid N" = w_n, method = list) %>%
    tab_pivot()

	#Total			Transmission
				Automatic			Manual
	Mean	Std. dev.	Valid N	Mean	Std. dev.	Valid N	Mean	Std. dev.	Valid N
Miles/(US) gallon	20.1	6.0	32	17.1	3.8	19	24.4	6.2	13
Displacement (cu.in.)	230.7	123.9	32	290.4	110.2	19	143.5	87.2	13
Gross horsepower	146.7	68.6	32	160.3	53.9	19	126.8	84.1	13
Weight (1000 lbs)	3.2	1.0	32	3.8	0.8	19	2.4	0.6	13
1/4 mile time	17.8	1.8	32	18.2	1.8	19	17.4	1.8	13

# Different statistics for different variables.
mtcars %>%
    tab_cols(total(), vs) %>%
    tab_cells(mpg) %>% 
    tab_stat_mean() %>% 
    tab_stat_valid_n() %>% 
    tab_cells(am) %>%
    tab_stat_cpct(total_row_position = "none", label = "col %") %>%
    tab_stat_rpct(total_row_position = "none", label = "row %") %>%
    tab_stat_tpct(total_row_position = "none", label = "table %") %>%
    tab_pivot(stat_position = "inside_rows")

		#Total	Engine
			V-engine	Straight engine
Miles/(US) gallon
Mean		20.1	16.6	24.6
Valid N		32.0	18.0	14.0
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

# Table with split by rows and with custom totals.
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()

			#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

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

	#Total			Transmission
				Automatic			Manual
	Coef.	2.5 %	97.5 %	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

Example of data processing with multiple-response variables

Here we use truncated dataset with data from product test of two samples of chocolate sweets. 150 respondents tested two kinds of sweets (codenames: VSX123 and SDF546). Sample was divided into two groups (cells) of 75 respondents in each group. In cell 1 product VSX123 was presented first and then SDF546. In cell 2 sweets were presented in reversed order. Questions about respondent impressions about first product are in the block A (and about second tested product in the block B). At the end of the questionnaire there was a question about the preferences between sweets.

List of variables:

id Respondent Id
cell First tested product (cell number)
s2a Age
a1_1-a1_6 What did you like in these sweets? Multiple response. First tested product
a22 Overall quality. First tested product
b1_1-b1_6 What did you like in these sweets? Multiple response. Second tested product
b22 Overall quality. Second tested product
c1 Preferences

data(product_test)

w = product_test # shorter name to save some keystrokes

# here we recode variables from first/second tested product to separate variables for each product according to their cells
# 'h' variables - VSX123 sample, 'p' variables - 'SDF456' sample
# also we recode preferences from first/second product to true names
# for first cell there are no changes, for second cell we should exchange 1 and 2.
w = w %>% 
    do_if(cell == 1, {
        recode(a1_1 %to% a1_6, other ~ copy) %into% (h1_1 %to% h1_6)
        recode(b1_1 %to% b1_6, other ~ copy) %into% (p1_1 %to% p1_6)
        recode(a22, other ~ copy) %into% h22
        recode(b22, other ~ copy) %into% p22
        c1r = c1
    }) %>% 
    do_if(cell == 2, {
        recode(a1_1 %to% a1_6, other ~ copy) %into% (p1_1 %to% p1_6)
        recode(b1_1 %to% b1_6, other ~ copy) %into% (h1_1 %to% h1_6)
        recode(a22, other ~ copy) %into% p22
        recode(b22, other ~ copy) %into% h22
        recode(c1, 1 ~ 2, 2 ~ 1, other ~ copy) %into% c1r
    }) %>% 
    compute({
        # recode age by groups
        age_cat = recode(s2a, lo %thru% 25 ~ 1, lo %thru% hi ~ 2)
        # count number of likes
        # codes 2 and 99 are ignored.
        h_likes = count_row_if(1 | 3 %thru% 98, h1_1 %to% h1_6) 
        p_likes = count_row_if(1 | 3 %thru% 98, p1_1 %to% p1_6) 
    })

