Do you see yourself working at the same institution in 5 years? (X17)

df %>% make_table(X17)
X17 n percent
Yes 146 73.7%
I don’t know 34 17.2%
No 15 7.6%
Other 3 1.5%
Total 198 100.0% 
df %>%
  count(X18)
## # A tibble: 4 × 2
##   X18                    n
##   <chr>              <int>
## 1 probably yes           1
## 2 retiring in 1 year     1
## 3 will retire            1
## 4 <NA>                 195

Do you intend to apply for a promotion within the next 5 years? (X19)

df %>% make_table(X19)
X19 n percent
Yes 82 41.4%
No 63 31.8%
Not applicable 28 14.1%
I don’t know 18 9.1%
Other 7 3.5%
Total 198 100.0%

Combine them

pdata <- df %>% 
  select(X17, X19) %>% 
  pivot_longer(everything(), names_to = "var", values_to = "val") %>% 
  count(var, val) %>% 
  group_by(var) %>% 
  mutate(percent = n/sum(n),
         val = fct_relevel(val, "Yes", "No", "Other", "I don't know", 
                           "Not applicable"))

pdata <- pdata %>% 
  mutate(var = case_when(
    var == "X17" ~ "Do you see yourself working at the same institution in 5 years?",
    TRUE ~ "Do you intend to apply for a promotion within the next 5 years?"),
         var = str_wrap(var, 30))

pdata %>% 
  ggplot(aes(var, percent, fill = val)) +
  geom_chicklet(width = .7) +
  coord_flip() +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = c(
    "Yes" = "#B96FB0",
    "No" = "#54984E",
    "Other" = "#E0C47C",
    "I don't know" = "grey80", #C9D1F9
    "Not applicable" = "#1C94BB"
  )) +
  labs(x = NULL, y = NULL, fill = NULL) +
  theme(legend.position = "top")

pdata %>% 
  mutate(var = str_replace_all(var, "\\n", " ")) %>% 
  arrange(var, val) %>% 
  knitr::kable()
var val n percent
Do you intend to apply for a promotion within the next 5 years? Yes 82 0.4141414
Do you intend to apply for a promotion within the next 5 years? No 63 0.3181818
Do you intend to apply for a promotion within the next 5 years? Other 7 0.0353535
Do you intend to apply for a promotion within the next 5 years? I don’t know 18 0.0909091
Do you intend to apply for a promotion within the next 5 years? Not applicable 28 0.1414141
Do you see yourself working at the same institution in 5 years? Yes 146 0.7373737
Do you see yourself working at the same institution in 5 years? No 15 0.0757576
Do you see yourself working at the same institution in 5 years? Other 3 0.0151515
Do you see yourself working at the same institution in 5 years? I don’t know 34 0.1717172

Familiarity with promotion policy (X21, X22)

df %>% 
  filter(X21 != "Not applicable") %>% 
  make_table(X21, sort = FALSE)
X21 n percent
Agree 76 40.9%
Completely agree 71 38.2%
Completely disagree 8 4.3%
Disagree 16 8.6%
I don’t know 3 1.6%
Neither agree nor disagree 12 6.5%
Total 186 100.0% 
df %>% 
  filter(X22 != "Not applicable") %>% 
  make_table(X22, sort = FALSE)
X22 n percent
Agree 72 39.3%
Completely agree 74 40.4%
Completely disagree 5 2.7%
Disagree 16 8.7%
I don’t know 2 1.1%
Neither agree nor disagree 14 7.7%
Total 183 100.0%

