Figure_2A
ZZ, APB, TFJ
2022-11-24
Last updated: 2022-11-24
Checks: 7 0
Knit directory:
workflowr-policy-landscape/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 60a6c61 | Andrew Beckerman | 2022-11-24 | Build site. |
| html | fb90a00 | Andrew Beckerman | 2022-11-24 | Build site. |
| Rmd | e08d7ac | Andrew Beckerman | 2022-11-24 | more organising and editing of workflowR mappings |
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Creating Figure 2A
Figure 2A shows gamma values (x-axis, means ± sd) that are the strength of association between a set of stakeholder documents (y-axis) and the corresponding topic.
High gamma values are strong associations.
We plotted the values for four main topics (side labels): Open Research (green), Community and Support (red), Innovation and Solutions (yellow) and Publication Process (blue).
The data for this are produced in 2_Topic_modeling
R Setup and packages
rm(list=ls())Libraries
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.5
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.2.1 ✔ stringr 1.4.1
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()Import data to create Figure 2A
The data are produced in 2_Topic_modeling
df_doc_level_stm_gamma <- read_csv(file = "./output/created_datasets/df_doc_level_stm_gamma.csv")Rows: 516 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): stakeholder, name
dbl (4): topic, total_topic, total_sent, prop
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Data management
df_figure1_gamma <- df_doc_level_stm_gamma %>%
select(-total_topic, -total_sent)
df_figure1_gamma$prop <- df_figure1_gamma$prop *100
df_figure1_gamma_wide <- df_figure1_gamma %>%
spread(topic, prop) %>%
rename(topic_1 = `1`, topic_2 = `2`, topic_3 = `3`, topic_4 = `4`)
# Topic/Category 1: Open Research
# Topic/Category 2: Community & Support
# Topic/Category 3: Innovation & Solution
# Topic/Category 4: Publication process (control)
df_figure1_gamma_wide2 = data.frame(
document = rep(df_figure1_gamma_wide$name,4),
stakeholder = rep(df_figure1_gamma_wide$stakeholder,4),
# type = c(rep("Topic 1",129), rep("Topic 2",129), rep("Topic 3",129), rep("Topic 4", 129)),
type = c(rep("1. Open Research",129), rep("2. Community and Support",129), rep("3. Innovation and Solutions",129), rep("4. Publication Process", 129)),
perc = c(df_figure1_gamma_wide$topic_1, df_figure1_gamma_wide$topic_2, df_figure1_gamma_wide$topic_3, df_figure1_gamma_wide$topic_4),
perc2 = c(df_figure1_gamma_wide$topic_1, df_figure1_gamma_wide$topic_2, df_figure1_gamma_wide$topic_3, df_figure1_gamma_wide$topic_4)
)
# adding quantiles values
sum_figure1_gamma_wide2 =
df_figure1_gamma_wide2 %>%
group_by(stakeholder, type) %>%
dplyr::summarize(lower = quantile(perc, probs = .25), upper = quantile(perc, probs = .75), perc_fin = mean(perc)) %>%
rename(perc = perc_fin)`summarise()` has grouped output by 'stakeholder'. You can override using the
`.groups` argument.Graph production
figure_topics <- ggplot() +
geom_point(data = df_figure1_gamma_wide2, aes(x = perc, y = stakeholder, colour = type), alpha = 0.2, position = position_jitter(), show.legend = FALSE) +
