Read Chapter 3 to help you complete the questions in this exercise.
1. As in previous exercises, either create a new R script or continue
with your previous R script in your RStudio Project. Again, make sure
you include any metadata you feel is appropriate (title, description of
task, date of creation etc) and don’t forget to comment out your
metadata with a # at the beginning of the line.
2. If you haven’t already, download the data file
‘whaledata.xlsx’ from the Data link
and save it to the data directory you created in Exercise 1
in your RStudio project.
3. Open the ‘whaledata.xlsx’ file in Microsoft Excel (or
even better use an open source equivalent - LibreOffice is
a good free alternative) and save it as a tab delimited file type (see
Section
3.3.1 of the Introduction to R book or watch this video
if you’re not sure how to do this). Name the file
‘whaledata.txt’ and save it to the data directory.
If you’re a Windows user be careful with file extensions (things like
.txt). By default, Windows doesn’t show you the file extension
(maybe the boffins at Microsoft don’t think you need to know complicated
things like this?) so if you enter ‘whaledata.txt’ as a
filename you might end up with a filename
’whaledata.txt.txt’!
4. Time for a quick description of the ‘whaledata.txt’ dataset to get your bearings. These data were collected during two research cruises in the North Atlantic in May and October 2003. During these two months the research vessel visited multiple stations (areas) and marine mammal observers recorded the number of whales (who doesn’t love whales!) at each of these stations. The time the vessel spent at each station was also recorded along with other site specific variables such as the latitude and longitude, water depth and gradient of the seabed. The researchers also recorded the ambient level of sub-surface noise with a hydrophone and categorised this variable into ‘low’, ‘medium’ or ‘high’. The structure of these data is known as a rectangular dataset (aka ‘tidy’ data by the cool kids) with no missing cells. Each row is an individual observation and each column a separate variable. The variable names are contained in the first row of the dataset (aka a header).
5. Now let’s import the ‘whaledata.txt’ file into R. To do
this you will use the workhorse function of data importing,
read.table(). This function is incredibly flexible and can
import many different file types (take a look at the help file)
including our tab delimited file. Don’t forget to include the
appropriate arguments when using the read.table() function
(remember that header?) and assign it a variable with an appropriate
name (such as whale). Take a look at Section
3.2.2 of the Introduction to R book or watch this video
if you need any further information.
6. Once you’ve imported your data file nothing much seems to happen
(don’t worry, this is normal). To examine the contents of the dataframe
one option would be to just type the variable name (whale)
into the console. This is probably not a good idea and doesn’t really
tell you anything about the dataframe other than there’s alot of data
(try it)! A slightly better option is to use the head()
function to display the first 5 rows of your dataframe. Again, this is
likely to just fill up your console. A better option would be to use the
names() function which will return a vector of variable
names from your dataset. However, all you get are the names of the
variables but no other information. A much, much better option is to use
the str() function to display the structure of the dataset
and a neat summary of your variables. Another advantage is that you can
copy this information from the console and paste it into your R script
(making sure it’s commented) for later reference. How many observations
does this dataset have? How many variables are in this dataset? What
type of variables are month and
water.noise?
head(whale) # display the first 5 rows
names(whale) # display the variable names
str(whale) # display the structure of the dataframe whale
# 'data.frame': 100 obs. of 8 variables:
# $ month : Factor w/ 2 levels "May","October": 1 1 1 1 ...
# $ time.at.station: int 1344 1633 743 1050 1764 580 459 ...
# $ water.noise : Factor w/ 3 levels "high","low","medium": 2 3 3 3 ...
# $ number.whales : int 7 13 12 10 12 10 5 8 11 12 ...
# $ latitude : num 60.4 60.4 60.5 60.3 60.4 ...
# $ longitude : num -4.18 -4.19 -4.62 -4.35 -5.2 ...
# $ depth : int 520 559 1006 540 1000 1000 993 988 ...
# $ gradient : int 415 405 88 409 97 173 162 162 245 161 ..
# the dataframe whale has 100 observations
# the dataframe whale has 8 variables
# the variables month and water.noise are factors
7. You can get another useful summary of your dataframe by using the
summary() function. This will provide you with some useful
summary statistics for each variable. Notice how the type of output
depends on whether the variable is a factor or a number. Another useful
feature of the summary() function is that it will also
count the number of missing values in each variable. Which variables
have missing values and how many?
