25 Rewriting R code in C++
25.1 Getting started with C++ (Exercises 25.2.6)
Q1. With the basics of C++ in hand, it’s now a great time to practice by reading and writing some simple C++ functions. For each of the following functions, read the code and figure out what the corresponding base R function is. You might not understand every part of the code yet, but you should be able to figure out the basics of what the function does.
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
double f1(NumericVector x) {
int n = x.size();
double y = 0;
for(int i = 0; i < n; ++i) {
y += x[i] / n;
}
return y;
}
// [[Rcpp::export]]
NumericVector f2(NumericVector x) {
int n = x.size();
NumericVector out(n);
out[0] = x[0];
for(int i = 1; i < n; ++i) {
out[i] = out[i - 1] + x[i];
}
return out;
}
// [[Rcpp::export]]
bool f3(LogicalVector x) {
int n = x.size();
for(int i = 0; i < n; ++i) {
if (x[i]) return true;
}
return false;
}
// [[Rcpp::export]]
int f4(Function pred, List x) {
int n = x.size();
for(int i = 0; i < n; ++i) {
LogicalVector res = pred(x[i]);
if (res[0]) return i + 1;
}
return 0;
}
// [[Rcpp::export]]
NumericVector f5(NumericVector x, NumericVector y) {
int n = std::max(x.size(), y.size());
NumericVector x1 = rep_len(x, n);
NumericVector y1 = rep_len(y, n);
NumericVector out(n);
for (int i = 0; i < n; ++i) {
out[i] = std::min(x1[i], y1[i]);
}
return out;
}
A1.
f1()
is the same as mean()
:
f2()
is the same as cumsum()
:
f3()
is the same as any()
:
x1 <- c(TRUE, FALSE, FALSE, TRUE)
x2 <- c(FALSE, FALSE)
f3(x1)
#> [1] TRUE
any(x1)
#> [1] TRUE
f3(x2)
#> [1] FALSE
any(x2)
#> [1] FALSE
f4()
is the same as Position()
:
f5()
is the same as pmin()
:
v1 <- c(1, 3, 4, 5, 6, 7)
v2 <- c(1, 2, 7, 2, 8, 1)
f5(v1, v2)
#> [1] 1 2 4 2 6 1
pmin(v1, v2)
#> [1] 1 2 4 2 6 1
Q2. To practice your function writing skills, convert the following functions into C++. For now, assume the inputs have no missing values.
diff()
. Start by assuming lag 1, and then generalise for lagn
.var()
. Read about the approaches you can take on Wikipedia. Whenever implementing a numerical algorithm, it’s always good to check what is already known about the problem.
A2. The performance benefits are not going to be observed if the function is primitive since those are already tuned to the max in R for performance. So, expect performance gain only for diff()
and var()
.
is.primitive(all)
#> [1] TRUE
is.primitive(cumprod)
#> [1] TRUE
is.primitive(diff)
#> [1] FALSE
is.primitive(range)
#> [1] TRUE
is.primitive(var)
#> [1] FALSE
#include <vector>
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
bool allC(std::vector<bool> x)
{
for (const auto& xElement : x)
{
if (!xElement) return false;
}
return true;
}
v1 <- rep(TRUE, 10)
v2 <- c(rep(TRUE, 5), rep(FALSE, 5))
all(v1)
#> [1] TRUE
allC(v1)
#> [1] TRUE
all(v2)
#> [1] FALSE
allC(v2)
