Some things about me

Content

I am interested in human driven impacts (with special attention to climate change) on fisheries resources in Latin America. How impacts of environmental change affect social structures, such as food security and local economies, how they can be measured, and how to create social adaptive capacity to such changes.

As of today, I’m on my second year of the Master program at the Bren School of Environmental Science & MAnagement. My group proyect looks to develop a Citizen Science program for the rocky intertidal zone

More about our project in: http://intertidalteam.weebly.com

Techniques

I think that GitHub will be a great tool for mi present and future work. At Bren, I’ve been envolved to coding projects. Learning how to use GitHub will provide me a new tool to organize my group-work.

Data

I still have no data for my proyect. I Could use some of the FAOStats available data. I would like to address climate change impacts to a certain species or may be a group of.

 # read csv
Data = read.csv("data/jepa_ReefPerch.csv")
      
# output summary
summary(Data)

##    SITE       FISHSIZE    
##  AQUE:20   Min.   : 5.00  
##  MOHK:20   1st Qu.: 8.00  
##  NAPL:20   Median :10.00  
##            Mean   :10.32  
##            3rd Qu.:12.00  
##            Max.   :15.00

plot(Data)

title: “Some things about me” author: Juliano Palacios Abrantes - http://www.laff.bren.ucsb.edu/laff-network/juliano-palacios-abrantes output: html_document: toc: true toc_depth: 6 —

I dont like cold

General Information

Content

I am interested in human driven impacts (with special attention to climate change) on fisheries resources in Latin America. How impacts of environmental change affect social structures, such as food security and local economies, how they can be measured, and how to create social adaptive capacity to such changes.

As of today, I’m on my second year of the Master program at the Bren School of Environmental Science & MAnagement. My group proyect looks to develop a Citizen Science program for the rocky intertidal zone

More about our project in: http://intertidalteam.weebly.com

Techniques

I think that GitHub will be a great tool for mi present and future work. At Bren, I’ve been envolved to coding projects. Learning how to use GitHub will provide me a new tool to organize my group-work.

Data

I still have no data for my proyect. I Could use some of the FAOStats available data. I would like to address climate change impacts to a certain species or may be a group of.

suppressWarnings(library(readr))
suppressWarnings(library(knitr))

 # read csv
Data = read.csv("data/jepa_ReefPerch.csv")
  
# output summary
summary(Data)

##    SITE       FISHSIZE    
##  AQUE:20   Min.   : 5.00  
##  MOHK:20   1st Qu.: 8.00  
##  NAPL:20   Median :10.00  
##            Mean   :10.32  
##            3rd Qu.:12.00  
##            Max.   :15.00

plot(Data)

Asignment Reading and Wrangling

EDAWR

suppressWarnings(library(EDAWR))
kable(storms)

storm	wind	pressure	date
Alberto	110	1007	2000-08-03
Alex	45	1009	1998-07-27
Allison	65	1005	1995-06-03
Ana	40	1013	1997-06-30
Arlene	50	1010	1999-06-11
Arthur	45	1010	1996-06-17

kable(cases)

country	2011	2012	2013
FR	7000	6900	7000
DE	5800	6000	6200
US	15000	14000	13000

kable(pollution)

city	size	amount
New York	large	23
New York	small	14
London	large	22
London	small	16
Beijing	large	121
Beijing	small	56

ratio <- storms$pressure/storms$wind

Tidyr

suppressWarnings(library(tidyr))
?gather
?spread

k<- gather(cases,"year","n",2:4)
kable(k)

country	year	n
FR	2011	7000
DE	2011	5800
US	2011	15000
FR	2012	6900
DE	2012	6000
US	2012	14000
FR	2013	7000
DE	2013	6200
US	2013	13000

PollutionTable <- spread(pollution,"size","amount")
kable(PollutionTable)

city	large	small
Beijing	121	56
London	22	16
New York	23	14

SepStorms<- separate(storms, date, c("year", "month", "day"), sep = "-")
kable(SepStorms)

storm	wind	pressure	year	month	day
Alberto	110	1007	2000	08	03
Alex	45	1009	1998	07	27
Allison	65	1005	1995	06	03
Ana	40	1013	1997	06	30
Arlene	50	1010	1999	06	11
Arthur	45	1010	1996	06	17

UniteStorms <- unite(SepStorms, "date", year, month, day, sep = "-")
kable(UniteStorms)

storm	wind	pressure	date
Alberto	110	1007	2000-08-03
Alex	45	1009	1998-07-27
Allison	65	1005	1995-06-03
Ana	40	1013	1997-06-30
Arlene	50	1010	1999-06-11
Arthur	45	1010	1996-06-17

Dplyr

suppressWarnings(library(dplyr))

