class: center, middle, inverse, title-slide .title[ # Marine Community Ecology 2024 ] .subtitle[ ## 01-Introduction to coding in R ] .author[ ### Simon J. Brandl ] .institute[ ### The University of Texas at Austin ] .date[ ### 2024/01/01 (updated: 2024-01-21) ] --- background-image: url("images/IMG_0743.jpg") background-size: cover class: center, top, inverse # Introducing basic operations and functions in R <style type="text/css"> .scrollable { height: 300px; overflow-y: auto; } .scrollable-auto { height: 75%; overflow-y: auto; } .remark-slide-scaler { overflow-y: auto; } </style> --- # Basics ## Slide setup Tips and explanations ⬅️ this type of text contains prompts, tips, and tricks .code-teal[ ```r # R code is delineated in these boxes # hashmarks in R code are annotations, not functional code "I am a penguin" # <- this is actual code ``` ``` ## [1] "I am a penguin" ``` ] ⬆️ Code outputs appear below each chunk of code ⬆️ --- ## Access your workspace To confirm that you're in the right place, you can write getwd() to get your working directory ```r # use getwd() to find out where you are getwd() ``` ``` ## [1] "/Users/simonjbrandl/Documents/SJB1/Projects/mce_xaringan_slides" ``` ➡️ If you're inside your project (as indicated on the upper right corner of your RStudio environment), your working directory should be automatically set to your project directory. ➡️ Think of your R-project as a box that contains everything you need to bake a cake. Within it, you may have smaller boxes that contain ingredients, instructions, or tools. --- ## Packages 📦 R is user maintained, and most of its functionality comes from **packages** that contain a set of different functions to accomplish tasks. Some packages (such as the _base_ package) come pre-installed, but the vast majority of packages need to be installed and loaded. Below, we are installing the _tidyverse_ package using the <span style="color:orange"> *install.packages()* </span> function ```r # you typically don't need to set repos when installing a package install.packages("tidyverse", repos = "http://cran.us.r-project.org") ``` Installing the package downloads its contents to your R library. Before you can start using it, however, you have to load it into your current R session. We can do this using the <span style="color:orange"> *library()* </span> function. ```r # load the tidyverse package in your current R session library(tidyverse) ``` You only need to install packages once, but you will have to re-load the packages you need each time you start a new R session. --- ## Common operators - "?" opens help - "<-" assigns the right side to the left side - "==" indicates whether the left side matches the right .pull-left[ Getting help and assigning values ```r # get help ?install.packages # assign value one <- 1 one ``` ``` ## [1] 1 ``` ```r five <- 5 five ``` ``` ## [1] 5 ``` ] .pull-right[ Check for matches ```r # check matching values (TRUE or FALSE) 5 == 5 ``` ``` ## [1] TRUE ``` ```r 5 == 6 ``` ``` ## [1] FALSE ``` ```r five == 5 ``` ``` ## [1] TRUE ``` ] --- class: middle ## R as a calculator - R works as a basic calculator, using the same annotation you would typically use. - It also understands a diverse range of mathematical functions as part of