This R Notebook supports the electronic laboratory notebook (ELN) suvey data shown in the publication: “Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment”, S.G. Higgins, A.A. Nogiwa-Valdez, M.M. Stevens (2021).

Configure environment

Load required packages:

library(here)
here() starts at /Users/stuart/OneDrive - Imperial College London/_Papers/ELN-essay/product_survey_revised
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.3.3     ✓ purrr   0.3.4
✓ tibble  3.0.6     ✓ dplyr   1.0.4
✓ tidyr   1.1.2     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(plotly)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout
library(htmlwidgets)

Import data

Load in survey data from file, recode ‘ongoing’ tags in the date_defunct column to the year 2021, calculate the total number of years active, and create a logical vector for each row determining whether the ELN is active or not in the year 2021: (Note: this Notebook expects file ‘ELN_Review_Higgins_2021_Survey.csv’ to be present in the same directory as the working directory identified by the here package)

data <-
  read_csv(here("ELN_Review_Higgins_2021_Survey.csv")) %>%
  mutate(date_defunct_numeric = as.numeric(replace(date_defunct, date_defunct == "ongoing", 2021)),
         years_active = date_defunct_numeric - date_released,
         defunct_in_2021 =
           case_when(
             date_defunct == "ongoing" ~ FALSE,
             date_defunct == 2021 ~ TRUE,
             TRUE ~ TRUE
           ),
         row_number = row_number())

── Column specification ─────────────────────────────────────────────────────────────────────────────────
cols(
  product_name = col_character(),
  manufacturer = col_character(),
  date_released = col_double(),
  date_defunct = col_character(),
  codebase = col_character(),
  notes = col_character(),
  reference_1 = col_character(),
  reference_2 = col_character(),
  reference_3 = col_character(),
  reference_4 = col_character(),
  references_accessed = col_character()
)

Generate statistics

How many ELNs were surveyed?

data %>%
  count()

How many of the ELNs surveyed are active (FALSE) or defunct (TRUE) in 2021?

data %>%
  count(defunct_in_2021)

What is the average (and spread) of the lifetime (years_active) of the ELNs surveyed? (Note: the median absolute estimate here has a default scaling constant of 1.4826, so that it acts as as a consistent estimator of the standard deviation)

data %>%
  summarise(mean_years_active = mean(years_active),
            sd_years_active = sd(years_active),
            median_years_active = median(years_active),
            mad_years_active = mad(years_active),
            iqr_years_active = IQR(years_active),
            range_years_active = max(years_active)-min(years_active))

What are the average and spread of the lifetimes of ELNs, sub-divided by codebase?

data %>%
  group_by(codebase) %>%
  summarise(mean_years_active = mean(years_active),
            sd_years_active = sd(years_active),
            median_years_active = median(years_active),
            mad_years_active = mad(years_active),
            iqr_years_active = IQR(years_active),
            range_years_active = max(years_active)-min(years_active))

How many of the ELNs surveyed have open-source or proprietary codebases?

data %>%
  count(codebase)

Which are the longest running proprietary and open source ELNs (in the survey data)?

data %>%
  group_by(codebase) %>%
  slice_max(n=1, order_by=years_active) %>%
  select(product_name, manufacturer, years_active, date_defunct, codebase)

Generate figures

Define a theme for plotting figures:

mytheme <-
  theme_bw() +
  theme(
    panel.background = element_rect(fill = "white", colour = "black", size = 2),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    text = element_text(size = 25, face = "plain", colour = "black"),
    axis.title.x = element_text(size = 25, face = "plain"),
    axis.title.y = element_text(size = 25),
    element_line(size = 2),
    axis.ticks.length = unit(0.15, "cm"))

Define functions for customising the appearance of plotted figures:

get_point_colour <- function(x){
  ifelse(x==TRUE, "grey", "grey30")
}

get_line_colour <- function(x){
  ifelse(x!="opensource", "#0072B2", "#CC79A7")
}

