From aa5e2a72af739af7c6238ed60d1e53e34e0ae9f2 Mon Sep 17 00:00:00 2001 From: Emma Rand Date: Thu, 11 Jan 2024 11:33:59 +0000 Subject: [PATCH] some babs 4 infrastructure --- .DS_Store | Bin 6148 -> 0 bytes pgt52m/.DS_Store | Bin 6148 -> 0 bytes r4babs4/r4babs4.qmd | 53 +- r4babs4/week-1/data-raw/adipocytes.txt | 31 + r4babs4/week-1/data-raw/beewing.txt | 101 +++ r4babs4/week-1/data-raw/neuron.txt | 9 + r4babs4/week-1/images/answer.png | Bin 0 -> 2473 bytes r4babs4/week-1/images/do_in_R.png | Bin 0 -> 2945 bytes r4babs4/week-1/images/do_on_internet.png | Bin 0 -> 3405 bytes r4babs4/week-1/images/do_on_your_computer.png | Bin 0 -> 3470 bytes r4babs4/week-1/overview.qmd | 51 ++ r4babs4/week-1/study_after_workshop.qmd | 56 ++ r4babs4/week-1/study_before_workshop.qmd | 12 + r4babs4/week-1/workshop.qmd | 256 +++++++ .../images/66I-exp-des-TNF-\316\261.png" | Bin 0 -> 63091 bytes r4babs4/week-2/images/answer.png | Bin 0 -> 2473 bytes r4babs4/week-2/images/do_in_R.png | Bin 0 -> 2945 bytes r4babs4/week-2/images/do_on_internet.png | Bin 0 -> 3405 bytes r4babs4/week-2/images/do_on_your_computer.png | Bin 0 -> 3470 bytes r4babs4/week-2/overview.qmd | 41 ++ r4babs4/week-2/study_after_workshop.qmd | 114 ++++ r4babs4/week-2/study_before_workshop.qmd | 14 + r4babs4/week-2/workshop.qmd | 386 +++++++++++ r4babs4/week-3/images/answer.png | Bin 0 -> 2473 bytes r4babs4/week-3/images/do_in_R.png | Bin 0 -> 2945 bytes r4babs4/week-3/images/do_on_internet.png | Bin 0 -> 3405 bytes r4babs4/week-3/images/do_on_your_computer.png | Bin 0 -> 3470 bytes r4babs4/week-3/overview.qmd | 47 ++ r4babs4/week-3/study_after_workshop.qmd | 125 ++++ r4babs4/week-3/study_before_workshop.qmd | 13 + r4babs4/week-3/workshop.qmd | 534 +++++++++++++++ .../week-4/data-raw/ECOLIGreen_ISOTYPE.fcs | Bin 0 -> 2207966 bytes r4babs4/week-4/data-raw/ECOLIGreen_TNFAPC.fcs | Bin 0 -> 2207966 bytes r4babs4/week-4/data-raw/LPS_ISOTYPE.fcs | Bin 0 -> 2207948 bytes r4babs4/week-4/data-raw/LPS_TNFAPC.fcs | Bin 0 -> 2207950 bytes r4babs4/week-4/data-raw/MEDIA_ISOTYPE.fcs | Bin 0 -> 1206954 bytes r4babs4/week-4/data-raw/MEDIA_TNFAPC.fcs | Bin 0 -> 2207956 bytes r4babs4/week-4/images/answer.png | Bin 0 -> 2473 bytes r4babs4/week-4/images/do_in_R.png | Bin 0 -> 2945 bytes r4babs4/week-4/images/do_on_internet.png | Bin 0 -> 3405 bytes r4babs4/week-4/images/do_on_your_computer.png | Bin 0 -> 3470 bytes r4babs4/week-4/overview.qmd | 42 ++ r4babs4/week-4/study_after_workshop.qmd | 279 ++++++++ r4babs4/week-4/study_before_workshop.qmd | 13 + r4babs4/week-4/workshop.qmd | 541 +++++++++++++++ r4babs4/week-5/images/answer.png | Bin 0 -> 2473 bytes r4babs4/week-5/images/do_in_R.png | Bin 0 -> 2945 bytes r4babs4/week-5/images/do_on_internet.png | Bin 0 -> 3405 bytes r4babs4/week-5/images/do_on_your_computer.png | Bin 0 -> 3470 bytes r4babs4/week-5/overview.qmd | 43 ++ r4babs4/week-5/study_after_workshop.qmd | 32 + r4babs4/week-5/study_before_workshop.qmd | 17 + r4babs4/week-5/workshop.qmd | 622 ++++++++++++++++++ r4babs4/week-6/images/answer.png | Bin 0 -> 2473 bytes r4babs4/week-6/images/do_in_R.png | Bin 0 -> 2945 bytes r4babs4/week-6/images/do_on_internet.png | Bin 0 -> 3405 bytes r4babs4/week-6/images/do_on_your_computer.png | Bin 0 -> 3470 bytes r4babs4/week-6/overview.qmd | 37 ++ r4babs4/week-6/study_after_workshop.qmd | 32 + r4babs4/week-6/study_before_workshop.qmd | 16 + r4babs4/week-6/workshop.qmd | 83 +++ update_notes.txt | 15 +- 62 files changed, 3597 insertions(+), 18 deletions(-) delete mode 100644 .DS_Store delete mode 100644 pgt52m/.DS_Store create mode 100644 r4babs4/week-1/data-raw/adipocytes.txt create mode 100644 r4babs4/week-1/data-raw/beewing.txt create mode 100644 r4babs4/week-1/data-raw/neuron.txt create mode 100644 r4babs4/week-1/images/answer.png create mode 100644 r4babs4/week-1/images/do_in_R.png create mode 100644 r4babs4/week-1/images/do_on_internet.png create mode 100644 r4babs4/week-1/images/do_on_your_computer.png create mode 100644 r4babs4/week-1/overview.qmd create mode 100644 r4babs4/week-1/study_after_workshop.qmd create mode 100644 r4babs4/week-1/study_before_workshop.qmd create mode 100644 r4babs4/week-1/workshop.qmd create mode 100644 "r4babs4/week-2/images/66I-exp-des-TNF-\316\261.png" create mode 100644 r4babs4/week-2/images/answer.png create mode 100644 r4babs4/week-2/images/do_in_R.png create mode 100644 r4babs4/week-2/images/do_on_internet.png create mode 100644 r4babs4/week-2/images/do_on_your_computer.png create mode 100644 r4babs4/week-2/overview.qmd create mode 100644 r4babs4/week-2/study_after_workshop.qmd create mode 100644 r4babs4/week-2/study_before_workshop.qmd create mode 100644 r4babs4/week-2/workshop.qmd create mode 100644 r4babs4/week-3/images/answer.png create mode 100644 r4babs4/week-3/images/do_in_R.png create mode 100644 r4babs4/week-3/images/do_on_internet.png create mode 100644 r4babs4/week-3/images/do_on_your_computer.png create mode 100644 r4babs4/week-3/overview.qmd create mode 100644 r4babs4/week-3/study_after_workshop.qmd create mode 100644 r4babs4/week-3/study_before_workshop.qmd create mode 100644 r4babs4/week-3/workshop.qmd create mode 100644 r4babs4/week-4/data-raw/ECOLIGreen_ISOTYPE.fcs create mode 100644 r4babs4/week-4/data-raw/ECOLIGreen_TNFAPC.fcs create mode 100644 r4babs4/week-4/data-raw/LPS_ISOTYPE.fcs create mode 100644 r4babs4/week-4/data-raw/LPS_TNFAPC.fcs create mode 100644 r4babs4/week-4/data-raw/MEDIA_ISOTYPE.fcs create mode 100644 r4babs4/week-4/data-raw/MEDIA_TNFAPC.fcs create mode 100644 r4babs4/week-4/images/answer.png create mode 100644 r4babs4/week-4/images/do_in_R.png create mode 100644 r4babs4/week-4/images/do_on_internet.png create mode 100644 r4babs4/week-4/images/do_on_your_computer.png create mode 100644 r4babs4/week-4/overview.qmd create mode 100644 r4babs4/week-4/study_after_workshop.qmd create mode 100644 r4babs4/week-4/study_before_workshop.qmd