May 31st, 2022
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.
Try out basic R Markdown features, as described here.
This text is bold.
This text is in italics.
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
x <- 5
y <- 7
z <- x * y
z## [1] 35Play around with some basic R code, trying the following:
# 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", "dim sum", "jambalaya", "breakfast tacos", "pizza")
fav_foods## [1] "sashimi" "dim sum" "jambalaya" "breakfast tacos"
## [5] "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 dim sum 13
## 3 jambalaya 21
## 4 breakfast tacos 51
## 5 pizza 63# calling a column in a dataframe
new_df$fav_foods## [1] "sashimi" "dim sum" "jambalaya" "breakfast tacos"
## [5] "pizza"A function is statement internally (i.e., "under the hood") coded to perform a specific task. For instance, the head() function displays the first several rows of a data frame or values in a vector.
R comes with many built-in functions and data sets. Type data() in the console to look at a list of all available data sets. Type ?iris in the console for more information about this specific data set. You can take a glance at the iris data set using the head() function. Run the code chunk below to test this.
# preview the first few rows a data frame
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaYou can also use the summary() function to see the summary statistics of a data set at a glance. Try this now with the iris data set.
# look at summary statistics for the iris data set
summary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## You can see the column names of iris from the code output above. Calculate the mean of the Petal.Length column using the mean() function. Calculate the range of the Petal.Width column using the range() function. Hint: call the columns the same way you did in Part 2 of the worksheet.
# calculate the mean of the `Petal.Length` column in the `iris` dataset
mean(iris$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.
Try the following:
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 now have an "Upload" function. These concepts are covered at the end of this section.
# read in the dataset from the working directory
mushrooms <- read_csv("mushrooms_small.csv")## Parsed with column specification:
## cols(
## class = col_character(),
## cap_shape = col_character(),
## cap_surface = col_character(),
## cap_color = col_character(),
## odor = col_character(),
## gill_spacing = col_character(),
## gill_size = col_character(),
## gill_color = col_character(),
## stalk_shape = col_character(),
## stalk_root = col_character(),
## veil_type = col_character(),
## veil_color = col_character(),
## ring_number = col_double(),
## ring_type = col_character(),
## spore_print_color = col_character(),
## population = col_character(),
## habitat = col_character()
## )# look at the first 10 rows of that dataset
head(mushrooms, 10)## # A tibble: 10 x 17
## class cap_shape cap_surface cap_color odor gill_spacing gill_size gill_color
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 pois~ convex scaly red spicy close narrow buff
## 2 edib~ convex smooth red none close broad white
## 3 edib~ convex smooth gray none crowded broad chocolate
## 4 edib~ flat scaly brown almo~ close broad pink
## 5 edib~ flat fibrous brown none crowded broad pink
## 6 pois~ convex fibrous yellow foul close broad chocolate
## 7 pois~ convex smooth brown spicy close narrow buff
## 8 edib~ bell scaly white almo~ close broad brown
## 9 edib~ knobbed smooth brown none close broad orange
## 10 edib~ bell smooth white anise close broad black
## # ... with 9 more variables: stalk_shape <chr>, stalk_root <chr>,
## # veil_type <chr>, veil_color <chr>, ring_number <dbl>, ring_type <chr>,
## # spore_print_color <chr>, population <chr>, habitat <chr># save the first 10 rows to a new dataframe
mushrooms_tiny <- head(mushrooms, 10)
# write the new dataframe to a 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] "C:/Users/Rachael/Google Drive/Teaching/IntroR_2022/worksheets"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("day1_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] "day1.html" "day1.Rmd" "day1_data"
## [4] "day1_solutions.html" "day1_solutions.Rmd" "day2.html"
## [7] "day2.Rmd" "day2_solutions.html" "day2_solutions.Rmd"
## [10] "day3.html" "day3.Rmd" "day3_solutions.html"
## [13] "day3_solutions.Rmd" "mushrooms.csv" "mushrooms_small.csv"
## [16] "mushrooms_tiny.csv"# list files in the sub-directory called "day1_data"
list.files("day1_data")## [1] "mushrooms_tiny.csv"# list the full path to the files in "day1_data"
list.files("day1_data")## [1] "mushrooms_tiny.csv"list.files("day1_data", full.names = TRUE) # this becomes very useful for reading many sub-directory files at once## [1] "day1_data/mushrooms_tiny.csv"Clear your global environment (the broom symbol in the top right window). Read the file in the sub-directory "day1_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("day1_data/mushrooms_tiny.csv")## Parsed with column specification:
## cols(
## class = col_character(),
## cap_shape = col_character(),
## cap_surface = col_character(),
## cap_color = col_character(),
## odor = col_character(),
## gill_spacing = col_character(),
## gill_size = col_character(),
## gill_color = col_character(),
## stalk_shape = col_character(),
## stalk_root = col_character(),
## veil_type = col_character(),
## veil_color = col_character(),
## ring_number = col_double(),
## ring_type = col_character(),
## spore_print_color = col_character(),
## population = col_character(),
## habitat = col_character()
## )## # A tibble: 10 x 17
## class cap_shape cap_surface cap_color odor gill_spacing gill_size gill_color
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 pois~ convex scaly red spicy close narrow buff
## 2 edib~ convex smooth red none close broad white
## 3 edib~ convex smooth gray none crowded broad chocolate
## 4 edib~ flat scaly brown almo~ close broad pink
## 5 edib~ flat fibrous brown none crowded broad pink
## 6 pois~ convex fibrous yellow foul close broad chocolate
## 7 pois~ convex smooth brown spicy close narrow buff
## 8 edib~ bell scaly white almo~ close broad brown
## 9 edib~ knobbed smooth brown none close broad orange
## 10 edib~ bell smooth white anise close broad black
## # ... with 9 more variables: stalk_shape <chr>, stalk_root <chr>,
## # veil_type <chr>, veil_color <chr>, ring_number <dbl>, ring_type <chr>,
## # spore_print_color <chr>, population <chr>, habitat <chr>