Vincent Schubert Malbas ®

cine europa

last week i queued up at the EDSA Shangrila mall for free tickets for cine europa.

the first film i was able to watch was 'Almanya - Welcome to Germany', a tale about an overseas Turkish worker in Germany who seeks to return to his roots together with his Deutschified family. the first half was a veritable, laudable comedy, but the second melodramatic half was kind of meh.

the second movie i saw was 'O Xenagos', a tale about a young adult working as a tour guide amidst the orderly chaos of contemporary Athens. Set against the backdrop of the Greek crisis and a rowdy group of erasmus architecture students, the character questions his maturity, sexuality, and lifegoals; quite a ride, i should say.

the film fest runs from september 5 to 15 at the shang before going to the provinces. i might be able to catch another round of free tickets this week.

residency options

During benign rotations such as family and community medicine, I preoccupy myself with thoughts of the future. I always say to myself, the future remains uncertain, but I'm pretty inclined to do research again; it seems that an endless sense of inquiry is in my very blood.

Anyway, as I was looking for job vacancies in PCHRD, Philhealth, and PIDS, and several hospitals offering residency training, I came across this website again, which I think I've encountered for the first time three years ago.

I answered the 130-item questionnaire that asks about life values, and I was surprised with the results!

PATHOLOGY

I only thought of pathology as an alternative; I never thought that I would fit in. Apparently, my life values are similar to most pathologists.

PSYCHIATRY

When I had psychiatric patients at the outpatient and ambulatory clinics last year, I was seriously considering a future in psychiatry. But recently, someone narrated to me her experience at the national center for mental health, which was quite revealing. There was one pavilion that housed criminal psychos, and I fear that my life will be put in danger if I was their psychiatrist. 'Nough said.

RADIOLOGY

I envy their lifestyle: regular work hours than most on-call doctors. If I were to train in radiology, I would like to apply in big private hospitals because that's where the latest technology is. However I am quite unsure if I can make it big in Davao or elsewhere, likely because radiology works like a monopoly in any hospital in the provinces.

INTERNAL MEDICINE

My batchmates see me as a future internist, but I am not sure about this. Ward work is stressful and the hours are long; I prefer rigid working hours and exploring life outside medicine.

OB-GYNE

I once wanted to become an obstetrician primarily because I want to see mothers give birth to healthy babies and witness God's gift of life in all its wonder and mystery. However, just like IM, you are always on-call, because many pregnant patients get fully dilated at the most unholy hours.

ANESTHESIOLOGY

Among all residency choices, this is my first. It's critical care at its best. I look to Florence Nightingale as my inspiration, among other people of course. She was an intensivist at the height of war, taking care of wounded soldiers, a historic scene that resembles the modern post-anesthesia care unit. She was also a self-taught statistician, taking notes for patient census; not a lot of people know this. Anesthesia as a field is a conglomerate of medicine and other specialties in the perioperative setting. It is 50% brains (drug effects, physiologic changes) and 50% skills (intubation, lining) ; that's why I like it a lot.

FAMILY PRACTICE

One thing that bothers me is that I get easily frustated with geriatric patients. They are stubborn when I give medical advice, or sometimes, I just don't feel like they're listening.

PEDIATRICS

No matter how much I deny it, I like giving advice to parents regarding their children. Also, I like to hear a kid cry during vaccination.

community health center

the community health center near Singalong, Malate, Manila, a two-storey mixed wood-concrete edifice with ostensibly quaint features, may reek of malady and gradual decay, but at the same time, it embodies the rich history and strong sense of community inspite of circumstances. in fact, the health center houses a large mural that seems rather expensive and would rather belong in a national museum; however even the philippine general hospital has a lot of expensive paintings in its lobby so i guess this phenomenon is not new.

the health center is just one of the many community health offices in the country reflecting the dilapidated health system today, something that is in really bad shape and in need of constant resuscitation. i guess the dose of monetary adrenaline injected into the system was just not enough, or probably, the selfless hands that were in charge of the system got tired eventually. indeed it is difficult to expect much when you have multiple organ failure.

ambulatory care

my ambulatory care rotation was relatively benign: no psychiatric patients this week (although i have this weird feeling that i will enjoy probing the endlessness of a patient's troubled psyche) and no endorsed cases. the ambu clinic takes care of patients that have been informally rejected from admission to the emergency room (at times due to inadequate space) but nonetheless required urgent medical attention.

last saturday i was deluged with pediatric patients; i had two very young sisters who both had bronchial asthma, but the intern i was with said that she was also entertaining the possibility of pulmonary tuberculosis, following lymphocytosis in the complete blood count, and the fact that both parents were coughing the whole week. thus she advised the parents to consult the family medicine clinic as well.