#> [1] FALSE
# performance benefits?
bench::mark(
all(c(rep(TRUE, 1000), rep(FALSE, 1000))),
allC(c(rep(TRUE, 1000), rep(FALSE, 1000))),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min
#> <bch:expr> <bch:tm>
#> 1 all(c(rep(TRUE, 1000), rep(FALSE, 1000))) 6.14µs
#> 2 allC(c(rep(TRUE, 1000), rep(FALSE, 1000))) 7.96µs
#> median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 6.56µs 146811. 15.8KB 0
#> 2 8.26µs 117171. 15.8KB 0
#include <vector>
// [[Rcpp::export]]
std::vector<double> cumprodC(const std::vector<double> &x)
{
std::vector<double> out{x};
for (std::size_t i = 1; i < x.size(); i++)
{
out[i] = out[i - 1] * x[i];
}
return out;
}
v1 <- c(10, 4, 6, 8)
cumprod(v1)
#> [1] 10 40 240 1920
cumprodC(v1)
#> [1] 10 40 240 1920
# performance benefits?
bench::mark(
cumprod(v1),
cumprodC(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 cumprod(v1) 120ns 140ns 6248225. 0B
#> 2 cumprodC(v1) 732ns 811ns 1118228. 4.12KB
#> `gc/sec`
#> <dbl>
#> 1 0
#> 2 0
cumminC()
#include <vector>
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::vector<double> cumminC(const std::vector<double> &x)
{
std::vector<double> out{x};
for (std::size_t i = 1; i < x.size(); i++)
{
out[i] = (out[i] < out[i - 1]) ? out[i] : out[i - 1];
}
return out;
}
v1 <- c(3:1, 2:0, 4:2)
cummin(v1)
#> [1] 3 2 1 1 1 0 0 0 0
cumminC(v1)
#> [1] 3 2 1 1 1 0 0 0 0
# performance benefits?
bench::mark(
cummin(v1),
cumminC(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cummin(v1) 130ns 184.98ns 5060404. 0B 0
#> 2 cumminC(v1) 831ns 1.04µs 894322. 4.12KB 0
cummaxC()
#include <vector>
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::vector<double> cummaxC(const std::vector<double> &x)
{
std::vector<double> out{x};
for (std::size_t i = 1; i < x.size(); i++)
{
out[i] = (out[i] > out[i - 1]) ? out[i] : out[i - 1];
}
return out;
}
v1 <- c(3:1, 2:0, 4:2)
cummax(v1)
#> [1] 3 3 3 3 3 3 4 4 4
cummaxC(v1)
#> [1] 3 3 3 3 3 3 4 4 4
# performance benefits?
bench::mark(
cummax(v1),
cummaxC(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 cummax(v1) 130ns 221ns 4352155. 0B 0
#> 2 cummaxC(v1) 821ns 1µs 949874. 4.12KB 0
#include <vector>
#include <functional>
#include <algorithm>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::vector<double> diffC(const std::vector<double> &x, int lag)
{
std::vector<double> vec_start;
std::vector<double> vec_lagged;
std::vector<double> vec_diff;
for (std::size_t i = lag; i < x.size(); i++)
{
vec_lagged.push_back(x[i]);
}
for (std::size_t i = 0; i < (x.size() - lag); i++)
{
vec_start.push_back(x[i]);
}
std::transform(
vec_lagged.begin(), vec_lagged.end(),
vec_start.begin(), std::back_inserter(vec_diff),
std::minus<double>());
return vec_diff;
}
v1 <- c(1, 2, 4, 8, 13)
v2 <- c(1, 2, NA, 8, 13)
diff(v1, 2)
#> [1] 3 6 9
diffC(v1, 2)
#> [1] 3 6 9
diff(v2, 2)
#> [1] NA 6 NA
diffC(v2, 2)
#> [1] NA 6 NA
# performance benefits?
bench::mark(
diff(v1, 2),
diffC(v1, 2),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 diff(v1, 2) 3.94µs 4.34µs 210594. 0B
#> 2 diffC(v1, 2) 1.12µs 1.2µs 771449. 0B
#> `gc/sec`
#> <dbl>
#> 1 0
#> 2 0
#include <iostream>
#include <vector>
#include <algorithm>
using namespace std;
// [[Rcpp::export]]
std::vector<double> rangeC(std::vector<double> x)
{
std::vector<double> rangeVec{0.0, 0.0};
rangeVec.at(0) = *std::min_element(x.begin(), x.end());
rangeVec.at(1) = *std::max_element(x.begin(), x.end());
return rangeVec;
}
v1 <- c(10, 4, 6, 8)
range(v1)
#> [1] 4 10
rangeC(v1)
