I don't know much about graphic devices etc. All I want to do is to save plots to PDF and to embed fonts.
I use cairo_pdf() for this, but I noticed that sometimes plot elements are printed outside of the box/plot region (see screenshots of the PDFs). I can reproduce the issue on different Windows machines, different R versions, using packages cairoDevice or Cairo, and with for example lines(). But plots saved via pdf() look fine.
My questions are:
Is this reproducible? If yes, is this a bug and where?
Are there any other situations were cairo_pdf()-plots look different compared to pdf()-plots? Are there any other disadvantages of using cairo_pdf()?
Below are screenshots from details of the whole PDFs illustrating the differences. Note that, in the left image, the axis overlap with some points.
capabilities("cairo")
#> cairo
#> TRUE
set.seed(123456)
N <- 10000
v1 <- rnorm(N)
v2 <- rnorm(N)
v3 <- ifelse(v1 > 1.02 | v2 > 1.02 | v1 < -.02 | v2 < -.02, 2, 1)
cairo_pdf("plot1.pdf")
plot(v1, v2, xlim = 0:1, ylim = 0:1, col = v3, pch = 16)
dev.off()
#> null device
#> 1
pdf("plot2.pdf")
plot(v1, v2, xlim = 0:1, ylim = 0:1, col = v3, pch = 16)
dev.off()
#> null device
#> 1
devtools::session_info()
#> Session info ------------------------------------------------------------------
#> setting value
#> version R version 3.4.2 (2017-09-28)
#> system x86_64, mingw32
#> ui Rgui
#> language (EN)
#> collate German_Germany.1252
#> tz Europe/Berlin
#> date 2018-03-09
#>
#> Packages ----------------------------------------------------------------------
#> package * version date source
#> base * 3.4.2 2017-09-28 local
#> compiler 3.4.2 2017-09-28 local
#> datasets * 3.4.2 2017-09-28 local
#> devtools 1.13.5 2018-02-18 CRAN (R 3.4.3)
#> digest 0.6.15 2018-01-28 CRAN (R 3.4.3)
#> graphics * 3.4.2 2017-09-28 local
#> grDevices * 3.4.2 2017-09-28 local
#> memoise 1.1.0 2017-04-21 CRAN (R 3.4.1)
#> methods * 3.4.2 2017-09-28 local
#> stats * 3.4.2 2017-09-28 local
#> utils * 3.4.2 2017-09-28 local
#> withr 2.1.1 2017-12-19 CRAN (R 3.4.3)
This bug is fixed in R 3.6.0.
From the NEWS:
The cairo_pdf graphics device (and other Cairo-based devices) now clip correctly to the right and bottom border.
There was an off-by-one-pixel bug, reported by Lee Kelvin.
Related
What I'd like to do
I am trying to build a model in tidymodels that will predict the efficacy of drugs on cell lines (like bacteria). The model will rank drugs by efficacy for a given cell line, so I want to use Spearman's correlation (ρ) as a metric. In the following example data set, each cell line (column Sample) is represented by a letter, Q, R, S, ..., Z, and each sample was treated with 50 drugs.
When I split the data for cross-validation, the training/test splits for each fold will have >1 cell line (e.g. Q, R in the test split for fold 1), but in calculating the metric (ρ), I want to calculate it for each cell line individually and then take the average across all the cell lines in the test split, rather than for all the observations in aggregate. For example, if the test split for fold 1 consists of Q, R, then I want to calculate ρ for the 50 drugs tested against Q, then a separate ρ for the 50 drugs tested against R, average these two ρ, and have that average be the metric calculated for fold 1.
What I've tried
I was thinking that I'd have to calculate the metric on a tibble/data.frame grouped by the Sample column, but I can't figure out how to pass that variable into tune_grid(). I don't think I can include the variable in add_formula() when creating the workflow object, since I don't want it as a predictor variable. I just discovered tidymodels yesterday, so maybe there's a straightforward solution I'm unaware of, but I haven't been able to find anything on Google so far. The code below is what I've tried, but obviously it doesn't work. Thank you in advance for any advice you can give.
