From 6c5d530c741bea4f1cf42df69d8489563c19e114 Mon Sep 17 00:00:00 2001 From: Daniel Roelfs Date: Wed, 22 Nov 2023 22:27:35 +0100 Subject: [PATCH 01/12] add first version election post --- config.toml | 6 +- .../peilingwijzer-plot-1.png | Bin 0 -> 184175 bytes .../plot-exit-polls-2100-1.png | Bin 0 -> 145066 bytes .../plot-political-spectrum-1.png | Bin 0 -> 153341 bytes .../unnamed-chunk-5-1.png | Bin 0 -> 227018 bytes .../unnamed-chunk-7-1.png | Bin 0 -> 120757 bytes .../unnamed-chunk-8-1.png | Bin 0 -> 102886 bytes content/blog/2023-dutch-elections/index.md | 562 ++++++++++++++++++ content/blog/2023-dutch-elections/index.qmd | 547 +++++++++++++++++ 9 files changed, 1113 insertions(+), 2 deletions(-) create mode 100644 content/blog/2023-dutch-elections/index.markdown_strict_files/figure-markdown_strict/peilingwijzer-plot-1.png create mode 100644 content/blog/2023-dutch-elections/index.markdown_strict_files/figure-markdown_strict/plot-exit-polls-2100-1.png create mode 100644 content/blog/2023-dutch-elections/index.markdown_strict_files/figure-markdown_strict/plot-political-spectrum-1.png create mode 100644 content/blog/2023-dutch-elections/index.markdown_strict_files/figure-markdown_strict/unnamed-chunk-5-1.png create mode 100644 content/blog/2023-dutch-elections/index.markdown_strict_files/figure-markdown_strict/unnamed-chunk-7-1.png create mode 100644 content/blog/2023-dutch-elections/index.markdown_strict_files/figure-markdown_strict/unnamed-chunk-8-1.png create mode 100644 content/blog/2023-dutch-elections/index.md create mode 100644 content/blog/2023-dutch-elections/index.qmd diff --git a/config.toml b/config.toml index a86d351..4fce0a4 100644 --- a/config.toml +++ b/config.toml @@ -2,7 +2,9 @@ baseurl = "/" title = "Daniel Roelfs" theme = "typography" -ignoreFiles = ["\\.qmd$", "\\.ipynb$", "\\.Rmd$", "\\.Rmarkdown$", "_cache$", "index\\.html", "about\\.html", "static/blog/\\*_files/", "README.md"] +ignoreFiles = ["\\.qmd$", "\\.ipynb$", "\\.Rmd$", 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+description: "Visualizing the Dutch elections throughout the night" +slug: visualizing-the-dutch-elections +categories: + - society +tags: + - R + - ggplot +editor_options: + chunk_output_type: console +--- + +So it's time again for the Netherlands to have another election for the Dutch parliament ([*Tweede Kamer*](https://en.wikipedia.org/wiki/House_of_Representatives_(Netherlands))) The previous one was only in 2021, and we had elections in 2023 already for the Provinces and indirectly for the Dutch "Senate" ([*Eerste Kamer*](https://en.wikipedia.org/wiki/Senate_(Netherlands))), but the parliamentary elections are the most important ones, usually taking place every four years (until one of the coalition partners believes they cannot support the government anymore, or if one of parties in government believe it's in their best interest to have another election sooner rather than later). + +Anyway, parliamentary elections are a prime example of how and why data vizualization is important. Data is used extensively throughout the political campaigns to get political points across (whether it's used justly or disingenuously). For data analysts and data journalists, vizualizing the polls, results, and trends as the data comes in is a key challenge throughout the night. Since this is a unique event that happens (hopefully) only once every four years, so I thought I'd jump on the opportunity to provide my (admittedly unrequested) take on the data visualizations that are important to share. + +The plots I'll share here are a bit more complicated than the usual ones (especially in cases where I actively try to recreate a plot), so I'll hide the code to each plot by default, but you can see the code by clicking "Show code" or "Show code for the plot". + +I'll also separate this post in when I added the new plots and data. I'll mark the (general) time I added a new section. + +{{< omission >}} + +## 21 November + +Some of the plots I could create beforehand, this data about the polls leading up to election dayy. I also made sure to create a few datasets that might help me speed up the work during election night. So I'll add color and accent colors and their political identity. First, I'll load the packages we'll use here. I'll also load a custom font I'll use for the plots. + +

+Show code + +``` r +library(tidyverse) +library(ggtext) +library(rvest) +library(showtext) + +font_add_google("Bitter", family = "custom") +showtext_auto() +``` + +
+ +{{< sidenote br="3em" >}} +There are a ton of voting advice websites, but only a few (including *KiesKompas*) are generally recommended by a variety of political parties across the spectrum +{{< /sidenote >}} + +Then we can create a data frame with the party names and their identifying colors. I'll also add their political identity, i.e. how far left or right the parties are and how progressive or conservative the parties are. This data comes from the webpage [*the political landscape*](https://tweedekamer2023.kieskompas.nl/nl/results/compass) from the Dutch voting advice website [*KiesKompas*](https://www.kieskompas.nl) which offers one of the most popular application that helps voters identify what parties their values most closely align with. It's a tool by the Vrije Universiteit Amsterdam (VU Amsterdam) and is generally well-respected. However, their political landscape is a bit controversial because it tends to "average" across a set of issues, while perhaps ignoring some of the key identifying issue a party is campaigning on (historically e.g. immigration and the PVV). + +One more complication here is that two parties, *GroenLinks* (GL) and the *Partij van de Arbeid* (PvdA), campaigned under a single banner, referred to here as *GL-PvdA*. When comparing to previous elections, polls and election outcomes should probably be summed across those two parties to better reflect the trends. + +
+Show code + +``` r +data_parties <- tribble( + ~party, ~leftright, ~progcon, ~color, ~text_color, + "VVD", 50, -32, "#EE6F2D", "#333333", + "D66", -5, 58, "#4DAA4F", "#333333", + "GL-PvdA", -55, 66, "#CD3029", "grey95", + "PVV", 9, -74, "grey92", "#333333", + "CDA", 23, -24, "#62C45D", "#333333", + "SP", -77, 21, "#DA3732", "grey95", + "FvD", 64, -92, "#9B2820", "grey95", + "PvdD", -95, 82, "#317142", "grey95", + "CU", -32, 21, "#46A4E2", "#333333", + "Volt", -14, 84, "#4A2975", "grey95", + "JA21", 77, -89, "#242C54", "grey95", + "SGP", 27, -42, "#DA632C", "#333333", + "DENK", -68, 32, "#50B4B1", "#333333", + "50PLUS", -23, -3, "#8C3D83", "grey95", + "BBB", 9, -42, "#9DBD43", "#333333", + "BIJ1", -95, 92, "#FFFD54", "#333333", + "BVNL", 100, -89, "#1D2540", "grey95", + "NSC", -9, -8, "#121439", "grey95" +) + +data_historical_parties <- tribble( + ~party, ~hist_leftright, ~hist_progcon, ~hist_color, ~hist_text_color, + "PvdA", -65, 50, "#D22C21", "grey95", + "GroenLinks", -70, 68, "#5BA546", "#333333", + "ChristenUnie", -32, 21, "#47A6E5", "#333333", +) +``` + +
+ +Recreating the political compass is fairly simple once the data is loaded. It's in principle simply a scatterplot. Making it look informative by labeling the points directly and adding the colors and annotations in an informative matter is the only challenge here. + +
+Show code + +``` r +compass_text <- tribble( + ~leftright, ~progcon, ~label, + -100, 5, "Left", + 80, 5, "Right", + 2, 96, "Progressive", + 2, -96, "Conservative" +) + +data_parties |> + mutate(party = str_replace(party, "-", "-\n")) |> + ggplot(aes(x = leftright, y = progcon, color = color)) + + geom_hline(yintercept = 0, color = "#333333") + + geom_vline(xintercept = 0, color = "#333333") + + geom_text( + data = compass_text, aes(label = label), + size = 5, color = "#333333", + hjust = 0, fontface = "bold", family = "custom" + ) + + geom_point(size = 16) + + geom_text( + aes(label = party, color = text_color), + size = 4, lineheight = 1, + fontface = "bold", family = "custom" + ) + + labs( + title = "**Dutch political spectrum**", + x = NULL, + y = NULL, + caption = "**Source**: KiesKompas" + ) + + scale_x_continuous( + limits = c(-100, 100), + labels = NULL, + expand = expansion(add = 0) + ) + + scale_y_continuous( + limits = c(-100, 100), + labels = NULL, + expand = expansion(add = 0) + ) + + scale_color_identity() + + coord_equal(clip = "off") + + theme_minimal(base_family = "custom") + + theme( + text = element_text(color = "#333333"), + plot.title.position = "plot", + plot.title = element_markdown(size = 32, margin = margin(l = -50)), + plot.caption.position = "plot", + plot.caption = element_markdown(margin = margin(t = 10, r = -50)), + panel.grid = element_blank() + ) +``` + +