#> [1] 4 10
# performance benefits?
bench::mark(
range(v1),
rangeC(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 range(v1) 2.59µs 2.87µs 339163. 0B 0
#> 2 rangeC(v1) 731.09ns 812.11ns 1139075. 4.12KB 0
#include <vector>
#include <cmath>
#include <numeric>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
double variance(std::vector<double> x)
{
double sumSquared{0};
double mean = std::accumulate(x.begin(), x.end(), 0.0) / x.size();
for (const auto& xElement : x)
{
sumSquared += pow(xElement - mean, 2.0);
}
return sumSquared / (x.size() - 1);
}
v1 <- c(1, 4, 7, 8)
var(v1)
#> [1] 10
variance(v1)
#> [1] 10
# performance benefits?
bench::mark(
var(v1),
variance(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 var(v1) 5.42µs 6.38µs 146796. 0B
#> 2 variance(v1) 730.97ns 801.05ns 880396. 4.12KB
#> `gc/sec`
#> <dbl>
#> 1 0
#> 2 0
25.2 Missing values (Exercises 25.4.5)
Q1. Rewrite any of the functions from Exercise 25.2.6 to deal with missing values. If na.rm
is true, ignore the missing values. If na.rm
is false, return a missing value if the input contains any missing values. Some good functions to practice with are min()
, max()
, range()
, mean()
, and var()
.
A1. We will only create a version of range()
that deals with missing values. The same principle applies to others:
#include <iostream>
#include <vector>
#include <algorithm>
#include <math.h>
#include <Rcpp.h>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::vector<double> rangeC_NA(std::vector<double> x, bool removeNA = true)
{
std::vector<double> rangeVec{0.0, 0.0};
bool naPresent = std::any_of(
x.begin(),
x.end(),
[](double d)
{ return isnan(d); });
if (naPresent)
{
if (removeNA)
{
std::remove(x.begin(), x.end(), NAN);
}
else
{
rangeVec.at(0) = NA_REAL; // NAN;
rangeVec.at(1) = NA_REAL; // NAN;
return rangeVec;
}
}
rangeVec.at(0) = *std::min_element(x.begin(), x.end());
rangeVec.at(1) = *std::max_element(x.begin(), x.end());
return rangeVec;
}
v1 <- c(10, 4, NA, 6, 8)
range(v1, na.rm = FALSE)
#> [1] NA NA
rangeC_NA(v1, FALSE)
#> [1] NA NA
range(v1, na.rm = TRUE)
#> [1] 4 10
rangeC_NA(v1, TRUE)
#> [1] 4 10
Q2. Rewrite cumsum()
and diff()
so they can handle missing values. Note that these functions have slightly more complicated behaviour.
A2. The cumsum()
docs say:
An
NA
value inx
causes the corresponding and following elements of the return value to beNA
, as does integer overflow in cumsum (with a warning).
Similarly, diff()
docs say:
NA
’s propagate.
Therefore, both of these functions don’t allow removing missing values and the NA
s propagate.
As seen from the examples above, diffC()
already behaves this way.
Similarly, cumsumC()
propagates NA
s as well.
25.3 Standard Template Library (Exercises 25.5.7)
Q1. To practice using the STL algorithms and data structures, implement the following using R functions in C++, using the hints provided:
A1.
-
median.default()
usingpartial_sort
.
