5_For_and_not_for_profit_comparison
ZZ, APB, TFJ
2022-11-24
Last updated: 2022-11-24
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workflowr-policy-landscape/
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There are two questions we did not address formally in the manuscript because of too small sample size.
- What language do for-profit and not-for-profit publishers use in their mission statements and aims?
- What organisations use more common language to the UNESCO Recommendations in Open Science and which use business words?
We conducted the same language analysis that were conducted on the main stakeholders groups to compare for-profit vs. not-for-profit publishers and nonOA journals vs. OA journals.
R Step and Packages
# Clearing R
rm(list=ls())
# Libraries used for text/data analysis
library(tidyverse)
library(dplyr)
library(tidytext)
# Libraries used to create plots
library(ggplot2)
# Library to create a table when converting to html
library("kableExtra") Importing data
# Documents data
df_corpuses <- read_csv("./output/created_datasets/cleaned_data.csv")Rows: 22822 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (10): txt, filename, name, doc_type, stakeholder, sentence_doc, orig_wor...
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_words <- df_corpuses
# Dictionary data 100 words per dictionary
# all_dict <- read_csv("./output/created_datasets/freq_dict_total_100.csv")
# All words
all_dict <- read_csv("./output/created_datasets/freq_dict_total_all.csv")Rows: 9587 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): word
dbl (5): present_in_dict, present_BUS, present_UNESCO, present_book, sum
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 Prep and Cleaning
# Data preparation
# Changing name of the dictionary variable
new_dictionary <- all_dict
## Getting a data set with the words
data_words <- df_corpuses
dim(data_words)[1] 22822 10colnames(data_words) [1] "txt" "filename" "name" "doc_type"
[5] "stakeholder" "sentence_doc" "orig_word" "word_mix"
[9] "word" "org_subgroups"unique(data_words$org_subgroups)[1] "advocates" "funders" "journals_nonOA"
[4] "journals_OA" "publishers_nonProfit" "publishers_Profit"
[7] "repositories" "societies" # Merging and removing duplicates
data_words <- data_words %>%
rename(document = name) %>%
select(document, org_subgroups, word) %>%
left_join(new_dictionary, by = c("word" = "word")) %>% # merging
filter(org_subgroups %in% c("publishers_nonProfit", "publishers_Profit", "journals_nonOA", "journals_OA"))
# Transforming the data frame
# data_words <- transform(data_words, present_BUS = as.numeric(present_BUS),
# present_UNESCO = as.numeric(present_UNESCO),
# present_book = as.numeric(present_book))
# Adding a column with stakeholder and word together to allow merging later
data_words$stake_word <- paste(data_words$org_subgroups, "_", data_words$word)
# Adding a column with a document name and word together to allow merging later
data_words$doc_word <- paste(data_words$document, "_", data_words$word)
# Adding a column that will be used later to calculate the total of unique words in the document (dictionaries without removed words)
data_words$doc_pres <- 1
# Adding two columns that will be used later to calculate the total of unique words in the document (new dictionaries with removed common words)
# Replace NAs with 0 in all absence/presence columns
data_words_ND <- data_words %>%
mutate_at(vars(present_BUS, present_UNESCO, present_book, sum), ~replace_na(., 0)) # replacing NAs
head(as.data.frame(data_words_ND,3)) document org_subgroups word present_in_dict present_BUS
1 American Naturalist journals_nonOA inception 1 0
