Schedule:
- ~30 min intro to apprenticeship
- ~30 min intro to R
- ~1.5 hours data wrangling
Introduction to the Apprenticeship program
Workshops
Detailed information about the workshop schedule is available in the course outline on Moodle. Any updates to the timing or location will also be announced there. Unless specified otherwise, workshops take place between 9:00-14:00 in classroom A-24.
For apprentices, active workshop participation is mandatory. Therefore, please bring your fully charged laptop to all workshops.
Other interested members of the UCR community may join in-person (please bring you laptop) or online. If interested, please enroll in the UCR Data Center Moodle course (enrollment key “Data-Center-1”) to receive Teams links and schedule updates.
Projects
You should work on your projects in the afternoons, preferably in room A-24, where the Data Center will be regularly available for support. We also encourage collaboration between apprentices: while all apprentices need to produce individual work, feel free to discuss your progress with each other and ask for help. Note that you are not expected to finish your projects by 26 January, but you should have some progress and a plan on how you’ll move forward. You will then have the rest of the semester to complete your project and submit your internship report (following the usual internship guidelines on the Intranet).
Presentation
On 26 January we will organize a session where all apprentices can showcase their work. You will have to prepare and briefly present a poster on your project; you will receive more detailed guidelines and expectations later. You do not need to have complete results, but you should demonstrate the progress and direction of your project.
Introduction to R
Installation and setup: prepare in advance
Before the first workshop, please make sure that you have access to R and RStudio on your laptop. If you need help with the installation, please follow this tutorial.
The R basics tutorial on the Data Center website explains the RStudio interface, and shows how to create a new project and how to install packages. Please make sure that you are familiar with these features before the workshop, and complete the following:
- create a new project for your apprenticeship work;
- install the
tidyversepackage.
Objects in R
One of the most basic types of objects in R is a vector. A vector is a
collection of values of the same type, such as numbers, characters, or
logicals (TRUE/FALSE). You can create a vector with the c() function,
which stands for concatenate. If you assign a vector to an object with
the assignment operator <-, your vector will be saved in your
environment so you can work with it within your current R session. Some
examples of creating vectors are:
v1 <- c("A", "B", "C")
v2 <- 25
v3 <- 1:10
To subset or extract elements from a vector, you can use square brackets
[ ] with an index. For example, v1[1] returns the first element of
v1, v3[2:5] returns the 2nd to 5th elements of v3, and
v3[-c(2, 4, 6)] returns all but the 2nd, 4th and 6th elements of v3.
v1[1]
## [1] "A"
v3[2:5]
## [1] 2 3 4 5
v3[-c(2, 4, 6)]
## [1] 1 3 5 7 8 9 10
A dataframe (or tibble in tidyverse) is a special type of object that
combines vectors into a rectangular table. Each column of a dataframe is
a vector, and each row is an observation. usually you would load data
from an external source, but you can create a dataframe with the
data.frame() and a tibble with the tibble() function. You can also
convert other data types such as matrices to tibbles with the
as_tibble() function. Both functions take vectors as their arguments.
Tibbles are preferred because they are more modern and have some
convenient features that dataframes don’t, but for the most part,
differences are minor and for the most part it does not matter whether
you work with tibbles or dataframes.
A simple example of creating a tibble is (make sure to load
tidyverse first):
library(tidyverse)
# define vectors within the tibble() function
tibble(
name = c("Alice", "Bob", "Chris"),
height = c(165, 180, 175)
)
## # A tibble: 3 × 2
## name height
## <chr> <dbl>
## 1 Alice 165
## 2 Bob 180
## 3 Chris 175
# define the vectors first, then combine them into a tibble
name <- c("Alice", "Bob", "Chris")
height <- c(165, 180, 175)
tibble(name, height)
## # A tibble: 3 × 2
## name height
## <chr> <dbl>
## 1 Alice 165
## 2 Bob 180
## 3 Chris 175
Functions in R
Functions are reusable pieces of code that perform a specific task. They take arguments as inputs and return one or more pieces of output. You will mostly work with functions loaded from various packages or from the base R distribution, and in some cases you may write your own functions to avoid repetition or improve the readability of your code. We will cover writing your own functions later in the program.