the _base_ package, such as <span style="color:orange"> *log()* </span>, <span style="color:orange"> *sqrt()* </span>, or <span style="color:orange"> *sin()* </span> ```r # four times four 4*4 ``` ``` ## [1] 16 ``` ```r # a much more complicated calculation 23*sin(19)/sqrt(12) + log(58) ``` ``` ## [1] 5.055557 ``` ```r # be careful to observe basic rules 2 * 2 + 5 == 2 * (2 + 5) ``` ``` ## [1] FALSE ``` --- ## Naming objects - you can name objects however you want, but misspelling names is one of the most common errors - try to develop a standardized system for naming objects! - for example, separate object names by "." or "_" and don't use capitals (e.g. "fish.data") .pull-left[ ```r # assign obj1 the value of 4*4 obj1 <- 4*4 obj1 ``` ``` ## [1] 16 ``` ```r # assign obj2 a more complex value obj2 <- 23/sqrt(58)*21^2 obj2 ``` ``` ## [1] 1331.841 ``` ```r # assign obj3 a non-numerical value obj3 <- "fish" obj3 ``` ``` ## [1] "fish" ``` ] .pull-right[ <img src="1-introduction_files/clown_meme.jpeg" width="75%" /> ] --- ## Combine objects - you can readily combine numeric objects, but you cannot combine a numeric and non-numeric objects ```r obj4 <- obj1 + obj2 # combine numeric objects obj4 ``` ``` ## [1] 1347.841 ``` ```r obj5 <- obj1 + obj3 # attempt to combine numeric and character ``` ``` ## Error in obj1 + obj3: non-numeric argument to binary operator ``` 😢 - to combine numbers and characters, you need to use a specialized function such as <span style="color:orange"> *paste()* </span> ```r obj6 <- paste(obj1,obj3) # using the paste function obj6 ``` ``` ## [1] "16 fish" ``` --- class: inverse, center, top # Exercise 1.1 🏋️‍♀️ ### a) Find your current working directory. ### b) Test whether: ```r (6 * 1^3 + 1)^3 - (6 * 1^3 - 1)^3 - (6 *1^2)^3 ``` ### yields the same value as ```r (sin(2) + cos(2))^2 + (sin(2) - cos(2))^2 ``` ### c) Create an object that contains the text 'MCE', an object that contains the number 2024, and a new object that combines the two. --- class: center, top # Solution 1.1a 🤓 ## a) Find your current working directory. ```r getwd() ``` ``` ## [1] "/Users/simonjbrandl/Documents/SJB1/Projects/mce_xaringan_slides" ``` --- class: center, top # Solution 1.1b 🤓 ## b) Compare the values of two equations ```r (6 * 1^3 + 1)^3 - (6 * 1^3 - 1)^3 - (6 *1^2)^3 ``` ``` ## [1] 2 ``` ```r (sin(2) + cos(2))^2 + (sin(2) - cos(2))^2 ``` ``` ## [1] 2 ``` Alternatively, you can use the '==' operator ```r (6 * 1^3 + 1)^3 - (6 * 1^3 - 1)^3 - (6 *1^2)^3 == (sin(2) + cos(2))^2 + (sin(2) - cos(2))^2 ``` ``` ## [1] TRUE ``` --- class: center, top # Solution 1.1c 🤓 ## c) Create 'MCE2024' from two objects named 'MCE' and '2024' ```r o1 <- "MCE" o2 <- 2024 paste(o1, o2) ``` ``` ## [1] "MCE 2024" ``` --- class: middle, center, inverse # Functions and vectors # ⚙️ --- ## Functions - denoted by parentheses - perform basic (or very sophisticated) operations with your objects - you have already used them! 