2 American Naturalist journals_nonOA maintain NA 0
3 American Naturalist journals_nonOA position NA 0
4 American Naturalist journals_nonOA world NA 0
5 American Naturalist journals_nonOA premier NA 0
6 American Naturalist journals_nonOA peer NA 0
present_UNESCO present_book sum stake_word
1 0 1 1 journals_nonOA _ inception
2 0 0 0 journals_nonOA _ maintain
3 0 0 0 journals_nonOA _ position
4 0 0 0 journals_nonOA _ world
5 0 0 0 journals_nonOA _ premier
6 0 0 0 journals_nonOA _ peer
doc_word doc_pres
1 American Naturalist _ inception 1
2 American Naturalist _ maintain 1
3 American Naturalist _ position 1
4 American Naturalist _ world 1
5 American Naturalist _ premier 1
6 American Naturalist _ peer 1colnames(data_words_ND) [1] "document" "org_subgroups" "word" "present_in_dict"
[5] "present_BUS" "present_UNESCO" "present_book" "sum"
[9] "stake_word" "doc_word" "doc_pres" ## Preparing columns used to creating ternary plots
# Select columns of my interest (stakeholders) and aggregate
data_words_ND <- data_words_ND %>%
select(document, org_subgroups, word, present_BUS, present_UNESCO, present_book, doc_pres, sum) # selecting columns, sum - column with the information about in how many dictionaries a certain word occurs
df_sum_pres_ND <- aggregate(x = data_words_ND[,4:8], by = list(data_words_ND$document), FUN = sum, na.rm = TRUE)
# By doing aggregate I lost info about the org_subgroups the doc come from, I want to add it
df_doc_ord <- data_words_ND %>%
select(document, org_subgroups) %>%
distinct(document, .keep_all = TRUE)
df_sum_pres_ND <- df_sum_pres_ND %>%
left_join(df_doc_ord, by = c("Group.1" = "document")) %>%
rename(document = Group.1)
# Creating % columns in a new df_sum_proc_ND data frame
df_sum_proc_ND <- df_sum_pres_ND
# View(df_sum_proc_ND)
df_sum_proc_ND$proc_BUS <-df_sum_proc_ND$present_BUS/df_sum_proc_ND$doc_pres*100
df_sum_proc_ND$proc_UNESCO <- df_sum_proc_ND$present_UNESCO/df_sum_proc_ND$doc_pres*100
df_sum_proc_ND$proc_book <- df_sum_proc_ND$present_book/df_sum_proc_ND$doc_pres*100
# Additional information about the data
# Stakeholders (2 from each of the stakeholders) that shared the highest no of words with UNESCO recommendation
UNESCO_stak_top <- df_sum_proc_ND %>%
group_by(org_subgroups) %>%
arrange(desc(proc_UNESCO)) %>%
slice_head(n=2) %>%
select(org_subgroups, document, proc_UNESCO)
UNESCO_stak_top %>%
kbl(caption = "Stakeholders that shared the higest no of words with UNESCO recommendation:") %>%
kable_classic("hover", full_width = T)| org_subgroups | document | proc_UNESCO |
|---|---|---|
| journals_nonOA | Ecology Letters | 9.677419 |
| journals_nonOA | Frontiers in Ecology and the Environment | 8.411215 |
| journals_OA | Frontiers in Ecology and Evolution | 13.636364 |
| journals_OA | Conservation Letters | 8.823529 |
| publishers_nonProfit | Resilience Alliance | 12.060301 |
| publishers_nonProfit | BioOne | 10.447761 |
| publishers_Profit | Cell Press | 9.292035 |
| publishers_Profit | Springer Nature | 8.196721 |
# Stakeholders (2 from each of the stakeholders) that shared the highest no of words with business dictionary
business_stak_top <- df_sum_proc_ND %>%
group_by(org_subgroups) %>%
arrange(desc(proc_BUS)) %>%
slice_head(n=2) %>%
select(org_subgroups, document, proc_BUS)
business_stak_top %>%
kbl(caption = "Stakeholders that shared the higest no of words with business dictionary:") %>%
kable_classic("hover", full_width = T)| org_subgroups | document | proc_BUS |
|---|---|---|
| journals_nonOA | Evolution | 21.212121 |
| journals_nonOA | Ecology | 8.212560 |
| journals_OA | Evolution Letters | 6.349206 |
| journals_OA | Biogeosciences | 5.917160 |
