This function allows embedding of interactive or static plots based on various types of data using tidyselect syntax for variable selection.
Usage
makeme(
  data,
  dep = tidyselect::everything(),
  indep = NULL,
  type = c("cat_plot_html", "int_plot_html", "cat_table_html", "int_table_html",
    "sigtest_table_html", "cat_prop_plot_docx", "cat_freq_plot_docx", "int_plot_docx"),
  ...,
  require_common_categories = TRUE,
  crowd = c("all"),
  mesos_var = NULL,
  mesos_group = NULL,
  simplify_output = TRUE,
  hide_for_crowd_if_all_na = TRUE,
  hide_for_crowd_if_valid_n_below = 0,
  hide_for_crowd_if_category_k_below = 2,
  hide_for_crowd_if_category_n_below = 0,
  hide_for_crowd_if_cell_n_below = 0,
  hide_for_all_crowds_if_hidden_for_crowd = NULL,
  hide_indep_cat_for_all_crowds_if_hidden_for_crowd = FALSE,
  add_n_to_dep_label = FALSE,
  add_n_to_indep_label = FALSE,
  add_n_to_label = FALSE,
  add_n_to_category = FALSE,
  totals = FALSE,
  categories_treated_as_na = NULL,
  label_separator = " - ",
  error_on_duplicates = TRUE,
  showNA = c("ifany", "always", "never"),
  data_label = c("percentage_bare", "percentage", "proportion", "count", "mean",
    "median"),
  data_label_position = c("center", "bottom", "top", "above"),
  html_interactive = TRUE,
  hide_axis_text_if_single_variable = TRUE,
  hide_label_if_prop_below = 0.01,
  inverse = FALSE,
  vertical = FALSE,
  digits = 0,
  data_label_decimal_symbol = ".",
  x_axis_label_width = 25,
  strip_width = 25,
  sort_dep_by = ".variable_position",
  sort_indep_by = ".factor_order",
  sort_by = NULL,
  descend = TRUE,
  descend_indep = FALSE,
  labels_always_at_top = NULL,
  labels_always_at_bottom = NULL,
  table_wide = TRUE,
  table_main_question_as_header = FALSE,
  n_categories_limit = 12,
  translations = list(last_sep = " and ", table_heading_N = "Total (N)",
    table_heading_data_label = "%", add_n_to_dep_label_prefix = " (N = ",
    add_n_to_dep_label_suffix = ")", add_n_to_indep_label_prefix = " (N = ",
    add_n_to_indep_label_suffix = ")", add_n_to_label_prefix = " (N = ",
    add_n_to_label_suffix = ")", add_n_to_category_prefix = " (N = [",
    add_n_to_category_infix = ",", add_n_to_category_suffix = "])", by_total =
    "Everyone", sigtest_variable_header_1 = "Var 1", sigtest_variable_header_2 = "Var 2",
    crowd_all = "All", 
     crowd_target = "Target", crowd_others = "Others"),
  plot_height = 15,
  colour_palette = NULL,
  colour_2nd_binary_cat = "#ffffff",
  colour_na = "grey",
  label_font_size = 6,
  main_font_size = 6,
  strip_font_size = 6,
  legend_font_size = 6,
  font_family = "sans",
  path = NULL,
  docx_template = NULL
)Arguments
- data
- Your data.frame/tibble or srvyr-object (experimental) - data.frame// required- The data to be used for plotting. 
- dep, indep
- Variable selections - < - tidyselect> // Default:- NULL, meaning everything for dep, nothing for indep.- Columns in - data.- depis compulsory.
- type
- Kind of output - scalar<character>// default:- "cat_plot_html"(- optional)- For a list of registered types in your session, use - get_makeme_types().
- ...
- Dynamic dots - Arguments forwarded to the corresponding functions that create the elements. 
- require_common_categories
- Check common categories - scalar<logical>// default:- TRUE(- optional)- Whether to check if all items share common categories. 
- crowd
- Which group(s) to display results for - vector<character>// default:- c("target", "others", "all")(- optional)- Choose whether to produce results for target (mesos) group, others, all, or combinations of these. 
