Slides 02
Working in R
Arvind Venkatadri
1

R Basics2

R BasicsR is an interpreter (>)
3

R Basics

R is an interpreter (>)
Name objects in R (i_like_snake_case <-)

R Basics

R is an interpreter (>)
Name objects in R (i_like_snake_case <-)
Know your object types (typeof())

R Basics

R is an interpreter (>)
Name objects in R (i_like_snake_case <-)
Know your object types (typeof())
Case matters (my_names != My_names)

R Basics

R is an interpreter (>)
Name objects in R (i_like_snake_case <-)
Know your object types (typeof())
Case matters (my_names != My_names)

Use comments! (# use the hashtag symbol)

R Basics

R is an interpreter (>)
Name objects in R (i_like_snake_case <-)
Know your object types (typeof())
Case matters (my_names != My_names)

Use comments! (# use the hashtag symbol)
Functions (fun!)

R Basics

R is an interpreter (>)
Name objects in R (i_like_snake_case <-)
Know your object types (typeof())
Case matters (my_names != My_names)

Use comments! (# use the hashtag symbol)
Functions (fun!)
Use packages ("install once per machine, load once per R session")

R Basics

R is an interpreter (>)
Name objects in R (i_like_snake_case <-)
Know your object types (typeof())
Case matters (my_names != My_names)

Use comments! (# use the hashtag symbol)
Functions (fun!)
Use packages ("install once per machine, load once per R session")
Use the %>% ("dataframe first, dataframe once")

R is an interpreter >11

R is an interpreter `>`

You enter commands line-by-line (as opposed to compiled languages).

R is an interpreter `>`

You enter commands line-by-line (as opposed to compiled languages).

The > means R is a ready for a command

R is an interpreter `>`

You enter commands line-by-line (as opposed to compiled languages).

The > means R is a ready for a command
The + means your last command isn't complete

R is an interpreter `>`

You enter commands line-by-line (as opposed to compiled languages).

The > means R is a ready for a command
The + means your last command isn't complete
- If you get stuck with a + use your escape key!

🐍Name Objects in Ri_like_snake_case <-16

🐍

Name Objects in R

`i_like_snake_case <-`

RStudio Keyboard Shortcuts:

OSX: Option + -

Else: Alt + -

(the + means and, not the + key)

Name your own objects

us <- c("Pratyush", "Anand", "Arvind") # combine strings
us

[1] "Pratyush" "Anand"    "Arvind"

Name your own objects

us <- c("Pratyush", "Anand", "Arvind") # combine strings
us

[1] "Pratyush" "Anand"    "Arvind"

num_labs <- c(1:10) # combine numbers
num_labs

 [1]  1  2  3  4  5  6  7  8  9 10

Name your own objects

us <- c("Pratyush", "Anand", "Arvind") # combine strings
us

[1] "Pratyush" "Anand"    "Arvind"

num_labs <- c(1:10) # combine numbers
num_labs

 [1]  1  2  3  4  5  6  7  8  9 10

mood <- rep("yippee", length(num_labs)) # replicate 10 times
mood

 [1] "yippee" "yippee" "yippee" "yippee" "yippee" "yippee" "yippee" "yippee" "yippee" "yippee"

Re-name others' objects

my_alpha <- letters # built-in, no package needed
my_alpha

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

Re-name others' objects

my_alpha <- letters # built-in, no package needed
my_alpha

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

my_names <- babynames # from the babynames package
my_names

# A tibble: 1,924,665 × 5
    year sex   name          n   prop
   <dbl> <chr> <chr>     <int>  <dbl>
 1  1880 F     Mary       7065 0.0724
 2  1880 F     Anna       2604 0.0267
 3  1880 F     Emma       2003 0.0205
 4  1880 F     Elizabeth  1939 0.0199
 5  1880 F     Minnie     1746 0.0179
 6  1880 F     Margaret   1578 0.0162
 7  1880 F     Ida        1472 0.0151
 8  1880 F     Alice      1414 0.0145
 9  1880 F     Bertha     1320 0.0135
10  1880 F     Sarah      1288 0.0132
# … with 1,924,655 more rows
# ℹ Use `print(n = ...)` to see more rows

What to name objects?

