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index_all.Rmd
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---
title: "FB Ads All"
author: "Ondrej Pekacek & TI CZ"
date: "`r format(Sys.Date(),'%d. %m. %Y')`"
output:
flexdashboard::flex_dashboard:
self_contained: false
lib_dir: "./lib"
mathjax: null
logo: "https://www.transparentnivolby.cz/hrad2023/wp-content/themes/prezident2023/images/logo2023.svg"
css: "./input/dashboard/style.css"
theme: readable
includes:
in_header: "./input/dashboard/header.html"
orientation: columns
vertical_layout: fill
navbar:
- {title: "Duelists", icon: "ion-person-stalker", href: "./index.html"}
- {title: "Candidates", icon: "ion-ios-people", href: "./index_candidates.html"}
- {title: "Project", icon: "ion-information-circled", href: "https://www.transparentnivolby.cz/hrad2023"}
- {title: "Author", icon: "ion-social-linkedin", href: "https://www.linkedin.com/in/ondrej-pekacek"}
- {title: "Data", icon: "ion-cloud", href: "https://www.facebook.com/ads/library"}
- {title: "Source Code", icon: "ion-social-github", href: "https://github.com/opop999/TI_monitoring_fb_political_ads_2023"}
params:
election_date: "2023-01-27"
start_date: "2022-07-01"
end_date: "2023-01-27"
packages: ["metathis", "dplyr", "ggplot2", "forcats", "tidyr", "reactable", "reactablefmtr", "stringr", "plotly", "flexdashboard", "htmlwidgets", "rmarkdown"]
data_dir: "output"
plots_dir: "plots"
plots_static_dir: "static_all"
plots_interactive_dir: "interactive_all"
spenders_limit_high: 50
spenders_limit_mobile_high: 20
spenders_limit_low: 10
spenders_limit_mobile_low: 10
min_ads_filter: 0
social_description: "Monitoring of political and media communication before 2023 presidential elections in the Czech Republic. This dashboard focuses on the analysis of the paid political advertising on Meta platforms through the use of the FB Ads library access."
social_url: "https://opop999.github.io/TI_monitoring_fb_political_ads_2023"
social_preview: "https://www.transparentnivolby.cz/hrad2023/wp-content/uploads/sites/13/2022/11/Cover-TV-hrad-2023.png"
social_preview_w: "1844"
social_preview_h: "902"
social_gh_repo: "opop999/TI_monitoring_fb_political_ads_2023"
---
```{r setup_load, include=FALSE}
# Disable scientific notation of numbers
options(scipen = 999)
# Install packages not yet installed
installed_packages <- params$packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
install.packages(params$packages[!installed_packages])
}
# Packages loading
invisible(lapply(params$packages, library, character.only = TRUE))
```
```{js, echo=FALSE}
// Add pull-left class to logo
document.querySelector("span.navbar-logo").classList.add("pull-left")
// Add link wrapper to logo
$("span.navbar-logo").wrap('<a href="https://www.transparentnivolby.cz/hrad2023/">');
```
```{r setup_meta, echo=FALSE}
# Add meta tags for easier dashboard link sharing on social networks
meta() %>%
meta_description(
params$social_description
) %>%
meta_name("github-repo" = params$social_gh_repo) %>%
meta_tag(
"property" = "og:url",
content = params$social_url
) %>%
meta_tag(
"property" = "og:description",
content = params$social_description
) %>%
meta_tag(
"property" = "og:image",
content = params$social_preview
) %>%
meta_tag(
"property" = "og:image:width",
content = params$social_preview_w
) %>%
meta_tag(
"property" = "og:image:height",
content = params$social_preview_h
) %>%
meta_tag(
"property" = "og:image:alt",