- mesos_var
- Variable in - dataindicating groups to tailor reports for- scalar<character>// default:- NULL(- optional)- Column name in data indicating the groups for which mesos reports will be produced. 
- mesos_group
- scalar<character>// default:- NULL(- optional)- String, target group. 
- simplify_output
- scalar<logical>// default:- TRUE- If TRUE, a list output with a single output element will return the element itself, whereas list with multiple elements will return the list. 
- hide_for_crowd_if_all_na
- Hide variable from output if containing all NA - scalar<boolean>// default:- TRUE- Whether to remove all variables (in particular useful for mesos) if all values are NA 
- hide_for_crowd_if_valid_n_below
- Hide variable if variable has < n observations - scalar<integer>// default:- 0- Whether to hide a variable for a crowd if variable contains fewer than n observations (always ignoring NA). 
- hide_for_crowd_if_category_k_below
- Hide variable if < k categories - scalar<integer>// default:- 2- Whether to hide a variable for a crowd if variable contains fewer than k used categories (always ignoring NA). Defaults to - 2because a unitary plot/table is rarely informative.
- hide_for_crowd_if_category_n_below
- Hide variable if having a category with < n observations - scalar<integer>// default:- 0- Whether to hide a variable for a crowd if variable contains a category with less than n observations (ignoring NA) Cells with a 0 count is not considered as these are usually not a problem for anonymity. 
- hide_for_crowd_if_cell_n_below
- Hide variable if having a cell with < n - scalar<integer>// default:- 0- Whether to hide a variable for a crowd if the combination of dep-indep results in a cell with less than n observations (ignoring NA). Cells with a 0 count is not considered as these are usually not a problem for anonymity. 
- Conditional hiding - scalar<character>// default:- NULL(- optional)- Select one of the - crowdoutput groups. If selected, will hide a variable across all- crowd-outputs if it for some reason is not displayed for- hide_for_all_if_hidden_for_crowd. For instance, say:- crowd = c("target", "others"), hide_variable_if_all_na = TRUE,- hide_for_all_if_hidden_for_crowd = "target"- will hide variables from both target and others-outputs if all are NA in the target-group. 
- Conditionally hide independent categories - scalar<logical>// default:- FALSE- If - hide_for_all_crowds_if_hidden_for_crowdis specified, should categories of the- indepvariable(s) be hidden for a crowd if it does not exist for the crowds specified in- hide_for_all_crowds_if_hidden_for_crowd? This is useful when e.g.- indepis academic disciplines,- mesos_varis institutions, and a specific institution is not interested in seeing academic disciplines they do not offer themselves.
- add_n_to_dep_label, add_n_to_indep_label
- Add N= to the variable label - scalar<logical>// default:- FALSE(- optional)- For some plots and tables it is useful to attach the - "N="to the end of the label of the dependent and/or independent variable. Whether it is- Nor- N_validdepends on your- showNA-setting. See also- translations$add_n_to_dep_label_prefix,- translations$add_n_to_dep_label_suffix,- translations$add_n_to_indep_label_prefix,- translations$add_n_to_indep_label_suffix.
- add_n_to_label
- Add N= to the variable label of both dep and indep - scalar<logical>// default:- FALSE(- optional)- For some plots and tables it is useful to attach the - "N="to the end of the label. Whether it is- Nor- N_validdepends on your- showNA-setting. See also- translations$add_n_to_label_prefixand- translations$add_n_to_label_suffix.
- add_n_to_category
- Add N= to the category - scalar<logical>// default:- FALSE(- optional)- For some plots and tables it is useful to attach the - "N="to the end of the category. This will likely produce a range across the variables, hence an infix (comma) between the minimum and maximum can be specified. Whether it is- Nor- N_validdepends on your- showNA-setting. See also- translations$add_n_to_category_prefix,- translations$add_n_to_category_infix, and- translations$add_n_to_category_suffix.
- totals
- Include totals - scalar<logical>// default:- FALSE(- optional)- Whether to include totals in the output. 
- categories_treated_as_na
- NA categories - vector<character>// default:- NULL(- optional)- Categories that should be treated as NA. 
- label_separator
- How to separate main question from sub-question - scalar<character>// default:- NULL(- optional)- Separator for main question from sub-question. 