Error
i Resample1: preprocessor 1/1
✓ Resample1: preprocessor 1/1
i Resample1: preprocessor 1/1, model 1/20
✓ Resample1: preprocessor 1/1, model 1/20
i Resample1: preprocessor 1/1, model 1/20 (predictions)
x Resample1: internal: Error: In metric: `spearman_cor`
unused arguments (truth = ~TargetVariable, estimate = ~.pred, na_rm ...
i Resample2: preprocessor 1/1
✓ Resample2: preprocessor 1/1
i Resample2: preprocessor 1/1, model 1/20
✓ Resample2: preprocessor 1/1, model 1/20
i Resample2: preprocessor 1/1, model 1/20 (predictions)
x Resample2: internal: Error: In metric: `spearman_cor`
unused arguments (truth = ~TargetVariable, estimate = ~.pred, na_rm ...
i Resample3: preprocessor 1/1
✓ Resample3: preprocessor 1/1
i Resample3: preprocessor 1/1, model 1/20
✓ Resample3: preprocessor 1/1, model 1/20
i Resample3: preprocessor 1/1, model 1/20 (predictions)
x Resample3: internal: Error: In metric: `spearman_cor`
unused arguments (truth = ~TargetVariable, estimate = ~.pred, na_rm ...
i Resample4: preprocessor 1/1
✓ Resample4: preprocessor 1/1
i Resample4: preprocessor 1/1, model 1/20
✓ Resample4: preprocessor 1/1, model 1/20
i Resample4: preprocessor 1/1, model 1/20 (predictions)
x Resample4: internal: Error: In metric: `spearman_cor`
unused arguments (truth = ~TargetVariable, estimate = ~.pred, na_rm ...
i Resample5: preprocessor 1/1
✓ Resample5: preprocessor 1/1
i Resample5: preprocessor 1/1, model 1/20
✓ Resample5: preprocessor 1/1, model 1/20
i Resample5: preprocessor 1/1, model 1/20 (predictions)
x Resample5: internal: Error: In metric: `spearman_cor`
unused arguments (truth = ~TargetVariable, estimate = ~.pred, na_rm ...
Warning message:
All models failed. See the `.notes` column.
Upon running glmnet_tuning_results:
Warning message:
This tuning result has notes. Example notes on model fitting include:
internal: Error: In metric: `spearman_cor`
unused arguments (truth = ~TargetVariable, estimate = ~.pred, na_rm = ~na_rm)
internal: Error: In metric: `spearman_cor`
unused arguments (truth = ~TargetVariable, estimate = ~.pred, na_rm = ~na_rm)
internal: Error: In metric: `spearman_cor`
unused arguments (truth = ~TargetVariable, estimate = ~.pred, na_rm = ~na_rm)
Code
Example data set
data = tibble(
Sample = rep(LETTERS[17:26], each = 50),
TargetVariable = rnorm(500, mean = 0, sd = 1),
PredictorVariable1 = rnorm(500, mean = 5, sd = 1),
PredictorVariable2 = rpois(500, lambda = 5)
)
Model
# Splitting for cross-validation.
set.seed(1026)
folds = group_vfold_cv(data, group = Sample, v = 5)
# Model specification.
glmnet_model = linear_reg(
mode = "regression",
penalty = tune(),
mixture = tune()
) %>%
set_engine("glmnet")
# Workflow.
glmnet_wf = workflow() %>%
add_model(glmnet_model) %>%
add_formula(TargetVariable ~ . - Sample)
# Grid specification.
glmnet_params = parameters(penalty(), mixture())
set.seed(1026)
glmnet_grid = grid_max_entropy(glmnet_params, size = 20)
# Hyperparameter tuning.
glmnet_tuning_results = tune_grid(
glmnet_wf,
resamples = folds,
grid = glmnet_grid,
metrics = metric_set(spearman_cor),
control = control_grid(verbose = TRUE)
)
glmnet_tuning_results %>% show_best(n = 10)