?make.names

Object names cannot:

Start with a number
Contain a space
Contain "reserved" words

What to name objects?

?make.names

Object names cannot:

Start with a number
Contain a space
Contain "reserved" words

Object names must:

Start with a letter
Contain letters, numbers, _ and .

Adopt a consistent naming style

i_use_snake_case # recommended
otherPeopleUseCamelCase
some.people.use.periods
And_aFew.People_RENOUNCEconvention

From: http://r4ds.had.co.nz/workflow-basics.html#whats-in-a-name

🔦Know Your Data Typestypeof()26

Know your data typesNumeric (2 subtypes)Integers (1, 50)
Double (1.5, 50.25, ?double)

Character ("hello")
Factor (grade = "A" | grade = "B")
Logical (TRUE | FALSE)
27

Know your data types

Numeric (2 subtypes)
- Integers (1, 50)
- Double (1.5, 50.25, ?double)
Character ("hello")
Factor (grade = "A" | grade = "B")
Logical (TRUE | FALSE)

typeof(num_labs) # numeric

[1] "integer"

typeof(mood) # "yippee" is a character

[1] "character"

typeof(mood == "yippee") # is mood equal to "yippee"- T or F?

[1] "logical"

Characters can be deceiving

my_things <- c(num_labs, mood)
my_things

 [1] "1"      "2"      "3"      "4"      "5"      "6"      "7"      "8"      "9"      "10"     "yippee" "yippee" "yippee" "yippee"
[15] "yippee" "yippee" "yippee" "yippee" "yippee" "yippee"

Characters can be deceiving

my_things <- c(num_labs, mood)
my_things

 [1] "1"      "2"      "3"      "4"      "5"      "6"      "7"      "8"      "9"      "10"     "yippee" "yippee" "yippee" "yippee"
[15] "yippee" "yippee" "yippee" "yippee" "yippee" "yippee"

typeof(my_things)

[1] "character"

`NA` is special

num_labs <- c(num_labs, NA)

`NA` is special

num_labs <- c(num_labs, NA)

typeof(num_labs)

[1] "integer"

`NA` is special

num_labs <- c(num_labs, NA)

typeof(num_labs)

[1] "integer"

num_labs*3

 [1]  3  6  9 12 15 18 21 24 27 30 NA

max(num_labs)

[1] NA

max(num_labs, na.rm = TRUE)

[1] 10

Case mattersmy_names != My_names34

Case matters

This works:

glimpse(babynames)

Rows: 1,924,665
Columns: 5
$ year <dbl> 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1…
$ sex  <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", …
$ name <chr> "Mary", "Anna", "Emma", "Elizabeth", "Minnie", "Margaret", "Ida", "Alice", "Bertha", "Sarah", "Annie", "Clara", "El…
$ n    <int> 7065, 2604, 2003, 1939, 1746, 1578, 1472, 1414, 1320, 1288, 1258, 1226, 1156, 1063, 1045, 1040, 1012, 995, 982, 949…
$ prop <dbl> 0.07238359, 0.02667896, 0.02052149, 0.01986579, 0.01788843, 0.01616720, 0.01508119, 0.01448696, 0.01352390, 0.01319…

Case matters

This works:

glimpse(babynames)

Rows: 1,924,665
Columns: 5
$ year <dbl> 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1…
$ sex  <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", …
$ name <chr> "Mary", "Anna", "Emma", "Elizabeth", "Minnie", "Margaret", "Ida", "Alice", "Bertha", "Sarah", "Annie", "Clara", "El…
$ n    <int> 7065, 2604, 2003, 1939, 1746, 1578, 1472, 1414, 1320, 1288, 1258, 1226, 1156, 1063, 1045, 1040, 1012, 995, 982, 949…
$ prop <dbl> 0.07238359, 0.02667896, 0.02052149, 0.01986579, 0.01788843, 0.01616720, 0.01508119, 0.01448696, 0.01352390, 0.01319…

