expss
Introduction
expss
computes and displays tables with support for 'SPSS'style labels, multiple / nested banners, weights, multipleresponse 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
 Online introduction  visit it to view code with its output
 expss on CRAN
 expss on Github
 expss on Stackoverflow
 Issues
Installation
expss
is on CRAN, so for installation you can print in the console
install.packages("expss")
.
Crosstablulation examples
We will use for demonstartion wellknown 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("Vengine" = 0,
"Straight engine" = 1),
am = "Transmission",
am = c("Automatic" = 0,
"Manual"=1),
gear = "Number of forward gears",
carb = "Number of carburetors"
)
For quick crosstabulation 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)
# Table column % with multiple banners
cro_cpct(mtcars$cyl, list(total(), mtcars$am, mtcars$vs))
# or, the same result with another notation
mtcars %>% calc_cro_cpct(cyl, list(total(), am, vs))
# Table with nested banners (column %).
mtcars %>% calc_cro_cpct(cyl, list(total(), am %nest% vs))
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()
# 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 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()
# 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")
# 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()
# 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()
Example of data processing with multipleresponse 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_1a1_6
What did you like in these sweets? Multiple response. First tested product 
a22
Overall quality. First tested product 
b1_1b1_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()
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()
# Overall liking
banner %>%
tab_cells(h22) %>%
tab_means_sig() %>%
tab_cpct_sig() %>%
tab_cells(p22) %>%
tab_means_sig() %>%
tab_cpct_sig() %>%
tab_pivot()
# 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()
# 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")
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")
Labels support for base R
Variable label is human readable description of the variable. R supports rather long variable names and these names can contain even spaces and punctuation but short variables names make coding easier. Variable label can give a nice, long description of variable. With this description it is easier to remember what those variable names refer to.
Value labels are similar to variable labels, but value labels are descriptions of the values a variable can take. Labeling values means we don’t have to remember if 1=Extremely poor and 7=Excellent or viceversa. We can easily get dataset description and variables summary with info
function.
The usual way to connect numeric data to labels in R is factor variables. However, factors miss important features which the value labels provide. Factors only allow for integers to be mapped to a text label, these integers have to be a count starting at 1 and every value need to be labelled. Also, we can’t calculate means or other numeric statistics on factors.
With labels we can manipulate short variable names and codes when we analyze our data but in the resulting tables and graphs we will see humanreadable text.
It is easy to store labels as variable attributes in R but most R functions cannot use them or even drop them. expss
package integrates value labels support into base R functions and into functions from other packages. Every function which internally converts variable to factor will utilize labels. Labels will be preserved during variables subsetting and concatenation. Additionally, there is a function (use_labels
) which greatly simplify variable labels usage. See examples below.
Getting and setting variable and value labels
First, apply value and variables labels to 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("Vengine" = 0,
"Straight engine" = 1),
am = "Transmission",
am = c("Automatic" = 0,
"Manual"=1),
gear = "Number of forward gears",
carb = "Number of carburetors"
)
In addition to apply_labels
we have SPSSstyle var_lab
and val_lab
functions:
nps = c(1, 0, 1, 1, 0, 1, 1, 1)
var_lab(nps) = "Net promoter score"
val_lab(nps) = num_lab("
1 Detractors
0 Neutralists
1 Promoters
")
We can read, add or remove existing labels:
var_lab(nps) # get variable label
val_lab(nps) # get value labels
# add new labels
add_val_lab(nps) = num_lab("
98 Other
99 Hard to say
")
# remove label by value
# %d%  diff, %n_d%  names diff
val_lab(nps) = val_lab(nps) %d% 98
# or, remove value by name
val_lab(nps) = val_lab(nps) %n_d% "Other"
Additionaly, there are some utility functions. They can applied on one variable as well as on the entire dataset.
drop_val_labs(nps)
drop_var_labs(nps)
unlab(nps)
drop_unused_labels(nps)
prepend_values(nps)
There is also prepend_names
function but it can be applied only to data.frame.
Labels with base R and ggplot2 functions
Base table
and plotting with value labels:
with(mtcars, table(am, vs))
with(mtcars,
barplot(
table(am, vs),
beside = TRUE,
legend = TRUE)
)
There is a special function for variables labels support  use_labels
. By now variables labels support available only for expression which will be evaluated inside data.frame.
# table with dimension names
use_labels(mtcars, table(am, vs))
# linear regression
use_labels(mtcars, lm(mpg ~ wt + hp + qsec)) %>% summary
# boxplot with variable labels
use_labels(mtcars, boxplot(mpg ~ am))
And, finally, ggplot2
graphics with variables and value labels. Note that with ggplot2 version 3.2.0 and higher you need to explicitly convert labelled variables to factors in the facet_grid
formula:
library(ggplot2, warn.conflicts = FALSE)
use_labels(mtcars, {
# '..data' is shortcut for all 'mtcars' data.frame inside expression
ggplot(..data) +
geom_point(aes(y = mpg, x = wt, color = qsec)) +
facet_grid(factor(am) ~ factor(vs))
})
Extreme value labels support
We have an option for extreme values lables support: expss_enable_value_labels_support_extreme()
. With this option factor
/as.factor
will take into account empty levels. However, unique
will give weird result for labelled variables: labels without values will be added to unique values. That's why it is recommended to turn off this option immediately after usage. See examples.
