I have a large df (only 2 columns here for example)
CancerVar<-c("CancerVar:9#Tier_II_potential","CancerVar:2#Tier_IV_benign","CancerVar:11#Tier_I_strong","CancerVar:2#Tier_IV_benign","CancerVar:2#Tier_IV_benign")
driver_mut_prediction<-c("not protein-affecting","TIER 1","passenger","TIER 2","passenger")
df<-data.frame(CancerVar,driver_mut_prediction)
df
CancerVar driver_mut_prediction
1 CancerVar:9#Tier_II_potential not protein-affecting
2 CancerVar:2#Tier_IV_benign TIER 1
3 CancerVar:11#Tier_I_strong passenger
4 CancerVar:2#Tier_IV_benign TIER 2
5 CancerVar:2#Tier_IV_benign passenger
I want to select rows using partial (different) string matches over two columns.
I want to select rows where EITHER (CancerVar contains Tier I OR Tier II) OR (driver_mut_prediction contains TIER 1 OR TIER 2)
I have tried:
df_sub<-df[with(df, grepl("TIER|Tier_I|Tier_II", paste(driver_mut_prediction, CancerVar,ignore.case=FALSE))),]
Which still has the last row (so neither conditional has worked)
I have tried:
df %>% select(contains("Tier_I|Tier_II|TIER 1|TIER 2"))
data frame with 0 columns and 5000 rows
Please help!
This approach should work:
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
CancerVar<-c("CancerVar:9#Tier_II_potential","CancerVar:2#Tier_IV_benign","CancerVar:11#Tier_I_strong","CancerVar:2#Tier_IV_benign","CancerVar:2#Tier_IV_benign")
driver_mut_prediction<-c("not protein-affecting","TIER 1","passenger","TIER 2","passenger")
df<-data.frame(CancerVar,driver_mut_prediction)
df %>%
filter(
grepl("Tier_I_|Tier_II_", CancerVar) |
grepl("TIER 1|TIER 2", driver_mut_prediction)
)
#> CancerVar driver_mut_prediction
#> 1 CancerVar:9#Tier_II_potential not protein-affecting
#> 2 CancerVar:2#Tier_IV_benign TIER 1
#> 3 CancerVar:11#Tier_I_strong passenger
#> 4 CancerVar:2#Tier_IV_benign TIER 2
Created on 2022-04-06 by the reprex package (v2.0.1)
Or, with base R:
CancerVar<-c("CancerVar:9#Tier_II_potential","CancerVar:2#Tier_IV_benign","CancerVar:11#Tier_I_strong","CancerVar:2#Tier_IV_benign","CancerVar:2#Tier_IV_benign")
driver_mut_prediction<-c("not protein-affecting","TIER 1","passenger","TIER 2","passenger")
df<-data.frame(CancerVar,driver_mut_prediction)
df[grepl("Tier_I_|Tier_II_", df$CancerVar) | grepl("TIER 1|TIER 2", df$driver_mut_prediction),]
#> CancerVar driver_mut_prediction
#> 1 CancerVar:9#Tier_II_potential not protein-affecting
#> 2 CancerVar:2#Tier_IV_benign TIER 1
#> 3 CancerVar:11#Tier_I_strong passenger
#> 4 CancerVar:2#Tier_IV_benign TIER 2
Created on 2022-04-06 by the reprex package (v2.0.1)
You can use str_detect:
library(tidyverse)
df %>%
filter(str_detect(CancerVar, "Tier_I_|Tier_II_") |
str_detect(driver_mut_prediction, "TIER 1|TIER 2"))
Output
CancerVar driver_mut_prediction
1 CancerVar:9#Tier_II_potential not protein-affecting
2 CancerVar:2#Tier_IV_benign TIER 1
3 CancerVar:11#Tier_I_strong passenger
4 CancerVar:2#Tier_IV_benign TIER 2
Data
df <- structure(list(CancerVar = c("CancerVar:9#Tier_II_potential",
"CancerVar:2#Tier_IV_benign", "CancerVar:11#Tier_I_strong", "CancerVar:2#Tier_IV_benign",
"CancerVar:2#Tier_IV_benign"), driver_mut_prediction = c("not protein-affecting",
"TIER 1", "passenger", "TIER 2", "passenger")), class = "data.frame", row.names = c(NA,
-5L))
Related
I have a factor that's words (instances of words that difference participants said). I want to collapse it so that there are the categories "that" (every instance of the word "that") and notThat (all other words combined into one category). Naturally there are a lot of other words, and I don't want to go through and type them all. I've tried using != in various places, but it won't work. Maybe I just have the syntax wrong?