🚥 ```r obj8 <- 9 sqrt(obj8) # sqrt() is a function ``` ``` ## [1] 3 ``` ```r obj9 <- paste("fish", "23") # paste() is a function obj9 ``` ``` ## [1] "fish 23" ``` --- ## Vectors - objects that contain multiple elements - simplest data structure in R ```r conc.vals <- c(1,2,3,4,5) # c() is the concatenate function - use it to combine numbers conc.vals # we have created a vector with five numbers ``` ``` ## [1] 1 2 3 4 5 ``` ```r conc.text <- c("fish1", "fish2", "fish3", "fish4") # or use it to string together words conc.text # we have created a vector with four words ``` ``` ## [1] "fish1" "fish2" "fish3" "fish4" ``` ```r conc.fish <- c("<º(((><", "<º(((><", "<º(((><") # you can concatenate almost anything conc.fish ``` ``` ## [1] "<º(((><" "<º(((><" "<º(((><" ``` --- ## Vectors 🤝 Functions ### Create vectors using functions - functions consist of **arguments** that need to be specified - the <span style="color:orange"> *seq()* </span> function creates sequence vectors - its basic arguments are **_from_**, **_to_**, and **_by_** ```r seq1 <- seq(from = 1, to = 10, by = 1) # create a sequence from 1 to 10 in intervals of 1 seq1 ``` ``` ## [1] 1 2 3 4 5 6 7 8 9 10 ``` ```r seq2 <- seq(1, 10, 1) # you don't have to spell out arguments but it's safer to do so seq2 ``` ``` ## [1] 1 2 3 4 5 6 7 8 9 10 ``` ```r seq3 <- 1:10 # with an easy sequence like this, you can also do this seq3 ``` ``` ## [1] 1 2 3 4 5 6 7 8 9 10 ``` --- .pull-left[ ### There are often many ways to perform the same operation! 🤡 As the tasks at hand get more complex, you're less likely to find alternative pathways .scrollable[ ```r seq4 <- seq(from = 0, to = 12167, by = 23) seq4 ``` ``` ## [1] 0 23 46 69 92 115 ## [7] 138 161 184 207 230 253 ## [13] 276 299 322 345 368 391 ## [19] 414 437 460 483 506 529 ## [25] 552 575 598 621 644 667 ## [31] 690 713 736 759 782 805 ## [37] 828 851 874 897 920 943 ## [43] 966 989 1012 1035 1058 1081 ## [49] 1104 1127 1150 1173 1196 1219 ## [55] 1242 1265 1288 1311 1334 1357 ## [61] 1380 1403 1426 1449 1472 1495 ## [67] 1518 1541 1564 1587 1610 1633 ## [73] 1656 1679 1702 1725 1748 1771 ## [79] 1794 1817 1840 1863 1886 1909 ## [85] 1932 1955 1978 2001 2024 2047 ## [91] 2070 2093 2116 2139 2162 2185 ## [97] 2208 2231 2254 2277 2300 2323 ## [103] 2346 2369 2392 2415 2438 2461 ## [109] 2484 2507 2530 2553 2576 2599 ## [115] 2622 2645 2668 2691 2714 2737 ## [121] 2760 2783 2806 2829 2852 2875 ## [127] 2898 2921 2944 2967 2990 3013 ## [133] 3036 3059 3082 3105 3128 3151 ## [139] 3174 3197 3220 3243 3266 3289 ## [145] 3312 3335 3358 3381 3404 3427 ## [151] 3450 3473 3496 3519 3542 3565 ## [157] 3588 3611 3634 3657 3680 3703 ## [163] 3726 3749 3772 3795 3818 3841 ## [169] 3864 3887 3910 3933 3956 3979 ## [175] 4002 4025 4048 4071 4094 4117 ## [181] 4140 4163 4186 4209 4232 4255 ## [187] 4278 4301 4324 4347 4370 4393 ## [193] 4416 4439 4462 4485 4508 4531 ## [199] 4554 4577 4600 4623 4646 4669 ## [205] 4692 4715 4738 4761 4784 4807 ## [211] 4830 4853 4876 4899 4922 4945 ## [217] 4968 4991 5014 5037 5060 5083 ## [223] 5106 5129 5152 5175 5198 5221 ## [229] 5244 5267 5290 5313 5336 5359 ## [235] 5382 5405 5428 5451 5474 5497 ## [241] 5520 5543 5566 5589 5612 5635 ## [247] 5658 5681 5704 5727 5750 5773 ## [253] 5796 5819 5842 5865 5888 5911 ## [259] 5934 5957 5980 6003 6026 6049 ## [265] 6072 6095 6118 6141 6164 6187 ## [271] 6210 6233 6256 6279 6302 6325 ## [277] 6348 6371 6394 6417 6440 6463 ## [283] 6486 6509 6532 6555 6578 6601 ## [289] 6624 6647 6670 6693 6716 6739 ## [295] 6762 6785 6808 6831 6854 6877 ## [301] 6900 