#include <iostream>
#include <vector>
#include <algorithm>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
double medianC(std::vector<double> &x)
{
int middleIndex = static_cast<int>(x.size() / 2);
std::partial_sort(x.begin(), x.begin() + middleIndex, x.end());
// for even number of observations
if (x.size() % 2 == 0)
{
return (x[middleIndex - 1] + x[middleIndex]) / 2;
}
return x[middleIndex];
}
v1 <- c(1, 3, 3, 6, 7, 8, 9)
v2 <- c(1, 2, 3, 4, 5, 6, 8, 9)
median.default(v1)
#> [1] 6
medianC(v1)
#> [1] 6
median.default(v2)
#> [1] 4.5
medianC(v2)
#> [1] 4.5
# performance benefits?
bench::mark(
median.default(v2),
medianC(v2),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 median.default(v2) 19.9µs 21.3µs 43684. 0B
#> 2 medianC(v2) 731.1ns 772.1ns 1200548. 0B
#> `gc/sec`
#> <dbl>
#> 1 441.
#> 2 0
#include <vector>
#include <unordered_set>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::vector<bool> matchC(const std::vector<double> &x, const std::vector<double> &table)
{
std::unordered_set<double> tableUnique(table.begin(), table.end());
std::vector<bool> out;
for (const auto &xElem : x)
{
out.push_back(tableUnique.find(xElem) != tableUnique.end() ? true : false);
}
return out;
}
x1 <- c(3, 4, 8)
x2 <- c(1, 2, 3, 3, 4, 4, 5, 6)
x1 %in% x2
#> [1] TRUE TRUE FALSE
matchC(x1, x2)
#> [1] TRUE TRUE FALSE
# performance benefits?
bench::mark(
x1 %in% x2,
matchC(x1, x2),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 x1 %in% x2 961.12ns 1.24µs 607028. 0B
#> 2 matchC(x1, x2) 1.31µs 1.43µs 565650. 4.12KB
#> `gc/sec`
#> <dbl>
#> 1 0
#> 2 0
-
unique()
using anunordered_set
(challenge: do it in one line!).
#include <unordered_set>
#include <vector>
#include <iostream>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::unordered_set<double> uniqueC(const std::vector<double> &x)
{
std::unordered_set<double> xSet(x.begin(), x.end());
return xSet;
}
Note that these functions are not comparable. As far as I can see, there is no way to get the same output as the R version of the function using the unordered_set
data structure.
We can make comparable version using set
data structure:
#include <set>
#include <vector>
#include <iostream>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::set<double> uniqueC2(const std::vector<double> &x)
{
std::set<double> xSet(x.begin(), x.end());
return xSet;
}
v1 <- c(1, 3, 3, 6, 7, 8, 9)
unique(v1)
#> [1] 1 3 6 7 8 9
uniqueC2(v1)
#> [1] 1 3 6 7 8 9
# performance benefits?
bench::mark(
unique(v1),
uniqueC2(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 unique(v1) 2.22µs 2.52µs 372051. 0B
#> 2 uniqueC2(v1) 981.03ns 1.12µs 826150. 4.12KB
#> `gc/sec`
#> <dbl>
#> 1 0
#> 2 0
#include <iostream>
#include <vector>
#include <algorithm>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
const double minC(const std::vector<double> &x)
{
return *std::min_element(x.begin(), x.end());
}
// [[Rcpp::export]]
const double maxC(std::vector<double> x)
{
return *std::max_element(x.begin(), x.end());
}
v1 <- c(3, 3, 6, 1, 9, 7, 8)
min(v1)
#> [1] 1
minC(v1)
#> [1] 1
# performance benefits?
bench::mark(
min(v1),
minC(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 min(v1) 241ns 251ns 3430855. 0B 0
#> 2 minC(v1) 721ns 757ns 1204027. 4.12KB 0
max(v1)
#> [1] 9
maxC(v1)
#> [1] 9
# performance benefits?
bench::mark(
max(v1),
maxC(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 max(v1) 230ns 261ns 3100500. 0B 0
#> 2 maxC(v1) 711ns 792ns 1183376. 4.12KB 0
-
which.min()
usingmin_element
, orwhich.max()
usingmax_element
.
