November 3rd, 2021
Computational analyses require methods and notes to be recorded the same way you would for wet lab experiments. An excellent way to do this is via R Markdown documents. R Markdown documents are documents that combine text, R code, and R code output, and figures. They are a great way to produce self-contained and documented statistical analyses.
In this first worksheet, you will learn how to do some basic markdown
editing in addition to the basic use of variables and functions in R.
After you have made a change to the document, press “Knit HTML” in R
Studio and see what kind of a result you get. Note: You
may have to disable pop-ups to get this to work; moreover, if the
document contains any erroneous code (e.g., typos), you will get an
error that prevents the knit. You can bypass this by debugging the code
or commenting it out with a #.
Below I have demonstrated some basic R Markdown features, as described here. In your own work, you can use Markdown syntax to organize your coding notebook.
This text is bold.
This text is in italics.
This is a numbered list:
A bulleted list:
A nested list:
Block quote:
“Science is magic that works.” — Kurt Vonnegut
R code embedded in R chunks will be executed and the output will be shown.
# R code is embedded into this chunk
# when we start a line with '#', that tells R not to interpret the line as code
# this is called commenting code, and documents its purpose
# the code below assigns integers to the variables 'x' and 'y'
x <- 7
y <- 1029
# we can perform operations with variables
z <- x * y
z## [1] 7203# this is a string assigned to the variable 'my_name'
my_name <- "Rachael"
# we can create vectors of integers and strings
nums <- c(4, 8, 3, 6, 9)
fruits <- c("strawberries", "bananas", "apples", "peaches", "mangos")
# and combine them into a table using the data.frame() function
grocery_list <- data.frame(fruits, nums)
grocery_list## fruits nums
## 1 strawberries 4
## 2 bananas 8
## 3 apples 3
## 4 peaches 6
## 5 mangos 9# there are a number of ways to extract specific information from a table
# for instance, selecting the first column:
grocery_list[1]## fruits
## 1 strawberries
## 2 bananas
## 3 apples
## 4 peaches
## 5 mangosgrocery_list['fruits']## fruits
## 1 strawberries
## 2 bananas
## 3 apples
## 4 peaches
## 5 mangosselect(grocery_list, fruits)## fruits
## 1 strawberries
## 2 bananas
## 3 apples
## 4 peaches
## 5 mangos# the following code also targets the first column, but extracts the information in a different way
# can you spot the difference?
grocery_list$fruits## [1] strawberries bananas apples peaches mangos
## Levels: apples bananas mangos peaches strawberriesProblem Set #1:
# assigning integers to variables
fav_num <- 6
second_fav_num <- 13
some_new_num <- second_fav_num / fav_num
# assigning strings to variables
fav_enzyme <- "cyclooxygenase"
# creating a vector of strings
fav_foods <- c("sashimi", "dumplings", "jambalaya", "tacos", "pizza")
fav_foods## [1] "sashimi" "dumplings" "jambalaya" "tacos" "pizza"# creating a vector of integers
random_nums <- c(6, 13, 21, 51, 63)
random_nums## [1] 6 13 21 51 63# combining vectors into a dataframe
new_df <- data.frame(fav_foods, random_nums)
new_df## fav_foods random_nums
## 1 sashimi 6
## 2 dumplings 13
## 3 jambalaya 21
## 4 tacos 51
## 5 pizza 63# calling a column in a dataframe
new_df$fav_foods## [1] sashimi dumplings jambalaya tacos pizza
## Levels: dumplings jambalaya pizza sashimi tacosA function is statement internally (i.e., “under the hood”) coded to
perform a specific task. For instance, the head() function
displays the first several rows of a data frame or values in a
vector.
R comes with many built-in functions and data sets. Type
data() in the console to look at a list of all available
data sets. Type ?iris in the console for more information
about this specific data set. Important: You can ask for help
with any built-in data set or function by typing
?<function> in the console; for example,
?head or ?summary.
You can take a glance at the iris data set using the
head() function. Run the code chunk below to test this.
# preview the first few rows a data frame
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaYou can also use the summary() function to see the
summary statistics of a data set at a glance.
# look at summary statistics for the iris data set
summary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## You can see the column names of iris from the code
output above. We can perform calculations on this data set using a
number of functions built into R. See the example below, which
calculates the median of the Sepal.Length column.
# calculate median sepal length across all species of iris
median(iris$Sepal.Length)## [1] 5.8Problem Set #2:
Petal.Length column using the
mean() function.Petal.Width column using the
range() function.# calculate the mean of the `Petal.Length` column in the `iris` dataset
mean(iris$Petal.Length)## [1] 3.758# calculate the range of the `Petal.Width` column in the `iris` dataset
range(iris$Petal.Width)## [1] 0.1 2.5There are several ways to upload data into your R environment. We covered one way in Part 1 of the worksheet: manual entry. However, this is clearly not feasible for big data sets–more often, we want to read in a file containing our data. Also, we tend to modify data frames and save them to a new file.
Problem Set #3:
mushrooms_small.csv from the
“Test data set” link on the class webpage.read_csv() function to read the file, and save
it as a data frame called mushrooms. Important: The
file name must be given to the function as a string.head() function to preview the first 10
rows of the new data frame. Specify the integer as the second
argument of the function.head() function as a new data
frame called mushrooms_tiny.write_csv function to write the data frame
mushrooms_tiny to a new .csv file.
