ETC5523: Communicating with Data

Clearly communicating with code

Lecturer: Emi Tanaka

Department of Econometrics and Business Statistics

emi.tanaka@monash.edu
Week 8
cwd.numbat.space

Aim

Understand and formulate code for communication
Understand R package structure
Build R package with R code, data, and launching shiny apps

Why

Sharing code makes your analysis transparent and reproducible to others.
Writing code that is readable by others make author intent explainable
An R package makes functions, data or apps accessible, thereby increasing impact of your work

Thanks to Stuart Lee for developing the initial content in this slide, which has been subsequently modified a fair amount by me.

Example data

Plant growth

An experiment to compare yields under control and two different treatment conditions on plants

str(PlantGrowth)

'data.frame':   30 obs. of  2 variables:
 $ weight: num  4.17 5.58 5.18 6.11 4.5 4.61 5.17 4.53 5.33 5.14 ...
 $ group : Factor w/ 3 levels "ctrl","trt1",..: 1 1 1 1 1 1 1 1 1 1 ...

Tooth growth

An experiment to study the effect of vitamin C on tooth growth in guinea pigs

str(ToothGrowth)

'data.frame':   60 obs. of  3 variables:
 $ len : num  4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
 $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
 $ dose: num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...

Which one do you prefer?

Code #1

with(PlantGrowth, tapply(weight, group, mean))

 ctrl  trt1  trt2 
5.032 4.661 5.526

Code #2

library(tidyverse)
PlantGrowth %>% 
  group_by(group) %>% 
  summarise(weight_avg = mean(weight))

# A tibble: 3 × 2
  group weight_avg
  <fct>      <dbl>
1 ctrl        5.03
2 trt1        4.66
3 trt2        5.53

What do you expect the output is?

Code #1

with(ToothGrowth, tapply(len, list(supp, dose), mean))

     0.5     1     2
OJ 13.23 22.70 26.06
VC  7.98 16.77 26.14

Code #2

library(tidyverse)
ToothGrowth %>% 
  group_by(supp, dose) %>% 
  summarise(length_avg = mean(len))

# A tibble: 6 × 3
# Groups:   supp [2]
  supp   dose length_avg
  <fct> <dbl>      <dbl>
1 OJ      0.5      13.2 
2 OJ      1        22.7 
3 OJ      2        26.1 
4 VC      0.5       7.98
5 VC      1        16.8 
6 VC      2        26.1

Naming matters

Syntactic sugar

Syntactic sugar means using function name or syntax in a programming language that is designed to make things easier to read or to express for humans.

my_function(x)

What do you think this function is doing?

compute_average(x)

A human reads your code, so write your function in a way that reads and works well for humans

Naming cases

camelCase

Capitalise all words after first word.
Common in R Shiny and JavaScript.

snake_case

All words are lower case and separated by an underscore.
Preferred by R programmers in general (except Shiny).

PascalCase

Capitalise all words.
Preferred by C programmers.

kebab-case

All words are lower case and separated by a dash.
Common in HTML attribute names and CSS property names.

Stick with the style convention of the language as much as possible!

Syntactically valid names in R

In R, a syntactically valid name consists of letters, numbers and the dot or underline characters and starts with a letter or the dot not followed by a number.
It also cannot be a reserved word, e.g. if, else, TRUE, FALSE, while, function. For full list see ?Reserved.
Anything else is a non-syntactic name in R and you can still use any name in R by surrounding it with backticks:

`4` <- 3

You can use make.names() to make syntatically valid names in R.

Object names

Variable and function names should use only snake case.
Strive for names that are concise and meaningful.

Good

day_one
day_1

Bad

DayOne
dayone
first_day_of_the_month
djm1

Function names

Function names should be verbs (with exceptions).
Avoid using . in the names…
…unless writing a function method for S3 object system.