#include <vector>
#include <algorithm>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
int which_maxC(std::vector<double> &x)
{
int maxIndex = std::distance(x.begin(), std::max_element(x.begin(), x.end()));
// R is 1-index based, while C++ is 0-index based
return maxIndex + 1;
}
// [[Rcpp::export]]
int which_minC(std::vector<double> &x)
{
int minIndex = std::distance(x.begin(), std::min_element(x.begin(), x.end()));
// R is 1-index based, while C++ is 0-index based
return minIndex + 1;
}
v1 <- c(3, 3, 6, 1, 9, 7, 8)
which.min(v1)
#> [1] 4
which_minC(v1)
#> [1] 4
# performance benefits?
bench::mark(
which.min(v1),
which_minC(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 which.min(v1) 391ns 421ns 2169373. 0B
#> 2 which_minC(v1) 711ns 742ns 1245602. 4.12KB
#> `gc/sec`
#> <dbl>
#> 1 0
#> 2 0
which.max(v1)
#> [1] 5
which_maxC(v1)
#> [1] 5
# performance benefits?
bench::mark(
which.max(v1),
which_maxC(v1),
iterations = 100
)
#> # A tibble: 2 × 6
#> expression min median `itr/sec` mem_alloc
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt>
#> 1 which.max(v1) 401ns 441ns 1954119. 0B
#> 2 which_maxC(v1) 691ns 732ns 1226240. 4.12KB
#> `gc/sec`
#> <dbl>
#> 1 0
#> 2 0
-
setdiff()
,union()
, andintersect()
for integers using sorted ranges andset_union
,set_intersection
andset_difference
.
Note that the following C++ implementations of given functions are not strictly equivalent to their R versions. As far as I can see, there is no way for them to be identical while satisfying the specifications mentioned in the question.
#include <algorithm>
#include <iostream>
#include <vector>
#include <set>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::set<int> unionC(std::vector<int> &v1, std::vector<int> &v2)
{
std::sort(v1.begin(), v1.end());
std::sort(v2.begin(), v2.end());
std::vector<int> union_vec(v1.size() + v2.size());
auto it = std::set_union(v1.begin(), v1.end(), v2.begin(), v2.end(), union_vec.begin());
union_vec.resize(it - union_vec.begin());
std::set<int> union_set(union_vec.begin(), union_vec.end());
return union_set;
}
v1 <- c(1, 4, 5, 5, 5, 6, 2)
v2 <- c(4, 1, 6, 8)
union(v1, v2)
#> [1] 1 4 5 6 2 8
unionC(v1, v2)
#> [1] 1 2 4 5 6 8
#include <algorithm>
#include <iostream>
#include <vector>
#include <set>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::set<int> intersectC(std::vector<int> &v1, std::vector<int> &v2)
{
std::sort(v1.begin(), v1.end());
std::sort(v2.begin(), v2.end());
std::vector<int> union_vec(v1.size() + v2.size());
auto it = std::set_intersection(v1.begin(), v1.end(), v2.begin(), v2.end(), union_vec.begin());
union_vec.resize(it - union_vec.begin());
std::set<int> union_set(union_vec.begin(), union_vec.end());
return union_set;
}
v1 <- c(1, 4, 5, 5, 5, 6, 2)
v2 <- c(4, 1, 6, 8)
intersect(v1, v2)
#> [1] 1 4 6
intersectC(v1, v2)
#> [1] 1 4 6
#include <algorithm>
#include <iostream>
#include <vector>
#include <set>