Combine both

pdata <- df %>% 
  select(X21, X22) %>% 
  pivot_longer(everything(), names_to = "var", values_to = "val") %>% 
  filter(val != "Not applicable") %>% 
  count(var, val) %>% 
  group_by(var) %>% 
  mutate(percent = n/sum(n),
         val = fct_relevel(val, "Completely agree", "Agree", 
                           "Neither agree nor disagree", "Disagree",
                           "Completely disagree", "I don't know"))

pdata <- pdata %>% 
  mutate(var = case_when(
    var == "X21" ~ "I know where to find the promotion policy relevant to my current career stage.",
    TRUE ~ "I am familiar with the promotion policy that applies to my current career stage"),
         var = str_wrap(var, 30))

pdata %>% 
  ggplot(aes(var, percent, fill = val)) +
  geom_chicklet(width = .7) +
  coord_flip() +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = c("#B96FB0", "#F9AEEF", "#E0C47C", "#94D790",
                               "#54984E", "grey80")) +
  guides(fill = guide_legend(byrow = TRUE)) +
  labs(x = NULL, y = NULL, fill = NULL) +
  theme(legend.position = "top")

pdata %>%
  mutate(var = str_replace_all(var, "\\n", " ")) %>% 
  arrange(var, val) %>% 
  knitr::kable()
var val n percent
I am familiar with the promotion policy that applies to my current career stage Completely agree 74 0.4043716
I am familiar with the promotion policy that applies to my current career stage Agree 72 0.3934426
I am familiar with the promotion policy that applies to my current career stage Neither agree nor disagree 14 0.0765027
I am familiar with the promotion policy that applies to my current career stage Disagree 16 0.0874317
I am familiar with the promotion policy that applies to my current career stage Completely disagree 5 0.0273224
I am familiar with the promotion policy that applies to my current career stage I don’t know 2 0.0109290
I know where to find the promotion policy relevant to my current career stage. Completely agree 71 0.3817204
I know where to find the promotion policy relevant to my current career stage. Agree 76 0.4086022
I know where to find the promotion policy relevant to my current career stage. Neither agree nor disagree 12 0.0645161
I know where to find the promotion policy relevant to my current career stage. Disagree 16 0.0860215
I know where to find the promotion policy relevant to my current career stage. Completely disagree 8 0.0430108
I know where to find the promotion policy relevant to my current career stage. I don’t know 3 0.0161290

Gender and other equality

Institutional opinion

base <- df %>%
  select(X1, X44:X45) %>%
  pivot_longer(-X1, names_to = "var", values_to = "val") 

# get labels
labels <- var_overview %>%
  filter(var_id %in% c("X44", "X45")) %>%
  mutate(label = str_extract(var_full, "(?<=\\[).*?(?=\\s?\\])")) %>%
  select(var_id, label)

combined <- base %>%
  left_join(labels, by = c("var" = "var_id")) %>%
  select(-var)

pdata <- combined %>% 
  count(label, val) %>% 
  make_proportion(group = label, var = val) %>% 
  mutate(val = fct_relevel(
    val,
    "Completely agree", "Agree", "Neither agree nor disagree", "Disagree", 
    "Completely disagree", "I don't know", "Not applicable"
  ))
pdata %>% 
  select(-order) %>% 
  arrange(label, val) %>% 
  knitr::kable()
label val n prop
Where possible, promotion committees include representative numbers of women and minorities Completely agree 43 0.2171717
Where possible, promotion committees include representative numbers of women and minorities Agree 64 0.3232323
Where possible, promotion committees include representative numbers of women and minorities Neither agree nor disagree 28 0.1414141
Where possible, promotion committees include representative numbers of women and minorities Disagree 24 0.1212121
Where possible, promotion committees include representative numbers of women and minorities Completely disagree 10 0.0505051
Where possible, promotion committees include representative numbers of women and minorities I don’t know 23 0.1161616
Where possible, promotion committees include representative numbers of women and minorities Not applicable 6 0.0303030
Where two candidates are equally qualified, candidates from underrepresented groups are preferred (e.g. women/men or minority groups) for promotion Completely agree 30 0.1515152
Where two candidates are equally qualified, candidates from underrepresented groups are preferred (e.g. women/men or minority groups) for promotion Agree 41 0.2070707
Where two candidates are equally qualified, candidates from underrepresented groups are preferred (e.g. women/men or minority groups) for promotion Neither agree nor disagree 35 0.1767677
Where two candidates are equally qualified, candidates from underrepresented groups are preferred (e.g. women/men or minority groups) for promotion Disagree 33 0.1666667
Where two candidates are equally qualified, candidates from underrepresented groups are preferred (e.g. women/men or minority groups) for promotion Completely disagree 20 0.1010101
Where two candidates are equally qualified, candidates from underrepresented groups are preferred (e.g. women/men or minority groups) for promotion I don’t know 33 0.1666667
Where two candidates are equally qualified, candidates from underrepresented groups are preferred (e.g. women/men or minority groups) for promotion Not applicable 6 0.0303030 
pdata %>% 
  ungroup() %>% 
  mutate(label = str_wrap(label, 40)) %>% 
  ggplot(aes(label, prop, fill = val)) +
  geom_chicklet(width = .7) +
  coord_flip() +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = c("#B96FB0", "#F9AEEF", "#E0C47C", "#94D790",
                               "#54984E", "grey80", "#1C94BB")) +
  guides(fill = guide_legend(byrow = TRUE)) +
  labs(x = NULL, y = NULL, fill = NULL) +
  theme(legend.position = "top")