Anyway, is there a way to do this? That is, collapse all words that aren't "that" into one group?
How about this:
library(forcats)
x <- c("that", "something", "else")
fct_collapse(x, that = c("that"), other_level="notThat")
#> [1] that notThat notThat
#> Levels: that notThat
Created on 2022-02-15 by the reprex package (v2.0.1)
Edit to show in a data frame
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(forcats)
dat <- data.frame(
gender = factor(c(1,0,1,1,1,0), labels=c("male", "female")),
age = round(runif(6, 18,85)),
word = c("that", "something", "altogether", "different", "entirely", "that"))
dat %>%
mutate(word_collapse = fct_collapse(word, that="that", other_level="notThat"))
#> gender age word word_collapse
#> 1 female 74 that that
#> 2 male 72 something notThat
#> 3 female 57 altogether notThat
#> 4 female 44 different notThat
#> 5 female 79 entirely notThat
#> 6 male 81 that that
Created on 2022-02-15 by the reprex package (v2.0.1)
I am trying to change some of my data that are stored as tibbles inside a list.
This list of tibbles was generated by a package.
I do not understand why my function does not work.
If I extract a tibble element manually, the function works but not inside a lapply.
my function:
changesomethingtaxize <- function(x, whatchange=NULL, applyfunction=NULL){
library(lazyeval) ;
mutate_call <- lazyeval::interp(~ a(b), a = match.fun(applyfunction), b = as.name(whatchange) )
x %<>% mutate_(.dots = setNames(list(mutate_call), whatchange) )
return(x)
}
I want to do
mydata <- lapply(mydata, function(x) changesomethingtaxize(x, whatchange=rank, applyfunction=str_to_sentence) )
I could use a loop to extract each tibbles (in this case I only have 5) but I would like to understand what I do wrong :)
From dput()
mydata <- structure(list(`Zostera marina` = structure(list(name = c("Plantae",
"Viridiplantae", "Streptophyta", "Embryophyta", "Tracheophyta",
"Spermatophytina", "Magnoliopsida", "Lilianae", "Alismatales",
"Zosteraceae", "Zostera", "Zostera marina"), rank = c("kingdom",
"subkingdom", "infrakingdom", "superdivision", "division", "subdivision",
"class", "superorder", "order", "family", "genus", "species"),
id = c("202422", "954898", "846494", "954900", "846496",
"846504", "18063", "846542", "38883", "39069", "39073", "39074"
)), row.names = c(NA, 12L), class = "data.frame"), `Vascular plants` = structure(list(
name = c("Plantae", "Viridiplantae", "Streptophyta", "Embryophyta",
"Tracheophyta"), rank = c("kingdom", "subkingdom", "infrakingdom",
"superdivision", "division"), id = c("202422", "954898",
"846494", "954900", "846496")), row.names = c(NA, 5L), class = "data.frame"),
`Fucus vesiculosus` = structure(list(name = c("Chromista",
"Chromista", "Phaeophyta", "Phaeophyceae", "Fucales", "Fucaceae",
"Fucus", "Fucus vesiculosus"), rank = c("kingdom", "subkingdom",
"division", "class", "order", "family", "genus", "species"
), id = c("630578", "590735", "660055", "10686", "11328",
"11329", "11334", "11335")), row.names = c(NA, 8L), class = "data.frame"),
Macroalgae = NA, `Filamentous algae` = NA), class = "classification", db = "itis")
I think I actually found why... :D
The lapply works but was not returning anything because of the NAs (empty elements of the list).
I added an if() that only mutates a tibble if the tibble actually contains something.
It is always an NA issue somewhere!
Well hope that piece of code could help someone someday.
The functions you provided aren't usable by themselves, but it looks like you're attempting to use a function meant to modify a data frame on non-dataframe objects, which mydata contains.