content = "TI Project website preview"
) %>%
meta_tag(
"property" = "og:type",
content = "website"
) %>%
meta_tag(
"property" = "og:locale",
content = "en_US"
) %>%
meta_tag(
"property" = "article:author",
content = metadata$author
) %>%
meta_tag(
"name" = "twitter:title",
content = metadata$title
) %>%
meta_tag(
"name" = "twitter:description",
content = params$social_description
) %>%
meta_tag(
"name" = "twitter:url",
content = params$social_url
) %>%
meta_tag(
"name" = "twitter:image",
content = params$social_preview
) %>%
meta_tag(
"name" = "twitter:image:alt",
content = "Project Logo"
) %>%
meta_tag(
"name" = "twitter:card",
content = "summary_large_image"
)
```
```{r setup_data, include=FALSE}
# Import datasets created in the previous script
summary_dataset <- readRDS(file.path(params$data_dir, "summary_tables", "merged_summary.rds"))
time_dataset <- readRDS(file.path(params$data_dir, "summary_tables", "time_summary.rds"))
regional_spenders <- readRDS(file.path(params$data_dir, "summary_tables", "regional_spenders.rds"))
# Select top spenders for different plot sizes
spenders_limit_high <- summary_dataset %>%
slice_max(avg_spend, n = params$spenders_limit_high) %>%
pull(page_id)
spenders_limit_low <- summary_dataset %>%
slice_max(avg_spend, n = params$spenders_limit_low) %>%
pull(page_id)
spenders_limit_mobile_high <- summary_dataset %>%
slice_max(avg_spend, n = params$spenders_limit_mobile_high) %>%
pull(page_id)
spenders_limit_mobile_low <- summary_dataset %>%
slice_max(avg_spend, n = params$spenders_limit_mobile_low) %>%
pull(page_id)
```
```{r setup_params, include=FALSE}
# Graph zoom date end & beginning + election date for vertical line in the time-plots
election_date <- as.Date(params$election_date)
start_date <- as.Date(params$start_date)
end_date <- as.Date(params$end_date)
# Set upper limit for the number of accounts that show in the graphs
min_ads <- params$min_ads_filter
# Set output dir for individual plots
output_dir <- file.path(params$data_dir, params$plots_dir)
static_dir <- file.path(params$data_dir, params$plots_dir, params$plots_static_dir)
interactive_dir <- file.path(params$data_dir, params$plots_dir, params$plots_interactive_dir)
```
```{r setup_dirs, include=FALSE}
# Check whether output directory exists to save individual plots
if (!dir.exists(output_dir)) {
dir.create(output_dir)
} else {
print("Output directory for all plots already exists")
}
# Repeat for subdirectories
if (!dir.exists(static_dir)) {
dir.create(static_dir)
} else {
print("Subdirectory for static plots already exists")
}
# Repeat for subdirectories
if (!dir.exists(interactive_dir)) {
dir.create(interactive_dir)
} else {
print("Subdirectory for interactive plots already exists")
}
```
Ads
=====================================
Column {.tabset}
-----------------------------------------------------------------------
### **TOTAL ADS** {.no-mobile}
```{r include=FALSE}
plot_name <- "total_ads"
fill_lab <- "Proportion unique ads"
x_lab <- "Total ads"
y_lab <- element_blank()
title_lab <- paste(
"Total ads of top",
length(spenders_limit_high),
"spenders since",
format(start_date, "%d.%m.%Y")
)
```
```{r}
plot <- summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_high) %>%
mutate(page_name = reorder(page_name, total_ads)) %>%
ggplot(
aes(
x = total_ads,
y = page_name,
fill = percent_unique,
text = paste(
"Page:",
page_name,
"<br>Total ads:",
total_ads,
