Gender (X84)

df %>% make_table(X84, label = "Gender")
Gender n percent
Male 140 70.7%
Female 56 28.3%
Prefer not to say 2 1.0%
Total 198 100.0%

Academic role (X85)

make_table(df, X85, label = "Academic role")
Academic role n percent
Professor 82 41.4%
Senior Lecturer / Associate Professor / Senior Researcher 72 36.4%
Lecturer / Assistant Professor / Research Fellow 29 14.6%
Post-doctoral researcher (up to 5 years post-PhD) 7 3.5%
Doctoral researcher 5 2.5%
Other 3 1.5%
Total 198 100.0%

Merge junior roles

df %>% 
  mutate(X85 = case_when(str_detect(X85, "Post-doc") ~ "Prae/Post-doc",
                         str_detect(X85, "Doctoral") ~ "Prae/Post-doc",
                         TRUE ~ X85)) %>% 
  make_table(X85, label = "Academic role")
Academic role n percent
Professor 82 41.4%
Senior Lecturer / Associate Professor / Senior Researcher 72 36.4%
Lecturer / Assistant Professor / Research Fellow 29 14.6%
Prae/Post-doc 12 6.1%
Other 3 1.5%
Total 198 100.0%

Year of first academic publication (X87)

df %>% 
  # fix mis-typed input
  mutate(X87 = case_when(X87 == 19999 ~ 1999,
                         X87 == 84 ~ 1984,
                         TRUE ~ X87)) %>% 
  ggplot(aes(X87)) +
  geom_histogram(binwidth = 2, fill = custom_blue) +
  labs(x = "Year of first publication", y = NULL)

Type of instiution (X88 + X89)

Q: “How would you characterise your institution?”

df %>% 
  make_table(X88)
X88 n percent
Both research and teaching intensive 117 59.1%
Research intensive 48 24.2%
Teaching intensive 23 11.6%
Other 10 5.1%
Total 198 100.0%

Q: “How would you characterise your institution?”

df %>% 
  count(X89) %>% 
  drop_na() %>% 
  knitr::kable()
X89 n
clinical hospital 1
intensive research, teaching and patient care activities 1
Medical Outfit 1
Nothing Just it is a political 1
Patient-care 1
Plundered and dying 1
R&D 1
Research and graduate student supervision 1
research and monitoring oriented; limited teaching 1
teaching intensive with a growing emphasis on research 1

Disciplines (X90 + X91)

df %>% 
  make_table(X90)
X90 n percent
Health 21 10.6%
Computer Science 19 9.6%
Biology 16 8.1%
Engineering 15 7.6%
Medicine 15 7.6%
Environmental Science 14 7.1%
Life Sciences 12 6.1%
Other 12 6.1%
Physics 10 5.1%
Economics 8 4.0%
Education 8 4.0%
Agriculture 5 2.5%
Chemistry 5 2.5%
Psychology 5 2.5%
Business 4 2.0%
Materials Science 4 2.0%
Astronomy 3 1.5%
Climate 3 1.5%
Linguistics 3 1.5%
Mathematics 3 1.5%
Archeology 2 1.0%
History 2 1.0%
Philosophy 2 1.0%
Sociology 2 1.0%
Statistics 2 1.0%
Geography 1 0.5%
Geology 1 0.5%
Public Health 1 0.5%
Total 198 100.0% 
df %>% 
  count(X91) %>% 
  drop_na() %>% 
  knitr::kable()
X91 n
Agroforestry 1
Demography 1
digital humanities 1
Ethics 1
Film Studies 1
Health sciences which is very different from medicine 1
Management information systems 1
Neuroscience 1
Science studies 1
Sport 1
transportation systems 1

Disciplines were manually grouped by using the topics from the Web of Science: https://images.webofknowledge.com/images/help/WOS/hp_research_areas_easca.html

df %>% 
  drop_na(disciplines_recoded_wos) %>% # there is one missing case
  make_table(disciplines_recoded_wos)
disciplines_recoded_wos n percent
Life Sciences & Biomedicine 88 44.7%
Technology 39 19.8%
Social Sciences 36 18.3%
Physical Sciences 27 13.7%
Arts & Humanities 7 3.6%
Total 197 100.0% 
plot_bar(df, disciplines_recoded_wos, nudge_y = .01) +
  labs(caption = "n = 197")
## Registered S3 methods overwritten by 'ggalt':
##   method                  from   
##   grid.draw.absoluteGrob  ggplot2
##   grobHeight.absoluteGrob ggplot2
##   grobWidth.absoluteGrob  ggplot2
##   grobX.absoluteGrob      ggplot2
##   grobY.absoluteGrob      ggplot2