- error_on_duplicates
- Error or warn on duplicate labels - scalar<logical>// default:- TRUE(- optional)- Whether to abort ( - TRUE) or warn (- FALSE) if the same label (suffix) is used across multiple variables.
- showNA
- Show NA categories - vector<character>// default:- c("ifany", "always", "never")(- optional)- Choose whether to show NA categories in the results. 
- data_label
- Data label - scalar<character>// default:- "proportion"(- optional)- One of "proportion", "percentage", "percentage_bare", "count", "mean", or "median". 
- data_label_position
- Data label position - scalar<character>// default:- "center"(- optional)- Position of data labels on bars. One of "center" (middle of bar), "bottom" (bottom but inside bar), "top" (top but inside bar), or "above" (above bar outside). 
- html_interactive
- Toggle interactive plot - scalar<logical>// default:- TRUE(- optional)- Whether the plot is to be interactive (ggiraph) or static (ggplot2). 
- hide_axis_text_if_single_variable
- Hide y-axis text if just a single variable - scalar<boolean>// default:- FALSE(- optional)- Whether to hide text on the y-axis label if just a single variable. 
- hide_label_if_prop_below
- Hide label threshold - scalar<numeric>// default:- NULL(- optional)- Whether to hide label if below this value. 
- inverse
- Flag to swap x-axis and faceting - scalar<logical>// default:- FALSE(- optional)- If TRUE, swaps x-axis and faceting. 
- vertical
- Display plot vertically - scalar<logical>// default:- FALSE(- optional)- If TRUE, display plot vertically. 
- digits
- Decimal places - scalar<integer>// default:- 0L(- optional)- Number of decimal places. 
- data_label_decimal_symbol
- Decimal symbol - scalar<character>// default:- "."(- optional)- Decimal marker, some might prefer a comma ',' or something else entirely. 
- x_axis_label_width, strip_width
- Label width of x-axis and strip texts in plots - scalar<integer>// default:- 20(- optional)- Width of the labels used for the categorical column names in x-axis texts and strip texts. 
- sort_dep_by
- What to sort dependent variables by - vector<character>// default:- ".variable_position"(- optional)- Sort dependent variables in output. When using - indep-argument, sorting differs between ordered factors and unordered factors: Ordering of ordered factors is always respected in output (their levels define the base order). Unordered factors will be reordered by- sort_dep_by.- NULL or ".variable_position"
- Sort by variable position in the supplied data frame (default). 
- ".variable_label"
- Sort by the variable labels. 
- ".variable_name"
- Sort by the variable names. 
- ".top"
- The proportion for the highest category available in the variable. 
- ".upper"
- The sum of the proportions for the categories above the middle category. 
- ".mid_upper"
- The sum of the proportions for the categories including and above the middle category. 
- ".mid_lower"
- The sum of the proportions for the categories including and below the middle category. 
- ".lower"
- The sum of the proportions for the categories below the middle category. 
- ".bottom"
- The proportions for the lowest category available in the variable. 
 
- sort_indep_by
- What to sort independent variable categories by - vector<character>// default:- ".factor_order"(- optional)- Sort independent variable categories in output. When - ".factor_order", preserves the original factor level order for the independent variable. Passing- NULLis accepted and treated as- ".factor_order".- NULL
- No sorting - preserves original factor level order (default). 
- ".top"
- The proportion for the highest category available. 
- ".upper"
- The sum of the proportions for the categories above the middle category. 
- ".mid_upper"
- The sum of the proportions for the categories including and above the middle category. 
- ".mid_lower"
- The sum of the proportions for the categories including and below the middle category. 
- ".lower"
- The sum of the proportions for the categories below the middle category. 
- ".bottom"
- The proportions for the lowest category available. 
- character()
- Character vector of category labels to sum together. 
 
- sort_by
- What to sort output by (legacy) - vector<character>// default:- NULL(- optional)- DEPRECATED: Use - sort_dep_byand- sort_indep_byinstead for clearer control. When specified, this parameter will be used for both dependent and independent sorting. If- NULL(default), dependent variables will be sorted by- .variable_position.- NULL
- Uses - .variable_positionfor dependent variables, no sorting for independent.