create mode 100644 r4babs4/week-4/workshop.qmd create mode 100644 r4babs4/week-5/images/answer.png create mode 100644 r4babs4/week-5/images/do_in_R.png create mode 100644 r4babs4/week-5/images/do_on_internet.png create mode 100644 r4babs4/week-5/images/do_on_your_computer.png create mode 100644 r4babs4/week-5/overview.qmd create mode 100644 r4babs4/week-5/study_after_workshop.qmd create mode 100644 r4babs4/week-5/study_before_workshop.qmd create mode 100644 r4babs4/week-5/workshop.qmd create mode 100644 r4babs4/week-6/images/answer.png create mode 100644 r4babs4/week-6/images/do_in_R.png create mode 100644 r4babs4/week-6/images/do_on_internet.png create mode 100644 r4babs4/week-6/images/do_on_your_computer.png create mode 100644 r4babs4/week-6/overview.qmd create mode 100644 r4babs4/week-6/study_after_workshop.qmd create mode 100644 r4babs4/week-6/study_before_workshop.qmd create mode 100644 r4babs4/week-6/workshop.qmd diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 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the Learning Objectives, a topic summary and the instructions for the week. You should read this first. + -- Some independent study on the "Prepare!" page to prepare you for the workshop. This will be reading from the course book ([Computational Analysis for Bioscientists](https://3mmarand.github.io/comp4biosci/)), watching a video, or doing some coding or set up. It is designed to take about 30-45 mins on average. You will most likely learn best if you can find people to study with. + -- A two-hour workshop using R. This will usually start with me doing a short demonstration of one or more of the examples that were in "Prepare!" but you will spend most of the session going through some exercises. Anything you have not done before is explained and guided but you will also have to use the skills gained in previous workshops. I often remind you to take care of future you by making notes so you can look up your previous work but you can also search the [R4BABS](https://3mmarand.github.io/R4BABS/) site (search is top right). Talking to other people in the workshop about the exercises and working together will really help you understand more. There will be plenty of help from me and my demonstrators. + -- Some independent study on the "Consolidate!" page to give you more practice. The exercises are usually similar to those in the workshop but with less guidance. Occasionally, there will be reading to do. It is designed to take about 30-45 mins on average but may be quicker if you understood the workshop very well or slower if you need to revisit the workshop. + -Learning Data Analysis in R is like learning to speak a new language or play an instrument or a technical sport - you can't really rush it or cram for it. You need regular practice. + -- a little bit of engagement and practice is always better than none + -- if you get behind, just pick up where you left off rather than jumping in. It is fine to work on a previous week's workshop + # Content -## All Strands +## Week 1 Core -many var and obs, examples, PCA, log fc transformation, normalisation, QC gating, missing values, excluding proteins id'd from fewer thaan two peptides, FDR, data structures +Workshop -## -## +All strands -## \ No newline at end of file +many var and obs, examples, use the emphasise equivalance +PCA, log fc transformation, normalisation, QC gating, missing values, excluding proteins id'd from fewer than two peptides, FDR, data structures + + +## Week 2 Immunobiology - 1 + +Workshop + +There are three different cell treatments: Media, Lipopolysaccharide (LPS) and E. coli (NeonGreen fluorescence)) +Two experimental conditions: Isotype antibody and TNFalpha antibody conjugated to Allophycocyanin (APC) + + + +## Week 3 + + + +## Week 4 Immunobiology - 2 + +Workshop + + + +## Week 5 + + +## Week 6 Immunobiology - 3 + +Workshop \ No newline at end of file diff --git a/r4babs4/week-1/data-raw/adipocytes.txt b/r4babs4/week-1/data-raw/adipocytes.txt new file mode 100644 index 00000000..1c4c608c --- /dev/null +++ b/r4babs4/week-1/data-raw/adipocytes.txt @@ -0,0 +1,31 @@ +adiponectin treatment +7.39 nicotinic +7.58 nicotinic +6.87 nicotinic +7.23 nicotinic +9.32 nicotinic +5.72 nicotinic +7.12 nicotinic +9.74 nicotinic +8.55 nicotinic +6.31 nicotinic +6.71 nicotinic +8.01 nicotinic +10.62 nicotinic +8.33 nicotinic +3.13 nicotinic +3.77 control +2.46 control +6.9 control +5.89 control +4.31 control +7.86 control +4.35 control +6.61 control +6.19 control +6.52 control +3.94 control +5.86 control +6.36 control +6.83 control +5.34 control diff --git a/r4babs4/week-1/data-raw/beewing.txt b/r4babs4/week-1/data-raw/beewing.txt new file mode 100644 index 00000000..62039e44 --- /dev/null +++ b/r4babs4/week-1/data-raw/beewing.txt @@ -0,0 +1,101 @@ +wing +3.6 +3.7 +3.8 +3.8 +4 +4 +4 +4 +4.1 +4.1 +4.1 +4.1 +4.1 +4.1 +4.2 +4.2 +4.2 +4.2 +4.2 +4.2 +4.2 +4.3 +4.3 +4.3 +4.3 +4.3 +4.3 +4.3 +4.3 +4.4 +4.4 +4.4 +4.4 +4.4 +4.4 +4.4 +4.4 +4.4 +5.1 +5.1 +5.1 +5.1 +4.5 +4.5 +4.5 +4.5 +4.5 +4.5 +4.5 +4.5 +4.5 +4.5 +4.9 +4.9 +4.9 +4.9 +4.9 +4.9 +4.9 +5.2 +5.2 +5.3 +5.3 +4.6 +4.6 +4.6 +4.6 +4.6 +4.6 +4.6 +4.6 +4.6 +4.6 +5 +5 +5 +5 +5 +5.4 +5.5 +4.7 +4.7 +4.7 +4.7 +4.7 +4.7 +4.7 +4.7 +4.7 +4.8 +4.8 +4.8 +4.8 +4.8 +4.8 +4.8 +4.8 +3.9 +3.9 +5 diff --git a/r4babs4/week-1/data-raw/neuron.txt b/r4babs4/week-1/data-raw/neuron.txt new file mode 100644 index 00000000..35c3f146 --- /dev/null +++ b/r4babs4/week-1/data-raw/neuron.txt @@ -0,0 +1,9 @@ +csa +160.6 +129.7 +153.7 +128.1 +137.4 +137.7 +149.6 +142 diff --git a/r4babs4/week-1/images/answer.png b/r4babs4/week-1/images/answer.png new file mode 100644 index 0000000000000000000000000000000000000000..77438911b4aee9685f3451ca0cec1c41f8ecd03e GIT binary patch literal 2473 zcmV;a30C%rP)Px#1ZP1_K>z@;j|==^1poj532;bRa{vGi!T$-kW`F#tb&bEVcn-(^3~qXoCTjRf2{VMM7ZIra>wK)kT$x%2E_jHBcgG5g_;h z2*lnZ6k_2JFfCP~K~R>O9R$TrSxn+E_Kc13HshIhd+vGfjb{wSX89A(hpyi}=iaw{ z_iXnuiXb4tAYWKCGypBF5N&OMq8I}Liq#4@9Z-^zpxA7>{sl-V0wZ5lqpj?