Code
#Prep of source data
# Package readr version 2.1.5 documented via https://readr.tidyverse.org/
library(readr)
####the source is real
real.data <- read.csv("google-political-ads-creative-stats.csv", sep=',')
View(real.data)Reaching_the_Voters_Supplementary_Material
#‘R version 4.4.1 (2024-06-14 ucrt) – “Race for Your Life” #Copyright (C) 2024 The R Foundation for Statistical Computing’ # Data Retrieval: Export via transparency library: https://adstransparency.google.com/political?region=DE&topic=political # original export date: 22/10/2021, 19:36 CET #working file: google-political-ads-creative-stats.csv #additional information: https://support.google.com/adspolicy/answer/6014595?hl=en#zippy=
Code
####creating working file
data.prep <- real.dataCode
####adjusting observation period
data.prep$Date_Range_Start <- gsub("-", "", data.prep$Date_Range_Start)
data.prep$Date_Range_End <- gsub("-", "", data.prep$Date_Range_End)
data.prep01 <- subset(data.prep, Date_Range_Start > 20210726 & Date_Range_End < 20210927)
View(data.prep01)
####adjusting geo area to D for Germany
data.prep02 <- subset(data.prep01, Regions != "US")
View(data.prep02)
data.DE<- subset(data.prep02, Regions == "DE, EU")
View(data.DE) #5728 obsCode
####cleaning from individual, non-party advertisers
##### this step was conducted in several single steps,
##### the documentation below includes info on the individual or non-party advertiser
#####which is provided as a justification for exclusion from the data set
######Sebastian Münzenmaier, AfD politician
data.clean <- subset(data.DE, Advertiser_Name != "Sebastian Münzenmaier") #5699 obs
######Urban Media GmbH, publisher
data.clean <- subset(data.clean, Advertiser_Name != "Urban Media GmbH") #5695 obs
######Sabine Buder, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Sabine Buder") #5687 obs
######Jorinde Schulz, LEFT politician
data.clean <- subset(data.clean, Advertiser_Name != "Jorinde Schulz") #5684 obs
######Julius Kröger, (Jusos Aachen?)
data.clean <- subset(data.clean, Advertiser_Name != "Julius Kröger") #5683 obs
######Mareen Theil, spokesperson for MP Heilmann, CDU
data.clean <- subset(data.clean, Advertiser_Name != "Mareen Theil") #5680 obs
######Lucas Brandes, Intern for MP Frank Oelsterhelweg, CDU
data.clean <- subset(data.clean, Advertiser_Name != "Lucas Brandes") #5663 obs
######Campact e.V., NGO supporting democracy and social justice mainly through petitions
data.clean <- subset(data.clean, Advertiser_Name != "Campact e.V.") #5629 obs
######Karsten Scherschanski, FDP politician
data.clean <- subset(data.clean, Advertiser_Name != "Karsten Scherschanski") #5619 obs
######Verlag der Tagesspiegel GmbH, media
data.clean <- subset(data.clean, Advertiser_Name != "Verlag der Tagesspiegel GmbH") #5565 obs
######Katharina Pötter, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Katharina Pötter") #5563 obs
######Peter Stein, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Peter Stein") #5550 obs
######Verena Ute Hubertz, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Verena Ute Hubertz") #5539 obs
######Zweites Deutsches Fernsehen, media
data.clean <- subset(data.clean, Advertiser_Name != "Zweites Deutsches Fernsehen") #5534 obs
######Dr. Franka Kretschmer, independent candidate for Magdeburg
data.clean <- subset(data.clean, Advertiser_Name != "Dr. Franka Kretschmer") #5533 obs
######Florian Fuchs, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Florian Fuchs") #5531 obs
######Katharina Bartsch, GREEN politician
data.clean <- subset(data.clean, Advertiser_Name != "Katharina Bartsch") #5529 obs
######Timo Schisanowski, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Timo Schisanowski") #5458 obs
######Daniel Föst, FDP politician
data.clean <- subset(data.clean, Advertiser_Name != "Daniel Föst") #5453 obs
######Jan-Philipp Knoop, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Jan-Philipp Knoop") #5451 obs
######Jens MATHIAS STEIN, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Jens MATHIAS STEIN") #5427 obs
######Volker Brauer, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Volker Brauer") #5423 obs
######Hans-Böckler-Stiftung, political foundation
data.clean <- subset(data.clean, Advertiser_Name != "Hans-Böckler-Stiftung") #5422 obs
######Philipp da Cunha, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Philipp da Cunha") #5421 obs
######DEMOCRACY Deutschland e.V., NGO supporting democracy
data.clean <- subset(data.clean, Advertiser_Name != "DEMOCRACY Deutschland e.V.") #5401 obs
######Dr. Gerold Eberhard Papsch, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Dr. Gerold Eberhard Papsch") #5399 obs
######Thore Stein, AfD politician
data.clean <- subset(data.clean, Advertiser_Name != "Thore Stein") #5396 obs
######Kai Whittaker, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Kai Whittaker") #5386 obs
######Die BürgerNahen e.V., local candidates for municipal election
data.clean <- subset(data.clean, Advertiser_Name != "Die BürgerNahen e.V.") #5385 obs
######AfD-Fraktion im Sächs. Landtag, Party in parliament
data.clean <- subset(data.clean, Advertiser_Name != "AfD-Fraktion im Sächs. Landtag") #5384 obs
######Ulrika Schöllner, independent candidate for Leipzig
data.clean <- subset(data.clean, Advertiser_Name != "Ulrika Schöllner") #5383 obs
######FDP-Fraktion im Deutschen Bundestag, Party in parliament
data.clean <- subset(data.clean, Advertiser_Name != "FDP-Fraktion im Deutschen Bundestag") #5380 obs
######Isabell Charlotte Steidel, GREEN politician
data.clean <- subset(data.clean, Advertiser_Name != "Isabell Charlotte Steidel") #5373 obs
######Niklas Teßmann, independent candidate
data.clean <- subset(data.clean, Advertiser_Name != "Niklas Teßmann") #5371 obs
######Sören Pellmann, LEFt politician
data.clean <- subset(data.clean, Advertiser_Name != "Sören Pellmann") #5369 obs
######David Hess, ?
data.clean <- subset(data.clean, Advertiser_Name != "David Hess") #5344 obs
######Verlag Lensing-Wolff GmbH & Co. KG, media
data.clean <- subset(data.clean, Advertiser_Name != "Verlag Lensing-Wolff GmbH & Co. KG") #5324 obs
######SPD-Fraktion im Bundestag, party in parliament
data.clean <- subset(data.clean, Advertiser_Name != "SPD-Fraktion im Bundestag") #5323 obs
######Ann-Veruschka Jurisch, fdp politician
data.clean <- subset(data.clean, Advertiser_Name != "Ann-Veruschka Jurisch") #5318 obs
######Kai Koeser, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Kai Koeser") #5306 obs
######Henrik Werner, GREEN politician
data.clean <- subset(data.clean, Advertiser_Name != "Henrik Werner") #5305 obs
######Cansel Kiziltepe, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Cansel Kiziltepe") #5303 obs
######Marcus Carsten Fuchs, independent candidate Sachsen
data.clean <- subset(data.clean, Advertiser_Name != "Marcus Carsten Fuchs") #5295 obs
######Florian Westerwalbesloh, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Florian Westerwalbesloh") #5294 obs
######The ONE Campaign gGmbH, campaign agency
data.clean <- subset(data.clean, Advertiser_Name != "The ONE Campaign gGmbH") #5293 obs
######campaigners Network GmbH, campaign agency
data.clean <- subset(data.clean, Advertiser_Name != "campaigners Network GmbH") #5291 obs
######Neue Wege für Berlin e.V., NGO supporting change in Berlin
data.clean <- subset(data.clean, Advertiser_Name != "Neue Wege für Berlin e.V.") #5283 obs
######Andrew Ullmann, FDP politician
data.clean <- subset(data.clean, Advertiser_Name != "Andrew Ullmann") #5273 obs
######Volker Mayer-Lay, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Volker Mayer-Lay") #5272 obs
######Christina Daniela Stumpp, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Christina Daniela Stumpp") #5269 obs
######Dirk Stamer, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Dirk Stamer") #5267 obs
######Roman Wick (?)
