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BeginneR Session
- Data Pipeline -
#75 Tokyo.R
2019.01.19
@kilometer00
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Who！？
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Who！？
名前： 三村 @kilometer
職業： ポスドク (こうがくはくし)
専⾨： ⾏動神経科学(霊⻑類)
脳イメージング
医療システム⼯学
R歴： ~ 10年ぐらい
流⾏: グリル付きコンロ
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BeginneR Session
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BeginneR
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BeginneR Advanced Hoxo_m
If I have seen further it is by standing on
the shoulders of Giants.
-- Sir Isaac Newton, 1676
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Before After
BeginneR Session
BeginneR BeginneR
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BeginneR Session
- Data Pipeline -
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Input
Output
Data Pipeline
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packages
you
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Input
Output
packages
Data Pipeline
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Output
Input
Input
Data Pipeline
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Output
Input
Input
Data Pipeline
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Output
Input
Input
Data Pipeline
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Data Pipeline
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Data Pipeline
readable
coding
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Programing
Write
Run
Read
Think
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Run!!!
https://www.amazon.co.jp/dp/B00Y0UI990/
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Programing
Write
Run
Read
Think
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Programing
Write
Run
Read
Think
coding style
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The tidyverse style guide
https://style.tidyverse.org/
"Good coding style is like correct punctuation:
you can manage without it, butitsuremakesthingseasiertoread."
Google's R Style Guide
https://style.tidyverse.org/
"The goal of the R Programming Style Guide is to make our
R code easier to read, share, and verify."
R coding style guides
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The tidyverse style guides
https://style.tidyverse.org/syntax.html#object-names
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The tidyverse style guides
https://style.tidyverse.org/syntax.html#object-names
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> data
function(..., list = character(), package = NULL,
lib.loc = NULL, verbose = getOption("verbose"),
envir = .GlobalEnv) {
fileExt <- function(x) {
db <- grepl("\\.[^.]+\\.(gz|bz2|xz)$", x)
ans <- sub(".*\\.", "", x)
...
"Where possible, avoid re-using names of common functions and variables.
This will cause confusion for the readers of your code."
# Good
df <- read.csv("hoge.csv")
dat <- read.csv("hoge.csv")
# Bad
data <- read.csv("hoge.csv")
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# Bad
for(i in 1:10){
print(i)
}
# Good
for(i in 1:10){
print(i)
}
copy (cut) & paste
Auto-indentation (in RStudio)
Details: RStudio > Preference > Code > Editing
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The tidyverse style guide
https://style.tidyverse.org/
"Good coding style is like correct punctuation:
you can manage without it, butitsuremakesthingseasiertoread."
Google's R Style Guide
https://style.tidyverse.org/
"The goal of the R Programming Style Guide is to make our
R code easier to read, share, and verify."
R coding style guides
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Programing
Write
Run
Read
Think
Write
Run
Read
Think
Share
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Text Figure
Information
Intention
Data
decode
encode
feedback
Programing
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ブール演算⼦ Boolean Algebra
A == B A != B
George Boole
1815 - 1864
A | B A & B
A %in% B
# equal to # not equal to
# or # and
# is A in B?
wikipedia
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"a" != "b"