geom_pointrange(data = sum_figure1_gamma_wide2, aes(x = perc, xmin = lower, xmax = upper, y = stakeholder, colour = type), show.legend = FALSE) +
facet_grid(.~type) +
labs(x = "Topics occuring in documents (%) gamma", y = "") +
scale_color_manual(values = c("dark green", "dark red", "darkgoldenrod", "dark blue")) +
theme_classic()
figure_topics
| Version | Author | Date |
|---|---|---|
| e08d7ac | Andrew Beckerman | 2022-11-24 |
# # UNCOMMENT TO SAVE FIGURES
# figure_name <- paste0("./output/Figure_2A/Figure_2A.png")
# png(file=figure_name,
# width= 3000, height= 2000, res=400)
# figure_topics
# dev.off()Session information
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10 purrr_0.3.5
[5] readr_2.1.3 tidyr_1.2.1 tibble_3.1.8 ggplot2_3.3.6
[9] tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.4 sass_0.4.2 bit64_4.0.5
[4] vroom_1.6.0 jsonlite_1.8.3 modelr_0.1.9
[7] bslib_0.4.0 assertthat_0.2.1 getPass_0.2-2
[10] highr_0.9 googlesheets4_1.0.1 cellranger_1.1.0
[13] yaml_2.3.6 pillar_1.8.1 backports_1.4.1
[16] glue_1.6.2 digest_0.6.29 promises_1.2.0.1
[19] rvest_1.0.3 colorspace_2.0-3 htmltools_0.5.3
[22] httpuv_1.6.6 pkgconfig_2.0.3 broom_1.0.1
[25] haven_2.5.1 scales_1.2.1 processx_3.7.0
[28] whisker_0.4 later_1.3.0 tzdb_0.3.0
[31] git2r_0.30.1 googledrive_2.0.0 generics_0.1.3
[34] farver_2.1.1 ellipsis_0.3.2 cachem_1.0.6
[37] withr_2.5.0 cli_3.4.1 magrittr_2.0.3
[40] crayon_1.5.2 readxl_1.4.1 evaluate_0.16
[43] ps_1.7.1 fs_1.5.2 fansi_1.0.3
[46] xml2_1.3.3 tools_4.2.1 hms_1.1.2
[49] gargle_1.2.1 lifecycle_1.0.3 munsell_0.5.0
[52] reprex_2.0.2 callr_3.7.2 compiler_4.2.1
[55] jquerylib_0.1.4 rlang_1.0.6 grid_4.2.1
[58] rstudioapi_0.14 labeling_0.4.2 rmarkdown_2.16
[61] gtable_0.3.1 DBI_1.1.3 R6_2.5.1
[64] lubridate_1.8.0 knitr_1.40 fastmap_1.1.0
[67] bit_4.0.4 utf8_1.2.2 rprojroot_2.0.3
[70] stringi_1.7.8 parallel_4.2.1 Rcpp_1.0.9
[73] vctrs_0.5.0 dbplyr_2.2.1 tidyselect_1.2.0
[76] xfun_0.33
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10 purrr_0.3.5
[5] readr_2.1.3 tidyr_1.2.1 tibble_3.1.8 ggplot2_3.3.6
[9] tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.4 sass_0.4.2 bit64_4.0.5
[4] vroom_1.6.0 jsonlite_1.8.3 modelr_0.1.9
[7] bslib_0.4.0 assertthat_0.2.1 getPass_0.2-2
[10] highr_0.9 googlesheets4_1.0.1 cellranger_1.1.0
[13] yaml_2.3.6 pillar_1.8.1 backports_1.4.1
[16] glue_1.6.2 digest_0.6.29 promises_1.2.0.1
[19] rvest_1.0.3 colorspace_2.0-3 htmltools_0.5.3
[22] httpuv_1.6.6 pkgconfig_2.0.3 broom_1.0.1
[25] haven_2.5.1 scales_1.2.1 processx_3.7.0
[28] whisker_0.4 later_1.3.0 tzdb_0.3.0
[31] git2r_0.30.1 googledrive_2.0.0 generics_0.1.3
[34] farver_2.1.1 ellipsis_0.3.2 cachem_1.0.6
[37] withr_2.5.0 cli_3.4.1 magrittr_2.0.3
[40] crayon_1.5.2 readxl_1.4.1 evaluate_0.16
[43] ps_1.7.1 fs_1.5.2 fansi_1.0.3
[46] xml2_1.3.3 tools_4.2.1 hms_1.1.2
[49] gargle_1.2.1 lifecycle_1.0.3 munsell_0.5.0
[52] reprex_2.0.2 callr_3.7.2 compiler_4.2.1
[55] jquerylib_0.1.4 rlang_1.0.6 grid_4.2.1
[58] rstudioapi_0.14 labeling_0.4.2 rmarkdown_2.16
[61] gtable_0.3.1 DBI_1.1.3 R6_2.5.1
[64] lubridate_1.8.0 knitr_1.40 fastmap_1.1.0
[67] bit_4.0.4 utf8_1.2.2 rprojroot_2.0.3
[70] stringi_1.7.8 parallel_4.2.1 Rcpp_1.0.9
[73] vctrs_0.5.0 dbplyr_2.2.1 tidyselect_1.2.0
[76] xfun_0.33