i also had one gynecologic case of vaginal discharge and polyposis, and a lot of geriatric procedures in between. the breadth of family medicine is indeed wide, and most, if not all, patients are willing to share info about their private lives for the sake of completing the medical notes. once they open up, it feels like half of the job is done, and the rest of the consult becomes a breeze.

my first pf

an amazing aloha burger from jollibee, with fries and coke! that was last june 18 on my post-duty. well the parents of my pedia patient probably saw me harrassed and all, and took a pity at my shagged appearance. i turned pink when they handed me my would-be dinner, and started a litany of thank-yous.

in retrospect they got to abuse me the next few days, i.e., run labs needed stat, schedule ctscan by badgering the radio resident 3x on a sunday, write multiple clinical abstracts for pcso, et cetera.

but i guess i did something right that day. thank you Lord.

to live in a hospital

this month I started to gain my first experiences with real-life round-the-clock patient care. It's a wonderful mess really. At certain times, you feel jubilant for being able to take care of your patient efficiently, but at other times, you feel stressed that it does not exactly pan out like what you have hoped.

some interns choose to spend their supposed time-off inside the hospital: many of these instances because you want to get things done. i salute them for being extraordinary beyond the call of duty, literally.

anyway, it was just a week long and i have just barely scraped the entire pgh experience, but i felt really tired and fulfilled at the same time. i guess, i have to end this blogpost now, because postduty people would wisely use their time off sleeping rather than writing another word in a blog.

tutorials in python v R

UPDATED:
Here's the BioPython Tutorial and Cookbook --> BioPython
It's made by Jeff Chang and colleagues, and it seems to be a very useful reference for bioinformatics.

Actually, I have now found the Documentation website for Python 2.6 --> Python Docs
It probably has almost all basic functions defined in there. Of course, there's always StackOverflow...

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

For future reference, I'm keeping this Python tutorial URL --> Tutorials Point

Obviously I'm new to this. I'm keeping tabs on useful websites for Python learning.
I am not aware yet of a function that easily calls on the documentation from the Python GUI.
This makes my learning very arduous, a "trial-and-error" kind of thing.
#imstillhavingfunatleast

In contrast, R has "?" and "??" functions, which will search the appropriate documentation for that queried function. For example, I can type ?spplot, which will look up the term 'spplot' from the local library, i.e., files in the computer, or ??spplot, which will look up the term from the internet, i.e., from the CRAN website. #obviouslyimafan

If you happen to know how to search for Python functions more easily, do give me a heads up!
Currently, looking for the right function to write is a P.I.A. I'm not complaining though; I thank God that for some reason I'm having fun with this particular challenge.

senate election results

click on the image to enlarge, or click this link to view this infographic on visual.ly

These data are sourced from the 16th Official Canvass Report from the Comelec Rappler Mirror Server. Retrieved May 23, 2013 4:30am. http://bit.ly/12AmEyO

you may also download the high-res dataviz from dropbox --> http://bit.ly/1a9KBPU

The following source code for making the heatmaps is working and functional in R v. 3.0.0

#this code was tested in
# R version 3.0.0 (2013-04-03) -- "Masked Marvel"
# Copyright (C) 2013 The R Foundation for Statistical Computing
# Platform: i386-w64-mingw32/i386 (32-bit)

getwd()
# this is your working directory

# setwd("")
# you can set your personal working directory here

getwd()

#  ------------------------------------------------------------------------------------------------
# # LOAD VOTES DATASETS from COMELEC's 16th canvass report (OFFICIAL TALLY)
# Accessed from http://www.rappler.com/nation/politics/elections-2013/features/rich-media/29126-official-tally-votes-2013-senatorial-race
# Retrieved May 23, 2013 4:30am


data1 <- "http://dl.dropboxusercontent.com/u/7911075/election%202013%20csv/namebyregion.csv"
download.file(data1, destfile="./datasets/namebyregion.csv")

data2 <- "http://dl.dropboxusercontent.com/u/7911075/election%202013%20csv/namebyprovince.csv"
download.file(data2, destfile="./datasets/namebyprovince.csv")

data3 <- "http://dl.dropboxusercontent.com/u/7911075/election%202013%20csv/provincetotalbyname.csv"
download.file(data3, destfile="./datasets/provincetotalbyname.csv")

data4 <- "http://dl.dropboxusercontent.com/u/7911075/election%202013%20csv/regiontotalbyname.csv"
download.file(data4, destfile="./datasets/regiontotalbyname.csv")

list.files(".")
dateDownloaded <-date()
dateDownloaded

byregion <- read.csv("./datasets/namebyregion.csv", header=TRUE)
str(byregion)

byprovince <- read.csv("./datasets/namebyprovince.csv", header=TRUE)
str(byprovince)

tprovince <- read.csv("./datasets/provincetotalbyname.csv", header=TRUE)
str(tprovince)

tregion <- read.csv("./datasets/regiontotalbyname.csv", header=TRUE)
str(tregion)

