inst/doc/tables-with-labels.R

In expss: Tables, Labels and Some Useful Functions from Spreadsheets and 'SPSS' Statistics

## ---- message=FALSE, warning=FALSE--------------------------------------------
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"
)


## -----------------------------------------------------------------------------
# 'cross_*' examples
# just simple crosstabulation, similar to base R 'table' function
cross_cases(mtcars, am, vs)

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

# magrittr pipe usage and nested banners
mtcars %>% 
    cross_cpct(cyl, list(total(), am %nest% vs))      


## -----------------------------------------------------------------------------
# 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() 

## -----------------------------------------------------------------------------

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
)


## -----------------------------------------------------------------------------
# '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()
    

## -----------------------------------------------------------------------------
# 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") 


## ---- eval=FALSE--------------------------------------------------------------
#  write_labelled_csv(w, file  filename = "product_test.csv")

## ---- eval=FALSE--------------------------------------------------------------
#  write_labelled_spss(w, file  filename = "product_test.csv")