Your take home final consists of 3 parts. First part is about some simple questions and their answers. These questions might include coding, brief comments or direct answers. Second part is about your group projects. You are asked to make a contribution to your project report with an additional analysis with two/three visualizations. Third part is about gathering real life data and conducting analysis on it.
Here are significant points that you should read carefully.
The purpose of this part is to gauge your apprehension about data manipulation, visualization and data science workflow in general. Most questions have no single correct answer, some don’t have good answers at all. It is possible to write many pages on the questions below but please keep it short. Constrain your answers to one or two paragraphs (7-8 lines tops).
1 - What is your opinion about two y-axis graphs? Do you use it at work? Is it a good practice, a necessary evil, or plain horrible? See Hadley Wickham’s point (and other discussion in the topic) before making your argument (https://stackoverflow.com/a/3101876/3608936). See an example of two y-axis graph on https://mef-bda503.github.io/gpj-rjunkies/files/project/index.html#comparing__of_accidents___of_departures
2 - What is your exploratory data analysis workflow? Suppose you are given a data set and a research question. Where do you start? How do you proceed? For instance, you are given the task to distribute funds from donations to public welfare projects in a wide range of subjects (e.g. education, gender equality, poverty, job creation, healthcare) with the objective of maximum positive impact on the society in general. Assume you have almost all the data you require. How do you measure impact? How do you form performance measures? What makes you think you find an interesting angle?
Would you present an argument for a policy that you are more inclined to (e.g. suppose you are more inclined to allocate budget to fix gender inequality than affordable healthcare) or would you just present what data says? In other words, would the (honest) title of your presentation be “Gender Inequality - The Most Important Social Problem Backed by Data” or “Pain Points in Our Society and Optimal Budget Allocation”?
3 - What are the differences between time series and non time series data in terms of analysis, modeling and validation? In other words what makes Bitcoin price movements analysis different from diamonds (or carat) data set?
4 - If you had to plot a single graph using the data below what would it be? Why? Make your argument, actually code the plot and provide the output. (You can find detailed info about the movies data set in its help file. Use ?movies
, after you load ggplot2movies
package.)
In this part you are going to extend your group project with an additional analysis supported by some visualizations. You are tasked with finding the best improvement on the top of your group project. About one page is enough, two pages tops.
As all of you know well enough; real life data is not readly available and it is messy. In this part, you are going to gather data from Higher Education Council’s (Y??K) data service. You can use all the data provided on https://istatistik.yok.gov.tr/ . Take some time to see what are offered in the data sets. Choose an interesting theme which can be analyzed with the given data and collect relevant data from the service. Some example themes can be as follows.
a - Gather the data, bind them together and save in an .RData file. Make .RData file available online for everybody. Provide the data link in your analysis. You can work together with your friends to provide one comprehensive .RData file if it is more convenient to you. (You don’t need to report any code in this part.)
b - Perform EDA on the data you collected based on the theme you decided on. Keep it short. One to two pages is enough, three pages tops. If you are interested and want to keep going, write a data blog post about it. I will not grade it but I can share it on social media.
library(ggplot2movies)
library(tidyverse)
library(gridExtra)
library(stringr)
library(knitr)
1 - In my opinion two axis graph is useful if it used to give more describtion about idea. I use it in work, but i do not use them like accidents and departures graph from R Junkies. If i were created graph from departures and accidents data :
I will use,
2 - You can find my exploratory data analysis workflow on below.
3 - Time series data has time in it. Analysis results can change with time. Non time series data has not time in it.
For Example:
Bitcoin data has time in dataset and bitcoin price change with events in that timeline. But diamonds dataset does not time in it. Diamonds prices are changed not with time, they are changed with diamonds features.
If we put time in diamonds data, analysis diamonds price change with diamonds features and time. We can say diamonds data is time series data.
4 - I want to analysis what is the evalution of ratings comedy and drama films by years. But I do not have columns in my data which rating is given which year.
