Tableplots

This vignette shows you how to plot tableplots from eatRep data. The workflow is optimized for Bildungstrend-graphs, but can be expanded for plotting other eatRep data as well.

library(eatPlot)

Geschlechterkapitel

Abbildung 6.5

First we prepare the data:

gender_prepped <- do.call(rbind, lapply(trend_gender, prep_tablebarplot, subgroup_var = "Kgender"))

Now, we only want to plot the comparison of männlich vs. weiblich. The direction is always the same, we can look that up in the plain data, therefore it doesn’t matter which one we filter:

gender_prepped_sub <- subset(gender_prepped, subgroup_var == "maennlich")

gender_prepped <- gender_prepped_sub[order(gender_prepped_sub$state_var), ]
gender_prepped$y_axis <- 1:nrow(gender_prepped)

gender_prepped$state_var <- process_bundesland(gender_prepped$state_var, linebreak = FALSE)

The tables need some more work, because they need to be connected with the barplot, but only have half the rows. Therefore we need to build separate variables for each table part, with the respective rows of the domain not plotted in the table half set to empty strings. Because we want to plot both competence areas next to each other, we have to do some minor data wrangling first:

gender_prepped_lesen <- gender_prepped
gender_prepped_lesen[gender_prepped_lesen$kb == "hoeren", !colnames(gender_prepped) %in% c("y_axis", "kb")] <- NA

gender_prepped_hoeren <- gender_prepped
gender_prepped_hoeren[gender_prepped_hoeren$kb == "lesen", !colnames(gender_prepped) %in% c("y_axis", "kb")] <- NA

gender_prepped_lh <- merge(gender_prepped_lesen, gender_prepped_hoeren, by = c("y_axis", "kb"), suffixes = c("_lesen", "_hören"))
gender_prepped_lh_final <- merge(
  gender_prepped_lh,
  gender_prepped[, c(
    "y_axis",
    "est_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022",
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022"
  )]
)


gender_prepped_lh_final <- gender_prepped_lh_final[order(gender_prepped_lh_final$y_axis), ]

Now we can plot the table like usual, with one exception: I can nudge the table contents a bit downwards, so they also fill the empty row.

tableplot_6.5 <- plot_tablebarplot(
  dat = gender_prepped_lh_final,
  bar_est = "est_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022",
  bar_fill = "kb",
  bar_sig = "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022",
  columns_table = list(
    "state_var_lesen",
    "est_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_lesen",
    "se_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_lesen",
    "es_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_lesen",
    "est_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_hören",
    "se_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_hören",
    "es_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_hören"
  ),
  columns_table_sig_bold = list(
    NULL,
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_lesen",
    NULL,
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_lesen",
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_hören",
    NULL,
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_hören"
  ),
  columns_table_sig_superscript = list(
    NULL,
    "sig_mean_comp_crossDiff_of_groupDiff_totalFacet_weiblichSubgroup_2022_lesen",
    NULL,
    "sig_mean_comp_crossDiff_of_groupDiff_totalFacet_weiblichSubgroup_2022_lesen",
    "sig_mean_comp_crossDiff_of_groupDiff_totalFacet_weiblichSubgroup_2022_hören",
    NULL,
    "sig_mean_comp_crossDiff_of_groupDiff_totalFacet_weiblichSubgroup_2022_hören"
  ),
  columns_round = list(NULL, 0, 1, 2, 0, 1, 2),
  columns_table_se = list(
    NULL,
    NULL,
    "se_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_lesen",
    NULL,
    NULL,
    "se_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022_hören",
    NULL
  ),
  headers = list(
    "**Land**",
    "*M*<sub>M</sub>-*M*<sub>J</sub>",
    "(*SE*)",
    "*d*",
    "*M*<sub>M</sub>-*M*<sub>J</sub>",
    "(*SE*)",
    "*d*",
    "**Vorsprung zugunsten der<br>Mädchen**"
  ),
  column_spanners = list("**Leseverstehen**" = c(2, 4), "**Hörverstehen**" = c(5, 7)),
  y_axis = "y_axis",
  plot_settings = plotsettings_tablebarplot(
    default_list = abb_6.5
  )
)