As with vectors, the output of a function is saved to your environment
only if you assign the result to an object. For example, sum(x) will
display the sum of the elements of the vector x, but sum <- sum(x)
will save this result to an object.
x <- c(1, 5, 6, 2, 1, 8)
sum(x)
## [1] 23
sum <- sum(x)
Some important functions on vectors are
mean(x) # return the mean; add the argument na.rm = TRUE if missing values should be excluded
## [1] 3.833333
length(x) # give the length of the vector (number of elements)
## [1] 6
unique(x) # list the unique elements of the vector
## [1] 1 5 6 2 8
To learn more about a function and its arguments, you can use the ?
operator or the help() function, for example by typing ?sum (or
equivalently, ?sum()). It is good practice to request help files from
your console and not you R script, since there is no need to save these
queries for the future.
Data wrangling in R
Importing data
In the following we will be working with a dataset on animal species
diversity and weights. You can load this data directly from this
link
by pasting the URL as the argument of the read_csv() function (make
sure you loaded tidyverse in your current R session). Pay attention to
the quotation marks around the URL so R treats the URL as a character
string to parse, and not an object defined in the R environment.
surveys <- read_csv("https://raw.githubusercontent.com/ucrdatacenter/projects/main/apprenticeship/2024h1/1_intro/surveys.csv")
After importing the data, the surveys object will show up in your
Environment tab. If you click on the object name, the full dataset will
be displayed in your data viewer. Looking at your console, you can see
that clicking on the object name automatically runs the View(surveys)
function. If you would like to get an overview of what variables are in
your data, you can use the summary function that gives you information
about each variable:
summary(surveys)
## record_id month day year plot_id
## Min. : 1 Min. : 1.000 Min. : 1.0 Min. :1977 Min. : 1.00
## 1st Qu.: 8964 1st Qu.: 4.000 1st Qu.: 9.0 1st Qu.:1984 1st Qu.: 5.00
## Median :17762 Median : 6.000 Median :16.0 Median :1990 Median :11.00
## Mean :17804 Mean : 6.474 Mean :16.1 Mean :1990 Mean :11.34
## 3rd Qu.:26655 3rd Qu.:10.000 3rd Qu.:23.0 3rd Qu.:1997 3rd Qu.:17.00
## Max. :35548 Max. :12.000 Max. :31.0 Max. :2002 Max. :24.00
##
## species_id sex hindfoot_length weight
## Length:34786 Length:34786 Min. : 2.00 Min. : 4.00
## Class :character Class :character 1st Qu.:21.00 1st Qu.: 20.00
## Mode :character Mode :character Median :32.00 Median : 37.00
## Mean :29.29 Mean : 42.67
## 3rd Qu.:36.00 3rd Qu.: 48.00
## Max. :70.00 Max. :280.00
## NA's :3348 NA's :2503
## genus species taxa plot_type
## Length:34786 Length:34786 Length:34786 Length:34786
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
Data wrangling
To learn the basics of data wrangling using the tidyverse (in
particular, the dplyer package within tidyverse), we follow Section 4
of the Data Carpentry course “Data Analysis and Visualization in R for
Ecologists”. It uses the species data we imported in the previous
section.
The tutorial covers
- how to select a subset of the variables in a dataframe;
- how to filter observations based on logical conditions (e.g. only keep observations from a particular area or removing missing values);
- how to create new variables or transform existing ones;
- how to analyze and summarize data within groups;
- how to convert data from wide to long format and vice versa;
- how to organize the data wrangling process into a tidy workflow using
pipes (
%>%or|>).
Please use this link to follow the relevant part of the tutorial.
A few notes on the contents of the tutorial:
- The Data Carpentry workshop uses the pipe from the
magrittrpackage (%>%). Now there is also an alternative, the base pipe (|>). For the most part, they are equivalent. In the workshops we will primarily use the base pipe. You can change your default pipe setting in RStudio -> Tools -> Global options -> Code -> Use native pipe operator. - The tutorial shows you how to filter out missing variables by
combining the
filter()andis.na()functions. An alternative is to use thedrop_na()function, especially if you would like to drop missing values from multiple or all variables. An example is below, showing how many observations remain in each case using thenrow()function. Note the character vector of variable names when using theall_of()selection helper function.
# drop all observations where at least one variable is missing
surveys |>
drop_na() |>
nrow()
## [1] 30676
# drop all observations where at least one of the listed variables is missing
surveys |>
drop_na(weight, hindfoot_length) |>
nrow()
## [1] 30738