| publishers_nonProfit | Resilience Alliance | 5.527638 |
| publishers_nonProfit | Annual Reviews | 4.666667 |
| publishers_Profit | Elsevier | 3.433476 |
| publishers_Profit | Pensoft | 3.092783 |
# Stakeholders (2 from each of the stakeholders) that shared the highest no of words with book dictionary (control)
book_stak_top <- df_sum_proc_ND %>%
group_by(org_subgroups) %>%
arrange(desc(proc_book)) %>%
slice_head(n=2) %>%
select(org_subgroups, document, proc_book)
book_stak_top %>%
kbl(caption = "Stakeholders that shared the higest no of words with book dictionary (control):") %>%
kable_classic("hover", full_width = T)| org_subgroups | document | proc_book |
|---|---|---|
| journals_nonOA | Conservation Biology | 18.81188 |
| journals_nonOA | Trends in Ecology & Evolution | 18.07229 |
| journals_OA | Evolution Letters | 22.22222 |
| journals_OA | Remote Sensing in Ecology and Conservation | 20.38835 |
| publishers_nonProfit | The University of Chicago Press | 22.36025 |
| publishers_nonProfit | The Royal Society Publishing | 20.83333 |
| publishers_Profit | Cell Press | 16.37168 |
| publishers_Profit | Wiley | 12.84916 |
Creating graphs
no <- nrow(df_sum_proc_ND)
no[1] 45# Plotting them separately book
df_sum_proc_ND_book = data.frame(
document = rep(df_sum_proc_ND$document,1),
org_subgroups = rep(df_sum_proc_ND$org_subgroups,1),
type = c(rep("Book",no)),
perc = c(df_sum_proc_ND$proc_book),
perc2 = c(df_sum_proc_ND$proc_book))
sum_df_sum_proc_ND_book =
df_sum_proc_ND_book %>%
group_by(org_subgroups, type) %>%
# dplyr::summarise(perc = mean(perc), SD = sd(perc2))
dplyr::summarise(perc = median(perc), SD = sd(perc2))`summarise()` has grouped output by 'org_subgroups'. You can override using the
`.groups` argument.ggplot() +
geom_point(data = df_sum_proc_ND_book, aes(x = perc, y = org_subgroups), alpha = 0.1, position = position_jitter()) +
geom_pointrange(data = sum_df_sum_proc_ND_book, aes(x = perc, xmin = perc - SD, xmax = perc + SD, y = org_subgroups)) +
facet_grid(type~.) +
labs(x = "Document words occuring in dictionary (%)", y = "") +
scale_colour_discrete(guide = "none") +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 796aa8e | zuzannazagrodzka | 2022-09-21 |
# Plotting them separately Business
df_sum_proc_ND_BUS = data.frame(
document = rep(df_sum_proc_ND$document,1),
org_subgroups = rep(df_sum_proc_ND$org_subgroups,1),
type = c(rep("Business",no)),
perc = c(df_sum_proc_ND$proc_BUS),
perc2 = c(df_sum_proc_ND$proc_BUS))
sum_df_sum_proc_ND_BUS =
df_sum_proc_ND_BUS %>%
group_by(org_subgroups, type) %>%
# dplyr::summarise(perc = mean(perc), SD = sd(perc2))
dplyr::summarise(perc = median(perc), SD = sd(perc2))`summarise()` has grouped output by 'org_subgroups'. You can override using the
`.groups` argument.ggplot() +
geom_point(data = df_sum_proc_ND_BUS, aes(x = perc, y = org_subgroups), alpha = 0.1, position = position_jitter()) +
geom_pointrange(data = sum_df_sum_proc_ND_BUS, aes(x = perc, xmin = perc - SD, xmax = perc + SD, y = org_subgroups)) +
facet_grid(type~.) +
labs(x = "Document words occuring in dictionary (%)", y = "") +
scale_colour_discrete(guide = "none") +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 796aa8e | zuzannazagrodzka | 2022-09-21 |
# Plotting them separately UNESCO
df_sum_proc_ND_UNESCO = data.frame(
document = rep(df_sum_proc_ND$document,1),
org_subgroups = rep(df_sum_proc_ND$org_subgroups,1),
type = c(rep("UNESCO",no)),
perc = c(df_sum_proc_ND$proc_UNESCO),
perc2 = c(df_sum_proc_ND$proc_UNESCO))
sum_df_sum_proc_ND_UNESCO =
df_sum_proc_ND_UNESCO %>%
group_by(org_subgroups, type) %>%