Custom metric
# Vector version.
spearman_cor_vec = function(truth, estimate, na_rm = TRUE) {
spearman_cor_impl = function(truth, estimate) {
cor(truth, estimate, method = "spearman")
}
metric_vec_template(
metric_impl = spearman_cor_impl,
truth = truth,
estimate = estimate,
na_rm = na_rm,
cls = "numeric"
)
}
# Data frame version.
spearman_cor = function(data) {
UseMethod("spearman_cor")
}
spearman_cor = new_numeric_metric(spearman_cor, direction = "maximize")
spearman_cor.data.frame = function(data, truth, estimate, na_rm = TRUE) {
data_grouped = data %>%
group_by(Sample)
metric_summarizer(
metric_nm = "spearman_cor",
metric_fn = spearman_cor_vec,
data = data_grouped,
truth = !! enquo(truth),
estimate = !! enquo(estimate),
na_rm = na_rm
)
}
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 3.6.3 (2020-02-29)
#> os macOS Catalina 10.15.7
#> system x86_64, darwin15.6.0
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/Chicago
#> date 2021-08-25
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
#> backports 1.1.6 2020-04-05 [1] CRAN (R 3.6.2)
#> cli 3.0.1 2021-07-17 [1] CRAN (R 3.6.2)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.0)
#> digest 0.6.25 2020-02-23 [1] CRAN (R 3.6.0)
#> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 3.6.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
#> fansi 0.4.1 2020-01-08 [1] CRAN (R 3.6.0)
#> fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.0)
#> glue 1.4.0 2020-04-03 [1] CRAN (R 3.6.2)
#> highr 0.8 2019-03-20 [1] CRAN (R 3.6.0)
#> htmltools 0.5.1.1 2021-01-22 [1] CRAN (R 3.6.2)
#> knitr 1.27 2020-01-16 [1] CRAN (R 3.6.0)
#> lifecycle 1.0.0 2021-02-15 [1] CRAN (R 3.6.2)
#> magrittr 2.0.1 2020-11-17 [1] CRAN (R 3.6.2)
#> pillar 1.6.2 2021-07-29 [1] CRAN (R 3.6.2)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 3.6.0)
#> purrr 0.3.4 2020-04-17 [1] CRAN (R 3.6.2)
#> Rcpp 1.0.4.6 2020-04-09 [1] CRAN (R 3.6.1)
#> reprex 2.0.1 2021-08-05 [1] CRAN (R 3.6.2)
#> rlang 0.4.10 2020-12-30 [1] CRAN (R 3.6.2)
#> rmarkdown 2.1 2020-01-20 [1] CRAN (R 3.6.0)
#> rstudioapi 0.13 2020-11-12 [1] CRAN (R 3.6.2)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
#> stringi 1.4.5 2020-01-11 [1] CRAN (R 3.6.0)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
#> styler 1.5.1 2021-07-13 [1] CRAN (R 3.6.2)
#> tibble 3.1.3 2021-07-23 [1] CRAN (R 3.6.2)
#> utf8 1.1.4 2018-05-24 [1] CRAN (R 3.6.0)
#> vctrs 0.3.8 2021-04-29 [1] CRAN (R 3.6.2)
#> withr 2.4.2 2021-04-18 [1] CRAN (R 3.6.2)
#> xfun 0.12 2020-01-13 [1] CRAN (R 3.6.0)
#> yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.0)
#>
#> [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library
To make your custom metric work, you were just missing some ... so arguments could be passed through:
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
spearman_cor_vec <- function(truth, estimate, na_rm = TRUE) {
spearman_cor_impl <- function(truth, estimate) {
cor(truth, estimate, method = "spearman")
}
metric_vec_template(
metric_impl = spearman_cor_impl,
truth = truth,
estimate = estimate,
na_rm = na_rm,
cls = "numeric"
)
}
spearman_cor <- function(data, ...) { ## these dots were missing
UseMethod("spearman_cor")
}
spearman_cor <- new_numeric_metric(spearman_cor, direction = "maximize")
spearman_cor.data.frame <- function(data, truth, estimate, na_rm = TRUE) {
data_grouped = data %>%
group_by(Sample)
metric_summarizer(
metric_nm = "spearman_cor",
metric_fn = spearman_cor_vec,
data = data_grouped,
truth = !! enquo(truth),
estimate = !! enquo(estimate),
na_rm = na_rm
)
}
This makes it so you can use this metric on a dataset like so:
df <- tibble(
Sample = rep(LETTERS[17:26], each = 50),
TargetVariable = rnorm(500, mean = 0, sd = 1),
Pred1 = rnorm(500, mean = 5, sd = 1),
Pred2 = rpois(500, lambda = 5)
)
df %>%