(Xe+;r z@R@T6|EB_4xB&)zOsong)WL&cEf@~_#0jvDc^kIxy`@ih4ibVu5e%Yv?Ro@{mLXDJ z4#nYulAHuM?105e2B=fWRA?Fy3_-NEKx?drH9H5^v4wDbuoSlZ@9?}>bbA6tID){C zVbH#=ftut3+-}`tyYSXRPHYWAv^3Lo!btl42XHT6YK-dW)&xeXAETyU2CS)>P+bl~ ztAk&=r3E5H)@Cpuc~$Hb_nb~B7K^SlpwT4l%P-)Z{T@6Y|Jl%XV*@g{>gk_W^(DfWt^lDRRIis$i9%Hosj7nOSH(!)yn$8S zl0a!yPu?w}MDZ`EJyQ&=4z00~GOPOx?ZHF7WQ|hBIVFux*vfWWzUA9pCNpbtjF8a(CP^xTvdVIWId|Pz+Hqt zGnZVxi4su!IpIf@NPSpEWlwiQO#q?KN}q9E5vYS>$~!Mbo`mO<0}yqO4aop4h5`%1 zSFUwG0>k(3(O;_NIwTIQsTN6VRvC(}!;&+Y;$W7(V~K^$jmYgg-H*WF!4mpQgf%?n zU@wz~M%Jggys%_u>62atRtqeBDVBC5P+B2Hzh?-nuC9iU0&QEz6TTr(Xdi1EHwm`F z?;GuHSVtAWx$qZIyqzbGXFwT3qk1tm88L`FO(FVZ5;3uJ=T2d>*+fc8irBMfkD*O$ zv{h6JADqJ9KS%fn4AEO=uArzWg8w)r;w*5@nl)^BPo6x%-o1O#OvPrRqv{D21HxpY2%9!f-(R8S3tk*Gr{(Lz@9654CY~H+?`zu$j zG)9FWf?3z6PaiR6%oy&Q?b4-7#w@-IH@n@=M|nMZIqvuG-`|)e#MG%%Syxbd3h`^T)2?s<;$0|yl>w=mInkH=mY4fBeHPjL%>zrk_j`Lps!?+}e2wi_0|(XnI4_(AgX^I1u; zPmmk4j@*NFl0c8g!wIIgww84lFJ9z1vuDp{dB%(xEKi<1S&+-I&V)Het}9AQOS!Ko z3KJ`_WXTfNtyr;w<%ts~vRqM7AUbaZrcLcc+ucR6H zV=;2%NFJLzcP_^+*>^gf#;6eG<>joC2}puSW-FU8VS;eETs$T%&xDzDWZSiC7wb&R zlZ{Fv)=9{iXX<6rlJQyg1tdugjFw3us`l9#rLYZP*tmV}>y`nZfQ?y;bY0P{+ z6k2(4>((t$A^^E3JySbmUABM!ezwjjQ>K7+Dj*jJ5o*i;RK8WBIS4~EO|8c z0(8p{s$Ip?dBq4G{1~c{6eUDUE0o@;NcsCwSSXZqhSyJD-(a+z`W*0h42dAix=;{~ z_kY?Y@+1(@hNg2QEP>j8?@@6_G{$Ce8O^9hwdM{n6rCe+ z39Jq178h3rDP)nBXZ}z4MMzizBQ>ty^@t3rJW3X&=cSMs@N`Hzq1*cd5u99d!{2T?f7iBrJj9>o*X- zaSOEdF*F9qDy_r63v2!`L(vH&fr*I5<-Ze;VG}-Q&-ezrXk7J?F{^VVY!-&w1;x{g zw{daJ;{{wUV^ZfxTmlWqIxyN;!Rjs};R%d`SPx#1ZP1_K>z@;j|==^1poj532;bRa{vGi!~g&e!~vBn4jTXf3l>R4K~!i%-CAjE zT-6ypvw6lIk9RxqN|vT5#10|O-mGLHq@gZQs)*1=3Y8$Ciz-^As%j+iucQis7AX=$ zpa2Rg>Lx%kC8eQGVj9R=N(jVB94E0I+haT9v1gxt=bL$s?RXj65IjBd^L_8W`|iAZ z-gm$4UQ<+nFA+v}CX-3aTZxGLc4to*9o`_$G z*ZV;uaxWx94u+x#gd*q;L@+(yfw{N2@Xg6i+)?Dzc8MbC3H;NsYeHO?N}5$A$t+~? 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More usually we might put is like this: +# the mean for the control group is 5.55 +/- 0.82 and that for the nicotinic group is 7.51 +/- 0.99 + + +``` + diff --git a/r4babs4/week-1/study_before_workshop.qmd b/r4babs4/week-1/study_before_workshop.qmd new file mode 100644 index 00000000..73888678 --- /dev/null +++ b/r4babs4/week-1/study_before_workshop.qmd @@ -0,0 +1,12 @@ +--- +title: "Independent Study to prepare for workshop" +subtitle: "The logic of hypothesis testing and confidence intervals" +toc: true +toc-location: right +--- + +1. 📖 Read [The logic of hyothesis testing](https://3mmarand.github.io/comp4biosci/logic_hyopthesis_testing.html) + +2. 📖 Read [Confidence Intervals](https://3mmarand.github.io/comp4biosci/confidence_intervals.html) + + diff --git a/r4babs4/week-1/workshop.qmd b/r4babs4/week-1/workshop.qmd new file mode 100644 index 00000000..f850bb2a --- /dev/null +++ b/r4babs4/week-1/workshop.qmd @@ -0,0 +1,256 @@ +--- +title: "Workshop" +subtitle: "The logic of hypothesis testing and confidence intervals" +toc: true +toc-location: right +--- + +```{r} +#| include: false +library(tidyverse) +library(kableExtra) +``` + +# Introduction + +![Artwork by @allison_horst: "love this class"](images/love-this-class.png){fig-alt="A little monster flying a biplane wearing aviator glasses, pulling a banner that says 'Fully expecting to hate this class.' Below, a teacher wearing a cheerleading outfit labeled 'STATS' with a bullhorn labeled 'CODE' cheering desperately with pom-poms, trying to help students believe stats is actually going to be awesomely life-changing." width="800"} + +## Session overview + +In this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples. + +## Philosophy + +Workshops are not a test. It is expected that you often don't know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips + +- don't worry about making mistakes +- don't let what you can not do interfere with what you can do +- discussing code with your neighbours will help +- look things up in the independent study material +- look things up in your own code from earlier +- there are no stupid questions + +::: callout-note +## Key + +These four symbols are used at the beginning of each instruction so you know where to carry out the instruction. + +![](images/do_on_your_computer.png) Something you need to do on your computer. It may be opening programs or documents or locating a file. + +![](images/do_in_R.png) Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files. + +![](images/do_on_internet.png) Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file. + +![](images/answer.png) A question for you to think about and answer. Record your answers in your script for future reference. +::: + +# Getting started + +![](images/do_on_your_computer.png) Start RStudio from the Start menu. + +![](images/do_in_R.png) Go the Files tab in the lower right pane and click on the `...` on the right. This will open a "Go to folder" window. Navigate to a place on your computer where you keep your work. Click Open. + +![](images/do_in_R.png) Also on the Files tab click on `New Folder`. Type "data-analysis-in-r-2" in to the box. This will be the folder that we work in throughout the Data Analysis in R part BABS2. + +![](images/do_in_R.png) Make an RStudio project for this workshop by clicking on the drop-down menu on top right where it says `Project: (None)` and choosing New Project, then New Directory, then New Project. Name the RStudio Project 'week-1'. + +![](images/do_in_R.png) Make a new script then save it with a name like analysis.R to carry out the rest of the work. + +![](images/do_in_R.png) Add a comment to the script: `# Introduction to RStudio and your first graph` and load the **`tidyverse`** [@tidyverse] package + +![](images/do_in_R.png) Make a new folder called `data-raw`. + +# Exercises + +## Remind yourself how to import files! + +[Importing data from files](../../r4babs1/week-8/workshop.html#importing-data-from-files) was covered in BABS 1 [@rand2023] if you need to remind yourself. + +## Confidence intervals (large samples) + +The data in [beewing.txt](data-raw/beewing.txt) are left wing widths of 100 honey bees (mm). The confidence interval for large samples is given by: + + +$\bar{x} \pm 1.96 \times s.e.