"<br>Proportion unique ads:",
percent_unique * 100,
"%"
)
)
) +
geom_col() +
scale_fill_gradient(
low = "#fbe8e7",
high = "#db1d0b",
limits = c(0, 1),
breaks = c(0, 0.25, 0.50, 0.75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")
) +
scale_x_continuous(
breaks = seq(0, 5000, 250),
labels = seq(0, 5000, 250)
) +
theme_minimal() +
labs(
fill = fill_lab,
x = x_lab,
y = y_lab,
title = title_lab
)
plot_interactive <- ggplotly(plot, tooltip = "text") %>% config(displaylogo = FALSE)
plot_interactive
```
```{r include=FALSE}
# Save plots
ggsave(paste0(file.path(static_dir, plot_name), ".png"), plot = plot, device = "png", bg = "white", units = "px", width = 3840, height = 2160)
saveWidget(plot_interactive, paste0(file.path(interactive_dir, plot_name), ".html"))
```
### **TOTAL SPENDING** {.no-mobile}
```{r include=FALSE}
plot_name <- "total_spending"
fill_lab <- "Per ad (CZK thousands)"
x_lab <- "Total spend (CZK thousands)"
y_lab <- element_blank()
title_lab <- paste(
"Spending on ads of top", length(spenders_limit_high), "spenders since",
format(start_date, "%d.%m.%Y")
)
```
```{r}
plot <- summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_high) %>%
mutate(
avg_spend_1000 = round(avg_spend / 1000, digits = 0),
per_ad_avg_spend_1000 = round(per_ad_avg_spend / 1000, digits = 1),
page_name = reorder(page_name, avg_spend)
) %>%
ggplot(aes(
x = avg_spend_1000,
y = page_name,
fill = per_ad_avg_spend_1000,
text = paste(
"Page:",
page_name,
"<br>Total spend:",
avg_spend_1000,
"thousand CZK",
"<br>Per ad avg spend:",
per_ad_avg_spend_1000,
"thousand CZK"
)
)) +
geom_col() +
scale_fill_gradient2(
low = "#e1e9ef",
mid = "#023059",
high = "#011c33",
midpoint = 35,
limits = c(0, 100)
) +
scale_x_continuous(
breaks = seq(0, 5000, 250),
labels = seq(0, 5000, 250)
) +
theme_minimal() +
labs(
fill = fill_lab,
x = x_lab,
y = y_lab,
title = title_lab
)
plot_interactive <-
ggplotly(plot, tooltip = "text") %>% config(displaylogo = FALSE)
plot_interactive
```
```{r include=FALSE}
# Save plots
ggsave(paste0(file.path(static_dir, plot_name), ".png"), plot = plot, device = "png", bg = "white", units = "px", width = 3840, height = 2160)
saveWidget(plot_interactive, paste0(file.path(interactive_dir, plot_name), ".html"))
```
### **AD LENGTH** {.no-mobile}
```{r include=FALSE}
plot_name <- "ad_length"
x_lab <- "Number of Words"
y_lab <- element_blank()
title_lab <- paste(
"Average ad length in words of top", length(spenders_limit_high), "spenders since",
format(start_date, "%d.%m.%Y")
)
```
```{r}
plot <- summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_high) %>%
mutate(page_name = reorder(page_name, avg_words)) %>%
ggplot(aes(
x = avg_words,
y = page_name,
text = paste(
"Page:",
page_name,
"<br>Avg length per ad:",
avg_words, "words"
)
)) +
geom_col(fill = "#320266") +
scale_x_continuous(
breaks = seq(0, 1500, 50),
labels = seq(0, 1500, 50)
) +
theme_minimal() +
labs(
x = x_lab,
y = y_lab,
title = title_lab
)
plot_interactive <-
ggplotly(plot, tooltip = "text") %>% config(displaylogo = FALSE)
plot_interactive
```
```{r include=FALSE}
# Save plots
ggsave(paste0(file.path(static_dir, plot_name), ".png"), plot = plot, device = "png", bg = "white", units = "px", width = 3840, height = 2160)
saveWidget(plot_interactive, paste0(file.path(interactive_dir, plot_name), ".html"))
```
### **AD RUNTIME** {.no-mobile}
```{r include=FALSE}
plot_name <- "ad_runtime"
x_lab <- "Days"
y_lab <- element_blank()
