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Analysis Examples

Source: vignettes/analysis-examples.Rmd
analysis-examples.Rmd

This vignette demonstrates three very simple analysis workflows using thesis metadata from the NTC. Each example starts with data collection and ends with a table or plot. The workflows cover research trends, institutional comparisons, and keyword mining.

Package builds show the code without running live requests. Set TEZR_LIVE_DOCS=true before rendering if you want to refresh all outputs.

Prerequisites: Familiarity with dplyr and ggplot2. See the Getting Started vignette for search function details.

library(tezr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(stringr)

rolling_mean_right <- function(x, k) {
  vapply(seq_along(x), function(i) {
    if (i < k) {
      return(NA_real_)
    }
    mean(x[(i - k + 1):i])
  }, numeric(1))
}

Suppose you want to track how interest in a topic has changed across decades. This is a standard starting point for bibliometric analysis. You can replace the search term with your own topic of interest.

Collecting Data

Let’s use search_advanced() with the search_field parameter set to all. The result is a tibble of matching records with year, author, university, and other metadata.

# Search for "iklim değişikliği" (climate change) in thesis titles
climate <- search_advanced(keyword = "iklim değişikliği",
                           search_field = "all",
                           max_search_results = Inf)
glimpse(climate)

Yearly Counts with Rolling Average

Let’s count theses per year and smooth with a 10-year rolling average. The rolling average reveals sustained growth versus one-off spikes. We can adjust k for a wider or narrower window.

# Count theses per year
yearly_counts <- climate |>
  count(year) |>
  arrange(year) |>
  mutate(
    year_numeric = as.numeric(year),
    # 10-year rolling average to smooth yearly variation
    rolling_avg = rolling_mean_right(n, k = 10)
  )

# Bar chart with rolling average overlay
yearly_counts |> 
  na.omit() |> 
  ggplot(aes(x = year_numeric)) +
  geom_col(aes(y = n), fill = "steelblue", alpha = 0.6) +
  geom_line(aes(y = rolling_avg), color = "red", linewidth = 1) +
  labs(
    title = "Climate Change Research in Turkish Universities",
    subtitle = "Annual thesis count with 10-year rolling average",
    x = "Year",
    y = "Number of Theses",
    caption = "Red line: 10-year moving average"
  ) +
  theme_minimal(base_size = 11)

We can split by degree type to see what drives growth. Filter to the two main types for a readable plot.

# Compare master's and PhD thesis counts over time
type_trends <- climate |>
  filter(thesis_type_en %in% c("Master", "Doctorate")) |>
  count(year, thesis_type_en) |>
  mutate(year = as.numeric(year))

type_trends |> 
  ggplot(aes(x = year, y = n, color = thesis_type_en)) +
  geom_line(linewidth = 1) +
  labs(
    title = "Climate Research by Degree Type",
    x = "Year",
    y = "Number of Theses",
    color = "Degree"
  ) +
  theme_minimal(base_size = 11)

Example 2: Comparing Universities

Suppose we want to identify which universities produce the most research in a given field. You can replace "Ekonometri" with any subject from list_subjects().

Collecting University-Level Data 

# All econometrics theses, counted by university
econ_theses <- search_detailed(subject = "Ekonometri",
                               max_search_results = Inf)

uni_counts <- econ_theses |>
  count(university, sort = TRUE)

uni_counts |> 
  head(10)

Top Universities Bar Chart

Let’s create a simple bar chart. Horizontal bars make long Turkish university names easy to read.

uni_counts |> 
  head(10) |> 
  ggplot(aes(x = n, y = reorder(university, n))) +
  geom_col() +
  labs(
    title = "Top 10 Universities for Econometrics Research",
    subtitle = "Total thesis count (all years)",
    x = "Number of Theses",
    y = NULL
  ) +
  theme_minimal(base_size = 11)

Let’s compare the top four universities from 2000 onward.

top4_unis <- uni_counts$university[1:4]

