A single loop is not enough. A collection of hello world control structures




As the post on “hello world” functions has been quite appreciated by the R community, here follows the second round of functions for wannabe R programmer.

# If else statement:
# See the code syntax below for if else statement
x=10
if(x>1){
print(“x is greater than 1”)
}else{
print(“x is less than 1”)
}

# See the code below for nested if else statement

x=10
if(x>1 & x<7){
print(“x is between 1 and 7”)} else if(x>8 & x< 15){
print(“x is between 8 and 15”)
}


# For loops:
# Below code shows for loop implementation
x = c(1,2,3,4,5)
for(i in 1:5){
print(x[i])
}


# While loop :
# Below code shows while loop in R
x = 2.987
while(x <= 4.987) {
x = x + 0.987
print(c(x,x-2,x-1))
}


# Repeat Loop:
# The repeat loop is an infinite loop and used in association with a break statement.

# Below code shows repeat loop:
a = 1
repeat{
print(a)
a = a+1
if (a > 4) {
break
}
}

# Break statement:
# A break statement is used in a loop to stop the iterations and flow the control outside of the loop.

#Below code shows break statement:
x = 1:10
for (i in x){
if (i == 6){
break
}
print(i)
}

# Next statement:
# Next statement enables to skip the current iteration of a loop without terminating it.

#Below code shows next statement
x = 1: 4
for (i in x) {
if (i == 2){
next
}
print(i)
}


# function

words = c(“R”, “datascience”, “machinelearning”,”algorithms”,”AI”)
words.names = function(x) {
for(name in x){
print(name)
}
}

words.names(words) # Calling the function


# extract the elements above the main diagonal of a (square) matrix
# example of a correlation matrix

cor_matrix <- matrix(c(1, -0.25, 0.89, -0.25, 1, -0.54, 0.89, -0.54, 1), 3,3)
rownames(cor_matrix) <- c(“A”,”B”,”C”)
colnames(cor_matrix) <- c(“A”,”B”,”C”)
cor_matrix

rho <- list()
name <- colnames(cor_matrix)
var1 <- list()
var2 <- list()
for (i in 1:ncol(cor_matrix)){
for (j in 1:ncol(cor_matrix)){
if (i != j & i<j){
rho <- c(rho,cor_matrix[i,j])
var1 <- c(var1, name[i])
var2 <- c(var2, name[j])
}
}
}

d <- data.frame(var1=as.character(var1), var2=as.character(var2), rho=as.numeric(rho))
d

var1 var2 rho
1 A B -0.25
2 A C 0.89
3 B C -0.54


As programming is the best way to learn and think, have fun programming awesome functions!

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R as learning tool: solving integrals




Integrals are so easy only math teachers could make them difficult.When I was in high school I really disliked math and, with hindsight, I would say it was just because of the the prehistoric teaching tools (when I saw this video I thought I’m not alone). I strongly believe that interaction CAUSES learning (I’m using “causes” here on purpose being quite aware of the difference between correlation and causation), practice should come before theory and imagination is not a skill you, as a teacher, could assume in your students. Here follows a short and simple practical explanation of integrals. The only math-thing I will write here is the following: f(x) = x + 7. From now on everything will be coded in R. So, first of all, what is a function? Instead of using the complex math philosophy let’s just look at it with a programming eye: it is a tool that takes something in input and returns something else as output. For example, if we use the previous tool with 2 as an input we get a 9. Easy peasy. Let’s look at the code:
# here we create the tool (called "f")
# it just takes some inputs and add it to 7
f <- function(x){x+7}

# if we apply it to 2 it returns a 9
f(2)
9

Then the second question comes by itself. What is an integral? Even simpler, it is just the sum of this tool applied to many inputs in a range. Quite complicated, let’s make it simpler with code: 
# first we create the range of inputs
# basically x values go from 4 to 6 
# with a very very small step (0.01)
# seq stands for sequence(start, end, step)


x <- seq(4, 6, 0.01) 
x
4.00 4.01 4.02 4.03 4.04 4.05 4.06 4.07...