- ".top"
- The proportion for the highest category available in the variable. 
- ".upper"
- The sum of the proportions for the categories above the middle category. 
- ".mid_upper"
- The sum of the proportions for the categories including and above the middle category. 
- ".mid_lower"
- The sum of the proportions for the categories including and below the middle category. 
- ".lower"
- The sum of the proportions for the categories below the middle category. 
- ".bottom"
- The proportions for the lowest category available in the variable. 
- ".variable_label"
- Sort by the variable labels. 
- ".variable_name"
- Sort by the variable names. 
- ".variable_position"
- Sort by the variable position in the supplied data frame. 
- ".by_group"
- The groups of the by argument. 
- character()
- Character vector of category labels to sum together. 
 
- descend
- Sorting order - scalar<logical>// default:- FALSE(- optional)- Reverse sorting of - sort_byin figures and tables. Works with both ordered and unordered factors - for ordered factors, it reverses the display order while preserving the inherent level ordering. See- arrange_section_byfor sorting of report sections.
- descend_indep
- Sorting order for independent variables - scalar<logical>// default:- FALSE(- optional)- Reverse sorting of - sort_indep_byin figures and tables. Works with both ordered and unordered factors - for ordered factors, it reverses the display order while preserving the inherent level ordering. See- arrange_section_byfor sorting of report sections.
- labels_always_at_top, labels_always_at_bottom
- Top/bottom variables - vector<character>// default:- NULL(- optional)- Column names in - datathat should always be placed at the top or bottom of figures/tables.
- table_wide
- Pivot table wider - scalar<logical>// default:- FALSE(- optional)- Whether to pivot table wider. 
- table_main_question_as_header
- Table main question as header - scalar<logical>// default:- FALSE(- optional)- Whether to include the main question as a header in the table. 
- n_categories_limit
- Limit for cat_table_ wide format - scalar<integer>// default:- 12(- optional)- If there are more than this number of categories in the categorical variable, cat_table_* will have a long format instead of wide format. 
- translations
- Localize your output - list<character>- A list of translations where the name is the code and the value is the translation. See the examples. 
- plot_height
- DOCX-setting - scalar<numeric>// default:- 12(- optional)- DOCX plots need a height, which currently cannot be set easily with a Quarto chunk option. 
- colour_palette
- Colour palette - vector<character>// default:- NULL(- optional)- Must contain at least the number of unique values (including missing) in the data set. 
- colour_2nd_binary_cat
- Colour for second binary category - scalar<character>// default:- "#ffffff"(- optional)- Colour for the second category in binary variables. Often useful to hide this. 
- colour_na
- Colour for NA category - scalar<character>// default:- NULL(- optional)- Colour as a single string for NA values, if showNA is "ifany" or "always". 
- main_font_size, label_font_size, strip_font_size, legend_font_size
- Font sizes - scalar<integer>// default:- 6(- optional)- ONLY FOR DOCX-OUTPUT. Other output is adjusted using e.g. ggplot2::theme() or set with a global theme (ggplot2::set_theme()). Font sizes for general text (6), data label text (3), strip text (6) and legend text (6). 
- font_family
- Font family - scalar<character>// default:- "sans"(- optional)- Word font family. See officer::fp_text. 
- path
- Output path for DOCX - scalar<character>// default:- NULL(- optional)- Path to save docx-output. 
- docx_template
- Filename or rdocx object - scalar<character>|<rdocx>-object// default:- NULL(- optional)- Can be either a valid character path to a reference Word file, or an existing rdocx-object in memory. 