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

suppressWarnings(library(nycflights13))

StormPressure<- select(storms, storm,pressure) #selects from "storms" data "storm and pressure"
select(storms,-storm) #sellects all except "storm"

## Source: local data frame [6 x 3]
## 
##    wind pressure       date
##   (int)    (int)     (date)
## 1   110     1007 2000-08-03
## 2    45     1009 1998-07-27
## 3    65     1005 1995-06-03
## 4    40     1013 1997-06-30
## 5    50     1010 1999-06-11
## 6    45     1010 1996-06-17

Avion<- select(planes,type:model)
kable(head(Avion))

type	manufacturer	model
Fixed wing multi engine	EMBRAER	EMB-145XR
Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214
Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214
Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214
Fixed wing multi engine	EMBRAER	EMB-145LR
Fixed wing multi engine	AIRBUS INDUSTRIE	A320-214

filter(Avion,manufacturer == "EMBRAER")

## Source: local data frame [299 x 3]
## 
##                       type manufacturer     model
##                      (chr)        (chr)     (chr)
## 1  Fixed wing multi engine      EMBRAER EMB-145XR
## 2  Fixed wing multi engine      EMBRAER EMB-145LR
## 3  Fixed wing multi engine      EMBRAER EMB-145XR
## 4  Fixed wing multi engine      EMBRAER EMB-145XR
## 5  Fixed wing multi engine      EMBRAER EMB-145XR
## 6  Fixed wing multi engine      EMBRAER EMB-145XR
## 7  Fixed wing multi engine      EMBRAER EMB-145XR
## 8  Fixed wing multi engine      EMBRAER EMB-145XR
## 9  Fixed wing multi engine      EMBRAER EMB-145XR
## 10 Fixed wing multi engine      EMBRAER EMB-145XR
## ..                     ...          ...       ...

filter(planes, manufacturer == "EMBRAER", model %in% c("EMB-145XR","EMB-145LR"))

## Source: local data frame [218 x 9]
## 
##    tailnum  year                    type manufacturer     model engines
##      (chr) (int)                   (chr)        (chr)     (chr)   (int)
## 1   N10156  2004 Fixed wing multi engine      EMBRAER EMB-145XR       2
## 2   N10575  2002 Fixed wing multi engine      EMBRAER EMB-145LR       2
## 3   N11106  2002 Fixed wing multi engine      EMBRAER EMB-145XR       2
## 4   N11107  2002 Fixed wing multi engine      EMBRAER EMB-145XR       2
## 5   N11109  2002 Fixed wing multi engine      EMBRAER EMB-145XR       2
## 6   N11113  2002 Fixed wing multi engine      EMBRAER EMB-145XR       2
## 7   N11119  2002 Fixed wing multi engine      EMBRAER EMB-145XR       2
## 8   N11121  2003 Fixed wing multi engine      EMBRAER EMB-145XR       2
## 9   N11127  2003 Fixed wing multi engine      EMBRAER EMB-145XR       2
## 10  N11137  2003 Fixed wing multi engine      EMBRAER EMB-145XR       2
## ..     ...   ...                     ...          ...       ...     ...
## Variables not shown: seats (int), speed (int), engine (chr)

Planes2 <- select(planes,manufacturer,engines,seats)
PlanesMut<- mutate(Planes2, PPlPerSeat = seats/engines)
kable(head(PlanesMut))

manufacturer	engines	seats	PPlPerSeat
EMBRAER	2	55	27.5
AIRBUS INDUSTRIE	2	182	91.0
AIRBUS INDUSTRIE	2	182	91.0
AIRBUS INDUSTRIE	2	182	91.0
EMBRAER	2	55	27.5
AIRBUS INDUSTRIE	2	182	91.0

planes %>% summarise(median = median(seats), variance = var(seats),mean=mean(seats),sd=sd(seats),n=n())

## Source: local data frame [1 x 5]
## 
##   median variance     mean       sd     n
##    (dbl)    (dbl)    (dbl)    (dbl) (int)
## 1    149 5425.055 154.3164 73.65497  3322

arrange(storms,wind)

## Source: local data frame [6 x 4]
## 
##     storm  wind pressure       date
##     (chr) (int)    (int)     (date)
## 1     Ana    40     1013 1997-06-30
## 2    Alex    45     1009 1998-07-27
## 3  Arthur    45     1010 1996-06-17
## 4  Arlene    50     1010 1999-06-11
## 5 Allison    65     1005 1995-06-03
## 6 Alberto   110     1007 2000-08-03

The Pipe Operator

select(airports, name,alt)