x[1]
4

x[2]
4.01
As you see, x has many values and each of them is indexed so it’s easy to find, e.g. the first element is 4 (x[1]). Now that we have many x values (201) within the interval from 4 to 6, we compute the integral.
# since we said that the integral is 
# just a sum, let's call it IntSum and 
# set it to the start value of 0
# in this way it will work as an accumulator
IntSum = 0
Differently from the theory in which the calculation of the integral produces a new non-sense formula (just kidding, but this seems to be what math teachers are supposed to explain), the integral does produce an output, i.e. a number. We find this number by summing the output of each input value we get from the tool (e.g. 4+7, 4.01+7, 4.02+7, etc) multiplied by the step between one value and the following (e.g. 4.01-4, 4.02-4.01, 4.03-4.02, etc). Let’s clarify this, look down here:
# for each value of x 
for(i in 2:201){
    
    # we do a very simple thing:
    # we cumulate with a sum
    # the output value of the function f 
    # multiplied by each steps difference
    
    IntSum = IntSum + f(x[i])*(x[i]-x[i-1])
    
    
    # So for example,  
    # with the first and second x values the numbers will be:
    #0.1101 = 0 + (4.01 + 7)*(4.01 - 4)
    
    # with the second and third:
    #0.2203 = 0.1101 + (4.02 + 7)*(4.02 - 4.01)
    
    # with the third and fourth:
    #0.3306 = 0.2203 + (4.03 + 7)*(4.03 - 4.02)
    
    # and so on... with the sum (integral) growing and growing
    # up until the last value
}

IntSum
24.01
Done! We have the integral but let’s have a look to the visualization of this because it can be represented and made crystal clear. Let’s add a short line of code to serve the purpose of saving the single number added to the sum each time. The reason why we decide to call it “bin” instead of, for example, “many_sum” will be clear in a moment.
# we need to store 201 calculation and we
# simply do what we did for IntSum but 201 times
bin = rep(0, 201)
bin
0 0 0 0 0 0 0 0 0 0 0 0 ...
Basically, we created a sort of memory to host each of the calculation as you see down here:
for (i in 2:201){
    
    # the sum as earlier
    IntSum = IntSum + f(x[i])*(x[i]-x[i-1])
    
    # overwrite each zero with each number
    bin[i] = f(x[i])*(x[i]-x[i-1])
}

IntSum
24.01

bin
0.0000 0.1101 0.1102 0.1103 0.1104 0.1105 ..

sum(bin)
24.01
Now if you look at the plot below you get the whole story: each bin is a tiny bar with a very small area and is the smallest part of the integral (i.e. the sum of all the bins).
# plotting them all
barplot(bin, names.arg=x)
This tells you a lot about the purpose of integral and the possibility of calculating areas of curvy surfaces. To have an idea of this just change the function f with, let’s say, sin(x) or log(x). What is happening? And what if you increase/decrease the number of bins? Have fun replicating the code changing some numbers and functions. Integrals should be clearer in the end. That’s all folks! #R #rstats #maRche #Rbloggers 

“Print hello”​ is not enough. A collection of Hello world functions.


I guess I wrote my R “hello world!” function 7 or 8 years ago while approaching R for the first time. And it is too little to illustrate the basic syntax of a programming language for a working program to a wannabe R programmer. Thus, here follows a collection of basic functions that may help a bit more than the famed piece of code.

