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00383_example_A.1_of_section_A.2.R
# example A.1 of section A.2
# (example A.1 of section A.2) : Starting with R and other tools : Starting with R
# Title: Trying a few R commands
1## [1] 1
## [1] 1
1/2## [1] 0.5
## [1] 0.5
'Joe'## [1] "Joe"
## [1] "Joe"
"Joe"## [1] "Joe"
## [1] "Joe"
"Joe"=='Joe'## [1] TRUE
## [1] TRUE
c()## NULL
## NULL
is.null(c())## [1] TRUE
## [1] TRUE
is.null(5)## [1] FALSE
## [1] FALSE
c(1)## [1] 1
## [1] 1
c(1, 2)## [1] 1 2
## [1] 1 2
c("Apple", 'Orange')## [1] "Apple" "Orange"
## [1] "Apple" "Orange"
length(c(1, 2))## [1] 2
## [1] 2
vec <- c(1, 2)
vec## [1] 1 2
## [1] 1 200384_informalexample_A.3_of_section_A.2.1.R
# informalexample A.3 of section A.2.1
# (informalexample A.3 of section A.2.1) : Starting with R and other tools : Starting with R : Primary features of R
x <- 2
x < - 3## [1] FALSE
## [1] FALSE
print(x)## [1] 2
## [1] 200385_example_A.2_of_section_A.2.1.R
# example A.2 of section A.2.1
# (example A.2 of section A.2.1) : Starting with R and other tools : Starting with R : Primary features of R
# Title: Binding values to function arguments
divide <- function(numerator,denominator) { numerator/denominator }
divide(1, 2)## [1] 0.5
## [1] 0.5
divide(2, 1)## [1] 2
## [1] 2
divide(denominator = 2, numerator = 1)## [1] 0.5
## [1] 0.5
divide(denominator <- 2, numerator <- 1) # wrong symbol <-, yields 2, a wrong answer!## [1] 2
## [1] 200386_example_A.3_of_section_A.2.1.R
# example A.3 of section A.2.1
# (example A.3 of section A.2.1) : Starting with R and other tools : Starting with R : Primary features of R
# Title: Demonstrating side effects
x<-1
good <- function() { x <- 5}
good()
print(x)## [1] 1
## [1] 1
bad <- function() { x <<- 5}
bad()
print(x)## [1] 5
## [1] 500387_example_A.4_of_section_A.2.1.R
# example A.4 of section A.2.1
# (example A.4 of section A.2.1) : Starting with R and other tools : Starting with R : Primary features of R
# Title: R truth tables for Boolean operators
c(TRUE, TRUE, FALSE, FALSE) == c(TRUE, FALSE, TRUE, FALSE)## [1] TRUE FALSE FALSE TRUE
## [1] TRUE FALSE FALSE TRUE
c(TRUE, TRUE, FALSE, FALSE) & c(TRUE, FALSE, TRUE, FALSE)## [1] TRUE FALSE FALSE FALSE
## [1] TRUE FALSE FALSE FALSE
c(TRUE, TRUE, FALSE, FALSE) | c(TRUE, FALSE, TRUE, FALSE)## [1] TRUE TRUE TRUE FALSE
## [1] TRUE TRUE TRUE FALSE00388_example_A.5_of_section_A.2.1.R
# example A.5 of section A.2.1
# (example A.5 of section A.2.1) : Starting with R and other tools : Starting with R : Primary features of R
# Title: Call-by-value effect
a <- c(1, 2)