So I changed my idea.
I analysis how many comedy and drama movies released each year and what are the ratio between them.
comedy <- movies %>%
filter(year > 1960, year <= 2000) %>%
group_by(year) %>% summarise(comedy_drama_ratio = sum(Comedy)/sum(Drama),
number = sum(Comedy), kind = 'Comedy'
)
drama <- movies %>%
filter(year > 1960, year <= 2000) %>%
group_by(year) %>% summarise(comedy_drama_ratio = sum(Comedy) / sum(Drama),
number = sum(Drama), kind = 'Drama'
)
movie2 <- union_all(comedy,drama) %>% arrange(year)
normalizer <- max(movie2$number)/max(movie2$comedy_drama_ratio)
ggplot(movie2, aes(x = year, y = number/normalizer, fill = kind)) +
geom_bar(aes(y = number),stat = "identity",position = position_dodge()) +
geom_line(aes(y = comedy_drama_ratio * normalizer)) +
ggtitle("Number of Comedy and Drama Movies(Bar) and Their Ratio(Line)") +
scale_y_continuous(sec.axis = sec_axis(trans = ~.* 1 / normalizer, name = 'Comedy/Drama Ratio'))+ labs(y = "Number of Movies", x = "Year") + theme_classic()
Results :
You can find Big Mart Sales project page here.
I thought missing piece of our projects is we do not know to data very well. We try to learn new language(R), new methods, new graphs and applied them our project. But we do not learn about our data, we do not own it because we lost in what we learn.
In my opinion, we needed to look our data with some select and filter functions, but we did not.
For example for “FDB02” item :
bigMart %>% select(Item_Identifier, Item_Weight, Item_Visibility
,Item_MRP,Item_Outlet_Sales) %>%
filter(Item_Identifier == "FDB02") %>%
kable()
Item_Identifier | Item_Weight | Item_Visibility | Item_MRP | Item_Outlet_Sales |
---|---|---|---|---|
FDB02 | 9.695 | 0.0291588 | 174.537 | 1235.059 |
FDB02 | 9.695 | 0.0292234 | 175.437 | 2999.429 |
FDB02 | 9.695 | 0.0291400 | 176.337 | 3528.740 |
FDB02 | 12.600 | 0.0290230 | 177.837 | 6704.606 |
FDB02 | 9.695 | 0.0292831 | 175.137 | 3705.177 |
median(bigMart$Item_Weight)
## [1] 12.6
If we look our data set with some filter and select, we can see “FD502” Item Weight is different in one row. Cause of this difference is we equalize it median of dataset.If we research our dataset before, we were find it and equalize 9.695 but we do not researh enought and we equalize it median of dataset. There were so many columns we equalize median of dataset.
And we do not descriptive analysis on our data. We do not have analysis like which kind of product sell which outlet mostly or which outlet sells how many number of products.
bigMart_Summary <- bigMart %>% group_by(Outlet_Type,Outlet_Identifier,Item_Identifier_Str2)%>%
summarise(Sales = round(sum(Item_Outlet_Sales))
, nProduct = n_distinct(Item_Identifier)
,nSales = round(sum(Item_Outlet_Sales/Item_MRP))
,avgPrize=round(Sales/nSales)
)
bigMart_Summary %>%
arrange(desc(Sales)) %>% filter(Item_Identifier_Str2 == "NC") %>% kable()
Outlet_Type | Outlet_Identifier | Item_Identifier_Str2 | Sales | nProduct | nSales | avgPrize |
---|---|---|---|---|---|---|
Supermarket Type3 | OUT027 | NC | 617898 | 174 | 4567 | 135 |
Supermarket Type1 | OUT035 | NC | 426131 | 168 | 2959 | 144 |
Supermarket Type1 | OUT049 | NC | 415435 | 164 | 2944 | 141 |
Supermarket Type1 | OUT013 | NC | 402420 | 180 | 2767 | 145 |
Supermarket Type1 | OUT046 | NC | 394250 | 181 | 2780 | 142 |
Supermarket Type1 | OUT017 | NC | 384667 | 172 | 2723 | 141 |
Supermarket Type1 | OUT045 | NC | 367225 | 174 | 2676 | 137 |
Supermarket Type2 | OUT018 | NC | 342185 | 173 | 2427 | 141 |
Grocery Store | OUT010 | NC | 42377 | 114 | 295 | 144 |
Grocery Store | OUT019 | NC | 33624 | 99 | 233 | 144 |
nSales : What is the average Non-Consumable product prize in each shop.