######################################################
############### Hello world functions ################
######################################################
##################################
# General info
fun <- function( arguments ) { body }


##################################
foo.add <- function(x,y){
  x+y
}

foo.add(7, 5)

----------------------------------

foo.above <- function(x){
  x[x>10]
}

foo.above(1:100)

----------------------------------

foo.above_n <- function(x,n){
  x[x>n]
}

foo.above_n(1:20, 12)

----------------------------------

foo = seq(1, 100, by=2)
foo.squared = NULL

for (i in 1:50 ) {
  foo.squared[i] = foo[i]^2
}

foo.squared

----------------------------------

a <- c(1,6,7,8,8,9,2)

s <- 0
for (i in 1:length(a)){
  s <- s + a[[i]]
}
s

----------------------------------

a <- c(1,6,7,8,8,9,2,100)

s <- 0
i <- 1
while (i <= length(a)){
  s <- s + a[[i]]
  i <- i+1
}
s

----------------------------------

FunSum <- function(a){
  s <- 0
  i <- 1
  while (i <= length(a)){
    s <- s + a[[i]]
    i <- i+1
  }
  print(s)
}

FunSum(a)

-----------------------------------

SumInt <- function(n){
  s <- 0
  for (i in 1:n){
    s <- s + i
  }
  print(s)  
}

SumInt(14)

-----------------------------------
# find the maximum
# right to left assignment
x <- c(3, 9, 7, 2)

# trick: it is necessary to use a temporary variable to allow the comparison by pairs of
# each number of the sequence, i.e. the process of comparison is incremental: each time
# a bigger number compared to the previous in the sequence is found, it is assigned as the
# temporary maximum
# Since the process has to start somewhere, the first (temporary) maximum is assigned to be
# the first number of the sequence

max <- x[1]
for(i in x){
  tmpmax = i
  if(tmpmax > max){
    max = tmpmax
  }
}

max

x <- c(-20, -14, 6, 2)
x <- c(-2, -24, -14, -7)

min <- x[1]
for(i in x){
  tmpmin = i
  if(tmpmin < min){
    min = tmpmin
  }
}

min

----------------------------------
# n is the nth Fibonacci number
# temp is the temporary variable

Fibonacci <- function(n){
  a <- 0
  b <- 1
  for(i in 1:n){
    temp <- b
    b <- a
    a <- a + temp
  }
  return(a)
}

Fibonacci(13)

----------------------------------
# R available factorial function
factorial(5)

# recursive function: ff
ff <- function(x) {
  if(x<=0) {
    return(1)
  } else {
    return(x*ff(x-1)) # function uses the fact it knows its own name to call itself
  }
}
ff(5)

----------------------------------

say_hello_to <- function(name){
  paste("Hello", name)
} 
say_hello_to("Roberto")

----------------------------------

foo.colmean <- function(y){
  nc <- ncol(y)
  means <- numeric(nc)
  for(i in 1:nc){
    means[i] <- mean(y[,i])
  }
  means
}

foo.colmean(airquality)

----------------------------------

foo.colmean <- function(y, removeNA=FALSE){
  nc <- ncol(y)
  means <- numeric(nc)
  for(i in 1:nc){
    means[i] <- mean(y[,i], na.rm=removeNA)
  }
  means
}

foo.colmean(airquality, TRUE)

----------------------------------

foo.contingency <- function(x,y){
  nc <- ncol(x)
  out <- list() 
  for (i in 1:nc){
    out[[i]] <- table(y, x[,i]) 
  }
  names(out) <- names(x)
  out
}

set.seed(123)
v1 <- sample(c(rep("a", 5), rep("b", 15), rep("c", 20)))
v2 <- sample(c(rep("d", 15), rep("e", 20), rep("f", 5)))
v3 <- sample(c(rep("g", 10), rep("h", 10), rep("k", 20)))

data <- data.frame(v1, v2, v3)

foo.contingency(data,v3)
That's all folks! #R #rstats #maRche #Rbloggers This post is also shared in LinkedIn and www.r-bloggers.com

Web data acquisition: from database to dataframe for data analysis and visualization (Part 4)