Produce the timeline plot featured in Figure 1 of the main manuscript:

p_timeline <-
  data %>%
  mutate(row_number = as_factor(row_number)) %>%
  mutate(row_number = fct_reorder(fct_reorder(row_number, years_active, .desc=FALSE), codebase, .desc=FALSE)) %>%
  mutate(row_number_new = as.numeric(row_number)) %>%
  ggplot() +
  geom_segment(aes(x=date_released, xend=date_defunct_numeric,y=row_number_new, yend=row_number_new),
               colour=get_line_colour(data$codebase),
               linetype="solid",
               size=0.5) +
  geom_point(aes(x=date_released, y=row_number_new), colour=get_point_colour(data$defunct_in_2021), shape=1, size=2 ) +
  geom_point(aes(x=date_defunct_numeric, y=row_number_new), colour=get_point_colour(data$defunct_in_2021), shape=16, size=0.5) + 
  scale_x_continuous(position="bottom", breaks=c(seq(1980,2021,5))) +
  coord_cartesian(xlim=c(1980,2021)) +
  theme_bw() +
  theme(
    plot.margin = margin(0.1, 0.1, 0.1, 0.1, "cm"),
    panel.border = element_blank(),
    panel.grid.major.y = element_line(colour="grey95", size=0.25),
    panel.grid.major.x = element_line(colour="grey95", size=0.25),
    panel.grid.minor.x = element_line(colour="grey95", size=0.25),
    axis.text.y = element_blank(),
    axis.text.x = element_blank(),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_blank(),
    legend.position = "bottom"
  )

print(p_timeline)

ggsave(here("ELN_Review_Higgins_2021_Timeline.pdf"), plot=p_timeline, width=18.0, height=10, device="pdf", dpi=600, units="cm")

Generate a separate interactive version of the timeline shown in Figure 1, and export as a standalone HTML file using plotly and htmlwidgets:

p_interactive <-
  data %>%
  mutate(row_number = as_factor(row_number)) %>%
  mutate(row_number = fct_reorder(fct_reorder(row_number, years_active, .desc=FALSE), codebase, .desc=FALSE)) %>%
  mutate(row_number_new = as.numeric(row_number)) %>%
  mutate(codebase_name = recode(codebase, proprietary = "Proprietary", opensource = "Open-source"),
         marker_colour = if_else(defunct_in_2021 == TRUE, "#C0C0C0", "#4d4d4d"),
         hovertext =
           paste0("<b>",
                  product_name,
                  "</b><br>Date released: ",
                  date_released,
                  "<br>Date defunct: ",
                  date_defunct,
                  "<extra></extra>")
  ) %>%
  plot_ly() %>%
  add_segments(
    x = ~date_released, y = ~row_number_new,
    xend = ~date_defunct_numeric, yend ~row_number_new,
    color = ~factor(codebase_name),
    colors = c("#CC79A7", "#0072B2")
  ) %>%
  add_markers(
    x = ~date_released,
    y = ~row_number_new,
    text = ~product_name,
    name = "Date released",
    customdata = ~hovertext,
    hovertemplate = "%{customdata}",
    marker =
      list(
        symbol = "circle-open",
        size = 10,
        color = ~marker_colour
      ),
    inherit = FALSE
  ) %>%
  add_markers(
    x = ~date_defunct_numeric,
    y = ~row_number_new,
    text = ~product_name,
    name = "Date defunct",
    customdata = ~hovertext,
    hovertemplate = "%{customdata}",
    marker =
      list(
        size = 3,
        color = ~marker_colour
      )
  ) %>%
  layout(
    title = "How long do electronic laboratory notebooks last?",
    xaxis = list(title = "Timeline of ELN products"),
    yaxis = list(title = "Lifetimes of surveyed ELN products",
                 showticklabels = FALSE,
                 zeroline = FALSE,
                 showline = FALSE)
  )
saveWidget(partial_bundle(p_interactive), here("ELN_Review_Higgins_2021_Lifetimes_Interactive_Figure1.html"), selfcontained = TRUE, title = "ELN survey")