#  ------------------------------------------------------------------------------------------------
# # LOAD COLOR PALETTES

library(RColorBrewer)

cols4v1 <- brewer.pal(5, "YlGnBu")
pal4v1 <- colorRampPalette(cols4v1)  # <- dark blue cyan white gradient

cols4v2 <- brewer.pal(5, "YlOrBr")
pal4v2 <- colorRampPalette(cols4v2)  # <- brown orange white gradient

cols4v3 <- brewer.pal(5, "PuRd")
pal4v3 <- colorRampPalette(cols4v3)  # <- purple pink white gradient

cols4v4 <- brewer.pal(5, "RdPu")
pal4v4 <- colorRampPalette(cols4v4)  # <- violet pink white gradient

cols8 <- brewer.pal(8, "Set3")
pal8 <- colorRampPalette(cols8)

cols12 <- brewer.pal(12, "Set3")
pal12 <- colorRampPalette(cols12)

#  ------------------------------------------------------------------------------------------------
# # DRAW HEATMAPS FOR REGION

row.names(byregion) <- byregion$X
byregion <- byregion[,2:19]
byregion_matrix <- data.matrix(byregion)

regions_heatmap1 <- heatmap(byregion_matrix, Rowv=NA, Colv=NA, col = cm.colors(256), scale="column", margins=c(5,10)) # <-- pink, blue
regions_heatmap2 <- heatmap(byregion_matrix, Rowv=NA, Colv=NA, col = heat.colors(256), scale="column", margins=c(5,10)) # <-- too much red
regions_heatmap3 <- heatmap(byregion_matrix, Rowv=NA, Colv=NA, col = pal4v2(5), scale="column", margins=c(5,10)) # <-- yellow brown gradient
regions_heatmap4 <- heatmap(byregion_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
regions_heatmap5 <- heatmap(byregion_matrix, Rowv=NA, Colv=NA, col = pal12(5), scale="column", margins=c(5,10)) # <- messy looking

regions_heatmap6 <- heatmap(byregion_matrix, Rowv=NA, Colv=NA, col = pal4v4(5), scale="column", margins=c(5,10)) # <-- purle pink gradient
# dev.copy2pdf(file="./output/votesperregion.pdf", height = 8, width = 11)

#  ------------------------------------------------------------------------------------------------
# # DRAW HEATMAPS PER PROVINCE

str(byprovince)
names(byprovince)

#  REGION 1 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region1
provincesregion1 <- byprovince[,33:37]
provincesregion1$X <- byprovince$X
str(provincesregion1)
row.names(provincesregion1) <- provincesregion1$X
provincesregion1 <- provincesregion1[,1:5]
provincesregion1_matrix <- data.matrix(provincesregion1)

#draw heatmap for provinces in region1
region1_heatmap <- heatmap(provincesregion1_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
dev.copy2pdf(file="./output/votesregion1.pdf", height = 8, width = 11)

#  REGION 2 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region2
provincesregion2 <- byprovince[,38:43]
provincesregion2$X <- byprovince$X
str(provincesregion2)
row.names(provincesregion2) <- provincesregion2$X
provincesregion2 <- provincesregion2[,1:6]
provincesregion2_matrix <- data.matrix(provincesregion2)

#draw heatmap for provinces in region2
region2_heatmap <- heatmap(provincesregion2_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion2.pdf", height = 8, width = 11)

#  REGION 3 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region3
provincesregion3 <- byprovince[,44:51]
provincesregion3$X <- byprovince$X
str(provincesregion3)
row.names(provincesregion3) <- provincesregion3$X
provincesregion3 <- provincesregion3[,1:8]
provincesregion3_matrix <- data.matrix(provincesregion3)

#draw heatmap for provinces in region3
region3_heatmap <- heatmap(provincesregion3_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion3.pdf", height = 8, width = 11)

#  REGION 4A ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region4a
provincesregion4a <- byprovince[,52:57]
provincesregion4a$X <- byprovince$X
str(provincesregion4a)
row.names(provincesregion4a) <- provincesregion4a$X

provincesregion4a <- provincesregion4a[,1:6]
provincesregion4a_matrix <- data.matrix(provincesregion4a)