Good

add_row()
permute()
add_column()

Bad

row_adder()
permutation()
add.column()

Variable names

Variable names should be nouns.
Consider using a list or data.frame to group variables in a similar context instead of assigning it as separate objects.

Good

origin
fit

where

fit[[1]] = fit1,
fit[[2]] = fit2,
and so on.

Bad

originate
fit1
fit2
fit3
fit4
fit5

Readable code

Consistency is key!

Consistency spacing

Always put a space after a comma, never before.

Good

x[, 1]

Bad

x[,1]
x[ ,1]
x[ , 1]

Do not put spaces inside or outside parentheses for regular function calls.

Good

mean(x, na.rm = TRUE)

Bad

mean (x, na.rm = TRUE)
mean( x, na.rm = TRUE )

Consistency spacing

Place a space before and after () when used with if, for, or while.

Good

if (debug) {
  show(x)
}

Bad

if(debug){
  show(x)
}

Place a space after () (but not before) used for function arguments:

Good

function(x) {}

Bad

function (x) {}
function(x){}

Consistency spacing

Most infix operators (+, -, <-, etc.) should be surrounded by spaces…

Good

height <- (feet * 12) + inches
mean(x, na.rm = TRUE)

Bad

height<-feet*12+inches
mean(x, na.rm=TRUE)

…with exceptions of operators with high precedence (::, :::, $, @, [, [[, ^, unary -, unary +, and :).

Good

sqrt(x^2 + y^2)
df$z
x <- 1:10

Bad

sqrt(x ^ 2 + y ^ 2)
df $ z
x <- 1 : 10

Consistency spacing

… with exceptions of single-sided formulas when the right-hand side is a single identifier.

Good

~foo
tribble(
  ~col1, ~col2,
  "a",   "b"
)

Bad

~ foo
tribble(
  ~ col1, ~ col2,
  "a", "b"
)

Single-sided formulas with a complex right-hand side do need a space.

Good

~ x + y

Bad

~x + y

Avoid long lines

Limit your code to 80 characters per line.
If the arguments to a function don’t all fit on one line, put each argument on its own line and indent.

Good

do_something_very_complicated(
  something = "that",
  requires = many,
  arguments = "some of which may be long"
)

Bad

do_something_very_complicated("that", requires, many, arguments, "some of which may be long")

Sequence of functions

Avoid deeply nesting functions in one line.
%>% should always have a space before it, and should usually be followed by a new line.
After the first step, each line should be indented by two spaces.

Good

shopping_list %>% 
  buy() %>% 
  prepare() %>% 
  cook()

Bad

cook(prepare(buy(shopping_list)))

shopping_list %>% buy() %>% 
  prepare() %>% cook()

R packages for styling code

styler allows you to interactively restyle selected text, files, or entire projects.
styler includes an RStudio add-in, the easiest way to re-style existing code.
lintr performs automated checks to confirm that you conform to the style guide.

What exactly are R packages?

R packages can be many things…

A container:

for a set of R functions,
to share data,
to share an app,
and more, e.g. Rmd templates (out of scope for this unit).

The anatomy of an R package

DESCRIPTION file
R/ directory for R files that contain your functions
NAMESPACE file (manual creation is out of scope for this unit)

Optionally,

data/: for binary data available to the user
data-raw/: for raw data
inst/: for arbitrary additional files that you want include in your package.
and others.

The `DESCRIPTION` file

Metadata for the package

Package name
Title and description of what the package does
Authors
Dependencies (depends, imports and suggests)
Licencing
Version number
Where to report bugs and so on