+ + + +{{< sidenote br="12em" >}} +Geert Wilders is also the party's sole member ([link](https://en.wikipedia.org/wiki/Party_for_Freedom#Organisation_and_support)) +{{< /sidenote >}} + +It's immediately visible that the political parties (as broken down by *KiesKompas*) are aligned across a diagonal from right and conservative to left and progressive. Parties that are both left-wing and conservative, or right-wing and progressive are not really present in the mainstream Dutch political system (or in most foreign ones as far as I'm aware). There are three parties in the lower-right corner (FvD, JA21, and BVNL). JA21 and BVNL are both factions that left the FvD after internal disagreement. The PVV is perhaps best known internationally for their leader [Geert Wilders](https://www.britannica.com/biography/Geert-Wilders). According to *KiesKompas* they are somewhere in the middle between left and right-wing, but in this case the party can probably be best described as *populist*, adapting both policies from the left and right. Internationally, they associate most with far-right parties and in the Netherlands they are also commonly called far-right. + +The only squarely left-wing party of significance this election is the coalition GL-PvdA, led by former EU Commissioner [Frans Timmermans](https://www.theguardian.com/world/2023/jul/20/eu-climate-chief-frans-timmermans-quits-to-run-in-dutch-elections). + +Before I move to the lastest polls, I can show how the seats have been distributed across the political parties over the past election cycles. For this I'll quickly scrape the [parlement.com](https://www.parlement.com) website for the seat distribution across the past few elections. + +
- + {{< sidenote >}} In contrast to [Geert Wilders](https://www.britannica.com/biography/Geert-Wilders), [Mark Rutte](https://www.britannica.com/biography/Mark-Rutte) does not have an elaborate entry in the Encyclopedia Brittanica @@ -309,13 +309,16 @@ data_polls_pre_new <- read_rds("./data/polls_wk_46.rds") |> ) |> group_by(party) |> summarise(across(everything(), sum)) |> - filter(!party %in% c("GL", "PvdA")) |> - mutate(party = case_when(str_detect(party, "50") ~ "50PLUS", - str_detect(party, "VOLT") ~ "Volt", - TRUE ~ party)) + filter(!party %in% c("GL", "PvdA")) |> + mutate(party = case_when( + str_detect(party, "50") ~ "50PLUS", + str_detect(party, "VOLT") ~ "Volt", + TRUE ~ party + )) data_polls_pre_new |> - pivot_longer(cols = contains("seats"), names_to = "seats_var", values_to = "n_seats") |> + pivot_longer(cols = contains("seats"), + names_to = "seats_var", values_to = "n_seats") |> filter(seats_var %in% c("current_seats", "polls_seats_wk_46")) |> ggplot(aes(x = n_seats, y = reorder(party, polls_perc_wk_46), fill = seats_var)) + geom_vline(xintercept = 0, color = "#333333") + @@ -356,7 +359,7 @@ data_polls_pre_new |> Warning: Removed 6 rows containing missing values (`geom_text()`). - + The biggest thing to notice here is the party NSC which was started by Pieter Omtzigt, formerly a member of the CDA. This party participates in the 2023 parliamentary elections for the first time, so it doesn't have anything to compare to. According to these polls, the NSC will go in one go to 26 seats. The VVD is still the biggest, but loses a few seats. Other than the VVD, the biggest losers in these polls are the D66, CDA, FvD, and SP. @@ -392,22 +395,29 @@ polls_peilingwijzer <- tribble( "50PLUS", 0, 1, ) -polls_peilingwijzer |> - inner_join(data_parties) |> - inner_join(data_polls_pre_new |> select(party, current_seats)) |> +polls_peilingwijzer |> + inner_join(data_parties) |> + inner_join(data_polls_pre_new |> + select(party, current_seats)) |> mutate( color = ifelse(color == "grey92", "grey", color), - range_max_label = ifelse(range_max <= 2, str_glue("{range_min}-{range_max}"), range_max) + range_max_label = ifelse(range_max <= 2, + str_glue("{range_min}-{range_max}"), range_max) ) |> - ggplot(aes(x = current_seats, y = reorder(party, range_max), - color = color, fill = color)) + + ggplot(aes( + x = current_seats, y = reorder(party, range_max), + color = color, fill = color + )) + geom_vline(xintercept = 0, color = "#333333", linewidth = 1) + geom_col(color = "transparent", width = 0.25, alpha = 0.5, just = 2.5) + geom_crossbar( - aes(x = range_min + round((range_max - range_min) / 2), - xmin = range_min, xmax = range_max, - color = colorspace::lighten(color, amount = 0.5)), - width = 0.5, linewidth = 1, fatten = 2) + + aes( + x = range_min + round((range_max - range_min) / 2), + xmin = range_min, xmax = range_max, + color = colorspace::lighten(color, amount = 0.5) + ), + width = 0.5, linewidth = 1, fatten = 2 + ) + geom_text( aes(x = range_min, label = range_min), color = "#333333", size = 4, family = "custom", @@ -422,7 +432,7 @@ polls_peilingwijzer |> data = tibble(), aes(x = 27, y = 12, label = "Previous\nelections"), inherit.aes = FALSE, - size = 4, family = "custom", + size = 4, family = "custom", hjust = 0, lineheight = 0.75 ) + geom_curve( @@ -479,7 +489,7 @@ The voting booths (at least in the mainland part of the Netherlands) close at 21 Show code for the plot ``` r -data_current_seats <- data_polls_pre_new |> +data_current_seats <- data_polls_pre_new |> select(party, current_perc, current_seats) exit_polls_2100 <- tribble( @@ -488,7 +498,7 @@ exit_polls_2100 <- tribble( "PVV", 35, "GL-PvdA", 26, "NSC", 20, - "D66", 10, + "D66", 10, "BBB", 7, "SP", 5, "PvdD", 4, @@ -504,19 +514,24 @@ exit_polls_2100 <- tribble( "50PLUS", 1 ) -exit_polls_2100 |> - inner_join(data_parties, by = "party") |> - inner_join(data_current_seats, by = "party") |> - pivot_longer(cols = c(polls_2100, current_seats), - names_to = "variable", values_to = "seats") |> +exit_polls_2100 |> + inner_join(data_parties, by = "party") |> + inner_join(data_current_seats, by = "party") |> + pivot_longer( + cols = c(polls_2100, current_seats), + names_to = "variable", values_to = "seats" + ) |> mutate( color = ifelse(str_detect(variable, "current"), "grey80", color), color = ifelse(color == "grey92", "grey40", color), - text_color = ifelse(str_detect(variable, "polls") & party == "PVV", "white", text_color), + text_color = ifelse(str_detect(variable, "polls") & party == "PVV", + "white", text_color), text_color = ifelse(str_detect(variable, "current"), "#333333", text_color) - ) |> - ggplot(aes(x = seats, y = reorder(party, current_perc), - group = variable, fill = color)) + + ) |> + ggplot(aes( + x = seats, y = reorder(party, current_perc), + group = variable, fill = color + )) + geom_col(position = position_dodge(), key_glyph = "point") + geom_vline(xintercept = 0, color = "#333333") + geom_text( @@ -530,7 +545,7 @@ exit_polls_2100 |> x = "Number of seats in parliament", y = NULL, fill = NULL, - caption = "**Source**: NOS" + caption = "**Source**: Ipsos, comissioned by NOS and RTL" ) + scale_x_continuous( position = "top", @@ -559,4 +574,89 @@ Out of the blue, contrary to basically any serious poll, the PVV party from Geer {{< omission >}} -*Data and analysis will be added throughout the night* +## 22 November 22:00 + +An hour later, at 22:00, the next exit polls was released. After the shock of the first one I think everyone was quite nervous to see whether these next exit polls were consistent with the first or if there was some course correction, but the second exit polls were very consistent. No party changed more than 1 seat in either direction compared to the polls released at 21:00. I'll try to visualize the difference, showing how flat the differences are. + +
+Show code for the plot + +``` r +exit_polls_2200 <- tribble( + ~party, ~polls_2200, + "VVD", 24, + "PVV", 35, + "GL-PvdA", 25, + "NSC", 20, + "D66", 10, + "BBB", 7, + "SP", 5, + "PvdD", 3, + "CU", 3, + "CDA", 5, + "FvD", 3, + "DENK", 3, + "Volt", 2, + "SGP", 3, + "JA21", 1, + "BVNL", 0, + "BIJ1", 0, + "50PLUS", 1 +) + +exit_polls_2200 |> + inner_join(exit_polls_2100) |> + inner_join(data_parties, by = "party") |> + pivot_longer(cols = starts_with("polls"), + names_to = "time", values_to = "seats") |> + mutate( + time = str_remove(time, "polls_"), + time = lubridate::parse_date_time(time, "HM", tz=""), + color = ifelse(party == "PVV", "grey40", color), + text_color = ifelse(party == "PVV", + "white", text_color), + ) |> + ggplot(aes(x = time, y = seats, color = color)) + + geom_path() + + geom_point(size = 3) + + geom_text( + aes(label = seats, color = text_color), + size = 2 + ) + + ggrepel::geom_text_repel( + data = . %>% group_by(party) %>% slice_max(time), + mapping = aes(label = party), + hjust = 0, size = 2, seed = 42, + max.overlaps = 20, + fontface = "bold", family = "custom" + ) + + labs( + title = "Difference in exit polls
between 21:00 and 22:00", + subtitle = "Little change across the board, maximum difference is 1 seat", + x = NULL, + y = "Number of seats", + caption = "**Source**: Ipsos, comissioned by NOS and RTL" + ) + + scale_x_datetime( + expand = expansion(add = c(50, 500)) + ) + + scale_color_identity() + + theme_minimal(base_family = "custom") + + coord_fixed(ratio = 120, clip = "off") + + theme( + plot.title.position = "plot", + plot.title = element_markdown(size = 16, face = "bold"), + plot.subtitle = element_markdown(lineheight = 0.67), + plot.caption.position = "plot", + plot.caption = element_markdown(), + legend.position = c(0.8, 0.2) + ) +``` + +