The previous post described how the deeply nested JSON data on fligths were parsed and stored in an R-friendly database structure. However, looking into the data, the information is not yet ready for statistical analysis and visualization and some further processing is necessary before extracting insights and producing nice plots. In the parsed batch, it is clearly visible the redundant structure of the data with the flight id repeted for each segment of each flight. This is also confirmed with the following simple check as the rows of the dataframe are more than the unique counts of the elements in the id column.
dim(data_items)
[1] 397  15

length(unique(data_items$id))
201

# real time changes of data could produce different results
This implies that the information of each segment of each flight has to be aggregated and merged in a dataset as single observations of a statistical analysis between, for example, price and distance. First, a unique primary key for each observation has to be used as reference variable to uniquely identify each element of the dataset.
library(plyr) # sql like functions
library(readr) # parse numbers from strings
 
data_items <- data.frame(data_items)
 
# id (primary key)
data <- data.frame(unique(data_items$id))
colnames(data) <- c('id')
 
# n° of segment
n_segment <- aggregate(data_items['segment.id'], by=data_items['id'], length)
data <- join(data, n_segment, by='id', type='left', match='first') # sql left join
# mileage
mileage <- aggregate(data_items['segment.leg.mileage'], by=data_items['id'], sum)
data <- join(data, mileage, by='id', type='left', match='first') # sql left join
# price
price <- data.frame('id'=data_items$id, 'price'=parse_number(data_items$saleTotal))
data <- join(data, price, by='id', type='left', match='first') # sql left join
# dataframe
colnames(data) <- c('id','segment', 'mileage', 'price')
head(data)

The aggregation of mileage and price using the unique primary key allows to set up a dataframe ready for statistical analysis and data visualization. Current data tells us that there is a maximum of three segments in the connection between FCO and LHR with a minimum price of around EUR 122 and a median around EUR 600.
# descriptive statistics
summary(data)
 
 
# histogram price & distance
g1 <- ggplot(data, aes(x=price)) + 
  geom_histogram(bins = 50) +  
  ylab("Distribution of the Price (EUR)") +
  xlab("Price (EUR)") 
 
g2 <- ggplot(data, aes(x=mileage)) + 
  geom_histogram(bins = 50) +  
  ylab("Distribution of the Distance") +
  xlab("Distance (miles)")
 
grid.arrange(g1, g2)
# price - distance relationship
s0 <- ggplot(data = data, aes(x = mileage, y = price)) +
    geom_smooth(method = "lm", se=FALSE, color="black") +
    geom_point() + labs(x = "Distance in miles", y = "Price (EUR)")
s0 <- ggMarginal(s0, type = "histogram", binwidth = 30)
s0

Of course, plenty of other analysis and graphical representations using flights features are possible given the large set of variables available in QPX Express API and the availability of data in real time.
To conclude the 4-step (flight) trip from data acquisition to data analysis, let's recap the most important concepts described in each of the post: 1) Client-Server connection 2) POST request in R 3) Data parsing and structuring 4) Data analysis and visualization
That's all folks! #R #rstats #maRche #json #curl #qpxexpress #Rbloggers This post is also shared in www.r-bloggers.com

Web data acquisition: parsing json objects with tidyjson (Part 3)