Finally we can save the plot:

save_plot(tableplot_6.5, filename = "C:/Users/hafiznij/Downloads/abb6.5.pdf", width = 160, height = 90)

tableplot_6.5

Abbildung 6.6

First the data has to be prepared:

gender_prepped_mean_sd <- lapply(trend_gender,
  prep_tablebarplot,
  subgroup_var = "Kgender",
  par = c("mean", "sd")
)

## Let's focus on:

gender_lesen <- gender_prepped_mean_sd[[1]]

Afterwards, we have to bring it in the format we want to plot it in:

gender_lesen <- subset(gender_lesen, subgroup_var %in% c("maennlich", "weiblich"))

## Mädchen sollen nach oben
gender_lesen <- gender_lesen[order(gender_lesen$subgroup_var, decreasing = TRUE), ]
gender_lesen <- gender_lesen[order(gender_lesen$state_var), ]


gender_lesen$state_var <- process_bundesland(gender_lesen$state_var, linebreak = TRUE)
## Depending on whether a column has a linebreak, they have to be nudged down. I define the according vector here already, because it might get confusing furhter down:
nudge_col1_y <- ifelse(grepl("<br>", gender_lesen$state_var), -0.25, -0.5)
  
gender_lesen$state_var[duplicated(gender_lesen$state_var)] <- " "
gender_lesen$subgroup_var <- gsub("maennlich", "Jungen", gender_lesen$subgroup_var)
gender_lesen$subgroup_var <- gsub("weiblich", "Mädchen", gender_lesen$subgroup_var)
gender_lesen$empty <- ""

We will have to plot multiple plots and combine them. To do that, we will have to set the column widths, so they are identical over the different plots:

column_widths_stand <- standardize_column_width(
  column_widths = list(
    p1 = c(0.085, 0.05, rep(0.035, 6), 0.015, rep(0.035, 3), NA),
    p2 = c(rep(0.035, 3), NA)
  ),
  plot_ranges = c(142, 142) # Ranges of the x-axes of both plots set in 'axis_x_lims'.
)

We can just use this object in the plot-settings to adjust the column widths.

p_1 <- plot_tablebarplot(
  dat = gender_lesen,
  bar_est = "est_mean_comp_trend_sameFacet_sameSubgroup_2009_2015",
  bar_label = NULL,
  bar_sig = "sig_mean_comp_trend_sameFacet_sameSubgroup_2009_2015",
  bar_fill = "subgroup_var",
  column_spanners = list(
    "**2009**" = c(3, 4),
    "**2015**" = c(5, 6),
    "**2022**" = c(7, 8),
    "**Differenz 2015-2009**" = c(10, 13)
  ),
  columns_table_se = list(NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, "se_mean_comp_trend_sameFacet_sameSubgroup_2009_2015", NULL),
  headers = list("**Land**", " ", "*M*", "*SD*", "*M*", "*SD*", "*M*", "*SD*", "", "*M*<sub>2015</sub>-<br>*M*<sub>2009</sub> ", "(*SE*)", "*d*", " "),
  columns_table = c(
    "state_var",
    "subgroup_var",
    "est_mean_comp_none_2009",
    "est_sd_comp_none_2009",
    "est_mean_comp_none_2015",
    "est_sd_comp_none_2015",
    "est_mean_comp_none_2022",
    "est_sd_comp_none_2022",
    "empty",
    "est_mean_comp_trend_sameFacet_sameSubgroup_2009_2015",
    "se_mean_comp_trend_sameFacet_sameSubgroup_2009_2015",
    "es_mean_comp_trend_sameFacet_sameSubgroup_2009_2015"
  ),
  columns_table_sig_bold = list(
    NULL,
    NULL,
    "sig_mean_comp_none_2009",
    NULL,
    "sig_mean_comp_none_2015",
    NULL,
    "sig_mean_comp_none_2022",
    NULL,
    NULL,
    "sig_mean_comp_trend_sameFacet_sameSubgroup_2009_2015",
    NULL,
    "sig_mean_comp_trend_sameFacet_sameSubgroup_2009_2015"
  ),
  columns_table_sig_superscript = list(NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, "sig_mean_comp_trend_crossDiff_totalFacet_sameSubgroup_2009_2015", NULL, NULL),
  y_axis = "y_axis",
  columns_round = c(rep(0, 11), 2),
  plot_settings = plotsettings_tablebarplot(
    bar_pattern_spacing = 0.01, ## Try out what look good. For own plots, take the template ones as orientation
    columns_alignment = c(0, 0, rep(2, 10)),
    columns_width = column_widths_stand$p1, ## This is the column-width object we set above
    columns_nudge_x = c(rep(0, 9), -1.5, 0, 0),
    columns_nudge_y = c(list(nudge_col1_y), rep(list(0), 11)),
    headers_alignment = c(0, 0, rep(0.5, 7), 0, 0.5, 0.5, 0),
    headers_nudge_x = c(rep(0, 9), 2, rep(0, 3)),
    default_list = abb_6.6
  )
)