Also we do not have any descriptive graph like product type sales percentage in each outlet or transpose.
g1 <- bigMart_Summary %>%
group_by(Outlet_Identifier, Item_Identifier_Str2) %>%
summarise(Sales = sum(Sales))%>%
ggplot(aes(x = Outlet_Identifier, y = Sales, fill = Item_Identifier_Str2)) +
geom_bar(stat = "identity", position = "fill", colors = "fill") +
theme_classic() + ggtitle("Portion of Total Product Sales by Outlet") +
labs(y="Proportion of Total Sales", x="Outlet Identifier") +
theme(axis.text.x = element_text(angle = 30))
g2 <- bigMart_Summary %>%
group_by(Outlet_Identifier, Item_Identifier_Str2) %>%
summarise(Sales = sum(Sales))%>%
ggplot(aes(x = Item_Identifier_Str2, y = Sales, fill = Outlet_Identifier)) +
geom_bar(stat = "identity", position = "fill", colors = "fill") +
theme_classic() + ggtitle("Portion of Total Sales in Each Outlet by Product") +
labs(y="Proportion of Total Sales", x="Product Type")
grid.arrange(g1,g2,nrow=2)
In conclusion, we were need to learn more about our dataset. If we did, our analysis were be more meaningful for us.
a - Below, you can find about my universities_34 dataset.
Description
universities_34 dataset has information about which university has how many students in 1987 to 2017 in 5 years periods in Istanbul. You can download data from (https://mef-bda503.github.io/pj-esera/files/universities_34.RData)
Details
A data frame with 192 rows and 17 columns
b - Project Aim : To see number of state and private universities, number of students in state and private university, percentage of female university students evalutions by years in Istanbul.
setwd("/Users/yetkineser/Desktop/mef R/final")
download.file("https://mef-bda503.github.io/pj-esera/files/universities_34.RData"
, "universities_34.RData")
universities_34 <- get(load("universities_34.RData"))
uni_34_summary <- universities_34 %>%
group_by(year,university_type) %>%
summarise(number_of_universities = n_distinct(university), number_of_students = sum(total)
, number_of_male_students = sum(male_total), number_of_female_students = sum(female_total)
)
g0 <- uni_34_summary %>%
ggplot(aes(x = year, y = number_of_universities, fill = university_type)) +
geom_bar(stat = "identity",position = position_dodge(),color=c("grey33")) +
theme_classic() + ggtitle("Number of Universities by Years") +
geom_text(aes(label=number_of_universities),color="grey11",size=5,vjust = -0.5
, position = position_dodge(width = 5)) +
scale_x_continuous(breaks = c(1987,1992,1997,2002,2007,2012,2017)) +
scale_y_continuous(limits = c(0,50)) + labs(y="Number", x="Year")
g1 <- uni_34_summary %>%
ggplot(aes(x = year, y = number_of_universities, fill = university_type)) +
geom_bar(stat = "identity",position = "fill") + ylab("proportion") + theme_classic() +
ggtitle("Portion of University Numbers by Years") +
scale_x_continuous(breaks = c(1987,1992,1997,2002,2007,2012,2017)) +
labs(y = "Proportion", x = "Year")
grid.arrange(g0,g1,nrow=2)
g0 <- uni_34_summary %>%
ggplot(aes(x = year, y = number_of_students/1000, fill = university_type)) +
geom_bar(stat = "identity",position = position_dodge(),color=c("grey33")) +
theme_classic() + ggtitle("Number of Students (1000)") +