# is A in B?
ブール演算⼦ Boolean Algebra
[1] TRUE
1 %in% 10:100
# is A in B?
[1] FALSE
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George Boole
1815 - 1864
A Class-Room Introduction to Logic
https://niyamaklogic.wordpress.co
m/category/laws-of-thoughts/
Mathematician
Philosopher
&
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ブール演算⼦ Boolean Algebra
A == B A != B
George Boole
1815 - 1864
A | B A & B
A %in% B
# equal to # not equal to
# or # and
# is A in B?
wikipedia
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Programing
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Programing
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Programing
Write
Run
Read
Think
Write
Run
Read
Think
Communicate
Share
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Programing
Write
Run
Read
Think
Write
Run
Read
Think
Communicate
Share
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Programing
Write
Run
Read
Think
Write
Run
Read
Think
Communicate
Share
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Input
Output
packages
Data Pipeline
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Integrated Development Environment
RStudio https://www.rstudio.com/
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Integrated Development Environment
RStudio https://www.rstudio.com/
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RStudio
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Projects
RStudio
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RStudio > Project
⼀説には2147483647個存在するとも⾔われるRStudioの
利点のなかでも、
‧Rなどのソースファイルをタブで並べて表⽰できる
‧そのタブの順番を保持できる
‧タブの内容をファイルを保存せずにRStudioを終了して
しまっても、編集途中の内容を保持してくれている
等が全国2147483647⼈のRユーザーのQOLを⼤いに向上
させたのは、おそらく異論がないことと思われます。
RStudioって...なんだ？ Projectって...なんだ????
@wakuteka
https://qiita.com/wakuteka/items/9599bb0a8985d98928d7
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File > New Project… > New Directory > New Project
hogehoge
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hogehoge
~/Documents/R
hogehoge.Rproj
.Rproj.user
Project Root Directory
Double click!!
.RData
.Rhistory
Auto saved
project information
Open project
New!!
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~/Documents/R
project1
project2
project3
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0. Introduction
1. data.frame
2. Pipe
4. Tidy data
3. Verbs
Agenda
済
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vector
in Excel
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vector
in R
in Excel
pre <- c(1, 2, 3, 4, 5)
post <- pre * 5
> pre
[1] 1 2 3 4 5
> post
[1] 5 10 15 20 25
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vector
vec1 <- c(1, 2, 3, 4, 5)
vec2 <- 1:5
vec3 <- seq(from = 1, to = 5, by = 1)
> vec1
[1] 1 2 3 4 5
> vec2
[1] 1 2 3 4 5
> vec3
[1] 1 2 3 4 5
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vector
vec1 <- seq(from = 1, to = 5, by = 1)
vec2 <- seq(1, 5, 1)
> vec1
[1] 1 2 3 4 5
> vec2
[1] 1 2 3 4 5
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> ?seq
vector
seq{base}
Sequence Generation
Description
Generate regular sequences. seq is a standard
generic with a default method. …
Usage
seq(...)
## Default S3 method:
seq(from = 1, to = 1, by = ((to - from)/(length.out - 1)),
length.out = NULL, along.with = NULL, ...)
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vector
vec1 <- rep(1:3, times = 2)
vec2 <- rep(1:3, each = 2)
vec3 <- rep(1:3, times = 2, each = 2)
> vec1
[1] 1 2 3 1 2 3
> vec2
[1] 1 1 2 2 3 3
> vec3
[1] 1 1 2 2 3 3 1 1 2 2 3 3
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vector
vec1 <- 11:15
> vec1
[1] 11 12 13 14 15
> vec1[1]
[1] 11
> vec1[3:5]
[1] 13 14 15
> vec1[c(1:2, 5)]
[1] 11 12 15
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list
list1 <- list(1:6, 11:15, c("a", "b", "c"))
> list1
[[1]]
[1] 1 2 3 4 5 6
[[2]]
[1] 11 12 13 14 15
[[3]]
[1] "a" "b" "c"
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list
list1 <- list(1:6, 11:15, c("a", "b", "c"))
> list1[[1]]
[1] 1 2 3 4 5 6
> list1[[3]][2:3]
[1] "b" "c"
> list1[[2]] * 3
[1] 33 36 39 42 45
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named list
list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
> list2
$A
[1] 1 2 3 4 5 6
$B
[1] 11 12 13 14 15
$C
[1] "a" "b" "c"
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> list2$A
[1] 1 2 3 4 5 6
> list2$C[2:3]
[1] "b" "c"