p_2 <- plot_tablebarplot(
  dat = gender_lesen,
  bar_est = "est_mean_comp_trend_sameFacet_sameSubgroup_2015_2022",
  bar_label = NULL,
  bar_sig = "sig_mean_comp_trend_sameFacet_sameSubgroup_2015_2022",
  bar_fill = "subgroup_var",
  column_spanners = list(
    "**Differenz 2022-2015**" = c(1, 4)
  ),
  headers = list(
    "*M*<sub>2022</sub>-<br>*M*<sub>2015</sub> ",
    "(*SE*)",
    "*d*",
    " "
  ),
  columns_table = c(
    "est_mean_comp_trend_sameFacet_sameSubgroup_2015_2022",
    "se_mean_comp_trend_sameFacet_sameSubgroup_2015_2022",
    "es_mean_comp_trend_sameFacet_sameSubgroup_2015_2022"
  ),
  columns_table_se = list(NULL, "se_mean_comp_trend_sameFacet_sameSubgroup_2015_2022", NULL),
  columns_table_sig_bold = list("sig_mean_comp_trend_sameFacet_sameSubgroup_2015_2022", 
                                NULL, 
                                "sig_mean_comp_trend_sameFacet_sameSubgroup_2015_2022"),
  columns_table_sig_superscript = list("sig_mean_comp_trend_crossDiff_totalFacet_sameSubgroup_2015_2022", NULL, NULL),
  y_axis = "y_axis",
  columns_round = c(0, 0, 2),
  plot_settings = plotsettings_tablebarplot(
    bar_pattern_spacing = 0.017,
    columns_alignment = c(2, 2, 2),
    columns_width = column_widths_stand$p2, ## This is the column-width object we set above
    headers_nudge_x = c(2, 0, 0, 0),
    headers_alignment = c(0, 0.5, 0.5, 0),
    default_list = abb_6.6
  )
)

These values have to be set in the plot-settings above.

Now we can combine the plots:

tableplot_6.6 <- combine_plots(list(p_1, p_2))

And save the result:

save_plot(tableplot_6.6, filename = "C:/Users/hafiznij/Downloads/abb_6.6.pdf", width = 235, height = 130)

Zuwanderungskapitel

Currently use the gender_trend data for demonstration purposes, the subgroups are of course named differently for the actual chapter.

gender_prepped_2 <- gender_prepped[gender_prepped$kb == "lesen", ]
gender_prepped_2$empty <- "" ## Needes as a empty space between column spanners
gender_prepped_2$state_var <- process_bundesland(gender_prepped_2$state_var, linebreak = FALSE)

column_widths_stand <- standardize_column_width(
  column_widths = list(
    p1 = c(0.16, rep(0.045, 5), NA),
    p2 = c(0.015, 0.07, 0.045, NA),
    p3 = c(0.07, 0.045, NA)
  ),
  plot_ranges = c(60, 60, 65) # Range of the x-axes of both plots set in 'axis_x_lims'.
)

