ggplot2 | 𝚃𝚛𝚊𝚗𝚜𝚙𝚘𝚗𝚜𝚝𝚎𝚛

CMU Sport Analytics Projects Slideshows

Wed, 21 Aug 2019 21:13:14 -0500

My CMSAC Experience

Jeremy Sanchez @_jsanchez1, Nathan Moss @CMU_Stats, and Kapil Khanal @Kapil71001628 working on soccer with @kpelechrinis pic.twitter.com/Ij2eFiJ8eH
— CMU Stats & DS (@CMU_Stats) July 26, 2019

Presenting our Final Project

My First Project at CMU Statistics :Sport Analytics Camp

The first week has been a good review of basic dplyr syntax and ggplot2 philosophy. I like how Professors and TA are always there for us. Small data manipulation problems or points being masked in scatterplots, i ran into all sort of problems.
These are a practice projects before we actually work with our choice of research projects.

Here is the schedule of this summer camp.

Last day of #CMSACamp! Jam-packed summer full of #datascience, #sportsanalytics, speakers, tours, amazing partners @TruMediaSports @albert_larcada @stat_sam @penguins @kpelechrinis @Stat_Ron @NFL @albertbayes @bklynmaks @ATLHawks @acthomasca @sarah_malle @nflscrapR @Pirates pic.twitter.com/feG2cZnGQR
— CMU Stats & DS (@CMU_Stats) July 26, 2019

Project1: Baseball

For this project, we looked into how similar the top 5 hitters are in baseball.Below is the slide we presented at the camp.

Similarly for project 2 , we did another project using tennis dataset.

Project 2: Tennis

What factors are best at predicting point ratio for a match during a Grand Slam?

Project 3:Simulating Office Environment in Analytics

This is a non-technical project but most fun project. Our class of 16 students were partitioned into 4 analytics department for a hypothetical team. There is a lot of romour on players market, where some players are up for grab who are extremely essential for our team. Also, we have to let go some players. The crazy part of this project is that time is ticking. Our boss changes her decision every few minutes as per the changes inmarket. We have to come up with a some numbers to back up some decisions we are about to recommend.

Below is the slide we prepared within 10 minutes with so many factora being changed while we were working on it.

This project shed some light on the life of working data scientists and data analysts. It’s not always about fancy graphs or complicated tongue twisting models. I learned that we start with the problem we have, collect necessary data, make new metrics as per problem, graph problems and proposed solutions so that intuitive to all concerned parties and then use models to test our hypothesis and take decision.

Project 3:

This is the final project i worked on for the half of this summer camp. We This is actually a work in progress. We will be changing a lot of things(i guess that is research, change until you no longer find a justification to change)

I chose this because soccer has been very interesting for me from my childhood. I played soccer in my high school extensively and it still fascinates me with all the complexity involved from Math ,Statistical and data point of view.

Presenting to class mates before poster presentation

Like i tweeted, I am extremely grateful for CMU Stats for letting me experience life as a data scientists.

The best 8 weeks. I got to learn so many things and enjoy Pittsburgh. The spirit at @CMU_Stats is amazing, like a Stat-Disney land. Thank you for everything especially all those free foods and tickets to game and Kennywood. #CMSACamp
— Kapil.Khanal (@almost_kapil) July 26, 2019

Sankey diagrams for Bacteria and antibiotics

Wed, 24 Jul 2019 21:13:14 -0500

Visually Classifying Bacteria and Antibiotics

After World War II, antibiotics earned the moniker “wonder drugs” for quickly treating previously-incurable diseases. Data was gathered to determine which drug worked best for each bacterial infection. Comparing drug performance was an enormous aid for practitioners and scientists alike. In the fall of 1951, Will Burtin published a graph showing the effectiveness of three popular antibiotics on 16 different bacteria, measured in terms of minimum inhibitory concentration.

image creidt: Ask a biologist

I am reproducing this wonderful visualization from my professor( Silas Bergen.) in ggplot2, who did this in Tableau

Let’s bring the datasets,

library(tidyverse)
library(knitr)
library(kableExtra)
df <- read.csv("https://cdn.rawgit.com/plotly/datasets/5360f5cd/Antibiotics.csv", stringsAsFactors = F)
#String as Factors is a demon. Better not bring it here ! We rarely need that beast.
#There are 16 bacteria so giving them ID to reference later..
df<-df %>% mutate(ID =seq(1:16) )

kable(head(df,n = 16))

