Get, Download, and Read City Bike Data from Norway
Functions to get, download, and read open data from each City Bike
website, and each City Bike API, in Norway that is made available under the
NLOD 2.0 <https://data.norge.no/nlod/en/2.0>. These functions speed up the
process of reading city bike data directly to R, and to download the data
to disk, so that the user can focus on data analysis. The data is
retrieved from the "developer" or "open data" pages of
Oslo City Bike <https://developer.oslobysykkel.no/>,
Oslo Winter Bike <https://oslovintersykkel.no/en/open-data>,
Bergen City Bike <https://bergenbysykkel.no/en/open-data>, and
Trondheim City Bike <https://trondheimbysykkel.no/en/open-data>.
person("Iman", "Ghayoornia", email = "ghayoornia.iman@gmail.com",
role = c("aut", "cre"))
data.table, glue (>= 1.3.0), httr, jsonlite, magrittr, tibble

Functions to get and download city bike data from
the website and API service of each city bike service in Norway. These
functions minimize time spent on getting Norwegian city bike data, and
lower barriers to start analyzing the data. The data is retrieved from
Oslo City Bike, Oslo Winter Bike, Bergen City Bike, and Trondheim City
Bike. The data is made available under
NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.
glue (>= 1.3.0), httr, jsonlite, lubridate, magrittr, tibble

Functions to get and download city bike data from
the website and API service of each city bike service in Norway. The
package aims to reduce time spent on getting Norwegian city bike data,
and lower barriers to start analyzing it. The data is retrieved from
Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is
made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.
glue (>= 1.3.0), httr, jsonlite, lubridate, tibble

Statistics About Bytes Contained in a File as a Circle Plot
person("Roberto", "S .Galende", email = "roberto.s.galende@gmail.com", role = c("aut", "cre"))
Shows statistics about bytes contained in a file
as a circle graph of deviations from mean in sigma increments.
The function can be useful for statistically analyze the content of files
in a glimpse: text files are shown as a green centered crown, compressed
and encrypted files should be shown as equally distributed variations with
a very low CV (sigma/mean), and other types of files can be classified between
these two categories depending on their text vs binary content, which can be
useful to quickly determine how information is stored inside them (databases,
multimedia files, etc).