Examples
makeme(
  data = ex_survey,
  dep = b_1:b_2
)
 makeme(
  data = ex_survey,
  dep = b_1:b_3, indep = c(x1_sex, x2_human),
  type = "sigtest_table_html"
)
#>   Var 1    Var 2                         .bi_test .p_value .variable_name_Males
#> 1   b_1   x1_sex Chi-squared Goodness-of-Fit Test    0.728                  b_1
#> 2   b_1 x2_human Chi-squared Goodness-of-Fit Test    0.536                 <NA>
#> 3   b_2   x1_sex Chi-squared Goodness-of-Fit Test    0.447                  b_2
#> 4   b_2 x2_human Chi-squared Goodness-of-Fit Test    0.955                 <NA>
#> 5   b_3   x1_sex Chi-squared Goodness-of-Fit Test    0.850                  b_3
#> 6   b_3 x2_human Chi-squared Goodness-of-Fit Test    0.260                 <NA>
#>   .variable_name_Females n_valid_Males n_valid_Females n_Males n_Females
#> 1                    b_1           151             149     151       149
#> 2                   <NA>            NA              NA      NA        NA
#> 3                    b_2           151             149     151       149
#> 4                   <NA>            NA              NA      NA        NA
#> 5                    b_3           151             149     151       149
#> 6                   <NA>            NA              NA      NA        NA
#>   .variable_position_Males .variable_position_Females
#> 1                       13                         13
#> 2                       NA                         NA
#> 3                       14                         14
#> 4                       NA                         NA
#> 5                       15                         15
#> 6                       NA                         NA
#>                       .variable_label_Males
#> 1   How much do you like living in - Bejing
#> 2                                      <NA>
#> 3 How much do you like living in - Brussels
#> 4                                      <NA>
#> 5 How much do you like living in - Budapest
#> 6                                      <NA>
#>                     .variable_label_Females
#> 1   How much do you like living in - Bejing
#> 2                                      <NA>
#> 3 How much do you like living in - Brussels
#> 4                                      <NA>
#> 5 How much do you like living in - Budapest
#> 6                                      <NA>
#>                .variable_label_prefix_Males
#> 1   How much do you like living in - Bejing
#> 2                                      <NA>
#> 3 How much do you like living in - Brussels
#> 4                                      <NA>
#> 5 How much do you like living in - Budapest
#> 6                                      <NA>
#>              .variable_label_prefix_Females .variable_name_Definitely humanoid
#> 1   How much do you like living in - Bejing                               <NA>
#> 2                                      <NA>                                b_1
#> 3 How much do you like living in - Brussels                               <NA>
#> 4                                      <NA>                                b_2
#> 5 How much do you like living in - Budapest                               <NA>
#> 6                                      <NA>                                b_3
#>   .variable_name_Robot? n_valid_Definitely humanoid n_valid_Robot?
#> 1                  <NA>                          NA             NA
#> 2                   b_1                         144            156
#> 3                  <NA>                          NA             NA
#> 4                   b_2                         144            156
#> 5                  <NA>                          NA             NA
#> 6                   b_3                         144            156
#>   n_Definitely humanoid n_Robot? .variable_position_Definitely humanoid
#> 1                    NA       NA                                     NA
#> 2                   144      156                                     13
#> 3                    NA       NA                                     NA
#> 4                   144      156                                     14
#> 5                    NA       NA                                     NA
#> 6                   144      156                                     15
#>   .variable_position_Robot?       .variable_label_Definitely humanoid
#> 1                        NA                                      <NA>
#> 2                        13   How much do you like living in - Bejing
#> 3                        NA                                      <NA>
#> 4                        14 How much do you like living in - Brussels
#> 5                        NA                                      <NA>
#> 6                        15 How much do you like living in - Budapest
#>                      .variable_label_Robot?
#> 1                                      <NA>
#> 2   How much do you like living in - Bejing
#> 3                                      <NA>
#> 4 How much do you like living in - Brussels
#> 5                                      <NA>
#> 6 How much do you like living in - Budapest
#>   .variable_label_prefix_Definitely humanoid
#> 1                                       <NA>
#> 2    How much do you like living in - Bejing
#> 3                                       <NA>
#> 4  How much do you like living in - Brussels
#> 5                                       <NA>
#> 6  How much do you like living in - Budapest
#>               .variable_label_prefix_Robot?