# here we prepare labels for future usage
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
")

overall_liking_scale = num_lab("
    1 Extremely poor 
    2 Very poor
    3 Quite poor
    4 Neither good, nor poor
    5 Quite good
    6 Very good
    7 Excellent
")

w = apply_labels(w, 
    c1r = "Preferences",
    c1r = num_lab("
        1 VSX123 
        2 SDF456
        3 Hard to say
    "),
    
    age_cat = "Age",
    age_cat = c("18 - 25" = 1, "26 - 35" = 2),
    
    h1_1 = "Likes. VSX123",
    p1_1 = "Likes. SDF456",
    h1_1 = codeframe_likes,
    p1_1 = codeframe_likes,
    
    h_likes = "Number of likes. VSX123",
    p_likes = "Number of likes. SDF456",
    
    h22 = "Overall quality. VSX123",
    p22 = "Overall quality. SDF456",
    h22 = overall_liking_scale,
    p22 = overall_liking_scale
)

Are there any significant differences between preferences? Yes, difference is significant.

# 'tab_mis_val(3)' remove 'hard to say' from vector 
w %>% tab_cols(total(), age_cat) %>% 
      tab_cells(c1r) %>% 
      tab_mis_val(3) %>% 
      tab_stat_cases() %>% 
      tab_last_sig_cases() %>% 
      tab_pivot()

	#Total	Age
		18 - 25	26 - 35
Preferences
VSX123	94.0	46.0	48.0
SDF456	50.0	22.0	28.0
Hard to say
#Chi-squared p-value	<0.05	(warn.)
#Total cases	144.0	68.0	76.0

Further we calculate distribution of answers in the survey questions.

# lets specify repeated parts of table creation chains
banner = w %>% tab_cols(total(), age_cat, c1r) 
# column percent with significance
tab_cpct_sig = . %>% tab_stat_cpct() %>% 
                    tab_last_sig_cpct(sig_labels = paste0("<b>",LETTERS, "</b>"))

# means with siginifcance
tab_means_sig = . %>% tab_stat_mean_sd_n(labels = c("<b><u>Mean</u></b>", "sd", "N")) %>% 
                      tab_last_sig_means(
                          sig_labels = paste0("<b>",LETTERS, "</b>"),   
                          keep = "means")

# Preferences
banner %>% 
    tab_cells(c1r) %>% 
    tab_cpct_sig() %>% 
    tab_pivot()

	#Total	Age		Preferences
		18 - 25	26 - 35	VSX123	SDF456	Hard to say
		A	B	A	B	C
Preferences
VSX123	62.7	65.7	60.0	100.0
SDF456	33.3	31.4	35.0		100.0
Hard to say	4.0	2.9	5.0			100.0
#Total cases	150	70	80	94	50	6

# Overall liking
banner %>%  
    tab_cells(h22) %>% 
    tab_means_sig() %>% 
    tab_cpct_sig() %>%  
    tab_cells(p22) %>% 
    tab_means_sig() %>% 
    tab_cpct_sig() %>%
    tab_pivot()

	#Total	Age		Preferences
		18 - 25	26 - 35	VSX123	SDF456	Hard to say
		A	B	A	B	C
Overall quality. VSX123
Mean	5.5	5.4	5.6	5.3	5.8 A	5.5
Extremely poor
Very poor
Quite poor	2.0	2.9	1.2	3.2
Neither good, nor poor	10.7	11.4	10.0	14.9 B	2.0	16.7
Quite good	39.3	45.7	33.8	40.4	38.0	33.3
Very good	33.3	24.3	41.2 A	30.9	38.0	33.3
Excellent	14.7	15.7	13.8	10.6	22.0	16.7
#Total cases	150	70	80	94	50	6
Overall quality. SDF456
Mean	5.4	5.3	5.4	5.4	5.3	5.7
Extremely poor
Very poor	0.7		1.2	1.1
Quite poor	2.7	4.3	1.2	2.1	4.0
Neither good, nor poor	16.7	20.0	13.8	18.1	14.0	16.7
Quite good	31.3	27.1	35.0	28.7	38.0	16.7
Very good	35.3	35.7	35.0	35.1	34.0	50.0
Excellent	13.3	12.9	13.8	14.9	10.0	16.7
#Total cases	150	70	80	94	50	6

