RLibs

RLibs is a library of various production tools I use in data processing. It is under construction, a lot of functions are/will be deprecated, some of them will be moved to other packages (like anything useful and related to plotting with ggplot2 will go to sciplotr).

Features

Equality & Comparison

Default R == operator performs strict comparison, which does not work very well for floating-point problems. What R considers unequal, technically can be equal within machine’s precision. The standard example is

0.1 + 0.2 == 0.3

## [1] FALSE

There is RLibs::are_equal_f, which performs more or less correct floating-point comparison (with some given tolerance).

library(RLibs, quietly = TRUE, warn.conflicts = FALSE)
are_equal_f(0.1 + 0.2, 0.3)

## [1] TRUE

Atop of this function there are several more built for comfortable use:

(0.1 + 0.2) %==% 0.3

## [1] TRUE

(0.1 + 0.2) %!=% 0.3

## [1] FALSE

Operators invoke floating-point method only if type of one operand is floating-point. Type/size stability is enforced by the vctrs package.

Cluster planning

A set of tools to create clusters to work with future and furrr packages.

# Checks cluster status
get_topology()

## [1] 1

# Create 2 workers, each spawning 2 workers (so 4 + 2 in total, max 4 working simultaneously)
plan_cluster(2, 2)

## Cluster: [2, 2]

unlist(furrr::future_map(1:2, ~list(Sys.getpid(), furrr::future_map(1:2, ~Sys.getpid()))))

## [1] 22324 16552 13264 20988 26340 21840

# Switch back to sequential execution
plan_cluster(1)

## Cluster: single process

unlist(furrr::future_map(1:2, ~list(Sys.getpid(), furrr::future_map(1:2, ~Sys.getpid()))))

## [1] 10160 10160 10160 10160 10160 10160

Tricky joins

dplyr can do various joins, like inner_join, left_join. Here is a way to do conditional joins (not really optimized):

library(dplyr, quietly = TRUE, warn.conflicts = FALSE)
tbl <- data.frame(Type = c("10-20", "20-30"), L = c(10, 20), U = c(20, 30))
# Subsetting mtcars to reduce output
left_join_cnd(mtcars[c(1:7, 18:20, 28:32),], tbl, .x$mpg >= .y$L, .x$mpg < .y$U) %>% select(Type, L, mpg, U, everything())

##     Type  L  mpg  U cyl  disp  hp drat    wt  qsec vs am gear carb
## 1  20-30 20 21.0 30   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## 2  20-30 20 21.0 30   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## 3  20-30 20 22.8 30   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## 4  20-30 20 21.4 30   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## 5  10-20 10 18.7 20   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## 6  10-20 10 18.1 20   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## 7  10-20 10 14.3 20   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## 8   <NA> NA 32.4 NA   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## 9   <NA> NA 30.4 NA   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## 10  <NA> NA 33.9 NA   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## 11  <NA> NA 30.4 NA   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## 12 10-20 10 15.8 20   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## 13 10-20 10 19.7 20   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## 14 10-20 10 15.0 20   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## 15 20-30 20 21.4 30   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Here ... accepts comma-separated conditions, similar to dplyr::filter, where .x refers to lhs table and .y refers to rhs table.

There are also *_join_safe, which perform exactly the same as dplyr::*_join joins, but beforehand key columns are converted to common types and a meaningful error message is displayed if conversion fails. No more casting factors to strings if the levels are different. vctrs to the rescue!

Utility methods

• %vec_in% invokes vctrs::vec_in(),
• %within% is non-inclusive check if vector is in range, %withini% include boundaries,
• fct_get gets values of factor (as in levels(factor)[factor]),
• len is an S3 that invokes vctrs::vec_size almost always, except for a few cases, where it calls length, e.g. for grid::unit.
• cc is short for vctrs::vec_c,
• vec_rbind_uq is an unquoted pipe-friendly version of vec_rbind, which accepts list as parameter, not .... It can be used to bind list of data frames / tibbles, like `tbl_list %>% map(mutate, A = 2
  • B) %>% vec_rbind_uq. It is currently preferred over dplyr::bind_rows`, which does horrible things to the types.
• lin does linear inter/extrapolation. It ensures type/size stability

IO

• write_fixed accepts a table, a path and format specifier (e.g. a vector of sprintf-like specifiers, one per each column), and outputs table in the plain text format. It is a great way to produce human-readable tables that can be read back by e.g. read.table or readr::read_table / read_table2.
• read/write_smart accepts a table, a path, and other arguments. It calls different methods based on the extension of the path provided. Currently supported types are feather, fth for feather type of data, rds for R’s rds, csv for comma-separated file and everything else is processed by write_fixed/read_table as plain text.