$ + + +Where 1.96 is the quantile for 95% confidence. + +![](images/do_on_your_computer.png) Save [beewing.txt](data-raw/beewing.txt) to your `data-raw` folder. + +![](images/do_in_R.png) Read in the data and check the structure of the resulting dataframe. + +```{r} +#| include: false + +#---CODING ANSWER--- + +bee <- read_table("data-raw/beewing.txt") +str(bee) +``` + +![](images/do_in_R.png) Calculate and assign to variables: the mean, standard deviation and standard error: + +```{r} +# mean +m <- mean(bee$wing) + +# standard deviation +sd <- sd(bee$wing) + +# sample size (needed for the se) +n <- length(bee$wing) + +# standard error +se <- sd / sqrt(n) +``` + +![](images/do_in_R.png) To calculate the 95% confidence interval we need to look up the quantile (multiplier) using `qnorm()` + +```{r} +q <- qnorm(0.975) +``` + +This should be about 1.96. + +![](images/do_in_R.png) Now we can use it in our confidence interval calculation + +```{r} +lcl <- m - q * se +ucl <- m + q * se +``` + +![](images/do_in_R.png) Print the values + +```{r} +lcl +ucl +``` + +This means we are 95% confident the population mean lies between 4.47 mm and 4.63 mm. The usual way of expressing this is that the mean is 4.55 +/- 0.07 mm + +![](images/do_in_R.png) Between what values would you be 99% confident of the population mean being? + +```{r} +#| include: false + +#---CODING ANSWER--- + +# qnorm(0.975) gives the quantile for 95%. For 99% we need qnorm(0.995) +q <- qnorm(0.995) +lcl <- m - q * se +ucl <- m + q * se + +``` + + + + + + +## Confidence intervals (small samples) + +The confidence interval for small samples is given by: + + +$\bar{x} \pm \sf t_{[d.f]} \times s.e.$ + +The only difference between the calculation for small and large sample is the multiple. For large samples we use the "the standard normal distribution" accessed with `qnorm()`; for small samples we use the "*t* distribution" assessed with `qt()`.The value returned by `q(t)` is larger than that returned by `qnorm()` which reflects the greater uncertainty we have on estimations of population means based on small samples. + + +The fatty acid Docosahexaenoic acid (DHA) is a major component of membrane phospholipids in nerve cells and deficiency leads to many behavioural and functional deficits. The cross sectional area of neurons in the CA 1 region of the hippocampus of normal rats is 155 $\mu m^2$. A DHA deficient diet was fed to 8 animals and the cross sectional area (csa) of neurons is given in [neuron.txt](data-raw/neuron.txt) + +![](images/do_on_your_computer.png) Save [neuron.txt](data-raw/neuron.txt) to your `data-raw` folder + +![](images/do_in_R.png) Read in the data and check the structure of the resulting dataframe + +```{r} +#| include: false + +#---CODING ANSWER--- +neur <- read_table("data-raw/neuron.txt") +``` + +![](images/do_in_R.png) Assign the mean to `m`. + +```{r} +#| include: false + +#---CODING ANSWER--- + +m <- mean(neur$csa) + +``` + +![](images/do_in_R.png) Calculate and assign the standard error to `se`. + +```{r} +#| include: false + +#---CODING ANSWER--- + +# I created intermediate variables for sd and n +sd <- sd(neur$csa) +n <- length(neur$csa) +se <- sd / sqrt(n) +``` + +To work out the confidence interval for our sample mean we need to use the *t* distribution because it is a small sample. This means we need to determine the degrees of freedom (the number in the sample minus one). + +![](images/do_in_R.png) We can assign this to a variable, `df`, using: + +```{r} +df <- length(neur$csa) - 1 +``` + +![](images/do_in_R.png) The *t* value is found by: + +```{r} +t <- qt(0.975, df = df) +``` + +Note that we are using `qt()` rather than `qnorm()` but that the probability, 0.975, used is the same. Finally, we need to put our mean, standard error and *t* value in the equation. $\bar{x} \pm \sf t_{[d.f]} \times s.e.$. + +![](images/do_in_R.png) The upper confidence limit is: + +```{r} +(m + t * se) |> round(2) +``` + +The first part of the command, `(m + t * se)` calculates the upper limit. This is 'piped' in to the `round()` function to round the result to two decimal places. + +![](images/do_in_R.png) Calculate the lower confidence limit: + +```{r include=FALSE} +(m - t * se) |> round(2) +``` + +![](images/answer.png) Given the upper and lower confidence values for the estimate of the population mean, what do you think about the effect of the DHA deficient diet? + + + + + + + + + +You're finished! + +# 🥳 Well Done! 🎉 + +![Artwork by @allison_horst: "We belive in you!"](images/we-believe.png){fig-alt="Header text 'R learners' above five friendly monsters holding up signs that together read 'we believe in you.'" width="800"} + +# Independent study following the workshop + +[Consolidate](study_after_workshop.qmd) + +# The Code file + +These contain all the code needed in the workshop even where it is not visible on the webpage. + +The [workshop.qmd](workshop.qmd) file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interweaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Rmd file in RStudio. Alternatively, [View in Browser](https://github.com/3mmaRand/R4BABS/blob/main/r4babs2/week-1/workshop.qmd).Coding and thinking answers are marked with `#---CODING ANSWER---` and `#---THINKING ANSWER---` + +Pages made with R [@R-core], Quarto [@allaire2022], `knitr` [@knitr], `kableExtra` [@kableExtra] + +# References diff --git "a/r4babs4/week-2/images/66I-exp-des-TNF-\316\261.png" "b/r4babs4/week-2/images/66I-exp-des-TNF-\316\261.png" new file mode 100644 index 0000000000000000000000000000000000000000..8d73a2608a74d5d871ea6a0786a93baff5ceaf11 GIT binary patch literal 63091 zcmaI8Wmr_-8#Rn{cY}loNP{9FU80gA-O}CNLyDwyGe~!LcS(qJ4&5zX&-V9!pX>Sd za?Pg!