mutate(.pred = TargetVariable + rnorm(500, mean = 0, sd = 0.2)) %>%
spearman_cor(TargetVariable, .pred)
#> # A tibble: 10 × 4
#> Sample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Q spearman_cor standard 0.980
#> 2 R spearman_cor standard 0.975
#> 3 S spearman_cor standard 0.983
#> 4 T spearman_cor standard 0.985
#> 5 U spearman_cor standard 0.978
#> 6 V spearman_cor standard 0.963
#> 7 W spearman_cor standard 0.975
#> 8 X spearman_cor standard 0.979
#> 9 Y spearman_cor standard 0.987
#> 10 Z spearman_cor standard 0.969
Created on 2021-08-31 by the reprex package (v2.0.1)
However, this doesn't totally solve your problem because for the tuning functions, we typically only pass the predictors and outcomes, not any extra variables with other roles. I worked with this a little bit and couldn't quite figure out a way to get the tuning function to have a variable only for computing metrics and not for fitting. I don't believe we support this right now; you might want to create a reprex, explain your use case, and post an issue on the tune repo so we can prioritize a new feature like this.
I am trying to build a function wrapping over haven::read_dta() similar to the wrap_function() defined in the code below.
My wrap_function() has a default variables = NULL, which should be able to pass NULL to haven::read_dta()'s col_select argument if no values are specified. However, passing the NULL from variables to col_select throws an error (i.e. 'Error: Can't find any columns matching col_select in data.').
Can someone help me understand why this happens and how could I build a wrap_function capable of passing a NULL default value to the lower-level function?
Thanks!
library(reprex)
library(haven)
df_ <- data.frame(a = 1:5,
b = letters[1:5])
haven::write_dta(df_,
path = "file.dta")
# works well:
haven::read_dta(file = "file.dta",
col_select = NULL)
#> # A tibble: 5 x 2
#> a b
#> <dbl> <chr>
#> 1 1 a
#> 2 2 b
#> 3 3 c
#> 4 4 d
#> 5 5 e
# does not work:
wrap_function <- function(file, variables = NULL){
haven::read_dta(file = file,
col_select = variables)
}
wrap_function("file.dta")
#> Note: Using an external vector in selections is ambiguous.
#> ℹ Use `all_of(variables)` instead of `variables` to silence this message.
#> ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
#> This message is displayed once per session.
#> Error: Can't find any columns matching `col_select` in data.
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.3 (2020-10-10)
#> os CentOS Linux 8
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> date 2021-05-14
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
#> cli 2.4.0 2021-04-05 [1] CRAN (R 4.0.3)
#> crayon 1.4.1.9000 2021-04-16 [1] Github (r-lib/crayon#965d1dc)
#> digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.3)
#> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.2)
#> fansi 0.4.2 2021-01-15 [1] CRAN (R 4.0.3)
#> forcats 0.5.1 2021-01-27 [1] CRAN (R 4.0.3)
#> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
#> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
#> haven * 2.3.1 2020-06-01 [1] CRAN (R 4.0.2)
TLDR: You just need to embrace the argument by wrapping it in double curly brackets{{ }}, previously called "curly-curly". This passes the variable properly. See the programming with dplyr vignette for more info.
wrap_function <- function(file, variables = NULL){
haven::read_dta(file = file,
col_select = {{ variables }})
}
wrap_function("file.dta")
#> # A tibble: 5 x 2
#> a b
#> <dbl> <chr>
#> 1 1 a
#> 2 2 b
#> 3 3 c
#> 4 4 d
#> 5 5 e
Unfortunately it's a little hard to understand that this is necessary without looking at the code. If you look up the haven repository, you can see that read_dta uses the double-curly around col_select as well. This is a pretty good indication that you need to use it in your wrapper function.