#> 1                                      <NA>
#> 2   How much do you like living in - Bejing
#> 3                                      <NA>
#> 4 How much do you like living in - Brussels
#> 5                                      <NA>
#> 6 How much do you like living in - Budapest
makeme(
  data = ex_survey,
  dep = p_1:p_4, indep = x2_human,
  type = "cat_table_html"
)
#> # A tibble: 8 × 8
#>   .variable_label `Is respondent human?` `Strongly disagree (%)`
#>   <ord>           <fct>                  <chr>                  
#> 1 Blue Party      Robot?                 26.28                  
#> 2 Blue Party      Definitely humanoid    19.44                  
#> 3 Yellow Party    Robot?                 16.03                  
#> 4 Yellow Party    Definitely humanoid    20.83                  
#> 5 Green Party     Robot?                 25.00                  
#> 6 Green Party     Definitely humanoid    13.89                  
#> 7 Red Party       Robot?                 19.23                  
#> 8 Red Party       Definitely humanoid    16.67                  
#> # ℹ 5 more variables: `Somewhat disagree (%)` <chr>,
#> #   `Somewhat agree (%)` <chr>, `Strongly agree (%)` <chr>, `NA (%)` <chr>,
#> #   `Total (N)` <int>
makeme(
  data = ex_survey,
  dep = c_1:c_2, indep = x1_sex,
  type = "int_table_html"
)
#> # A tibble: 4 × 12
#>   .variable_label Gender      N N_valid N_missing  Mean    SD Median   MAD   IQR
#>   <fct>           <fct>   <int>   <int>     <int> <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1 Company A       Males     151     151         0  20.6  4.62   20.5  4.74  6.45
#> 2 Company A       Females   149     149         0  20.4  4.96   20.6  5.34  6.9 
#> 3 Company B       Males     151     151         0  20.1  4.54   20.2  4.74  6.45
#> 4 Company B       Females   149     149         0  19.7  4.79   19.9  4.45  6.7 
#> # ℹ 2 more variables: Min <dbl>, Max <dbl>
makeme(
  data = ex_survey,
  dep = b_1:b_2,
  crowd = c("target", "others"),
  mesos_var = "f_uni",
  mesos_group = "Uni of A"
)
#> $Target
makeme(
  data = ex_survey,
  dep = b_1:b_3, indep = c(x1_sex, x2_human),
  type = "sigtest_table_html"
)
#>   Var 1    Var 2                         .bi_test .p_value .variable_name_Males
#> 1   b_1   x1_sex Chi-squared Goodness-of-Fit Test    0.728                  b_1
#> 2   b_1 x2_human Chi-squared Goodness-of-Fit Test    0.536                 <NA>
#> 3   b_2   x1_sex Chi-squared Goodness-of-Fit Test    0.447                  b_2
#> 4   b_2 x2_human Chi-squared Goodness-of-Fit Test    0.955                 <NA>
#> 5   b_3   x1_sex Chi-squared Goodness-of-Fit Test    0.850                  b_3
#> 6   b_3 x2_human Chi-squared Goodness-of-Fit Test    0.260                 <NA>
#>   .variable_name_Females n_valid_Males n_valid_Females n_Males n_Females
#> 1                    b_1           151             149     151       149
#> 2                   <NA>            NA              NA      NA        NA
#> 3                    b_2           151             149     151       149
#> 4                   <NA>            NA              NA      NA        NA
#> 5                    b_3           151             149     151       149
#> 6                   <NA>            NA              NA      NA        NA
#>   .variable_position_Males .variable_position_Females
#> 1                       13                         13
#> 2                       NA                         NA
#> 3                       14                         14
#> 4                       NA                         NA
#> 5                       15                         15
#> 6                       NA                         NA
#>                       .variable_label_Males
#> 1   How much do you like living in - Bejing
#> 2                                      <NA>
#> 3 How much do you like living in - Brussels
#> 4                                      <NA>
#> 5 How much do you like living in - Budapest
#> 6                                      <NA>
#>                     .variable_label_Females
#> 1   How much do you like living in - Bejing
#> 2                                      <NA>
#> 3 How much do you like living in - Brussels
#> 4                                      <NA>
#> 5 How much do you like living in - Budapest
#> 6                                      <NA>
#>                .variable_label_prefix_Males
#> 1   How much do you like living in - Bejing
#> 2                                      <NA>
#> 3 How much do you like living in - Brussels
#> 4                                      <NA>
#> 5 How much do you like living in - Budapest
#> 6                                      <NA>
#>              .variable_label_prefix_Females .variable_name_Definitely humanoid
#> 1   How much do you like living in - Bejing                               <NA>
#> 2                                      <NA>                                b_1
#> 3 How much do you like living in - Brussels                               <NA>
#> 4                                      <NA>                                b_2
#> 5 How much do you like living in - Budapest                               <NA>
#> 6                                      <NA>                                b_3
#>   .variable_name_Robot? n_valid_Definitely humanoid n_valid_Robot?
