From c8cfc305e99cb1cd1640d7103edce237d288c536 Mon Sep 17 00:00:00 2001 From: SudhakarKuma Date: Wed, 4 Mar 2020 15:38:44 +0530 Subject: Add Mumbai report --- Mumbai/FOSSEE.png | Bin 0 -> 79661 bytes Mumbai/IIT_Bombay.png | Bin 0 -> 81741 bytes Mumbai/Industry Commodities.xlsx | Bin 0 -> 12148 bytes Mumbai/Industry.csv | 28 +++++ Mumbai/Mumbai Suburban households.xls | Bin 0 -> 35840 bytes Mumbai/MumbaiReport.Rmd | 175 +++++++++++++++++++++++++++ Mumbai/MumbaiReport.pdf | Bin 0 -> 464136 bytes Mumbai/Railway employment country data.xlsx | Bin 0 -> 12757 bytes Mumbai/SwachB country data.xlsx | Bin 0 -> 9369 bytes Mumbai/Tourism Statistics country data .xlsx | Bin 0 -> 10310 bytes Mumbai/abb.csv | 28 +++++ Mumbai/caste.csv | 5 + Mumbai/caste.xlsx | Bin 0 -> 8357 bytes Mumbai/mumbai.png | Bin 0 -> 204829 bytes Mumbai/total.csv | 3 + 15 files changed, 239 insertions(+) create mode 100644 Mumbai/FOSSEE.png create mode 100644 Mumbai/IIT_Bombay.png create mode 100644 Mumbai/Industry Commodities.xlsx create mode 100644 Mumbai/Industry.csv create mode 100644 Mumbai/Mumbai Suburban households.xls create mode 100644 Mumbai/MumbaiReport.Rmd create mode 100644 Mumbai/MumbaiReport.pdf create mode 100644 Mumbai/Railway employment country data.xlsx create mode 100644 Mumbai/SwachB country data.xlsx create mode 100644 Mumbai/Tourism Statistics country data .xlsx create mode 100644 Mumbai/abb.csv create mode 100644 Mumbai/caste.csv create mode 100644 Mumbai/caste.xlsx create mode 100644 Mumbai/mumbai.png create mode 100644 Mumbai/total.csv diff --git a/Mumbai/FOSSEE.png b/Mumbai/FOSSEE.png new file mode 100644 index 0000000..6feda2d Binary files /dev/null and b/Mumbai/FOSSEE.png differ diff --git a/Mumbai/IIT_Bombay.png b/Mumbai/IIT_Bombay.png new file mode 100644 index 0000000..34a0eb0 Binary files /dev/null and b/Mumbai/IIT_Bombay.png differ diff --git a/Mumbai/Industry Commodities.xlsx b/Mumbai/Industry Commodities.xlsx new file mode 100644 index 0000000..24d1b34 Binary files /dev/null and b/Mumbai/Industry Commodities.xlsx differ diff --git a/Mumbai/Industry.csv b/Mumbai/Industry.csv new file mode 100644 index 0000000..8250ad1 --- /dev/null +++ b/Mumbai/Industry.csv @@ -0,0 +1,28 @@ +Principal Commodity,2012,2013 +OC,1592,1715 +GG,8917,8179 +OM,698,0 +BI,3388,3642 +DB,4432,4734 +SS,610,786 +OC,753,0 +RP,1297,1159 +OF,0,532 +BP,1446,750 +PS,880,958 +AC,0,562 +HM,760,939 +MT,642,777 +MI,4530,4848 +AW,910,936 +EE,3015,2947 +IP,622,602 +IP,3407,3295 +ME,654,701 +OI,1034,1083 +AP,3156,4015 +CP,4739,5245 +CE,1715,2295 +EC,3767,3943 +EI,3779,4141 +TI,6570,8999 diff --git a/Mumbai/Mumbai Suburban households.xls b/Mumbai/Mumbai Suburban households.xls new file mode 100644 index 0000000..c3e0f88 Binary files /dev/null and b/Mumbai/Mumbai Suburban households.xls differ diff --git a/Mumbai/MumbaiReport.Rmd b/Mumbai/MumbaiReport.Rmd new file mode 100644 index 0000000..7e52b2c --- /dev/null +++ b/Mumbai/MumbaiReport.Rmd @@ -0,0 +1,175 @@ +--- +title: '**Task-B: Data Collection and Analysis**' +subtitle: '**District: Mumbai**' +author: "Lalith Dupathi" +date: "10 March 2019" +output: + pdf_document: default + html_document: + df_print: paged +bibliography: bibliography.bib +urlcolor: blue +--- + +```{r,echo=FALSE,warning=FALSE,fig.align = "center"} +knitr::include_graphics("FOSSEE.png") + + +``` + + +```{r,echo=FALSE,warning=FALSE,fig.align = "center"} +knitr::include_graphics("IIT_Bombay.png") +``` +\newpage + +\tableofcontents + +\newpage + +> Abstract + +In this report, data from the various government departments of Coimbatore district were collected by visiting respective departments in person and by searching for required data on online government portals. Data included in this report are on **Population of the Area** and **Industries**. Collected data was analyzed and conclusions were drawn. Analysis was done completely using a **Free-Libre / Open Source Software (FLOSS)** known as **'R'** as a part of **[FOSSEE](https://fossee.in/) Project** by **IIT Bombay** and **MHRD, Government of India.