R interface to InfluxDB
This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.
Installation is easy thanks to CRAN:
install.packages("influxdbr")You can install the dev version from github with:
# install.packages("remotes")
remotes::install_github("dleutnant/influxdbr@dev")This is a basic example which shows you how to communicate (i.e. query and write data) with the InfluxDB server.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(influxdbr)
library(xts)
#> Loading required package: zoo
#>
#> Attaching package: 'zoo'
#> The following objects are masked from 'package:base':
#>
#> as.Date, as.Date.numeric
#>
#> Attaching package: 'xts'
#> The following objects are masked from 'package:dplyr':
#>
#> first, lastLet’s create first some sample data from the xts package and assign arbitrary attributes:
# attach data "sample_matrix"
data("sample_matrix")
# create xts object
xts_data <- xts::as.xts(x = sample_matrix)
# assign some attributes
xts::xtsAttributes(xts_data) <- list(info = "SampleDataMatrix",
UnitTesting = TRUE,
n = 180,
source = "xts")
# print structure to inspect the object
str(xts_data)
#> An 'xts' object on 2007-01-02/2007-06-30 containing:
#> Data: num [1:180, 1:4] 50 50.2 50.4 50.4 50.2 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : NULL
#> ..$ : chr [1:4] "Open" "High" "Low" "Close"
#> Indexed by objects of class: [POSIXct,POSIXt] TZ:
#> xts Attributes:
#> List of 4
#> $ info : chr "SampleDataMatrix"
#> $ UnitTesting: logi TRUE
#> $ n : num 180
#> $ source : chr "xts"To connect to an InfluxDB server, we need a connection object. A
connection object can be created by providing usual server details (e.g.
host, port, …) or with help of a group file,
which conveniently holds all information for us (s. package
documentation):
# create connection object
# (here: based on a config file with group "admin" in it (s. package documentation))
con <- influx_connection(group = "admin")
#> Success: (204) No ContentThe influxdbr package provides handy wrappers to manage
a remote InfluxDB:
# create new database
create_database(con = con, db = "mydb")
# list all databases
show_databases(con = con)
#> # A tibble: 10 x 1
#> name
#> <chr>
#> 1 _internal
#> 2 stbmod
#> 3 wasig
#> 4 wasig-fr
#> 5 wasig-h
#> 6 test
#> 7 oscar_test
#> 8 longterm
#> 9 deznwba
#> 10 mydbWriting an xts-object to the server can be achieved with
influx_write. In this case, columnnames of the
xts object are used as InfluxDB’s field keys,
xts’s coredata represent field values. Attributes are
preserved and written as tag keys and values, respectively.
# write example xts-object to database
influx_write(con = con,
db = "mydb",
x = xts_data,
measurement = "sampledata")Writing a data.frame (or tibble) to the server can also be achieved
with influx_write. In this case, we need to specify which
columns of the data.frame represent time and tags. Fields are
automatically determined.Each row represents a unique data point.
NA’s are not supported and need to be removed. Timestamps
should be located in column time.
Remember that time and tags are optional: InfluxDB uses the server’s local nanosecond timestamp in UTC if the timestamp is not included with the point.