title_lab <- paste(
"Average ad runtime of top", length(spenders_limit_high), "spenders since",
format(start_date, "%d.%m.%Y")
)
```
```{r}
plot <- summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_high) %>%
mutate(page_name = reorder(page_name, avg_ad_runtime)) %>%
ggplot(aes(
x = avg_ad_runtime,
y = page_name,
text = paste(
"Page:",
page_name,
"<br>Avg runtime:",
avg_ad_runtime, "days"
)
)) +
geom_col(fill = "#026663") +
scale_x_continuous(
breaks = seq(0, 150, 5),
labels = seq(0, 150, 5)
) +
theme_minimal() +
labs(
x = x_lab,
y = y_lab,
title = title_lab
)
plot_interactive <-
ggplotly(plot, tooltip = "text") %>% config(displaylogo = FALSE)
plot_interactive
```
```{r include=FALSE}
# Save plots
ggsave(paste0(file.path(static_dir, plot_name), ".png"), plot = plot, device = "png", bg = "white", units = "px", width = 3840, height = 2160)
saveWidget(plot_interactive, paste0(file.path(interactive_dir, plot_name), ".html"))
```
### **IMPRESSIONS** {.no-mobile}
```{r include=FALSE}
plot_name <- "total_impressions"
fill_lab <- "Per Ad impressions (thousands)"
x_lab <- "Total impressions (millions)"
y_lab <- element_blank()
title_lab <- paste(
"Total ads impressions of top", length(spenders_limit_high), "spenders since",
format(start_date, "%d.%m.%Y")
)
```
```{r}
plot <- summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_high) %>%
mutate(
avg_impressions_million = round(total_avg_impressions / 1000000, digits = 3),
per_ad_avg_impressions_1000 = round(per_ad_avg_impression / 1000, digits = 1),
page_name = reorder(page_name, avg_impressions_million)
) %>%
ggplot(
aes(
x = avg_impressions_million,
y = page_name,
fill = per_ad_avg_impressions_1000,
text = paste(
"Page:",
page_name,
"<br>Total impressions:",
avg_impressions_million,
"million",
"<br>Avg impressions per ad:",
per_ad_avg_impressions_1000,
"thousand"
)
)
) +
geom_col() +
scale_fill_gradient2(
low = "#f9fefa",
mid = "#84cc8a",
high = "#011603",
midpoint = 100,
limits = c(0, 1000)
) +
scale_x_continuous(
breaks = seq(0, 1000, 5),
labels = seq(0, 1000, 5)
) +
theme_minimal() +
labs(
fill = fill_lab,
x = x_lab,
y = y_lab,
title = title_lab
)
plot_interactive <-
ggplotly(plot, tooltip = "text") %>% config(displaylogo = FALSE)
plot_interactive
```
```{r include=FALSE}
# Save plots
ggsave(paste0(file.path(static_dir, plot_name), ".png"), plot = plot, device = "png", bg = "white", units = "px", width = 3840, height = 2160)
saveWidget(plot_interactive, paste0(file.path(interactive_dir, plot_name), ".html"))
```
### **AVERAGE AUDIENCE** {.no-mobile}
```{r include=FALSE}
plot_name <- "avg_audience"
x_lab <- "Average targeted audience size (thousands)"
y_lab <- element_blank()
title_lab <- paste(
"Average minimal estimated audience of top",
length(spenders_limit_high),
"spenders since",
format(start_date, "%d.%m.%Y")
)
```
```{r}
plot <- summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_high) %>%
mutate(
avg_ad_min_audience_1000 = round(avg_ad_min_audience / 1000, digits = 0),
page_name = reorder(page_name, avg_ad_min_audience_1000)
) %>%
ggplot(
aes(
x = avg_ad_min_audience_1000,
y = page_name,
text = paste(
"Page:",
page_name,
"<br>Average minimal audience:",
avg_ad_min_audience_1000, "thousand people"
)
)
) +
geom_col(fill = "#94d9ee") +
scale_x_continuous(
breaks = seq(0, 2000, 100),
labels = seq(0, 2000, 100)
) +
theme_minimal() +
labs(
x = x_lab,
y = y_lab,
title = title_lab
)
plot_interactive <-
