<-
for assignment, NOT =
.# GOOD
x <- 1
# BAD
x = 1
1 -> 1
# GOOD
mean(x, na.rm = TRUE)
# BAD
mean(x, na = TRUE)
# GOOD
raise_to_power(x, power = 2.7)
# BAD
raise_to_power(power = 2.7, x)
...
argument should either be in the beginning or in the end.# GOOD
standardize(..., scale = TRUE, center = TRUE)
# BAD
standardize(scale = TRUE, ..., center = TRUE)
NULL
idiom, and avoid dependence between arguments.base
package.# GOOD
ggplot2::ggplot()
dplyr::select()
# BAD
ggplot()
select()
# GOOD
x <- 1
x <- x + 1
# BAD
x <- 1; x <- x + 1
As a general rule, abbreviations must be avoided when naming.
.R
extension.# GOOD
plot.R
# BAD
plot
# GOOD
plot.R
# BAD
Untitled1.R
/
and spaces. Instead, a dash (-
) or underscore (_
) must be used.# GOOD
read_csv.R
plot-methods.R
# BAD
read csv.R
# GOOD
plot.R
# BAD
Plot.R
données.R
# GOOD
fit_model.R
# BAD
addition.R
AllClasses.R
stores all S4 classes definitions.AllGenerics.R
stores all S4 generic functions.zzz.R
contains .onLoad()
and friends.-methods
suffix for S4 class methods..
(reserved for an S3 dispatch) or use PascalCase (reserved for S4 classes definitions). Instead, use an underscore (_
).# GOOD
std_dev <- 3
# BAD
std.dev <- 3
StdDev <- 3
# GOOD
std_dev <- 3
# BAD
T <- 1
c <- 2 * 2
mean <- 10
.
only for dispatching S3 generic.object_verb()
naming scheme should be prefered as often as possible. This scheme is easy to read and auto-complete.# GOOD
peak_detect()
# BAD
addition()
readFile()
=
, +
, *
, ==
, &&
, <-
, %*%
, etc.).# GOOD
x == y
z <- 2 + 1
# BAD
x==y
z<-2+1
=
in function calls.# GOOD
mean(x = c(1, 2, 3), na.rm = TRUE)
# BAD
mean(x=c(1, 2, NA), na.rm=TRUE)
$
and @
), namespace manipulation (::
and :::
), and for sequence generation (:
).# GOOD
car$cyl
dplyr::select()
1:10
# BAD
car $cyl
dplyr:: select()
1: 10
# GOOD
mtcars[1, ]
mean(x = c(1, NA, 2), na.rm = TRUE)
# BAD
mtcars[1 ,]
mean(x = c(1,NA,2),na.rm = TRUE)
# GOOD
for (element in element_list)
if (total == 5)
sum(1:10)
# BAD
for(element in element_list)
if(total == 5)
sum (1:10)
# GOOD
if (is_true) message("Hello!")
species["tiger", ]
# BAD
if ( is_true ) message("Hello!")
species[ "tiger" ,]
# GOOD
if (is_true) {
# do something
}
if (is_true) {
# do something
} else {
# do something else
}
# BAD
if (is_true)
{
# do something
}
if (is_true) { # do something }
else { # do something else }
else
.# GOOD
if (is_true) {
# do something
} else {
# do something else
}
# BAD
if (is_true) {
# do something
}
else {
# do something else
}
# GOOD
if (is_true) {
# do something
# and then something else
}
# BAD
if (is_true) {
# do something
# and then something else
}
if
is very short.# Good
if (is_true) return(value)
# GOOD
long_function_name <- function(arg1, arg2, arg3, arg4,
long_argument_name1 = TRUE)
plot(table(rpois(100, 5)), type = "h", col = "red", lwd = 10,
main = "rpois(100, lambda = 5)")
# GOOD
long_function_name <- function(long_argument_name1 = c("value1", "value2"),
long_argument_name2 = TRUE,
long_argument_name3 = NULL,
long_argument_name4 = FALSE)
list(
mean = mean(x),
sd = sd(x),
var = var(x),
min = min(x),
max = max(x),
median = median(x)
)
if
statement expands into several lines, than each condition must end with a logical operator, NOT start with it.# GOOD
if (some_very_long_name_1 == 1 &&
some_very_long_name_2 == 1 ||
some_very_long_name_3 %in% some_very_long_name_4)
# BAD
if (some_very_long_name_1 == 1
&& some_very_long_name_2 == 1
|| some_very_long_name_3 %in% some_very_long_name_4)
# GOOD
normal_pdf <- 1 / sqrt(2 * pi * d_sigma ^ 2) *
exp(-(x - d_mean) ^ 2 / 2 / s ^ 2)
# BAD
normal_pdf <- 1 / sqrt(2 * pi * d_sigma ^ 2)
* exp(-(x - d_mean) ^ 2 / 2 / d_sigma ^ 2)
dplyr
(after %>%
) and ggplot2
(after +
) should start with a new line.#
followed by space and text of the comment.# This is a comment.
# GOOD
# define iterator
i <- 1
# BAD
# set i to 1
i <- 1
plot(price, weight) # Plot a scatter chart of price and weight
#
followed by -
or =
.# Read data --------------------------------------------------------------------
# Clean data -------------------------------------------------------------------
This coding style guide is adapted from the the tidyverse style guide, the rOpenSci Packages book and Iegor Rudnytskyi's R Coding Style Guide.
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