data.clean <- subset(data.clean, Advertiser_Name != "Roman Wick") #5264 obs
######Joachim Horst Krämer, FDP politician
data.clean <- subset(data.clean, Advertiser_Name != "Joachim Horst Krämer") #5261 obs
######Saskia Ludwig, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Saskia Ludwig") #5248 obs
######Julia Söhne, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Julia Söhne") #5239 obs
######Thomas Bareiß, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Thomas Bareiß") #5236 obs
######Franziska Hoppermann, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Franziska Hoppermann") #5234 obs
######Konrad-Adenauer-Stiftung e.V., political foundation
data.clean <- subset(data.clean, Advertiser_Name != "Konrad-Adenauer-Stiftung e.V.") #5229 obs
######Dr. Jens Zimmermann, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Dr. Jens Zimmermann") #5227 obs
######DIE FAMILIENUNTERNEHMER e. V., Lobbying for family owned businesses
data.clean <- subset(data.clean, Advertiser_Name != "DIE FAMILIENUNTERNEHMER e. V.") #5218 obs
######Zentrum Liberale Moderne, Think Tank
data.clean <- subset(data.clean, Advertiser_Name != "Zentrum Liberale Moderne") #5217 obs
######Marcus Finselberger, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Marcus Finselberger") #5214 obs
######Alexander Engelhard, CSU politician
data.clean <- subset(data.clean, Advertiser_Name != "Alexander Engelhard") #5213 obs
######Petra Nicolaisen, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Petra Nicolaisen") #5197 obs
######Ronald Härtlein, Republik in Bewegung politician
data.clean <- subset(data.clean, Advertiser_Name != "Ronald Härtlein") #5190 obs
######Thomas Knott, Mittelstandsinitiative (?)
data.clean <- subset(data.clean, Advertiser_Name != "Thomas Knott") #5180 obs
######Oliver Sieke, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Oliver Sieke") #5166 obs
######Matthias Katsch, SPD politician
data.clean <- subset(data.clean, Advertiser_Name != "Matthias Katsch") #5161 obs
######Matthias Hauer, CDU politician
data.clean <- subset(data.clean, Advertiser_Name != "Matthias Hauer") #5159 obs
######Susanne Ferschl, LEFT politician
data.clean <- subset(data.clean, Advertiser_Name != "Susanne Ferschl") #5158 obs
######Forum für politischen und gesellschaftlichen Informationsaustausch, Think Tank
data.clean <- subset(data.clean, Advertiser_Name != "Forum für politischen und gesellschaftlichen Informationsaustausch") #5155 obs
######Jens Teutrine, FDP politician
data.clean <- subset(data.clean, Advertiser_Name != "Jens Teutrine") #5148 obs
######AfD Fraktion i.d. Hbg. Bürgers, Parliamentary group
data.clean <- subset(data.clean, Advertiser_Name != "AfD Fraktion i.d. Hbg. Bürgers") #5131 obsCode
####data.clean is maintained as milestone
View(data.clean ) #5131 obs
write.csv(data.clean, "data_clean.csv")Code
######
#### second access option
#library(readr)
#df <- read.csv("data_clean.csv", sep=',')
df <- data.clean
View(df)Code
##################
# Package openxlsx version 4.2.5.2 documented via https://github.com/ycphs/openxlsx
library(openxlsx)
# Package dplyr version 1.1.4 documented via https://dplyr.tidyverse.org/
library(dplyr)
Attache Paket: 'dplyr'Die folgenden Objekte sind maskiert von 'package:stats':
filter, lagDie folgenden Objekte sind maskiert von 'package:base':
intersect, setdiff, setequal, unionCode
###########
####stepwise pooling each party
#Recode Parties
afd <- c(
"AfD Landesverband Berlin",
"Alternative für Deutschland",
"Alternative für Deutschland (AfD)",
"AfD Baden-Württemberg")
b90 <- c(
"BÜNDNIS 90 / DIE GRÜNEN Charlottenburg Wilmersdorf",
"Bündnis 90/ Die Grünen Mecklenburg-Vorpommern",
"Bündnis 90/Die Grünen Berlin",
"BÜNDNIS 90/DIE GRÜNEN Kreisverband Aachen",
"BÜNDNIS 90/DIE GRÜNEN Kreisverband Schweinfurt",
"Bündnis 90/Die Grünen KV Stuttgart",
"Bündnis 90/Die Grünen Ortsverband Gütersloh Bündnis 90/Die Grünen Gütersloh",
"BÜNDNIS 90/DIE GRÜNEN Ortsverband Willich",
"Bündnis 90/Die Grünen/GAL Münster",
"Bündnis90/ Die Grünen",
"Bündnis90/Die Grünen Kreisverband Bodensee")
buc <- "Bündnis C - Christen für Deutschland"
cdu <- c(
"CDU Christlich Demokratische Union",
"CDU Landesverband Mecklenburg/Vorpommern",
"CDU Deutschlands",
"Christlich Demokratische Union (CDU) im Wahlkreis 208 Neustadt - Speyer",
"CDU-Kreisverband Pinneberg",
"CDU Landesverband Berlin",
"CDU Kreisverband Kassel-Land",
"CDU Kreisverband Cuxhaven",
"CDU Stadtverband Schwalmstadt",
"CDU-Landesverband Baden-Württemberg",
"CDU Kreisverband Görlitz",
"Christlich Demokratische Union (CDU) Landesverband Thüringen",
"CDU Landesverband Rheinland-Pfalz",
"CDU Kreisverband Main-Tauber",
"CDU Kreisverband Bremen-Nord",
"CDU Kreisverband Mainz",
"CDU Schwalm-Eder"
)
csu <- c(
"Christlich-Soziale Union Bayern eingetragener Verein",
"CSU"
)
fdp <- c(
"Freie Demokratische Partei (FDP), Landesverband Bayern eingetragener Verein",
"FDP Kreisverband Ravensburg",
"Freie Demokratische Partei",
"FDP Freiburg",
"Freie Demokratische Partei (FDP)",
"FDP Landesverband Saarland",
"FDP Kreisverband Bielefeld",
"Junge Liberale e.V.",
"FDP Kreisverband Bochum",
"FDP Kreisverband Speyer",
"FDP Oberallgäu",
"FDP Heidelberg",
"FDP Kreisverband Heidekreis",
"FDP Bayern Kreisverband München-Land Ortsverband (OV) Grünwald",
"FDP Nürnberg",
"FDP Kreisverband Regensburg-Stadt"
)
fw <- c(
"FREIE WÄHLER Niedersachsen",
"Landesvereinigung FREIE WÄHLER Bayern e.V."