summary(whale)
# NOTE: I have removed the last 3 column information to save space!
# month time.at.station water.noise number.whales latitude
# May :50 Min. : 60.0 high :15 Min. : 0.00 Min. :60.29
# October:50 1st Qu.: 693.8 low :28 1st Qu.: 9.00 1st Qu.:60.69
# Median :1077.5 medium:57 Median :11.00 Median :61.29
# Mean :1064.7 Mean :11.56 Mean :61.16
# 3rd Qu.:1349.2 3rd Qu.:14.00 3rd Qu.:61.59
# Max. :2158.0 Max. :28.00 Max. :62.10
# NA's :1
# the variable number.whales has one missing value (NA)
8. Summarising and manipulating dataframes is a key skill to acquire
when learning R. Although there are many ways to do this, we will
concentrate on using the square bracket [ ] notation which
you used previously with vectors. The key thing to remember when using
[ ] with dataframes is that dataframes have two dimensions
(think rows and columns) so you always need to specify which rows and
which columns you want inside the [ ] (see Section
3.4.1 and this video
for some additional background information and a few examples). Let’s
practice.
Extract all the elements of the first 10 rows and the first 4
columns of the whale dataframe and assign to a new variable
called whale.sub.
Next, extract all observations (remember - rows) from the
whale dataframe and the columns month,
water.noise and number.whales and assign to a
variable called whale.num.
Now, extract the first 50 rows and all columns form the original
dataframe and assign to a variable whale.may (there’s a
better way to do this with conditional statements - see below).
Finally, extract all rows except the first 10 rows and all columns except the last column. Remember, for some of these questions you can specify the columns you want either by position or by name. Practice both ways. Do you have a preference? If so why?
# first 10 rows and first 4 columns
whale.sub <- whale[1:10, 1:4]
# all rows and columns 1, 3 and 6
whale.num <- whale[, c(1, 3, 4)]
# alternative way of indexing columns with named indexes
whale.num <- whale[, c("month", "water.noise", "number.whales")]
# first 50 rows and all columns
whale.may <- whale[1:50, ]
# excluding first 10 rows and last column using negative indexing
whale.last <- whale[-c(1:10), -8]
# more general way if you have lots of columns
whale.last <- whale[-c(1:10), -c(ncol(whale))]
# NOTE: this doesn't work for named columns
whale.last <- whale[-c(1:10), -c("gradient")]
9. In addition to extracting rows and columns from your dataframe by position you can also use conditional statements to select particular rows based on some logical criteria. This is very useful but takes a bit of practice to get used to (see Section 3.4.2 for an introduction). Extract rows from your dataframe (all columns by default) based on the following criteria (note: you will need to assign the results of these statements to appropriately named variables, I’ll leave it up to you to use informative names!):
whale.1200 <- whale[whale$depth > 1200, ]
whale.200 <- whale[whale$gradient > 200, ]
whale.low <- whale[whale$water.noise == "low", ]
whale.h.may <- whale[whale$water.noise == "high" & whale$month == "May", ]
whale.subset <- whale[whale$month == "October" & whale$water.noise == "low" & whale$gradient > 132, ]
whale.lat.long <- whale[whale$latitude > 60 & whale$latitude < 61 & whale$longitude > -6 & whale$longitude <
-4, ]
whale.nomed <- whale[whale$water.noise != "medium", ]
10. A really neat feature of extracting rows based on conditional
statements is that you can include R functions within the statement
itself. To practice this, modify your answer to the gradient question in
Q9 to use the median() function rather than hard coding the
value 132.
whale.subset <- whale[whale$month == "October" & whale$water.noise == "low" & whale$gradient > median(whale$gradient),
]
11. However, when using functions in conditional statements you need
to be careful. For example, write some code to extract all rows from the
dataframe whale with depths greater than 1500 m and with a
greater number of whales spotted than average (hint: use the
mean() function in your conditional statement). Can you see
a problem with the output? Discuss the cause of this problem with an
instructor and explore possible solutions.
# results in a dataframe filled with NAs.
whale.new <- whale[whale$depth > 1500 & whale$number.whales > mean(whale$number.whales), ]
# the variable number.whales contains 1 NA value. By default the mean function will return an NA.
# use the na.rm argument to ignore NAs
whale.new <- whale[whale$depth > 1500 & whale$number.whales > mean(whale$number.whales, na.rm = TRUE),
]
12. Although you have concentrated on using the square bracket
[ ] notation to extract rows and columns from your
dataframe, there are of course many other approaches. One such approach
is to use the subset() function (see ?subset
or search for the subset function in the Introduction to R
book to find more information). Use the subset() function
to extract all rows in ‘May’ with a time at station less than 1000
minutes and a depth greater than 1000 m. Also use subset()
to extract data collected in ‘October’ from latitudes greater than 61
degrees but only include the columns month,
latitude, longitude and
number.whales.
subset(whale, month == "May" & time.at.station < 1000 & depth > 1000)
subset(whale, month == "October" & latitude > 61, select = c("month", "latitude", "longitude", "number.whales"))
13. Another useful way to manipulated your dataframes is to sort the
rows based on the value of a variable (or combinations of variables).
