[ and [[Given a vector of random numbers (picked from a normal distribution) vec <- rnorm(100), retrieve the 5th, 10th and 15th elements from it.
R has a special vector called letters that stores the letter of the alphabet. Use it in combination with the function rep() to create a vector of 100 that contains only a and e.
Starting with vec <- 1:100, and using the function seq() create a vector that doesn’t contain the multiples of 5 (i.e., 5, 10, … 100).
Typically, rows are not associated with names, so to remove them from the
data.frame, you can do:
surveys <- read.csv(file="data/surveys.csv")
surveys_missingRows <- surveys[-c(10, 50:70), ] # removing rows 10, and 50 to 70
Let’s get a closer look at our data. For instance, we might want to know how many animals we trapped in each plot, or how many of each species were caught.
To get a vector of all the species, we are going to use the unique()
function that tells us the unique values in a given vector:
unique(surveys$species_id)
## [1] NL DM PF PE DS PP SH OT DO OX SS OL RM SA PM AH DX AB CB CM CQ RF
## [24] PC PG PH PU CV UR UP ZL UL CS SC BA SF RO AS SO PI ST CU SU RX PB PL
## [47] PX CT US
## 49 Levels: AB AH AS BA CB CM CQ CS CT CU CV DM DO DS DX NL OL OT ... ZL
The function table(), tells us how many of each species we have:
table(surveys$species_id)
##
## AB AH AS BA CB CM CQ CS CT CU CV
## 763 303 437 2 46 50 13 16 1 1 1 1
## DM DO DS DX NL OL OT OX PB PC PE PF
## 10596 3027 2504 40 1252 1006 2249 12 2891 39 1299 1597
## PG PH PI PL PM PP PU PX RF RM RO RX
## 8 32 9 36 899 3123 5 6 75 2609 8 2
## SA SC SF SH SO SS ST SU UL UP UR US
## 75 1 43 147 43 248 1 5 4 8 10 4
## ZL
## 2
R has a lot of built in statistical functions, like mean(), median(),
max(), min(). Let’s start by calculating the average weight of all the
animals using the function mean():
mean(surveys$wgt)
## [1] NA
Hmm, we just get NA. That’s because we don’t have the weight for every animal
and missing data is recorded as NA. By default, all R functions operating on a
vector that contains missing data will return NA. It’s a way to make sure that
users know they have missing data, and make a conscious decision on how to deal
with it.
When dealing with simple statistics like the mean, the easiest way to ignore
NA (the missing data) is to use na.rm=TRUE (rm stands for remove):
mean(surveys$wgt, na.rm=TRUE)
## [1] 42.67243
In some cases, it might be useful to remove the missing data from the
vector. For this purpose, R comes with the function na.omit:
wgt_noNA <- na.omit(surveys$wgt)
For some applications, it’s useful to keep all observations, for others, it
might be best to remove all observations that contain missing data. The function
complete.cases() removes any rows that contain at least one missing
observation:
surveys_complete <- surveys[complete.cases(surveys), ]
If you want to remove only the observations that are missing data for one
variable, you can use the function is.na(). For instance, to create a new
dataset that only contains individuals that have been weighted:
surveys_with_weights <- surveys[!is.na(surveys$weight), ]
## Warning in is.na(surveys$weight): is.na() applied to non-(list or vector)
## of type 'NULL'
To determine the number of elements found in a vector, we can use
use the function length() (e.g., length(surveys$wgt)). Using length(), how
many animals have not had their weights recorded?
What is the median weight for the males?
What is the range (minimum and maximum) weight?
Bonus question: what is the standard error for the weight? (hints: there is
no built-in function to compute standard errors, and the function for the
square root is sqrt()).
What if we want the maximum weight for all animals, or the average for each plot?
R comes with convenient functions to do this kind of operations, functions in
the apply family.
For instance, tapply() allows us to repeat a function across each level of a
factor. The format is:
tapply(columns_to_do_the_calculations_on, factor_to_sort_on, function)
If we want to calculate the mean for each species (using the complete dataset):
tapply(surveys_complete$wgt, surveys_complete$species_id, mean)
## AB AH AS BA CB
## NA NA NA NA 8.600000 NA
## CM CQ CS CT CU CV
## NA NA NA NA NA NA
## DM DO DS DX NL OL
## 43.157864 48.870523 120.130546 NA 159.245660 31.575258
## OT OX PB PC PE PF
## 24.230556 21.000000 31.735943 NA 21.586508 7.923127
## PG PH PI PL PM PP
## NA 31.064516 19.250000 19.138889 21.364155 17.173942
## PU PX RF RM RO RX
## NA 19.000000 13.386667 10.585010 10.250000 15.500000
## SA SC SF SH SO SS
## NA NA 58.878049 73.148936 55.414634 93.500000
## ST SU UL UP UR US
## NA NA NA NA NA NA
## ZL
## NA
This produces some NA because R “remembers” all species that were found in the
original dataset, even if they didn’t have any weight data associated with them
in the current dataset. To remove the NA and make things clearer, we can
redefine the levels for the factor “species” before calculating the means. Let’s
also create an object to store these values:
surveys_complete$species_id <- factor(surveys_complete$species_id)
species_mean <- tapply(surveys_complete$wgt, surveys_complete$species_id, mean)
surveys_summary) that contains as columns:
species the 2 letter code for the species namesmean_wgt the mean weight for each speciessd_wgt the standard deviation for each speciesmin_wgt the minimum weight for each speciesmax_wgt the maximum weight for each speciesAnswers
species_max <- tapply(surveys_complete$wgt, surveys_complete$species_id, max)
species_min <- tapply(surveys_complete$wgt, surveys_complete$species_id, min)
species_sd <- tapply(surveys_complete$wgt, surveys_complete$species_id, sd)
nlevels(surveys_complete$species_id) # or length(species_mean)
## [1] 25
surveys_summary <- data.frame(species=levels(surveys_complete$species_id),
mean_wgt=species_mean,
sd_wgt=species_sd,
min_wgt=species_min,
max_wgt=species_max)