)
humanisten <- "Partei der Humanisten"
linke <- c(
"DIE LINKE. Mecklenburg-Vorpommern",
"DIE LINKE",
"DIE LINKE. Kreisverband Hildesheim",
"DIE LINKE Landesverband Berlin",
"Die Linke Hessen"
)
odp <- "Ökologisch-Demokratische Partei (ÖDP)"
piraten <- c(
"Piratenpartei Deutschland",
"Piratenpartei Deutschland Landesverband Hessen"
)
sgp <- "Sozialistische Gleichheitspartei (SGP)"
spd <- c(
"Sozialdemokratische Partei Deutschlands (SPD) SPD Ortsverein Neu Wulmstorf",
"Sozialdemokratische Partei Deutschlands SPD",
"Sozialdemokratische Partei Deutschlands Unterbezirk Bonn",
"Sozialdemokratische Partei Deutschlands, Landesverband Berlin",
"SPD-Kreisverband Rastatt/Baden-Baden",
"SPD-Ortsverein Uelzen",
"SPD-UB Landkreis Harburg",
"SPD-Unterbezirk Aachen-Stadt",
"SPD-Unterbezirk Coesfeld",
"SPD-Unterbezirk Main-Taunus",
"SPD Baden-Württemberg",
"SPD Eimsbüttel",
"SPD Kreisverband Gütersloh",
"SPD Kreisverband Warendorf",
"SPD Landesorganisation Hamburg",
"SPD Mecklenburg Vorpommern",
"SPD Ortsverein Augustfehn",
"SPD Ortsverein Löningen",
"SPD Ortsverein Uchte",
"SPD Osnabrück",
"SPD Rhein-Neckar",
"SPD Schwalm-Eder"
)
tierschutz <- "PARTEI MENSCH UMWELT TIERSCHUTZ (Tierschutzpartei)"
volt <- "Volt Deutschland"Code
df <- df |>
mutate(advertiser = case_when(
Advertiser_Name %in% spd ~ 1,
Advertiser_Name %in% cdu ~ 2,
Advertiser_Name %in% csu ~ 3,
Advertiser_Name %in% b90 ~ 4,
Advertiser_Name %in% afd ~ 5,
Advertiser_Name %in% fdp ~ 6,
Advertiser_Name %in% linke ~ 7,
Advertiser_Name %in% fw ~ 8,
Advertiser_Name %in% odp ~ 9,
Advertiser_Name %in% tierschutz ~ 10,
Advertiser_Name %in% piraten ~ 11,
Advertiser_Name %in% humanisten ~ 12,
Advertiser_Name %in% buc ~ 13,
Advertiser_Name %in% volt ~ 14,
Advertiser_Name %in% sgp ~ 15,
TRUE ~ NA
))
table(df$advertiser)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
374 301 35 185 2494 1257 76 17 38 30 9 3 8 299 3 Parties
| Code | Party |
|---|---|
| 1 | SPD |
| 2 | CDU |
| 3 | CSU |
| 4 | Die Grünen |
| 5 | AfD |
| 6 | FDP |
| 7 | Die Linke |
| 8 | Freie Wähler |
| 9 | ÖDP |
| 10 | Tierschutzpartei |
| 11 | Piraten |
| 12 | Humanisten |
| 13 | Bündnis C - Christen für Deutschland |
| 14 | Volt |
| 15 | Sozialistische Gleichheitspartei |
Code
############
#Recode Ad type
library(dplyr)
df <- df |>
mutate(Ad_Type_num = as.numeric(factor(Ad_Type)))
table(df$Ad_Type_num)
1 2 3
3084 748 1299 Code
detach("package:dplyr", unload = TRUE)Ad Type
| Code | Meaning |
|---|---|
| 1 | Image |
| 2 | Text |
| 3 | Video |
#Figure 1 in the Text
Code
#Package ggplot; version: 3.5.1; full documentation: https://ggplot2.tidyverse.org/
library(ggplot2)
# Create a data frame
df.plot <- data.frame(
category = c("SPD", "CDU", "CSU", "Greens", "AfD", "FDP", "Left", "FW", "ÖDP", "Tierschutz", "Pirates", "Humanists", "BUC", "Volt", "SGP"),
values = c(374, 301, 35, 185, 2494, 1257, 76, 17, 38, 30, 9, 3, 8, 299, 3)
)
#Prepare category for plotting (factor maintains order)
df.plot$category <- factor(df.plot$category, levels = df.plot$category)
# Create the bar plot
ggplot(df.plot, aes(x = category, y = values)) +
geom_bar(stat = "identity", fill = "darkslategray") +
labs(title = "Parties advertising via Google services", x = "Party", y = "N of Ads") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Code
detach("package:ggplot2", unload = TRUE)#Table 1 in the text
Code
library(dplyr)
Attache Paket: 'dplyr'Die folgenden Objekte sind maskiert von 'package:stats':
filter, lagDie folgenden Objekte sind maskiert von 'package:base':
intersect, setdiff, setequal, unionCode
df <- df |>
mutate(advertiser_reduced = case_when(
Advertiser_Name %in% spd ~ 1,
Advertiser_Name %in% cdu ~ 2,
Advertiser_Name %in% csu ~ 3,
Advertiser_Name %in% b90 ~ 4,
Advertiser_Name %in% afd ~ 5,
Advertiser_Name %in% fdp ~ 6,
Advertiser_Name %in% linke ~ 7,
Advertiser_Name %in% volt ~ 14,
TRUE ~ NA
),
advertiser_label = factor(case_when(
advertiser_reduced == 1 ~ "SPD",
advertiser_reduced == 2 ~ "CDU",
advertiser_reduced == 3 ~ "CSU",
advertiser_reduced == 4 ~ "Greens",
advertiser_reduced == 5 ~ "AfD",
advertiser_reduced == 6 ~ "FDP",
advertiser_reduced == 7 ~ "Left",
advertiser_reduced == 14 ~ "Volt",
TRUE ~ "Other"
), levels = c("SPD", "CDU", "CSU", "Greens", "AfD", "FDP", "Left", "Volt", "Other")))
table(df$advertiser_label)
SPD CDU CSU Greens AfD FDP Left Volt Other
374 301 35 185 2494 1257 76 299 110 Code
table01 <- table(df$Ad_Type_num, df$advertiser_label)
print(table01)
SPD CDU CSU Greens AfD FDP Left Volt Other
1 108 42 18 83 2191 493 21 122 6
2 105 48 5 81 4 433 19 31 22
3 161 211 12 21 299 331 36 146 82Parties
| Code | Party |
|---|---|
| 1 | SPD |
| 2 | CDU |
| 3 | CSU |
| 4 | Die Grünen |
| 5 | AfD |
| 6 | FDP |
| 7 | Die Linke |
| 14 | Volt |
#Figure 2 in the text
Code
table(df$advertiser)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
374 301 35 185 2494 1257 76 17 38 30 9 3 8 299 3 Code
library(ggplot2)
#Package reshape 2; version: 1.4.4, full documentation: https://github.com/hadley/reshape
library(reshape2)
# Create a data frame
matrix1 <- matrix(c(108, 42, 18, 83, 2191, 493, 21, 122,
105, 48, 5, 81, 4, 433, 19, 31,
161, 211, 12, 21, 299, 331, 36, 146),
ncol = 8, byrow = TRUE)
rownames(matrix1) <- c("Image", "Text", "Video")
colnames (matrix1) <- c("SPD (n=374)", "CDU (n=301)", "CSU (n=35)", "Greens (n=185)", "AfD (n=2494)", "FDP (n=1257)", "Left (n=76)", "Volt (n=299)")
# calculating percentages per column
prop_data <- prop.table(matrix1, margin =2)
print(prop_data) SPD (n=374) CDU (n=301) CSU (n=35) Greens (n=185) AfD (n=2494)
Image 0.2887701 0.1395349 0.5142857 0.4486486 0.878508420
Text 0.2807487 0.1594684 0.1428571 0.4378378 0.001603849
Video 0.4304813 0.7009967 0.3428571 0.1135135 0.119887731
FDP (n=1257) Left (n=76) Volt (n=299)
Image 0.3922037 0.2763158 0.4080268
Text 0.3444710 0.2500000 0.1036789
Video 0.2633254 0.4736842 0.4882943Code
# DataFrame for ggplot2
df.plot01 <- as.data.frame(prop_data)
df.plot01$category <-rownames(df.plot01)
df_melted <- melt(df.plot01, id.vars = "category")
colors <- c("Image" = "darkslategray", "Text" = "darksalmon", "Video" = "deepskyblue4")