Bacteria	Penicillin	Streptomycin	Neomycin	Gram	ID
Mycobacterium tuberculosis	800.000	5.00	2.000	negative	1
Salmonella schottmuelleri	10.000	0.80	0.090	negative	2
Proteus vulgaris	3.000	0.10	0.100	negative	3
Klebsiella pneumoniae	850.000	1.20	1.000	negative	4
Brucella abortus	1.000	2.00	0.020	negative	5
Pseudomonas aeruginosa	850.000	2.00	0.400	negative	6
Escherichia coli	100.000	0.40	0.100	negative	7
Salmonella (Eberthella) typhosa	1.000	0.40	0.008	negative	8
Aerobacter aerogenes	870.000	1.00	1.600	negative	9
Brucella antracis	0.001	0.01	0.007	positive	10
Streptococcus fecalis	1.000	1.00	0.100	positive	11
Staphylococcus aureus	0.030	0.03	0.001	positive	12
Staphylococcus albus	0.007	0.10	0.001	positive	13
Streptococcus hemolyticus	0.001	14.00	10.000	positive	14
Streptococcus viridans	0.005	10.00	40.000	positive	15
Diplococcus pneumoniae	0.005	11.00	10.000	positive	16

Before proceeding further with the data manipulation we need to think about the format of the visualization. Here we will be making our visualization on the bacteria level, that means we will have information for each bacteria, their gram stain , and the concentration of drug required .

If you look at the table above, we do have all the data we need but not on the format we are thinking. We want one information per row for each bacteria unlike above where each row has all the information of each bacteria on one single row. Let’s change the format of the data,

key_value = df %>% gather("Drug","Concentration",Penicillin:Neomycin,-Bacteria)
kable(head(key_value))

Bacteria	Gram	ID	Drug	Concentration
Mycobacterium tuberculosis	negative	1	Penicillin	800
Salmonella schottmuelleri	negative	2	Penicillin	10
Proteus vulgaris	negative	3	Penicillin	3
Klebsiella pneumoniae	negative	4	Penicillin	850
Brucella abortus	negative	5	Penicillin	1
Pseudomonas aeruginosa	negative	6	Penicillin	850

okay so, now what we need to do is add a minimum concentration information for each bacteria for each stain type. so basically a column on the gathered table above. The only thing to keep note of is that here we should group all these bacteria and select the minimum concentration. We could have done this first[basically for eacg ] and gather like above but this is my thought process.

df_min<- key_value  %>% 
  group_by(Bacteria) %>% summarise(Min = min(Concentration))
kable(head(df_min))

Bacteria	Min
Aerobacter aerogenes	1.000
Brucella abortus	0.020
Brucella antracis	0.001
Diplococcus pneumoniae	0.005
Escherichia coli	0.100
Klebsiella pneumoniae	1.000

so now, let’s join this df_min dataframe from above with df to have that minimum information in the dataframe.

df<- inner_join(df,df_min,by = "Bacteria")
df<- df %>% mutate(Best = case_when(
  Penicillin == Min~ "Penicillin",
  Neomycin == Min~ "Neomycin",
  Streptomycin == Min~ "Streptomycin"
))

Now, since the data is ready and in the format we want,

kable(head(df))

Bacteria	Penicillin	Streptomycin	Neomycin	Gram	ID	Min	Best
Mycobacterium tuberculosis	800	5.0	2.00	negative	1	2.00	Neomycin
Salmonella schottmuelleri	10	0.8	0.09	negative	2	0.09	Neomycin
Proteus vulgaris	3	0.1	0.10	negative	3	0.10	Neomycin
Klebsiella pneumoniae	850	1.2	1.00	negative	4	1.00	Neomycin
Brucella abortus	1	2.0	0.02	negative	5	0.02	Neomycin
Pseudomonas aeruginosa	850	2.0	0.40	negative	6	0.40	Neomycin

Okay, this step might be a little unintuitive but if we think with grammer of graphics philosophy this will make sense.

seq1 <- rep(1:16,each=100)
seq2 <-rep(seq(-6,6,length=100),16)
newdat <-data.frame(ID=seq1,T=seq2)
write.csv(newdat,"new_data.csv",row.names=FALSE)

We are making a new dataframe that has data point for the sigmoid curve(you can just draw sigmoid curve in R but this way it is linked with our data with ID)

#Joining the data by ID
final_df<-inner_join(df,newdat,by = "ID")
kable(head(final_df))