If you look further, it is using them to pass the argument to a function skip_cols, which uses it inside tidyselect::vars_select. The reason this is needed is so that you can delay evaluation of the argument until the point that you actually need it. In other words, it lets you call the function like this:
wrap_function("file.dta", variables = a)
instead of forcing you to do something like
wrap_function("file.dta", variables = "a")
and saves you a lot of typed quotes, especially with a lot of columns. You see this pattern in dplyr and other tidyverse functions a lot, especially any time an argument refers to a dataframe column rather than a variable.
In other words, you want to not have the code check exactly what a is until you reach skip_cols, which knows that a refers to a column inside the file you're reading. If you don't use the curly braces, it will think that a is some object in your working environment.
I am currently reviewing this question on SO and see that the OP stated that by adding more for loops you can expand the polynomials. How exactly would you do so? I am trying to expand to polyorder 5.
Polynomial feature expansion in R
Here is the code below:
polyexp = function(df){
df.polyexp = df
colnames = colnames(df)
for (i in 1:ncol(df)){
for (j in i:ncol(df)){
colnames = c(colnames, paste0(names(df)[i],'.',names(df)[j]))
df.polyexp = cbind(df.polyexp, df[,i]*df[,j])
}
}
names(df.polyexp) = colnames
return(df.polyexp)
}
Ultimately, I'd like to order the matrix so that it expands in order of degree. I tried using the poly function but I'm not sure if you can order the result so that it returns a matrix that starts with degree 1 then moves to degree 2, then 3, 4, and 5.
To "sort by degree" is a little ambiguous. x^2 and x*y both have degree 2. I'll assume you want to sort by total degree, and then within each of those, by degree of the 1st column; within that, by degree of the second column, etc. (I believe the default is to ignore total degree and sort by degree of the last column, within that the second last, and so on, but this is not documented so I won't count on it.)
Here's how to use polym to do this. The columns are named things like "2.0" or "1.1". You could sort these alphabetically and it would be fine up to degree 9, but if you convert those names using as.numeric_version, there's no limit. So convert the column names to version names, get the sort order, and use that plus degree to re-order the columns of the result. For example,
df <- data.frame(x = 1:6, y = 0:5, z = -(1:6))
expanded <- polym(as.matrix(df), degree = 5)
o <- order(attr(expanded, "degree"),
as.numeric_version(colnames(expanded)))
sorted <- expanded[,o]
# That lost the attributes, so put them back
attr(sorted, "degree") <- attr(expanded, "degree")[o]
attr(sorted, "coefs") <- attr(expanded, "coefs")
class(sorted) <- class(expanded)
# If you call predict(), it comes out in the default order,
# so will need sorting too:
predict(sorted, newdata = as.matrix(df[1,]))[, o]
#> 0.0.1 0.1.0 1.0.0 0.0.2 0.1.1 0.2.0
#> 0.59761430 -0.59761430 -0.59761430 0.54554473 -0.35714286 0.54554473
#> 1.0.1 1.1.0 2.0.0 0.0.3 0.1.2 0.2.1
#> -0.35714286 0.35714286 0.54554473 0.37267800 -0.32602533 0.32602533
#> 0.3.0 1.0.2 1.1.1 1.2.0 2.0.1 2.1.0
#> -0.37267800 -0.32602533 0.21343368 -0.32602533 0.32602533 -0.32602533
#> 3.0.0 0.0.4 0.1.3 0.2.2 0.3.1 0.4.0
#> -0.37267800 0.18898224 -0.22271770 0.29761905 -0.22271770 0.18898224
#> 1.0.3 1.1.2 1.2.1 1.3.0 2.0.2 2.1.1
#> -0.22271770 0.19483740 -0.19483740 0.22271770 0.29761905 -0.19483740
#> 2.2.0 3.0.1 3.1.0 4.0.0 0.0.5 0.1.4
#> 0.29761905 -0.22271770 0.22271770 0.18898224 0.06299408 -0.11293849
#> 0.2.3 0.3.2 0.4.1 0.5.0 1.0.4 1.1.3
#> 0.20331252 -0.20331252 0.11293849 -0.06299408 -0.11293849 0.13309928
#> 1.2.2 1.3.1 1.4.0 2.0.3 2.1.2 2.2.1
#> -0.17786140 0.13309928 -0.11293849 0.20331252 -0.17786140 0.17786140
#> 2.3.0 3.0.2 3.1.1 3.2.0 4.0.1 4.1.0
#> -0.20331252 -0.20331252 0.13309928 -0.20331252 0.11293849 -0.11293849
#> 5.0.0
#> -0.06299408
Created on 2020-03-21 by the reprex package (v0.3.0)
I'm puzzled by the behaviour of the uq() function. The behavior is not the same when I use uq() or lazyeval::uq().
