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")

Code for generating the statistics and data figures presented in the publication ‘Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment’

Stuart G. Higgins

03 August 2021

Configure environment

Import data

Generate statistics

Generate figures