# dplyr::summarise(perc = mean(perc), SD = sd(perc2))
dplyr::summarise(perc = median(perc), SD = sd(perc2))`summarise()` has grouped output by 'org_subgroups'. You can override using the
`.groups` argument.ggplot() +
geom_point(data = df_sum_proc_ND_UNESCO, aes(x = perc, y = org_subgroups), alpha = 0.1, position = position_jitter()) +
geom_pointrange(data = sum_df_sum_proc_ND_UNESCO, aes(x = perc, xmin = perc - SD, xmax = perc + SD, y = org_subgroups)) +
facet_grid(type~.) +
labs(x = "Document words occuring in dictionary (%)", y = "") +
scale_colour_discrete(guide = "none") +
theme_classic()
| Version | Author | Date |
|---|---|---|
| 796aa8e | zuzannazagrodzka | 2022-09-21 |
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] kableExtra_1.3.4 tidytext_0.3.4 forcats_0.5.2 stringr_1.4.1
[5] dplyr_1.0.10 purrr_0.3.5 readr_2.1.3 tidyr_1.2.1
[9] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 lubridate_1.8.0 bit64_4.0.5
[4] webshot_0.5.4 httr_1.4.4 rprojroot_2.0.3
[7] SnowballC_0.7.0 tools_4.2.1 backports_1.4.1
[10] bslib_0.4.0 utf8_1.2.2 R6_2.5.1
[13] DBI_1.1.3 colorspace_2.0-3 withr_2.5.0
[16] tidyselect_1.2.0 processx_3.7.0 bit_4.0.4
[19] compiler_4.2.1 git2r_0.30.1 cli_3.4.1
[22] rvest_1.0.3 xml2_1.3.3 labeling_0.4.2
[25] sass_0.4.2 scales_1.2.1 callr_3.7.2
[28] systemfonts_1.0.4 digest_0.6.29 rmarkdown_2.16
[31] svglite_2.1.0 pkgconfig_2.0.3 htmltools_0.5.3
[34] highr_0.9 dbplyr_2.2.1 fastmap_1.1.0
[37] rlang_1.0.6 readxl_1.4.1 rstudioapi_0.14
[40] farver_2.1.1 jquerylib_0.1.4 generics_0.1.3
[43] jsonlite_1.8.3 vroom_1.6.0 tokenizers_0.2.3
[46] googlesheets4_1.0.1 magrittr_2.0.3 Matrix_1.4-1
[49] Rcpp_1.0.9 munsell_0.5.0 fansi_1.0.3
[52] lifecycle_1.0.3 stringi_1.7.8 whisker_0.4
[55] yaml_2.3.6 grid_4.2.1 parallel_4.2.1
[58] promises_1.2.0.1 crayon_1.5.2 lattice_0.20-45
[61] haven_2.5.1 hms_1.1.2 knitr_1.40
[64] ps_1.7.1 pillar_1.8.1 reprex_2.0.2
[67] glue_1.6.2 evaluate_0.16 getPass_0.2-2
[70] modelr_0.1.9 vctrs_0.5.0 tzdb_0.3.0
[73] httpuv_1.6.6 cellranger_1.1.0 gtable_0.3.1
[76] assertthat_0.2.1 cachem_1.0.6 xfun_0.33
[79] broom_1.0.1 janeaustenr_1.0.0 later_1.3.0
[82] googledrive_2.0.0 viridisLite_0.4.1 gargle_1.2.1
[85] ellipsis_0.3.2
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] kableExtra_1.3.4 tidytext_0.3.4 forcats_0.5.2 stringr_1.4.1
[5] dplyr_1.0.10 purrr_0.3.5 readr_2.1.3 tidyr_1.2.1
[9] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 lubridate_1.8.0 bit64_4.0.5
[4] webshot_0.5.4 httr_1.4.4 rprojroot_2.0.3
[7] SnowballC_0.7.0 tools_4.2.1 backports_1.4.1
[10] bslib_0.4.0 utf8_1.2.2 R6_2.5.1
[13] DBI_1.1.3 colorspace_2.0-3 withr_2.5.0
[16] tidyselect_1.2.0 processx_3.7.0 bit_4.0.4
[19] compiler_4.2.1 git2r_0.30.1 cli_3.4.1
[22] rvest_1.0.3 xml2_1.3.3 labeling_0.4.2
[25] sass_0.4.2 scales_1.2.1 callr_3.7.2
[28] systemfonts_1.0.4 digest_0.6.29 rmarkdown_2.16
[31] svglite_2.1.0 pkgconfig_2.0.3 htmltools_0.5.3
[34] highr_0.9 dbplyr_2.2.1 fastmap_1.1.0
[37] rlang_1.0.6 readxl_1.4.1 rstudioapi_0.14
[40] farver_2.1.1 jquerylib_0.1.4 generics_0.1.3
[43] jsonlite_1.8.3 vroom_1.6.0 tokenizers_0.2.3
[46] googlesheets4_1.0.1 magrittr_2.0.3 Matrix_1.4-1
[49] Rcpp_1.0.9 munsell_0.5.0 fansi_1.0.3
[52] lifecycle_1.0.3 stringi_1.7.8 whisker_0.4
[55] yaml_2.3.6 grid_4.2.1 parallel_4.2.1
[58] promises_1.2.0.1 crayon_1.5.2 lattice_0.20-45
[61] haven_2.5.1 hms_1.1.2 knitr_1.40
[64] ps_1.7.1 pillar_1.8.1 reprex_2.0.2
[67] glue_1.6.2 evaluate_0.16 getPass_0.2-2
[70] modelr_0.1.9 vctrs_0.5.0 tzdb_0.3.0
[73] httpuv_1.6.6 cellranger_1.1.0 gtable_0.3.1
[76] assertthat_0.2.1 cachem_1.0.6 xfun_0.33
[79] broom_1.0.1 janeaustenr_1.0.0 later_1.3.0
[82] googledrive_2.0.0 viridisLite_0.4.1 gargle_1.2.1
[85] ellipsis_0.3.2