Statistical Computing 1, Stat 590 Fall 2015

Transcrição

Statistical Computing 1, Stat 590
Fall 2015
Name: Answer Key
Prof. Erik B. Erhardt
Homework 03
Part I. (60 points) Do all calculations in LATEX + R + knitr. For this assignment, all R code
should well commented and be visible (echo=TRUE) in the document where you have written
it. Every time you create or modify an object, please show the results with the appropriate
function. Please do not display complete objects when they are large.
Weather data: This is an exercise in manipulating data. We will be using daily weather
data from the Albuquerque International Airport (KABQ).
(30pts )
1. Read and manipulate
(a) (10 pts) Read in the data directly from the KABQ weather website: http://www.
wunderground.com/, history, KABQ, date rante, etc.
Solution:
fn.list <- c(
"http://www.wunderground.com/history/airport/KABQ/2009/1/1/CustomHistory.html?dayend=31&monthend=12&yearend=2009&r
, "http://www.wunderground.com/history/airport/KABQ/2010/1/1/CustomHistory.html?dayend=31&monthend=12&yearend=2010&r
)
# list to hold all the data files
all.dat <- as.list(new.env())
# read all data
for (i.dat in 1:length(fn.list)) {
# read each year's data
dat.temp <- read.csv( fn.list[i.dat]
, stringsAsFactors = FALSE
)
# put data into it's own list
all.dat[[i.dat]] <- dat.temp
}
# all the column names match except for the last data.frame
colnames(all.dat[[1]]) == colnames(all.dat[[7]])
## [1] FALSE
## [12] TRUE
## [23] TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
# change that column name to match the others
colnames(all.dat[[7]])[1] <- "MST"
# combine all data together into one data.frame
dat <- do.call("rbind", all.dat)
str(dat)
## 'data.frame': 2441 obs. of
## $ MST
## $ Max.TemperatureF
## $ Mean.TemperatureF
23 variables:
: chr "2009-1-1" "2009-1-2" "2009-1-3" "2009-1-4" ...
: int 56 57 57 42 40 41 49 57 54 46 ...
: int 42 43 44 34 31 34 36 43 41 37 ...
SC1/HW03
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– Page 2 of 6 –
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
Min.TemperatureF
:
Max.Dew.PointF
:
MeanDew.PointF
:
Min.DewpointF
:
Max.Humidity
:
Mean.Humidity
:
Min.Humidity
:
Max.Sea.Level.PressureIn :
Mean.Sea.Level.PressureIn:
Min.Sea.Level.PressureIn :
Max.VisibilityMiles
:
Mean.VisibilityMiles
:
Min.VisibilityMiles
:
Max.Wind.SpeedMPH
:
Mean.Wind.SpeedMPH
:
Max.Gust.SpeedMPH
:
PrecipitationIn
:
CloudCover
:
Events
:
WindDirDegrees.br...
:
int
int
int
int
int
int
int
num
num
num
int
int
int
int
int
int
chr
int
chr
chr
Name: Answer Key
28 28 31 25 21 27 23 28 27 28 ...
23 21 26 28 23 23 23 25 26 23 ...
20 19 19 17 16 18 20 23 23 16 ...
18 15 16 6 10 14 17 21 17 11 ...
69 69 53 70 68 69 69 69 75 69 ...
47 45 37 55 53 52 53 50 54 47 ...
24 20 20 39 37 34 36 30 32 25 ...
30.1 30 29.9 30.2 30.2 ...
30 29.9 29.8 30 30 ...
29.9 29.7 29.7 29.8 29.8 ...
10 10 10 10 10 10 10 10 10 10 ...
10 10 10 9 10 10 10 10 10 10 ...
10 10 10 2 10 10 10 10 10 10 ...
10 9 22 36 22 25 22 14 23 18 ...
3 3 7 18 5 14 10 6 8 7 ...