#draw heatmap for provinces in region4a
region4a_heatmap <- heatmap(provincesregion4a_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion4a.pdf", height = 8, width = 11)

#  REGION4B ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region4b
provincesregion4b <- byprovince[,58:63]
provincesregion4b$X <- byprovince$X
str(provincesregion4b)
row.names(provincesregion4b) <- provincesregion4b$X

provincesregion4b <- provincesregion4b[,1:6]
provincesregion4b_matrix <- data.matrix(provincesregion4b)

#draw heatmap for provinces in region4b
region4b_heatmap <- heatmap(provincesregion4b_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion4b.pdf", height = 8, width = 11)

#  REGION5 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region5
provincesregion5 <- byprovince[,64:70]
provincesregion5$X <- byprovince$X
str(provincesregion5)
row.names(provincesregion5) <- provincesregion5$X

provincesregion5 <- provincesregion5[,1:7]
provincesregion5_matrix <- data.matrix(provincesregion5)

#draw heatmap for provinces in region5
region5_heatmap <- heatmap(provincesregion5_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion5.pdf", height = 8, width = 11)

#  REGION6 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region6
provincesregion6 <- byprovince[,71:79]
provincesregion6$X <- byprovince$X
str(provincesregion6)
row.names(provincesregion6) <- provincesregion6$X

provincesregion6 <- provincesregion6[,1:9]
provincesregion6_matrix <- data.matrix(provincesregion6)

#draw heatmap for provinces in region6
region6_heatmap <- heatmap(provincesregion6_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion6.pdf", height = 8, width = 11)

#  REGION7 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region7
provincesregion7 <- byprovince[,80:86]
provincesregion7$X <- byprovince$X
str(provincesregion7)
row.names(provincesregion7) <- provincesregion7$X

provincesregion7 <- provincesregion7[,1:7]
provincesregion7_matrix <- data.matrix(provincesregion7)

#draw heatmap for provinces in region7
region7_heatmap <- heatmap(provincesregion7_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion7.pdf", height = 8, width = 11)

#  REGION8 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region8
provincesregion8 <- byprovince[,87:93]
provincesregion8$X <- byprovince$X
str(provincesregion8)
row.names(provincesregion8) <- provincesregion8$X

provincesregion8 <- provincesregion8[,1:7]
provincesregion8_matrix <- data.matrix(provincesregion8)

#draw heatmap for provinces in region8
region8_heatmap <- heatmap(provincesregion8_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion8.pdf", height = 8, width = 11)

#  REGION9 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region9
provincesregion9 <- byprovince[,94:98]
provincesregion9$X <- byprovince$X
str(provincesregion9)
row.names(provincesregion9) <- provincesregion9$X

provincesregion9 <- provincesregion9[,1:5]
provincesregion9_matrix <- data.matrix(provincesregion9)

#draw heatmap for provinces in region9
region9_heatmap <- heatmap(provincesregion9_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion9.pdf", height = 8, width = 11)

#  REGION10 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region10
provincesregion10 <- byprovince[,99:106]
provincesregion10$X <- byprovince$X
str(provincesregion10)
row.names(provincesregion10) <- provincesregion10$X

provincesregion10 <- provincesregion10[,1:7]
provincesregion10_matrix <- data.matrix(provincesregion10)

#draw heatmap for provinces in region10
region10_heatmap <- heatmap(provincesregion10_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion10.pdf", height = 8, width = 11)

#  REGION11 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region11
provincesregion11 <- byprovince[,107:112]
provincesregion11$X <- byprovince$X
str(provincesregion11)
row.names(provincesregion11) <- provincesregion11$X

provincesregion11 <- provincesregion11[,1:6]
provincesregion11_matrix <- data.matrix(provincesregion11)

#draw heatmap for provinces in region11
region11_heatmap <- heatmap(provincesregion11_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion11.pdf", height = 8, width = 11)

#  REGION12 ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region12
provincesregion12 <- byprovince[,113:117]
provincesregion12$X <- byprovince$X
str(provincesregion12)
row.names(provincesregion12) <- provincesregion12$X

provincesregion12 <- provincesregion12[,1:5]
provincesregion12_matrix <- data.matrix(provincesregion12)

#draw heatmap for provinces in region12
region12_heatmap <- heatmap(provincesregion12_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion12.pdf", height = 8, width = 11)

