class: center, middle, inverse, title-slide .title[ # R basics and workflows ] .author[ ### ] --- layout: true --- ## RStudio <img src="img/rstudio-anatomy.png" width="80%" style="display: block; margin: auto;" /> --- ## R basics Basic use of R (the code is in the subfolder _lecture_ in the project): ``` r 8738787213 / 1653 # as a calculator ``` ``` ## [1] 5286623 ``` ``` r "Lars" # a character/string ``` ``` ## [1] "Lars" ``` ``` r c(1,4) # a vector ``` ``` ## [1] 1 4 ``` ``` r 1:4 # a vector (sequence) ``` ``` ## [1] 1 2 3 4 ``` --- ## R basics ``` r age <- c(12, 56, 34) # assignment to object name <- c("Hans", "Sille", "Bo") # character vector people <- data.frame(Name = name, Age = age) # data frame people # print object ``` ``` ## Name Age ## 1 Hans 12 ## 2 Sille 56 ## 3 Bo 34 ``` ``` r people[1,] # subsetting 1. row ``` ``` ## Name Age ## 1 Hans 12 ``` ``` r people$Name # column Name ``` ``` ## [1] "Hans" "Sille" "Bo" ``` --- ## R basics ``` r lst <- list(p = people, status = 0, log = "Okay") # a list (most abstract object) lst ``` ``` ## $p ## Name Age ## 1 Hans 12 ## 2 Sille 56 ## 3 Bo 34 ## ## $status ## [1] 0 ## ## $log ## [1] "Okay" ``` - The most commonly used way to store data is using a data frame, where each row represents an observation, and each column a variable. --- ## Your Turn Open the [R project][r-cloud-mod8] for Teaching Module 8 (TM8) on RStudio Cloud and open the file in the lecture folder. .your-turn[ Use R to - Extract column `Age` from `people`. - Extract the age of Hans from `people`. - Use `class` to find the data type of `p` in `lst`. - Why does this code not work? ```r x <- 2 Y <- 4 x + y ``` - Define a list with a vector, a number, a string and a boolean. ]
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??? ``` r people$Age ``` ``` ## [1] 12 56 34 ``` ``` r people[1, 2] # or ``` ``` ## [1] 12 ``` ``` r people[people$Name == "Hans", "Age"] ``` ``` ## [1] 12 ``` ``` r class(lst$p) ``` ``` ## [1] "data.frame" ``` ``` r # R is case sensitive list(v = c(1,2), n = 3, s = "foo", l = TRUE) ``` ``` ## $v ## [1] 1 2 ## ## $n ## [1] 3 ## ## $s ## [1] "foo" ## ## $l ## [1] TRUE ``` --- ## Functions - A function have inputs and outputs. - Functions are often used to encapsulate a sequence of expressions that need to be executed numerous times, perhaps under slightly different conditions. - In programming, functional programming is a programming paradigm, a style how code is written. Rather than repeating code, functions and control structures allow one to build code in blocks. - Functions are (often) verbs, followed by what they will be applied to in parentheses. ``` r do_this(to_this) do_that(to_this, to_that, with_those) ``` --- ## Your Turn .your-turn[ - Create a vector `v` with numbers 2, 4, 6, 8 [see the help for function `seq` by writing `?seq` in the console and having a look at the examples]. - What is the sum of `v`? - What is the sum of the numbers in `x <- c(1:4, NA)` [see `?sum`]? - Does `x` contain a missing value (`NA`) [see `?is.na`] - Convert `s <- "1.2"` to a number [see `?as.numeric`]. - What is the return value of `class(x)`? - Set `y <- NULL` and check if `y` is null [see `?NULL`]. - Set `lst <- list(x = 3, y = "foo")` and check if `lst` contains an object `z` [use `is.null`]. ]
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--- ## R packages - In R, the fundamental unit of shareable code is the package. - As of April 2024, there are over 21,000 packages available on the **C**omprehensive **R** **A**rchive **N**etwork (CRAN), the public clearing house for R packages. - This huge variety of packages is one reason why R is so successful: the chances are that someone has already solved a problem that you’re working on, and you can benefit from their work by downloading their package. - Using R packages: - Install them from CRAN with `install.packages("x")`. - Install from GitHub using `remotes::install_github(path)`. - Use them in R with `library(x)` or `x::<function>`. - Use `?x` the see the help pages. --- ## Pipes Use the pipe `|>` operator (or `%>%`) for expressing a sequence of multiple operations (Ctrl+Shift+M, Shift+Cmd+M). Value `log2(sqrt(16))` using pipes: ``` r # library(tidyverse) # must be loaded if use the %>% pipe operator x <- 16 x |> sqrt() |> log2() ``` ``` ## [1] 2 ``` The pipe _sends_ the result of the left side of the pipe to be the first argument of the function on the right side of the pipe. $$ \mbox{original data (x)} \rightarrow \mbox{ sqrt } \rightarrow \mbox{ log2 } $$ That is, we take what is left of the arrow (the object `x`) and put it into the function on the right of the arrow. --- ## Your Turn .your-turn[ Use pipes to calculate ``` r x <- c(1:4, NA, 34) x <- x^2 x <- sum(x, na.rm = TRUE) x <- sqrt(x) x ``` ``` ## [1] 34.43835 ``` ]
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??? ``` r x <- c(1:4, NA, 34)^2 |> sum(na.rm = TRUE) |> sqrt() # or x ``` ``` ## [1] 34.43835 ``` [BSS]: https://bss.au.dk/en/ [course-help]: https://github.com/bss-osca/tfa/issues [cran]: https://cloud.r-project.org [cheatsheet-readr]: https://rawgit.com/rstudio/cheatsheets/master/data-import.pdf [course-welcome-to-the-tidyverse]: https://github.com/rstudio-education/welcome-to-the-tidyverse [DataCamp]: https://www.datacamp.com/ [datacamp-signup]: https://www.datacamp.com/groups/shared_links/c90b55dfb7c72d4f8184f5e53ac5c2521e67a220a9e40778ee28178b284eef77 [datacamp-r-intro]: https://learn.datacamp.com/courses/free-introduction-to-r [datacamp-r-rmarkdown]: https://campus.datacamp.com/courses/reporting-with-rmarkdown [datacamp-r-communicating]: https://learn.datacamp.com/courses/communicating-with-data-in-the-tidyverse [datacamp-r-communicating-chap3]: https://campus.datacamp.com/courses/communicating-with-data-in-the-tidyverse/introduction-to-rmarkdown [datacamp-r-communicating-chap4]: 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