We have label 'Hard to say' for which there are no values in nps
:
nps = c(1, 0, 1, 1, 0, 1, 1, 1)
var_lab(nps) = "Net promoter score"
val_lab(nps) = num_lab("
1 Detractors
0 Neutralists
1 Promoters
99 Hard to say
")
Here we disable labels support and get results without labels:
expss_disable_value_labels_support()
table(nps) # there is no labels in the result
unique(nps)
Results with default value labels support  three labels are here but "Hard to say" is absent.
expss_enable_value_labels_support()
# table with labels but there are no label "Hard to say"
table(nps)
unique(nps)
And now extreme value labels support  we see "Hard to say" with zero counts. Note the weird unique
result.
expss_enable_value_labels_support_extreme()
# now we see "Hard to say" with zero counts
table(nps)
# weird 'unique'! There is a value 99 which is absent in 'nps'
unique(nps)
Return immediately to defaults to avoid issues:
expss_enable_value_labels_support()
Labels are preserved during common operations on the data
There are special methods for subsetting and concatenating labelled variables. These methods preserve labels during common operations. We don't need to restore labels on subsetted or sorted data.frame.
mtcars
with labels:
str(mtcars)
Make subset of the data.frame:
mtcars_subset = mtcars[1:10, ]
Labels are here, nothing is lost:
str(mtcars_subset)
Interaction with 'haven'
To use expss
with haven
you need to load expss
strictly after haven
(or other package with implemented 'labelled' class) to avoid conflicts. And it is better to use read_spss
with explict package specification: haven::read_spss
. See example below.
haven
package doesn't set 'labelled' class for variables which have variable label but don't have value labels. It leads to labels losing during subsetting and other operations. We have a special function to fix this: add_labelled_class
. Apply it to dataset loaded by haven
.
# we need to load packages strictly in this order to avoid conflicts
library(haven)
library(expss)
spss_data = haven::read_spss("spss_file.sav")
# add missing 'labelled' class
spss_data = add_labelled_class(spss_data)
Export to Microsoft Excel
To export expss
tables to *.xlsx you need to install excellent openxlsx
package. To install it just type in the console install.packages("openxlsx")
. On Windows system you may need also install RTools. It can be downloaded from CRAN: RTools.
First we apply labels on the mtcars dataset and build simple table with caption.
library(expss)
library(openxlsx)
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 (lb/1000)",
qsec = "1/4 mile time",
vs = "Engine",
vs = c("Vengine" = 0,
"Straight engine" = 1),
am = "Transmission",
am = c("Automatic" = 0,
"Manual"=1),
gear = "Number of forward gears",
carb = "Number of carburetors"
)
mtcars_table = mtcars %>%
calc_cro_cpct(
cell_vars = list(cyl, gear),
col_vars = list(total(), am, vs)
) %>%
set_caption("Table 1")
mtcars_table
Then we create workbook and add worksheet to it.
wb = createWorkbook()
sh = addWorksheet(wb, "Tables")
Export  we should specify workbook and worksheet.
xl_write(mtcars_table, wb, sh)
And, finally, we save workbook with table to the xlsx file.
saveWorkbook(wb, "table1.xlsx", overwrite = TRUE)
Screenshot of the exported table:
Automation of the report generation
First of all, we create banner which we will use for all our tables.
banner = calc(mtcars, list(total(), am, vs))
Then we generate list with all tables. If variables have small number of discrete values we create column percent table. In other cases we calculate table with means. For both types of tables we mark significant differencies between groups.
list_of_tables = lapply(mtcars, function(variable) {
if(length(unique(variable))<7){
cro_cpct(variable, banner) %>% significance_cpct()
} else {
# if number of unique values greater than seven we calculate mean
cro_mean_sd_n(variable, banner) %>% significance_means()
}
})
Create workbook:
wb = createWorkbook()
sh = addWorksheet(wb, "Tables")
Here we export our list with tables with additional formatting. We remove '#' sign from totals and mark total column with bold. You can read about formatting options in the manual fro xl_write
(?xl_write
in the console).
xl_write(list_of_tables, wb, sh,
# remove '#' sign from totals
col_symbols_to_remove = "#",
row_symbols_to_remove = "#",
# format total column as bold
other_col_labels_formats = list("#" = createStyle(textDecoration = "bold")),
other_cols_formats = list("#" = createStyle(textDecoration = "bold")),
)
Save workbook:
saveWorkbook(wb, "report.xlsx", overwrite = TRUE)
Screenshot of the generated report:
Excel functions translation guide
library(expss)
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:
w = text_to_columns("
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 = text_to_columns("
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
cro_cpct(w$b, w$a) # Column percent of cost by fruits
cro_mean(sheet(w$b, w$c), w$a) # Mean cost and price by fruits