These do not:

Glimpse(babynames) # no function

Error in Glimpse(babynames): could not find function "Glimpse"

glimpse(Babynames) # no data

Error in glimpse(Babynames): object 'Babynames' not found

📢Comments# go here37

Text behind a `#` is a comment

num_labs + 2 # add 2 here

 [1]  3  4  5  6  7  8  9 10 11 12 NA

num_weeks <- num_labs + 2 # save as new object

Text behind a `#` is a comment

num_labs + 2 # add 2 here

 [1]  3  4  5  6  7  8  9 10 11 12 NA

num_weeks <- num_labs + 2 # save as new object

# I can say anything I want here...
num_weeks

 [1]  3  4  5  6  7  8  9 10 11 12 NA

Text behind a `#` is a comment

num_labs + 2 # add 2 here

 [1]  3  4  5  6  7  8  9 10 11 12 NA

num_weeks <- num_labs + 2 # save as new object

# I can say anything I want here...
num_weeks

 [1]  3  4  5  6  7  8  9 10 11 12 NA

but not here

Error: <text>:1:5: unexpected symbol
1: but not
        ^

🍰Functions41

Functions

Sometimes abbreviated funs in documentation, which is a little ironic 😉.

Functions can come from:

base R (these functions are "built in")
packages
you

Base R Functions

seq(1, 12, 1) # base R

 [1]  1  2  3  4  5  6  7  8  9 10 11 12

Functions from Packages

babynames %>% count(sex) # count is from dplyr

# A tibble: 2 × 2
  sex         n
  <chr>   <int>
1 F     1138293
2 M      786372

Roll Your Own Functions

greet <- function(name) {
  glue::glue("Welcome to SMI, {name}!")
}
greet("Kanishka")

Welcome to SMI, Kanishka!

Function help

?seq
?count

Pay attention to:

Function help

?seq
?count

Pay attention to:

Usage (recipe)

Function help

?seq
?count

Pay attention to:

Usage (recipe)
Arguments (ingredients)

Function help

?seq
?count

Pay attention to:

Usage (recipe)
Arguments (ingredients)
Examples

Function help

?seq
?count

Pay attention to:

Usage (recipe)
Arguments (ingredients)
Examples

📦

Packages

"install once per machine, load once per R session"

Packages!

Install once per machine

install.packages("dplyr")

Packages!

Install once per machine

install.packages("dplyr")

Load once per R work session

library(dplyr)

Packages!

Install once per machine

install.packages("dplyr")

Load once per R work session

library(dplyr)

also: quotes matter, sorry

The `tidyverse` package ecosystem

https://www.tidyverse.org

"The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures."

install.packages("tidyverse")
library(tidyverse)

See packages included here: https://www.tidyverse.org/packages/

`%>%`

The pipe

"dataframe first, dataframe once"

`%>%`

The pipe

"dataframe first, dataframe once"

library(dplyr)

`%>%`

The pipe

"dataframe first, dataframe once"

library(dplyr)

RStudio Keyboard Shortcuts:

OSX: CMD + SHIFT + M

Else: CTRL + SHIFT + M

Nesting a dataframe inside a function is hard to read.

slice(babynames, 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 F     Mary   7065 0.0724

Nesting a dataframe inside a function is hard to read.

slice(babynames, 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 F     Mary   7065 0.0724

Here, the "sentence" starts with a verb.

Nesting a dataframe inside a function is hard to read.

slice(babynames, 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 F     Mary   7065 0.0724

Here, the "sentence" starts with a verb.

Piping a dataframe into a function lets you read L to R

babynames %>% slice(1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 F     Mary   7065 0.0724

Nesting a dataframe inside a function is hard to read.

slice(babynames, 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 F     Mary   7065 0.0724

Here, the "sentence" starts with a verb.

Piping a dataframe into a function lets you read L to R

babynames %>% slice(1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 F     Mary   7065 0.0724

Now, the "sentence" starts with a noun.

