# Tables with labels in R

#### 2019-08-25

## 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 (‘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
- 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")`.

## 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

    # 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 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 = 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

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