** [FOSSEE](https://fossee.in/) project is part of the National Mission on Education through ICT with the thrust area being adaptation and deployment of open source simulation packages equivalent to proprietary software, funded by MHRD, based at the Indian Institute of Technology Bombay (IITB). + + + + +\newpage + +>#1.) Introduction + + +Mumbai (also known as Bombay, the official name until 1995) is the capital city of the Indian state of Maharashtra. + +Mumbai lies on the Konkan coast on the west coast of India and has a deep natural harbor. In 2008, Mumbai was named an alpha world city. It is also the wealthiest city in India and has the highest number of millionaires and billionaires among all cities in India. + +The seven islands that came to constitute Mumbai were home to communities of fishing colonies of the Koli people. For centuries, the islands were under the control of successive indigenous empires before being ceded to the Portuguese Empire and subsequently to the East India Company when in 1661 Charles II of England married Catherine of Braganza and as part of her dowry, Charles received the ports of Tangier and Seven Islands of Bombay. During the mid-18th century, Bombay was reshaped by the Hornby Vellard project, which undertook reclamation of the area between the seven islands from the sea. Along with construction of major roads and railways, the reclamation project, completed in 1845, transformed Bombay into a major seaport on the Arabian Sea. Bombay in the 19th century was characterized by economic and educational development. During the early 20th century it became a strong base for the Indian independence movement. Upon India's independence in 1947 the city was incorporated into Bombay State. In 1960, following the Samyukta Maharashtra Movement, a new state of Maharashtra was created with Bombay as the capital. + + + +source: [@mum] + +```{r,echo=FALSE,warning=FALSE,fig.align = "center"} +knitr::include_graphics("mumbai.png") +``` + +| *Fig: Map of Mumbai District * + +\newpage + +>#2.) Population + +1 of every 6 people on the planet live in India, and between the 2001 and 2011 censuses, the country grew by 17.7%, adding 181.5 million people. The country has doubled in size in just 40 years and is expected to unseat China as the world's most populated country in the next couple of decades. India's current yearly growth rate is 1.02%. + +The country as a whole has a population density of 416 people per square kilometer, which ranks 31st in the world. In Mumbai, the population density is 21,000 people per square kilometer (54,000/square mile). + +source: [@pop] + +###Total Population + +```{r echo=FALSE,warning=FALSE,message=FALSE,error=FALSE} +library("ggplot2") # Data visualization +library("dplyr") # Data manipulation + +totalpop<-read.csv("total.csv") + +totalpop <- totalpop %>% + arrange(desc(Gender)) %>% + mutate(lab.ypos = cumsum(Population) - 0.5*Population) + +mycols <- c("#0073C2FF", "#EFC000FF") + +ggplot(totalpop, aes(x = 2, y = Population, fill = Gender)) + + geom_bar(stat = "identity", color = "white") + + coord_polar(theta = "y", start = 0)+ + geom_text(aes(y = lab.ypos, label = Population), color = "white")+ + scale_fill_manual(values = mycols) + + labs(title = "Total Population of the District")+ + theme_void()+ + xlim(0.5, 2.5) + + + + +``` +We can observe from the above donut plot that the male population of the district is 86,40,419 and the female population of the district is 47,41,720. There are just 55 females on every 100 males. The female to male ratio of the district is very low. + +###Scheduled Caste and Scheduled Tribe + +The Scheduled Castes (SCs) and Scheduled Tribes (STs) are officially designated groups of historically disadvantaged people in India. The terms are recognized in the Constitution of India and the groups are designated in one or other of the categories. For much of the period of British rule in the Indian subcontinent, they were known as the Depressed Classes. + +Since the independence of India, the Scheduled Castes and Scheduled Tribes were given Reservation status, guaranteeing political representation. The Constitution lays down the general principles of positive discrimination for SCs and STs. Pallan of Tamil Nadu is considered the oldest community of Schedule Castes, perhaps in all of India. + + +```{r echo=FALSE,warning=FALSE,message=FALSE,error=FALSE} + + +caste<-read.csv("caste.csv") + +caste <- caste %>% + arrange(desc(Caste)) %>% + mutate(lab.ypos = cumsum(Population) - 0.5*Population) + +mycols2<- c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF") + +ggplot(caste, aes(x = "", y = Population, fill = Caste)) + + geom_bar(width = 1, stat = "identity", color = "white") + + coord_polar("y", start = 0)+ + geom_text(aes(y = lab.ypos, label = Population), color = "white")+ + scale_fill_manual(values = mycols2) + + labs(title="Scheduled Caste in the District")+ + theme_void() + + + + +``` + +###Conclusion + +We can conclude from the above pie chart that Mumbai has more population of Scheduled Caste than Scheduled Tribe. The male population in both Scheduled Caste and Scheduled Tribe is more than the female population. There are 90 Scheduled Caste females on every 100 Scheduled Caste males and 88 Scheduled Tribe females on every 100 Scheduled Tribe males. +Female to male ratio is much better in Scheduled Caste and Scheduled Tribe than the total population of Mumbai. + +\newpage + +>#2.) Industry + +Four key industrial economic sectors are identified in India. The primary sector, largely extract raw material and they are mining and farming industries. In the secondary sector, refining, construction, and manufacturing are included. The tertiary sector deals with services and distribution of manufactured goods. India's service industry accounts for 57.2% of the country's GDP while the industrial and agricultural sector contributes 28% and 14.6% respectively. Agriculture is the predominant occupation in India, accounting for about 52% of direct and indirect employment. The service sector makes up a further 34% and industrial sector around 14%. The labour force totals around half a billion workers. Industry accounts for 28% of the GDP and employs 14% of the total workforce. + +source: [@indus] + +```{r echo=FALSE,warning=FALSE,message=FALSE,error=FALSE} +library(reshape2) + +industry<-read.csv("Industry.csv",header =TRUE) +colnames(industry)<-c("PrincipalCommodity","2012","2013") +ggplot(melt(industry, id.vars='PrincipalCommodity'), aes(x=PrincipalCommodity, y=value, fill=variable)) + + geom_bar(stat='identity', position='dodge') + + labs(title="Commodity Values ", + y="Value (in Crore)", + x="Principal Commodity",fill="Year" + ,caption = "Please Ref. Below Table for Abbreviations ")+ + theme(axis.text.x = element_text(angle=60, hjust=1)) + + +``` + +*Table: Abbreviations regarding Principal Commodity * + +```{r,echo=FALSE,warning=F} +library(knitr) +# Creating table. +abb<-read.csv("abb.csv") +knitr::kable(abb, format = "markdown") + +``` + + +###Conclusion + +The above bar graph concludes that in the year 2012, the value of almost every commodity for different industries is mostly less than 2000 crores and limited commodities have value above 6000 crores. For the year 2013, the number of commodities having a commodity value less than 2000 crores has decreased and commodities having commodity value more than 6000 crores has increased. Thus, suggesting a better market in 2013. + + +\newpage + +#References \ No newline at end of file diff --git a/Mumbai/MumbaiReport.pdf b/Mumbai/MumbaiReport.pdf new file mode 100644 index 0000000..f3caef0 Binary files /dev/null and b/Mumbai/MumbaiReport.pdf differ diff --git a/Mumbai/Railway employment country data.xlsx b/Mumbai/Railway employment country data.xlsx new file mode 100644 index 0000000..a90eac0 Binary files /dev/null and b/Mumbai/Railway employment country data.xlsx differ diff --git a/Mumbai/SwachB country data.xlsx b/Mumbai/SwachB country data.xlsx new file mode 100644 index 0000000..627d336 Binary files /dev/null and b/Mumbai/SwachB country data.xlsx differ diff --git a/Mumbai/Tourism Statistics country data .xlsx b/Mumbai/Tourism Statistics country data .xlsx new file mode 100644 index 0000000..87a5b5d Binary files /dev/null and b/Mumbai/Tourism Statistics country data .xlsx differ diff --git a/Mumbai/abb.csv b/Mumbai/abb.csv new file mode 100644 index 0000000..0230a40 --- /dev/null +++ b/Mumbai/abb.csv @@ -0,0 +1,28 @@ +Full Name ,abbreviation +OTHER COMMODITIES,OC +GOLD,GG +OTHER PRECIOUS AND BASE METALS,OM +BULK DRUGS DRUG INTERMEDIATES,BI +DRUG FORMULATIONS BIOLOGICALS,DB +SURGICALS,SS +ORGANIC CHEMICALS,OC +RESIDUAL CHEMICALS AND ALLIED PRODUCTS,RP +OTHER RUBBER PRODUCTS EXCEPT FOOTWEAR,OF +"BOOKS, PUBLICATIONS AND PRINTING",BP +PRODUCTS OF IRON AND STEEL,PS +AUTO COMPONENTS/PARTS,AC +"HAND TOOLS, CUTTING TOOLS AND IMPLEMENTS MADE OF METALS",HM +MACHINE TOOLS,MT +MEDICAL AND SCIENTIFIC INSTRUMENTS,MI +"AIR CONDITION AND REFRIGERATION MACHINERY AND PARTS, INDUSTRIAL FURNACES, WATER",AW +ELECTRIC MACHINERY AND EQUIPMENT,EE +IC ENGINES AND PARTS,IP +"INDUSTRIAL MACHINERY FOR DAIRY, AGRICULTURE, FOOD PROCESSING, TEXTILES, PAPER",IP +"MACHINERY FOR ATMS, INJECTING MOULDING MACHINERY, VALVES, ETC",ME +OTHER MISC. ENGINEERING ITEMS,OI +"AIRCRAFTS, SPACECRAFTS AND PARTS",AP +COMPUTER HARDWARE PERIPHERALS,CP +CONSUMER ELECTRONICS,CE +ELECTRONICS COMPONENTS,EC +ELECTRONICS INSTRUMENTS,EI +TELECOM INSTRUMENTS,TI diff --git a/Mumbai/caste.csv b/Mumbai/caste.csv new file mode 100644 index 0000000..8f969d3 --- /dev/null +++ b/Mumbai/caste.csv @@ -0,0 +1,5 @@ +Caste,Population +SC Male,211010 +SC Female,190559 +ST Male,37335 +ST Female,33119 diff --git a/Mumbai/caste.xlsx b/Mumbai/caste.xlsx new file mode 100644 index 0000000..4de781e Binary files /dev/null and b/Mumbai/caste.xlsx differ diff --git a/Mumbai/mumbai.png b/Mumbai/mumbai.png new file mode 100644 index 0000000..2c7151f Binary files /dev/null and b/Mumbai/mumbai.png differ diff --git a/Mumbai/total.csv b/Mumbai/total.csv new file mode 100644 index 0000000..83c786d --- /dev/null +++ b/Mumbai/total.csv @@ -0,0 +1,3 @@ +Gender,Population +Male,8640419 +Female,4741720 -- cgit