tklebel

library(tidyverse)

df <- read_csv("~\\NPL_DOI_FOS.csv")

df_restructured <- df %>% 
  select(doi, matches("fos_\\d_name$")) %>% 
  group_by(doi) %>% 
  mutate(id = seq_along(doi)) %>% 
  pivot_longer(starts_with("fos")) %>% 
  filter(!is.na(value)) %>% 

tklebel

library(tidyverse)
library(rcrossref)


# determine the maximum number of records that will be retrieved
initial_res <- cr_members(member_ids = 297,  works = T,
                         filter = list(type = "journal"), limit = 1)

# query all of them
res <- cr_members(member_ids = 297,  works = T,

tklebel

# save test result as object
test_res <- t.test(examgrades$MeanGrade ~ examgrades$Semester)

# extract t and p values and format them
label <- paste(paste("t =", round(test_res$statistic, 2)),
               paste("p =", format.pval(test_res$p.value, eps = .001)),
               sep = ", ")

# add text with t-test result to figure
q1 <- q + annotate("text", x = 1, y = 85, label = label, size = 8)

tklebel

### cpd beta1 Coverage
# hinzufügen zu Zeile 335 (ca)

library(ggplot2)
df <- as.data.frame(cpd.coverB1)

ggplot(df, aes(cpd.coverB1, fill = cpd.coverB1)) +
  geom_bar() +
  geom_hline(yintercept = 950, linetype = 'dashed', colour = "blue") +
  ylim(c(0, 1000)) +