# convert the existing xts-object to data.frame
df_data <- dplyr::bind_cols(time = zoo::index(xts_data), # timestamp
data.frame(xts_data)) %>% # coredata
dplyr::mutate(info = "SampleDataMatrix", # add tag 'info'
UnitTesting = TRUE, # add tag 'UnitTesting'
n = row_number(), # add tag 'n'
source = "df") # add source 'df'
df_data
#> # A tibble: 180 x 9
#> time Open High Low Close info UnitT… n sour…
#> <dttm> <dbl> <dbl> <dbl> <dbl> <chr> <lgl> <int> <chr>
#> 1 2007-01-02 00:00:00 50.0 50.1 50.0 50.1 SampleD… T 1 df
#> 2 2007-01-03 00:00:00 50.2 50.4 50.2 50.4 SampleD… T 2 df
#> 3 2007-01-04 00:00:00 50.4 50.4 50.3 50.3 SampleD… T 3 df
#> 4 2007-01-05 00:00:00 50.4 50.4 50.2 50.3 SampleD… T 4 df
#> 5 2007-01-06 00:00:00 50.2 50.2 50.1 50.2 SampleD… T 5 df
#> 6 2007-01-07 00:00:00 50.1 50.2 50.0 50.0 SampleD… T 6 df
#> 7 2007-01-08 00:00:00 50.0 50.1 50.0 50.0 SampleD… T 7 df
#> 8 2007-01-09 00:00:00 50.0 50.0 49.8 49.9 SampleD… T 8 df
#> 9 2007-01-10 00:00:00 49.9 50.1 49.9 50.0 SampleD… T 9 df
#> 10 2007-01-11 00:00:00 49.9 50.2 49.9 50.2 SampleD… T 10 df
#> # ... with 170 more rows
# write example data.frame to database
influx_write(con = con,
db = "mydb",
x = df_data,
time_col = "time", tag_cols = c("info", "UnitTesting", "n", "source"),
measurement = "sampledata")We can now check if the time series were succefully written:
# check if measurements were succefully written
show_measurements(con = con, db = "mydb")
#> # A tibble: 1 x 1
#> name
#> <chr>
#> 1 sampledataTo query the database, two functions influx_query and
influx_select are available. influx_select
wraps around influx_query and can be useful for simple
requests because it provides default query parameters. The return type
can be configured to be of class tibble or of class
xts.
If return_xts = FALSE a list of tibbles per query
statement is returned. Each tibble contains columns with statement_id,
series_names, tags, time and fields.
# fetch time series data by using the helper function `influx_select`
result <- influx_select(con = con,
db = "mydb",
field_keys = "Open, High",
measurement = "sampledata",
where = "source = 'df'",
group_by = "*",
limit = 10,
order_desc = TRUE,
return_xts = FALSE)
result
#> [[1]]
#> # A tibble: 180 x 10
#> state… serie… serie… Unit… info n sour… time Open
#> <int> <chr> <lgl> <chr> <chr> <chr> <chr> <dttm> <dbl>
#> 1 0 sampl… F TRUE Sampl… 99 df 2007-04-09 22:00:00 49.6
#> 2 0 sampl… F TRUE Sampl… 98 df 2007-04-08 22:00:00 49.4
#> 3 0 sampl… F TRUE Sampl… 97 df 2007-04-07 22:00:00 49.5
#> 4 0 sampl… F TRUE Sampl… 96 df 2007-04-06 22:00:00 49.5
#> 5 0 sampl… F TRUE Sampl… 95 df 2007-04-05 22:00:00 49.3
#> 6 0 sampl… F TRUE Sampl… 94 df 2007-04-04 22:00:00 49.4
#> 7 0 sampl… F TRUE Sampl… 93 df 2007-04-03 22:00:00 49.2
#> 8 0 sampl… F TRUE Sampl… 92 df 2007-04-02 22:00:00 49.1
#> 9 0 sampl… F TRUE Sampl… 91 df 2007-04-01 22:00:00 48.9
#> 10 0 sampl… F TRUE Sampl… 90 df 2007-03-31 22:00:00 48.9
#> # ... with 170 more rows, and 1 more variable: High <dbl>If return_xts = TRUE a list of xts objects per query
statement is returned. Because xts objects are basically matrices (which
can store one data type only), a single xts object is created for each
InfluxDB field. This ensures a correct representation of the field
values data type (instead of getting all into a “character” matrix).
InfluxDB tags are now xts attributes.