Rather counter-intuitively you should use the order()
function to sort your dataframes, not the sort() function
(see Section
3.4.3 of the Introduction to R book for an explanation). Ordering
dataframes uses the same logic you practised in Q14 in Exercise 2. Let’s
practice with a straight forward example. Use the order()
function to sort all rows in the whale dataframe in
ascending order of depth (shallowest to deepest). Store this sorted
dataframe in a variable called whale.depth.sort.
14. Now for something a little more complicated. Sort all rows in the
whale dataframe by ascending order of depth within each
level of water noise. The trick here is to remember that you can order
by more than one variable when using the order() function
(see Section
3.4.3 again). Don’t forget to assign your sorted dataframe to a new
variable with a sensible name. Repeat the previous ordering but this
time order by descending order of depth within each
level of water noise.
# notice how the variable water.noise has been ordered - why?
whale.sorted <- whale[order(whale$water.noise, whale$depth), ]
# use '-' to reverse the order of depth
whale.rev.sorted <- whale[order(whale$water.noise, -whale$depth), ]
15. Often, we would like to summarise variables by, for example, calculating a mean, median or counting the number of observations. To do this for a single variable it’s fairly straight forward :
mean(whale$time.at.station) # mean time at station
median(whale$depth) # median depth
length(whale$number.whales) # number of observations
15. (cont) Perhaps more interestingly, you might want summarise one variable conditional on the level of another factor variable. For example, write some R code to calculate the mean number of whales sighted at each of the three levels of water noise (see Section 3.5 for a few hints). Next, calculate the median number of whales sighted at each level of water noise and for each month.
tapply(whale$number.whales, whale$water.noise, mean) # notice the NA?
# use the na.rm argument again
tapply(whale$number.whales, whale$water.noise, mean, na.rm = TRUE)
# alternative method using the with() function. see ?with
with(whale, tapply(number.whales, water.noise, mean, na.rm = TRUE))
# when using multiple factors these need to be supplied as a list
tapply(whale$number.whales, list(whale$water.noise, whale$month), median, na.rm = TRUE)
16. Another useful function for summarising dataframes is
aggregate(). Search in the R book for the function
aggregate to see how to use this function (or see
?aggregate). Use the aggregate() function to
calculate the mean of time at station, number of whales, depth and
gradient for each level of water noise (don’t forget about that sneaky
NA value). Next calculate the mean of time at station, number of whales,
depth and gradient for each level of water noise for each month.
(Optional): For an extra bonus point see if you can figure out how to
modify your previous code to display the mean values to 2 decimal places
rather than the default of 3 decimal places.
aggregate(whale[, c(2, 4, 7, 8)], by = list(water.noise = whale$water.noise), mean, na.rm = TRUE)
aggregate(whale[, c(2, 4, 7, 8)], by = list(water.noise = whale$water.noise, month = whale$month), mean,
na.rm = TRUE)
# optional question. Need to specify a function 'on the fly' using function(x){}
aggregate(whale[, c(2, 4, 7, 8)], by = list(water.noise = whale$water.noise, month = whale$month), function(x) {
round(mean(x, na.rm = TRUE), digits = 2)
})
17. Knowing how many observations are present for each factor level
(or combinations of factor levels) is useful to determine whether you
have an adequate sample size (for subsequent modelling for example). Use
the table() function to determine the number of
observations for each level of water noise (see Section
3.5 again for more information). Next use the same function to
display the number of observations for each combination of water noise
and month. (Optional): The xtabs() function is very
flexible for creating tables of counts for factor combinations (aka
contingency tables). Take a look at the Introduction to R book, the help
file or Google to figure out how to use the xtabs()
function to replicate your use of the table() function.
# using table
table(whale$water.noise)
table(whale$water.noise, whale$month)
# using xtabs
xtabs(~ water.noise, data = whale)
xtabs(~ month + water.noise, data = whale)
18. Ok, we have spent quite a bit of time (and energy) learning how
to import and manipulate dataframes. The last thing we need to cover is
how to export dataframes from R to an external file (see Section
3.6 of the book for more details). Let’s say you want to export the
dataframe whale.num you created previously (see Q8) to a
file called ‘whale_num.txt’ in your output directory which
you created in Exercise 1. To do this you will need to use the
write.table() function. You want to include the the
variable names in the first row of the file, but you don’t want to
include the row names. Also, make sure the file is a tab delimited file.
Once you have create your file, try to open it in Microsoft Excel (or
open source equivalent).
End of Exercise 3