Here is my reproducible example :
First, I generate a fake dataset
library(tibble)
library(lazyeval)
fruits <- c("apple", "banana", "peanut")
price <- c(5,6,4)
table_fruits <- tibble(fruits, price)
Then I write a toy function, toy_function_v1, using only uq() :
toy_function_v1 <- function(data, var) {
lazyeval::f_eval(f = ~ uq(var), data = data)
}
and a second function using lazyeval::uq() :
toy_function_v2 <- function(data, var) {
lazyeval::f_eval(f = ~ lazyeval::uq(var), data = data)
}
Surprisingly, the output of v1 and v2 is not the same :
> toy_function_v1(data = table_fruits, var = ~ price)
[1] 5 6 4
> toy_function_v2(data = table_fruits, var = ~ price)
price
Is there any explanation ?
I know it's a good practice to use the syntaxe package::function() to use the function inside a new package. So what's the best solution in that case ?
Here is my session_info :
> devtools::session_info()
Session info ----------------------------------------------------------------------------------------------------------------------------------------------------
setting value
version R version 3.3.1 (2016-06-21)
system x86_64, linux-gnu
ui RStudio (1.0.35)
language (EN)
collate C
tz <NA>
date 2016-11-07
Packages --------------------------------------------------------------------------------------------------------------------------------------------------------
package * version date source
Rcpp 0.12.7 2016-09-05 CRAN (R 3.2.3)
assertthat 0.1 2013-12-06 CRAN (R 3.2.2)
devtools 1.12.0 2016-06-24 CRAN (R 3.2.3)
digest 0.6.10 2016-08-02 CRAN (R 3.2.3)
lazyeval * 0.2.0.9000 2016-10-14 Github (hadley/lazyeval#c155c3d)
memoise 1.0.0 2016-01-29 CRAN (R 3.2.3)
tibble * 1.2 2016-08-26 CRAN (R 3.2.3)
withr 1.0.2 2016-06-20 CRAN (R 3.2.3)
It's just a bug in the uq() function. The issue is open on Github : https://github.com/hadley/lazyeval/issues/78.
At the end of an R book, I'd like to show the versions of main R packages used to compile the book. I'm wondering if there is anything I could do better than just use sessionInfo() in a chunk, e.g.,
\section*{Colophon}
This book was produced using \Sexpr{R.version.string},
\pkg{knitr} (\Sexpr{packageDescription("knitr")[["Version"]]})
and other package versions listed below.
<<session-info, size='footnotesize',R.options=list(width=90)>>=
print(sessionInfo(), locale = FALSE)
#
In particular, sessionInfo() lists all packages loaded indirectly as well as those loaded directly.
```{r}
library(knitr)
p = devtools::loaded_packages()
p$version = unlist(lapply(p$package, function(x) as.character(packageVersion(x))))
kable(p[order(p$package),], row.names=FALSE)
```
If you do not have devtools installed, steal the code from loaded_packages.
This will give a comma separated list of the packages loaded into the current session:
pkgs <- sort(sub("package:", "", grep("package:", search(), value = TRUE)));
toString(Map(function(p) sprintf("%s (%s)", p, packageVersion(p)), pkgs))
giving this string which you can insert by placing the code above in a \Sexpr:
[1] "base (3.2.0), datasets (3.2.0), graphics (3.2.0), grDevices (3.2.0), methods (3.2.0), stats (3.2.0), utils (3.2.0)"
Only core R functions are used in this code.