+ + + +{{< omission >}} + +It looks like the results per municipality will take some time longer, so I'll go to bed in the meantime and I'll update this post tomorrow with results per municipality and/or province. diff --git a/content/blog/2023-dutch-elections/index.qmd b/content/blog/2023-dutch-elections/index.qmd index 4e72a5f..1297848 100644 --- a/content/blog/2023-dutch-elections/index.qmd +++ b/content/blog/2023-dutch-elections/index.qmd @@ -178,6 +178,7 @@ data_tk_history <- "https://www.parlement.com/id/vh8lnhronvx6/zetelverdeling_twe Then we can merge this dataset with the metadata tibble I created earlier to get the colors so I can use them in plotting. This plot is a simple "time-series"-like plot using `geom_path()`. The biggest challenge here was to annotate all the parties properly. I chose for a combination of both labeling each line explicitly and adding a legend with the specification for each color. However, since many parties use very similar colors this legend alone was not enough. ```{r} +#| label: plot-seat-historical-distributions #| warning: false #| code-fold: true #| code-summary: "Show code" @@ -261,6 +262,7 @@ I scraped this data earlier in the week, so the current table online does not re Let's now also look at some polls to see what election night might bring. For this I'll scrape the results from [Ipsos](https://www.ipsos.com). It contains the polls from week 44 and week 46. ```{r} +#| label: scrape-ipsos-polls-wk46 #| eval: false #| code-fold: true #| code-summary: "Show code" @@ -289,6 +291,7 @@ data_polls_pre |> write_rds("./data/polls_wk_46.rds") From this table I'll use only the polls from week 46 (latest available at the time). To better compare the polls to the current situation without drowning out the most important data I'll show the current seat distribution in grey, and the predicted seats in red. ```{r} +#| label: plot-ipsos-polls-wk46 #| code-fold: true #| code-summary: "Show code for the plot" @@ -534,7 +537,7 @@ exit_polls_2100 |> x = "Number of seats in parliament", y = NULL, fill = NULL, - caption = "**Source**: NOS" + caption = "**Source**: Ipsos, comissioned by NOS and RTL" ) + scale_x_continuous( position = "top", @@ -559,4 +562,89 @@ Out of the blue, contrary to basically any serious poll, the PVV party from Geer {{{< omission >}}} -_Data and analysis will be added throughout the night_ +## 22 November 22:00 + +An hour later, at 22:00, the next exit polls was released. After the shock of the first one I think everyone was quite nervous to see whether these next exit polls were consistent with the first or if there was some course correction, but the second exit polls were very consistent. No party changed more than 1 seat in either direction compared to the polls released at 21:00. I'll try to visualize the difference, showing how flat the differences are. + +```{r} +#| label: plot-exit-polls-2200 +#| warning: false +#| fig-height: 6 +#| code-fold: true +#| code-summary: "Show code for the plot" + +exit_polls_2200 <- tribble( + ~party, ~polls_2200, + "VVD", 24, + "PVV", 35, + "GL-PvdA", 25, + "NSC", 20, + "D66", 10, + "BBB", 7, + "SP", 5, + "PvdD", 3, + "CU", 3, + "CDA", 5, + "FvD", 3, + "DENK", 3, + "Volt", 2, + "SGP", 3, + "JA21", 1, + "BVNL", 0, + "BIJ1", 0, + "50PLUS", 1 +) + +exit_polls_2200 |> + inner_join(exit_polls_2100) |> + inner_join(data_parties, by = "party") |> + pivot_longer(cols = starts_with("polls"), + names_to = "time", values_to = "seats") |> + mutate( + time = str_remove(time, "polls_"), + time = lubridate::parse_date_time(time, "HM", tz=""), + color = ifelse(party == "PVV", "grey40", color), + text_color = ifelse(party == "PVV", + "white", text_color), + ) |> + ggplot(aes(x = time, y = seats, color = color)) + + geom_path() + + geom_point(size = 3) + + geom_text( + aes(label = seats, color = text_color), + size = 2 + ) + + ggrepel::geom_text_repel( + data = . %>% group_by(party) %>% slice_max(time), + mapping = aes(label = party), + hjust = 0, size = 2, seed = 42, + max.overlaps = 20, + fontface = "bold", family = "custom" + ) + + labs( + title = "Difference in exit polls
between 21:00 and 22:00", + subtitle = "Little change across the board, maximum difference is 1 seat", + x = NULL, + y = "Number of seats", + caption = "**Source**: Ipsos, comissioned by NOS and RTL" + ) + + scale_x_datetime( + expand = expansion(add = c(50, 500)) + ) + + scale_color_identity() + + theme_minimal(base_family = "custom") + + coord_fixed(ratio = 120, clip = "off") + + theme( + plot.title.position = "plot", + plot.title = element_markdown(size = 16, face = "bold"), + plot.subtitle = element_markdown(lineheight = 0.67), + plot.caption.position = "plot", + plot.caption = element_markdown(), + legend.position = c(0.8, 0.2) + ) +``` + +{{{< omission >}}} + +It looks like the results per municipality will take some time longer, so I'll go to bed in the meantime and I'll update this post tomorrow with results per municipality and/or province. + From b3b26eb4c8afd26a04c53fdcfe4cb204bda2b8c2 Mon Sep 17 00:00:00 2001 From: Daniel Roelfs Date: Thu, 23 Nov 2023 00:28:28 +0100 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+Show code + +``` r +data_tk_history <- "https://www.parlement.com/id/vh8lnhronvx6/zetelverdeling_tweede_kamer" |> + read_html() |> + html_elements("table") |> + html_table() |> + first() |> + janitor::clean_names() |> + rename_with(~ str_glue("election_{str_sub(.x, 2, 5)}")) |> + rename(party = election_arti) |> + mutate( + party = str_extract(party, "[^ + +Then we can merge this dataset with the metadata tibble I created earlier to get the colors so I can use them in plotting. This plot is a simple "time-series"-like plot using `geom_path()`. The biggest challenge here was to annotate all the parties properly. I chose for a combination of both labeling each line explicitly and adding a legend with the specification for each color. However, since many parties use very similar colors this legend alone was not enough. + +
+Show code + +``` r +data_tk_hist <- data_tk_history |> + left_join(data_parties, by = "party") |> + left_join(data_historical_parties, by = "party") |> + mutate( + color = coalesce(color, hist_color), + text_color = coalesce(text_color, hist_text_color) + ) + +legend_list <- data_tk_hist |> + select(color, party) |> + deframe() + +data_tk_hist |> + pivot_longer(starts_with("election"), + names_to = "year", values_to = "seats" + ) |> + mutate( + year = parse_number(year) + ) |> + ggplot(aes(x = year, y = seats, color = color)) + + geom_hline(yintercept = 0, color = "#333333", alpha = 0.5) + + geom_vline( + xintercept = na.omit(names(data_tk_hist) |> parse_number()), + color = "#333333", alpha = 0.5 + ) + + geom_path(linewidth = 1, key_glyph = "point") + + geom_point(size = 3) + + ggrepel::geom_text_repel( + data = . %>% slice_max(year), + aes(label = party), + xlim = c(2021, NA), min.segment.length = 0, + hjust = 0, size = 3, fontface = "bold", family = "custom" + ) + + labs( + title = "Historical seat distribution
in the past few elections", + x = NULL, + y = "Number of seats in parliament", + color = NULL, + caption = "**Source**: parlement.com" + ) + + scale_x_continuous( + breaks = na.omit(names(data_tk_hist) |> parse_number()), + expand = expansion(add = c(0, 1.5)) + ) + + scale_y_continuous( + expand = expansion(add = c(0, 2)) + ) + + scale_color_identity( + labels = legend_list, + guide = guide_legend(override.aes = list(size = 5), nrow = 3) + ) + + coord_cartesian(clip = "off") + + theme_minimal(base_family = "custom") + + theme( + text = element_text(color = "#333333"), + plot.title = element_markdown(size = 20, face = "bold"), + plot.title.position = "plot", + plot.caption = element_markdown(), + plot.caption.position = "plot", + plot.margin = margin(l = 10, t = 10), + legend.position = "bottom", + panel.grid.major.x = element_blank(), + panel.grid.minor.x = element_blank() + ) +``` + +
+ + + +{{< sidenote >}} +In contrast to [Geert Wilders](https://www.britannica.com/biography/Geert-Wilders), [Mark Rutte](https://www.britannica.com/biography/Mark-Rutte) does not have an elaborate entry in the Encyclopedia Brittanica +{{< /sidenote >}} + +This plot immediately shows the dominance of the VVD, the party of [Mark Rutte](https://en.wikipedia.org/wiki/Mark_Rutte) who is quite well-known internationally. The PvdA, which internationally associates with traditional labour parties, used to be the biggest opponent to the centre-right CDA and the right-wing VVD, but have since then failed to connect with the voters. + +{{< sidenote >}} +I scraped this data earlier in the week, so the current table online does not reflect the data used here +{{< /sidenote >}} + +Let's now also look at some polls to see what election night might bring. For this I'll scrape the results from [Ipsos](https://www.ipsos.com). It contains the polls from week 44 and week 46. + +
+Show code + +``` r +data_polls_pre <- "https://www.ipsos.com/sites/default/files/nl/nl/politiekebarometer/Link_Tabel.html" |> + read_html() |> + html_elements("table") |> + html_table(header = TRUE) |> + first() |> + janitor::clean_names() |> + filter(nchar(x) > 0) |> + rename( + party = x, + current_perc = tweede_kamer, + current_seats = tweede_kamer_2, + polls_perc_wk_44 = week_441_nov_23, + polls_seats_wk_44 = week_441_nov_23_2, + polls_perc_wk_46 = week_4615_nov_23, + polls_seats_wk_46 = week_4615_nov_23_2 + ) |> + mutate(across(-party, as.numeric)) + +data_polls_pre |> write_rds("./data/polls_wk_46.rds") +``` + +