I'm using dplyr::mutate() just to illustrate here.
Your data contain NAs (which in this case are logical). dplyr::mutate() doesnt' have a method for logicals and I'm assuming the function you're trying to use doesn't either (or simply doesn't have a way of handling NA values).
You should be getting an error that's at least conceptually similar to the following ...
lapply(mydata, function(x) dplyr::mutate(x, col_to_modify = toupper(rank)))
#> Error in UseMethod("mutate_"): no applicable method for 'mutate_' applied to an object of class "logical"
To get around this, you can check your list ahead of time and note which elements are indeed data frames.
df_indices <- vapply(mydata, is.data.frame, logical(1L))
df_indices
#> Zostera marina Vascular plants Fucus vesiculosus Macroalgae
#> TRUE TRUE TRUE FALSE
#> Filamentous algae
#> FALSE
Using df_indices, we can modify only those elements in mydata like so...
mydata[df_indices] <- lapply(
mydata[df_indices],
function(x) dplyr::mutate(x, col_to_modify = toupper(rank))
)
mydata
#> $`Zostera marina`
#> name rank id col_to_modify
#> 1 Plantae kingdom 202422 KINGDOM
#> 2 Viridiplantae subkingdom 954898 SUBKINGDOM
#> 3 Streptophyta infrakingdom 846494 INFRAKINGDOM
#> 4 Embryophyta superdivision 954900 SUPERDIVISION
#> 5 Tracheophyta division 846496 DIVISION
#> 6 Spermatophytina subdivision 846504 SUBDIVISION
#> 7 Magnoliopsida class 18063 CLASS
#> 8 Lilianae superorder 846542 SUPERORDER
#> 9 Alismatales order 38883 ORDER
#> 10 Zosteraceae family 39069 FAMILY
#> 11 Zostera genus 39073 GENUS
#> 12 Zostera marina species 39074 SPECIES
#>
#> $`Vascular plants`
#> name rank id col_to_modify
#> 1 Plantae kingdom 202422 KINGDOM
#> 2 Viridiplantae subkingdom 954898 SUBKINGDOM
#> 3 Streptophyta infrakingdom 846494 INFRAKINGDOM
#> 4 Embryophyta superdivision 954900 SUPERDIVISION
#> 5 Tracheophyta division 846496 DIVISION
#>
#> $`Fucus vesiculosus`
#> name rank id col_to_modify
#> 1 Chromista kingdom 630578 KINGDOM
#> 2 Chromista subkingdom 590735 SUBKINGDOM
#> 3 Phaeophyta division 660055 DIVISION
#> 4 Phaeophyceae class 10686 CLASS
#> 5 Fucales order 11328 ORDER
#> 6 Fucaceae family 11329 FAMILY
#> 7 Fucus genus 11334 GENUS
#> 8 Fucus vesiculosus species 11335 SPECIES
#>
#> $Macroalgae
#> [1] NA
#>
#> $`Filamentous algae`
#> [1] NA
#>
#> attr(,"class")
#> [1] "classification"
#> attr(,"db")
#> [1] "itis"
Note that {purrr} has a nice map() variant designed to handle this very situation. purrr::map_if() takes a .p (predicate) argument to which you can provide a function that it applies to .x and returns TRUE or FALSE. Only those elements that return TRUE are modified by the function you provide to .f
purrr::map_if(.x = mydata, .p = is.data.frame,
.f = ~ dplyr::mutate(.x, col_to_modify = toupper(rank)))
#> $`Zostera marina`
#> name rank id col_to_modify
#> 1 Plantae kingdom 202422 KINGDOM
#> 2 Viridiplantae subkingdom 954898 SUBKINGDOM
#> 3 Streptophyta infrakingdom 846494 INFRAKINGDOM
#> 4 Embryophyta superdivision 954900 SUPERDIVISION
#> 5 Tracheophyta division 846496 DIVISION
#> 6 Spermatophytina subdivision 846504 SUBDIVISION
#> 7 Magnoliopsida class 18063 CLASS
#> 8 Lilianae superorder 846542 SUPERORDER
#> 9 Alismatales order 38883 ORDER
#> 10 Zosteraceae family 39069 FAMILY
#> 11 Zostera genus 39073 GENUS
#> 12 Zostera marina species 39074 SPECIES
#>
#> $`Vascular plants`
#> name rank id col_to_modify
#> 1 Plantae kingdom 202422 KINGDOM
#> 2 Viridiplantae subkingdom 954898 SUBKINGDOM
#> 3 Streptophyta infrakingdom 846494 INFRAKINGDOM