# creating bar plot ggplot2
ggplot(df_melted, aes(x = variable, y = value, fill = category)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Share of Media Types by Party",
x = "Party",
y = "%",
fill = "Media Type") +
scale_fill_manual(values = colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Party Level or unit as advertiser
Code
##########################
####stepwise pooling
#Recode Party Level
fed_party <- c(
"CDU Christlich Demokratische Union",
"Volt Deutschland",
"CDU Deutschlands",
"Freie Demokratische Partei",
"Alternative für Deutschland",
"Freie Demokratische Partei (FDP)",
"Sozialdemokratische Partei Deutschlands SPD",
"DIE LINKE",
"Alternative für Deutschland (AfD)",
"Ökologisch-Demokratische Partei (ÖDP)",
"Bündnis90/ Die Grünen",
"Sozialistische Gleichheitspartei (SGP)",
"PARTEI MENSCH UMWELT TIERSCHUTZ (Tierschutzpartei)",
"Piratenpartei Deutschland",
"Ökologisch Demokratische Partei",
"Bündnis C - Christen für Deutschland",
"Partei der Humanisten"
)
state_party <- c(
"AfD Landesverband Berlin",
"AfD Baden-Württemberg",
"Bündnis 90/ Die Grünen Mecklenburg-Vorpommern",
"Bündnis 90/Die Grünen Berlin",
"CDU Landesverband Mecklenburg/Vorpommern",
"CDU Landesverband Berlin",
"CDU Landesverband Rheinland-Pfalz",
"Christlich-Soziale Union Bayern eingetragener Verein",
"CSU",
"Freie Demokratische Partei (FDP), Landesverband Bayern eingetragener Verein",
"FDP Landesverband Saarland",
"FREIE WÄHLER Niedersachsen",
"Landesvereinigung FREIE WÄHLER Bayern e.V.",
"DIE LINKE Landesverband Berlin",
"Die Linke Hessen",
"DIE LINKE. Mecklenburg-Vorpommern",
"Piratenpartei Deutschland Landesverband Hessen",
"SPD Baden-Württemberg",
"SPD Landesorganisation Hamburg",
"SPD Mecklenburg Vorpommern",
"Sozialdemokratische Partei Deutschlands, Landesverband Berlin"
)
local_party <- c(
"BÜNDNIS 90 / DIE GRÜNEN Charlottenburg Wilmersdorf",
"BÜNDNIS 90/DIE GRÜNEN Kreisverband Aachen",
"BÜNDNIS 90/DIE GRÜNEN Kreisverband Schweinfurt",
"Bündnis 90/Die Grünen KV Stuttgart",
"Bündnis 90/Die Grünen Ortsverband Gütersloh Bündnis 90/Die Grünen Gütersloh",
"BÜNDNIS 90/DIE GRÜNEN Ortsverband Willich",
"Bündnis 90/Die Grünen/GAL Münster",
"Bündnis90/Die Grünen Kreisverband Bodensee",
"Christlich Demokratische Union (CDU) im Wahlkreis 208 Neustadt - Speyer",
"CDU-Kreisverband Pinneberg",
"CDU Kreisverband Kassel-Land",
"CDU Kreisverband Cuxhaven",
"CDU Stadtverband Schwalmstadt",
"CDU Kreisverband Görlitz",
"CDU Kreisverband Main-Tauber",
"CDU Kreisverband Bremen-Nord",
"CDU Kreisverband Mainz",
"CDU Schwalm-Eder",
"FDP Kreisverband Ravensburg",
"FDP Freiburg",
"FDP Kreisverband Bielefeld",
"FDP Kreisverband Bochum",
"FDP Kreisverband Speyer",
"FDP Oberallgäu",
"FDP Heidelberg",
"FDP Kreisverband Heidekreis",
"FDP Nürnberg",
"FDP Kreisverband Regensburg-Stadt",
"DIE LINKE. Kreisverband Hildesheim",
"Sozialdemokratische Partei Deutschlands (SPD) SPD Ortsverein Neu Wulmstorf",
"Sozialdemokratische Partei Deutschlands Unterbezirk Bonn",
"SPD-Kreisverband Rastatt/Baden-Baden",
"SPD-Ortsverein Uelzen",
"SPD-UB Landkreis Harburg",
"SPD-Unterbezirk Aachen-Stadt",
"SPD-Unterbezirk Coesfeld",
"SPD-Unterbezirk Main-Taunus",
"SPD Eimsbüttel",
"SPD Kreisverband Gütersloh",
"SPD Kreisverband Warendorf",
"SPD Ortsverein Augustfehn",
"SPD Ortsverein Löningen",
"SPD Ortsverein Uchte",
"SPD Osnabrück",
"SPD Rhein-Neckar",
"SPD Schwalm-Eder"
)
youth <- c(
"Junge Liberale e.V."
)
library(dplyr)
####creation of new variable
df <- df |>
mutate(party_level = case_when(
Advertiser_Name %in% fed_party ~ 1,
Advertiser_Name %in% state_party ~ 2,
Advertiser_Name %in% local_party ~ 3,
Advertiser_Name %in% youth ~ 4,
TRUE ~ NA
))
table(df$party_level)
1 2 3 4
4351 247 519 12 Code
prop.table(table(df$party_level))
1 2 3 4
0.848313511 0.048157536 0.101189316 0.002339637 Party Level
| Code | Party |
|---|---|
| 1 | Federal level |
| 2 | State level |
| 3 | Local Level |
| 4 | Youth Organisation |
Code
library(ggplot2)
# Create a data frame
df.plot1 <- data.frame(
category = c("Federal Party", "State Party", "Local Party", "Youth Organisation"),
values = c(84.8, 4.8, 10.1, 0.2)
)
# Create the bar plot
ggplot(df.plot1, aes(x = category, y = values)) +
geom_bar(stat = "identity") +
labs(title = "Advertising Level or Unit", x = "Level or Unit", y = "%")+
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Code
detach("package:ggplot2", unload = TRUE)Gender Targeting
Code
##########################
#Recode Gender Target
df <- df |>
mutate(target_gender = as.numeric(factor(Gender_Targeting)))
table(df$target_gender)
1 2
4908 223 Code
table(df$target_gender, df$Gender_Targeting)
Male, Female, Unknown gender Not targeted
1 4908 0
2 0 223Code
# 1= 'male,female, unknown", 2= 'Not targeted'
# "Male, Female, Unknown gender" includes all genders. Therefore, this is not targeted. Gender Codes
| Code | Party |
|---|---|
| 1 | Male, Female, Unknown gender |
| 2 | Not targeted |
Age targeting
Code
#Age
table(df$Age_Targeting)
18-24, 25-34, 35-44, 45-54, 55-64, ≥65, Unknown age
49
18-24, 25-34, 35-44, 45-54, 55-64, ≥65, Unknown age, ≤18
4904
18-24, 25-34, Unknown age
5
35-44, 45-54, 55-64, ≥65, Unknown age
16
Not targeted
157 Code
#Explanation of Findings: 4953 ads were not focused on a certain group, another 157 were explicitly not targeted; 5 focused on the younger demographic (18-24, 25-34, unknown age) and 16 targeted the age groups 35 and older (+ unknown age)
table(df$Age_Targeting, df$advertiser_label)
SPD CDU CSU
18-24, 25-34, 35-44, 45-54, 55-64, ≥65, Unknown age 0 0 0
18-24, 25-34, 35-44, 45-54, 55-64, ≥65, Unknown age, ≤18 374 301 35
18-24, 25-34, Unknown age 0 0 0
35-44, 45-54, 55-64, ≥65, Unknown age 0 0 0
Not targeted 0 0 0
Greens AfD FDP
18-24, 25-34, 35-44, 45-54, 55-64, ≥65, Unknown age 0 49 0
18-24, 25-34, 35-44, 45-54, 55-64, ≥65, Unknown age, ≤18 185 2392 1134
18-24, 25-34, Unknown age 0 0 5
35-44, 45-54, 55-64, ≥65, Unknown age 0 0 16
Not targeted 0 53 102
Left Volt Other
18-24, 25-34, 35-44, 45-54, 55-64, ≥65, Unknown age 0 0 0
18-24, 25-34, 35-44, 45-54, 55-64, ≥65, Unknown age, ≤18 74 299 110
18-24, 25-34, Unknown age 0 0 0
35-44, 45-54, 55-64, ≥65, Unknown age 0 0 0
Not targeted 2 0 0Code