## Source: local data frame [1,397 x 2]
## 
##                              name   alt
##                             (chr) (int)
## 1               Lansdowne Airport  1044
## 2   Moton Field Municipal Airport   264
## 3             Schaumburg Regional   801
## 4                 Randall Airport   523
## 5           Jekyll Island Airport    11
## 6  Elizabethton Municipal Airport  1593
## 7         Williams County Airport   730
## 8   Finger Lakes Regional Airport   492
## 9    Shoestring Aviation Airfield  1000
## 10          Jefferson County Intl   108
## ..                            ...   ...

airports %>% select(name,alt)

## Source: local data frame [1,397 x 2]
## 
##                              name   alt
##                             (chr) (int)
## 1               Lansdowne Airport  1044
## 2   Moton Field Municipal Airport   264
## 3             Schaumburg Regional   801
## 4                 Randall Airport   523
## 5           Jekyll Island Airport    11
## 6  Elizabethton Municipal Airport  1593
## 7         Williams County Airport   730
## 8   Finger Lakes Regional Airport   492
## 9    Shoestring Aviation Airfield  1000
## 10          Jefferson County Intl   108
## ..                            ...   ...

A<- airports %>% filter(alt >= 2000)
kable(head(A))

faa	name	lat	lon	alt	tz	dst
2G9	Somerset County Airport	40.03887	-79.0150	2275	-5	A
36U	Heber City Municipal Airport	40.48181	-111.4288	5637	-6	A
4U9	Dell Flight Strip	44.73575	-112.7200	6007	-7	A
6S0	Big Timber Airport	45.80639	-109.9811	4492	-7	A
ABQ	Albuquerque International Sunport	35.04022	-106.6092	5355	-7	A
AIA	Alliance Municipal Airport	42.05333	-102.8039	3931	-7	A

Unit of Analysis

pollution %>% group_by(city) %>% summarise(mean = mean(amount), sum = sum(amount), n = n())

## Source: local data frame [3 x 4]
## 
##       city  mean   sum     n
##      (chr) (dbl) (dbl) (int)
## 1  Beijing  88.5   177     2
## 2   London  19.0    38     2
## 3 New York  18.5    37     2

planes %>% group_by(manufacturer) %>% summarise(mean=mean(seats),sd=sd(seats),n=n())

## Source: local data frame [35 x 4]
## 
##              manufacturer     mean         sd     n
##                     (chr)    (dbl)      (dbl) (int)
## 1              AGUSTA SPA   8.0000         NA     1
## 2                  AIRBUS 221.2024 81.4309017   336
## 3        AIRBUS INDUSTRIE 187.4025 23.8565154   400
## 4   AMERICAN AIRCRAFT INC   2.0000  0.0000000     2
## 5      AVIAT AIRCRAFT INC   2.0000         NA     1
## 6  AVIONS MARCEL DASSAULT  12.0000         NA     1
## 7           BARKER JACK L   2.0000         NA     1
## 8                   BEECH   9.5000  0.7071068     2
## 9                    BELL   8.0000  4.2426407     2
## 10                 BOEING 175.1877 59.4688097  1630
## ..                    ...      ...        ...   ...

Joining Data

Colors<-bind_cols(y,z)
kable(Colors)

x1	x2	x1	x2
A	1	B	2
B	2	C	3
C	3	D	4

kable(Colors)

x1	x2	x1	x2
A	1	B	2
B	2	C	3
C	3	D	4

Cr<- bind_rows(y, z)  
kable(Cr)

x1	x2
A	1
B	2
C	3
B	2
C	3
D	4

Un<-union(y, z)
kable(Un)

x1	x2
D	4
C	3
B	2
A	1

In<-intersect(y, z)
kable(In)

x1	x2
B	2
C	3

Set<-setdiff(y, z)
kable(Set)

x1	x2
A	1

AName <- left_join(songs, artists, by = "name")
kable(AName)

song	name	plays
Across the Universe	John	guitar
Come Together	John	guitar
Hello, Goodbye	Paul	bass
Peggy Sue	Buddy	NA

LFL<- left_join(songs2, artists2, by = c("first", "last"))
kable(LFL)

song	first	last	plays
Across the Universe	John	Lennon	guitar
Come Together	John	Lennon	guitar
Hello, Goodbye	Paul	McCartney	bass
Peggy Sue	Buddy	Holly	NA

InnerN<- inner_join(songs, artists, by = "name")
kable(InnerN)

song	name	plays
Across the Universe	John	guitar
Come Together	John	guitar
Hello, Goodbye	Paul	bass

SemNa<- semi_join(songs, artists, by = "name")
kable(SemNa)

song	name
Across the Universe	John
Come Together	John
Hello, Goodbye	Paul

AJ<-anti_join(songs, artists, by = "name")
kable(AJ)

song	name
Peggy Sue	Buddy