Cormac O'Keeffe ocaoimh

## index.html
<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <meta http-equiv="X-UA-Compatible" content="ie=edge" />
    <title>Spotify Clone</title>
    <link rel="stylesheet" href="styles/style.css" />
  </head>

## try_except_else2.py
try:
    request = requests.get('put your link here')
    request.raise_for_status()  # returns an HTTPError if the response is not OK
    print("All good! Response code is", request.status_code)
except requests.exceptions.HTTPError as err:
    if request.status_code == 404:
        print("404: Oops, sorry we can't find that page!")
    else:
        print("The error code is", err.args[0]) # look up the 1st argument from HTTPError

## try_except.py
from requests import codes # the code names from requests
from http.client import responses #it lets you look up the response

try:
    r = requests.get('https://www.abc.net.au/triplej/hottest100/22/1-100', timeout=1)
    r.raise_for_status() # returns an HTTPError object if an error has occurred during the process
except requests.exceptions.HTTPError as errh: # explains the errror
    print("HTTP Error")
    print(errh.args[0]) # look up the 1st argument from HTTPError
else:

## putting_boxplots_inagrid.py
fig, axes = plt.subplots(5, 3, sharex=True, figsize=(18,40)) # 5 rows  x 3 columns

fig.subplots_adjust(hspace=0.1, wspace=.5) # ensure that the plots do not overlap

fig.suptitle('Insurance claims')

sns.boxplot(ax=axes[0, 0], data=data, x="total_claim_amount", y="state")
sns.boxplot(ax=axes[0, 1], data=data, x="total_claim_amount", y="response")
sns.boxplot(ax=axes[0, 2], data=data, x="total_claim_amount", y="coverage")
sns.boxplot(ax=axes[1, 0], data=data, x="total_claim_amount", y="education")

## rename-conditional.py
#Create a new dataframe with language ids and language names.
#Hindi, Bengali, Marathi, Telugu, Tamil, Gujarati, Urdu
#Corresponding to these language ids: 171,  330,  345,   49,  206,  260,  161

conditions = [
    (data2['LanguageID'] ==  171),
    (data2['LanguageID'] == 330),
    (data2['LanguageID'] == 345) ,
    (data2['LanguageID'] == 49),
    (data2['LanguageID'] == 206),

## likert calc + plot
# Create likert list object
LXP.data.items.likert <- likert(LXP.data.items)

# Summarize the data
summary(LXP.data.items.likert)

# Plot the data
plot(LXP.data.items.likert)

# Plot the data with colours to match our visual identity

## organising levels
# Remove full stops and replace with them with blanks
names(LXP.data.items) <- gsub("\\.", " ", names(LXP.data.items))

#ordering levels
#Q1
LXP.data.items$`Access my timetable`<- factor(LXP.data.items$`Access my timetable`, levels = c("Very unimportant","Quite unimportant", "Neither important nor unimportant","Important","Very important"), ordered = T)

#Q2
LXP.data.items$`Particpate in collaborative activities  forums  peer assessment  group work ` <- factor(LXP.data.items$`Particpate in collaborative activities  forums  peer assessment  group work ` , levels = c("Very unimportant","Quite unimportant", "Neither important nor unimportant","Important","Very important"), ordered = T)

## likert_libraries_data
# Import libraries
library(plyr)
library(dplyr)
library(likert)
library(grid)
library(ggplot2)

#Import data. You'll need to change the path here and point it to where your data are.
LXP.data <- read_excel("LMSdummyDataShort_en.xlsx")