# Likes
banner %>% 
    tab_cells(h_likes) %>% 
    tab_means_sig() %>% 
    tab_cells(mrset(h1_1 %to% h1_6)) %>% 
    tab_cpct_sig() %>% 
    tab_cells(p_likes) %>% 
    tab_means_sig() %>% 
    tab_cells(mrset(p1_1 %to% p1_6)) %>% 
    tab_cpct_sig() %>%
    tab_pivot()

	#Total	Age		Preferences
		18 - 25	26 - 35	VSX123	SDF456	Hard to say
		A	B	A	B	C
Number of likes. VSX123
Mean	2.0	2.0	2.1	1.9	2.2	2.3
Likes. VSX123
Liked everything
Disliked everything	3.3	1.4	5.0	4.3	2.0
Chocolate	34.0	38.6	30.0	35.1	32.0	33.3
Appearance	29.3	21.4	36.2 A	25.5	38.0	16.7
Taste	32.0	38.6	26.2	23.4	48.0 A	33.3
Stuffing	27.3	20.0	33.8	28.7	26.0	16.7
Nuts	66.7	72.9	61.3	69.1	60.0	83.3
Consistency	12.0	4.3	18.8 A	8.5	14.0	50.0 A B
Other
Hard to answer
#Total cases	150	70	80	94	50	6
Number of likes. SDF456
Mean	2.0	2.0	2.1	2.0	2.0	2.0
Likes. SDF456
Liked everything
Disliked everything	1.3	1.4	1.2	2.1
Chocolate	32.0	27.1	36.2	29.8	34.0	50.0
Appearance	32.0	35.7	28.7	34.0	30.0	16.7
Taste	39.3	42.9	36.2	36.2	44.0	50.0
Stuffing	27.3	24.3	30.0	31.9	20.0	16.7
Nuts	61.3	60.0	62.5	58.5	68.0	50.0
Consistency	10.0	5.7	13.8	11.7	6.0	16.7
Other	0.7		1.2	1.1
Hard to answer
#Total cases	150	70	80	94	50	6

# below more complicated table where we compare likes side by side
# Likes - side by side comparison
w %>% 
    tab_cols(total(label = "#Total| |"), c1r) %>% 
    tab_cells(list(unvr(mrset(h1_1 %to% h1_6)))) %>% 
    tab_stat_cpct(label = var_lab(h1_1)) %>% 
    tab_cells(list(unvr(mrset(p1_1 %to% p1_6)))) %>% 
    tab_stat_cpct(label = var_lab(p1_1)) %>% 
    tab_pivot(stat_position = "inside_columns")

	#Total		Preferences
			VSX123		SDF456		Hard to say
	Likes. VSX123	Likes. SDF456	Likes. VSX123	Likes. SDF456	Likes. VSX123	Likes. SDF456	Likes. VSX123	Likes. SDF456
Liked everything
Disliked everything	3.3	1.3	4.3	2.1	2
Chocolate	34.0	32.0	35.1	29.8	32	34	33.3	50.0
Appearance	29.3	32.0	25.5	34.0	38	30	16.7	16.7
Taste	32.0	39.3	23.4	36.2	48	44	33.3	50.0
Stuffing	27.3	27.3	28.7	31.9	26	20	16.7	16.7
Nuts	66.7	61.3	69.1	58.5	60	68	83.3	50.0
Consistency	12.0	10.0	8.5	11.7	14	6	50.0	16.7
Other		0.7		1.1
Hard to answer
#Total cases	150	150	94	94	50	50	6	6

We can save labelled dataset as *.csv file with accompanying R code for labelling.

write_labelled_csv(w, file  filename = "product_test.csv")

Or, we can save dataset as *.csv file with SPSS syntax to read data and apply labels.

write_labelled_spss(w, file  filename = "product_test.csv")

Excel functions translation guide

Let us consider Excel toy table:

	A	B	C
1	2	15	50
2	1	70	80
3	3	30	40
4	2	30	40

Code for creating the same table in R:

library(expss)
w = from_text("
        a  b  c
        2 15 50
        1 70 80
        3 30 40
        2 30 40
")

w is the name of our table.