#the FDP was the only party targeting for different age groupsFindings on geotargeting (included areas)
Code
########################
#####pooling the different target areas was conducted by a series of steps
### first step: generating a new variable based on the geo-targeting (included areas)
##Recode Geo Target
df <- df |>
mutate(target_geo = as.numeric(factor(Geo_Targeting_Included)))
table(df$target_geo)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
3 1 3 1 1 3 1 1 3 1 3 3 1 1 3 6
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
7 1 3 7 1 2 246 17 1 1 1 1 3 1 1 2
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
1 1 1 1 2 2 2 2 8 1 3 8 7 12 8 8
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
10 2 2 8 2 8 8 3 8 9 1 1 6 1 1 10
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
1 1 49 1 1 110 1 13 59 11 1 1 1 41 2 3
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
1 1 2 21 2 1 8 6 1 2 3 4 4 4 2 4
97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
35 1 15 2 2 1 1 1 1 1 1 4 4 4 1 20
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
5 2 4 3 1 16 37 4 4 45 1 1 399 5 63 19
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
11 27 15 1 4 1 4 189 10 1 48 11 31 4 2 1
145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
1 3 1 4 2 1 1 1 15 48 3 3 9 1 1 1
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
2821 9 1 1 4 1 1 1 1 1 6 23 1 9 6 2
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
1 1 4 1 3 9 11 1 27 8 1 2 3 1 86 18
193 194 195 196 197 198 199 200 201 202 203 204 205 206
2 3 1 1 1 28 6 1 5 4 9 5 2 1 Code
#write.csv(df, "df_geo.csv")Code
#####second step: the original information included numeric and character codes in the same cell, which required looking up the respective zip-codes (web research; ChatGPT)
#####and grouping them on the state level and adding the codes already indicating a town, district or state
#####other codes: 161 for Germany;
df$target_BL[df$target_geo %in% c(115, 79, 80, 81, 84, 85, 114, 137, 150, 151, 152, 153, 159, 160, 189, 190)] <- 1001 #Baden-Württemberg
df$target_BL[df$target_geo %in% c(119, 82, 83, 86, 99, 100, 101, 117, 120, 129, 178, 184, 188, 192, 193, 194, 195, 201, 202)] <- 1002 #Bayern
df$target_BL[df$target_geo %in% c(122, 5, 6, 7, 8, 9, 10, 11, 12, 13, 123, 146)] <- 1003 #Berlin
df$target_BL[df$target_geo %in% c(135, 25)] <- 1004 #Brandenburg
df$target_BL[df$target_geo %in% c(138, 142, 143)] <- 1005 #Bremen
df$target_BL[df$target_geo %in% c(171, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 154, 172)] <- 1006 #Hamburg
df$target_BL[df$target_geo %in% c(174, 69, 77, 106, 107, 112, 158, 180, 181)] <- 1007 #Hessen
df$target_BL[df$target_geo %in% c(183, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 197)] <- 1008 #Mecklenburg-Vorpommern
df$target_BL[df$target_geo %in% c(43, 60, 61, 62, 63, 64, 65, 70, 74, 97, 98, 113, 144, 145, 147, 167, 173, 175, 186, 200, 203, 204, 205)] <- 1009 #Niedersachsen
df$target_BL[df$target_geo %in% c(66, 67, 68, 71, 72, 73, 75, 76, 88, 89, 90, 91, 111, 130, 131, 132, 134, 157, 168, 169, 170, 185, 206)] <- 1010 #Nordrhein-Westfalen
df$target_BL[df$target_geo %in% c(92, 93, 94, 96, 102, 103, 104, 105, 108, 109, 110, 121, 133, 148, 177, 179, 196)] <- 1011 #Rheinland-Pfalz
df$target_BL[df$target_geo %in% c(198, 78, 182)] <- 1012 #Saarland
df$target_BL[df$target_geo %in% c(199, 1, 2, 3, 95, 128, 149, 166)] <- 1013 #Sachsen
df$target_BL[df$target_geo %in% c()] <- 1014 #Sachen-Anhalt
df$target_BL[df$target_geo %in% c(59, 118, 155)] <- 1015 #Schleswig-Holstein
df$target_BL[df$target_geo %in% c(156, 165)] <- 1016 #Thüringen
df$target_BL[df$target_geo %in% c(161, 87, 125, 126, 127, 136)] <- 1000 #Germany
df$target_BL[df$target_geo %in% c(4, 124, 164)] <- 999 #Germany_East
df$target_BL[df$target_geo %in% c(163, 176)] <- 998 #Germany-West
df$target_BL[df$target_geo %in% c(116)] <- 997 #Germany-East_West_mix
df$target_BL[df$target_geo %in% c(14, 15, 16, 17, 18, 19, 20, 22, 21, 23, 24, 139, 140, 141, 162, 191)] <- 900 #unspezifisch
df$target_BL[df$target_geo %in% c(187)] <- 9999 #not targeted
table(df$target_BL)
900 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011
479 3 3 3 3485 69 113 66 5 7 197 38 26 221 223 50
1012 1013 1015 1016 9999
78 37 20 7 1 Geo_Targeting_Inclusive
| Code | Party |
|---|---|
| 900 | unspecific |
| 997 | Germany-East_West_mix |
| 998 | Germany-West_mix |
| 999 | Germany-East_mix |
| 1000 | Germany |
| 1001 | Baden-Württemberg |
| 1002 | Bayern |
| 1003 | Berlin |
| 1004 | Brandenburg |
| 1005 | Bremen |
| 1006 | Hamburg |
| 1007 | Hessen |
| 1008 | Mecklenburg-Vorpommern |
| 1009 | Niedersachsen |
| 1010 | Nordrhein-Westfalen |
| 1011 | Rheinland-Pfalz |
| 1012 | Saarland |
| 1013 | Sachsen |
| 1014 | Sachsen-Anhalt |
| 1015 | Schleswig-Holstein |
| 1016 | Thüringen |
| 9999 | not targeted |
Code
#############
####Package labelled, version: 2.13.0; full documentation: https://larmarange.github.io/labelled/
library(labelled)
####Package expss, version: 0.11.6; full documentation: https://gdemin.github.io/expss/
library(expss)Lade nötiges Paket: maditr
To aggregate data: take(mtcars, mean_mpg = mean(mpg), by = am)
Attache Paket: 'maditr'Die folgenden Objekte sind maskiert von 'package:reshape2':
dcast, meltDie folgenden Objekte sind maskiert von 'package:dplyr':
between, coalesce, first, lastDas folgende Objekt ist maskiert 'package:readr':
colsDas folgende Objekt ist maskiert 'package:base':
sort_by
Use 'expss_output_rnotebook()' to display tables inside R Notebooks.
To return to the console output, use 'expss_output_default()'.
Attache Paket: 'expss'Das folgende Objekt ist maskiert 'package:labelled':
is.labelledDie folgenden Objekte sind maskiert von 'package:dplyr':
compute, contains, na_if, recode, vars, whereCode
val_lab(df$target_BL) <- make_labels("900 unspezifisch
997 West_East_mix
998 West_mix
999 East_mix
1000 Deutschland
1001 Baden-Württemberg
1002 Bayern
1003 Berlin
1004 Brandenburg
1005 Bremen
1006 Hamburg
1007 Hessen
1008 Mecklenburg-Vorpommern
1009 Niedersachsen
1010 Nordrhein-Westfalen
1011 Rheinland-Pfalz
1012 Saarland
1013 Sachsen
1014 Sachen-Anhalt
1015 Schleswig-Holstein
1016 Thüringen
9999 not_targeted")
table(df$target_BL)