6923 6946 6969 6992 7015 ## [307] 7038 7061 7084 7107 7130 7153 ## [313] 7176 7199 7222 7245 7268 7291 ## [319] 7314 7337 7360 7383 7406 7429 ## [325] 7452 7475 7498 7521 7544 7567 ## [331] 7590 7613 7636 7659 7682 7705 ## [337] 7728 7751 7774 7797 7820 7843 ## [343] 7866 7889 7912 7935 7958 7981 ## [349] 8004 8027 8050 8073 8096 8119 ## [355] 8142 8165 8188 8211 8234 8257 ## [361] 8280 8303 8326 8349 8372 8395 ## [367] 8418 8441 8464 8487 8510 8533 ## [373] 8556 8579 8602 8625 8648 8671 ## [379] 8694 8717 8740 8763 8786 8809 ## [385] 8832 8855 8878 8901 8924 8947 ## [391] 8970 8993 9016 9039 9062 9085 ## [397] 9108 9131 9154 9177 9200 9223 ## [403] 9246 9269 9292 9315 9338 9361 ## [409] 9384 9407 9430 9453 9476 9499 ## [415] 9522 9545 9568 9591 9614 9637 ## [421] 9660 9683 9706 9729 9752 9775 ## [427] 9798 9821 9844 9867 9890 9913 ## [433] 9936 9959 9982 10005 10028 10051 ## [439] 10074 10097 10120 10143 10166 10189 ## [445] 10212 10235 10258 10281 10304 10327 ## [451] 10350 10373 10396 10419 10442 10465 ## [457] 10488 10511 10534 10557 10580 10603 ## [463] 10626 10649 10672 10695 10718 10741 ## [469] 10764 10787 10810 10833 10856 10879 ## [475] 10902 10925 10948 10971 10994 11017 ## [481] 11040 11063 11086 11109 11132 11155 ## [487] 11178 11201 11224 11247 11270 11293 ## [493] 11316 11339 11362 11385 11408 11431 ## [499] 11454 11477 11500 11523 11546 11569 ## [505] 11592 11615 11638 11661 11684 11707 ## [511] 11730 11753 11776 11799 11822 11845 ## [517] 11868 11891 11914 11937 11960 11983 ## [523] 12006 12029 12052 12075 12098 12121 ## [529] 12144 12167 ``` ] ] .pull-right[ <img src="1-introduction_files/samething.jpeg" width="95%" /> ] --- ### Create vectors with repetitions - the <span style="color:orange"> *rep()* </span> function creates repititions - its basic arguments are **_x_** (the element to be replicated) and your choice of **_times_** and/or **_each_** ```r rep1 <- rep(1:5, times = 10) # replicate the sequence 1:5 ten times rep1 ``` ``` ## [1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 ## [49] 4 5 ``` ```r rep2 <- rep(1:5, each = 10) # replicate each element in 1:5 ten times rep2 # notice the difference between 'times' and 'each' ``` ``` ## [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 ## [49] 5 5 ``` ```r rep3 <- rep("fish", times = 8) # you can also repeat text rep3 ``` ``` ## [1] "fish" "fish" "fish" "fish" "fish" "fish" "fish" "fish" ``` --- ### Combining functions to create vectors - you can use functions within functions (like the <span style="color:orange"> *seq()* </span> function within the <span style="color:orange"> *rep()* </span> function) - there's no real limit to how many functions you can nest within each other (but going over board may get a little messy) ```r rep4 <- rep(seq(from = 0, to = 10, by = 2), times = 3) # use seq() within rep() rep4 ``` ``` ## [1] 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 ``` ```r mean.rep <- mean(rep(seq(from = 0, to = 10, by = 2), times = 3)) # use mean(), seq(), and rep() mean.rep ``` ``` ## [1] 5 ``` --- ### Performing functions on vectors - as the previous operation suggests, you can use functions across vectors ```r reps.comb <- c(rep2, rep4) # combine two numerical vectors reps.comb ``` ``` ## [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 ## [33] 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 0 2 4 6 8 10 0 2 4 6 8 10 0 2 ## [65] 4 6 8 10 ``` ```r log.rep4 <- log(rep4) # take the log of rep2 log.rep4 ``` ``` ## [1] -Inf 0.6931472 1.3862944 1.7917595 2.0794415 2.3025851 -Inf 0.6931472 1.3862944 ## [10] 1.7917595 2.0794415 2.3025851 -Inf 0.6931472 1.3862944 1.7917595 2.0794415 2.3025851 ``` ```r reps.diff1 <- paste(rep2, rep3) # using paste() to combine vectors of different classes reps.diff1 ``` ``` ## [1] "1 fish" "1 fish" "1 fish" "1 fish" "1 fish" "1 fish" "1 fish" "1 fish" "1 fish" "1 fish" ## [11] "2 fish" "2 fish" "2 fish" "2 fish" "2 fish" "2 fish" "2 fish" "2 fish" "2 fish" "2 fish" ## [21] "3 fish" "3 fish" "3 fish" "3 fish" "3 fish" "3 fish" "3 fish" "3 fish" "3 fish" "3 fish" ## [31] "4 fish" "4 fish" "4 fish" "4 fish" "4 fish" "4 fish" "4 fish" "4 fish" "4 fish" "4 fish" ## [41] "5 fish" "5 fish" "5 fish" "5 fish" "5 fish" "5 fish" "5 fish" "5 fish" "5 fish" "5 fish" ``` --- ### Elements within vectors - you can isolate specific elements in your vectors using square brackets ```r seq1 # call one of your vectors to see it ``` ``` ## [1] 1 2 3 4 5 6 7 8 9 10 ``` ```r element3 <- seq1[3] # select only the third element element3 ``` ``` ## [1] 3 ``` - this also works for multiple elements at a time using the <span style="color:orange"> *c()* </span> function or ranges ```r three.elements <- seq1[c(3,4,5)] # using c() to get elements 3, 4, and 5 three.elements ``` ``` ## [1] 3 4 5 ``` ```r last.eight <- seq1[2:10] # using x:y to get the last 8 elements last.eight ``` ``` ## [1] 2 3 4 5 6 7 8 9 10 ``` --- ## Apply logic - you can also extract values based on a condition with logical operators - operators are pretty standard and works as below - the '==' is a common source of error 🤦🏽 ### Overview of common operators Operator | Meaning ---------|--------- >= | values equal or greater <= | values equal or smaller > | values greater < | values smaller == | value equals != | value does not equal --- ## Logic in practice - to demonstrate logic, we need a somewhat more complex vector - we're creating this using one of the distributional functions, in this case a selection of integers using the <span style="color:orange"> *rpois()* </span> function ```r random.vals <- rpois(30, lambda = 5) # create a new vector with 30 integers random.vals ``` ``` ## [1] 6 4 1 5 4 5 6 3 2 8 7 5 5 1 2 12 1 6 2 4 4 6 7 2 5 3 3 4 7 7 ``` ```r selected.vals <- random.vals[random.vals > 4] # select values that are greater than 4 selected.vals ``` ``` ## [1] 6 5 5 6 8 7 5 5 12 6 6 7 5 7 7 ``` ```r values.not.6 <- random.vals[random.vals != 6] # select values that are not 6 values.not.6 ``` ``` ## [1] 4 1 5 4 5 3 2 8 7 5 5 1 2 12 1 2 4 4 7 2 5 3 3 4 7 7 ``` - note that the [] requires you to re-specify the vector you want to use for the operation ☝️ --- ## Combining logical statements - we can use Boolean expressions to combine logical statements - three symbols: "&" = AND, "|" = OR, "!" = NOT - can be applied separately and in combination - NOT ("!") has precedence over AND ("&"), which has precedence over OR ("|") ```r boolean.and <- random.vals[random.vals >= 4 & random.vals <= 8] # values between 4 and 8 