#> 4 Embryophyta superdivision 954900 SUPERDIVISION
#> 5 Tracheophyta division 846496 DIVISION
#>
#> $`Fucus vesiculosus`
#> name rank id col_to_modify
#> 1 Chromista kingdom 630578 KINGDOM
#> 2 Chromista subkingdom 590735 SUBKINGDOM
#> 3 Phaeophyta division 660055 DIVISION
#> 4 Phaeophyceae class 10686 CLASS
#> 5 Fucales order 11328 ORDER
#> 6 Fucaceae family 11329 FAMILY
#> 7 Fucus genus 11334 GENUS
#> 8 Fucus vesiculosus species 11335 SPECIES
#>
#> $Macroalgae
#> [1] NA
#>
#> $`Filamentous algae`
#> [1] NA
I have sample data frame as below:
quoteiD <- c("q1","q2","q3","q4", "q5")
quote <- c("Unthinking respect for authority is the greatest enemy of truth.",
"In the middle of difficulty lies opportunity.",
"Intelligence is the ability to adapt to change.",
"Science is not only a disciple of reason but, also, one of romance and passion.",
"If I have seen further it is by standing on the shoulders of Giants.")
library(dplyr)
quotes <- tibble(quoteiD = quoteiD, quote= quote)
quotes
I have created some tidy text as below
library(tidytext)
data(stop_words)
tidy_words <- quotes %>%
unnest_tokens(word, quote) %>%
anti_join(stop_words) %>%
count( word, sort = TRUE)
tidy_words
Further, I have searched the synonyms using qdap package as below
library(qdap)
syns <- synonyms(tidy_words$word)
The qdap out put is a list , and I am looking to pick the first 5 synonym for each word in the tidy data frame and create a column called synonyms as below:
word n synonyms
ability 1 adeptness, aptitude, capability, capacity, competence
adapt 1 acclimatize, accommodate, adjust, alter, apply,
authority 1 ascendancy, charge, command, control, direction
What is an elegant way of merging the list of 5 words from qdap synonym function and separate by commas?
One way this can be done using a tidyverse solution is
library(plyr)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:plyr':
#>
#> arrange, count, desc, failwith, id, mutate, rename, summarise,
#> summarize
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidytext)
library(qdap)
#> Loading required package: qdapDictionaries
#> Loading required package: qdapRegex
#>
#> Attaching package: 'qdapRegex'
#> The following object is masked from 'package:dplyr':
#>
#> explain
#> Loading required package: qdapTools
#>
#> Attaching package: 'qdapTools'
#> The following object is masked from 'package:dplyr':
#>
#> id
#> The following object is masked from 'package:plyr':
#>
#> id
#> Loading required package: RColorBrewer
#>
#> Attaching package: 'qdap'
#> The following object is masked from 'package:dplyr':
#>
#> %>%
#> The following object is masked from 'package:base':
#>
#> Filter
library(tibble)
library(tidyr)
#>
#> Attaching package: 'tidyr'
#> The following object is masked from 'package:qdap':
#>
#> %>%
quotes <- tibble(quoteiD = paste0("q", 1:5),
quote= c(".\n\nthe ebodac consortium consists of partners: janssen (efpia), london school of hygiene and tropical medicine (lshtm),",
"world vision) mobile health software development and deployment in resource limited settings grameen\n\nas such, the ebodac consortium is well placed to tackle.",
"Intelligence is the ability to adapt to change.",
"Science is a of reason of romance and passion.",
"If I have seen further it is by standing on ."))
quotes
#> # A tibble: 5 x 2
#> quoteiD quote
#> <chr> <chr>
#> 1 q1 ".\n\nthe ebodac consortium consists of partners: janssen (efpia~
#> 2 q2 "world vision) mobile health software development and deployment~
#> 3 q3 Intelligence is the ability to adapt to change.