unspezifisch West_East_mix West_mix
479 3 3
East_mix Deutschland Baden-Württemberg
3 3485 69
Bayern Berlin Brandenburg
113 66 5
Bremen Hamburg Hessen
7 197 38
Mecklenburg-Vorpommern Niedersachsen Nordrhein-Westfalen
26 221 223
Rheinland-Pfalz Saarland Sachsen
50 78 37
Schleswig-Holstein Thüringen not_targeted
20 7 1 Code
# Which Party tageted which State?
table(df$target_BL, df$advertiser_label)
SPD CDU CSU Greens AfD FDP Left Volt Other
unspezifisch 0 37 0 0 0 266 0 176 0
West_East_mix 0 0 0 0 0 0 0 0 3
West_mix 0 0 0 0 0 2 0 0 1
East_mix 0 1 0 0 0 1 1 0 0
Deutschland 69 161 12 106 2343 596 31 112 55
Baden-Württemberg 22 0 0 6 1 32 0 1 7
Bayern 0 0 23 23 0 62 0 2 3
Berlin 6 2 0 11 20 0 16 7 4
Brandenburg 0 0 0 0 0 1 0 0 4
Bremen 0 6 0 0 0 0 0 0 1
Hamburg 67 0 0 0 125 1 0 0 4
Hessen 22 6 0 0 0 1 5 0 4
Mecklenburg-Vorpommern 12 3 0 2 0 0 6 0 3
Niedersachsen 100 1 0 0 0 112 6 0 2
Nordrhein-Westfalen 75 0 0 37 0 110 0 1 0
Rheinland-Pfalz 0 45 0 0 0 5 0 0 0
Saarland 0 0 0 0 0 67 0 0 11
Sachsen 0 19 0 0 1 1 8 0 8
Schleswig-Holstein 0 20 0 0 0 0 0 0 0
Thüringen 0 0 0 0 4 0 3 0 0
not_targeted 1 0 0 0 0 0 0 0 0Code
#Explanation of major findings: CSU only runs in Bavaria; VOLT and FDP targeted cities (unspecific for States); AfD competed for certain districts in Hamburg; Left defended two out of four direct mandates in Berlin (Gysi and Lötzsche) and one in Leipzig, Sachsen (Pellmann); Findings on geotargeting (excluded areas)
Code
##Recode Geo Target
df <- df |>
mutate(target_geo_ex = as.numeric(factor(Geo_Targeting_Excluded)))
table(df$target_geo_ex)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
51 14 138 1 10 2 1 12 21 4 1 196 16 63 1 1
17 18 19 20 21 22
1 1 1 4491 69 36 Code
library(dplyr)
df$target_BL_ex[df$target_geo_ex %in% c(20)] <- 100 # not targeted
df$target_BL_ex[df$target_geo_ex %in% c(1, 3, 19)] <- 101 # unspezifisch Land
df$target_BL_ex[df$target_geo_ex %in% c(13, 14, 15, 16)] <- 102 # unspezifisch Stadt
df$target_BL_ex[df$target_geo_ex %in% c(2)] <- 103 #Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg
df$target_BL_ex[df$target_geo_ex %in% c(4)] <- 104 # Stolzenau Niedersachsen
df$target_BL_ex[df$target_geo_ex %in% c(5)] <- 105 # Petershagen (NRW), Stolzenau (NieS)
df$target_BL_ex[df$target_geo_ex %in% c(6,8)] <- 106 # Austria, Switzerland
df$target_BL_ex[df$target_geo_ex %in% c(7, 9)] <- 107 # Austria, Italy, Baden-Württemberg, Argenbrühl
df$target_BL_ex[df$target_geo_ex %in% c(10)] <- 108 # Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen
df$target_BL_ex[df$target_geo_ex %in% c(11)] <- 109 # Baden-Württemberg
df$target_BL_ex[df$target_geo_ex %in% c(12)] <- 110 # Bayern
df$target_BL_ex[df$target_geo_ex %in% c(17)] <- 111 # Konstanz
df$target_BL_ex[df$target_geo_ex %in% c(18)] <- 112 # Mannheim, Frankenthal, Ludwigshafen
df$target_BL_ex[df$target_geo_ex %in% c(21)] <- 113 # Saarland
df$target_BL_ex[df$target_geo_ex %in% c(22)] <- 114 # Schleswig-Holstein, Saarland
library(labelled)
library (expss)
val_lab(df$target_BL_ex) <- make_labels("100 not targeted
101 unspezifisch Land
102 unspezifisch Stadt
103 Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg
104 Stolzenau Niedersachsen
105 Petershagen (NRW), Stolzenau (NieS)
106 Austria, Switzerland
107 Austria, Italy, Baden-Württemberg, Argenbrühl
108 Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen
109 Baden-Württemberg
110 Bayern
111 Konstanz
112 Mannheim, Frankenthal, Ludwigshafen
113 Saarland
114 Schleswig-Holstein, Saarland")
detach("package:expss", unload = TRUE)
detach("package:labelled", unload = TRUE)
table(df$target_BL_ex)
not targeted
4491
unspezifisch Land
190
unspezifisch Stadt
81
Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg
14
Stolzenau Niedersachsen
1
Petershagen (NRW), Stolzenau (NieS)
10
Austria, Switzerland
14
Austria, Italy, Baden-Württemberg, Argenbrühl
22
Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen
4
Baden-Württemberg
1
Bayern
196
Konstanz
1
Mannheim, Frankenthal, Ludwigshafen
1
Saarland
69
Schleswig-Holstein, Saarland
36 Geo_Targeting_Excluded
| Code | Party |
|---|---|
| 100 | not targeted |
| 101 | unspecific State |
| 102 | unspecific City |
| 103 | Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg |
| 104 | Stolzenau Niedersachsen |
| 105 | Petershagen (NRW), Stolzenau (NieS) |
| 106 | Austria, Switzerland |
| 107 | Austria, Italy, Baden-Württemberg, Argenbrühl |
| 108 | Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen |
| 109 | Baden-Württemberg |
| 110 | Bayern |
| 111 | Konstanz |
| 112 | Mannheim, Frankenthal, Ludwigshafen |
| 113 | Saarland |
| 114 | Schleswig-Holstein, Saarland |
Code
# Which party excluded which areas?
table(df$target_BL_ex, df$advertiser_label)
SPD CDU
not targeted 363 104
unspezifisch Land 0 1
unspezifisch Stadt 0 0
Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg 0 0
Stolzenau Niedersachsen 1 0
Petershagen (NRW), Stolzenau (NieS) 10 0
Austria, Switzerland 0 0
Austria, Italy, Baden-Württemberg, Argenbrühl 0 0
Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen 0 0
Baden-Württemberg 0 0
Bayern 0 196
Konstanz 0 0
Mannheim, Frankenthal, Ludwigshafen 0 0
Saarland 0 0
Schleswig-Holstein, Saarland 0 0
CSU
not targeted 19
unspezifisch Land 0
unspezifisch Stadt 0
Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg 0
Stolzenau Niedersachsen 0
Petershagen (NRW), Stolzenau (NieS) 0
Austria, Switzerland 12
Austria, Italy, Baden-Württemberg, Argenbrühl 0
Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen 4
Baden-Württemberg 0
Bayern 0
Konstanz 0
Mannheim, Frankenthal, Ludwigshafen 0
Saarland 0
Schleswig-Holstein, Saarland 0
Greens
not targeted 79
unspezifisch Land 0
unspezifisch Stadt 0
Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg 0
Stolzenau Niedersachsen 0
Petershagen (NRW), Stolzenau (NieS) 0
Austria, Switzerland 0
Austria, Italy, Baden-Württemberg, Argenbrühl 0
Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen 0
Baden-Württemberg 0