boolean.and ``` ``` ## [1] 6 4 5 4 5 6 8 7 5 5 6 4 4 6 7 5 4 7 7 ``` ```r boolean.or <- random.vals[random.vals == 3 | random.vals == 5] # values that are 3 or 5 boolean.or ``` ``` ## [1] 5 5 3 5 5 5 3 3 ``` ```r boolean.not <- random.vals[random.vals != 4 & random.vals > 2 & random.vals < 8] # between 2 and 8 but not 4 boolean.not ``` ``` ## [1] 6 5 5 6 3 7 5 5 6 6 7 5 3 3 7 7 ``` --- ## Assigning new values - you can also replace values using the same principle - works for single values and values selected by logic ```r random.vals[1] <- 1000 # replace the first value of the vector with 1000 random.vals ``` ``` ## [1] 1000 4 1 5 4 5 6 3 2 8 7 5 5 1 2 12 1 6 2 ## [20] 4 4 6 7 2 5 3 3 4 7 7 ``` ```r random.vals[random.vals > 5] <- 1000 # replace values >5 with 1000 random.vals ``` ``` ## [1] 1000 4 1 5 4 5 1000 3 2 1000 1000 5 5 1 2 1000 1 1000 2 ## [20] 4 4 1000 1000 2 5 3 3 4 1000 1000 ``` --- ## Non-numerical vectors 🐠 - vectors can also consist of different character strings - same principles as for numbers apply, but logical operations are limited ```r fish.comm <- paste(rep(c("whitefish", "bluefish", "yellowfish"), 5)) # create a vector of words fish.comm ``` ``` ## [1] "whitefish" "bluefish" "yellowfish" "whitefish" "bluefish" "yellowfish" "whitefish" ## [8] "bluefish" "yellowfish" "whitefish" "bluefish" "yellowfish" "whitefish" "bluefish" ## [15] "yellowfish" ``` ```r white.extinct <- fish.comm[fish.comm != "whitefish"] # remove whitefish white.extinct ``` ``` ## [1] "bluefish" "yellowfish" "bluefish" "yellowfish" "bluefish" "yellowfish" "bluefish" ## [8] "yellowfish" "bluefish" "yellowfish" ``` ```r white.extinct[white.extinct == "yellowfish"] <- "greenfish" # replace yellowfish with greenfish white.extinct ``` ``` ## [1] "bluefish" "greenfish" "bluefish" "greenfish" "bluefish" "greenfish" "bluefish" "greenfish" ## [9] "bluefish" "greenfish" ``` --- ## Ordering vectors - sometimes it's useful to sort and order your vectors - this can be accomplished using the <span style="color:orange"> *sort()* </span> function ```r more.vals <- rpois(20, 5) # use the rpois() function again to create a vector more.vals ``` ``` ## [1] 8 9 8 5 4 5 4 5 6 7 7 3 5 4 7 6 4 10 4 8 ``` ```r more.vals.sorted <- sort(more.vals) # use sort() to sort values in increasing order more.vals.sorted ``` ``` ## [1] 3 4 4 4 4 4 5 5 5 5 6 6 7 7 7 8 8 8 9 10 ``` ```r more.vals.sorted.dec <- rev(sort(more.vals)) # sort the values in decreasing order using "decreasing = TRUE" or the rev() function more.vals.sorted.dec ``` ``` ## [1] 10 9 8 8 8 7 7 7 6 6 5 5 5 5 4 4 4 4 4 3 ``` --- ## Missing data - sometimes, vectors contain elements that aren't there or possible, called 'NAs' - this is not always a problem, but can lead to errors in functions that need numeric values ```r fish.numbers <- rpois(20, 5) # create vector with rpois() fish.numbers ``` ``` ## [1] 4 6 4 8 7 6 4 3 4 8 3 5 2 3 4 6 7 3 10 3 ``` ```r fish.numbers[c(5,15)] <- NA # replace some values with NAs fish.numbers ``` ``` ## [1] 4 6 4 8 NA 6 4 3 4 8 3 5 2 3 NA 6 7 3 10 3 ``` ```r fish.mean <- mean(fish.numbers) # calculate the mean fish number fish.mean ``` ``` ## [1] NA ``` ## 😭 - luckily, it is easy to remove NAs from some functions, or exclude them from your vector ```r