Introduction
expss computes and displays tables 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 and ‘Excel’ (‘RECODE’,
‘COUNT’, ‘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.
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
cross family of function. For simplicity we demonstrate
here only cross_cpct which calculates column percent.
Documentation for other functions, such as cross_cases for
counts, cross_rpct for row percent, cross_tpct
for table percent and cross_fun for custom summary
functions can be seen by typing ?cross_cpct and
?cross_fun in the console.
# 'cross_*' examples
# just simple crosstabulation, similar to base R 'table' function
cross_cases(mtcars, am, vs)
|
|
Engine
|
|
|
V-engine
|
Straight engine
|
|
Transmission
|
|
Automatic
|
12
|
7
|
|
Manual
|
6
|
7
|
|
#Total cases
|
18
|
14
|
# Table column % with multiple banners
cross_cpct(mtcars, 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
|
# magrittr pipe usage and nested banners
mtcars %>%
cross_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
|
# table with caption
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() %>%
set_caption("Table with summary statistics and significance marks.")
|
Table with summary statistics and significance marks.
|
|
|
#Total
|
|
Transmission
|
|
|
|
|
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 = as.name(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 Idcell First tested product (cell number)s2a Agea1_1-a1_6 What did you like in these sweets? Multiple
response. First tested producta22 Overall quality. First tested productb1_1-b1_6 What did you like in these sweets? Multiple
response. Second tested productb22 Overall quality. Second tested productc1 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 %>%
let_if(cell == 1,
h1_1 %to% h1_6 := recode(a1_1 %to% a1_6, other ~ copy),
p1_1 %to% p1_6 := recode(b1_1 %to% b1_6, other ~ copy),
h22 := recode(a22, other ~ copy),
p22 := recode(b22, other ~ copy),
c1r = c1
) %>%
let_if(cell == 2,
p1_1 %to% p1_6 := recode(a1_1 %to% a1_6, other ~ copy),
h1_1 %to% h1_6 := recode(b1_1 %to% b1_6, other ~ copy),
p22 := recode(a22, other ~ copy),
h22 := recode(b22, other ~ copy),
c1r := recode(c1, 1 ~ 2, 2 ~ 1, other ~ copy)
) %>%
let(
# 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")