Example: `dplyr` `DESCRIPTION` file

Type: Package
Package: dplyr
Title: A Grammar of Data Manipulation
Version: 1.0.99.9000
Authors@R: 
    c(person(given = "Hadley",
             family = "Wickham",
             role = c("aut", "cre"),
             email = "hadley@rstudio.com",
             comment = c(ORCID = "0000-0003-4757-117X")),
      person(given = "Romain",
             family = "François",
             role = "aut",
             comment = c(ORCID = "0000-0002-2444-4226")),
      person(given = "Lionel",
             family = "Henry",
             role = "aut"),
      person(given = "Kirill",
             family = "Müller",
             role = "aut",
             comment = c(ORCID = "0000-0002-1416-3412")),
      person(given = "RStudio",
             role = c("cph", "fnd")))
Description: A fast, consistent tool for working with data frame
    like objects, both in memory and out of memory.
License: MIT + file LICENSE
URL: https://dplyr.tidyverse.org,
    https://github.com/tidyverse/dplyr
BugReports: https://github.com/tidyverse/dplyr/issues
Depends: 
    R (>= 3.4.0)
Imports: 
    generics,
    glue (>= 1.3.2),
    lifecycle (>= 1.0.1.9001),
    magrittr (>= 1.5),
    methods,
    R6,
    rlang (>= 1.0.5),
    tibble (>= 2.1.3),
    tidyselect (>= 1.1.2.9000),
    utils,
    vctrs (>= 0.4.1.9000),
    pillar (>= 1.5.1)
Suggests: 
    bench,
    broom,
    callr,
    covr,
    DBI,
    dbplyr (>= 2.2.1),
    ggplot2,
    knitr,
    Lahman,
    lobstr,
    microbenchmark,
    nycflights13,
    purrr,
    rmarkdown,
    RMySQL,
    RPostgreSQL,
    RSQLite,
    stringi (>= 1.7.6),
    testthat (>= 3.1.1),
    tidyr, 
    withr
Remotes: 
    r-lib/tidyselect,
    r-lib/vctrs,
    r-lib/lifecycle
VignetteBuilder: 
    knitr
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.1
Config/testthat/edition: 3
Config/Needs/website:
    tidyverse,
    shiny,
    pkgdown,
    tidyverse/tidytemplate

The R/ directory

The functions you create are stored as R scripts that live in the R/ directory.
Functions can be internal to the package or exported so other users have access to them.
See for example the dplyr R directory.

The `NAMESPACE` file

The file contains a directive that describes whether an R object is exported from this package or imported from others.
These directives can be automatically created by roxygen2 (covered next lecture).