p_1 <- plot_tablebarplot(
  dat = gender_prepped_2,
  bar_est = "est_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022",
  bar_sig = "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022",
  column_spanners = list(
    "**2009**" = c(2),
    "**2015**" = c(3),
    "**2022**" = c(4, 7)
  ),
  column_spanners_2 = list(
    "**Differenz Schüler:innen mit - Schüler:innen ohne <br> Zuwanderungshintergrund**" = c(2, 7)
  ),
  columns_table_se = list(NULL, NULL, NULL, NULL, "se_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022", NULL),
  headers = list(
    "bold(Land)",
    "Delta * italic(M)",
    "Delta * italic(M)",
    "Delta * italic(M)",
    "(italic(SE))",
    "italic(d)",
    " "
  ),
  columns_table = c(
    "state_var",
    "est_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2009",
    "est_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2015",
    "est_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022",
    "se_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022",
    "es_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022"
  ),
  columns_table_sig_bold = list(
    NULL,
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2009",
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2015",
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022",
    NULL,
    "sig_mean_comp_groupDiff_sameFacet_weiblichSubgroup_2022"
  ),
  y_axis = "y_axis",
  columns_round = c(0, 0, 0, 0, 0, 2),
  plot_settings = plotsettings_tablebarplot(
    bar_pattern_spacing = 0.0274,
    columns_alignment = c(0, 0.5, 0.5, 0.5, 0.5, 0.5),
    columns_width = column_widths_stand$p1,
    default_list = abb_8.4
  )
)

p_2 <- plot_tablebarplot(
  dat = gender_prepped_2,
  bar_est = "est_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2009_2015",
  bar_sig = "sig_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2009_2015",
  column_spanners = list(
    "**2015-2009**" = c(2, 4)
  ),
  column_spanners_2 = list(
    "**Veränderung der Differenz<br>**" = c(2, 4)
  ),
  columns_table_se = list(NULL, NULL, "se_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2009_2015"),
  headers = list(
    " ",
    list("paste(Delta * italic(M[2015]), '-')",
          "Delta * italic(M[2009])"),
    "(italic(SE))",
    " "
  ),
  columns_table = c(
    "empty",
    "est_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2009_2015",
    "se_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2009_2015"
  ),
  columns_table_sig_bold = list(
    NULL, "sig_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2009_2015", NULL
  ),
  y_axis = "y_axis",
  plot_settings =
    plotsettings_tablebarplot(
      bar_pattern_spacing = 0.0334,
      columns_alignment = c(2, 2, 2),
      headers_alignment = c(0, 0, 0.5, 0),
      headers_nudge_x = c(0, 10, -2, 0),
      columns_width = column_widths_stand$p2,
      default_list = abb_8.4
    )
)

p_3 <- plot_tablebarplot(
  dat = gender_prepped_2,
  bar_est = "est_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2015_2022",
  bar_sig = "sig_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2015_2022",
  column_spanners = list(
    "**2022-2015**" = c(1, 3)
  ),
  column_spanners_2 = list(
    "**Veränderung der Differenz<br>**" = c(1, 3)
  ),
  columns_table_se = list(NULL, "se_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2015_2022"),
  headers = list(
    list("paste(Delta * italic(M[2022]), '-')",
          "Delta * italic(M[2015])"),
    "(italic(SE))",
    " "
  ),
  columns_table = c(
    "est_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2015_2022",
    "se_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2015_2022"
  ),
  columns_table_sig_bold = list(
    "sig_mean_comp_trend_groupDiff_sameFacet_weiblichSubgroup_2015_2022", NULL
  ),
  y_axis = "y_axis",
  plot_settings = plotsettings_tablebarplot(
    bar_pattern_spacing = 0.0392,
    axis_x_lims = c(-20, 45),
    columns_alignment = c(2, 2),
    headers_alignment = c(0, 0.5, 0),
    headers_nudge_x = c(10, -3, 0),
    columns_width = column_widths_stand$p3,
    default_list = abb_8.4
  )
)

tableplot_8.4 <- combine_plots(list(p_1, p_2, p_3))

save_plot(tableplot_8.4,
  filename = "C:/Users/hafiznij/Downloads/abb_8.4.pdf",
  width = 160, height = 100
)

tableplot_8.4

Geschlechterkapitel

Abbildung 6.5

Abbildung 6.6

Zuwanderungskapitel

Stacked barplot