#  REGION13 CARAGA ------------------------------------------------------------------------------------------------
# for example, we take the provinces in region13
provincesregion13 <- byprovince[,10:15]
provincesregion13$X <- byprovince$X
str(provincesregion13)
row.names(provincesregion13) <- provincesregion13$X

provincesregion13 <- provincesregion13[,1:6]
provincesregion13_matrix <- data.matrix(provincesregion13)

#draw heatmap for provinces in region13
region13_heatmap <- heatmap(provincesregion13_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesregion13.pdf", height = 8, width = 11)

#  NCR  ------------------------------------------------------------------------------------------------
# for example, we take the provinces in ncr
provincesncr <- byprovince[,16:32]
provincesncr$X <- byprovince$X
str(provincesncr)
row.names(provincesncr) <- provincesncr$X

provincesncr <- provincesncr[,1:17]
provincesncr_matrix <- data.matrix(provincesncr)

#draw heatmap for provinces in ncr
ncr_heatmap <- heatmap(provincesncr_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesncr.pdf", height = 8, width = 11)

#  CAR  ------------------------------------------------------------------------------------------------
# for example, we take the provinces in car
provincescar <- byprovince[,2:9]
provincescar$X <- byprovince$X
str(provincescar)
row.names(provincescar) <- provincescar$X

provincescar <- provincescar[,1:8]
provincescar_matrix <- data.matrix(provincescar)

#draw heatmap for provinces in car
car_heatmap <- heatmap(provincescar_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votescar.pdf", height = 8, width = 11)

#  ARMM  ------------------------------------------------------------------------------------------------
# for example, we take the provinces in armm
provincesarmm <- byprovince[,118:123]
provincesarmm$X <- byprovince$X
str(provincesarmm)

row.names(provincesarmm) <- provincesarmm$X
provincesarmm <- provincesarmm[,1:6]
provincesarmm_matrix <- data.matrix(provincesarmm)

#draw heatmap for provinces in armm
armm_heatmap <- heatmap(provincesarmm_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesarmm.pdf", height = 8, width = 11)

#  OAV OVERSEAS ABSENTEE VOTING COUNTRIES  ------------------------------------------------------------------------------------------------
# for example, we take the provinces in oav
provincesoav <- byprovince[,124:146]
provincesoav$X <- byprovince$X
str(provincesoav)

row.names(provincesoav) <- provincesoav$X
provincesoav <- provincesoav[,1:23]
provincesoav_matrix <- data.matrix(provincesoav)

#draw heatmap for provinces in oav
oav_heatmap <- heatmap(provincesoav_matrix, Rowv=NA, Colv=NA, col = pal4v1(5), scale="column", margins=c(5,10)) # <-- yellow blue gradient
# dev.copy2pdf(file="./output/votesoav.pdf", height = 8, width = 11)


# # ---------------------------
# END OF CODE

dataviz on antenatal care

in a previous post, i showed you how to make choropleth maps, particularly both world and Philippine maps, using R (see previous post). In this case we used the data we collected from the websites of the World Health Organization and the Philippine Department of Health.

improving antenatal care infographic

infographic newborn screening

what is newborn screening infographic

ironman3 v startrek2

this news report was brought to you by xkcd

i hate doing reviews; one can argue that films don't need them.
reviews exist because of movies, not vice versa.

don't get me wrong; star trek is pretty solid work from the reliable jj abrams.
it had a tight script, crazy visuals, character buildup, and some bromance on the side.

that being said, i agonized for 2 hours inside the cinema.
i was hoping to find salvation for startrek2 after much anticipation.
i wanted that one break-out moment that can topple iron man 3.
there was none.
to be fair, my pre-expectations were quite high for star trek, and a bit low for iron man.
there was a very high chance that it will fail my expectations.

as a trekkie, it pains me to say that ironman3 has edged out startrek2 in the emotions department.
where did my good ol' jj abrams go?
ironman3 felt, surprisingly, more human.

you can do the review on your own.
think about 3 themes:
infallibility: which felt more vulnerable?
sense of threat: what was the main goal of the antagonist? was it killing or was it just escaping?
consequences: do you really think that the protagonist will die?

having said that, startrek is best seen on 3D and with fellow trekkies.
live long and prosper, b*tches.

choropleth maps in R

below is the source code for making the choropleths for the infographic about newborns. you can run the following code in R. By the way, I'm using version 3.0.0; some library packages may have a different syntax and output in older versions of R.