# Generated by roxygen2: do not edit by hand

S3method("$<-",grouped_df)
S3method("[",fun_list)
S3method("[",grouped_df)
S3method("[",rowwise_df)
S3method("[<-",grouped_df)
S3method("[<-",rowwise_df)
S3method("[[<-",grouped_df)
S3method("names<-",grouped_df)
S3method("names<-",rowwise_df)
S3method(add_count,data.frame)
S3method(add_count,default)
S3method(anti_join,data.frame)
S3method(arrange,data.frame)
S3method(arrange_,data.frame)
S3method(arrange_,tbl_df)
S3method(as.data.frame,grouped_df)
S3method(as.tbl,data.frame)
S3method(as.tbl,tbl)
S3method(as_join_by,character)
S3method(as_join_by,default)
S3method(as_join_by,dplyr_join_by)
S3method(as_join_by,list)
S3method(as_tibble,grouped_df)
S3method(as_tibble,rowwise_df)
S3method(auto_copy,data.frame)
S3method(cbind,grouped_df)
S3method(collapse,data.frame)
S3method(collect,data.frame)
S3method(common_by,"NULL")
S3method(common_by,character)
S3method(common_by,default)
S3method(common_by,list)
S3method(compute,data.frame)
S3method(copy_to,DBIConnection)
S3method(copy_to,src_local)
S3method(count,data.frame)
S3method(distinct,data.frame)
S3method(distinct_,data.frame)
S3method(distinct_,grouped_df)
S3method(distinct_,tbl_df)
S3method(do,"NULL")
S3method(do,data.frame)
S3method(do,grouped_df)
S3method(do,rowwise_df)
S3method(do_,"NULL")
S3method(do_,data.frame)
S3method(do_,grouped_df)
S3method(do_,rowwise_df)
S3method(dplyr_col_modify,data.frame)
S3method(dplyr_col_modify,grouped_df)
S3method(dplyr_col_modify,rowwise_df)
S3method(dplyr_reconstruct,data.frame)
S3method(dplyr_reconstruct,grouped_df)
S3method(dplyr_reconstruct,rowwise_df)
S3method(dplyr_row_slice,data.frame)
S3method(dplyr_row_slice,grouped_df)
S3method(dplyr_row_slice,rowwise_df)
S3method(filter,data.frame)
S3method(filter,ts)
S3method(filter_,data.frame)
S3method(filter_,tbl_df)
S3method(filter_bullets,"dplyr:::filter_incompatible_size")
S3method(filter_bullets,"dplyr:::filter_incompatible_type")
S3method(filter_bullets,default)
S3method(format,src_local)
S3method(full_join,data.frame)
S3method(group_by,data.frame)
S3method(group_by_,data.frame)
S3method(group_by_,rowwise_df)
S3method(group_by_drop_default,default)
S3method(group_by_drop_default,grouped_df)
S3method(group_data,data.frame)
S3method(group_data,grouped_df)
S3method(group_data,rowwise_df)
S3method(group_data,tbl_df)
S3method(group_indices,data.frame)
S3method(group_indices_,data.frame)
S3method(group_indices_,grouped_df)
S3method(group_indices_,rowwise_df)
S3method(group_keys,data.frame)
S3method(group_map,data.frame)
S3method(group_modify,data.frame)
S3method(group_modify,grouped_df)
S3method(group_nest,data.frame)
S3method(group_nest,grouped_df)
S3method(group_size,data.frame)
S3method(group_split,data.frame)
S3method(group_split,grouped_df)
S3method(group_split,rowwise_df)
S3method(group_trim,data.frame)
S3method(group_trim,grouped_df)
S3method(group_vars,data.frame)
S3method(groups,data.frame)
S3method(inner_join,data.frame)
S3method(intersect,data.frame)
S3method(left_join,data.frame)
S3method(mutate,data.frame)
S3method(mutate_,data.frame)
S3method(mutate_,tbl_df)
S3method(mutate_bullets,"dplyr:::error_incompatible_combine")
S3method(mutate_bullets,"dplyr:::mutate_constant_recycle_error")
S3method(mutate_bullets,"dplyr:::mutate_incompatible_size")
S3method(mutate_bullets,"dplyr:::mutate_mixed_null")
S3method(mutate_bullets,"dplyr:::mutate_not_vector")
S3method(mutate_bullets,default)
S3method(n_groups,data.frame)
S3method(nest_by,data.frame)
S3method(nest_by,grouped_df)
S3method(nest_join,data.frame)
S3method(print,all_vars)
S3method(print,any_vars)
S3method(print,dplyr_join_by)
S3method(print,dplyr_sel_vars)
S3method(print,fun_list)
S3method(print,src)
S3method(pull,data.frame)
S3method(rbind,grouped_df)
S3method(recode,character)
S3method(recode,factor)
S3method(recode,numeric)
S3method(relocate,data.frame)
S3method(rename,data.frame)
S3method(rename_,data.frame)
S3method(rename_,grouped_df)
S3method(rename_with,data.frame)
S3method(right_join,data.frame)
S3method(rows_append,data.frame)
S3method(rows_delete,data.frame)