#> 4 q4 Science is a of reason of romance and passion.
#> 5 q5 If I have seen further it is by standing on .
data(stop_words)
tidy_words <- quotes %>%
unnest_tokens(word, quote) %>%
anti_join(stop_words) %>%
count( word, sort = TRUE)
#> Joining, by = "word"
tidy_words
#> # A tibble: 33 x 2
#> word n
#> <chr> <int>
#> 1 consortium 2
#> 2 ebodac 2
#> 3 ability 1
#> 4 adapt 1
#> 5 change 1
#> 6 consists 1
#> 7 deployment 1
#> 8 development 1
#> 9 efpia 1
#> 10 grameen 1
#> # ... with 23 more rows
syns <- synonyms(tidy_words$word)
#> no match for the following:
#> consortium, ebodac, consists, deployment, efpia, grameen, janssen, london, lshtm, partners, settings, software, tropical
#> ========================
syns %>%
plyr::ldply(data.frame) %>% # Change the list to a dataframe (See https://stackoverflow.com/questions/4227223/r-list-to-data-frame)
rename("Word_DefNumber" = 1, "Syn" = 2) %>% # Rename the columns with a name that is more intuitive
separate(Word_DefNumber, c("Word", "DefNumber"), sep = "\\.") %>% # Find the word part of the word and definition number
group_by(Word) %>% # Group by words, so that when we select rows it is done for each word
slice(1:5) %>% # Keep the first 5 rows for each word
summarise(synonyms = paste(Syn, collapse = ", ")) %>% # Combine the synonyms together comma separated using paste
ungroup() # So there are not unintended effects of having the data grouped when using the data later
#> # A tibble: 20 x 2
#> Word synonyms
#> <chr> <chr>
#> 1 ability adeptness, aptitude, capability, capacity, competence
#> 2 adapt acclimatize, accommodate, adjust, alter, apply
#> 3 change alter, convert, diversify, fluctuate, metamorphose
#> 4 development advance, advancement, evolution, expansion, growth
#> 5 health fitness, good condition, haleness, healthiness, robustness
#> 6 hygiene cleanliness, hygienics, sanitary measures, sanitation
#> 7 intelligence acumen, alertness, aptitude, brain power, brains
#> 8 limited bounded, checked, circumscribed, confined, constrained
#> 9 medicine cure, drug, medicament, medication, nostrum
#> 10 mobile ambulatory, itinerant, locomotive, migrant, motile
#> 11 passion animation, ardour, eagerness, emotion, excitement
#> 12 reason apprehension, brains, comprehension, intellect, judgment
#> 13 resource ability, capability, cleverness, ingenuity, initiative
#> 14 romance affair, affaire (du coeur), affair of the heart, amour, at~
#> 15 school academy, alma mater, college, department, discipline
#> 16 science body of knowledge, branch of knowledge, discipline, art, s~
#> 17 standing condition, credit, eminence, estimation, footing
#> 18 tackle accoutrements, apparatus, equipment, gear, implements
#> 19 vision eyes, eyesight, perception, seeing, sight
#> 20 world earth, earthly sphere, globe, everybody, everyone
Created on 2019-04-05 by the reprex package (v0.2.1)
Please note that plyr should be loaded before dplyr
I'm using a Tableau Fixed LOD function in a report, and was looking for ways to mimic this functionality in R.
Data set looks like:
Soldto<-c("123456","122456","123456","122456","124560","125560")
Shipto<-c("123456","122555","122456","124560","122560","122456")
IssueDate<-as.Date(c("2017-01-01","2017-01-02","2017-01-01","2017-01-02","2017-01-01","2017-01-01"))
Method<-c("Ground","Ground","Ground","Air","Ground","Ground")
Delivery<-c("000123","000456","000123","000345","000456","000555")
df1<-data.frame(Soldto,Shipto,IssueDate,Method,Delivery)
What I'm looking to do is "For each Sold-to/Ship-to/Method count the number of unique delivery IDs".
The intent is to find the number of unique deliveries that could potentially be "aggregated."