14 12 29 43 29 33 29 17 29 25 ...
"0.00" "0.00" "0.00" "T" ...
1 1 4 7 6 4 4 1 3 2 ...
"" "" "" "Snow" ...
"27<br />" "119<br />" "298<br />" "77<br />" ...
(b) (5 pts) Create a date column from the MST column (should be in POSIXct format).
Use as.Date() or a function from the lubridate package.
Solution:
library(lubridate)
dat$Date <- ymd(dat$MST)
str(dat[, c("MST", "Date")])
## 'data.frame': 2441 obs. of 2 variables:
## $ MST : chr "2009-1-1" "2009-1-2" "2009-1-3" "2009-1-4" ...
## $ Date: POSIXct, format: "2009-01-01" ...
(c) (10 pts) Create (numeric) month and year columns from the MST column. Use ?sub
and try of the examples there; also, google “regular expression R” for more info. Or
try package stringr and function str_split() and rbind().
Solution:
library(stringr)
# the result of spliting the strings is a list of lists
dat.ymd <- str_split(dat$MST, "-")
head(dat.ymd)
## [[1]]
## [1] "2009" "1"
"1"
##
## [[2]]
## [1] "2009" "1"
"2"
##
## [[3]]
## [1] "2009" "1"
"3"
SC1/HW03
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– Page 3 of 6 –
[[4]]
[1] "2009" "1"
"4"
[[5]]
[1] "2009" "1"
"5"
[[6]]
[1] "2009" "1"
"6"
Name: Answer Key
# bind the lists together using rbind
dat.ymd <- do.call("rbind", str_split(dat$MST, "-"))
head(dat.ymd)
##
##
##
##
##
##
##
[1,]
[2,]
[3,]
[4,]
[5,]
[6,]
[,1]
"2009"
"2009"
"2009"
"2009"
"2009"
"2009"
[,2]
"1"
"1"
"1"
"1"
"1"
"1"
[,3]
"1"
"2"
"3"
"4"
"5"
"6"
dat.ymd <- data.frame(Year = dat.ymd[,1], Month = dat.ymd[,2], stringsAsFactors = FALSE)
str(dat.ymd)
## $ Year : chr "2009" "2009" "2009" "2009" ...
## $ Month: chr "1" "1" "1" "1" ...
# combine all data together into one data.frame
dat <- cbind(dat, dat.ymd)
# order the date factor variables
dat$Year <- factor(dat$Year)
dat$Month <- factor(dat$Month, levels = 1:12, labels = as.character(1:12), ordered = TRUE)
(d) (5 pts) Replace all occurances of T with 0 in the PrecipitationIn column make
convert to a numeric variable using as.numeric().
Solution:
str(dat$PrecipitationIn)
## chr [1:2441] "0.00" "0.00" "0.00"
table(dat$PrecipitationIn)
##
## 0.00 0.01 0.02 0.03 0.04 0.05 0.06
## 1744
56
40
21
26
20
7
## 0.14 0.15 0.16 0.17 0.18 0.19 0.20
##
9
5
5
1
3
4
5
## 0.28 0.29 0.31 0.32 0.34 0.35 0.37
##
2
1
2
2
2
1
1
## 0.51 0.52 0.56 0.57 0.58 0.59 0.60
##
2
1
2
2
1
1
1
## 0.98 1.06 1.22 1.36 1.77 1.82
T
##
1
1
1
1
1
1 349
# replace "T" with 0
"T" "0.00" "0.00" ...
0.07
9
0.21
3
0.38
2
0.63
1
0.08
17
0.22
4
0.41
2
0.66
1
0.09
9
0.23
3
0.42
3
0.69
2
0.10
12
0.24
4
0.43
1
0.75
1
0.11
15
0.25
5
0.44
1
0.85
1
0.12
7
0.26
3
0.46
1
0.91
1
0.13
6
0.27
2
0.50
1
0.96
2
SC1/HW03
– Page 4 of 6 –
Name: Answer Key
dat$PrecipitationIn[(dat$PrecipitationIn == "T")] <- 0
# change to numeric
dat$PrecipitationIn <- as.numeric(dat$PrecipitationIn)
str(dat$PrecipitationIn)
##
(30pts )
num [1:2441] 0 0 0 0 0 0 0 0 0 0 ...
2. Subset and plot
(a) (10 pts) Use subset to keep only the columns related to the dates (including those
created above), and mean precipitation, temperature, and wind.
Solution:
# subset data
dat.sub <- subset(dat, select = c("Mean.TemperatureF"
, "Mean.Wind.SpeedMPH"
, "PrecipitationIn"
, "Date"
, "Year"
, "Month"
))
str(dat.sub)
## $ Mean.TemperatureF : int 42 43 44 34 31 34 36 43 41 37 ...
## $ Mean.Wind.SpeedMPH: int 3 3 7 18 5 14 10 6 8 7 ...
## $ PrecipitationIn
: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Date
: POSIXct, format: "2009-01-01" ...
## $ Year
: Factor w/ 7 levels "2009","2010",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Month
: Ord.factor w/ 12 levels "1"<"2"<"3"<"4"<..: 1 1 1 1 1 1 1 1 1 1 ...
(b) (10 pts) Use the melt() function to transform the subsetted object from wide form
into long form. What are the id.vars here?
Solution:
library(reshape2)
dat.sub.long <- melt(dat.sub, id.vars = c("Date", "Year", "Month"))
str(dat.sub.long)
## $ Date
: POSIXct, format: "2009-01-01" ...
## $ Year
: Factor w/ 7 levels "2009","2010",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Month
: Ord.factor w/ 12 levels "1"<"2"<"3"<"4"<..: 1 1 1 1 1 1 1 1 1 1 ...
## $ variable: Factor w/ 3 levels "Mean.TemperatureF",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value
: num 42 43 44 34 31 34 36 43 41 37 ...
(c) (10 pts) Create a plot of the measured variables over time using geom_point() and
facet_wrap(, scales = ). Organize your facets (small multiples) so they are easily
comparible.
Solution:
SC1/HW03
– Page 5 of 6 –
Name: Answer Key
library(ggplot2)
p <- ggplot(dat.sub.long, aes(x = Date, y = value))
p <- p + geom_point()
p <- p + facet_wrap( ~ variable, ncol = 1, scales = "free_y")
print(p)
## Warning:
## Warning:
Removed 1 rows containing missing values (geom point).
Removed 1 rows containing missing values (geom point).
Mean.TemperatureF
75
50
25
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2010
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2012
2014
2016
Date
(d) (0 pts) EXTRA +5: Add a local trend line with geom_smooth(), and explain what
this does, and improve other aspects of your plot. You’ll see that the the default
smoother doesn’t capture the patterns that well. Improve by clicking through the
help: follow geom_smooth() to stat_smooth() to the method used, to loess() and
the span parameter.
Solution:
library(ggplot2)
p <- ggplot(dat.sub.long, aes(x = Date, y = value))
p <- p + geom_line()
# free_y for separate axes
p <- p + facet_wrap( ~ variable, ncol = 1, scales = "free_y")
# span small to be sensitive to small changes
p <- p + geom_smooth(size = 2, method = loess, span = 0.1)
print(p)
##
##
##
##
Warning:
Warning:
Warning:
Warning:
Removed
Removed
Removed
Removed
1
1
1
1
rows
rows
rows
rows
containing
containing
containing
containing
missing
missing
missing
missing
values
values
values
values
(stat
(stat
(geom
(geom
smooth).
smooth).
path).
path).
SC1/HW03
– Page 6 of 6 –
Name: Answer Key
Mean.TemperatureF
75
50
25
Mean.Wind.SpeedMPH
value
30
20
10
0
PrecipitationIn
1.5
1.0
0.5
0.0
2010
2012
2014
Date
2016

Statistical Computing 1, Stat 590 Fall 2015

Transcrição

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