The collection of example flight data in json format available in part 2, described the libraries and the structure of the POST request necessary to collect data in a json object. Despite the process generated and transferred locally a proper response, the data collected were neither in a suitable structure for data analysis nor immediately readable. They appears as just a long string of information nested and separated according to the JavaScript object notation syntax. Thus, to visualize the deeply nested json object and make it human readable and understandable for further processing, the json content could be copied and pasted in a common online parser. The tool allows to select each node of the tree and observe the data structure up to the variables and data of interest for the statistical analysis. The bulk of the relevant information for the purpose of the analysis on flight prices are hidden in the tripOption node as shown in the following figure (only 50 flight solutions were requested). However, looking deeply into the object, several other elements are provided as the distance in mile, the segment, the duration, the carrier, etc. The R parser to transform the json structure in a usable dataframe requires the dplyr library for using the pipe operator (%>%) to streamline the code and make the parser more readable. Nevertheless, the library actually wrangling through the lines is tidyjson and its powerful functions:
  • enter_object: enters and dives into a data object;
  • gather_array: stacks a JSON array;
  • spread_values: creates new columns from values assigning specific type (e.g. jstring, jnumber).
library(dplyr) # for pipe operator %>% and other dplyr functions library(tidyjson) # https://cran.r-project.org/web/packages/tidyjson/vignettes/introduction-to-tidyjson.html data_items <- datajson %>% spread_values(kind = jstring("kind")) %>% spread_values(trips.kind = jstring("trips","kind")) %>% spread_values(trips.rid = jstring("trips","requestId")) %>% enter_object("trips","tripOption") %>% gather_array %>% spread_values( id = jstring("id"), saleTotal = jstring("saleTotal")) %>% enter_object("slice") %>% gather_array %>% spread_values(slice.kind = jstring("kind")) %>% spread_values(slice.duration = jstring("duration")) %>% enter_object("segment") %>% gather_array %>% spread_values( segment.kind = jstring("kind"), segment.duration = jnumber("duration"), segment.id = jstring("id"), segment.cabin = jstring("cabin")) %>% enter_object("leg") %>% gather_array %>% spread_values( segment.leg.aircraft = jstring("aircraft"), segment.leg.origin = jstring("origin"), segment.leg.destination = jstring("destination"), segment.leg.mileage = jnumber("mileage")) %>% select(kind, trips.kind, trips.rid, saleTotal,id, slice.kind, slice.duration, segment.kind, segment.duration, segment.id, segment.cabin, segment.leg.aircraft, segment.leg.origin, segment.leg.destination, segment.leg.mileage) head(data_items) kind trips.kind trips.rid saleTotal 1 qpxExpress#tripsSearch qpxexpress#tripOptions UnxCOx4nKIcIOpRiG0QBOe EUR178.38 2 qpxExpress#tripsSearch qpxexpress#tripOptions UnxCOx4nKIcIOpRiG0QBOe EUR178.38 3 qpxExpress#tripsSearch qpxexpress#tripOptions UnxCOx4nKIcIOpRiG0QBOe EUR235.20 4 qpxExpress#tripsSearch qpxexpress#tripOptions UnxCOx4nKIcIOpRiG0QBOe EUR235.20 5 qpxExpress#tripsSearch qpxexpress#tripOptions UnxCOx4nKIcIOpRiG0QBOe EUR248.60 6 qpxExpress#tripsSearch qpxexpress#tripOptions UnxCOx4nKIcIOpRiG0QBOe EUR248.60 id slice.kind slice.duration 1 ftm7QA6APQTQ4YVjeHrxLI006 qpxexpress#sliceInfo 510 2 ftm7QA6APQTQ4YVjeHrxLI006 qpxexpress#sliceInfo 510 3 ftm7QA6APQTQ4YVjeHrxLI009 qpxexpress#sliceInfo 490 4 ftm7QA6APQTQ4YVjeHrxLI009 qpxexpress#sliceInfo 490 5 ftm7QA6APQTQ4YVjeHrxLI007 qpxexpress#sliceInfo 355 6 ftm7QA6APQTQ4YVjeHrxLI007 qpxexpress#sliceInfo 355 segment.kind segment.duration segment.id segment.cabin 1 qpxexpress#segmentInfo 160 GixYrGFgbbe34NsI COACH 2 qpxexpress#segmentInfo 235 Gj1XVe-oYbTCLT5V COACH 3 qpxexpress#segmentInfo 190 Grt369Z0shJhZOUX COACH 4 qpxexpress#segmentInfo 155 GRvrptyoeTfrSqg8 COACH 5 qpxexpress#segmentInfo 100 GXzd3e5z7g-5CCjJ COACH 6 qpxexpress#segmentInfo 105 G8axcks1R8zJWKrN COACH segment.leg.aircraft segment.leg.origin segment.leg.destination segment.leg.mileage 1 320 FCO IST 859 2 77W IST LHR 1561 3 73H FCO ARN 1256 4 73G ARN LHR 908 5 319 FCO STR 497 6 319 STR LHR 469 Data are now in an R-friendly structure despite not yet ready for analysis. As can be observed from the first rows, each record has information on a single segment of the flight selected. A further step of aggregation using some SQL is needed in order to end up with a dataframe of flights data suitable for statistical analysis. Next up, the aggregation, some data analysis and data visualization to complete the journey through the web data acquisition using R. #R #rstats #maRche #json #curl #tidyjson #Rbloggers This post is also shared in www.r-bloggers.com and LinkedIn