b <- a
print(b)## [1] 1 2
a[[1]] <- 5 # Note: 1
print(a)## [1] 5 2
print(b) # Note: 2## [1] 1 2
# Note 1:
# “Alter a”. Actually this is
# implemented by building an entirely new vector and
# reassigning a to refer to this new vector. The old
# value remains as it was, and any references
# continue to see the old unaltered value.
# Note 2:
# Notice b’s value is not
# changed. 00389_informalexample_A.4_of_section_A.2.2.R
# informalexample A.4 of section A.2.2
# (informalexample A.4 of section A.2.2) : Starting with R and other tools : Starting with R : Primary R data types
vec <- c(2, 3)
vec[[2]] <- 5
print(vec)## [1] 2 5
## [1] 2 500390_example_A.6_of_section_A.2.2.R
# example A.6 of section A.2.2
# (example A.6 of section A.2.2) : Starting with R and other tools : Starting with R : Primary R data types
# Title: Examples of R indexing operators
x <- list('a' = 6, b = 'fred')
names(x)## [1] "a" "b"
## [1] "a" "b"
x$a## [1] 6
## [1] 6
x$b## [1] "fred"
## [1] "fred"
x[['a']]## [1] 6
## $a
## [1] 6
x[c('a', 'a', 'b', 'b')]## $a
## [1] 6
##
## $a
## [1] 6
##
## $b
## [1] "fred"
##
## $b
## [1] "fred"
## $a
## [1] 6
##
## $a
## [1] 6
##
## $b
## [1] "fred"
##
## $b
## [1] "fred"00391_example_A.7_of_section_A.2.2.R
# example A.7 of section A.2.2
# (example A.7 of section A.2.2) : Starting with R and other tools : Starting with R : Primary R data types
# Title: R’s treatment of unexpected factor levels
factor('red', levels = c('red', 'orange'))## [1] red
## Levels: red orange
## [1] red
## Levels: red orange
factor('apple', levels = c('red', 'orange'))## [1] <NA>
## Levels: red orange
## [1] <NA>
## Levels: red orange00392_example_A.8_of_section_A.2.2.R
# example A.8 of section A.2.2
# (example A.8 of section A.2.2) : Starting with R and other tools : Starting with R : Primary R data types
# Title: Confirm lm() encodes new strings correctly
d <- data.frame(x=factor(c('a','b','c')),
y=c(1,2,3))
m <- lm(y~0+x,data=d) # Note: 1
print(predict(m,
newdata=data.frame(x='b'))[[1]]) # Note: 2 ## [1] 2
# [1] 2
print(predict(m,
newdata=data.frame(x=factor('b',levels=c('b'))))[[1]]) # Note: 3 ## [1] 2
# [1] 2
# Note 1:
# Build a data frame and linear model mapping
# a,b,c to 1,2,3.
# Note 2:
# Show that model gets correct prediction for
# b as a string.
# Note 3:
# Show that model gets correct prediction for
# b as a factor, encoded with a different number of
# levels. This shows that lm() is correctly treating
# factors as strings. 00393_informalexample_A.5_of_section_A.3.1.R
# informalexample A.5 of section A.3.1
# (informalexample A.5 of section A.3.1) : Starting with R and other tools : Using databases with R : Running database queries using a query generator
library("rquery")
raw_connection <- DBI::dbConnect(RPostgres::Postgres(),
host = 'localhost',
port = 5432,
user = 'johnmount',
password = '') # Note: 1
dbopts <- rq_connection_tests(raw_connection) # Note: 2
db <- rquery_db_info(
connection = raw_connection,
is_dbi = TRUE,
connection_options = dbopts)
data_handle <- rq_copy_to( # Note: 3
db,
'offers',
wrapr::build_frame(
"user_name" , "product" , "discount", "predicted_offer_affinity" |
"John" , "Pandemic Board Game" , 0.1 , 0.8596 |
"Nina" , "Pandemic Board Game" , 0.2 , 0.1336 |
"John" , "Dell XPS Laptop" , 0.1 , 0.2402 |
"Nina" , "Dell XPS Laptop" , 0.05 , 0.3179 |
"John" , "Capek's Tales from Two Pockets", 0.05 , 0.2439 |
"Nina" , "Capek's Tales from Two Pockets", 0.05 , 0.06909 |
"John" , "Pelikan M200 Fountain Pen" , 0.2 , 0.6706 |
"Nina" , "Pelikan M200 Fountain Pen" , 0.1 , 0.616 ),
temporary = TRUE,
overwrite = TRUE)
# Note 1:
# Use DBI to connect to a database. In this case it creates a new in-memory SQLite.
# Note 2:
# Build an rquery wrapper of the connection.
# Note 3:
# Copy some example data into the database. 00394_informalexample_A.6_of_section_A.3.1.R
# informalexample A.6 of section A.3.1
# (informalexample A.6 of section A.3.1) : Starting with R and other tools : Using databases with R : Running database queries using a query generator
data_handle %.>% # Note: 1
extend(.,
simple_rank = rank(), # Note: 2
partitionby = "user_name", # Note: 3
orderby = "predicted_offer_affinity", # Note: 4
reverse = "predicted_offer_affinity") %.>%
execute(db, .) %.>% # Note: 5
knitr::kable(.) # Note: 6 | user_name | product | discount | predicted_offer_affinity | simple_rank |