Bayern 0
Konstanz 1
Mannheim, Frankenthal, Ludwigshafen 0
Saarland 69
Schleswig-Holstein, Saarland 36
AfD FDP
not targeted 2480 1042
unspezifisch Land 0 189
unspezifisch Stadt 0 0
Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg 14 0
Stolzenau Niedersachsen 0 0
Petershagen (NRW), Stolzenau (NieS) 0 0
Austria, Switzerland 0 2
Austria, Italy, Baden-Württemberg, Argenbrühl 0 22
Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen 0 0
Baden-Württemberg 0 1
Bayern 0 0
Konstanz 0 0
Mannheim, Frankenthal, Ludwigshafen 0 1
Saarland 0 0
Schleswig-Holstein, Saarland 0 0
Left Volt
not targeted 76 220
unspezifisch Land 0 0
unspezifisch Stadt 0 79
Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg 0 0
Stolzenau Niedersachsen 0 0
Petershagen (NRW), Stolzenau (NieS) 0 0
Austria, Switzerland 0 0
Austria, Italy, Baden-Württemberg, Argenbrühl 0 0
Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen 0 0
Baden-Württemberg 0 0
Bayern 0 0
Konstanz 0 0
Mannheim, Frankenthal, Ludwigshafen 0 0
Saarland 0 0
Schleswig-Holstein, Saarland 0 0
Other
not targeted 108
unspezifisch Land 0
unspezifisch Stadt 2
Berlin Prenzl, Mitte, Friedrichshain, Kreuzberg 0
Stolzenau Niedersachsen 0
Petershagen (NRW), Stolzenau (NieS) 0
Austria, Switzerland 0
Austria, Italy, Baden-Württemberg, Argenbrühl 0
Austria, Switzerland, Baden-Württemberg, Hessen, Sachsen, Thüringen 0
Baden-Württemberg 0
Bayern 0
Konstanz 0
Mannheim, Frankenthal, Ludwigshafen 0
Saarland 0
Schleswig-Holstein, Saarland 0Code
#explanation of major findings: CDU: Does not compete in Bavaria; CSU: Bavaria borders Switzerland and Austria; Greens were not able to run in Saarland dur to a formal mistake when submitting the list to the electoral commission; AfD very week in These Berlin districts)Figure 3 in the text
Code
table(df$Date_Range_Start)
20210727 20210728 20210730 20210801 20210802 20210803 20210804 20210805
22 1 1 25 53 23 11 8
20210806 20210807 20210808 20210809 20210810 20210811 20210812 20210813
37 16 3 49 6 52 11 27
20210814 20210815 20210816 20210817 20210818 20210819 20210820 20210821
12 42 56 30 10 303 117 128
20210822 20210823 20210824 20210825 20210826 20210827 20210828 20210829
40 120 47 303 311 31 14 15
20210830 20210831 20210901 20210902 20210903 20210904 20210905 20210906
88 89 914 209 246 34 27 31
20210907 20210908 20210909 20210910 20210911 20210912 20210913 20210914
85 37 38 278 72 42 72 49
20210915 20210916 20210917 20210918 20210919 20210920 20210921 20210922
308 105 36 22 35 158 57 72
20210923 20210924 20210925
22 49 32 Code
df$Date_Range_Start1 <- as.Date(df$Date_Range_Start, format = "%Y%m%d")
table(df$Date_Range_Start1)
2021-07-27 2021-07-28 2021-07-30 2021-08-01 2021-08-02 2021-08-03 2021-08-04
22 1 1 25 53 23 11
2021-08-05 2021-08-06 2021-08-07 2021-08-08 2021-08-09 2021-08-10 2021-08-11
8 37 16 3 49 6 52
2021-08-12 2021-08-13 2021-08-14 2021-08-15 2021-08-16 2021-08-17 2021-08-18
11 27 12 42 56 30 10
2021-08-19 2021-08-20 2021-08-21 2021-08-22 2021-08-23 2021-08-24 2021-08-25
303 117 128 40 120 47 303
2021-08-26 2021-08-27 2021-08-28 2021-08-29 2021-08-30 2021-08-31 2021-09-01
311 31 14 15 88 89 914
2021-09-02 2021-09-03 2021-09-04 2021-09-05 2021-09-06 2021-09-07 2021-09-08
209 246 34 27 31 85 37
2021-09-09 2021-09-10 2021-09-11 2021-09-12 2021-09-13 2021-09-14 2021-09-15
38 278 72 42 72 49 308
2021-09-16 2021-09-17 2021-09-18 2021-09-19 2021-09-20 2021-09-21 2021-09-22
105 36 22 35 158 57 72
2021-09-23 2021-09-24 2021-09-25
22 49 32 Code
library(ggplot2)
# Convert date to Date format and create a data frame
dfplot2 <- data.frame(
date = as.Date(c("2021-07-27", "2021-07-28", "2021-07-30", "2021-08-01", "2021-08-02", "2021-08-03", "2021-08-04", "2021-08-05", "2021-08-06", "2021-08-07", "2021-08-08", "2021-08-09", "2021-08-10", "2021-08-11", "2021-08-12", "2021-08-13", "2021-08-14", "2021-08-15", "2021-08-16", "2021-08-17", "2021-08-18", "2021-08-19", "2021-08-20", "2021-08-21", "2021-08-22", "2021-08-23", "2021-08-24", "2021-08-25", "2021-08-26", "2021-08-27", "2021-08-28", "2021-08-29", "2021-08-30", "2021-08-31", "2021-09-01", "2021-09-02", "2021-09-03", "2021-09-04", "2021-09-05", "2021-09-06", "2021-09-07", "2021-09-08", "2021-09-09", "2021-09-10", "2021-09-11", "2021-09-12", "2021-09-13", "2021-09-14", "2021-09-15", "2021-09-16", "2021-09-17", "2021-09-18", "2021-09-19", "2021-09-20", "2021-09-21", "2021-09-22", "2021-09-23", "2021-09-24", "2021-09-25")),
values = c(22, 1, 1, 25, 53, 23, 11, 8, 37, 16, 3, 49, 6, 52, 11, 27, 12, 42, 56, 30, 10, 303, 117, 128, 40, 120, 47, 303, 311, 31, 14, 15, 88, 89, 914, 209, 246, 34, 27, 31, 85, 37, 38, 278, 72, 42, 72, 49, 308, 105, 36, 22, 35, 158, 57, 72, 22, 49, 32)
)
dfplot2$date <- as.Date(dfplot2$date, format = "%Y-%m-%d")
min <- as.Date(as.factor("2021-07-27"))
max <- NA
# Plot Line plot for new ads across observation period
ggplot(data = dfplot2, aes(x = date, y = values)) +
geom_line(color = "darkslategray", linewidth = 1) +
scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "2 days", limits = c(min, max)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(x = "Date", y = "N New Ads", title = "New Ads per Day across observation period")
Figure 4 in the text
Code
library(dplyr)
df <- df |>
mutate(advertiser_reduced = case_when(
Advertiser_Name %in% spd ~ 1,
Advertiser_Name %in% cdu ~ 2,
Advertiser_Name %in% csu ~ 3,
Advertiser_Name %in% b90 ~ 4,
Advertiser_Name %in% afd ~ 5,
Advertiser_Name %in% fdp ~ 6,
Advertiser_Name %in% linke ~ 7,
Advertiser_Name %in% volt ~ 14,
TRUE ~ NA
),
advertiser_label = factor(case_when(
advertiser_reduced == 1 ~ "SPD",
advertiser_reduced == 2 ~ "CDU",
advertiser_reduced == 3 ~ "CSU",
advertiser_reduced == 4 ~ "Greens",
advertiser_reduced == 5 ~ "AfD",
advertiser_reduced == 6 ~ "FDP",
advertiser_reduced == 7 ~ "Left",
advertiser_reduced == 14 ~ "Volt",
TRUE ~ "Other"
), levels = c("SPD", "CDU", "CSU", "Greens", "AfD", "FDP", "Left", "Volt", "Other")))
table(df$advertiser_label)