S3method(rows_insert,data.frame)
S3method(rows_patch,data.frame)
S3method(rows_update,data.frame)
S3method(rows_upsert,data.frame)
S3method(rowwise,data.frame)
S3method(rowwise,grouped_df)
S3method(same_src,data.frame)
S3method(sample_frac,data.frame)
S3method(sample_frac,default)
S3method(sample_n,data.frame)
S3method(sample_n,default)
S3method(select,data.frame)
S3method(select,list)
S3method(select_,data.frame)
S3method(select_,grouped_df)
S3method(semi_join,data.frame)
S3method(setdiff,data.frame)
S3method(setequal,data.frame)
S3method(slice,data.frame)
S3method(slice_,data.frame)
S3method(slice_,tbl_df)
S3method(slice_head,data.frame)
S3method(slice_max,data.frame)
S3method(slice_min,data.frame)
S3method(slice_sample,data.frame)
S3method(slice_tail,data.frame)
S3method(src_tbls,src_local)
S3method(summarise,data.frame)
S3method(summarise,grouped_df)
S3method(summarise,rowwise_df)
S3method(summarise_,data.frame)
S3method(summarise_,tbl_df)
S3method(summarise_bullets,"dplyr:::error_incompatible_combine")
S3method(summarise_bullets,"dplyr:::summarise_incompatible_size")
S3method(summarise_bullets,"dplyr:::summarise_mixed_null")
S3method(summarise_bullets,"dplyr:::summarise_unsupported_type")
S3method(summarise_bullets,default)
S3method(symdiff,data.frame)
S3method(symdiff,default)
S3method(tally,data.frame)
S3method(tbl,DBIConnection)
S3method(tbl,src_local)
S3method(tbl_ptype,default)
S3method(tbl_sum,grouped_df)
S3method(tbl_sum,rowwise_df)
S3method(tbl_vars,data.frame)
S3method(transmute,data.frame)
S3method(transmute_,data.frame)
S3method(ungroup,data.frame)
S3method(ungroup,grouped_df)
S3method(ungroup,rowwise_df)
S3method(union,data.frame)
S3method(union_all,data.frame)
S3method(union_all,default)
export("%>%")
export(.data)
export(across)
export(add_count)
export(add_count_)
export(add_row)
export(add_rownames)
export(add_tally)
export(add_tally_)
export(all_equal)
export(all_of)
export(all_vars)
export(anti_join)
export(any_of)
export(any_vars)
export(arrange)
export(arrange_)
export(arrange_all)
export(arrange_at)
export(arrange_if)
export(as.tbl)
export(as_data_frame)
export(as_label)
export(as_tibble)
export(auto_copy)
export(bench_tbls)
export(between)
export(bind_cols)
export(bind_rows)
export(c_across)
export(case_match)
export(case_when)
export(changes)
export(check_dbplyr)
export(coalesce)
export(collapse)
export(collect)
export(combine)
export(common_by)
export(compare_tbls)
export(compare_tbls2)
export(compute)
export(consecutive_id)
export(contains)
export(copy_to)
export(count)
export(count_)
export(cumall)
export(cumany)
export(cume_dist)
export(cummean)
export(cur_column)
export(cur_data)
export(cur_data_all)
export(cur_group)
export(cur_group_id)
export(cur_group_rows)
export(current_vars)
export(data_frame)
export(db_analyze)
export(db_begin)
export(db_commit)
export(db_create_index)
export(db_create_indexes)
export(db_create_table)
export(db_data_type)
export(db_desc)
export(db_drop_table)
export(db_explain)
export(db_has_table)
export(db_insert_into)
export(db_list_tables)
export(db_query_fields)
export(db_query_rows)
export(db_rollback)
export(db_save_query)
export(db_write_table)
export(dense_rank)
export(desc)
export(dim_desc)
export(distinct)
export(distinct_)
export(distinct_all)
export(distinct_at)
export(distinct_if)
export(distinct_prepare)
export(do)
export(do_)
export(dplyr_col_modify)
export(dplyr_reconstruct)
export(dplyr_row_slice)
export(ends_with)
export(enexpr)
export(enexprs)
export(enquo)
export(enquos)
export(ensym)
export(ensyms)
export(eval_tbls)
export(eval_tbls2)
export(everything)
export(explain)
export(expr)
export(failwith)
export(filter)
export(filter_)
export(filter_all)
export(filter_at)
export(filter_if)
export(first)
export(full_join)
export(funs)
export(funs_)
export(glimpse)
export(group_by)
export(group_by_)
export(group_by_all)
export(group_by_at)
export(group_by_drop_default)
export(group_by_if)
export(group_by_prepare)
export(group_cols)
export(group_data)
export(group_indices)
export(group_indices_)
export(group_keys)
export(group_map)