geom_text(aes(label=round(number_of_students/1000))
,color="grey11",size = 4, vjust = -0.5, position = position_dodge(width = 5)) +
scale_x_continuous(breaks = c(1987, 1992, 1997, 2002, 2007, 2012, 2017)) +
scale_y_continuous(limits = c(0,500)) + labs(y="Number (1000)", x = "Year")
g1 <- uni_34_summary %>%
ggplot(aes(x = year, y = number_of_students, fill = university_type)) +
geom_bar(stat = "identity",position = "fill") + ylab("proportion") + theme_classic() +
ggtitle("Portion of Students based on University Type") +
scale_x_continuous(breaks = c(1987, 1992, 1997, 2002,2007,2012,2017)) + labs(y="Proportion", x = "Year")
grid.arrange(g0 ,g1 ,nrow = 2)
uni_34_summary <- universities_34 %>%
group_by(year) %>% summarise(number_of_students = sum(total),
number_of_male_students = sum(male_total),
number_of_female_students = sum(female_total))
g0 <- uni_34_summary %>%
mutate(female_ratio = round((number_of_female_students/number_of_students) * 100), 4) %>%
ggplot(aes(x = year, y = female_ratio) ) + geom_line() + theme_classic() +
ggtitle("Female Students / Total Students(%) by Years") +
geom_line(size = 1.2,color = c("grey33")) + scale_y_continuous(limits = c(35,55)) +
geom_text(aes(label = female_ratio), color = "grey11", size = 4, vjust = 0, nudge_y = 0.5) +
scale_x_continuous(breaks = c(1987,1992,1997,2002,2007,2012,2017)) +
labs(y="Female Ratio(%)", x = "Year")
uni_34_summary <- universities_34 %>%
group_by(year,university_type) %>% summarise(number_of_students = sum(total),
number_of_male_students = sum(male_total),
number_of_female_students = sum(female_total))
g1 <- uni_34_summary %>%
mutate(female_ratio = round((number_of_female_students/number_of_students)*100),4) %>%
filter(university_type == "STATE") %>%
ggplot(aes(x = year, y = female_ratio)) + geom_line() + theme_classic() +
ggtitle("Female Students / Total Students(%) by Years in State University") +
geom_line(size=1.2,color=c("grey33")) +
geom_text(aes(label = female_ratio), color = "grey11", size = 4, vjust = 0, nudge_y = 0.5) +
scale_y_continuous(limits = c(35,55)) + labs(y = "Female Ratio(%)", x = "Year") +
scale_x_continuous(breaks = c(1987,1992,1997,2002,2007,2012,2017))
g2 <- uni_34_summary %>%
mutate(female_ratio = round((number_of_female_students/number_of_students)*100),4) %>%
filter(university_type == "PRIVATE") %>%
ggplot(aes(x = year, y = female_ratio)) +geom_line() +theme_classic() +
ggtitle("Female Students / Total Students(%) by Years in Private University") +
geom_line(size = 1.2, color = c("grey33")) +
geom_text(aes(label = female_ratio),color = "grey11", size = 4,vjust = 0, nudge_y = 0.5) +
scale_y_continuous(limits = c(35,55)) + labs(y = "Female Ratio(%)", x = "Year") +
scale_x_continuous(breaks = c(1987,1992,1997,2002,2007,2012,2017),limits = c(1987,2017))
grid.arrange(g0,g1,g2,ncol=1)
In first graph in 1987 female university students is just 35% of students, but it increase year by year and in 2017 it will 47%. especially 2007 to 2012 its increase is 5%.
In second and third graph shows that there is no big difference percentage of female students in state and private universities(just 2% in 2017).