ggplotly(plot, tooltip = "text") %>% config(displaylogo = FALSE)
plot_interactive
```
```{r include=FALSE}
# Save plots
ggsave(paste0(file.path(static_dir, plot_name), ".png"), plot = plot, device = "png", bg = "white", units = "px", width = 3840, height = 2160)
saveWidget(plot_interactive, paste0(file.path(interactive_dir, plot_name), ".html"))
```
### **ADS SUMMARY TABLE** {.no-mobile}
```{r}
summary_dataset %>%
select(1:12) %>%
reactable(
defaultPageSize = 15,
theme = nytimes(),
highlight = TRUE,
defaultSorted = c("page_name"),
filterable = TRUE,
striped = TRUE,
defaultColDef = colDef(filterable = FALSE),
columns = list(
"page_name" = colDef(name = "Page Name", sticky = "left", filterable = TRUE, width = 120),
"page_id" = colDef(name = "Page ID", sticky = "left", width = 120),
"total_ads" = colDef(name = "Total Ads", ),
"unique_ads" = colDef(name = "Unique Ads"),
"percent_unique" = colDef(name = "Proportion Unique", format = colFormat(percent = TRUE, digits = 1)),
"avg_words" = colDef(name = "Average Words"),
"avg_spend" = colDef(name = "Spend", format = colFormat(prefix = "CZK ", separators = TRUE, digits = 0)),
"per_ad_avg_spend" = colDef(name = "Per Ad Spend", format = colFormat(prefix = "CZK ", separators = TRUE, digits = 0)),
"total_avg_impressions" = colDef(name = "Total Impressions", format = colFormat(separators = TRUE, digits = 0)),
"per_ad_avg_impression" = colDef(name = "Per Ad Impressions", format = colFormat(separators = TRUE, digits = 0)),
"avg_ad_min_audience" = colDef(name = "Average Ad Min Audience", format = colFormat(separators = TRUE, digits = 0)),
"avg_ad_runtime" = colDef(name = "Average Runtime", format = colFormat(suffix = " days"))
)
)
```
Column {.tabset .mobile}
-----------------------------------------------------------------------
### **TOTAL ADS**
```{r}
ggplotly(
summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_mobile_high) %>%
mutate(page_name = reorder(page_name, total_ads)) %>%
ggplot(
aes(
x = total_ads,
y = page_name,
fill = percent_unique,
text = paste(
"Page:",
page_name,
"<br>Total ads:",
total_ads,
"<br>Proportion unique ads:",
percent_unique * 100,
"%"
)
)
) +
geom_col() +
scale_fill_gradient(
low = "#fbe8e7",
high = "#db1d0b",
limits = c(0, 1),
breaks = c(0, 0.25, 0.50, 0.75, 1),
labels = c("0%", "25%", "50%", "75%", "100%")
) +
scale_y_discrete(label = function(x) str_trunc(x, 16)) +
scale_x_continuous(
breaks = seq(0, 5000, 500),
labels = seq(0, 5000, 500)
) +
theme_minimal() +
theme(legend.position = "none") +
labs(
x = element_blank(),
y = element_blank(),
fill = element_blank()
),
tooltip = "text"
) %>%
config(displayModeBar = FALSE)
```
### **TOTAL SPENDING**
```{r}
ggplotly(
summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_mobile_high) %>%
mutate(
avg_spend_1000 = round(avg_spend / 1000, digits = 0),
per_ad_avg_spend_1000 = round(per_ad_avg_spend / 1000, digits = 1),
page_name = reorder(page_name, avg_spend)
) %>%
ggplot(
aes(x = avg_spend_1000, y = page_name, fill = per_ad_avg_spend_1000, text = paste(
"Page:",
page_name,
"<br>Total spend:",
avg_spend_1000, "thousand CZK",
"<br>Per ad avg spend:",
per_ad_avg_spend_1000, "thousand CZK"
))
) +
geom_col() +
scale_fill_gradient2(
low = "#e1e9ef",
mid = "#023059",
high = "#011c33",
midpoint = 35,
limits = c(0, 100)
) +
scale_x_continuous(
breaks = seq(0, 5000, 500),
labels = seq(0, 5000, 500)
) +
scale_y_discrete(label = function(x) str_trunc(x, 16)) +