getwd()
# this is your working directory on your computer

# DOWNLOAD THE FOLLOWING DATA FROM THE INTERNET
provinces <- "http://dl.dropboxusercontent.com/u/7911075/csv%20newborn/provinces.csv"
download.file(provinces, destfile="./provinces.csv")

phattended <- "http://dl.dropboxusercontent.com/u/7911075/csv%20newborn/dataPH2003-2008attended.csv"
download.file(phattended, destfile="./dataPH2003-2008attended.csv")

phbirthshome <- "http://dl.dropboxusercontent.com/u/7911075/csv%20newborn/dataPH2003-2008facility.csv"
download.file(phbirthshome, destfile="./dataPH2003-2008facility.csv")

countries <- "http://dl.dropboxusercontent.com/u/7911075/csv%20newborn/datacountries2005-2011attended.csv"
download.file(countries, destfile="./datacountries2005-2011attended.csv")

# this is a large file from the gadm website, might take a while to finish downloading
phil1 <- "http://gadm.org/data/rda/PHL_adm1.RData"
download.file(phil1, destfile="./phil1.RData")

list.files(".")
dateDownloaded <-date()
dateDownloaded

#  ------------------------------------------------------------------------------------------------
# # LOAD GEO MAPS

library(sp)

con <- "./phil1.RData"
print(load(con))
close(con)
str(gadm, max.level=2)
# phil1.RData has 82 observations of 16 variables

names(gadm)
#  [1] "ID_0"       "ISO"        "NAME_0"     "ID_1"       "NAME_1"    
# [6] "VARNAME_1"  "NL_NAME_1"  "HASC_1"     "CC_1"       "TYPE_1"    
# [11] "ENGTYPE_1"  "VALIDFR_1"  "VALIDTO_1"  "REMARKS_1"  "Shape_Leng"
# [16] "Shape_Area"

#  ------------------------------------------------------------------------------------------------
# # LOAD PROVINCES DATASET
provinces <- read.csv("./provinces.csv")
str(provinces)

#  ------------------------------------------------------------------------------------------------
# # LOAD DATA BIRTHS ATTENDED BY SKILLED PERSONNEL
phattended <- read.csv("./dataPH2003-2008attended.csv", header=TRUE)
str(phattended)

#calculate percent births with skilled attendants, i.e., doctors, nurses, and midwives only
phattended$skilled2008percent <- (phattended$X2008doctorspercent + phattended$X2008nursespercent + phattended$X2008midwivespercent)
phattended$skilled2007percent <- (phattended$X2007doctorspercent + phattended$X2007nursespercent + phattended$X2007midwivespercent)
phattended$skilled2006percent <- (phattended$X2006doctorspercent + phattended$X2006nursespercent + phattended$X2006midwivespercent)
phattended$skilled2005percent <- (phattended$X2005doctorspercent + phattended$X2005nursespercent + phattended$X2005midwivespercent)
phattended$skilled2004percent <- (phattended$X2004doctorspercent + phattended$X2004nursespercent + phattended$X2004midwivespercent)
phattended$skilled2003percent <- (phattended$X2003doctorspercent + phattended$X2003nursespercent + phattended$X2003midwivespercent)
#example: barplot(phattended$X2008midwivespercent, col=unique(phattended$REGION), xlab="", ylab="", cex.axis=0.8)

#multiply by 100
phattended$skilled2008percent <- phattended$skilled2008percent*100
phattended$skilled2007percent <- phattended$skilled2007percent*100
phattended$skilled2006percent <- phattended$skilled2006percent*100
phattended$skilled2005percent <- phattended$skilled2005percent*100
phattended$skilled2004percent <- phattended$skilled2004percent*100
phattended$skilled2003percent <- phattended$skilled2003percent*100

#  ------------------------------------------------------------------------------------------------
# # LOAD DATA PHL HOMEBIRTHS, NORMAL SPONTANEOUS DELIVERY
phbirthshome <- read.csv("./dataPH2003-2008facility.csv", header=TRUE)
str(phbirthshome)
names(phbirthshome)

#  ------------------------------------------------------------------------------------------------
# # FIRST MERGE: INNER JOIN FOR DATASETS OF % HOME BIRTHS & % ATTENDED BY SKILLED PERSONNEL
phbirthdata <- merge(phattended, phbirthshome, by="REGION")
str(phbirthdata)
 
#  ------------------------------------------------------------------------------------------------
# # SECOND MERGE: INNER JOIN TO PROVINCE DATASET
provattended1 <- merge(provinces, phbirthdata, by="REGION")
str(provattended1)
# write.csv(provattended1, file="provattended1.csv")

# SORT MERGED DATASET TO ARRANGE ACCDG TO GADM MAP's ORDER
provattended1.sorted <- provattended1[order(provattended1$PROVINCEORDER) , ]
# write.csv(provattended1.sorted, file="provattended1.sorted.csv")

