Problem Set # 2

Frank Annie

Due Date: September 17, 2015 Total Points: 46

1 The following ten observations, taken during the years 1970-1979, are on October snow cover for Eurasia in units of millions of square kilometers. Follow the instructions and answer questions by typing the appropriate R commands.

Year Snow 1970 6.5 1971 12.0 1972 14.9 1973 10.0 1974 10.7 1975 7.9 1976 21.9 1977 12.5 1978 14.5 1979 9.2

1. Create a data frame from these data. (2)

year = c(1970:1979)
snow = c(6.5,12.0,14.9,10.0,10.7,7.9,21.9,12.5,14.5,9.2)
oct.snow = data.frame (year, snow)

2. What are the mean and median snow cover over this decade? (2)

mean(oct.snow$snow)

## [1] 12.01

median(oct.snow$snow)

## [1] 11.35

3. What is the standard deviation of the snow cover over this decade? (2)

sd(oct.snow$snow)

## [1] 4.390761

4. How many Octobers had snow cover greater than 10 million km\(^2\)? (2)

length(which(oct.snow$snow > 10))

## [1] 6

2 The data set rivers contains the lengths (miles) of 141 major rivers in North America.

1. What proportion of the rivers are shorter than 500 miles long? (2)

length(which(rivers < 500))/length(rivers)

## [1] 0.5815603

2. What proportion of the rivers are shorter than the mean length? (2)

length(which(rivers < mean (rivers)))/length(rivers)

## [1] 0.6666667

3. What is the 75th percentile river length? (2)

quantile(sort(rivers), pros = 0.75)

##   0%  25%  50%  75% 100% 
##  135  310  425  680 3710

4. What is the interquartile range in river length? (2)

IQR(rivers)

## [1] 370

3 The dataset hflights from the hflights package contains all 227,496 flights that departed Houston in 2011. Using the functions in the dplyr package

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

library (hflights)

1. Create a data frame from hflights containing only those flights that departed on September 11th of that year. (4)

hflights.df <-tbl_df(hflights)
hflights_filtered <- filter(hflights.df, Month == 9, DayofMonth ==11)
hflights_filtered

## Source: local data frame [602 x 21]
## 
##     Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier
##    (int) (int)      (int)     (int)   (int)   (int)         (chr)
## 1   2011     9         11         7    1546    1651            AA
## 2   2011     9         11         7     551     904            AA
## 3   2011     9         11         7    1936    2036            AA
## 4   2011     9         11         7    1438    1544            AA
## 5   2011     9         11         7    1720    2030            AA
## 6   2011     9         11         7    1142    1258            AA
## 7   2011     9         11         7    1006    1333            AA
## 8   2011     9         11         7     712     815            AA
## 9   2011     9         11         7     858    1003            AA
## 10  2011     9         11         7    1833    2051            AS
## ..   ...   ...        ...       ...     ...     ...           ...
## Variables not shown: FlightNum (int), TailNum (chr), ActualElapsedTime
##   (int), AirTime (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest
##   (chr), Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),
##   CancellationCode (chr), Diverted (int)

2. How many flights departed on that day? (2)

dim(hflights_filtered)

## [1] 602  21

4 Consider the SSN.txt file from http://myweb.fsu.edu/jelsner/data/SSN.txt. The file contains monthly sunspot numbers for since 1851.

1. Get the data into R. (4)

con = "http://myweb.fsu.edu/jelsner/data/SSN.txt"
SSN = read.table(con, na.string = "-9.900", header = TRUE)

2. Create a histogram of the September sunspot numbers. (2

library (ggplot2)

ggplot(SSN, aes(Sep)) + 
  geom_histogram(fill = "green") +
  xlab("Sun Spot") +
  theme_gray()

## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

3. Create a boxplot of the June sunspot numbers. Label the axis. (4)

boxplot(SSN$Jun, ylab = "Sun Spot" )
f = fivenum(SSN$Jun)
text(rep(1.3, 5), f, labels = c("Minimum","Maximum"))

4. Create a scatter plot placing the June sunspot numbers on the horizontal axis and September sunspot numbers on the vertical axis. Label the axes. (4)

ggplot(SSN, aes(x = Jun, y = Sep)) + geom_point() + xlab("June") + 
    ylab("Sun Spots")