+ +From this table I'll use only the polls from week 46 (latest available at the time). To better compare the polls to the current situation without drowning out the most important data I'll show the current seat distribution in grey, and the predicted seats in red. + +
+Show code for the plot + +``` r +data_polls_pre_new <- read_rds("./data/polls_wk_46.rds") |> + bind_rows( + read_rds("./data/polls_wk_46.rds") |> + filter(party %in% c("GL", "PvdA")) |> + summarise( + party = "GL-PvdA", across(-party, sum) + ) + ) |> + group_by(party) |> + summarise(across(everything(), sum)) |> + filter(!party %in% c("GL", "PvdA")) |> + mutate(party = case_when(str_detect(party, "50") ~ "50PLUS", + str_detect(party, "VOLT") ~ "Volt", + TRUE ~ party)) + +data_polls_pre_new |> + pivot_longer(cols = contains("seats"), names_to = "seats_var", values_to = "n_seats") |> + filter(seats_var %in% c("current_seats", "polls_seats_wk_46")) |> + ggplot(aes(x = n_seats, y = reorder(party, polls_perc_wk_46), fill = seats_var)) + + geom_vline(xintercept = 0, color = "#333333") + + geom_col(position = position_dodge(), key_glyph = "point") + + geom_text( + aes(x = n_seats - 0.5, label = n_seats), + position = position_dodge(width = 1), + color = "white", size = 3, family = "custom" + ) + + labs( + title = "Predicted seat distribution from Week 46", + x = NULL, + y = NULL, + fill = NULL, + caption = "**Source**: Ipsos" + ) + + scale_x_continuous( + position = "top", + limits = c(0, NA), + expand = expansion(add = c(0, NA)) + ) + + scale_fill_manual( + values = c("current_seats" = "grey60", "polls_seats_wk_46" = "firebrick"), + labels = c("Current seats", "Predicted seats (wk 46)"), + guide = guide_legend(override.aes = list(shape = 21, size = 6)) + ) + + theme_minimal(base_family = "custom") + + theme( + plot.title.position = "plot", + plot.title = element_markdown(size = 24, face = "bold"), + plot.caption.position = "plot", + plot.caption = element_markdown(), + legend.position = c(0.8, 0.2) + ) +``` + +
+ + Warning: Removed 6 rows containing missing values (`geom_text()`). + + + +The biggest thing to notice here is the party NSC which was started by Pieter Omtzigt, formerly a member of the CDA. This party participates in the 2023 parliamentary elections for the first time, so it doesn't have anything to compare to. According to these polls, the NSC will go in one go to 26 seats. The VVD is still the biggest, but loses a few seats. Other than the VVD, the biggest losers in these polls are the D66, CDA, FvD, and SP. + +{{< sidenote >}} +Tom Louwerse also contributes to the Irish Polling Indicator using the same methods, which describes methods in English +{{< /sidenote >}} + +This is just one plot from one pollster. There is however a polling aggregator, called the [*Peilingwijzer*](https://peilingwijzer.tomlouwerse.nl), which is maintained by political scientist [Tom Louwerse](https://www.tomlouwerse.nl) at Leiden University. It uses a Bayesian approach to weigh a collection of polls from various sources (description in [Dutch](https://peilingwijzer.tomlouwerse.nl/methode.html#statistisch-model) and [English](https://pollingindicator.com/method/)). This way he gets a better estimate of the uncertainty across several pollsters and polling dates. I know he does a lot of his analyses in R, so I'll try to recreate his plot on the [main website](https://peilingwijzer.tomlouwerse.nl) just as a challenge (and perhaps make one or two things a bit more aesthetically pleasing). It seems he uses the somewhat niche (at least in my field) `geom_crossbar()`. + +
+Show code + +``` r +polls_peilingwijzer <- tribble( + ~party, ~range_min, ~range_max, + "VVD", 26, 30, + "PVV", 25, 29, + "GL-PvdA", 23, 27, + "NSC", 19, 23, + "D66", 8, 11, + "BBB", 4, 7, + "SP", 4, 6, + "PvdD", 4, 6, + "CU", 3, 5, + "CDA", 3, 5, + "FvD", 3, 5, + "DENK", 3, 5, + "Volt", 2, 4, + "SGP", 2, 4, + "JA21", 0, 2, + "BVNL", 0, 1, + "BIJ1", 0, 1, + "50PLUS", 0, 1, +) + +polls_peilingwijzer |> + inner_join(data_parties) |> + inner_join(data_polls_pre_new |> select(party, current_seats)) |> + mutate( + color = ifelse(color == "grey92", "grey", color), + range_max_label = ifelse(range_max <= 2, str_glue("{range_min}-{range_max}"), range_max) + ) |> + ggplot(aes(x = current_seats, y = reorder(party, range_max), + color = color, fill = color)) + + geom_vline(xintercept = 0, color = "#333333", linewidth = 1) + + geom_col(color = "transparent", width = 0.25, alpha = 0.5, just = 2.5) + + geom_crossbar( + aes(x = range_min + round((range_max - range_min) / 2), + xmin = range_min, xmax = range_max, + color = colorspace::lighten(color, amount = 0.5)), + width = 0.5, linewidth = 1, fatten = 2) + + geom_text( + aes(x = range_min, label = range_min), + color = "#333333", size = 4, family = "custom", + nudge_x = -0.75 + ) + + geom_text( + aes(x = range_max, label = range_max_label), + color = "#333333", size = 4, family = "custom", + nudge_x = ifelse(polls_peilingwijzer$range_max <= 2, 1, 0.75) + ) + + geom_text( + data = tibble(), + aes(x = 27, y = 12, label = "Previous\nelections"), + inherit.aes = FALSE, + size = 4, family = "custom", + hjust = 0, lineheight = 0.75 + ) + + geom_curve( + data = tibble(), + aes(x = 26.75, y = 12, xend = 24.2, yend = 13.2), + inherit.aes = FALSE, curvature = -0.1, + arrow = arrow(length = unit(0.4, "lines")) + ) + + labs( + title = "Latest _Peilingwijzer_", + subtitle = "_Peilingwijzer_ combines the polls from I&O Research & Ipsos/EenVandaag", + x = "Seats", + y = NULL, + caption = "_Peilingwijzer_, based on I&O Research & Ipsos&EenVandaag
+ Tom Louwerse, Universiteit Leiden,
+ Last change: 21-11-2023" + ) + + scale_x_continuous( + limits = c(0, NA), + expand = expansion(add = c(0, NA)) + ) + + scale_y_discrete( + expand = expansion(add = c(1, 0)) + ) + + scale_color_identity() + + scale_fill_identity() + + theme_minimal(base_family = "custom") + + theme( + plot.title.position = "plot", + plot.title = element_markdown(size = 32, face = "bold"), + plot.subtitle = element_markdown(size = 14), + plot.caption.position = "plot", + plot.caption = element_markdown(lineheight = 0.75), + axis.text.y = element_markdown(size = 10, margin = margin(t = 10, r = 5)), + panel.grid.major.y = element_blank() + ) +``` + +
+ + + +{{< omission >}} + +## 22 November 21:00 + +{{< sidenote >}} +There are three municipalities in the Carribean that can vote until 02:00 CET +{{< /sidenote >}} + +The voting booths (at least in the mainland part of the Netherlands) close at 21:00. This also marks the release of the first exit polls. I'm just copying the numbers from the TV broadcast as they come in and saving them in a `tibble()` using the `tribble()` function. + +
+Show code for the plot + +``` r +data_current_seats <- data_polls_pre_new |> + select(party, current_perc, current_seats) + +exit_polls_2100 <- tribble( + ~party, ~polls_2100, + "VVD", 23, + "PVV", 35, + "GL-PvdA", 26, + "NSC", 20, + "D66", 10, + "BBB", 7, + "SP", 5, + "PvdD", 4, + "CU", 3, + "CDA", 5, + "FvD", 3, + "DENK", 2, + "Volt", 2, + "SGP", 3, + "JA21", 1, + "BVNL", 0, + "BIJ1", 0, + "50PLUS", 1 +) + +exit_polls_2100 |> + inner_join(data_parties, by = "party") |> + inner_join(data_current_seats, by = "party") |> + pivot_longer(cols = c(polls_2100, current_seats), + names_to = "variable", values_to = "seats") |> + mutate( + color = ifelse(str_detect(variable, "current"), "grey80", color), + color = ifelse(color == "grey92", "grey40", color), + text_color = ifelse(str_detect(variable, "polls") & party == "PVV", "white", text_color), + text_color = ifelse(str_detect(variable, "current"), "#333333", text_color) + ) |> + ggplot(aes(x = seats, y = reorder(party, current_perc), + group = variable, fill = color)) + + geom_col(position = position_dodge(), key_glyph = "point") + + geom_vline(xintercept = 0, color = "#333333") + + geom_text( + aes(x = seats - 0.5, label = seats, color = text_color), + position = position_dodge(width = 1), + size = 3, family = "custom" + ) + + labs( + title = "Exit polls from 22 November 22:00", + subtitle = "Colored bars show the predicted number of seats,
grey bars indicate current seats", + x = "Number of seats in parliament", + y = NULL, + fill = NULL, + caption = "**Source**: NOS" + ) + + scale_x_continuous( + position = "top", + limits = c(0, NA), + expand = expansion(add = c(0, 2)) + ) + + scale_color_identity() + + scale_fill_identity() + + theme_minimal(base_family = "custom") + + theme( + plot.title.position = "plot", + plot.title = element_markdown(size = 24, face = "bold"), + plot.subtitle = element_markdown(lineheight = 0.67), + plot.caption.position = "plot", + plot.caption = element_markdown(), + axis.text.y = element_markdown(size = 10), + legend.position = c(0.8, 0.2) + ) +``` + +