# ------------------------------------------------------------------------------------------------------------
# # NOW WE CAN MERGE THE COMBINED DATASET TO THE GADM MAP

gadm$region <- as.factor(provattended1.sorted$REGION)
gadm$capital <- as.factor(provattended1.sorted$CAPITAL)
gadm$popln2010 <- as.numeric(provattended1.sorted$POPLN2010)
gadm$landarea <- as.numeric(provattended1.sorted$LANDAREA.km2)
gadm$popdense <- as.numeric(provattended1.sorted$POPDENS2010)
gadm$livebirths2008 <- as.numeric(provattended1.sorted$X2008total)
gadm$skilled2008 <- as.numeric(provattended1.sorted$skilled2008percent)
gadm$homebirths2008 <- as.numeric(provattended1.sorted$X2008HOMEpercent)
gadm$hospbirths2008 <- as.numeric(provattended1.sorted$X2008HOSPpercent)

library(Hmisc)
library(RColorBrewer)

cols4v1 <- brewer.pal(5, "YlGnBu")
pal4v1 <- colorRampPalette(cols4v1)  # <- dark blue cyan white gradient

cols4v2 <- brewer.pal(5, "YlOrBr")
pal4v2 <- colorRampPalette(cols4v2)  # <- brown orange white gradient

cols4v3 <- brewer.pal(5, "PuRd")
pal4v3 <- colorRampPalette(cols4v3)  # <- purple pink white gradient

cols8 <- brewer.pal(8, "Set3")
pal8 <- colorRampPalette(cols8)

cols12 <- brewer.pal(12, "Set3")
pal12 <- colorRampPalette(cols12)

str(gadm$region)
str(gadm$capital)
str(gadm$popln2010)
str(gadm$landarea)
str(gadm$popdense)
str(gadm$livebirths2008)
str(gadm$skilled2008)
str(gadm$homebirths2008)

# --------------------------------------------------------------------
#  PARSE REGIONS INTO LOW-AVERAGE-HIGH

gadm$g4area <- cut2(gadm$landarea, g = 4)
table(gadm$g4area)

gadm$g4popln <- cut2(gadm$popln2010, g = 4)
table(gadm$g4popln)

gadm$g4popdense <- cut2(gadm$popdense, g = 4)
table(gadm$g4popdense)

gadm$g4livebirths2008 <-cut2(gadm$livebirths2008, g = 4)
table(gadm$g4livebirths2008)

gadm$g4skilled2008 <-cut2(gadm$skilled2008, g=4)
table(gadm$g4skilled2008)

gadm$g4homebirths2008 <- cut2(gadm$homebirths2008, g=4)
table(gadm$g4homebirths2008)

gadm$g4hospbirths2008 <- cut2(gadm$hospbirths2008, g=4)
table(gadm$g4hospbirths2008)

gadm$g8popln <- cut2(gadm$popln2010, g = 8)
table(gadm$g8popln)

gadm$g8popdense <- cut2(gadm$popdense, g = 8)
table(gadm$g8popdense)

colg4skilled4v1 = pal4v1(length(levels(gadm$g4skilled2008)))
spplot(gadm, "g4skilled2008", col.regions=colg4skilled4v1, main="% Births with Skilled Attendants, 2008 by Region")
# dev.copy2pdf(file="PH percent births attended 2008.pdf", height =11, width = 8)

colg4home4v2 = pal4v2(length(levels(gadm$g4homebirths2008)))
spplot(gadm, "g4homebirths2008", col.regions=colg4home4v2, main="% Births at Home, Normal Spontaneous Delivery, 2008 by Region")
# dev.copy2pdf(file="PH percent births at home 2008.pdf", height =11, width = 8)

colg4hosp4v3 = pal4v3(length(levels(gadm$g4hospbirths2008)))
spplot(gadm, "g4hospbirths2008", col.regions=colg4hosp4v3, main="% Births at Hospital, Normal Spontaneous Delivery, 2008 by Region")
# dev.copy2pdf(file="PH percent births at hospital 2008.pdf", height =11, width = 8)

colpopln4v1 = pal4v1(length(levels(gadm$g4popln))) # <- dark blue cyan white gradient
colpopln4v2 = pal4v2(length(levels(gadm$g4popln))) # <- brown orange white gradient 
colpopln4v3 = pal4v3(length(levels(gadm$g4popln))) # <- purple pink white gradient
# spplot(gadm, "g4popln", col.regions=colpopln4v1, main="Population by Province")