# Filter to top 4 universities, 2000 onward
uni_trends <- econ_theses |>
  filter(university %in% top4_unis) |>
  mutate(year = as.numeric(year)) |>
  filter(year >= 2000) |>
  count(year, university)

uni_trends |> 
  ggplot(aes(x = year, y = n, color = university)) +
  geom_line() +
  labs(
    title = "Econometrics Research Trends at Top Universities",
    subtitle = "2000-present",
    x = "Year",
    y = "Number of Theses",
    color = "University"
  ) +
  facet_wrap(~university, scales = "free_y") +
  theme_minimal(base_size = 11) +
  theme(legend.position = "none")

PhD-to-Total Ratio

Let’s assume a higher PhD ratio suggests a more research-intensive program.

# Compute PhD share at each top university
top_unis <- uni_counts$university[1:10]

degree_comparison <- econ_theses |>
  filter(university %in% top_unis) |>
  filter(thesis_type_en %in% c("Master", "Doctorate")) |>
  count(university, thesis_type_en) |>
  pivot_wider(names_from = thesis_type_en, values_from = n, values_fill = 0) |>
  mutate(phd_ratio = Doctorate / (Doctorate + Master)) |> 
  arrange(desc(phd_ratio))

degree_comparison

Example 3: Keyword and Abstract Analysis

You can extract research themes from thesis abstracts and keywords. Detail records include keywords_tr, keywords_en, abstract_original, and abstract_translation. This example fetches details for a small sample so the article builds quickly.

Collecting Detailed Metadata 

# Search for machine learning theses
ml_search <- search_basic("makine öğrenmesi",
                          max_search_results = Inf)

# Fetch full details (abstracts, keywords, advisor, PDF URLs)
ml_search_sample <- ml_search |> 
  slice_head(n = 5)

ml_details <- ml_search_sample$detail_id |>
  lapply(detail) |>
  bind_rows()

Keyword Frequency

The keywords_tr field contains semicolon separated terms. Let’s split them, trim whitespace, and count.

# Parse comma-separated keywords into individual rows
keywords <- ml_details |>
  filter(!is.na(keywords_tr)) |>
  select(thesis_no, keywords_tr) |>
  mutate(keywords_tr = str_split(keywords_tr, ";")) |>
  unnest(keywords_tr) |>
  mutate(keyword = str_trim(keywords_tr)) |>
  filter(keyword != "")

# Top 5 keywords
keyword_freq <- keywords |>
  count(keyword, sort = TRUE) |>
  head(5)

keyword_freq |> 
  ggplot(aes(x = n, y = reorder(keyword, n))) +
  geom_col() +
  labs(
    title = "Most Common Keywords in Machine Learning Theses",
    x = "Frequency",
    y = NULL
  ) +
  theme_minimal(base_size = 11)

Tips for Large-Scale Analysis

Saving Results Locally

Save search results to disk after the first fetch. Load them in later sessions to skip network calls. RDS preserves column types and CSV is useful for sharing.

# Save after first fetch
saveRDS(econ_theses, "econ_theses.rds")
readr::write_csv(econ_theses, "econ_theses.csv")

# Load in a later session
econ_theses <- readRDS("econ_theses.rds")

Incremental Detail Retrieval

For large result sets, fetch details in batches and save each batch. This protects against interruptions — if the process stops, you only lose the current batch.

batch_size <- 50
all_results <- search_basic("panel data")

for (i in seq(1, nrow(all_results), by = batch_size)) {
  batch_end <- min(i + batch_size - 1, nrow(all_results))
  batch <- all_results[i:batch_end, ]

  # detail() uses built-in rate limiting
  details <- detail(batch$detail_id)

  # Save each batch to disk
  saveRDS(details, paste0("details_batch_", i, ".rds"))

  # Optional short pause between batches
  Sys.sleep(2)
}

Rate Limiting

tezr uses a built-in 2-second rate limit for request setup. detail() fetches uncached records in parallel (up to 5 active requests), and large jobs can still take time. Process in batches and cache results when possible.