&YZLN9qV3e?Fm(sm&QUTM~8!h!}|C^QV9+Yp#u&Mo)Q%vyi+Hl$O;GN2lr7@ zOxaEMpfyB`=;M6D!<|Ii{*w2~Qt}*{fZ-@9-Y@=fqtB9JxOifN-(GQ|qNBzj65`@T z)6npnq97qjN#Y?QA`X&`Ud@ip|8dC-@Pod-TU682c2aS6c1{=GUR231x_$bH>QD3E zD~a<-fm9S1QS9F<(CPye?SH>8z>gLs_}{xdFKHkQ|NHxIA4TEi{=Ywt^+Qbg-!*6$ zo}qF7cL78(S$KT2|9fe~m*fAt0wNdM|KEl4GAk=9BO@b=ii-OB`m(ZUzk;jvSOx#P 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Do these seem to be met? + + + + + + + + + + + + + + +### Applying, interpreting and reporting + +![](images/do_in_R.png) We now carry out a regression assigning the result of the `lm()` procedure to a variable and examining it with `summary()`. +```{r} +mod <- lm(data = plant, mass ~ day) +summary(mod) +``` + +The Estimates in the Coefficients table give the intercept (first line) and the slope (second line) of the best fitting straight line. The *p*-values on the same line are tests of whether that coefficient is different from zero. + +The *F* value and *p*-value in the last line are a test of whether the model as a whole explains a significant amount of variation in the dependent variable. For a single linear regression this is exactly equivalent to the test of the slope against zero. + +![](images/answer.png) What is the equation of the line? What do you conclude from the analysis? + + + + + + + + + +![](images/answer.png) Does the line go through (0,0)? + + + + + + + +![](images/answer.png) What percentage of variation is explained by the line? + + + + + +It might be useful to assign the slope and the intercept to variables in case we need them later. The can be accessed in the `mod$coefficients` variable: + +```{r} +mod$coefficients +``` + +![](images/do_in_R.png) Assign `mod$coefficients[1]` to `b0` and `mod$coefficients[1]` to `b1`: + +```{r} +b0 <- mod$coefficients[1] |> round(2) +b1 <- mod$coefficients[2] |> round(2) +``` +I also rounded the values to two decimal places. + + + +### Checking assumptions + +We need to examine the residuals. Very conveniently, the object which is created by `lm()` contains a variable called `$residuals`. Also conveniently, the R's `plot()` function can used on the output objects of `lm()`. The assumptions demand that each y is drawn from a normal distribution for each x and these normal distributions have the same variance. Therefore we plot the residuals against the fitted values to see if the variance is the same for all the values of x. +The fitted - predicted - values are the values on the line of best fit. Each residual is the difference between the fitted values and the observed value. + +![](images/do_in_R.png) Plot the model residuals against the fitted values like this: +```{r} +plot(mod, which = 1) +``` + + +![](images/answer.png) What to you conclude? + + + + + + +To examine normality of the model residuals we can plot them as a histogram and do a normality test on them. + +![](images/do_in_R.png) Plot a histogram of the residuals: +```{r} + +ggplot(mapping = aes(x = mod$residuals)) + + geom_histogram(bins = 10) +``` + + + +![](images/do_in_R.png) Use the `shapiro.test()` to test the normality of the model residuals +```{r} +shapiro.test(mod$residuals) +``` + +Usually, when we are doing statistical tests we would like the the test to be significant because it means we have evidence of a biological effect. However, when doing normality tests we hope it will not be significant. A non-significant result means that there is no significant difference between the distribution of the residuals and a normal distribution and that indicates the assumptions are met. + +![](images/answer.png) What to you conclude? + + + + + + + + + +### Illustrating + +We want a figure with the points and the statistical model, i.e., the best fitting straight line. + +![](images/do_in_R.png) Create a scatter plot using `geom_point()` + + +```{r} +ggplot(plant, aes(x = day, y = mass)) + + geom_point() + + theme_classic() +``` + + ![](images/do_in_R.png) The `geom_smooth()` function will had a variety of fitted lines to a plot. We want a line so we need to specify `method = "lm"`: + +```{r} +ggplot(plant, aes(x = day, y = mass)) + + geom_point() + + geom_smooth(method = lm, + se = FALSE, + colour = "black") + + theme_classic() +``` + +![](images/do_in_R.png) What do the `se` and `colour` arguments do? Try changing them. + + +![](images/do_in_R.png) Let's add the equation of the line to the figure using `annotate()`: + +```{r} +ggplot(plant, aes(x = day, y = mass)) + + geom_point() + + geom_smooth(method = lm, + se = FALSE, + colour = "black") + + annotate("text", x = 20, y = 110, + label = "mass = 1.61 * day - 8.68") + + theme_classic() + +``` + +We have to tell `annotate()` what type of geom we want - `text` in this case, - where to put it, and the text we want to appear. + +```{r} +#| include: false + +#---BONUS CODING ANSWER--- +# I'm not expecting you to know about this but it's a neat trick. The glue package allows +# us to use R objects in strings so their values are printed. +ggplot(plant, aes(x = day, y = mass)) + + geom_point() + + geom_smooth(method = lm, + se = FALSE, + colour = "black") + + scale_x_continuous(name = "Day", + limits = c(0, 65), + expand = c(0,0)) + + scale_y_continuous(name = "Mass (g)", + limits = c(0, 120), + expand = c(0,0)) + + annotate("text", x = 20, y = 110, + label = glue::glue("mass = {b1} day {b0}")) + + theme_classic() +``` + + +![](images/do_in_R.png) Improve the axes. You may need to refer back +[Changing the axes](../../r4babs1/week-7/workshop.html#changing-the-axes) from the Week 7 workshop in BABS1 [@rand2023] + +```{r} +#| include: false + +#---CODING ANSWER--- +# + +ggplot(plant, aes(x = day, y = mass)) + + geom_point() + + geom_smooth(method = lm, + se = FALSE, + colour = "black") + + scale_x_continuous(name = "Day", + limits = c(0, 65), + expand = c(0,0)) + + scale_y_continuous(name = "Mass (g)", + limits = c(0, 120), + expand = c(0,0)) + + annotate("text", x = 20, y = 110, + label = "mass = 1.61 * day - 8.68") + + theme_classic() + + +# note that we need to use the continuous versions of both scale_ +``` + + +![](images/do_in_R.png) Save your figure to your `figures` folder. + +```{r} +#| include: false + +#---CODING ANSWER--- + +# Assign the figure to a variable +fig2 <- ggplot(plant, aes(x = day, y = mass)) + + geom_point() + + geom_smooth(method = lm, + se = FALSE, + colour = "black") + + scale_x_continuous(name = "Day", + limits = c(0, 65), + expand = c(0,0)) + + scale_y_continuous(name = "Mass (g)", + limits = c(0, 120), + expand = c(0,0)) + + annotate("text", x = 20, y = 110, + label = "mass = 1.61 * day - 8.68") + + theme_classic() + +``` + + +```{r} +#| include: false + +#---CODING ANSWER--- + +# save figure to figures/plant-growth.png +ggsave("figures/plant-growth.png", + plot = fig2, + width = 3.5, + height = 3.5, + units = "in", + dpi = 300) + +``` + +## Look after future you! + + +You're finished! + +# 🥳 Well Done! 🎉 + +![Artwork by @allison_horst: "length typos"](images/lenght-length.png){fig-alt="Comic panels of an alligator trying to debug some code. First panel: A confident looking alligator gets an error message. Second panel: a few minutes later, the error remains and the alligator is looking carefully at their code. Third panel: 10 minutes after that, the error remains and the alligator is giving a frustrated 'RAAAR' while desperately typing. Fourth panel: The error remains, and the alligator looks exhausted and exasperated, and a thought bubble reads 'maybe it's a bug'. Fifth panel: A friendly flamingo comes over to take a look, and reads aloud from the problematic code a spelling error: 'L-E-N-G-H-T'. Only the tail of the alligator is visible as it stomp stomp stomps out of the panel roaring." width="800"} + +# Independent study following the workshop + +[Consolidate](study_after_workshop.qmd) + +# The Code file + +These contain all the code needed in the workshop even where it is not visible on the webpage. + +The [workshop.qmd](workshop.qmd) file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interweaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Rmd file in RStudio. Alternatively, [View in Browser](https://github.com/3mmaRand/R4BABS/blob/main/r4babs2/week-2/workshop.qmd).Coding and thinking answers are marked with `#---CODING ANSWER---` and `#---THINKING ANSWER---` + +Pages made with R [@R-core], Quarto [@allaire2022], `knitr` [@knitr], `kableExtra` [@kableExtra] + +# References diff --git a/r4babs4/week-3/images/answer.png b/r4babs4/week-3/images/answer.png new file mode 100644 index 0000000000000000000000000000000000000000..77438911b4aee9685f3451ca0cec1c41f8ecd03e GIT binary patch literal 2473 zcmV;a30C%rP)Px#1ZP1_K>z@;j|==^1poj532;bRa{vGi!T$-kW`F#tb&bEVcn-(^3~qXoCTjRf2{VMM7ZIra>wK)kT$x%2E_jHBcgG5g_;h z2*lnZ6k_2JFfCP~K~R>O9R$TrSxn+E_Kc13HshIhd+vGfjb{wSX89A(hpyi}=iaw{ z_iXnuiXb4tAYWKCGypBF5N&OMq8I}Liq#4@9Z-^zpxA7>{sl-V0wZ5lqpj?(Xe+;r z@R@T6|EB_4xB&)zOsong)WL&cEf@~_#0jvDc^kIxy`@ih4ibVu5e%Yv?Ro@{mLXDJ z4#nYulAHuM?105e2B=fWRA?Fy3_-NEKx?drH9H5^v4wDbuoSlZ@9?}>bbA6tID){C zVbH#=ftut3+-}`tyYSXRPHYWAv^3Lo!btl42XHT6YK-dW)&xeXAETyU2CS)>P+bl~ ztAk&=r3E5H)@Cpuc~$Hb_nb~B7K^SlpwT4l%P-)Z{T@6Y|Jl%XV*@g{>gk_W^(DfWt^lDRRIis$i9%Hosj7nOSH(!)yn$8S zl0a!yPu?w}MDZ`EJyQ&=4z00~GOPOx?ZHF7WQ|hBIVFux*vfWWzUA9pCNpbtjF8a(CP^xTvdVIWId|Pz+Hqt zGnZVxi4su!IpIf@NPSpEWlwiQO#q?KN}q9E5vYS>$~!Mbo`mO<0}yqO4aop4h5`%1 zSFUwG0>k(3(O;_NIwTIQsTN6VRvC(}!;&+Y;$W7(V~K^$jmYgg-H*WF!4mpQgf%?n zU@wz~M%Jggys%_u>62atRtqeBDVBC5P+B2Hzh?-nuC9iU0&QEz6TTr(Xdi1EHwm`F z?;GuHSVtAWx$qZIyqzbGXFwT3qk1tm88L`FO(FVZ5;3uJ=T2d>*+fc8irBMfkD*O$ zv{h6JADqJ9KS%fn4AEO=uArzWg8w)r;w*5@nl)^BPo6x%-o1O#OvPrRqv{D21HxpY2%9!f-(R8S3tk*Gr{(Lz@9654CY~H+?`zu$j zG)9FWf?3z6PaiR6%oy&Q?b4-7#w@-IH@n@=M|nMZIqvuG-`|)e#MG%%Syxbd3h`^T)2?s<;$0|yl>w=mInkH=mY4fBeHPjL%>zrk_j`Lps!?+}e2wi_0|(XnI4_(AgX^I1u; zPmmk4j@*NFl0c8g!wIIgww84lFJ9z1vuDp{dB%(xEKi<1S&+-I&V)Het}9AQOS!Ko z3KJ`_WXTfNtyr;w<%ts~vRqM7AUbaZrcLcc+ucR6H zV=;2%NFJLzcP_^+*>^gf#;6eG<>joC2}puSW-FU8VS;eETs$T%&xDzDWZSiC7wb&R zlZ{Fv)=9{iXX<6rlJQyg1tdugjFw3us`l9#rLYZP*tmV}>y`nZfQ?y;bY0P{+ z6k2(4>((t$A^^E3JySbmUABM!ezwjjQ>K7+Dj*jJ5o*i;RK8WBIS4~EO|8c z0(8p{s$Ip?dBq4G{1~c{6eUDUE0o@;NcsCwSSXZqhSyJD-(a+z`W*0h42dAix=;{~ z_kY?Y@+1(@hNg2QEP>j8?@@6_G{$Ce8O^9hwdM{n6rCe+ z39Jq178h3rDP)nBXZ}z4MMzizBQ>ty^@t3rJW3X&=cSMs@N`Hzq1*cd5u99d!{2T?f7iBrJj9>o*X- zaSOEdF*F9qDy_r63v2!`L(vH&fr*I5<-Ze;VG}-Q&-ezrXk7J?F{^VVY!-&w1;x{g zw{daJ;{{wUV^ZfxTmlWqIxyN;!Rjs};R%d`SPx#1ZP1_K>z@;j|==^1poj532;bRa{vGi!~g&e!~vBn4jTXf3l>R4K~!i%-CAjE zT-6ypvw6lIk9RxqN|vT5#10|O-mGLHq@gZQs)*1=3Y8$Ciz-^As%j+iucQis7AX=$ zpa2Rg>Lx%kC8eQGVj9R=N(jVB94E0I+haT9v1gxt=bL$s?RXj65IjBd^L_8W`|iAZ z-gm$4UQ<+nFA+v}CX-3aTZxGLc4to*9o`_$G z*ZV;uaxWx94u+x#gd*q;L@+(yfw{N2@Xg6i+)?Dzc8MbC3H;NsYeHO?N}5$A$t+~? 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Maybe a bit higher in the wild plants (with the higher mean) + +hist(mod$residuals) +shapiro.test(mod$residuals) +# On balance the use of lm() is probably justifiable The variance isn't quite equal +# and the histogram looks a bit off normal but the normality test is NS and the +# effect (in the figure) is clear. +``` + +```{r} +#| output: false + +# A figure +fig1 <- ggplot() + + geom_point(data = csativa, aes(x = plant, y = omega), + position = position_jitter(width = 0.1, height = 0), + colour = "gray50") + + geom_errorbar(data = csativa_summary, + aes(x = plant, ymin = mean - se, ymax = mean + se), + width = 0.3) + + geom_errorbar(data = csativa_summary, + aes(x = plant, ymin = mean, ymax = mean), + width = 0.2) + + scale_x_discrete(name = "Plant type", labels = c("GMO", "WT")) + + scale_y_continuous(name = "Amount of Omega 3 (units)", + expand = c(0, 0), + limits = c(0, 90)) + + annotate("segment", x = 1, xend = 2, + y = 80, yend = 80, + colour = "black") + + annotate("text", x = 1.5, y = 85, + label = expression(italic(p)~"< 0.001")) + + theme_classic() + +# save figure to figures/csativa.png +ggsave("figures/csativa.png", + plot = fig1, + width = 3.5, + height = 3.5, + units = "in", + dpi = 300) + +``` + +1. 