|---|---|---|---|---|
| John | Pandemic Board Game | 0.10 | 0.85960 | 1 |
| John | Pelikan M200 Fountain Pen | 0.20 | 0.67060 | 2 |
| John | Capek's Tales from Two Pockets | 0.05 | 0.24390 | 3 |
| John | Dell XPS Laptop | 0.10 | 0.24020 | 4 |
| Nina | Pelikan M200 Fountain Pen | 0.10 | 0.61600 | 1 |
| Nina | Dell XPS Laptop | 0.05 | 0.31790 | 2 |
| Nina | Pandemic Board Game | 0.20 | 0.13360 | 3 |
| Nina | Capek's Tales from Two Pockets | 0.05 | 0.06909 | 4 |
# |user_name |product | discount| predicted_offer_affinity| simple_rank|
# |:---------|:------------------------------|--------:|------------------------:|-----------:|
# |Nina |Pelikan M200 Fountain Pen | 0.10| 0.61600| 1|
# |Nina |Dell XPS Laptop | 0.05| 0.31790| 2|
# |Nina |Pandemic Board Game | 0.20| 0.13360| 3|
# |Nina |Capek's Tales from Two Pockets | 0.05| 0.06909| 4|
# |John |Pandemic Board Game | 0.10| 0.85960| 1|
# |John |Pelikan M200 Fountain Pen | 0.20| 0.67060| 2|
# |John |Capek's Tales from Two Pockets | 0.05| 0.24390| 3|
# |John |Dell XPS Laptop | 0.10| 0.24020| 4|
# Note 1:
# Pipe our data into the
# execute() method. Notice we are using the “wrapr dot
# pipe.”
# Note 2:
# We are going to calculate rank() or the order of the data rows.
# Note 3:
# The ranking will be re-calculated for each user (our window partition).
# Note 4:
# The window ordering that controls the rank is to be from predicted_offer_affinity, reversed (largest first).
# Note 5:
# Translate the operation plan into SQL, send it to the database for execution, and bring the results back to R.
# Note 6:
# Pretty print the results. 00395_informalexample_A.7_of_section_A.3.1.R
# informalexample A.7 of section A.3.1
# (informalexample A.7 of section A.3.1) : Starting with R and other tools : Using databases with R : Running database queries using a query generator
ops <- data_handle %.>% # Note: 1
extend(., # Note: 2
simple_rank = rank(),
partitionby = "user_name",
orderby = "predicted_offer_affinity",
reverse = "predicted_offer_affinity") %.>%
select_rows(., # Note: 3
simple_rank <= 2) %.>%
orderby(., c("user_name", "simple_rank")) # Note: 4
result_table <- materialize(db, ops) # Note: 5
DBI::dbReadTable(db$connection, result_table$table_name) %.>% # Note: 6
knitr::kable(.)| user_name | product | discount | predicted_offer_affinity | simple_rank |
|---|---|---|---|---|
| John | Pandemic Board Game | 0.10 | 0.8596 | 1 |
| John | Pelikan M200 Fountain Pen | 0.20 | 0.6706 | 2 |
| Nina | Pelikan M200 Fountain Pen | 0.10 | 0.6160 | 1 |
| Nina | Dell XPS Laptop | 0.05 | 0.3179 | 2 |
# |user_name |product | discount| predicted_offer_affinity| simple_rank|
# |:---------|:-------------------------|--------:|------------------------:|-----------:|
# |John |Pandemic Board Game | 0.10| 0.8596| 1|
# |John |Pelikan M200 Fountain Pen | 0.20| 0.6706| 2|
# |Nina |Pelikan M200 Fountain Pen | 0.10| 0.6160| 1|
# |Nina |Dell XPS Laptop | 0.05| 0.3179| 2|
# Note 1:
# Define our sequence of operations
# Note 2:
# Mark each row with its simple per-user rank
# Note 3:
# Select the two rows with highest rank for each
# user
# Note 4:
# Order the rows by user and product rank
# Note 5:
# Run the result in the database, instantiating a new
# result table
# Note 6:
# Copy the result back to R and pretty print it. 00396_informalexample_A.8_of_section_A.3.1.R
# informalexample A.8 of section A.3.1
# (informalexample A.8 of section A.3.1) : Starting with R and other tools : Using databases with R : Running database queries using a query generator
ops %.>%
to_sql(., db) %.>%
cat(.)## SELECT * FROM (
## SELECT * FROM (
## SELECT
## "user_name",
## "product",
## "discount",
## "predicted_offer_affinity",
## rank ( ) OVER ( PARTITION BY "user_name" ORDER BY "predicted_offer_affinity" DESC ) AS "simple_rank"
## FROM (
## SELECT
## "user_name",
## "product",
## "discount",
## "predicted_offer_affinity"
## FROM
## "offers"
## ) tsql_96970161089718632318_0000000000
## ) tsql_96970161089718632318_0000000001
## WHERE "simple_rank" <= 2
## ) tsql_96970161089718632318_0000000002 ORDER BY "user_name", "simple_rank"
## SELECT * FROM (
## SELECT * FROM (
## SELECT
## "user_name",
## "product",
## "discount",
## "predicted_offer_affinity",
## rank ( ) OVER ( PARTITION BY "user_name" ORDER BY "predicted_offer_affinity" DESC ) AS "simple_rank"
## FROM (
## SELECT
## "user_name",
## "product",
## "discount",
## "predicted_offer_affinity"
## FROM
## "offers"
## ) tsql_17135820721167795865_0000000000
## ) tsql_17135820721167795865_0000000001
## WHERE "simple_rank" <= 2
## ) tsql_17135820721167795865_0000000002 ORDER BY "user_name", "simple_rank"