In Tableau that function looks like:
{FIXED [Soldto],[Shipto],[IssueDate],[Method],:countd([Delivery])
Could this be done with aggregate or summarize as in an example below:
df.new<-ddply(df,c("Soldto","Shipto","Method"),summarise,
Deliveries = n_distinct(Delivery))
This is fairly easy with dplyr. You are looking for the number of unique delivery for each combination of soldto, shipto and method, which is just group_by and then summarise:
library(tidyverse)
tbl <- tibble(
soldto = c("123456","122456","123456","122456","124560","125560"),
shipto = c("123456","122555","122456","124560","122560","122456"),
issuedate = as.Date(c("2017-01-01","2017-01-02","2017-01-01","2017-01-02","2017-01-01","2017-01-01")),
method = c("Ground","Ground","Ground","Air","Ground","Ground"),
delivery = c("000123","000456","000123","000345","000456","000555")
)
tbl %>%
group_by(soldto, shipto, method) %>%
summarise(uniques = n_distinct(delivery))
#> # A tibble: 6 x 4
#> # Groups: soldto, shipto [?]
#> soldto shipto method uniques
#> <chr> <chr> <chr> <int>
#> 1 122456 122555 Ground 1
#> 2 122456 124560 Air 1
#> 3 123456 122456 Ground 1
#> 4 123456 123456 Ground 1
#> 5 124560 122560 Ground 1
#> 6 125560 122456 Ground 1
Created on 2018-03-02 by the reprex package (v0.2.0).
I am new to R.
I need to reformat the following data frame:
`Sample Name` `Target Name` 'CT values'
<chr> <chr> <dbl>
1 Sample 1 actin 19.69928
2 Sample 1 Ho-1 27.71864
3 Sample 1 Nrf-2 26.00012
9 Sample 9 Ho-1 25.31180
10 Sample 9 Nrf-2 26.41421
11 Sample 9 C3 26.16980
...
15 Sample 1 actin 19.49202
Actually, I want to have the different 'Target Names' as column names, and the individual 'Sample Names' as row names. The table should then display the respective CT values.
But note that there are duplicates, e.g., Sample 1 exists twice, as the corresponding Target name, e.g. "actin" does. What I want to have is that the table later only shows these duplicates once, with the means of the two different CT values.
I guess this is a very basic R data frame manipulation, but as I said, I am quite new to R and messing around with different tutorials.
Thank you very much in advance!
One way of doing that using the tidyverse ecosystem of packages:
library(tidyverse)
tab <- tribble(
~`Sample Name`, ~`Target Name`, ~ `CT values`,
"Sample 1", "actin", 19.69928,
"Sample 1", "Ho-1", 27.71864,
"Sample 1", "Nrf-2", 26.00012,
"Sample 9", "Ho-1", 25.31180,
"Sample 9", "Nrf-2", 26.41421,
"Sample 9", "C3", 26.16980,
"Sample 1", "actin", 19.49202
)
tab %>%
# calculate the mean of your dpulicate
group_by(`Sample Name`, `Target Name`) %>%
summarise(`CT values` = mean(`CT values`)) %>%
# reshape the data
spread(`Target Name`, `CT values`)
#> # A tibble: 2 x 5
#> # Groups: Sample Name [2]
#> `Sample Name` actin C3 `Ho-1` `Nrf-2`
#> * <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Sample 1 19.6 NA 27.7 26.0
#> 2 Sample 9 NA 26.2 25.3 26.4
you can also use data.table to a more consise way of doing this with
dcast reshape function
library(data.table)
#>
#> Attachement du package : 'data.table'
#> The following objects are masked from 'package:dplyr':
#>
#> between, first, last
#> The following object is masked from 'package:purrr':
#>
#> transpose
setDT(tab)
dcast(tab, `Sample Name` ~ `Target Name`, fun.aggregate = mean)
#> Using 'CT values' as value column. Use 'value.var' to override
#> Sample Name C3 Ho-1 Nrf-2 actin
#> 1: Sample 1 NaN 27.71864 26.00012 19.59565
#> 2: Sample 9 26.1698 25.31180 26.41421 NaN
Created on 2018-01-13 by the reprex package (v0.1.1.9000).