fish.mean.nona <- mean(fish.numbers, na.rm = TRUE) # na.rm = TRUE removes NAs within a function fish.mean.nona ``` ``` ## [1] 4.944444 ``` ```r fish.mean.nona <- mean(fish.numbers[!is.na(fish.numbers)]) # !is.na(fish.density) excludes them from the vector prior to the function fish.mean.nona ``` ``` ## [1] 4.944444 ``` --- class: middle, center, inverse # Data # 📉 --- class: left, top ## Data classes There are several different types of data classes in R #### - numeric: all numbers 🧮 #### - integer: whole numbers without decimals 🔢 #### - logical: TRUE or FALSE (and NA) ✅ #### - character: character strings and factors (letters, symbols, numbers) 🔠 --- ## Working with data classes - it is absolutely critical to understand what class your data are - mixups in data classes are a common source of error ❌ ```r numbers <- seq(1:20) # create vector of numbers class(numbers) # find data class using class() or str() ``` ``` ## [1] "integer" ``` ```r str(numbers) ``` ``` ## int [1:20] 1 2 3 4 5 6 7 8 9 10 ... ``` ```r is.numeric(numbers) # check data class using "is" ``` ``` ## [1] TRUE ``` ```r numbers.char <- as.character(numbers) # turn data into characters numbers.char # note the quotes around the numbers ``` ``` ## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" ## [20] "20" ``` --- ### Matrices - there are several types of data structures in R - scalars, vectors, matrices, lists, data frames, arrays ```r scalar <- 23 # a single value is called a scalar scalar ``` ``` ## [1] 23 ``` ```r many.scalars <- rep(1:23, 2) # a collection of scalars is a vector many.scalars ``` ``` ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1 2 3 4 5 6 7 8 9 ## [33] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ``` ```r the.matrix <- matrix(1:24, nrow = 4, byrow = FALSE) # when it has two dimensions, it's a matrix the.matrix ``` ``` ## [,1] [,2] [,3] [,4] [,5] [,6] ## [1,] 1 5 9 13 17 21 ## [2,] 2 6 10 14 18 22 ## [3,] 3 7 11 15 19 23 ## [4,] 4 8 12 16 20 24 ``` --- ### Rows and columns - in two-dimensional arrangements, we can work with columns and rows - for example, we can create a dataset of three fish species across six sites 🐟 🐡 🐠 - matrices can only hold one type of data (usually numeric) ```r the.matrix <- matrix(rpois(18, 5), nrow = 6, byrow = FALSE) # create a matrix the.matrix ``` ``` ## [,1] [,2] [,3] ## [1,] 7 6 4 ## [2,] 8 5 6 ## [3,] 5 4 7 ## [4,] 6 3 6 ## [5,] 7 7 3 ## [6,] 2 3 5 ``` ```r colnames(the.matrix) <- c("bluefish", "blowfish", "yellowfish") # assign column names rownames(the.matrix) <- c("site1", "site2", "site3", "site4", "site5", "site6") # assign row names the.matrix ``` ``` ## bluefish blowfish yellowfish ## site1 7 6 4 ## site2 8 5 6 ## site3 5 4 7 ## site4 6 3 6 ## site5 7 7 3 ## site6 2 3 5 ``` --- ## Data frames - to combine data of different classes, you need to turn it into a data.frame - selecting elements within data.frames can be done using $ and [x,y] ```r fish.df <- as.data.frame(the.matrix) # convert matrix to data frame fish.df$blowfish # select a specific column using the $ sign ``` ``` ## [1] 6 5 4 3 7 3 ``` ```r fish.df[1,] # show the first row ``` ``` ## bluefish blowfish yellowfish ## site1 7 6 4 ``` ```r locations <- c("Australia", "Indonesia", "Philippines", "Fiji", "Solomons", "Papua