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))) 6.19µs
#> 2 allC(c(rep(TRUE, 1000), rep(FALSE, 1000))) 7.99µs
#> median `itr/sec` mem_alloc `gc/sec`
#> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 6.58µs 151250. 15.8KB 0
#> 2 8.33µs 114758. 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) 100ns 110ns 7461333. 0B
#> 2 cumprodC(v1) 741ns 862ns 1068599. 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) 110ns 120ns 6548454. 0B 0
#> 2 cumminC(v1) 801ns 862ns 1011132. 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) 110ns 200ns 4697984. 0B 0
#> 2 cummaxC(v1) 802ns 972ns 981431. 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) 6.09µs 6.61µs 144075. 0B
#> 2 diffC(v1, 2) 1.86µs 2.02µs 475791. 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) 2.5µs 2.83µs 340289. 0B 0
#> 2 rangeC(v1) 740.9ns 821.95ns 1147617. 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) 5.37µs 5.97µs 161310. 0B
#> 2 variance(v1) 692.09ns 792.09ns 1172727. 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) 21µs 22.3µs 44297. 0B
#> 2 medianC(v2) 731ns 831.1ns 1039459. 0B
#> `gc/sec`
#> <dbl>
#> 1 0
#> 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 1.07µs 1.42µs 670298. 0B
#> 2 matchC(x1, x2) 1.29µs 1.44µs 656512. 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) 2.13µs 2.33µs 408724. 0B
#> 2 uniqueC2(v1) 942.03ns 1.07µs 709492. 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) 220ns 241ns 3318025. 0B 0
#> 2 minC(v1) 701ns 732ns 1131072. 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 331ns 2958639. 0B 0
#> 2 maxC(v1) 701ns 792ns 1128823. 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) 390ns 421ns 2132032. 0B
#> 2 which_minC(v1) 701ns 802ns 1175801. 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) 400ns 421ns 2039134. 0B
#> 2 which_maxC(v1) 711ns 812ns 1132684. 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 4.4.1 (2024-06-14)
#> os Ubuntu 22.04.4 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C.UTF-8
#> ctype C.UTF-8
#> tz UTC
#> date 2024-09-15
#> pandoc 3.4 @ /opt/hostedtoolcache/pandoc/3.4/x64/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────
#> package * version date (UTC) lib source
#> base * 4.4.1 2024-08-22 [3] local
#> bench 1.1.3 2023-05-04 [1] RSPM
#> bookdown 0.40 2024-07-02 [1] RSPM
#> bslib 0.8.0 2024-07-29 [1] RSPM
#> cachem 1.1.0 2024-05-16 [1] RSPM
#> cli 3.6.3 2024-06-21 [1] RSPM
#> compiler 4.4.1 2024-08-22 [3] local
#> datasets * 4.4.1 2024-08-22 [3] local
#> digest 0.6.37 2024-08-19 [1] RSPM
#> downlit 0.4.4 2024-06-10 [1] RSPM
#> evaluate 0.24.0 2024-06-10 [1] RSPM
#> fansi 1.0.6 2023-12-08 [1] RSPM
#> fastmap 1.2.0 2024-05-15 [1] RSPM
#> fs 1.6.4 2024-04-25 [1] RSPM
#> glue 1.7.0 2024-01-09 [1] RSPM
#> graphics * 4.4.1 2024-08-22 [3] local
#> grDevices * 4.4.1 2024-08-22 [3] local
#> htmltools 0.5.8.1 2024-04-04 [1] RSPM
#> jquerylib 0.1.4 2021-04-26 [1] RSPM
#> jsonlite 1.8.8 2023-12-04 [1] RSPM
#> knitr 1.48 2024-07-07 [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 * 4.4.1 2024-08-22 [3] local
#> pillar 1.9.0 2023-03-22 [1] RSPM
#> pkgconfig 2.0.3 2019-09-22 [1] RSPM
#> profmem 0.6.0 2020-12-13 [1] RSPM
#> R6 2.5.1 2021-08-19 [1] RSPM
#> Rcpp * 1.0.13 2024-07-17 [1] RSPM
#> rlang 1.1.4 2024-06-04 [1] RSPM
#> rmarkdown 2.28 2024-08-17 [1] RSPM
#> sass 0.4.9 2024-03-15 [1] RSPM
#> sessioninfo 1.2.2 2021-12-06 [1] RSPM
#> stats * 4.4.1 2024-08-22 [3] local
#> tibble 3.2.1 2023-03-20 [1] RSPM
#> tools 4.4.1 2024-08-22 [3] local
#> utf8 1.2.4 2023-10-22 [1] RSPM
#> utils * 4.4.1 2024-08-22 [3] local
#> vctrs 0.6.5 2023-12-01 [1] RSPM
#> withr 3.0.1 2024-07-31 [1] RSPM
#> xfun 0.47 2024-08-17 [1] RSPM
#> xml2 1.3.6 2023-12-04 [1] RSPM
#> yaml 2.3.10 2024-07-26 [1] RSPM
#>
#> [1] /home/runner/work/_temp/Library
#> [2] /opt/R/4.4.1/lib/R/site-library
#> [3] /opt/R/4.4.1/lib/R/library
#>
#> ──────────────────────────────────────────────────────────