SPD CDU CSU Greens AfD FDP Left Volt Other
374 301 35 185 2494 1257 76 299 110 Code
table_data1 <- table(df$Num_of_Days, df$advertiser_label)
print(table_data1)
SPD CDU CSU Greens AfD FDP Left Volt Other
1 16 37 1 5 460 59 4 6 1
2 17 14 0 5 79 47 5 5 11
3 7 10 2 7 327 85 2 12 9
4 16 4 0 1 256 45 1 45 3
5 18 16 0 0 121 43 2 65 1
6 9 8 0 6 218 53 21 0 8
7 47 8 1 7 162 24 2 6 6
8 11 20 3 0 24 45 3 2 0
9 12 8 0 0 74 82 2 5 5
10 22 19 0 1 118 80 0 7 12
11 5 9 0 2 131 62 5 10 6
12 6 13 5 1 14 83 1 46 3
13 3 9 1 16 32 23 7 1 4
14 10 6 1 0 10 66 1 0 10
15 1 0 1 7 26 7 1 1 3
16 4 1 0 1 19 15 1 1 1
17 5 34 0 1 22 38 2 0 5
18 9 16 3 3 24 21 3 1 5
19 17 1 3 2 39 16 1 47 3
20 9 2 0 2 149 9 1 0 2
21 5 14 0 0 183 70 1 1 1
22 11 28 0 3 0 37 0 0 0
23 5 0 0 2 4 19 0 0 1
24 7 0 3 17 0 25 0 2 3
25 3 0 7 0 0 100 0 1 1
26 6 1 0 0 1 8 3 12 0
27 1 3 0 6 1 3 0 1 0
28 2 1 0 0 0 1 0 0 0
29 29 2 0 2 0 2 0 8 0
30 10 5 0 0 0 2 0 14 1
31 2 0 0 0 0 32 0 0 0
32 23 0 0 0 0 18 1 0 0
33 1 0 0 3 0 6 1 0 1
34 2 0 1 2 0 1 0 0 0
35 5 0 1 1 0 3 0 0 0
36 2 0 0 0 0 7 4 0 0
37 2 1 0 3 0 3 0 0 0
38 0 0 0 1 0 5 0 0 4
39 1 0 0 1 0 0 0 0 0
40 0 0 0 1 0 5 0 0 0
41 0 0 0 1 0 4 0 0 0
42 0 0 0 25 0 0 0 0 0
43 1 0 2 0 0 0 0 0 0
44 0 0 0 6 0 0 0 0 0
45 2 1 0 12 0 0 0 0 0
46 0 8 0 6 0 0 0 0 0
47 7 0 0 2 0 0 1 0 0
48 1 0 0 0 0 0 0 0 0
49 0 1 0 0 0 0 0 0 0
50 1 0 0 0 0 0 0 0 0
51 1 1 0 3 0 3 0 0 0
52 0 0 0 8 0 0 0 0 0
53 0 0 0 7 0 0 0 0 0
54 0 0 0 4 0 0 0 0 0
55 0 0 0 2 0 0 0 0 0Code
#conversion into data frame
df_table1 <- as.data.frame(table_data1)
colnames(df_table1) <- c("Num_of_Days", "Advertiser", "Frequency")
df_table1$Num_of_Days <- as.numeric(as.character(df_table1$Num_of_Days))
df_table1 <- df_table1 %>%
group_by(Num_of_Days) %>%
mutate(Share = Frequency / sum(Frequency))
library(ggplot2)
ggplot(df_table1, aes(x = Num_of_Days, y = Share, fill = Advertiser)) +
geom_area(alpha = 0.6, linewidth = 0.5, colour = "white") +
labs(x = "Number of Days", y = "Share", title = "Number of Days Ads were displayed per Party") +
scale_x_continuous(breaks = seq(1, 55, by = 5), limits = c(1, 55)) +
scale_fill_manual(values = c("SPD" = "#FF0000", "CDU" = "#000000", "CSU" = "#666666", "Greens" = "#009900",
"AfD" = "#3366FF", "FDP" = "#FFFF00", "Left" = "#990066", "Volt" = "#663399", "Other" = "white"),
labels = c("SPD", "CDU", "CSU", "Greens", "AfD", "FDP", "Left", "Volt", "Other")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Figure 5 in the text
Code
df$Date_Range_Startc <- as.character(df$Date_Range_Start)
# Convert to Date format
df$Date_Range_Start2 <- as.Date(df$Date_Range_Startc, format = "%Y%m%d")
# Create table
table_data2 <- table(df$Date_Range_Start2, df$advertiser_label)
# Print the table
print(table_data2)
SPD CDU CSU Greens AfD FDP Left Volt Other
2021-07-27 1 0 0 0 0 21 0 0 0
2021-07-28 0 0 0 0 0 0 1 0 0
2021-07-30 0 0 0 0 0 0 0 0 1
2021-08-01 3 0 0 14 0 8 0 0 0
2021-08-02 3 0 0 45 1 4 0 0 0
2021-08-03 1 0 0 19 2 1 0 0 0
2021-08-04 0 8 0 2 0 1 0 0 0
2021-08-05 4 1 0 2 0 0 1 0 0
2021-08-06 5 26 0 0 0 6 0 0 0
2021-08-07 1 15 0 0 0 0 0 0 0
2021-08-08 3 0 0 0 0 0 0 0 0
2021-08-09 3 1 0 1 43 1 0 0 0
2021-08-10 4 1 0 0 0 0 1 0 0
2021-08-11 13 2 0 0 36 0 0 0 1
2021-08-12 4 0 0 0 0 7 0 0 0
2021-08-13 9 0 0 0 13 1 0 0 4
2021-08-14 5 0 0 0 7 0 0 0 0
2021-08-15 4 0 2 0 14 22 0 0 0
2021-08-16 32 0 0 0 17 6 1 0 0
2021-08-17 12 0 0 2 0 16 0 0 0
2021-08-18 3 1 0 0 0 6 0 0 0
2021-08-19 7 19 0 0 252 25 0 0 0
2021-08-20 7 2 0 0 105 0 2 0 1
2021-08-21 1 2 0 0 124 1 0 0 0
2021-08-22 27 3 0 2 1 1 6 0 0
2021-08-23 10 0 2 5 0 24 0 75 4
2021-08-24 15 0 0 3 20 8 0 0 1
2021-08-25 12 0 0 1 244 30 2 12 2
2021-08-26 30 18 0 0 235 21 1 2 4
2021-08-27 8 7 5 2 2 6 0 0 1
2021-08-28 2 2 0 0 3 5 0 1 1
2021-08-29 7 6 0 0 0 1 0 0 1
2021-08-30 7 2 0 0 6 13 1 49 10
2021-08-31 3 8 0 7 2 50 1 18 0
2021-09-01 7 4 0 10 735 152 3 2 1
2021-09-02 1 1 7 7 9 178 0 4 2
2021-09-03 13 0 3 16 184 25 1 0 4
2021-09-04 3 1 0 0 13 16 0 0 1
2021-09-05 5 0 0 1 0 14 2 1 4
2021-09-06 10 0 3 2 2 12 0 1 1
2021-09-07 10 5 0 4 6 44 1 0 15
2021-09-08 9 11 0 1 5 8 1 0 2
2021-09-09 6 1 4 4 6 7 3 1 6
2021-09-10 3 39 0 0 0 118 2 110 6
2021-09-11 3 0 1 0 43 16 1 7 1
2021-09-12 1 12 0 0 17 8 1 3 0
2021-09-13 4 10 0 17 13 9 6 3 10
2021-09-14 3 0 0 2 15 21 3 1 4
2021-09-15 3 7 5 3 147 138 2 3 0
2021-09-16 0 23 3 0 33 38 4 0 4
2021-09-17 6 5 0 0 2 15 1 1 6
2021-09-18 2 0 0 1 8 9 1 1 0
2021-09-19 21 7 0 1 0 6 0 0 0
2021-09-20 3 9 0 3 73 47 22 0 1
2021-09-21 10 13 0 6 13 9 1 3 2
2021-09-22 1 15 0 0 0 55 1 0 0
2021-09-23 3 3 0 1 7 2 1 1 4
2021-09-24 0 5 0 0 16 22 1 0 5
2021-09-25 1 6 0 1 20 3 1 0 0Code
# Summarize the data to get the share per day for each advertiser
#Package tidyr version 1.3.1 full documentation:https://tidyr.tidyverse.org/
library(tidyr)
Attache Paket: 'tidyr'Das folgende Objekt ist maskiert 'package:reshape2':
smithsCode
df_summary <- df %>%
group_by(Date_Range_Start2, advertiser_label) %>%
summarise(Count = n()) %>%
mutate(Share1 = Count / sum(Count)) %>%
ungroup() %>%
complete(Date_Range_Start2, advertiser_label) %>%
mutate(Share1 = ifelse(is.na(Share1), 0, Share1))`summarise()` has grouped output by 'Date_Range_Start2'. You can override using
the `.groups` argument.Code
# Create the proportional stacked area plot
ggplot(df_summary, aes(x = Date_Range_Start2, y = Share1, fill = advertiser_label)) +
geom_area(alpha = 0.6, linewidth = 0.5, colour = "white", position= "fill") +
labs(x = "Date", y = "Share", title = "Proportional Share of Ads Displayed per Party Over Time") +
scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "5 days") +
scale_fill_manual(values = c("SPD" = "#FF0000", "CDU" = "#000000", "CSU" = "#666666",
"Greens" = "#009900", "AfD" = "#3366FF", "FDP" = "#FFFF00",
"Left" = "#990066", "Volt" = "#663399", "Other" = "white"),
labels = c("SPD", "CDU", "CSU", "Greens", "AfD", "FDP", "Left", "Volt", "Other")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Code
write.csv(df, "reaching_the_voter_complete.csv")