Revisiting an old post
Last September I wrote a post entitled Is the Government realising its ambition for SMEs on G-Cloud? Six months on, I wanted to revisit and update this article, fold in a second Digital Marketplace framework, and share the R code here. Revisiting an old post also provides an opportunity to see if one can simplify and improve older code.
Every 7.8 months, on average, suppliers are asked to resubmit their G-Cloud offerings with the lastest pricing and service descriptions. It’s a chance for new suppliers, often smaller ones, to join the existing list and increase overall competitiveness for Cloud services.
Last September’s article showed signs of a weakening in the Small & Medium Enterprise (SME) supplier share of G-Cloud sales by value. This plot shows this trend to have persisted, and reflects the Digital Outcomes & Specialists (DOS) framework also exhibiting a downward trend.
Overall spending through the two frameworks however continues to grow across all parts of Public Sector.
With the weakening in SME share reflected in most sectors.
Revisiting older code prompted me to try automating the routinely, albeit to date manually, produced R toolkit summary. See the code for the automated version. This also now incorporates a count of function usage, arranged with the most-used listed first for each package.
I’m excluding the
stats packages. I’m doing that because, for example,
stats::filter(), and the toolkit summary would imply I’ve used both when my first instinct is “all things tidyverse”. And I’m anyway more interested in my usage of “non-base” functions.
|purrr||map; map2_df; possibly; set_names|
|readr||guess_encoding; locale; read_csv; parse_number|
|dplyr||filter; group_by; mutate; select; summarize; arrange; as_tibble; bind_rows; data_frame; desc; if_else|
|tibble||as_tibble; data_frame; enframe|
|stringr||fixed; str_c; str_replace; str_remove; str_count; str_detect; str_extract|
|lubridate||myd; days_in_month; month|
|ggplot2||labs; aes; geom_line; geom_smooth; ggplot; ggtitle; scale_x_date; theme; element_rect; facet_wrap; scale_y_continuous|
View the code here.
R Development Core Team (2008). R: A language and environment for
statistical computing. R Foundation for Statistical Computing,
Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
Contains public sector information licensed under the Open Government Licence v3.0.