## ---- 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("",LETTERS, ""))
# means with siginifcance
tab_means_sig = . %>% tab_stat_mean_sd_n(labels = c("Mean", "sd", "N")) %>%
tab_last_sig_means(
sig_labels = paste0("",LETTERS, ""),
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")