## merge_excel_files.R
#this cleans up your global environment (optional)
rm(list = ls())
# Version notes
## Usung  data.table_1.12.8 and readxl_1.3.1, R version 3.6.1 (2019-07-05)


library(readxl) #this allows you import and extract data from your Excel workbooks
library(data.table) #this library is the most crucial one and applies the functions that list and recombine the data
library(xlsx) # this is optional. It's a tricky library that can break if you don't have the Java Development kit installed
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8" />
	<meta name="viewport" content="width=device-width, initial-scale=1.0" />
	<meta http-equiv="X-UA-Compatible" content="ie=edge" />
	<title>Spotify Clone</title>
	<link rel="stylesheet" href="styles/style.css" />
	</head>
	try:
	request = requests.get('put your link here')
	request.raise_for_status() # returns an HTTPError if the response is not OK
	print("All good! Response code is", request.status_code)
	except requests.exceptions.HTTPError as err:
	if request.status_code == 404:
	print("404: Oops, sorry we can't find that page!")
	else:
	print("The error code is", err.args[0]) # look up the 1st argument from HTTPError
	from requests import codes # the code names from requests
	from http.client import responses #it lets you look up the response

	try:
	r = requests.get('https://www.abc.net.au/triplej/hottest100/22/1-100', timeout=1)
	r.raise_for_status() # returns an HTTPError object if an error has occurred during the process
	except requests.exceptions.HTTPError as errh: # explains the errror
	print("HTTP Error")
	print(errh.args[0]) # look up the 1st argument from HTTPError
	else:
	fig, axes = plt.subplots(5, 3, sharex=True, figsize=(18,40)) # 5 rows x 3 columns

	fig.subplots_adjust(hspace=0.1, wspace=.5) # ensure that the plots do not overlap

	fig.suptitle('Insurance claims')

	sns.boxplot(ax=axes[0, 0], data=data, x="total_claim_amount", y="state")
	sns.boxplot(ax=axes[0, 1], data=data, x="total_claim_amount", y="response")
	sns.boxplot(ax=axes[0, 2], data=data, x="total_claim_amount", y="coverage")
	sns.boxplot(ax=axes[1, 0], data=data, x="total_claim_amount", y="education")
	#Create a new dataframe with language ids and language names.
	#Hindi, Bengali, Marathi, Telugu, Tamil, Gujarati, Urdu
	#Corresponding to these language ids: 171, 330, 345, 49, 206, 260, 161

	conditions = [
	(data2['LanguageID'] == 171),
	(data2['LanguageID'] == 330),
	(data2['LanguageID'] == 345) ,
	(data2['LanguageID'] == 49),
	(data2['LanguageID'] == 206),
	# Create likert list object
	LXP.data.items.likert <- likert(LXP.data.items)

	# Summarize the data
	summary(LXP.data.items.likert)

	# Plot the data
	plot(LXP.data.items.likert)

	# Plot the data with colours to match our visual identity
	# Remove full stops and replace with them with blanks
	names(LXP.data.items) <- gsub("\\.", " ", names(LXP.data.items))

	#ordering levels
	#Q1
	LXP.data.items$`Access my timetable`<- factor(LXP.data.items$`Access my timetable`, levels = c("Very unimportant","Quite unimportant", "Neither important nor unimportant","Important","Very important"), ordered = T)

	#Q2
	LXP.data.items$`Particpate in collaborative activities forums peer assessment group work ` <- factor(LXP.data.items$`Particpate in collaborative activities forums peer assessment group work ` , levels = c("Very unimportant","Quite unimportant", "Neither important nor unimportant","Important","Very important"), ordered = T)
	# Import libraries
	library(plyr)
	library(dplyr)
	library(likert)
	library(grid)
	library(ggplot2)

	#Import data. You'll need to change the path here and point it to where your data are.
	LXP.data <- read_excel("LMSdummyDataShort_en.xlsx")
	#this cleans up your global environment (optional)
	rm(list = ls())
	# Version notes
	## Usung data.table_1.12.8 and readxl_1.3.1, R version 3.6.1 (2019-07-05)


	library(readxl) #this allows you import and extract data from your Excel workbooks
	library(data.table) #this library is the most crucial one and applies the functions that list and recombine the data
	library(xlsx) # this is optional. It's a tricky library that can break if you don't have the Java Development kit installed