Sequences of functions make you read inside out

slice(filter(babynames, sex == "M"), 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

Sequences of functions make you read inside out

slice(filter(babynames, sex == "M"), 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

Chaining functions together lets you read L to R

babynames %>% filter(sex == "M") %>% slice(1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

"dataframe first, dataframe once"

babynames %>% filter(sex == "M") %>% slice(1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

babynames %>% filter(sex == "M") %>% slice(1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

This does the same thing:

babynames %>% filter(.data = ., sex == "M") %>% slice(.data = ., 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

babynames %>% filter(sex == "M") %>% slice(1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

This does the same thing:

babynames %>% filter(.data = ., sex == "M") %>% slice(.data = ., 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

So does this:

babynames %>% filter(., sex == "M") %>% slice(., 1)

# A tibble: 1 × 5
   year sex   name      n   prop
  <dbl> <chr> <chr> <int>  <dbl>
1  1880 M     John   9655 0.0815

I know...

I promise, it gets better.

🏃🏽

Resources for Working in R:

http://r4ds.had.co.nz/workflow-basics.html

http://moderndive.com/2-getting-started.html

https://bookdown.org/chesterismay/rbasics/3-rstudiobasics.html

https://github.com/rstudio/cheatsheets/blob/master/rstudio-ide.pdf

Install & load multiple R packages

This can get to be a long list if we want to use a lot of new packages in our work session. We can make a function to load a list of packages, and install them if not already installed (more on functions later).

pkgs <- c("readr", "dplyr", "tidyr") # list packages needed

Install & load multiple R packages

pkgs <- c("readr", "dplyr", "tidyr") # list packages needed

ipak <- function(pkg){
    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
    if (length(new.pkg)) 
        install.packages(new.pkg, dependencies = TRUE)
    sapply(pkg, require, character.only = TRUE)
}

Install & load multiple R packages

pkgs <- c("readr", "dplyr", "tidyr") # list packages needed

ipak <- function(pkg){
    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
    if (length(new.pkg)) 
        install.packages(new.pkg, dependencies = TRUE)
    sapply(pkg, require, character.only = TRUE)
}

ipak(pkgs) # take function, and give it that list

Function from: https://gist.github.com/stevenworthington/3178163

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Slides 02

Working in R

Arvind Venkatadri

R Basics

R Basics

R Basics

R Basics

R Basics

R Basics

R Basics

R Basics

R Basics

R is an interpreter >

R is an interpreter >

R is an interpreter >

R is an interpreter >

R is an interpreter >

🐍

Name Objects in R

i_like_snake_case <-

🐍

Name Objects in R

i_like_snake_case <-

Name your own objects

Name your own objects

Name your own objects

Re-name others' objects

Re-name others' objects

What to name objects?

What to name objects?

Adopt a consistent naming style

🔦

Know Your Data Types

typeof()

Know your data types

Know your data types

Characters can be deceiving

Characters can be deceiving

NA is special

NA is special

NA is special

Case matters

my_names != My_names

Case matters

Case matters

📢

Comments

# go here

Text behind a # is a comment

Text behind a # is a comment

Text behind a # is a comment

🍰

Functions

Functions

Base R Functions

Functions from Packages

Roll Your Own Functions

Function help

Function help

Function help

Function help

Function help

📦

Packages

Packages!

Packages!

Packages!

The tidyverse package ecosystem

%>%

The pipe

%>%

The pipe

%>%

The pipe

"dataframe first, dataframe once"

I know...

I promise, it gets better.

🏃🏽

Resources for Working in R:

Install & load multiple R packages

R is an interpreter `>`

R is an interpreter `>`

R is an interpreter `>`

R is an interpreter `>`

R is an interpreter `>`

`i_like_snake_case <-`

`i_like_snake_case <-`

`typeof()`

`NA` is special

`NA` is special

`NA` is special

`my_names != My_names`

`# go here`

Text behind a `#` is a comment

Text behind a `#` is a comment

Text behind a `#` is a comment

The `tidyverse` package ecosystem

`%>%`

`%>%`

`%>%`