# fetch time series data by using the helper function `influx_select`
result <- influx_select(con = con,
db = "mydb",
field_keys = "Open, High",
measurement = "sampledata",
where = "source = 'xts'",
group_by = "*",
limit = 10,
order_desc = TRUE,
return_xts = TRUE)
str(result)
#> List of 1
#> $ :List of 2
#> ..$ sampledata:An 'xts' object on 2007-06-20 22:00:00/2007-06-29 22:00:00 containing:
#> Data: num [1:10, 1] 47.7 47.6 47.2 47.2 47.2 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : NULL
#> ..$ : chr "Open"
#> Indexed by objects of class: [POSIXct,POSIXt] TZ: GMT
#> xts Attributes:
#> List of 7
#> .. ..$ statement_id : int 0
#> .. ..$ series_names : chr "sampledata"
#> .. ..$ series_partial: logi FALSE
#> .. ..$ UnitTesting : chr "TRUE"
#> .. ..$ info : chr "SampleDataMatrix"
#> .. ..$ n : chr "180"
#> .. ..$ source : chr "xts"
#> ..$ sampledata:An 'xts' object on 2007-06-20 22:00:00/2007-06-29 22:00:00 containing:
#> Data: num [1:10, 1] 47.7 47.6 47.2 47.3 47.4 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : NULL
#> ..$ : chr "High"
#> Indexed by objects of class: [POSIXct,POSIXt] TZ: GMT
#> xts Attributes:
#> List of 7
#> .. ..$ statement_id : int 0
#> .. ..$ series_names : chr "sampledata"
#> .. ..$ series_partial: logi FALSE
#> .. ..$ UnitTesting : chr "TRUE"
#> .. ..$ info : chr "SampleDataMatrix"
#> .. ..$ n : chr "180"
#> .. ..$ source : chr "xts"In case the InfluxDB response is expected to be a single series only,
we can flatten the list (simplifyList = TRUE) to directly
get to the data. This enhances a pipeable work flow.
result <- influx_select(con = con,
db = "mydb",
field_keys = "Open",
measurement = "sampledata",
where = "source = 'df'",
group_by = "*",
limit = 10,
order_desc = TRUE,
return_xts = FALSE,
simplifyList = TRUE)
str(result)
#> List of 1
#> $ :Classes 'tbl_df', 'tbl' and 'data.frame': 180 obs. of 9 variables:
#> ..$ statement_id : int [1:180] 0 0 0 0 0 0 0 0 0 0 ...
#> ..$ series_names : chr [1:180] "sampledata" "sampledata" "sampledata" "sampledata" ...
#> ..$ series_partial: logi [1:180] FALSE FALSE FALSE FALSE FALSE FALSE ...
#> ..$ UnitTesting : chr [1:180] "TRUE" "TRUE" "TRUE" "TRUE" ...
#> ..$ info : chr [1:180] "SampleDataMatrix" "SampleDataMatrix" "SampleDataMatrix" "SampleDataMatrix" ...
#> ..$ n : chr [1:180] "99" "98" "97" "96" ...
#> ..$ source : chr [1:180] "df" "df" "df" "df" ...
#> ..$ time : POSIXct[1:180], format: "2007-04-09 22:00:00" ...
#> ..$ Open : num [1:180] 49.6 49.4 49.5 49.5 49.3 ...This Git repository uses the Git
Flow branching model (the git flow
extension is useful for this). The dev
branch contains the latest contributions and other code that will appear
in the next release, and the master
branch contains the code of the latest release, which is exactly what is
currently on CRAN.
Contributing to this package is easy. Just send a pull
request. When you send your PR, make sure dev is the
destination branch on the influxdbr repository.
Your PR should pass R CMD check --as-cran, which will also
be checked by Travis
CI when the PR is submitted.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
To cite package ‘influxdbr’ in publications use:
Dominik Leutnant (2018). influxdbr: R Interface to InfluxDB. R package version 0.14.2. https://github.com/dleutnant/influxdbr
A BibTeX entry for LaTeX users is
@Manual{, title = {influxdbr: R Interface to InfluxDB}, author = {Dominik Leutnant}, year = {2018}, note = {R package version 0.14.2}, url = {https://github.com/dleutnant/influxdbr}, }