Generate a plot showing the total number of new proprietary and open-source ELNs per year, as featured in Figure 1: (Note: axes for this plot were manually appended late in graphics software)

data_summarised <-
  data %>%
  group_by(date_released, codebase) %>%
  summarise(count = n(), .groups="drop_last")

p_releases <-
  data_summarised %>%
  ggplot(aes(x=date_released, y=as.factor(codebase), size=count)) +
  geom_point(shape=21, fill=get_line_colour(data_summarised$codebase),alpha=0.5) +
  scale_size(range=c(1,10)) +
  scale_x_continuous(position="bottom", breaks=c(seq(1980,2021,5))) +
  coord_cartesian(xlim=c(1980,2021)) +
  theme_bw() +
  theme(
    plot.margin = margin(0.1, 0.1, 0.1, 0.1, "cm"),
    panel.border = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(colour="grey95", size=0.25),
    panel.grid.minor.x = element_line(colour="grey95", size=0.25),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.y = element_blank(),
    legend.position = "none"
  )

print(p_releases)

ggsave(here("ELN_Review_Higgins_2021_Releases-Per-Year.pdf"), plot=p_releases, width=18.0, height=2.5, device="pdf", dpi=600, units="cm")

Session information

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] htmlwidgets_1.5.3 plotly_4.9.3      forcats_0.5.1     stringr_1.4.0     dplyr_1.0.4      
 [6] purrr_0.3.4       readr_1.4.0       tidyr_1.1.2       tibble_3.0.6      ggplot2_3.3.3    
[11] tidyverse_1.3.0   here_1.0.1       

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.20         haven_2.3.1       colorspace_2.0-0  vctrs_0.3.6      
 [6] generics_0.1.0    htmltools_0.5.1.1 viridisLite_0.3.0 yaml_2.2.1        rlang_0.4.10     
[11] pillar_1.4.7      glue_1.4.2        withr_2.4.1       DBI_1.1.1         dbplyr_2.1.0     
[16] modelr_0.1.8      readxl_1.3.1      lifecycle_0.2.0   munsell_0.5.0     gtable_0.3.0     
[21] cellranger_1.1.0  rvest_0.3.6       labeling_0.4.2    knitr_1.31        crosstalk_1.1.1  
[26] curl_4.3          broom_0.7.4       Rcpp_1.0.6        scales_1.1.1      backports_1.2.1  
[31] jsonlite_1.7.2    farver_2.0.3      fs_1.5.0          hms_1.0.0         digest_0.6.27    
[36] stringi_1.5.3     grid_4.0.3        rprojroot_2.0.2   cli_2.3.0         tools_4.0.3      
[41] magrittr_2.0.1    lazyeval_0.2.2    crayon_1.4.0      pkgconfig_2.0.3   ellipsis_0.3.1   
[46] data.table_1.13.6 xml2_1.3.2        reprex_1.0.0      lubridate_1.7.9.2 assertthat_0.2.1 
[51] httr_1.4.2        rstudioapi_0.13   R6_2.5.0          compiler_4.0.3   
---
title: "Code for generating the statistics and data figures presented in the publication 'Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment'"
author: Stuart G. Higgins
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  html_notebook:
    theme: sandstone
    toc: true
    toc_depth: 5
    toc_float: true
---

This R Notebook supports the electronic laboratory notebook (ELN) suvey data shown in the publication: "Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment", S.G. Higgins, A.A. Nogiwa-Valdez, M.M. Stevens (2021).

# Configure environment

Load required packages:
```{r}
library(here)
library(tidyverse)
library(plotly)
library(htmlwidgets)
```

# Import data

Load in survey data from file, recode 'ongoing' tags in the `date_defunct` column to the year 2021, calculate the total number of years active, and create a logical vector for each row determining whether the ELN is active or not in the year 2021: (Note: this Notebook expects file 'ELN_Review_Higgins_2021_Survey.csv' to be present in the same directory as the working directory identified by the `here` package)
```{r}
data <-
  read_csv(here("ELN_Review_Higgins_2021_Survey.csv")) %>%
  mutate(date_defunct_numeric = as.numeric(replace(date_defunct, date_defunct == "ongoing", 2021)),
         years_active = date_defunct_numeric - date_released,
         defunct_in_2021 =
           case_when(
             date_defunct == "ongoing" ~ FALSE,
             date_defunct == 2021 ~ TRUE,
             TRUE ~ TRUE
           ),
         row_number = row_number())
```