Personal opinion

base <- df %>%
  select(X1, X66:X67) %>%
  pivot_longer(-X1, names_to = "var", values_to = "val") 

# get labels
labels <- var_overview %>%
  filter(var_id %in% c("X66", "X67")) %>%
  mutate(label = str_extract(var_full, "(?<=\\[).*?(?=\\s?\\])")) %>%
  select(var_id, label)

combined <- base %>%
  left_join(labels, by = c("var" = "var_id")) %>%
  select(-var)

pdata <- combined %>% 
  count(label, val) %>% 
  make_proportion(group = label, var = val) %>% 
  mutate(val = fct_relevel(
    val,
    "Completely agree", "Agree", "Neither agree nor disagree", "Disagree", 
    "Completely disagree", "I don't know", "Not applicable"
  ))
pdata %>% 
  select(-order) %>% 
  arrange(label, val) %>% 
  knitr::kable()
label val n prop
Promotion committees should where possible include representative numbers of women/men and minorities. Completely agree 96 0.4848485
Promotion committees should where possible include representative numbers of women/men and minorities. Agree 58 0.2929293
Promotion committees should where possible include representative numbers of women/men and minorities. Neither agree nor disagree 26 0.1313131
Promotion committees should where possible include representative numbers of women/men and minorities. Disagree 5 0.0252525
Promotion committees should where possible include representative numbers of women/men and minorities. Completely disagree 8 0.0404040
Promotion committees should where possible include representative numbers of women/men and minorities. I don’t know 3 0.0151515
Promotion committees should where possible include representative numbers of women/men and minorities. Not applicable 2 0.0101010
Where two candidates are equally qualified, candidates from underrepresented groups should be preferred (e.g. women/men or minority groups) for promotion Completely agree 49 0.2474747
Where two candidates are equally qualified, candidates from underrepresented groups should be preferred (e.g. women/men or minority groups) for promotion Agree 57 0.2878788
Where two candidates are equally qualified, candidates from underrepresented groups should be preferred (e.g. women/men or minority groups) for promotion Neither agree nor disagree 41 0.2070707
Where two candidates are equally qualified, candidates from underrepresented groups should be preferred (e.g. women/men or minority groups) for promotion Disagree 18 0.0909091
Where two candidates are equally qualified, candidates from underrepresented groups should be preferred (e.g. women/men or minority groups) for promotion Completely disagree 23 0.1161616
Where two candidates are equally qualified, candidates from underrepresented groups should be preferred (e.g. women/men or minority groups) for promotion I don’t know 3 0.0151515
Where two candidates are equally qualified, candidates from underrepresented groups should be preferred (e.g. women/men or minority groups) for promotion Not applicable 7 0.0353535 
pdata %>% 
  ungroup() %>% 
  mutate(label = str_wrap(label, 40)) %>% 
  ggplot(aes(label, prop, fill = val)) +
  geom_chicklet(width = .7) +
  coord_flip() +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = c("#B96FB0", "#F9AEEF", "#E0C47C", "#94D790",
                               "#54984E", "grey80", "#1C94BB")) +
  guides(fill = guide_legend(byrow = TRUE)) +
  labs(x = NULL, y = NULL, fill = NULL) +
  theme(legend.position = "top")