export(group_modify)
export(group_nest)
export(group_rows)
export(group_size)
export(group_split)
export(group_trim)
export(group_vars)
export(group_walk)
export(grouped_df)
export(groups)
export(id)
export(ident)
export(if_all)
export(if_any)
export(if_else)
export(inner_join)
export(intersect)
export(is.grouped_df)
export(is.src)
export(is.tbl)
export(is_grouped_df)
export(join_by)
export(lag)
export(last)
export(last_col)
export(lead)
export(left_join)
export(location)
export(lst)
export(make_tbl)
export(matches)
export(min_rank)
export(mutate)
export(mutate_)
export(mutate_all)
export(mutate_at)
export(mutate_each)
export(mutate_each_)
export(mutate_if)
export(n)
export(n_distinct)
export(n_groups)
export(na_if)
export(near)
export(nest_by)
export(nest_join)
export(new_grouped_df)
export(new_rowwise_df)
export(nth)
export(ntile)
export(num_range)
export(one_of)
export(order_by)
export(percent_rank)
export(progress_estimated)
export(pull)
export(quo)
export(quo_name)
export(quos)
export(recode)
export(recode_factor)
export(relocate)
export(rename)
export(rename_)
export(rename_all)
export(rename_at)
export(rename_if)
export(rename_vars)
export(rename_vars_)
export(rename_with)
export(right_join)
export(row_number)
export(rows_append)
export(rows_delete)
export(rows_insert)
export(rows_patch)
export(rows_update)
export(rows_upsert)
export(rowwise)
export(same_src)
export(sample_frac)
export(sample_n)
export(select)
export(select_)
export(select_all)
export(select_at)
export(select_if)
export(select_var)
export(select_vars)
export(select_vars_)
export(semi_join)
export(setdiff)
export(setequal)
export(show_query)
export(slice)
export(slice_)
export(slice_head)
export(slice_max)
export(slice_min)
export(slice_sample)
export(slice_tail)
export(sql)
export(sql_escape_ident)
export(sql_escape_string)
export(sql_join)
export(sql_select)
export(sql_semi_join)
export(sql_set_op)
export(sql_subquery)
export(sql_translate_env)
export(src)
export(src_df)
export(src_local)
export(src_mysql)
export(src_postgres)
export(src_sqlite)
export(src_tbls)
export(starts_with)
export(summarise)
export(summarise_)
export(summarise_all)
export(summarise_at)
export(summarise_each)
export(summarise_each_)
export(summarise_if)
export(summarize)
export(summarize_)
export(summarize_all)
export(summarize_at)
export(summarize_each)
export(summarize_each_)
export(summarize_if)
export(sym)
export(symdiff)
export(syms)
export(tally)
export(tally_)
export(tbl)
export(tbl_df)
export(tbl_nongroup_vars)
export(tbl_ptype)
export(tbl_vars)
export(tibble)
export(top_frac)
export(top_n)
export(transmute)
export(transmute_)
export(transmute_all)
export(transmute_at)
export(transmute_if)
export(tribble)
export(type_sum)
export(ungroup)
export(union)
export(union_all)
export(validate_grouped_df)
export(validate_rowwise_df)
export(vars)
export(with_groups)
export(with_order)
export(wrap_dbplyr_obj)
import(rlang)
import(vctrs, except = data_frame)
importFrom(R6,R6Class)
importFrom(generics,intersect)
importFrom(generics,setdiff)
importFrom(generics,setequal)
importFrom(generics,union)
importFrom(glue,glue)
importFrom(glue,glue_collapse)
importFrom(glue,glue_data)
importFrom(lifecycle,deprecated)
importFrom(magrittr,"%>%")
importFrom(methods,is)
importFrom(pillar,glimpse)
importFrom(pillar,tbl_sum)
importFrom(pillar,type_sum)
importFrom(stats,setNames)
importFrom(stats,update)
importFrom(tibble,add_row)
importFrom(tibble,as_data_frame)
importFrom(tibble,as_tibble)
importFrom(tibble,data_frame)
importFrom(tibble,is_tibble)
importFrom(tibble,lst)
importFrom(tibble,new_tibble)
importFrom(tibble,tibble)
importFrom(tibble,tribble)
importFrom(tibble,view)
importFrom(tidyselect,all_of)
importFrom(tidyselect,any_of)
importFrom(tidyselect,contains)
importFrom(tidyselect,ends_with)
importFrom(tidyselect,everything)
importFrom(tidyselect,last_col)
importFrom(tidyselect,matches)
importFrom(tidyselect,num_range)
importFrom(tidyselect,one_of)
importFrom(tidyselect,starts_with)
importFrom(utils,head)
importFrom(utils,tail)
useDynLib(dplyr, .registration = TRUE)