> list2$B * 3
[1] 33 36 39 42 45
list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
named list
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list1 <- list(1:6, 11:15, c("a", "b", "c"))
> class(list1)
[1] "list"
> names(list1)
NULL
list2 <- list(A = 1:6, B = 11:15, C = c("a", "b", "c"))
> class(list2)
[1] "list"
> names(list2)
[1] "A" "B" "C"
named list
list
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list3 <- list(A = 1:3, B = 11:13)
> class(list3)
[1] "list"
> names(list3)
[1] "A" "B"
df1 <- data.frame(A = 1:3, B = 11:13)
> class(df1)
[1] "data.frame"
> names(df1)
[1] "A" "B"
named list & data.frame
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> str(list3)
List of 2
$ A: int [1:3] 1 2 3
$ B: int [1:3] 11 12 13
> str(df1)
'data.frame': 3 obs. of 2 variables:
$ A: int 1 2 3
$ B: int 11 12 13
list3 <- list(A = 1:3, B = 11:13)
df1 <- data.frame(A = 1:3, B = 11:13)
named list & data.frame
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> list3
$A
[1] 1 2 3
$B
[1] 11 12 13
> df1
A B
1 1 11
2 2 12
3 3 13
named list & data.frame
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> list3
$A
[1] 1 2 3
$B
[1] 11 12 13
> df1
A B
1 1 11
2 2 12
3 3 13
named list & data.frame
observation
variable
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data.frame v.s. matrix
A B
1 1 11
2 2 12
3 3 13
[,1] [,2]
[1,] 1 11
[2,] 2 12
[3,] 3 13
df1 <- data.frame(A = 1:3, B = 11:13)
> str(mat1)
int [1:3, 1:2] 1 2 3 11 12 13
> str(df1)
'data.frame': 3 obs. of 2 vars.:
$ A: int 1 2 3
$ B: int 11 12 13
mat1 <- matrix(c(1:3, 11:13), 3, 2)
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data.frame v.s. matrix
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data.frame v.s. matrix
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…(省前) …
そんなわけで、「data.frame」は、我々の⼼の中にしかありません。
あの四⾓い感じの、みんなが「data.frame」と呼んでいるものこそが
「data.frame」なのです。
... So, "data.frame" is only in our mind.
Something like square, everyone calls "data.frame", is the "data.frame".
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0. Introduction
1. data.frame
2. Tidy data
4. Verbs
3. Pipe
Agenda
済
済
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http://vita.had.co.nz/papers/tidy-data.html
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https://r4ds.had.co.nz/
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In tidy data:
1. Each variable forms a column.
2. Each observation forms a row.
3. Each value must have its own cell.
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In tidy data:
1. Each variable forms a column.
2. Each observation forms a row.
3. Each value must have its own cell.
> df1
A B
1 1 11
2 2 12
3 3 13
observation
variable
df1 <- data.frame(A = 1:3, B = 11:13)
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In tidy data:
1. Each variable forms a column.
2. Each observation forms a row.
3. Each value must have its own cell.
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In tidy data:
1. Each variable forms a column.
2. Each observation forms a row.
3. Each value must have its own cell.
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In tidy data:
1. Each variable forms a column.
2. Each observation forms a row.
3. Each value must have its own cell.
Different observation data
Value
Label
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In tidy data:
1. Each variable forms a column.
2. Each observation forms a row.
3. Each value must have its own cell.
data tidying
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"Horizontal" tidy data
variables
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"Horizontal" tidy data
"Vertical" tidy data
gather(df, key, value, -c(obsid, group)) {tidyr}
variables
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"Horizontal" style
"Vertical" style
gather(df, key, value, -c(obsid, group)) {tidyr}
variables
variables
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"Horizontal" style
"Nested" style
nest(group_by(df, group)) {tidyr}
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"Nested" style
df2 <- nest(group_by(df, group)) {tidyr}
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"Horizontal" data
"Nested" data
"Vertical" data
nest
unnest
gather spread
input output
visualization
Data style manipulation in {tidyr}
Loops,
Summarization,
Feature extractions et al., ...