IF

Excel: IF(B1>60, 1, 0)

R: Here we create new column with name d with results. ifelse function is from base R not from ‘expss’ package but included here for completeness.

w$d = ifelse(w$b>60, 1, 0)

If we need to use multiple transformations it is often convenient to use compute function. Inside compute we can put arbitrary number of the statements:

w = compute(w, {
    d = ifelse(b>60, 1, 0)
    e = 42
    abc_sum = sum_row(a, b, c)
    abc_mean = mean_row(a, b, c)
})

COUNTIF

Count 1’s in the entire dataset.

Excel: COUNTIF(A1:C4, 1)

R:

count_if(1, w)

or

calculate(w, count_if(1, a, b, c))

Count values greater than 1 in each row of the dataset.

Excel: COUNTIF(A1:C1, ">1")

R:

w$d = count_row_if(gt(1), w)

or

w = compute(w, {
    d = count_row_if(gt(1), a, b, c) 
})

Count values less than or equal to 1 in column A of the dataset.

Excel: COUNTIF(A1:A4, "<=1")

R:

count_col_if(le(1), w$a)

Table of criteria:

Excel	R
<1	lt(1)
<=1	le(1)
<>1	ne(1)
=1	eq(1)
>=1	ge(1)
>1	gt(1)

SUM/AVERAGE

Sum all values in the dataset.

Excel: SUM(A1:C4)

R:

sum(w, na.rm = TRUE)

Calculate average of each row of the dataset.

Excel: AVERAGE(A1:C1)

R:

w$d = mean_row(w)

or

w = compute(w, {
    d = mean_row(a, b, c) 
})

Sum values of column A of the dataset.

Excel: SUM(A1:A4)

R:

sum_col(w$a)

SUMIF/AVERAGEIF

Sum values greater than 40 in the entire dataset.

Excel: SUMIF(A1:C4, ">40")

R:

sum_if(gt(40), w)

or

calculate(w, sum_if(gt(40), a, b, c))

Sum values less than 40 in the each row of the dataset.

Excel: SUMIF(A1:C1, "<40")

R:

w$d = sum_row_if(lt(40), w)

or

w = compute(w, {
    d = sum_row_if(lt(40), a, b, c) 
})

Calculate average of B column with column A values less than 3.

Excel: AVERAGEIF(A1:A4, "<3", B1:B4)

R:

mean_col_if(lt(3), w$a, data = w$b)

or, if we want calculate means for both b and c columns:

calculate(w, mean_col_if(lt(3), a, data = sheet(b, c)))

VLOOKUP

Our dictionary for lookup:

	X	Y
1	1	apples
2	2	oranges
3	3	peaches

Code for creating the same dictionary in R:

dict = from_text("
    x  y
    1  apples
    2  oranges
    3  peaches
")

Excel: VLOOKUP(A1, $X$1:$Y$3, 2, FALSE)

R:

w$d = vlookup(w$a, dict, 2)

or, we can use column names:

w$d = vlookup(w$a, dict, "y")

SPSS functions translation guide

COMPUTE

SPSS:

COMPUTE d = 1.

R:

w$d = 1

or, in the specific data.frame

w = compute(w, {
    d = 1
})

There can be arbitrary number of statements inside compute.

IF

SPSS:

IF(a = 3) d = 2.

R:

w = compute(w, {
    d = ifelse(a == 3, 2, NA)
})

or,

w = compute(w, {
    d = ifs(a == 3 ~ 2)
})

DO IF

SPSS:

DO IF (a>1).
    COMPUTE d = 4.
END IF.

R:

w = do_if(w, a>1, {
    d = 4
})

There can be arbitrary number of statements inside do_if.

COUNT

SPSS:

COUNT cnt = a1 TO a5 (LO THRU HI).

R:

cnt = count_row_if(lo %thru% hi, a1 %to% a5)

SPSS:

COUNT cnt = a1 TO a5 (SYSMIS).