💻 another example \ No newline at end of file diff --git a/r4babs4/week-3/study_before_workshop.qmd b/r4babs4/week-3/study_before_workshop.qmd new file mode 100644 index 00000000..584e4153 --- /dev/null +++ b/r4babs4/week-3/study_before_workshop.qmd @@ -0,0 +1,13 @@ +--- +title: "Independent Study to prepare for workshop" +subtitle: "Two-sample tests" +toc: true +toc-location: right +--- + +1. Prepare + + i. 📖 Read [Two-Sample tests](https://3mmarand.github.io/comp4biosci/two_sample_tests.html) + + + diff --git a/r4babs4/week-3/workshop.qmd b/r4babs4/week-3/workshop.qmd new file mode 100644 index 00000000..8c0ed2ed --- /dev/null +++ b/r4babs4/week-3/workshop.qmd @@ -0,0 +1,534 @@ +--- +title: "Workshop" +subtitle: "Two-sample tests" +toc: true +toc-location: right +--- + +```{r} +#| include: false +library(tidyverse) +library(kableExtra) +``` + +# Introduction + +![Artwork by @allison_horst: "How much I think I know about R"](images/how-much-i-know.png){fig-alt="Illustrated line plot of 'How much I think I know about R' on the y-axis, and 'Time' on the x-axis. Along the line are emoji-style faces, showing the non-linear progression of R knowledge over time. At first, a nervous face becomes a happy face early on in learning, then a grimace face at an intermediate peak before a steep decline (with an exhausted face at the local minimum). Then, a determined face charges back up a hill, reaching another peak with a mind-blown face and text annotation 'join R community on twitter' followed by another decline, but this time the faces look happy even though their 'How much I think I know about R' value is declining." width="800"} + +## Session overview + +In this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R. + +## Philosophy + +Workshops are not a test. It is expected that you often don't know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips + +- don't worry about making mistakes +- don't let what you can not do interfere with what you can do +- discussing code with your neighbours will help +- look things up in the independent study material +- look things up in your own code from earlier +- there are no stupid questions + +::: callout-note +## Key + +These four symbols are used at the beginning of each instruction so you know where to carry out the instruction. + +![](images/do_on_your_computer.png) Something you need to do on your computer. It may be opening programs or documents or locating a file. + +![](images/do_in_R.png) Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files. + +![](images/do_on_internet.png) Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file. + +![](images/answer.png) A question for you to think about and answer. Record your answers in your script for future reference. +::: + +# Getting started + +![](images/do_on_your_computer.png) Start RStudio from the Start menu. + +![](images/do_in_R.png) Go the Files tab in the lower right pane and click on the `...` on the right. This will open a "Go to folder" window. Navigate to a place on your computer where you keep your work. Click Open. + +![](images/do_in_R.png) Make an RStudio project for this workshop by clicking on the drop-down menu on top right where it says `Project: (None)` and choosing New Project, then New Directory, then New Project. Navigate to the `data-analysis-in-r-2` folder and name the RStudio Project `week-3`. + +![](images/do_in_R.png) Make new folders called `data-raw` and `figures`. You can do this on the Files Pane by clicking New Folder and typing into the box that appears. + +![](images/do_in_R.png) Make a new script then save it with a name like `two-sample-tests.R` to carry out the rest of the work. + +![](images/do_in_R.png) Add a comment to the script: `# Two-sample tests` and load the **`tidyverse`** [@tidyverse] package + +# Exercises + +## Adiponectin secretion + +Adiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in [adipocytes.txt](data-raw/adipocytes.txt). Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not. + +![](images/do_on_your_computer.png) Save a copy of [adipocytes.txt](data-raw/adipocytes.txt) to `data-raw` + +![](images/do_in_R.png) Read in the data and check the structure. I used the name `adip` for the dataframe/tibble. + +```{r} +#| include: false + +#| #---CODING ANSWER--- +adip <- read_table("data-raw/adipocytes.txt") +str(adip) +``` + +We have a tibble containing two variables: `adiponectin` is the response and is continuous and `treatment` is explanatory. `treatment` is categorical with two levels (groups). The first task is visualise the data to get an overview. For continuous response variables with categorical explanatory variables you could use `geom_point()`, `geom_boxplot()` or a variety of other geoms. I often use `geom_violin()` which allows us to see the distribution - the violin is fatter where there are more data points. + + + +![](images/do_in_R.png) Do a quick plot of the data: +```{r} +ggplot(data = adip, aes(x = treatment, y = adiponectin)) + + geom_violin() +``` + + +### Summarising the data + +Summarising the data for each treatment group is the next sensible step. The most useful summary statistics are the means, standard deviations, sample sizes and standard errors. + +![](images/do_in_R.png) Create a data frame called `adip_summary` that contains the means, standard