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0. Introduction
1. data.frame
2. Tidy data
4. Verbs
3. Pipe
Agenda
済
済
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1JQF
X %>% f
X %>% f(y)
X %>% f %>% g
X %>% f(y, .)
f(X)
f(X, y)
g(f(X))
f(y, X)
%>% {magrittr}
「dplyr再⼊⾨（基本編）」yutanihilation
https://speakerdeck.com/yutannihilation/dplyrzai-ru-men-ji-ben-bian
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{magrittr}
「最近パイプしか打ってないです」
「パイプ、あれはいいよなって
他の⾔語の⼈も皆んな思ってますよ」
「1年ぐらいかけてゆっくりこっち
（パイプ）にシフトしましたね」
【中毒 愛⽤者たちの声】
「Rコミュニティ四⽅⼭話」https://rlangradio.org/
1JQF %>%
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①
②
③
④
lift
take
pour
put
Bring milk from the kitchen!
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①
lift
Bring milk from the kitchen!
lift(Robot, glass, table) -> Robot'
take
②
take(Robot', fridge, milk) -> Robot''
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Bring milk from the kitchen!
Robot' <- lift(Robot, glass, table)
Robot'' <- take(Robot', fridge, milk)
Robot''' <- pour(Robot'', milk, glass)
result <- put(Robot''', glass, table)
result <- Robot %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
by using pipe,
# ①
# ②
# ③
# ④
# ①
# ②
# ③
# ④
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The tidyverse style guides
https://style.tidyverse.org/syntax.html#object-names
"There are only two hard things in Computer Science:
cache invalidation and naming things"
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Bring milk from the kitchen!
Robot' <- lift(Robot, glass, table)
Robot'' <- take(Robot', fridge, milk)
Robot''' <- pour(Robot'', milk, glass)
result <- put(Robot''', glass, table)
result <- Robot %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
by using pipe,
# ①
# ②
# ③
# ④
# ①
# ②
# ③
# ④
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Robot' <- lift(Robot, glass, table)
Robot'' <- take(Robot', fridge, milk)
Robot''' <- pour(Robot'', milk, glass)
result <- put(Robot''', glass, table)
result <- Robot %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
by using pipe,
# ①
# ②
# ③
# ④
# ①
# ②
# ③
# ④
Thinking Reading
Bring milk from the kitchen!
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Programing
Write
Run
Read
Think
Write
Run
Read
Think
Communicate
Share
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0. Introduction
1. data.frame
2. Tidy data
4. Verbs
3. Pipe
Agenda
済
済
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①
②
③
④
lift
take
pour
put
Bring milk from the kitchen!
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①
②
③
④
lift
take
pour
put
Bring milk from the kitchen!
result <- Robot %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
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please_bring <- function(someone, milk, glass,
table = dining_table,
fridge= kitchen_fridge){
someone %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
}
RobotA %>% please_bring(milk, my_glass)
Define an original function
Usage
RobotB %>% please_bring(cold_tea, her_glass)
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• nouns for variables
• verbs for functions
General naming guidance to naming things
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• nouns for variables
• verbs for functions
General naming guidance to naming things
https://www.grinchcentral.com/function-names-to-verb-or-not-to-verb
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• nouns for variables
• verbs for functions
General naming guidance to naming things
• variables are nouns
• functions are verbs
Conversely,
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Functions are verbs.
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filter(df, x == "a", y == 1)
df[df$x == "a" & df$y == 1, ]
# verb (動詞的)
# noun (名詞的)
{dplyr}
df %>%
filter(x == "a", y == 1)
# verb with pipe
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mutate
select
filter
arrange
summaries
# add column
# select column
# select row
# arrange row
# summary of vars
{dplyr}
WFSCT WFSCGVODUJPOT
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It (dplyr) provides simple “verbs” to help
you translate your thoughts into code.
functions that correspond to the most
common data manipulation tasks
Introduction to dplyr
https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
WFSCT {dplyr}
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dplyrは、あなたの考えをコードに翻訳
するための【動詞】を提供する。
データ操作における基本のキを、
シンプルに実⾏できる関数 (群)
Introduction to dplyr
https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
WFSCT {dplyr}
※ かなり意訳
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WFSCT {dplyr}
mutate # カラムの追加
+
mutate
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library(dplyr)
iris %>% mutate(a = 1:nrow(.)) %>% str
'data.frame': 150 obs. of 6 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 ...