#> 1                  <NA>                          NA             NA
#> 2                   b_1                         144            156
#> 3                  <NA>                          NA             NA
#> 4                   b_2                         144            156
#> 5                  <NA>                          NA             NA
#> 6                   b_3                         144            156
#>   n_Definitely humanoid n_Robot? .variable_position_Definitely humanoid
#> 1                    NA       NA                                     NA
#> 2                   144      156                                     13
#> 3                    NA       NA                                     NA
#> 4                   144      156                                     14
#> 5                    NA       NA                                     NA
#> 6                   144      156                                     15
#>   .variable_position_Robot?       .variable_label_Definitely humanoid
#> 1                        NA                                      <NA>
#> 2                        13   How much do you like living in - Bejing
#> 3                        NA                                      <NA>
#> 4                        14 How much do you like living in - Brussels
#> 5                        NA                                      <NA>
#> 6                        15 How much do you like living in - Budapest
#>                      .variable_label_Robot?
#> 1                                      <NA>
#> 2   How much do you like living in - Bejing
#> 3                                      <NA>
#> 4 How much do you like living in - Brussels
#> 5                                      <NA>
#> 6 How much do you like living in - Budapest
#>   .variable_label_prefix_Definitely humanoid
#> 1                                       <NA>
#> 2    How much do you like living in - Bejing
#> 3                                       <NA>
#> 4  How much do you like living in - Brussels
#> 5                                       <NA>
#> 6  How much do you like living in - Budapest
#>               .variable_label_prefix_Robot?
#> 1                                      <NA>
#> 2   How much do you like living in - Bejing
#> 3                                      <NA>
#> 4 How much do you like living in - Brussels
#> 5                                      <NA>
#> 6 How much do you like living in - Budapest
makeme(
  data = ex_survey,
  dep = p_1:p_4, indep = x2_human,
  type = "cat_table_html"
)
#> # A tibble: 8 × 8
#>   .variable_label `Is respondent human?` `Strongly disagree (%)`
#>   <ord>           <fct>                  <chr>                  
#> 1 Blue Party      Robot?                 26.28                  
#> 2 Blue Party      Definitely humanoid    19.44                  
#> 3 Yellow Party    Robot?                 16.03                  
#> 4 Yellow Party    Definitely humanoid    20.83                  
#> 5 Green Party     Robot?                 25.00                  
#> 6 Green Party     Definitely humanoid    13.89                  
#> 7 Red Party       Robot?                 19.23                  
#> 8 Red Party       Definitely humanoid    16.67                  
#> # ℹ 5 more variables: `Somewhat disagree (%)` <chr>,
#> #   `Somewhat agree (%)` <chr>, `Strongly agree (%)` <chr>, `NA (%)` <chr>,
#> #   `Total (N)` <int>
makeme(
  data = ex_survey,
  dep = c_1:c_2, indep = x1_sex,
  type = "int_table_html"
)
#> # A tibble: 4 × 12
#>   .variable_label Gender      N N_valid N_missing  Mean    SD Median   MAD   IQR
#>   <fct>           <fct>   <int>   <int>     <int> <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1 Company A       Males     151     151         0  20.6  4.62   20.5  4.74  6.45
#> 2 Company A       Females   149     149         0  20.4  4.96   20.6  5.34  6.9 
#> 3 Company B       Males     151     151         0  20.1  4.54   20.2  4.74  6.45
#> 4 Company B       Females   149     149         0  19.7  4.79   19.9  4.45  6.7 
#> # ℹ 2 more variables: Min <dbl>, Max <dbl>
makeme(
  data = ex_survey,
  dep = b_1:b_2,
  crowd = c("target", "others"),
  mesos_var = "f_uni",
  mesos_group = "Uni of A"
)
#> $Target
 #> 
#> $Others
#> 
#> $Others
 #>
#>