Creating an R package

usethis::create_package("mypkg") for creating a skeleton R package
Add things to your R package
devtools::load_all() for loading the functions in the R/ directory to the current environment

Adding functions to an R package

usethis::use_r("new-r-file") for creating a new R file in the R/ directory

- mypackage
  |- R
     |- new-r-file.R
     |- ...
  |- DESCRIPTION
  |- ...

Distribute data via an R package

usethis::use_data_raw("filename") for adding a file to data-raw/ directory to include code to reproduce data.
usethis::use_data(mydata) for creating a binary data file in data/ directory.
More information on this at R Packages (2e) Chapter 8 Data.

Launcing shiny app via an R package

- mypackage
  |- inst
     |- myapp
        |- app.R
  |- R
     |- run-app.R
     |- ...
  |- DESCRIPTION
  |- ...

run-app.R

#' @export
run_app <- function() {
  app_dir <- system.file("myapp", package = "mypackage")
  shiny::runApp(app_dir, display.mode = "normal")
}

Note: for this to work, you need to first install the package!

Installing the package

First run devtools::document()
(we will cover this more next lecture).
Then run devtools::install().
Now you can call library(mypackage) to use exported functions or data in your package!

Master the keyboard shortcuts

Cmd/Ctrl + Shift + L: Load all
Cmd/Ctrl + Shift + D: Document
Cmd/Ctrl + Shift + B: Build and Reload
plus more… see RStudio IDE > Tools > Keyboard Shortcuts Help

Week 8 Lesson

Summary

How to enhance communication in code by adopting a consistent styling.
R packages are flexible containers to share R code, data, and app amongst other things.
An R package can be set up, documented, and tried out with usethis and devtools.

Resources

Filazzola & Lortie (2022) A call for clean code to effectively communicate science
R Packages 2nd edition by Hadley Wickham and Jenny Bryan

ETC5523: Communicating with Data

Clearly communicating with code

Example data

Which one do you prefer?

What do you expect the output is?

Naming matters

Syntactic sugar

Naming cases

Syntactically valid names in R

Object names

Function names

Variable names

Readable code

Consistency spacing

Consistency spacing

Consistency spacing

Consistency spacing

Avoid long lines

Sequence of functions

R packages for styling code

What exactly are R packages?

R packages can be many things…

The anatomy of an R package

The DESCRIPTION file

Example: dplyr DESCRIPTION file

The R/ directory

The NAMESPACE file

Creating an R package

Adding functions to an R package

Distribute data via an R package

Launcing shiny app via an R package

Installing the package

Master the keyboard shortcuts

Week 8 Lesson

The `DESCRIPTION` file

Example: `dplyr` `DESCRIPTION` file

The `NAMESPACE` file