New Guinea") # vector with 6 site locations fish.df$location <- locations # add a character vector to a data frame fish.df ``` ``` ## bluefish blowfish yellowfish location ## site1 7 6 4 Australia ## site2 8 5 6 Indonesia ## site3 5 4 7 Philippines ## site4 6 3 6 Fiji ## site5 7 7 3 Solomons ## site6 2 3 5 Papua New Guinea ``` --- ## Tibbles - tibbles are similar to data.frames but offer more information ```r str(fish.df) # check structure of data frame ``` ``` ## 'data.frame': 6 obs. of 4 variables: ## $ bluefish : int 7 8 5 6 7 2 ## $ blowfish : int 6 5 4 3 7 3 ## $ yellowfish: int 4 6 7 6 3 5 ## $ location : chr "Australia" "Indonesia" "Philippines" "Fiji" ... ``` ```r fish.tibble <- as_tibble(fish.df) # convert data frame to tibble fish.tibble # view information provided by tibble ``` ``` ## # A tibble: 6 × 4 ## bluefish blowfish yellowfish location ## <int> <int> <int> <chr> ## 1 7 6 4 Australia ## 2 8 5 6 Indonesia ## 3 5 4 7 Philippines ## 4 6 3 6 Fiji ## 5 7 7 3 Solomons ## 6 2 3 5 Papua New Guinea ``` --- # Loading and saving data - you'll often import your data from csv files or save them as such - use the <span style="color:orange"> *write.csv()* </span> function to save a data frame or tibble in your working directory - use the <span style="color:orange"> *read.csv()* </span> function to import data ```r write.csv(fish.tibble, file = "data/fishtibble.csv", row.names = FALSE) # row.names = FALSE specifies to not include a column of row numbers in the csv loaded.fish.tibble <- read.csv(file = "data/fishtibble.csv") # load that csv file back into R loaded.fish.tibble ``` ``` ## bluefish blowfish yellowfish location ## 1 7 6 4 Australia ## 2 8 5 6 Indonesia ## 3 5 4 7 Philippines ## 4 6 3 6 Fiji ## 5 7 7 3 Solomons ## 6 2 3 5 Papua New Guinea ``` --- class: inverse, center, top # Exercise 1.2 🏋️‍♀️ ### a) Create a sequence of even numbers from 2 to 20 ### b) Create a vector with random integer values using rpois(100, 10) and obtain its mean ### c) Retain only values <6 and calculate the mean again ### d) Download the dataset called "coralreefherbivores.csv" from the link below, and put it in your working directory in a folder called "data" ### e) Read in the data using the <span style="color:orange"> *read_csv()* </span> function, specifying the correct path to the "data" folder --- class: center, top # Solution 1.2a 🤓 ## a) Create a sequence of even numbers from 2 to 20 ```r s1 <- seq(from = 2, to = 20, by = 2) ``` --- class: center, top # Solution 1.2b 🤓 ## b) Create a vector with random integer values using rpois(100, 10) and obtain its mean ```r v1 <- rpois(100, 10) mean(v1) ``` ``` ## [1] 10.75 ``` --- class: center, top # Solution 1.2c 🤓 ## c) Retain only values <6 and calculate the mean again ```r v2 <- v1[v1 < 6] mean(v2) ``` ``` ## [1] 4.2 ``` --- class: center, top # Solution 1.2d 🤓 .pull-left[ ## d) Download "coralreefherbivores.csv" to a folder called "data" ] .pull-right[ <img src="images/download_fail.jpg" width="80%" /> ] --- class: center, top # Solution 1.2e 🤓 ## e) Read in the data using the <span style="color:orange"> *read_csv()* </span> function ```r herbivores <- read.csv(file = "data/coralreefherbivores.csv") ``` --- class: center, middle # The end