Important: The file name must be given to the function as a
string.Note: If you are coding on a local installation of R, you will have to specify a path to the location of the file or move the file to the working directory. Local installations of R do not have an “Upload” function. These concepts are covered at the end of this section.
# read a file
mushrooms <- read_csv("mushrooms_small.csv")## Rows: 100 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): class, cap_shape, cap_surface, cap_color, odor, gill_spacing, gill...
## dbl (1): ring_number
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# look at the first 10 rows of that dataset
head(mushrooms, 10)## # A tibble: 10 × 17
## class cap_s…¹ cap_s…² cap_c…³ odor gill_…⁴ gill_…⁵ gill_…⁶ stalk…⁷ stalk…⁸
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 poison… convex scaly red spicy close narrow buff taperi… missing
## 2 edible convex smooth red none close broad white enlarg… missing
## 3 edible convex smooth gray none crowded broad chocol… taperi… equal
## 4 edible flat scaly brown almo… close broad pink enlarg… rooted
## 5 edible flat fibrous brown none crowded broad pink taperi… equal
## 6 poison… convex fibrous yellow foul close broad chocol… enlarg… bulbous
## 7 poison… convex smooth brown spicy close narrow buff taperi… missing
## 8 edible bell scaly white almo… close broad brown enlarg… club
## 9 edible knobbed smooth brown none close broad orange enlarg… missing
## 10 edible bell smooth white anise close broad black enlarg… club
## # … with 7 more variables: veil_type <chr>, veil_color <chr>,
## # ring_number <dbl>, ring_type <chr>, spore_print_color <chr>,
## # population <chr>, habitat <chr>, and abbreviated variable names ¹cap_shape,
## # ²cap_surface, ³cap_color, ⁴gill_spacing, ⁵gill_size, ⁶gill_color,
## # ⁷stalk_shape, ⁸stalk_root# save the first 10 rows to a new dataframe
mushrooms_tiny <- head(mushrooms, 10)
# write to file
write_csv(mushrooms_tiny, "mushrooms_tiny.csv")For this class, we are using a computer server where everyone has a
preset working directory associated with your unique student ID number.
Type getwd() to see the file path to your working
directory. On a local installation, the output of this function might
look something like C:/Users/Rachael/Documents.
# output the file path associated with the current working directory
getwd()## [1] "/stor/home/student20"This is the directory R will default to for reading and writing files. Ideally, for real life projects, we keep all the information we need organized into folders (aka sub-directories). More often than not, we have to tell R which sub-directory we want to read a file from or write a file to. Perform the following steps to familiarize yourself with file paths and R’s perception of where files are:
mushrooms_tiny.csv by checking the box.list.files() to see all the files in the current
working directory.list.files("new_data") to see the files in the new
sub-directory.full.names = TRUE as the second argument in the
function.# list files in current working directory
list.files()## [1] "clean_salary_data" "mushrooms_small.csv"
## [3] "mushrooms_tiny.csv" "new_data"
## [5] "R" "worksheet1_blank.Rmd"
## [7] "worksheet1_reference.html" "worksheet1_reference.Rmd"
## [9] "worksheet1_solutions.Rmd" "worksheet1.Rmd"
## [11] "worksheet2_blank.html" "worksheet2_blank.Rmd"
## [13] "worksheet2_reference.html" "worksheet2_reference.Rmd"
## [15] "worksheet2_solutions.html" "worksheet2_solutions.Rmd"
## [17] "worksheet2.Rmd"# list files in the sub-directory called "new_data"
list.files("new_data")## [1] "mushrooms_tiny.csv"# list the full path to the files in "new_data"
list.files("new_data")## [1] "mushrooms_tiny.csv"list.files("new_data", full.names = TRUE) # this becomes very useful for reading many sub-directory files at once## [1] "new_data/mushrooms_tiny.csv"Clear your global environment (the broom symbol in the top right
window). Read the file in the sub-directory “new_data” using
read_csv. The function will need the full path given by the
output from the code chunk above.
# read in data from a sub-directory
read_csv("new_data/mushrooms_tiny.csv")## Rows: 10 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (16): class, cap_shape, cap_surface, cap_color, odor, gill_spacing, gill...
## dbl (1): ring_number
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 10 × 17
## class cap_s…¹ cap_s…² cap_c…³ odor gill_…⁴ gill_…⁵ gill_…⁶ stalk…⁷ stalk…⁸
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 poison… convex scaly red spicy close narrow buff taperi… missing
## 2 edible convex smooth red none close broad white enlarg… missing
## 3 edible convex smooth gray none crowded broad chocol… taperi… equal
## 4 edible flat scaly brown almo… close broad pink enlarg… rooted
## 5 edible flat fibrous brown none crowded broad pink taperi… equal
## 6 poison… convex fibrous yellow foul close broad chocol… enlarg… bulbous
## 7 poison… convex smooth brown spicy close narrow buff taperi… missing
## 8 edible bell scaly white almo… close broad brown enlarg… club
## 9 edible knobbed smooth brown none close broad orange enlarg… missing
## 10 edible bell smooth white anise close broad black enlarg… club
## # … with 7 more variables: veil_type <chr>, veil_color <chr>,
## # ring_number <dbl>, ring_type <chr>, spore_print_color <chr>,
## # population <chr>, habitat <chr>, and abbreviated variable names ¹cap_shape,
## # ²cap_surface, ³cap_color, ⁴gill_spacing, ⁵gill_size, ⁶gill_color,
## # ⁷stalk_shape, ⁸stalk_root