Bacteria	Penicillin	Streptomycin	Neomycin	Gram	ID	Min	Best	T
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-6.000000
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.878788
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.757576
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.636364
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.515151
Mycobacterium tuberculosis	800	5	2	negative	1	2	Neomycin	-5.393939

#ggplot
final_df <- final_df %>% mutate(Sigmoid = 1/(1 + exp(-T)))

okay so now we have the final dataset, we can get in the ggplot2 land.

p <- ggplot(data = final_df , aes(x = T , y = Sigmoid ))
p + geom_point()

#Making best slope
#Different slop will separate our curves
final_df<-final_df %>% mutate(bestBacSlope = case_when(
  Best =="Streptomycin" ~ 4 - ID,
  Best =="Neomycin" ~ 9 - ID,
  Best =="Penicillin" ~ 14 - ID
))

final_df<-final_df %>% mutate(curveBest = ID + bestBacSlope * Sigmoid)
#Figuring out ID and labels

label_df<-final_df %>% dplyr::select(c(ID, Bacteria))%>% group_by(Bacteria,ID) %>% summarise(count = n()) %>% dplyr::select(Bacteria,ID) %>% arrange(ID)

Below are the label we will use in y-axis

label_y= c("Mycobacterium tuberculosis" ,  "Salmonella schottmuelleri"  ,    
           "Proteus vulgaris"        ,        "Klebsiella pneumoniae"  ,        
           "Brucella abortus"      ,          "Pseudomonas aeruginosa"    ,     
           "Escherichia coli"    ,            "Salmonella (Eberthella) typhosa",
           "Aerobacter aerogenes"     ,       "Brucella antracis"    ,          
           "Streptococcus fecalis"    ,       "Staphylococcus aureus"      ,    
           "Staphylococcus albus"    ,        "Streptococcus hemolyticus"      ,
           "Streptococcus viridans"    ,      "Diplococcus pneumoniae")

Now it’s a plotting time !

#Plotting the sigmoid plots
library(ggthemes)

## Warning: package 'ggthemes' was built under R version 3.5.2

sankey <- ggplot(data = final_df, aes(x = T , y = curveBest, color =Gram,size = Min,alpha = 0.9,group = Bacteria)) + geom_line() +scale_fill_manual(values=c("green","red")) + 
    scale_y_continuous(breaks = seq(1:16) , labels = label_y)   + theme(axis.title.y = element_blank() , axis.line.x  = element_blank() , axis.ticks.x = element_blank(), axis.title.x =element_blank() , axis.text.x.bottom = element_blank() ) + 
  annotate("text", x = 6, y = 14, label = "Penicillin") +
  annotate("text", x = 6, y = 9, label = "Neomycin") +
  annotate("text", x = 6, y = 4, label = "Streptomycin") +
  annotate("text",x = 5.5,y = 15,label = "Best Antibiotics" ,size = 5, colour = 'blue')+
  theme_minimal()

sankey

Figure 1: Classification of Bacteria

Watershed Quality in Minnesota

Sat, 23 Jun 2018 21:13:14 -0500

** Data Product Below… **

Link of the Competition the data is of : http://minneanalytics.org/minnemudac-2016/data/ Our Submission as a freshmen : MINNEMUDAC:2016
Water Quality Analytics Competiton:
A blog on Data Analytics Competition that we recently participated . We were Ranked 5th out of 19th team that participated from regional universities of Midwest USA This is the Analysis Report of a Analytics Competition that i participated in Minnesota on Nov 4 and Nov 5 in Eden Praire, Optum Technologies. This Competition was Organized by Minneanalytics[biggest analytics Group in Minneapolis], MUDAC[ Yearly analytics event of Winonat State University] and Social Data Science[a Data Science for Social Good Platform based in Minneapolis]

Thanks to my Wonderful team for collaboration and Professor for Helping this happen ! For interactive Dashboard of our Report: https://public.tableau.com/profile/malek.hakim#!/vizhome/PARCELS_Story/WaterQualityVisualizationsintheTwinCities

My first Data Analytics competition and we got honourable mention

This project is divided into two parts.

Data Cleaning and Data Management

Data Product and Presentation

First Part of this project was done in Python. This is the link of the code: https://github.com/KapilKhanal/DSCI430/blob/master/project_data_khanal.py

The Data was sponsored by MinneMUDAC as part of the Fall Data Challenge

The second part of the project was focused on the making a usable data product. The link of the code: https://github.com/KapilKhanal/DSCI430/blob/master/app.R

Below you can use this product