$ Species : Factor w/ 3 levels "setosa", ...
$ a : int 1 2 3 4 5 6 7 8 9 10 ...
WFSCT {dplyr}
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library(dplyr)
iris %>% mutate(a = 1:nrow(.),
a = a * 5/3 %>% round)
'data.frame': 150 obs. of 6 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 ...
$ Species : Factor w/ 3 levels “setosa”, ...
$ a : num 1.67 3.33 5 6.67 8.33 ... ...
WFSCT {dplyr}
over write
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WFSCT {dplyr}
select # カラムの選択
select
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library(dplyr)
iris %>% select(Sepal.Length, Sepal.Width)
'data.frame': 150 obs. of 6 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 ...
WFSCT {dplyr}
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library(dplyr)
iris %>% select(contains(“Width”))
'data.frame': 150 obs. of 6 variables:
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 ...
WFSCT {dplyr}
Select help functions
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WFSCT {dplyr}
# Select help functions
starts_with("s") ends_with("s")
contains("se") matches("^.e")
one_of(c("Sepal.Length", "Species"))
everything()
https://kazutan.github.io/blog/2017/04/dplyr-select-memo/
「dplyr::selectの活⽤例メモ」kazutan
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mutate
select
filter
arrange
summaries
# カラムの追加
# カラムの選択
# ⾏の絞り込み
# ⾏の並び替え
# 値の集約
{dplyr}
WFSCT WFSCؔ܈
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WFSCT {dplyr}
filter # ⾏の絞り込み
filter
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library(dplyr)
iris %>% filter(Species == "versicolor")
WFSCT {dplyr}
'data.frame': 50 obs. of 5 variables:
$ Sepal.Length: num 7 6.4 6.9 5.5 6.5 5.7 6.3 ...
$ Sepal.Width : num 3.2 3.2 3.1 2.3 2.8 2.8 ...
$ Petal.Length: num 4.7 4.5 4.9 4 4.6 4.5 4.7 ...
$ Petal.Width : num 1.4 1.5 1.5 1.3 1.5 1.3 ...
$ Species : Factor w/ 3 levels
"setosa","versicolor",..: 2 2 2 2 2 2 2 2 2 2 ...
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library(dplyr)
iris %>% filter(Species == "versicolor")
WFSCT {dplyr}
NSE (Non-Standard Evaluation)
'data.frame': 50 obs. of 5 variables:
$ Sepal.Length: num 7 6.4 6.9 5.5 6.5 5.7 6.3 ...
$ Sepal.Width : num 3.2 3.2 3.1 2.3 2.8 2.8 ...
$ Petal.Length: num 4.7 4.5 4.9 4 4.6 4.5 4.7 ...
$ Petal.Width : num 1.4 1.5 1.5 1.3 1.5 1.3 ...
$ Species : Factor w/ 3 levels
"setosa","versicolor",..: 2 2 2 2 2 2 2 2 2 2 ...
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filter(df, x == "a", y == 1)
/4&ͷ
NSE (Non-Standard Evaluation)
df[df$x == "a" & df$y == 1, ]
SE (Standard Evaluation)
http://dplyr.tidyverse.org/articles/programming.html
Programming with dplyr
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filter(df, x == "a", y == 1)
/4&ͷ
NSEを使うと、
‧dfの名前を何回も書かなくていいよ
‧SQLっぽく書けるよ
http://dplyr.tidyverse.org/articles/programming.html
Programming with dplyr
df[df$x == "a" & df$y == 1, ]
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filter(df, x == "a", y == 1)
/4&ͷ
NSEを使うと、
df[df$x == "a" & df$y == 1, ]
http://dplyr.tidyverse.org/articles/programming.html
Programming with dplyr
⾊々あるけどスッキリしているのは正義 (私⾒)
書きやすく、読みやすく。
思考と実装の距離を近く。
# verb (動詞的)
# noun (名詞的)
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df <- data.frame(x = 1:3, y = 1:3)
filter(df, x == 1)
/4&ͷ
Because of NSE..