theme_minimal() +
theme(legend.position = "none") +
labs(
x = element_blank(),
y = element_blank(),
fill = element_blank()
),
tooltip = "text"
) %>%
config(displayModeBar = FALSE)
```
### **AD LENGTH**
```{r}
ggplotly(
summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_mobile_high) %>%
mutate(page_name = reorder(page_name, avg_words)) %>%
ggplot(aes(
x = avg_words, y = page_name,
text = paste(
"Page:",
page_name,
"<br>Avg length per ad:",
avg_words, "words"
)
)) +
geom_col(fill = "#320266") +
scale_x_continuous(
breaks = seq(0, 1500, 100),
labels = seq(0, 1500, 100)
) +
scale_y_discrete(label = function(x) str_trunc(x, 16)) +
theme_minimal() +
labs(
x = element_blank(),
y = element_blank()
),
tooltip = "text"
) %>%
config(displayModeBar = FALSE)
```
### **AD RUNTIME**
```{r}
ggplotly(
summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_mobile_high) %>%
mutate(page_name = reorder(page_name, avg_ad_runtime)) %>%
ggplot(aes(
x = avg_ad_runtime,
y = page_name,
text = paste(
"Page:",
page_name,
"<br>Avg runtime:",
avg_ad_runtime, "days"
)
)) +
geom_col(fill = "#026663") +
scale_x_continuous(
breaks = seq(0, 150, 5),
labels = seq(0, 150, 5)
) +
scale_y_discrete(
label = function(x) {
str_trunc(x, 16)
}
) +
theme_minimal() +
labs(
x = element_blank(),
y = element_blank()
),
tooltip = "text"
) %>%
config(displayModeBar = FALSE)
```
### **IMPRESSIONS**
```{r}
ggplotly(
summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_mobile_high) %>%
mutate(
avg_impressions_million = round(total_avg_impressions / 1000000, digits = 3),
per_ad_avg_impressions_1000 = round(per_ad_avg_impression / 1000, digits = 1),
page_name = reorder(page_name, avg_impressions_million)
) %>%
ggplot(
aes(
x = avg_impressions_million,
y = page_name,
fill = per_ad_avg_impressions_1000,
text = paste(
"Page:",
page_name,
"<br>Total impressions:",
avg_impressions_million,
"million",
"<br>Avg impressions per ad:",
per_ad_avg_impressions_1000,
"thousand"
)
)
) +
geom_col() +
scale_fill_gradient2(
low = "#f9fefa",
mid = "#84cc8a",
high = "#011603",
midpoint = 100,
limits = c(0, 1000)
) +
scale_x_continuous(
breaks = seq(0, 1000, 10),
labels = seq(0, 1000, 10)
) +
scale_y_discrete(
label = function(x) {
str_trunc(x, 16)
}
) +
theme_minimal() +
theme(legend.position = "none") +
labs(
x = element_blank(),
y = element_blank(),
fill = element_blank()
),
tooltip = "text"
) %>%
config(displayModeBar = FALSE)
```
### **AVERAGE AUDIENCE**
```{r}
ggplotly(
summary_dataset %>%
filter(total_ads > min_ads & page_id %in% spenders_limit_mobile_high) %>%
mutate(
avg_ad_min_audience_1000 = round(avg_ad_min_audience / 1000, digits = 0),
page_name = reorder(page_name, avg_ad_min_audience_1000)
) %>%
ggplot(aes(
x = avg_ad_min_audience_1000,
y = page_name,
text = paste(
"Page:",
page_name,
"<br>Average minimal audience:",
avg_ad_min_audience_1000,
"thousand people"
)
)) +
geom_col(fill = "#94d9ee") +
scale_x_continuous(
breaks = seq(0, 2000, 250),
labels = seq(0, 2000, 250)
) +
scale_y_discrete(
label = function(x) {
str_trunc(x, 16)
}
) +
theme_minimal() +
labs(
x = element_blank(),
y = element_blank()
),
tooltip = "text"
) %>%
config(displayModeBar = FALSE)
```
### **ADS SUMMARY TABLE**
```{r}
summary_dataset %>%
select(1:12) %>%
reactable(
defaultPageSize = 15,
theme = nytimes(),
highlight = TRUE,
defaultSorted = c("page_name"),
filterable = TRUE,
striped = TRUE,
defaultColDef = colDef(filterable = FALSE),
columns = list(