# Generate statistics

How many ELNs were surveyed?
```{r}
data %>%
  count()
```

How many of the ELNs surveyed are active (FALSE) or defunct (TRUE) in 2021?
```{r}
data %>%
  count(defunct_in_2021)
```

What is the average (and spread) of the lifetime (`years_active`) of the ELNs surveyed? (Note: the median absolute estimate here has a default scaling constant of 1.4826, so that it acts as as a consistent estimator of the standard deviation)
```{r}
data %>%
  summarise(mean_years_active = mean(years_active),
            sd_years_active = sd(years_active),
            median_years_active = median(years_active),
            mad_years_active = mad(years_active),
            iqr_years_active = IQR(years_active),
            range_years_active = max(years_active)-min(years_active))
```

What are the average and spread of the lifetimes of ELNs, sub-divided by codebase?
```{r}
data %>%
  group_by(codebase) %>%
  summarise(mean_years_active = mean(years_active),
            sd_years_active = sd(years_active),
            median_years_active = median(years_active),
            mad_years_active = mad(years_active),
            iqr_years_active = IQR(years_active),
            range_years_active = max(years_active)-min(years_active))
```

How many of the ELNs surveyed have open-source or proprietary codebases?
```{r}
data %>%
  count(codebase)
```

Which are the longest running proprietary and open source ELNs (in the survey data)?
```{r}
data %>%
  group_by(codebase) %>%
  slice_max(n=1, order_by=years_active) %>%
  select(product_name, manufacturer, years_active, date_defunct, codebase)
```


# Generate figures

Define a theme for plotting figures:
```{r}
mytheme <-
  theme_bw() +
  theme(
    panel.background = element_rect(fill = "white", colour = "black", size = 2),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    text = element_text(size = 25, face = "plain", colour = "black"),
    axis.title.x = element_text(size = 25, face = "plain"),
    axis.title.y = element_text(size = 25),
    element_line(size = 2),
    axis.ticks.length = unit(0.15, "cm"))
```

Define functions for customising the appearance of plotted figures:
```{r}
get_point_colour <- function(x){
  ifelse(x==TRUE, "grey", "grey30")
}

get_line_colour <- function(x){
  ifelse(x!="opensource", "#0072B2", "#CC79A7")
}
```

Produce the timeline plot featured in Figure 1 of the main manuscript:
```{r}
p_timeline <-
  data %>%
  mutate(row_number = as_factor(row_number)) %>%
  mutate(row_number = fct_reorder(fct_reorder(row_number, years_active, .desc=FALSE), codebase, .desc=FALSE)) %>%
  mutate(row_number_new = as.numeric(row_number)) %>%
  ggplot() +
  geom_segment(aes(x=date_released, xend=date_defunct_numeric,y=row_number_new, yend=row_number_new),
               colour=get_line_colour(data$codebase),
               linetype="solid",
               size=0.5) +
  geom_point(aes(x=date_released, y=row_number_new), colour=get_point_colour(data$defunct_in_2021), shape=1, size=2 ) +
  geom_point(aes(x=date_defunct_numeric, y=row_number_new), colour=get_point_colour(data$defunct_in_2021), shape=16, size=0.5) + 
  scale_x_continuous(position="bottom", breaks=c(seq(1980,2021,5))) +
  coord_cartesian(xlim=c(1980,2021)) +
  theme_bw() +
  theme(
    plot.margin = margin(0.1, 0.1, 0.1, 0.1, "cm"),
    panel.border = element_blank(),
    panel.grid.major.y = element_line(colour="grey95", size=0.25),
    panel.grid.major.x = element_line(colour="grey95", size=0.25),
    panel.grid.minor.x = element_line(colour="grey95", size=0.25),
    axis.text.y = element_blank(),
    axis.text.x = element_blank(),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_blank(),
    legend.position = "bottom"
  )

print(p_timeline)

ggsave(here("ELN_Review_Higgins_2021_Timeline.pdf"), plot=p_timeline, width=18.0, height=10, device="pdf", dpi=600, units="cm")
```

