x0A_Starting_with_R_and_other_tools.Rmd

---
output: github_document
---



00383_example_A.1_of_section_A.2.R


```{r 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
1/2
## [1] 0.5
'Joe'
## [1] "Joe"
"Joe"
## [1] "Joe"
"Joe"=='Joe'
## [1] TRUE
c()
## NULL
is.null(c())
## [1] TRUE
is.null(5)
## [1] FALSE
c(1)
## [1] 1
c(1, 2)
## [1] 1 2
c("Apple", 'Orange')
## [1] "Apple"  "Orange"
length(c(1, 2))
## [1] 2
vec <- c(1, 2)
vec
## [1] 1 2



```




00384_informalexample_A.3_of_section_A.2.1.R


```{r 00384_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
print(x)
## [1] 2



```




00385_example_A.2_of_section_A.2.1.R


```{r 00385_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

divide(2, 1)
## [1] 2

divide(denominator = 2, numerator = 1)
## [1] 0.5

divide(denominator <- 2, numerator <- 1)  # wrong symbol <-, yields 2, a wrong answer!
## [1] 2



```




00386_example_A.3_of_section_A.2.1.R


```{r 00386_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

bad <- function() { x <<- 5}
bad()
print(x)
## [1] 5



```




00387_example_A.4_of_section_A.2.1.R


```{r 00387_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

c(TRUE, TRUE, FALSE, FALSE) & c(TRUE, FALSE, TRUE, FALSE)
## [1]  TRUE FALSE FALSE FALSE

c(TRUE, TRUE, FALSE, FALSE) | c(TRUE, FALSE, TRUE, FALSE)
## [1]  TRUE  TRUE  TRUE FALSE



```




00388_example_A.5_of_section_A.2.1.R


```{r 00388_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)

a[[1]] <- 5            	# Note: 1 

print(a)

print(b)               	# Note: 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


```{r 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



```




00390_example_A.6_of_section_A.2.2.R


```{r 00390_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"
x$a
## [1] 6
x$b
## [1] "fred"
x[['a']]
## $a
## [1] 6

x[c('a', 'a', 'b', 'b')]
## $a
## [1] 6
##
## $a
## [1] 6
##
## $b
## [1] "fred"
##
## $b
## [1] "fred"



```




00391_example_A.7_of_section_A.2.2.R


```{r 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

factor('apple', levels = c('red', 'orange'))
## [1] <NA>
## Levels: red orange



```




00392_example_A.8_of_section_A.2.2.R


```{r 00392_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
print(predict(m,
   newdata=data.frame(x=factor('b',levels=c('b'))))[[1]])        	# Note: 3 
# [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


```{r 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


```{r 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|
# |:---------|:------------------------------|--------:|------------------------:|-----------:|
# |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


```{r 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|

# 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


```{r 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_17135820721167795865_0000000000
##  ) tsql_17135820721167795865_0000000001
##  WHERE "simple_rank" <= 2
## ) tsql_17135820721167795865_0000000002 ORDER BY "user_name", "simple_rank"



```