+ + + +Out of the blue, contrary to basically any serious poll, the PVV party from Geert Wilders appears to become the largest party, followed by GL-PvdA. This is a major upset that would open the door for a very right and conservative government. This would unfortunately mean the latest blow to meaningful climate action, an increase anti-immigrant policy, decreased government support for Ukraine, increased support for Israel and the atrocities they commit in Gaza and the rest of Palestine, and possibly a host of challenges to the rule of law in the Netherlands. + +{{< omission >}} + +*Data and analysis will be added throughout the night* diff --git a/content/blog/2023-dutch-elections/index.qmd b/content/blog/2023-dutch-elections/index.qmd new file mode 100644 index 0000000..1bdebf6 --- /dev/null +++ b/content/blog/2023-dutch-elections/index.qmd @@ -0,0 +1,547 @@ +--- +title: Visualizing the Dutch elections throughout the night +date: 2023-11-22 +description: "Visualizing the Dutch elections throughout the night" +slug: visualizing-the-dutch-elections +categories: + - society +tags: + - R + - ggplot +editor_options: + chunk_output_type: console +--- + +So it's time again for the Netherlands to have another election for the Dutch parliament ([_Tweede Kamer_](https://en.wikipedia.org/wiki/House_of_Representatives_(Netherlands))) The previous one was only in 2021, and we had elections in 2023 already for the Provinces and indirectly for the Dutch "Senate" ([_Eerste Kamer_](https://en.wikipedia.org/wiki/Senate_(Netherlands))), but the parliamentary elections are the most important ones, usually taking place every four years (until one of the coalition partners believes they cannot support the government anymore, or if one of parties in government believe it's in their best interest to have another election sooner rather than later). + +Anyway, parliamentary elections are a prime example of how and why data vizualization is important. Data is used extensively throughout the political campaigns to get political points across (whether it's used justly or disingenuously). For data analysts and data journalists, vizualizing the polls, results, and trends as the data comes in is a key challenge throughout the night. Since this is a unique event that happens (hopefully) only once every four years, so I thought I'd jump on the opportunity to provide my (admittedly unrequested) take on the data visualizations that are important to share. + +The plots I'll share here are a bit more complicated than the usual ones (especially in cases where I actively try to recreate a plot), so I'll hide the code to each plot by default, but you can see the code by clicking "Show code" or "Show code for the plot". + +I'll also separate this post in when I added the new plots and data. I'll mark the (general) time I added a new section. + +{{{< omission >}}} + +## 21 November + +Some of the plots I could create beforehand, this data about the polls leading up to election dayy. I also made sure to create a few datasets that might help me speed up the work during election night. So I'll add color and accent colors and their political identity. First, I'll load the packages we'll use here. I'll also load a custom font I'll use for the plots. + +```{r} +#| label: pkgs +#| message: false +#| code-fold: true +#| code-summary: "Show code" + +library(tidyverse) +library(ggtext) +library(rvest) +library(showtext) + +font_add_google("Bitter", family = "custom") +showtext_auto() +``` + +{{{< sidenote br="3em" >}}} +There are a ton of voting advice websites, but only a few (including _KiesKompas_) are generally recommended by a variety of political parties across the spectrum +{{{< /sidenote >}}} + +Then we can create a data frame with the party names and their identifying colors. I'll also add their political identity, i.e. how far left or right the parties are and how progressive or conservative the parties are. This data comes from the webpage [_the political landscape_](https://tweedekamer2023.kieskompas.nl/nl/results/compass) from the Dutch voting advice website [_KiesKompas_](https://www.kieskompas.nl) which offers one of the most popular application that helps voters identify what parties their values most closely align with. It's a tool by the Vrije Universiteit Amsterdam (VU Amsterdam) and is generally well-respected. However, their political landscape is a bit controversial because it tends to "average" across a set of issues, while perhaps ignoring some of the key identifying issue a party is campaigning on (historically e.g. immigration and the PVV). + +One more complication here is that two parties, _GroenLinks_ (GL) and the _Partij van de Arbeid_ (PvdA), campaigned under a single banner, referred to here as _GL-PvdA_. When comparing to previous elections, polls and election outcomes should probably be summed across those two parties to better reflect the trends. + +```{r} +#| label: define-party-settings +#| code-fold: true +#| code-summary: "Show code" + +data_parties <- tribble( + ~party, ~leftright, ~progcon, ~color, ~text_color, + "VVD", 50, -32, "#EE6F2D", "#333333", + "D66", -5, 58, "#4DAA4F", "#333333", + "GL-PvdA", -55, 66, "#CD3029", "grey95", + "PVV", 9, -74, "grey92", "#333333", + "CDA", 23, -24, "#62C45D", "#333333", + "SP", -77, 21, "#DA3732", "grey95", + "FvD", 64, -92, "#9B2820", "grey95", + "PvdD", -95, 82, "#317142", "grey95", + "CU", -32, 21, "#46A4E2", "#333333", + "Volt", -14, 84, "#4A2975", "grey95", + "JA21", 77, -89, "#242C54", "grey95", + "SGP", 27, -42, "#DA632C", "#333333", + "DENK", -68, 32, "#50B4B1", "#333333", + "50PLUS", -23, -3, "#8C3D83", "grey95", + "BBB", 9, -42, "#9DBD43", "#333333", + "BIJ1", -95, 92, "#FFFD54", "#333333", + "BVNL", 100, -89, "#1D2540", "grey95", + "NSC", -9, -8, "#121439", "grey95" +) + +data_historical_parties <- tribble( + ~party, ~hist_leftright, ~hist_progcon, ~hist_color, ~hist_text_color, + "PvdA", -65, 50, "#D22C21", "grey95", + "GroenLinks", -70, 68, "#5BA546", "#333333", + "ChristenUnie", -32, 21, "#47A6E5", "#333333", +) +``` + +Recreating the political compass is fairly simple once the data is loaded. It's in principle simply a scatterplot. Making it look informative by labeling the points directly and adding the colors and annotations in an informative matter is the only challenge here. + +```{r} +#| label: plot-political-spectrum +#| fig-height: 6 +#| code-fold: true +#| code-summary: "Show code" + +compass_text <- tribble( + ~leftright, ~progcon, ~label, + -100, 5, "Left", + 80, 5, "Right", + 2, 96, "Progressive", + 2, -96, "Conservative" +) + +data_parties |> + mutate(party = str_replace(party, "-", "-\n")) |> + ggplot(aes(x = leftright, y = progcon, color = color)) + + geom_hline(yintercept = 0, color = "#333333") + + geom_vline(xintercept = 0, color = "#333333") + + geom_text( + data = compass_text, aes(label = label), + size = 5, color = "#333333", + hjust = 0, fontface = "bold", family = "custom" + ) + + geom_point(size = 16) + + geom_text( + aes(label = party, color = text_color), + size = 4, lineheight = 1, + fontface = "bold", family = "custom" + ) + + labs( + title = "**Dutch political spectrum**", + x = NULL, + y = NULL, + caption = "**Source**: KiesKompas" + ) + + scale_x_continuous( + limits = c(-100, 100), + labels = NULL, + expand = expansion(add = 0) + ) + + scale_y_continuous( + limits = c(-100, 100), + labels = NULL, + expand = expansion(add = 0) + ) + + scale_color_identity() + + coord_equal(clip = "off") + + theme_minimal(base_family = "custom") + + theme( + text = element_text(color = "#333333"), + plot.title.position = "plot", + plot.title = element_markdown(size = 32, margin = margin(l = -50)), + plot.caption.position = "plot", + plot.caption = element_markdown(margin = margin(t = 10, r = -50)), + panel.grid = element_blank() + ) +``` + +{{{< sidenote br="12em" >}}} +Geert Wilders is also the party's sole member ([link](https://en.wikipedia.org/wiki/Party_for_Freedom#Organisation_and_support)) +{{{< /sidenote >}}} + +It's immediately visible that the political parties (as broken down by _KiesKompas_) are aligned across a diagonal from right and conservative to left and progressive. Parties that are both left-wing and conservative, or right-wing and progressive are not really present in the mainstream Dutch political system (or in most foreign ones as far as I'm aware). There are three parties in the lower-right corner (FvD, JA21, and BVNL). JA21 and BVNL are both factions that left the FvD after internal disagreement. The PVV is perhaps best known internationally for their leader [Geert Wilders](https://www.britannica.com/biography/Geert-Wilders). According to _KiesKompas_ they are somewhere in the middle between left and right-wing, but in this case the party can probably be best described as _populist_, adapting both policies from the left and right. Internationally, they associate most with far-right parties and in the Netherlands they are also commonly called far-right. + +The only squarely left-wing party of significance this election is the coalition GL-PvdA, led by former EU Commissioner [Frans Timmermans](https://www.theguardian.com/world/2023/jul/20/eu-climate-chief-frans-timmermans-quits-to-run-in-dutch-elections). + +Before I move to the lastest polls, I can show how the seats have been distributed across the political parties over the past election cycles. For this I'll quickly scrape the [parlement.com](https://www.parlement.com) website for the seat distribution across the past few elections. + +```{r} +#| label: define-current-distribution +#| warning: false +#| code-fold: true +#| code-summary: "Show code" + +data_tk_history <- "https://www.parlement.com/id/vh8lnhronvx6/zetelverdeling_tweede_kamer" |> + read_html() |> + html_elements("table") |> + html_table() |> + first() |> + janitor::clean_names() |> + rename_with(~ str_glue("election_{str_sub(.x, 2, 5)}")) |> + rename(party = election_arti) |> + mutate( + party = str_extract(party, "[^ + left_join(data_parties, by = "party") |> + left_join(data_historical_parties, by = "party") |> + mutate( + color = coalesce(color, hist_color), + text_color = coalesce(text_color, hist_text_color) + ) + +legend_list <- data_tk_hist |> + select(color, party) |> + deframe() + +data_tk_hist |> + pivot_longer(starts_with("election"), + names_to = "year", values_to = "seats" + ) |> + mutate( + year = parse_number(year) + ) |> + ggplot(aes(x = year, y = seats, color = color)) + + geom_hline(yintercept = 0, color = "#333333", alpha = 0.5) + + geom_vline( + xintercept = na.omit(names(data_tk_hist) |> parse_number()), + color = "#333333", alpha = 0.5 + ) + + geom_path(linewidth = 1, key_glyph = "point") + + geom_point(size = 3) + + ggrepel::geom_text_repel( + data = . %>% slice_max(year), + aes(label = party), + xlim = c(2021, NA), min.segment.length = 0, + hjust = 0, size = 3, fontface = "bold", family = "custom" + ) + + labs( + title = "Historical seat distribution