Generate a separate interactive version of the timeline shown in Figure 1, and export as a standalone HTML file using `plotly` and `htmlwidgets`:
```{r}
p_interactive <-
  data %>%
  mutate(row_number = as_factor(row_number)) %>%
  mutate(row_number = fct_reorder(fct_reorder(row_number, years_active, .desc=FALSE), codebase, .desc=FALSE)) %>%
  mutate(row_number_new = as.numeric(row_number)) %>%
  mutate(codebase_name = recode(codebase, proprietary = "Proprietary", opensource = "Open-source"),
         marker_colour = if_else(defunct_in_2021 == TRUE, "#C0C0C0", "#4d4d4d"),
         hovertext =
           paste0("<b>",
                  product_name,
                  "</b><br>Date released: ",
                  date_released,
                  "<br>Date defunct: ",
                  date_defunct,
                  "<extra></extra>")
  ) %>%
  plot_ly() %>%
  add_segments(
    x = ~date_released, y = ~row_number_new,
    xend = ~date_defunct_numeric, yend ~row_number_new,
    color = ~factor(codebase_name),
    colors = c("#CC79A7", "#0072B2")
  ) %>%
  add_markers(
    x = ~date_released,
    y = ~row_number_new,
    text = ~product_name,
    name = "Date released",
    customdata = ~hovertext,
    hovertemplate = "%{customdata}",
    marker =
      list(
        symbol = "circle-open",
        size = 10,
        color = ~marker_colour
      ),
    inherit = FALSE
  ) %>%
  add_markers(
    x = ~date_defunct_numeric,
    y = ~row_number_new,
    text = ~product_name,
    name = "Date defunct",
    customdata = ~hovertext,
    hovertemplate = "%{customdata}",
    marker =
      list(
        size = 3,
        color = ~marker_colour
      )
  ) %>%
  layout(
    title = "How long do electronic laboratory notebooks last?",
    xaxis = list(title = "Timeline of ELN products"),
    yaxis = list(title = "Lifetimes of surveyed ELN products",
                 showticklabels = FALSE,
                 zeroline = FALSE,
                 showline = FALSE)
  )
saveWidget(partial_bundle(p_interactive), here("ELN_Review_Higgins_2021_Lifetimes_Interactive_Figure1.html"), selfcontained = TRUE, title = "ELN survey")
```

Generate a plot showing the total number of new proprietary and open-source ELNs per year, as featured in Figure 1: (Note: axes for this plot were manually appended late in graphics software)
```{r}
data_summarised <-
  data %>%
  group_by(date_released, codebase) %>%
  summarise(count = n(), .groups="drop_last")

p_releases <-
  data_summarised %>%
  ggplot(aes(x=date_released, y=as.factor(codebase), size=count)) +
  geom_point(shape=21, fill=get_line_colour(data_summarised$codebase),alpha=0.5) +
  scale_size(range=c(1,10)) +
  scale_x_continuous(position="bottom", breaks=c(seq(1980,2021,5))) +
  coord_cartesian(xlim=c(1980,2021)) +
  theme_bw() +
  theme(
    plot.margin = margin(0.1, 0.1, 0.1, 0.1, "cm"),
    panel.border = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_line(colour="grey95", size=0.25),
    panel.grid.minor.x = element_line(colour="grey95", size=0.25),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.y = element_blank(),
    legend.position = "none"
  )

print(p_releases)

ggsave(here("ELN_Review_Higgins_2021_Releases-Per-Year.pdf"), plot=p_releases, width=18.0, height=2.5, device="pdf", dpi=600, units="cm")
```

# Session information

```{r}
sessionInfo()
```