I don't want to list all packages loaded (base packages, dependencies) in the current session, so I came up with a better solution for my needs. Maybe this will be useful to someone else.
Find all packages explicitly loaded via library() in the .Rnw files for the book.
Use devtools:::package_info() for formatting
For (1.), I used the following pipe of shell commands, all standard except for my trusty tcgrep perl script that find strings in files recursively
tcgrep -E Rnw '^library(.*)' . \
| grep '/ch' \
| perl -p -e 's/^.*://; s/\s*#.*//' \
| perl -p -e 's/library\(([\w\d]+)\)/"$1"/g; s/;/, /' \
| sort -u | perl -p -e 's/\n/, /' > packages-used.R
This gave me
packages <- c(
"AER", "ca", "car", "colorspace", "corrplot", "countreg", "directlabels", "effects", "ggparallel", "ggplot2", "ggtern", "gmodels", "gnm", "gpairs", "heplots", "Lahman", "lattice", "lmtest", "logmult", "MASS", "MASS", "countreg", "mgcv", "nnet", "plyr", "pscl", "RColorBrewer", "reshape2", "rms", "rsm", "sandwich", "splines", "UBbipl", "vcd", "vcdExtra", "VGAM", "xtable")
Then for (2.),
library(devtools)
pkg_info <- devtools:::package_info(packages)
# clean up unwanted
pkg_info$source <- sub(" \\(R.*\\)", "", pkg_info$source)
pkg_info <- pkg_info[,-2]
pkg_info
I like the result because it also identifies non-CRAN (development version) packages. I could also format this with kable:
> pkg_info
package version date source
AER 1.2-3 2015-02-24 CRAN
ca 0.60 2015-03-01 R-Forge
car 2.0-25 2015-03-03 R-Forge
colorspace 1.2-6 2015-03-11 CRAN
corrplot 0.73 2013-10-15 CRAN
countreg 0.1-2 2014-10-17 R-Forge
directlabels 2013.6.15 2013-07-23 CRAN
effects 3.0-4 2015-03-22 R-Forge
ggparallel 0.1.1 2012-09-09 CRAN
ggplot2 1.0.1 2015-03-17 CRAN
ggtern 1.0.5.0 2015-04-15 CRAN
gmodels 2.15.4.1 2013-09-21 CRAN
gnm 1.0-8 2015-04-22 CRAN
gpairs 1.2 2014-03-09 CRAN
heplots 1.0-15 2015-04-18 CRAN
Lahman 3.0-1 2014-09-13 CRAN
lattice 0.20-31 2015-03-30 CRAN
lmtest 0.9-33 2014-01-23 CRAN
logmult 0.6.2 2015-04-22 CRAN
MASS 7.3-40 2015-03-21 CRAN
mgcv 1.8-6 2015-03-31 CRAN
nnet 7.3-9 2015-02-11 CRAN
plyr 1.8.2 2015-04-21 CRAN
pscl 1.4.9 2015-03-29 CRAN
RColorBrewer 1.1-2 2014-12-07 CRAN
reshape2 1.4.1 2014-12-06 CRAN
rms 4.3-1 2015-05-01 CRAN
rsm 2.7-2 2015-05-13 CRAN
sandwich 2.3-3 2015-03-26 CRAN
UBbipl 3.0.4 2013-10-13 local
vcd 1.4-0 2015-04-20 local
vcdExtra 0.6-8 2015-04-16 CRAN
VGAM 0.9-8 2015-05-11 CRAN
xtable 1.7-4 2014-09-12 CRAN
If you're using LaTeX you could simply generate bibliography for all the packages using:
%% begin.rcode rubber, results = 'asis', cache = FALSE
% write_bib(file = "generated.bib")
%% end.rcode
You can put this after your \end{document} and add corresponding \bibliography{mybib,generated} entry. This way you could also reference them with usual \cite{}