R:

cnt = count_row_if(NA, a1 %to% a5)

SPSS:

COUNT cnt = a1 TO a5 (1 THRU 5).

R:

cnt = count_row_if(1 %thru% 5, a1 %to% a5)

SPSS:

COUNT cnt = a1 TO a5 (1 THRU HI).

R:

cnt = count_row_if(1 %thru% hi, a1 %to% a5)

or,

cnt = count_row_if(ge(1), a1 %to% a5)

SPSS:

COUNT cnt = a1 TO a5 (LO THRU 1).

R:

cnt = count_row_if(lo %thru% 1, a1 %to% a5)

or,

cnt = count_row_if (le(1), a1 %to% a5)

SPSS:

COUNT cnt = a1 TO a5 (1 THRU 5, 99).

R:

cnt = count_row_if(1 %thru% 5 | 99, a1 %to% a5)

SPSS:

COUNT cnt = a1 TO a5(1,2,3,4,5, SYSMIS).

R:

cnt = count_row_if(c(1:5, NA), a1 %to% a5)

count_row_if can be used inside compute.

RECODE

SPSS:

RECODE V1 (0=1) (1=0) (2, 3=-1) (9=9) (ELSE=SYSMIS)

R:

recode(v1) = c(0 ~ 1, 1 ~ 0, 2:3 ~ -1, 9 ~ 9, other ~ NA)

SPSS:

RECODE QVAR(1 THRU 5=1)(6 THRU 10=2)(11 THRU HI=3)(ELSE=0).

R:

recode(qvar) = c(1 %thru% 5 ~ 1, 6 %thru% 10 ~ 2, 11 %thru% hi ~ 3, other ~ 0)

SPSS:

RECODE STRNGVAR ('A', 'B', 'C'='A')('D', 'E', 'F'='B')(ELSE=' ').

R:

recode(strngvar) = c(c('A', 'B', 'C') ~ 'A', c('D', 'E', 'F') ~ 'B', other ~ ' ')

SPSS:

RECODE AGE (MISSING=9) (18 THRU HI=1) (0 THRU 18=0) INTO VOTER.

R:

voter = recode(age, NA ~ 9, 18 %thru% hi ~ 1, 0 %thru% 18 ~ 0)
# or
recode(age, NA ~ 9, 18 %thru% hi ~ 1, 0 %thru% 18 ~ 0) %into% voter

recode can be used inside compute.

VARIABLE LABELS

SPSS:

VARIABLE LABELS a "Fruits"
                b "Cost"
                c "Price".

R:

w = apply_labels(w,
                 a = "Fruits",
                 b = "Cost",
                 c = "Price"
)

VALUE LABELS

SPSS:

VALUE LABELS a
    1 "apples"
    2 "oranges"
    3 "peaches".

R:

w = apply_labels(w, 
                 a = num_lab("
                        1 apples
                        2 oranges
                        3 peaches 
                    ")
)

or,

val_lab(w$a) = num_lab("
    1 apples
    2 oranges
    3 peaches 
")

Tables

R:

fre(w$a) # Frequency of fruits

Fruits	Count	Valid percent	Percent	Responses, %	Cumulative responses, %
apples	1	25	25	25	25
oranges	2	50	50	50	75
peaches	1	25	25	25	100
#Total	4	100	100	100
<NA>	0		0

cro_cpct(w$b, w$a) # Column percent of cost by fruits

	Fruits
	apples	oranges	peaches
Cost
15		50
30		50	100
70	100
#Total cases	1	2	1

cro_mean(sheet(w$b, w$c), w$a) # Mean cost and price by fruits

	Fruits
	apples	oranges	peaches
Cost	70	22.5	30
Price	80	45.0	40

Tables with labels in R

2018-06-14

Introduction

Links

Installation

Cross-tablulation examples

Example of data processing with multiple-response variables

Excel functions translation guide

IF

COUNTIF

SUM/AVERAGE

SUMIF/AVERAGEIF

VLOOKUP

SPSS functions translation guide

COMPUTE

IF

DO IF

COUNT

RECODE

VARIABLE LABELS

VALUE LABELS

Tables