http://dplyr.tidyverse.org/articles/programming.html
Programming with dplyr
my_var <- "x"
filter(df, my_var == 1)
This do NOT work
There is No “my_var” column in df
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/4&ͷ
my_var <- quo(x)
filter(df, (!! my_var) == 1)
ど〜〜〜してもやりたければ、
何故こうなるかは、
「dplyr再⼊⾨（Tidyval編）」を参照。
https://speakerdeck.com/yutannihilation/dplyrzai-ru-men-tidyevalbian
「dplyr再⼊⾨（Tidyval編）」yutanihilation
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/4&ͷ
my_var <- quo(x)
filter(df, (!! my_var) == 1)
ど〜〜〜してもやりたければ、
何故こうなるかは、
「dplyr再⼊⾨（Tidyval編）」を参照。
https://speakerdeck.com/yutannihilation/dplyrzai-ru-men-tidyevalbian
可読性が上がる？下がる？
それは、あなたと読み⼿次第。
「dplyr再⼊⾨（Tidyval編）」yutanihilation
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mutate
select
filter
arrange
summaries
# カラムの追加
# カラムの選択
# ⾏の絞り込み
# ⾏の並び替え
# 値の集約
{dplyr}
WFSCT WFSCؔ܈
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(SBNNBSPGEBUBNBOJQVMBUJPO
By constraining your options,
it helps you think about your
data manipulation challenges.
Introduction to dplyr
https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
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選択肢を制限することで、
データ解析のステップを
シンプルに考えられますヨ。
（めっちゃ意訳）
Introduction to dplyr
https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
※ まさに意訳
(SBNNBSPGEBUBNBOJQVMBUJPO
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より多くの制約を課す事で、
魂の⾜枷から、より⾃由になる。
Igor Stravinsky
И
́ горь Ф Страви́нский
The more constraints one imposes,
the more one frees one's self of the
chains that shackle the spirit.
1882 - 1971
※ 割と意訳
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0. Introduction
1. data.frame
2. Tidy data
4. Verbs
3. Pipe
Agenda
済
済
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Summary
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> list3
$A
[1] 1 2 3
$B
[1] 11 12 13
> df1
A B
1 1 11
2 2 12
3 3 13
observation
variable
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In tidy data:
1. Each variable forms a column.
2. Each observation forms a row.
3. Each value must have its own cell.
data tidying
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"Horizontal" data
"Nested" data
"Vertical" data
nest
unnest
gather spread
input output
visualization
Data style manipulation in {tidyr}
Loops,
Summarization,
Feature extractions et al., ...
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1JQF
X %>% f
X %>% f(y)
X %>% f %>% g
X %>% f(y, .)
f(X)
f(X, y)
g(f(X))
f(y, X)
%>% {magrittr}
「dplyr再⼊⾨（基本編）」yutanihilation
https://speakerdeck.com/yutannihilation/dplyrzai-ru-men-ji-ben-bian
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①
②
③
④
lift
take
pour
put
Functions are verbs
result <- Robot %>%
lift(glass, table) %>%
take(fridge, milk) %>%
pour(milk, glass) %>%
put(glass, table)
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filter(df, x == "a", y == 1)
df[df$x == "a" & df$y == 1, ]
# verb (動詞的)
# noun (名詞的)
{dplyr}
df %>%
filter(x == "a", y == 1)
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mutate
select
filter
arrange
summaries
# add column
# select column
# select row
# arrange row
# summary of vars
{dplyr}
WFSCT WFSCGVODUJPOT
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Data Pipeline
readable
coding
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https://www.tidyverse.org/
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Programing languages are language
Write
Run
Read
Think
Write
Run
Read
Think
Communicate
Share
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“Life shrinks or expands
to one’s courage.”
-- Anaïs Nin, 2000
http://theamericanreader.com
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Before After
BeginneR Session
BeginneR BeginneR
？
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