in the past few elections", + x = NULL, + y = "Number of seats in parliament", + color = NULL, + caption = "**Source**: parlement.com" + ) + + scale_x_continuous( + breaks = na.omit(names(data_tk_hist) |> parse_number()), + expand = expansion(add = c(0, 1.5)) + ) + + scale_y_continuous( + expand = expansion(add = c(0, 2)) + ) + + scale_color_identity( + labels = legend_list, + guide = guide_legend(override.aes = list(size = 5), nrow = 3) + ) + + coord_cartesian(clip = "off") + + theme_minimal(base_family = "custom") + + theme( + text = element_text(color = "#333333"), + plot.title = element_markdown(size = 20, face = "bold"), + plot.title.position = "plot", + plot.caption = element_markdown(), + plot.caption.position = "plot", + plot.margin = margin(l = 10, t = 10), + legend.position = "bottom", + panel.grid.major.x = element_blank(), + panel.grid.minor.x = element_blank() + ) +``` + +{{{< sidenote >}}} +In contrast to [Geert Wilders](https://www.britannica.com/biography/Geert-Wilders), [Mark Rutte](https://www.britannica.com/biography/Mark-Rutte) does not have an elaborate entry in the Encyclopedia Brittanica +{{{< /sidenote >}}} + +This plot immediately shows the dominance of the VVD, the party of [Mark Rutte](https://en.wikipedia.org/wiki/Mark_Rutte) who is quite well-known internationally. The PvdA, which internationally associates with traditional labour parties, used to be the biggest opponent to the centre-right CDA and the right-wing VVD, but have since then failed to connect with the voters. + +{{{< sidenote >}}} +I scraped this data earlier in the week, so the current table online does not reflect the data used here +{{{< /sidenote >}}} + +Let's now also look at some polls to see what election night might bring. For this I'll scrape the results from [Ipsos](https://www.ipsos.com). It contains the polls from week 44 and week 46. + +```{r} +#| eval: false +#| code-fold: true +#| code-summary: "Show code" + +data_polls_pre <- "https://www.ipsos.com/sites/default/files/nl/nl/politiekebarometer/Link_Tabel.html" |> + read_html() |> + html_elements("table") |> + html_table(header = TRUE) |> + first() |> + janitor::clean_names() |> + filter(nchar(x) > 0) |> + rename( + party = x, + current_perc = tweede_kamer, + current_seats = tweede_kamer_2, + polls_perc_wk_44 = week_441_nov_23, + polls_seats_wk_44 = week_441_nov_23_2, + polls_perc_wk_46 = week_4615_nov_23, + polls_seats_wk_46 = week_4615_nov_23_2 + ) |> + mutate(across(-party, as.numeric)) + +data_polls_pre |> write_rds("./data/polls_wk_46.rds") +``` + +From this table I'll use only the polls from week 46 (latest available at the time). To better compare the polls to the current situation without drowning out the most important data I'll show the current seat distribution in grey, and the predicted seats in red. + +```{r} +#| code-fold: true +#| code-summary: "Show code for the plot" + +data_polls_pre_new <- read_rds("./data/polls_wk_46.rds") |> + bind_rows( + read_rds("./data/polls_wk_46.rds") |> + filter(party %in% c("GL", "PvdA")) |> + summarise( + party = "GL-PvdA", across(-party, sum) + ) + ) |> + group_by(party) |> + summarise(across(everything(), sum)) |> + filter(!party %in% c("GL", "PvdA")) |> + mutate(party = case_when(str_detect(party, "50") ~ "50PLUS", + str_detect(party, "VOLT") ~ "Volt", + TRUE ~ party)) + +data_polls_pre_new |> + pivot_longer(cols = contains("seats"), names_to = "seats_var", values_to = "n_seats") |> + filter(seats_var %in% c("current_seats", "polls_seats_wk_46")) |> + ggplot(aes(x = n_seats, y = reorder(party, polls_perc_wk_46), fill = seats_var)) + + geom_vline(xintercept = 0, color = "#333333") + + geom_col(position = position_dodge(), key_glyph = "point") + + geom_text( + aes(x = n_seats - 0.5, label = n_seats), + position = position_dodge(width = 1), + color = "white", size = 3, family = "custom" + ) + + labs( + title = "Predicted seat distribution from Week 46", + x = NULL, + y = NULL, + fill = NULL, + caption = "**Source**: Ipsos" + ) + + scale_x_continuous( + position = "top", + limits = c(0, NA), + expand = expansion(add = c(0, NA)) + ) + + scale_fill_manual( + values = c("current_seats" = "grey60", "polls_seats_wk_46" = "firebrick"), + labels = c("Current seats", "Predicted seats (wk 46)"), + guide = guide_legend(override.aes = list(shape = 21, size = 6)) + ) + + theme_minimal(base_family = "custom") + + theme( + plot.title.position = "plot", + plot.title = element_markdown(size = 24, face = "bold"), + plot.caption.position = "plot", + plot.caption = element_markdown(), + legend.position = c(0.8, 0.2) + ) +``` + +The biggest thing to notice here is the party NSC which was started by Pieter Omtzigt, formerly a member of the CDA. This party participates in the 2023 parliamentary elections for the first time, so it doesn't have anything to compare to. According to these polls, the NSC will go in one go to 26 seats. The VVD is still the biggest, but loses a few seats. Other than the VVD, the biggest losers in these polls are the D66, CDA, FvD, and SP. + +{{{< sidenote >}}} +Tom Louwerse also contributes to the Irish Polling Indicator using the same methods, which describes methods in English +{{{< /sidenote >}}} + +This is just one plot from one pollster. There is however a polling aggregator, called the [_Peilingwijzer_](https://peilingwijzer.tomlouwerse.nl), which is maintained by political scientist [Tom Louwerse](https://www.tomlouwerse.nl) at Leiden University. It uses a Bayesian approach to weigh a collection of polls from various sources (description in [Dutch](https://peilingwijzer.tomlouwerse.nl/methode.html#statistisch-model) and [English](https://pollingindicator.com/method/)). This way he gets a better estimate of the uncertainty across several pollsters and polling dates. I know he does a lot of his analyses in R, so I'll try to recreate his plot on the [main website](https://peilingwijzer.tomlouwerse.nl) just as a challenge (and perhaps make one or two things a bit more aesthetically pleasing). It seems he uses the somewhat niche (at least in my field) `geom_crossbar()`. + +```{r} +#| label: peilingwijzer-plot +#| warning: false +#| fig-height: 7 +#| code-fold: true +#| code-summary: "Show code" + +polls_peilingwijzer <- tribble( + ~party, ~range_min, ~range_max, + "VVD", 26, 30, + "PVV", 25, 29, + "GL-PvdA", 23, 27, + "NSC", 19, 23, + "D66", 8, 11, + "BBB", 4, 7, + "SP", 4, 6, + "PvdD", 4, 6, + "CU", 3, 5, + "CDA", 3, 5, + "FvD", 3, 5, + "DENK", 3, 5, + "Volt", 2, 4, + "SGP", 2, 4, + "JA21", 0, 2, + "BVNL", 0, 1, + "BIJ1", 0, 1, + "50PLUS", 0, 1, +) + +polls_peilingwijzer |> + inner_join(data_parties) |> + inner_join(data_polls_pre_new |> select(party, current_seats)) |> + mutate( + color = ifelse(color == "grey92", "grey", color), + range_max_label = ifelse(range_max <= 2, str_glue("{range_min}-{range_max}"), range_max) + ) |> + ggplot(aes(x = current_seats, y = reorder(party, range_max), + color = color, fill = color)) + + geom_vline(xintercept = 0, color = "#333333", linewidth = 1) + + geom_col(color = "transparent", width = 0.25, alpha = 0.5, just = 2.5) + + geom_crossbar( + aes(x = range_min + round((range_max - range_min) / 2), + xmin = range_min, xmax = range_max, + color = colorspace::lighten(color, amount = 0.5)), + width = 0.5, linewidth = 1, fatten = 2) + + geom_text( + aes(x = range_min, label = range_min), + color = "#333333", size = 4, family = "custom", + nudge_x = -0.75 + ) + + geom_text( + aes(x = range_max, label = range_max_label), + color = "#333333", size = 4, family = "custom", + nudge_x = ifelse(polls_peilingwijzer$range_max <= 2, 1, 0.75) + ) + + geom_text( + data = tibble(), + aes(x = 27, y = 12, label = "Previous\nelections"), + inherit.aes = FALSE, + size = 4, family = "custom", + hjust = 0, lineheight = 0.75 + ) + + geom_curve( + data = tibble(), + aes(x = 26.75, y = 12, xend = 24.2, yend = 13.2), + inherit.aes = FALSE, curvature = -0.1, + arrow = arrow(length = unit(0.4, "lines")) + ) + + labs( + title = "Latest _Peilingwijzer_", + subtitle = "_Peilingwijzer_ combines the polls from I&O Research & Ipsos/EenVandaag", + x = "Seats", + y = NULL, + caption = "_Peilingwijzer_, based on I&O Research & Ipsos&EenVandaag