colregions = pal12(length(levels(gadm$region)))
# spplot(gadm, "region", col.regions=colregions, main="Regions in the Philippines")
# dev.copy2pdf(file="PH regions.pdf", height =11, width = 8)

colpopln8 = pal8(length(levels(gadm$g8popln)))
# spplot(gadm, "g8popln", col.regions=colpopln8, main="Population by Province")
# dev.copy2pdf(file="PH population per province.pdf", height =11, width = 8)

colpopdense8 = pal8(length(levels(gadm$g8popdense)))
# spplot(gadm, "g8popdense", col.regions=colpopdense8, main="Population Density by Province")
# dev.copy2pdf(file="PH population density per province.pdf", height =11, width = 8)

collivebirth4v2 = pal4v2(length(levels(gadm$g4livebirths2008)))
# spplot(gadm, "g4livebirths2008", col.regions=collivebirth4v2, main="Live Births 2008")
# dev.copy2pdf(file="live births 2008.pdf", height =11, width = 8)

# ------------------------------------------------------------------------------------------
# # MAKE WORLD MAP

countries <- read.csv("./datacountries2005-2011attended.csv", header=TRUE)
str(countries)
countries$skilled100 <- countries$birthsattendedpercent*100
countries$g8skilled100 <- cut2(countries$skilled100, g = 8)
table(countries$g8skilled100)


library(Hmisc)
library(RColorBrewer)

cols4v1 <- brewer.pal(5, "YlGnBu")
pal4v1 <- colorRampPalette(cols4v1)  # <- dark blue cyan white gradient

cols4v2 <- brewer.pal(5, "YlOrBr")
pal4v2 <- colorRampPalette(cols4v2)  # <- brown orange white gradient

cols4v3 <- brewer.pal(5, "PuRd")
pal4v3 <- colorRampPalette(cols4v3)  # <- purple pink white gradient

cols8 <- brewer.pal(8, "Set3")
pal8 <- colorRampPalette(cols8)

cols8v2 <- brewer.pal(8, "YlOrRd")
pal8v2 <- colorRampPalette(cols8v2)

cols8v3 <- brewer.pal(8, "YlGnBu")
pal8v3 <- colorRampPalette(cols8v3)

cols8v4 <- brewer.pal(8, "RdPu")
pal8v4 <- colorRampPalette(cols8v4)

cols12 <- brewer.pal(12, "Set3")
pal12 <- colorRampPalette(cols12)

library(rworldmap)

mapattended <- joinCountryData2Map(countries, joinCode = "ISO3", nameJoinColumn = "ISO3V10")
str(mapattended, max.level=2)

par(mai=c(0,0,0.2,0),xaxs="i",yaxs="i")
mapParams <- mapCountryData(mapattended, nameColumnToPlot="skilled100", catMethod = "pretty", numCats = 8, colourPalette = cols8v3, mapTitle="", addLegend=FALSE)
do.call( addMapLegend, c(mapParams, legendWidth=0.5, legendMar = 2, legendLabels="all", legendIntervals="data"))
# dev.copy2pdf(file="countries births skilled attendants 2005-2011 v2.pdf", height =8, width = 11)

network analysis

my osce recipe

i practiced suturing and vaccination using pork sisig!

sterile water vaccine station

sinulid at karayom suturing station

t'was the most fun review session i ever had.

masked marvel is out

R version 3.0.0, codename masked marvel, is now ready for download! (link) See the newest iteration of the open-source community's beloved environment for statistics computing and graphics. (homepage) The stable version was officially released on April 3, 2013.

I've been waiting for this for the past few months. I've been number-crunching during January to March using version2.15, and I was hesitant to download v2.16, because I knew for a fact that v3.0 is due for release this year. I had difficulty using certain packages, such as randomForest, because they won't work on v2.15.

So here now my patience has paid off, and such a great timing this was: school year's almost over, and I can install the software and upgrade packages to my heart's content. I have all the time that I need.

Professor Peter Dalgaard of the Copenhagen Business School precisely wrote, in behalf of the R core team, in his letter to the community mailing list saying that R v3.0.0 is not so much about adding a lot of new features from v2.0 iterations, but rather it was decided that the codebase has "developed to a new level of maturity" that warrants a major release.

Version 3.0.0, as of this writing, contains only really major new feature: The inclusion of long vectors... More changes are likely to make it into the final release, but the main reason for having it as a new major release is that R over the last 8.5 years has reached a new level: we now have 64 bit support on all platforms, support for parallel processing, the Matrix package, and much more.

Thank you R core team! Happy data crunching.

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