using namespace std;
// [[Rcpp::plugins(cpp11)]]
// [[Rcpp::export]]
std::set<int> setdiffC(std::vector<int> &v1, std::vector<int> &v2)
{
std::sort(v1.begin(), v1.end());
std::sort(v2.begin(), v2.end());
std::vector<int> union_vec(v1.size() + v2.size());
auto it = std::set_difference(v1.begin(), v1.end(), v2.begin(), v2.end(), union_vec.begin());
union_vec.resize(it - union_vec.begin());
std::set<int> union_set(union_vec.begin(), union_vec.end());
return union_set;
}
25.4 Session information
sessioninfo::session_info(include_base = TRUE)
#> ─ Session info ───────────────────────────────────────────
#> setting value
#> version R version 4.5.1 (2025-06-13)
#> os Ubuntu 24.04.2 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C.UTF-8
#> ctype C.UTF-8
#> tz UTC
#> date 2025-06-22
#> pandoc 3.7.0.2 @ /opt/hostedtoolcache/pandoc/3.7.0.2/x64/ (via rmarkdown)
#> quarto NA
#>
#> ─ Packages ───────────────────────────────────────────────
#> package * version date (UTC) lib source
#> base * 4.5.1 2025-06-13 [3] local
#> bench 1.1.4 2025-01-16 [1] RSPM
#> bookdown 0.43 2025-04-15 [1] RSPM
#> bslib 0.9.0 2025-01-30 [1] RSPM
#> cachem 1.1.0 2024-05-16 [1] RSPM
#> cli 3.6.5 2025-04-23 [1] RSPM
#> compiler 4.5.1 2025-06-13 [3] local
#> datasets * 4.5.1 2025-06-13 [3] local
#> digest 0.6.37 2024-08-19 [1] RSPM
#> downlit 0.4.4 2024-06-10 [1] RSPM
#> emoji 16.0.0 2024-10-28 [1] RSPM
#> evaluate 1.0.4 2025-06-18 [1] RSPM
#> fastmap 1.2.0 2024-05-15 [1] RSPM
#> fs 1.6.6 2025-04-12 [1] RSPM
#> glue 1.8.0 2024-09-30 [1] RSPM
#> graphics * 4.5.1 2025-06-13 [3] local
#> grDevices * 4.5.1 2025-06-13 [3] local
#> htmltools 0.5.8.1 2024-04-04 [1] RSPM
#> jquerylib 0.1.4 2021-04-26 [1] RSPM
#> jsonlite 2.0.0 2025-03-27 [1] RSPM
#> knitr 1.50 2025-03-16 [1] RSPM
#> lifecycle 1.0.4 2023-11-07 [1] RSPM
#> magrittr * 2.0.3 2022-03-30 [1] RSPM
#> memoise 2.0.1 2021-11-26 [1] RSPM
#> methods * 4.5.1 2025-06-13 [3] local
#> pillar 1.10.2 2025-04-05 [1] RSPM
#> pkgconfig 2.0.3 2019-09-22 [1] RSPM
#> profmem 0.7.0 2025-05-02 [1] RSPM
#> R6 2.6.1 2025-02-15 [1] RSPM
#> Rcpp * 1.0.14 2025-01-12 [1] RSPM
#> rlang 1.1.6 2025-04-11 [1] RSPM
#> rmarkdown 2.29 2024-11-04 [1] RSPM
#> sass 0.4.10 2025-04-11 [1] RSPM
#> sessioninfo 1.2.3 2025-02-05 [1] RSPM
#> stats * 4.5.1 2025-06-13 [3] local
#> stringi 1.8.7 2025-03-27 [1] RSPM
#> stringr 1.5.1 2023-11-14 [1] RSPM
#> tibble 3.3.0 2025-06-08 [1] RSPM
#> tools 4.5.1 2025-06-13 [3] local
#> utf8 1.2.6 2025-06-08 [1] RSPM
#> utils * 4.5.1 2025-06-13 [3] local
#> vctrs 0.6.5 2023-12-01 [1] RSPM
#> withr 3.0.2 2024-10-28 [1] RSPM
#> xfun 0.52 2025-04-02 [1] RSPM
#> xml2 1.3.8 2025-03-14 [1] RSPM
#> yaml 2.3.10 2024-07-26 [1] RSPM
#>
#> [1] /home/runner/work/_temp/Library
#> [2] /opt/R/4.5.1/lib/R/site-library
#> [3] /opt/R/4.5.1/lib/R/library
#> * ── Packages attached to the search path.
#>
#> ──────────────────────────────────────────────────────────