+ Tom Louwerse, Universiteit Leiden,
+ Last change: 21-11-2023" + ) + + scale_x_continuous( + limits = c(0, NA), + expand = expansion(add = c(0, NA)) + ) + + scale_y_discrete( + expand = expansion(add = c(1, 0)) + ) + + scale_color_identity() + + scale_fill_identity() + + theme_minimal(base_family = "custom") + + theme( + plot.title.position = "plot", + plot.title = element_markdown(size = 32, face = "bold"), + plot.subtitle = element_markdown(size = 14), + plot.caption.position = "plot", + plot.caption = element_markdown(lineheight = 0.75), + axis.text.y = element_markdown(size = 10, margin = margin(t = 10, r = 5)), + panel.grid.major.y = element_blank() + ) +``` + +{{{< omission >}}} + +## 22 November 21:00 + +{{{< sidenote >}}} +There are three municipalities in the Carribean that can vote until 02:00 CET +{{{< /sidenote >}}} + +The voting booths (at least in the mainland part of the Netherlands) close at 21:00. This also marks the release of the first exit polls. I'm just copying the numbers from the TV broadcast as they come in and saving them in a `tibble()` using the `tribble()` function. + +```{r} +#| label: plot-exit-polls-2100 +#| warning: false +#| fig-height: 6 +#| code-fold: true +#| code-summary: "Show code for the plot" + +data_current_seats <- data_polls_pre_new |> + select(party, current_perc, current_seats) + +exit_polls_2100 <- tribble( + ~party, ~polls_2100, + "VVD", 23, + "PVV", 35, + "GL-PvdA", 26, + "NSC", 20, + "D66", 10, + "BBB", 7, + "SP", 5, + "PvdD", 4, + "CU", 3, + "CDA", 5, + "FvD", 3, + "DENK", 2, + "Volt", 2, + "SGP", 3, + "JA21", 1, + "BVNL", 0, + "BIJ1", 0, + "50PLUS", 1 +) + +exit_polls_2100 |> + inner_join(data_parties, by = "party") |> + inner_join(data_current_seats, by = "party") |> + pivot_longer(cols = c(polls_2100, current_seats), + names_to = "variable", values_to = "seats") |> + mutate( + color = ifelse(str_detect(variable, "current"), "grey80", color), + color = ifelse(color == "grey92", "grey40", color), + text_color = ifelse(str_detect(variable, "polls") & party == "PVV", "white", text_color), + text_color = ifelse(str_detect(variable, "current"), "#333333", text_color) + ) |> + ggplot(aes(x = seats, y = reorder(party, current_perc), + group = variable, fill = color)) + + geom_col(position = position_dodge(), key_glyph = "point") + + geom_vline(xintercept = 0, color = "#333333") + + geom_text( + aes(x = seats - 0.5, label = seats, color = text_color), + position = position_dodge(width = 1), + size = 3, family = "custom" + ) + + labs( + title = "Exit polls from 22 November 22:00", + subtitle = "Colored bars show the predicted number of seats,
grey bars indicate current seats", + x = "Number of seats in parliament", + y = NULL, + fill = NULL, + caption = "**Source**: NOS" + ) + + scale_x_continuous( + position = "top", + limits = c(0, NA), + expand = expansion(add = c(0, 2)) + ) + + scale_color_identity() + + scale_fill_identity() + + theme_minimal(base_family = "custom") + + theme( + plot.title.position = "plot", + plot.title = element_markdown(size = 24, face = "bold"), + plot.subtitle = element_markdown(lineheight = 0.67), + plot.caption.position = "plot", + plot.caption = element_markdown(), + axis.text.y = element_markdown(size = 10), + legend.position = c(0.8, 0.2) + ) +``` + +Out of the blue, contrary to basically any serious poll, the PVV party from Geert Wilders appears to become the largest party, followed by GL-PvdA. This is a major upset that would open the door for a very right and conservative government. This would unfortunately mean the latest blow to meaningful climate action, an increase anti-immigrant policy, decreased government support for Ukraine, increased support for Israel and the atrocities they commit in Gaza and the rest of Palestine, and possibly a host of challenges to the rule of law in the Netherlands. + +{{{< omission >}}} + +_Data and analysis will be added throughout the night_ \ No newline at end of file From 15cd51ab9f3b25667ba9ae914c4296b80c3b608f Mon Sep 17 00:00:00 2001 From: Daniel Roelfs Date: Wed, 22 Nov 2023 22:47:05 +0100 Subject: [PATCH 02/12] fix linting --- .lintr | 2 +- content/about/index.qmd | 2 +- content/blog/2023-dutch-elections/index.qmd | 75 ++++++++++++--------- content/blog/2023-nyt-books-api/index.md | 2 +- content/blog/2023-nyt-books-api/index.qmd | 2 +- 5 files changed, 49 insertions(+), 34 deletions(-) diff --git a/.lintr b/.lintr index cde0b3b..c8693f5 100644 --- a/.lintr +++ b/.lintr @@ -1,5 +1,5 @@ linters: linters_with_defaults( - line_length_linter(80), + line_length_linter(120), commented_code_linter = NULL ) exclusions: list( diff --git a/content/about/index.qmd b/content/about/index.qmd index b0db240..1093ac8 100644 --- a/content/about/index.qmd +++ b/content/about/index.qmd @@ -12,4 +12,4 @@ Hi, I'm Daniel Roelfs! I'm currently working as a Data Scientist at the Norwegia Born and raised in the Netherlands, I have also studied in France and Sweden before moving to Norway. In my free time I run, ski, hike, and take photos. This blog serves as a way to share some of the code I write in R and Python for personal and professional interests. -If you have any questions or comments about the tutorials and posts here, or just want to get in touch, please send me an e-mail at [daniel.roelfs[a]medisin.uio.no](mailto:daniel.roelfs@medisin.uio.no). \ No newline at end of file +If you have any questions or comments about the tutorials and posts here, or just want to get in touch, please send me an e-mail at [daniel.roelfs[a]medisin.uio.no](mailto:daniel.roelfs@medisin.uio.no). diff --git a/content/blog/2023-dutch-elections/index.qmd b/content/blog/2023-dutch-elections/index.qmd index 1bdebf6..4e72a5f 100644 --- a/content/blog/2023-dutch-elections/index.qmd +++ b/content/blog/2023-dutch-elections/index.qmd @@ -101,7 +101,7 @@ compass_text <- tribble( ) data_parties |> - mutate(party = str_replace(party, "-", "-\n")) |> + mutate(party = str_replace(party, "-", "-\n")) |> ggplot(aes(x = leftright, y = progcon, color = color)) + geom_hline(yintercept = 0, color = "#333333") + geom_vline(xintercept = 0, color = "#333333") + @@ -113,7 +113,7 @@ data_parties |> geom_point(size = 16) + geom_text( aes(label = party, color = text_color), - size = 4, lineheight = 1, + size = 4, lineheight = 1, fontface = "bold", family = "custom" ) + labs( @@ -302,13 +302,16 @@ data_polls_pre_new <- read_rds("./data/polls_wk_46.rds") |> ) |> group_by(party) |> summarise(across(everything(), sum)) |> - filter(!party %in% c("GL", "PvdA")) |> - mutate(party = case_when(str_detect(party, "50") ~ "50PLUS", - str_detect(party, "VOLT") ~ "Volt", - TRUE ~ party)) + filter(!party %in% c("GL", "PvdA")) |> + mutate(party = case_when( + str_detect(party, "50") ~ "50PLUS", + str_detect(party, "VOLT") ~ "Volt", + TRUE ~ party + )) data_polls_pre_new |> - pivot_longer(cols = contains("seats"), names_to = "seats_var", values_to = "n_seats") |> + pivot_longer(cols = contains("seats"), + names_to = "seats_var", values_to = "n_seats") |> filter(seats_var %in% c("current_seats", "polls_seats_wk_46")) |> ggplot(aes(x = n_seats, y = reorder(party, polls_perc_wk_46), fill = seats_var)) + geom_vline(xintercept = 0, color = "#333333") + @@ -382,22 +385,29 @@ polls_peilingwijzer <- tribble( "50PLUS", 0, 1, ) -polls_peilingwijzer |> - inner_join(data_parties) |> - inner_join(data_polls_pre_new |> select(party, current_seats)) |> +polls_peilingwijzer |> + inner_join(data_parties) |> + inner_join(data_polls_pre_new |> + select(party, current_seats)) |> mutate( color = ifelse(color == "grey92", "grey", color), - range_max_label = ifelse(range_max <= 2, str_glue("{range_min}-{range_max}"), range_max) + range_max_label = ifelse(range_max <= 2, + str_glue("{range_min}-{range_max}"), range_max) ) |> - ggplot(aes(x = current_seats, y = reorder(party, range_max), - color = color, fill = color)) + + ggplot(aes( + x = current_seats, y = reorder(party, range_max), + color = color, fill = color + )) + geom_vline(xintercept = 0, color = "#333333", linewidth = 1) + geom_col(color = "transparent", width = 0.25, alpha = 0.5, just = 2.5) + geom_crossbar( - aes(x = range_min + round((range_max - range_min) / 2), - xmin = range_min, xmax = range_max, - color = colorspace::lighten(color, amount = 0.5)), - width = 0.5, linewidth = 1, fatten = 2) + + aes( + x = range_min + round((range_max - range_min) / 2), + xmin = range_min, xmax = range_max, + color = colorspace::lighten(color, amount = 0.5) + ), + width = 0.5, linewidth = 1, fatten = 2 + ) + geom_text( aes(x = range_min, label = range_min), color = "#333333", size = 4, family = "custom", @@ -412,7 +422,7 @@ polls_peilingwijzer |> data = tibble(), aes(x = 27, y = 12, label = "Previous\nelections"), inherit.aes = FALSE, - size = 4, family = "custom", + size = 4, family = "custom", hjust = 0, lineheight = 0.75 ) + geom_curve( @@ -468,7 +478,7 @@ The voting booths (at least in the mainland part of the Netherlands) close at 21 #| code-fold: true #| code-summary: "Show code for the plot" -data_current_seats <- data_polls_pre_new |> +data_current_seats <- data_polls_pre_new |> select(party, current_perc, current_seats) exit_polls_2100 <- tribble( @@ -477,7 +487,7 @@ exit_polls_2100 <- tribble( "PVV", 35, "GL-PvdA", 26, "NSC", 20, - "D66", 10, + "D66", 10, "BBB", 7, "SP", 5, "PvdD", 4, @@ -493,19 +503,24 @@ exit_polls_2100 <- tribble( "50PLUS", 1 ) -exit_polls_2100 |> - inner_join(data_parties, by = "party") |> - inner_join(data_current_seats, by = "party") |> - pivot_longer(cols = c(polls_2100, current_seats), - names_to = "variable", values_to = "seats") |> +exit_polls_2100 |> + inner_join(data_parties, by = "party") |> + inner_join(data_current_seats, by = "party") |> + pivot_longer( + cols = c(polls_2100, current_seats), + names_to = "variable", values_to = "seats" + ) |> mutate( color = ifelse(str_detect(variable, "current"), "grey80", color), color = ifelse(color == "grey92", "grey40", color), - text_color = ifelse(str_detect(variable, "polls") & party == "PVV", "white", text_color), + text_color = ifelse(str_detect(variable, "polls") & party == "PVV", + "white", text_color), text_color = ifelse(str_detect(variable, "current"), "#333333", text_color) - ) |> - ggplot(aes(x = seats, y = reorder(party, current_perc), - group = variable, fill = color)) + + ) |> + ggplot(aes( + x = seats, y = reorder(party, current_perc), + group = variable, fill = color + )) + geom_col(position = position_dodge(), key_glyph = "point") + geom_vline(xintercept = 0, color = "#333333") + geom_text( @@ -544,4 +559,4 @@ Out of the blue, contrary to basically any serious poll, the PVV party from Geer {{{< omission >}}} -_Data and analysis will be added throughout the night_ \ No newline at end of file +_Data and analysis will be added throughout the night_ diff --git a/content/blog/2023-nyt-books-api/index.md b/content/blog/2023-nyt-books-api/index.md index 44aa0dc..fb66dcf 100644 --- a/content/blog/2023-nyt-books-api/index.md +++ b/content/blog/2023-nyt-books-api/index.md @@ -379,7 +379,7 @@ for (i in seq_len(nrow(titles_w_adaptation))) { rplot[[i]] <- data |> right_join(titles_w_adaptation |> slice(i), by = "title") |> - right_join(data |> select(list_publication_date) |> distinct(), + right_join(data |> select(list_publication_date) |> distinct(), by = "list_publication_date" ) |> replace_na(list(rank = 16)) |> diff --git a/content/blog/2023-nyt-books-api/index.qmd b/content/blog/2023-nyt-books-api/index.qmd index d7962e2..55fb492 100644 --- a/content/blog/2023-nyt-books-api/index.qmd +++ b/content/blog/2023-nyt-books-api/index.qmd @@ -378,7 +378,7 @@ for (i in seq_len(nrow(titles_w_adaptation))) { rplot[[i]] <- data |> right_join(titles_w_adaptation |> slice(i), by = "title") |> - right_join(data |> select(list_publication_date) |> distinct(), + right_join(data |> select(list_publication_date) |> distinct(), by = "list_publication_date" ) |> replace_na(list(rank = 16)) |> From fb24fbd069f0d9247223dcfc8af7a8b538d4c477 Mon Sep 17 00:00:00 2001 From: Daniel Roelfs Date: Wed, 22 Nov 2023 22:50:01 +0100 Subject: [PATCH 03/12] lint notebook --- content/blog/2023-nyt-books-api/index.md | 6 +++--- content/blog/2023-nyt-books-api/index.qmd | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/content/blog/2023-nyt-books-api/index.md b/content/blog/2023-nyt-books-api/index.md index fb66dcf..a866857 100644 --- a/content/blog/2023-nyt-books-api/index.md +++ b/content/blog/2023-nyt-books-api/index.md @@ -378,9 +378,9 @@ for (i in seq_len(nrow(titles_w_adaptation))) { rplot[[i]] <- data |> right_join(titles_w_adaptation |> - slice(i), by = "title") |> - right_join(data |> select(list_publication_date) |> distinct(), - by = "list_publication_date" + slice(i), by = "title") |> + right_join(data |> select(list_publication_date) |> distinct(), + by = "list_publication_date" ) |> replace_na(list(rank = 16)) |> ggplot(aes(x = list_publication_date, y = rank)) + diff --git a/content/blog/2023-nyt-books-api/index.qmd b/content/blog/2023-nyt-books-api/index.qmd index 55fb492..876d5b8 100644 --- a/content/blog/2023-nyt-books-api/index.qmd +++ b/content/blog/2023-nyt-books-api/index.qmd @@ -377,9 +377,9 @@ for (i in seq_len(nrow(titles_w_adaptation))) { rplot[[i]] <- data |> right_join(titles_w_adaptation |> - slice(i), by = "title") |> - right_join(data |> select(list_publication_date) |> distinct(), - by = "list_publication_date" + slice(i), by = "title") |> + right_join(data |> select(list_publication_date) |> distinct(), + by = "list_publication_date" ) |> replace_na(list(rank = 16)) |> ggplot(aes(x = list_publication_date, y = rank)) + From d506deafc9d92a4756973f4f183ba675e24015ce Mon Sep 17 00:00:00 2001 From: Daniel Roelfs Date: Wed, 22 Nov 2023 22:55:26 +0100 Subject: [PATCH 04/12] lint --- content/blog/2023-nyt-books-api/index.md | 2 +- content/blog/2023-nyt-books-api/index.qmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/content/blog/2023-nyt-books-api/index.md b/content/blog/2023-nyt-books-api/index.md index a866857..2a8e6f6 100644 --- a/content/blog/2023-nyt-books-api/index.md +++ b/content/blog/2023-nyt-books-api/index.md @@ -380,7 +380,7 @@ for (i in seq_len(nrow(titles_w_adaptation))) { right_join(titles_w_adaptation |> slice(i), by = "title") |> right_join(data |> select(list_publication_date) |> distinct(), - by = "list_publication_date" + by = "list_publication_date" ) |> replace_na(list(rank = 16)) |> ggplot(aes(x = list_publication_date, y = rank)) + diff --git a/content/blog/2023-nyt-books-api/index.qmd b/content/blog/2023-nyt-books-api/index.qmd index 876d5b8..46ad1c6 100644 --- a/content/blog/2023-nyt-books-api/index.qmd +++ b/content/blog/2023-nyt-books-api/index.qmd @@ -379,7 +379,7 @@ for (i in seq_len(nrow(titles_w_adaptation))) { right_join(titles_w_adaptation |> slice(i), by = "title") |> right_join(data |> select(list_publication_date) |> distinct(), - by = "list_publication_date" + by = "list_publication_date" ) |> replace_na(list(rank = 16)) |> ggplot(aes(x = list_publication_date, y = rank)) + From 7e1c1ddffc5381041364a09bac7f29cb07d6e837 Mon Sep 17 00:00:00 2001 From: Daniel Roelfs Date: Wed, 22 Nov 2023 22:59:10 +0100 Subject: [PATCH 05/12] lint --- content/blog/2023-nyt-books-api/index.md | 2 +- content/blog/2023-nyt-books-api/index.qmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/content/blog/2023-nyt-books-api/index.md b/content/blog/2023-nyt-books-api/index.md index 2a8e6f6..a41e5d4 100644 --- a/content/blog/2023-nyt-books-api/index.md +++ b/content/blog/2023-nyt-books-api/index.md @@ -380,7 +380,7 @@ for (i in seq_len(nrow(titles_w_adaptation))) { right_join(titles_w_adaptation |> slice(i), by = "title") |> right_join(data |> select(list_publication_date) |> distinct(), - by = "list_publication_date" + by = "list_publication_date" ) |> replace_na(list(rank = 16)) |> ggplot(aes(x = list_publication_date, y = rank)) + diff --git a/content/blog/2023-nyt-books-api/index.qmd b/content/blog/2023-nyt-books-api/index.qmd index 46ad1c6..caf3994 100644 --- a/content/blog/2023-nyt-books-api/index.qmd +++ b/content/blog/2023-nyt-books-api/index.qmd @@ -379,7 +379,7 @@ for (i in seq_len(nrow(titles_w_adaptation))) { right_join(titles_w_adaptation |> slice(i), by = "title") |> right_join(data |> select(list_publication_date) |> distinct(), - by = "list_publication_date" + by = "list_publication_date" ) |> replace_na(list(rank = 16)) |> ggplot(aes(x = list_publication_date, y = rank)) + From dbdf8d8f27d0874bbff715f8c95ce12071300a4d Mon Sep 17 00:00:00 2001 From: Daniel Roelfs Date: Wed, 22 Nov 2023 23:47:45 +0100 Subject: [PATCH 06/12] readd umami --- config.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/config.toml b/config.toml index 4fce0a4..694865e 100644 --- a/config.toml +++ b/config.toml @@ -4,7 +4,8 @@ theme = "typography" ignoreFiles = ["\\.qmd$", "\\.ipynb$", "\\.Rmd$", "\\.Rmarkdown$", "_cache$", "index\\.html", "about\\.html", "static/blog/\\*_files/", "README.md", "content/blog/2022-dutch-performance-olympic-speed-skating/.venv/", - "content/blog/2023-nyt-books-api/.venv"] + "content/blog/2023-nyt-books-api/.venv", + "content/blog/2023-scientific-publishing/.venv"] [markup] defaultMarkdownHandler = "goldmark" @@ -27,7 +28,7 @@ ignoreFiles = ["\\.qmd$", "\\.ipynb$", "\\.Rmd$", "\\.Rmarkdown$", "_cache$", "i customcss = ["custom_style.css"] - umami = false + umami = true umamilink = "https://analytics-danielroelfs.netlify.app/script.js" umamiid = "da48a88a-2e87-4024-8c99-639222aab54d" From 73987a62e2260a467d146615a6e300aa85434432 Mon Sep 17 00:00:00 2001 From: Daniel Roelfs Date: Thu, 23 Nov 2023 00:19:17 +0100 Subject: [PATCH 07/12] last update for the night --- .../plot-exit-polls-2100-1.png | Bin 145066 -> 150240 bytes .../plot-exit-polls-2200-1.png | Bin 0 -> 151376 bytes ...nk-7-1.png => plot-ipsos-polls-wk46-1.png} | Bin 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"-\n")) |> + mutate(party = str_replace(party, "-", "-\n")) |> ggplot(aes(x = leftright, y = progcon, color = color)) + geom_hline(yintercept = 0, color = "#333333") + geom_vline(xintercept = 0, color = "#333333") + @@ -112,7 +112,7 @@ data_parties |> geom_point(size = 16) + geom_text( aes(label = party, color = text_color), - size = 4, lineheight = 1, + size = 4, lineheight = 1, fontface = "bold", family = "custom" ) + labs( @@ -252,7 +252,7 @@ data_tk_hist |>