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small fixes to the figures
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@mlagisz
mlagisz committed Oct 31, 2023
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147 changes: 135 additions & 12 deletions R/SI_methods_results.Rmd
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@@ -1,7 +1,7 @@
---
title: "SI_methods_results"
author: "ML"
date: "21/09/2023"
date: "27/10/2023"
output:
html_document:
toc: true
Expand Down Expand Up @@ -245,13 +245,13 @@ BPindiv_meta <- read_csv(here("data", "BP_awards_lists_SHAREDCOPY - indiv_winner
skip = 1) #load individual winners data
```

Load country-level productivity SCImago data set and an associated meta-data table:
Load country-level productivity SCImago data set:

```{r}
#load SCImago 2021 country productivity (documents) data downloaded from https://www.scimagojr.com/countryrank.php?year=2021&min=0&min_type=it
COprod <- read_csv(here("data", "scimagojr country rank 2021.csv"), skip = 0) #load data
#create meta-data with columns: data type [options]
#create meta-data with columns:
COprod_meta <- tibble("column name" = names(COprod),
"description" = c("Rank of a given country across all Scimago Subject Areas.",
"Country name.",
Expand All @@ -274,6 +274,73 @@ COprod_meta <- tibble("column name" = names(COprod),
))
```

Load aythor contributorship data set:

```{r}
#load people and tasks log table country productivity
AUcontr <- read_csv(here("data", "BP_awards_lists_SHAREDCOPY - people_tasks log.csv"), skip = 0, n_max = 19) #load data
#simplify column names
names(AUcontr) <- c("name",
"piloting_documentation",
"register_plan",
"plan_checks_feedback",
"journal_screening_(SA)",
"journal_screening_checks_(SA)",
"awards_data_extraction_(SA)",
"awards_data_extraction_checks_(SA)",
"awardees_data_extraction_(SA)",
"awardees_data_extraction_checks (SA)",
"data_wrangling",
"data_wrangling_checks",
"draft_writing",
"draft_checks_feedback",
"finalising_manuscript",
"finalising_manuscript_checks_feedback",
"author_order",
"author_position")
#create meta-data for all columns:
AUcontr_meta <- tibble("column name" = names(AUcontr),
"description" = c("Name of the author",
"Contribution to project piloting and documentation (1 = yes, 0 = no)",
"Contribution to registering the project plan (1 = yes, 0 = no)",
"Contribution to checks and feedback on the project plan (1 = yes, 0 = no)",
"Contribution to journal screening and awards shortlisting (number of Subject Areas)",
"Contribution to cross-checking of journal screening and awards shortlisting (number of Subject Areas)",
"Contribution to awards data extractions (number of Subject Areas)",
"Contribution to cross-checking of awards data extractiosn (number of Subject Areas)",
"Contribution to gender and affiliation data extraction (number of Subject Areas)",
"Contribution to cross-checking of gender and affiliation data extraction (number of Subject Areas)",
"Contribution to data cleaning and pilot analyses (1 = yes, 0 = no)",
"Contribution to cross-checking ofdata cleaning and pilot analyses (1 = yes, 0 = no)",
"Contribution to draft writing (1 = yes, 0 = no)",
"Contribution to draft - checks and feedback (1 = yes, 0 = no)",
"Contribution to finalising draft (1 = yes, 0 = no)",
"Contribution to finalising draft - checks and feedback (1 = yes, 0 = no)",
"Numeric position on the authorship list",
"Position on the authorship list (first, middle, last)"),
"data type [options]" = c("text",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"integer",
"text"
))
```


## Meta-data tables.

Expand Down Expand Up @@ -308,6 +375,15 @@ kable(BPindiv_meta, "html") #%>%
#scroll_box(width = "100%", height = "1500px")
```

#### Table S4.
*Meta-data for the author contributions dataset*.

```{r}
## making a html table:
kable(AUcontr_meta, "html") #%>%
#kable_styling("striped", position = "left") #%>%
#scroll_box(width = "100%", height = "1500px")
```

# Supplementary Results

Expand Down Expand Up @@ -995,8 +1071,8 @@ Summary by focus on individuals (“individual award”) or whole article (“te