Type of contract

# X15 = Are you on a limited-term contract?
df %>% make_table(X15)
X15 n percent
No 158 79.8%
Yes 35 17.7%
Other 4 2.0%
I don’t know 1 0.5%
Total 198 100.0% 
df %>% 
  filter(X15 == "Other") %>% 
  select(X16)
## # A tibble: 4 × 1
##   X16                                                                           
##   <chr>                                                                         
## 1 non-contracted grant researcher                                               
## 2 I am a Complex General Surgical Oncology Fellow and have a postdoctoral resea…
## 3 I retire 4 yrs ago. my fdbk may be irrelevant, but based on my experience aft…
## 4 Definition of my contract is 'permanent' but this subject to the external fun…
# one of the "others" is technically on a permanent contract
total_unlimited <- {df %>% filter(X15 == "No") %>% nrow()} + 1
share <- total_unlimited/nrow(df)

glue::glue("Number and share of researchers on unlimited contract: 
           {total_unlimited} ({scales::percent(share, .1)})")
## Number and share of researchers on unlimited contract: 
## 159 (80.3%)

Country

# checking for others
stopifnot(identical(nrow(filter(df, X12 == "Other")), 0L))

# n for country
nrow(df)
## [1] 198
# inspect country
df %>% make_table(X12, label = "Country")
Country n percent
United Kingdom 33 16.7%
United States 23 11.6%
Italy 21 10.6%
Netherlands 12 6.1%
Sweden 11 5.6%
Canada 9 4.5%
France 7 3.5%
Germany 7 3.5%
Spain 7 3.5%
Switzerland 7 3.5%
Australia 5 2.5%
Indonesia 5 2.5%
Hungary 4 2.0%
Romania 4 2.0%
Austria 3 1.5%
Brazil 3 1.5%
Iran 3 1.5%
Norway 3 1.5%
Poland 3 1.5%
Denmark 2 1.0%
Lithuania 2 1.0%
Russia 2 1.0%
Slovakia 2 1.0%
Burkina Faso 1 0.5%
China 1 0.5%
Ethiopia 1 0.5%
Finland 1 0.5%
Ghana 1 0.5%
Iceland 1 0.5%
India 1 0.5%
Ireland 1 0.5%
Japan 1 0.5%
Malaysia 1 0.5%
Mexico 1 0.5%
Montenegro 1 0.5%
New Zealand 1 0.5%
Portugal 1 0.5%
Serbia 1 0.5%
Slovenia 1 0.5%
Taiwan 1 0.5%
Tanzania 1 0.5%
Turkey 1 0.5%
Uganda 1 0.5%
Total 198 100.0% 
# number of countries
df %>% 
  summarise(n_countries = n_distinct(X12))
## # A tibble: 1 × 1
##   n_countries
##         <int>
## 1          43
# lumping together
country <- df %>% 
  mutate(country_lumped = fct_lump_min(X12, min = 4)) %>% 
  select(X12, country_lumped)
country %>% 
  count(country_lumped) %>% 
  mutate(prop = n / sum(n),
         labels = scales::percent(prop, .1)) %>% 
  mutate(country_ordered = fct_reorder(country_lumped, n, .fun = max,
                                       .desc = TRUE) %>% 
           fct_relevel("Other", after = Inf)) %>% 
  ggplot(aes(country_ordered, prop)) +
  geom_text(aes(label = labels), nudge_y = .01, size = 3.8, family = "Hind") +
  geom_col(width = .7, fill = custom_blue) +
  # geom_chicklet(width = .8, radius = unit(7, "pt")) +
  scale_x_discrete(guide = guide_axis(angle = 45, )) +
  scale_y_continuous(labels = scales::percent) +
  labs(x = NULL, y = NULL) +
  hrbrthemes::theme_ipsum_rc(base_family = "Hind", grid = "Y")

Alternative with dotplot

plot_bar(country, country_lumped, nudge_y = .005, last_val = "Other")

Further classify countries per WP categories. Categories from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups

# computations were moved higher up the pipeline
df %>% 
  count(Region) %>% 
  mutate(prop = n / sum(n),
         labels = scales::percent(prop, .1)) %>% 
  mutate(country_ordered = fct_reorder(Region, n, .fun = max,
                                       .desc = TRUE)) %>% 
  ggplot(aes(country_ordered, prop)) +
  geom_text(aes(label = labels), nudge_y = .03, size = 3.8, family = "Hind") +
  geom_col(width = .7, fill = custom_blue) +
  # geom_chicklet(width = .8, radius = unit(7, "pt")) +
  scale_x_discrete(guide = guide_axis(angle = 45, )) +
  scale_y_continuous(labels = scales::percent) +
  labs(x = NULL, y = NULL) +
  hrbrthemes::theme_ipsum_rc(base_family = "Hind", grid = "Y")

alternative with n

df %>% 
  count(Region) %>% 
  mutate(prop = n / sum(n),
         labels = n) %>% 
  mutate(country_ordered = fct_reorder(Region, n, .fun = max,
                                       .desc = TRUE)) %>% 
  ggplot(aes(country_ordered, prop)) +
  geom_text(aes(label = labels), nudge_y = .03, size = 3.8, family = "Hind") +
  geom_col(width = .7, fill = custom_blue) +
  # geom_chicklet(width = .8, radius = unit(7, "pt")) +
  scale_x_discrete(guide = guide_axis(angle = 45, )) +
  scale_y_continuous(labels = scales::percent) +
  labs(x = NULL, y = NULL) +
  hrbrthemes::theme_ipsum_rc(base_family = "Hind", grid = "Yy")

plot_bar(df, Region)