Web data acquisition: the structure of RCurl request (Part 2)

The acquisition of data in json structure presented in part 1 clearly showed the functioning of the client-server connection and the possibility to collect the data of interest. However, the json output appeares as a set of raw data in a json string that needs to be structured and stored in a suitable form for data processing and statistical analysis.

For this reason, it makes sense to develop the entire process using #R in order to have the data directly queried, collected, parsed, structured and made usable in a unique environment. Of course, this will be the one used in the process “last mile”, i.e. data analysis. The curl library adopted in the command line process described in the previous post has its alter ego in the RCurl library. Together with jsonlite for ‘R-JSON translation’ these are the necessay packages for the development of the request as presented in the following code. [splus] # before loading the libraries rememeber to install them – install.packages(‘library here’) library(RCurl) library(jsonlite) # save the url of the request in an object (same as -X POST in the curl request) url <- ‘https://www.googleapis.com/qpxExpress/v1/trips/search?key={SERVER_KEY}&alt=json’ # headers (same as -H) headers <- list(‘Accept’ = ‘application/json’, ‘Content-Type’ = ‘application/json’, ‘charset’ = ‘UTF-8’) # R structure of the input for the request (same as -d + JSON) x = list( request = list( slice = list( list(origin = ‘FCO’, destination = ‘LHR’, date = ‘2017-06-30’)), passengers = list(adultCount = 1, infantInLapCount = 0, infantInSeatCount = 0, childCount = 0, seniorCount = 0), solutions = 500, refundable = F)) # url, headers and x are the parameters to be used in R functions to send the request # and save the output data in the datajson object # postForm is the RCurl function to send the request using the POST method # toJSON is the jsonlite function to convert the R structure of the request in JSON input datajson <- postForm(url, .opts=list(postfields=toJSON(x), httpheader=headers)) datajson [/splus] After few seconds from the POST request necessary to send the request and collect the response, all the information related to the flights with origin FCO (Fiumicino – Rome) and destination LHR (London Heathrow) will be hosted in the datajson object, similarly to the command line procedure. The json string holds and hides all the observations and variables of interest for the statistical analysis inlcuding the most important, i.e. the flight prices. The next post will explain how to parse the json object and structure the information in a suitable dataframe for analysis using the powerful library #tidyjson. #R #rstats #maRche #json #curl #qpxexpress #Rbloggers This post is also shared in www.r-bloggers.com and LinkedIn

Web data acquisition: understanding RCurl from the command line (Part 1)

After the short presentation here, let’s start using R seriously, i.e. with every day data.