- Total number of awards for individual authors: `r length(grep("yes", BPdata$Award_individual))` (`r round(length(grep("yes", BPdata$Award_individual)) / length(BPdata$Award_individual) * 100, 0)`%).
- Total number of awards for individual authors with any career stage being eligible: `r length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Career_stage == "any career stage"])` (`r round(length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Career_stage == "any career stage"]) / sum(str_count(BPdata$Award_individual, "yes")) * 100, 0)`%).
- Total number of awards for individual authors with inflexible time limits for eligibility: `r length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Flexible_eligibility == "no"])` (`r round(length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Flexible_eligibility == "no"]) / sum(str_count(BPdata$Award_individual, "yes")) * 100, 0)`%).
- Total number of awards for individual authors with flexible time limits for eligibility: `r length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Flexible_eligibility == "yes"])` (`r round(length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Flexible_eligibility == "yes"]) / sum(str_count(BPdata$Award_individual, "yes")) * 100, 0)`%). Note: remaining are "not applicable", usually because tehre are no time-related limits for eligibility, e.g. due to any career stage being eligible.
- Total number of awards for individual authors with inflexible time limits for eligibility: `r length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Flexible_eligibility == "no"])` (`r round(length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Flexible_eligibility == "no"]) / sum(str_count(BPdata$Award_individual, "yes")) * 100, 0)`% of all individual awards).
- Total number of awards for individual authors with flexible time limits for eligibility: `r length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Flexible_eligibility == "yes"])` (`r round(length(BPdata$Award_name[BPdata$Award_individual == "yes" & BPdata$Flexible_eligibility == "yes"]) / sum(str_count(BPdata$Award_individual, "yes")) * 100, 0)`% of all individual awards). Note: remaining are "not applicable", usually because there were no time-related limits for eligibility, e.g. due to any career stage being eligible.

Summary by basic award characteristics:

Expand Down Expand Up @@ -2890,7 +2966,7 @@ figure7A <- BPindiv %>%
pattern_alpha = 0.5,
pattern_fill = 'white',
pattern_colour = 'white') +
scale_fill_manual(values = c("#748A52", "#8A6172")) +
scale_fill_manual(values = c("#8A6172", "#748A52")) +
theme_bw() +
labs(fill = "Awardee profile shown:",
pattern = "Awardee profile shown:",
Expand All @@ -2905,11 +2981,12 @@ figure7A <- BPindiv %>%

```{r}
#table(BPindiv$awardee_photo_shown, useNA = "always")
#table(BPindiv$awardee_photo_shown, BPindiv$award_year, useNA = "always")
#plot overall
figure7B <- BPindiv %>%
mutate(awardee_photo_shown = factor(awardee_photo_shown, levels = (c("yes",
"no")))) %>% #reorder value levels
mutate(awardee_photo_shown = factor(awardee_photo_shown, levels = (c("no",
"yes")))) %>% #reorder value levels
group_by(award_year) %>%
count(awardee_photo_shown) %>%
ggplot(aes(x = award_year,
Expand All @@ -2925,11 +3002,11 @@ figure7B <- BPindiv %>%
pattern_alpha = 0.5,
pattern_fill = 'white',
pattern_colour = 'white') +
scale_fill_manual(values = c("#748A52", "#8A6172")) +
scale_fill_manual(values = c("#8A6172", "#748A52")) +
theme_bw() +
labs(fill = "Awardee profile shown:",
pattern = "Awardee profile shown:",
pattern_angle = "Awardee profile shown:",
labs(fill = "Awardee photo shown:",
pattern = "Awardee photo shown:",
pattern_angle = "Awardee photo shown:",
x = "Year",
y = "Awardee count") +
theme(legend.position = "top",
Expand Down Expand Up @@ -3552,6 +3629,52 @@ summary(model_Open_science) #slope ns
```


## Author contributions

```{r}
#save vector for reordering in the plot
position_order <- AUcontr$author_order
#reorder in reverse author position
AUcontr <- AUcontr %>% arrange(desc(position_order))
#make names a factor
AUcontr$name <- factor(AUcontr$name, levels = AUcontr$name)
#change to long format
AUcontr_long <- gather(AUcontr, contribution, counts, piloting_documentation:finalising_manuscript_checks_feedback, factor_key = TRUE)
#remove rows with 0 counts
AUcontr_long <- filter_if(AUcontr_long, is.numeric, all_vars((.) != 0))
# #reinforce factor orders
AUcontr_long$name <- factor(AUcontr_long$name, levels = AUcontr$name)
#make bubble plot
ggplot(AUcontr_long,
aes(x = fct_inorder(contribution),
y = name,
colour = counts,
size = counts)) +
geom_point() +
geom_text(data = subset(AUcontr_long, grepl("(SA)", AUcontr_long$contribution)),
aes(label = counts),
colour = "white",
size = 3) +
scale_x_discrete(position = "top", expand = expansion(mult = 0.1)) +
scale_y_discrete(expand = expansion(mult = 0.1)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0)) +
scale_size_continuous(range = c(2, 6)) +
labs(x = NULL, y = NULL) +
theme(legend.position = "none")
```

#### Figure S54.
*Plot of individual author contributions. Authors are listed in the manuscript authorship order. Contributions are listed in chronological order. Dots without numbers indicate contributions. Numbers within dots show numbers of completed Subject Areas, as relevant*.


## Session Info

```{r}
Expand Down
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