"page_name" = colDef(name = "Page Name", sticky = "left", filterable = TRUE, width = 70),
"page_id" = colDef(name = "Page ID", sticky = "left", width = 50),
"total_ads" = colDef(name = "Total Ads", ),
"unique_ads" = colDef(name = "Unique Ads"),
"percent_unique" = colDef(name = "Proportion Unique", format = colFormat(percent = TRUE, digits = 1)),
"avg_words" = colDef(name = "Average Words"),
"avg_spend" = colDef(name = "Spend", format = colFormat(prefix = "CZK ", separators = TRUE, digits = 0)),
"per_ad_avg_spend" = colDef(name = "Per Ad Spend", format = colFormat(prefix = "CZK ", separators = TRUE, digits = 0)),
"total_avg_impressions" = colDef(name = "Total Impressions", format = colFormat(separators = TRUE, digits = 0)),
"per_ad_avg_impression" = colDef(name = "Per Ad Impressions", format = colFormat(separators = TRUE, digits = 0)),
"avg_ad_min_audience" = colDef(name = "Average Ad Min Audience", format = colFormat(separators = TRUE, digits = 0)),
"avg_ad_runtime" = colDef(name = "Average Runtime", format = colFormat(suffix = " days"))
)
)
```
Trends
=====================================
Column {.tabset}
-----------------------------------------------------------------------
### **SPENDING OVER TIME** {.no-mobile}
```{r include=FALSE}
plot_name <- "spend_over_time"
color_lab <- ""
x_lab <- element_blank()
y_lab <- "CZK million"
title_lab <- paste(
"Spending on ads of top",
length(spenders_limit_low),
"spenders since",
format(start_date, "%d.%m.%Y")
)
```
```{r}
plot <- time_dataset %>%
filter(page_id %in% spenders_limit_low) %>%
mutate(
ad_creation_time_week = as.Date(cut(
ad_creation_time,
breaks = "week",
start.on.monday = TRUE
)) + 6,
cumulative_spend_million = round((cumulative_spend / 1000000), digits = 3)
) %>%
group_by(page_name, ad_creation_time_week) %>%
summarise(end_of_week_million_spend = max(cumulative_spend_million)) %>%
ungroup() %>%
ggplot(aes(x = ad_creation_time_week, y = end_of_week_million_spend, color = page_name)) +
geom_line() +
geom_point(size = 0.8, aes(
text = paste(
"Page:",
page_name,
"<br>Week:",
ad_creation_time_week,
"<br>Cumulative spend:",
end_of_week_million_spend,
"million CZK"
)
)) +
geom_vline(
aes(xintercept = as.numeric(election_date)),
color = "#563a3a",
linetype = 2,
linewidth = 0.2
) +
geom_text(
aes(x = election_date, y = 0, label = "elections"),
color = "#22209F",
vjust = -1,
size = 3,
fontface = "bold",
check_overlap = TRUE
) +
theme_minimal() +
scale_y_continuous(
breaks = seq(0, 10, 0.5),
labels = seq(0, 10, 0.5)
) +
scale_x_date(date_breaks = "1 months", date_labels = "%d.%m.%y") +
coord_cartesian(xlim = c(start_date, end_date), expand = TRUE) +
theme(
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
labs(
color = color_lab,
x = x_lab,
y = y_lab,
title = title_lab
)
plot_interactive <-
ggplotly(plot, tooltip = "text") %>% config(displaylogo = FALSE)
plot_interactive
```
```{r include=FALSE}
# Save plots
ggsave(paste0(file.path(static_dir, plot_name), ".png"), plot = plot, device = "png", bg = "white", units = "px", width = 3840, height = 2160)
saveWidget(plot_interactive, paste0(file.path(interactive_dir, plot_name), ".html"))
```
### **SPENDING WEEKLY** {.no-mobile}
```{r include=FALSE}
plot_name <- "spend_weekly"
fill_lab <- ""
x_lab <- element_blank()
y_lab <- "CZK thousand"
title_lab <- paste(
"Weekly spending on ads of top",
length(spenders_limit_low),
"spenders since",
format(start_date, "%d.%m.%Y")
)
```