Being a frequent flyer, I often search the web to book flights and organise my trip. Being a data analyst, it’s natural to look at price data with interest, especially the most convenient, most expensive and “average” flights. Being a PhD student in software engineering the (study) programme includes some on the structure of applications, client-server data flows and the web languages running the processes. Nothing like Google flight API QPX express would have been better to combine all these three. I discovered it by chance delving into the first results of my flight searches on Google and it is perfect for some web scraping and analysis. As described in the manual, QPX Express is part of the Google suite of publicly-available REST APIs, designed for easy integration, fast response and high reliability. QPX Express searches combinations of airline schedules, fares and seat availability and includes search parameters such as origin, destination, dates and travel features such as maximum price and earliest departure. The API returns up to 500 itineraries for each request, including all data elements needed for customer display and a booking process. Moreover, it also allows to clearly see the structure of the data in #json both in input (JSON request) and output (Unformatted Response). For a simple test, use the Demo with a Google account. Before start using the QPX Express API, you need to get a Google API key. To get a key, follow these steps: 1. Go to the Google Developers Console / 2. Create or select a project / 3. Click Continue to enable the API and any related services / 4. On the Credentials page, get an API key. With a single API key, it is possible to make 50 free requests per day (then the cost per single query is $ 0.035.) The first thing I tried before writing the code in #R for data acquisition, manipulation and analysis was to test the client-server connection through a command line instruction using the cURL library (you can download here – add the certificate in the same directory otherwise the server will deny you access). Let’s play a bit with the command line (Windows 10) before running the request:
# open the command prompt and the default folder should be system32 of the OS
C:\WINDOWS\system32>

# set a convenient current directory (cd) for storing data - \ (enter) and / (exit) the path
cd C:\Users\Roberto\Desktop

# your new position should be in 
C:\Users\Roberto\Desktop>


# I prefer using a partition of the HD dedicated to data instead of a messy desktop
cd /D D:\

# create a new working directory (use "" for separated names otherwise two folders will be created)
mkdir "flight data"
# set the current directory to the newly created folder
cd "flight data"
# make sub-folders of the cd
mkdir "curl test 1"
mkdir "curl test 2"
# check the cd, its features and its new sub-folders
cd

dir

# finally, check the proper installation of curl using help
# the list of the function parameters should appear with some description

curl -help
After the creation of a repository, cURL can be used for testing the connection to QPX express and the collection of data in json using cURL parameters for HTTP requests and the structure of the JSON request the API proposes:
# Parameters
# -X POST: parameter for POST request -> see: https://en.wikipedia.org/wiki/POST_(HTTP)
# -H: Pass custom header LINE to server (H) -> see: https://en.wikipedia.org/wiki/List_of_HTTP_header_fields
# -d: HTTP POST data, i.e. the input data in JSON structure 
# >: "send to" destination file

curl -X POST "https://www.googleapis.com/qpxExpress/v1/trips/search?key={SERVER_KEY}&alt=json" -H "Content-Type: application/json; charset=UTF-8" -d "{ \"request\": { \"slice\": [ { \"origin\": \"{ORIGIN_CODE}\", \"destination\": \"{DESTINATION_CODE}\", \"date\": \"{DATE_YYYY-MM-DD}\" } ], \"passengers\": { \"adultCount\": 1, \"infantInLapCount\": 0, \"infantInSeatCount\": 0, \"childCount\": 0, \"seniorCount\": 0 }, \"solutions\": 500, \"refundable\": false }}" > "{RESULT_FILE}.json"
# Mac syntax
curl -X POST 'https://www.googleapis.com/qpxExpress/v1/trips/search?key={SERVER_KEY}&alt=json' -H 'Content-Type: application/json; charset=UTF-8' -d '{ "request": { "slice": [ { "origin": "{ORIGIN_CODE}", "destination": "{DESTINATION_CODE}", "date": "{DATE_YYYY-MM-DD}" } ], "passengers": { "adultCount": 1, "infantInLapCount": 0, "infantInSeatCount": 0, "childCount": 0, "seniorCount": 0 }, "solutions": 500, "refundable": false }}'> '{RESULT_FILE}.json'
After a second, the prompt should show the downloading process stats and a new json file should appear in the cd “flight data” close to the previously created subfolders. The connection works! Next up – a post with code to translate the same commands in R – to manipulate the data comfortably after acquisition from the API. #R #rstats #maRche #json #curl #qpxexpress #Rbloggers This post is also shared in www.r-bloggers.com and LinkedIn