deviations, sample sizes and standard errors for the control and nicotinic acid treated samples. You may need to the [Summarise](../../r4babs1/week-9/workshop.html#summarise) from the Week 9 workshop of BABS1 [@rand2023] + +```{r} +#| include: false + +#| #---CODING ANSWER--- + +adip_summary <- adip %>% + group_by(treatment) %>% + summarise(mean = mean(adiponectin), + std = sd(adiponectin), + n = length(adiponectin), + se = std/sqrt(n)) + +``` + +You should get the following numbers: + +```{r} +#| echo: false +knitr::kable(adip_summary) %>% + kableExtra::kable_styling() +``` + + +### Selecting a test + +![](images/answer.png) Do you think this is a paired-sample test or two-sample test? + + + + + + + + + +### Applying, interpreting and reporting + +![](images/do_in_R.png) Create a two-sample model like this: + +```{r} +mod <- lm(data = adip, + adiponectin ~ treatment) +``` + + +![](images/do_in_R.png) Examine the model with: + +```{r} +summary(mod) +``` + + + +![](images/answer.png) What do you conclude from the test? Write your conclusion in a form suitable for a report. + + + + + + + + + + +### Check assumptions + +The assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the `mod$residuals` variable. You may want to refer to [Checking assumptions](../week-2/workshop.html#checking-assumptions) in the "Single regression" workshop. + +![](images/do_in_R.png) Plot the model residuals against the fitted values. +```{r} +#| include: false + +#---CODING ANSWER- + +plot(mod, which = 1) +``` + + +![](images/answer.png) What to you conclude? + + + + + + +To examine normality of the model residuals we can plot them as a histogram and do a normality test on them. + +![](images/do_in_R.png) Plot a histogram of the residuals. +```{r} +#| include: false + +#---CODING ANSWER- + +ggplot(mapping = aes(x = mod$residuals)) + + geom_histogram(bins = 10) +``` + + + +![](images/do_in_R.png) Use the `shapiro.test()` to test the normality of the model residuals +```{r} +#| include: false + +#---CODING ANSWER- + +shapiro.test(mod$residuals) +``` + +![](images/answer.png) What to you conclude? + + + + + + + + +### Illustrating + +![](images/do_in_R.png) Create a figure like the one below. You may need to refer to [Visualise](../../r4babs1/week-9/workshop.html#visualise) from the "Summarising data with several variables" workshop [@rand2023] + +```{r} +#| echo: false + +#---CODING ANSWER--- +ggplot() + + geom_point(data = adip, aes(x = treatment, y = adiponectin), + position = position_jitter(width = 0.1, height = 0), + colour = "gray50") + + geom_errorbar(data = adip_summary, + aes(x = treatment, ymin = mean - se, ymax = mean + se), + width = 0.3) + + geom_errorbar(data = adip_summary, + aes(x = treatment, ymin = mean, ymax = mean), + width = 0.2) + + scale_y_continuous(name = "Adiponectin (pg/mL)", + limits = c(0, 12), + expand = c(0, 0)) + + scale_x_discrete(name = "Treatment", + labels = c("Control", "Nicotinic acid")) + + theme_classic() + +``` + + +We now need to annotate the figure with the results from the statistical test. This most commonly done with a line linking the means being compared and the *p*-value. The `annotate()` function can be used to draw the line and then to add the value. The line is a `segment` and the *p*-value is a `text`. + +![](images/do_in_R.png) Add annotation to the figure by adding: +```r +...... + + annotate("segment", x = 1, xend = 2, + y = 11.3, yend = 11.3, + colour = "black") + + annotate("text", x = 1.5, y = 11.7, + label = expression(italic(p)~"= 0.003")) + + theme_classic() +``` + +```{r} +#| echo: false + +ggplot() + + geom_point(data = adip, aes(x = treatment, y = adiponectin), + position = position_jitter(width = 0.1, height = 0), + colour = "gray50") + + geom_errorbar(data = adip_summary, + aes(x = treatment, ymin = mean - se, ymax = mean + se), + width = 0.3) + + geom_errorbar(data = adip_summary, + aes(x = treatment, ymin = mean, ymax = mean), + width = 0.2) + + scale_y_continuous(name = "Adiponectin (pg/mL)", + limits = c(0, 12), + expand = c(0, 0)) + + scale_x_discrete(name = "Treatment", + labels = c("Control", "Nicotinic acid")) + + annotate("segment", x = 1, xend = 2, + y = 11.3, yend = 11.3, + colour = "black") + + annotate("text", x = 1.5, y = 11.7, + label = expression(italic(p)~"= 0.003")) + + theme_classic() +``` + + + + +For the segment, `annotate()` needs the *x* and *y* coordinates for the start and the finish of the line. + +The use of `expression()` allows you to specify formatting or special characters. `expression()` takes strings or [LaTeX](https://en.wikipedia.org/wiki/LaTeX) formatting. Each string or piece of LaTeX is separated by a `*` or a `~`. The `*` concatenates the strings without a space, `~` does so with a space. +It will generate a warning message "In is.na(x) : is.na() applied to non-(list or vector) of type 'expression'" which can be ignored. + + + +![](images/do_in_R.png) Save your figure to your figures folder. +```{r} +#| include: false + +#---CODING ANSWER--- + +# Assign the figure to a variable +fig1 <- ggplot() + + geom_point(data = adip, aes(x = treatment, y = adiponectin), + position = position_jitter(width = 0.1, height = 0), + colour = "gray50") + + geom_errorbar(data = adip_summary, + aes(x = treatment, ymin = mean - se, ymax = mean + se), + width = 0.3) + + geom_errorbar(data = adip_summary, + aes(x = treatment, ymin = mean, ymax = mean), + width = 0.2) + + scale_y_continuous(name = "Adiponectin (pg/mL)", + limits = c(0, 12), + expand = c(0, 0)) + + scale_x_discrete(name = "Treatment", + labels = c("Control", "Nicotinic acid")) + + annotate("segment", x = 1, xend = 2, + y = 11.3, yend = 11.3, + colour = "black") + + annotate("text", x = 1.5, y = 11.7, + label = expression(italic(p)~"= 0.003")) + + theme_classic() + +``` + + +```{r} +#| include: false + +#---CODING ANSWER--- + +# save figure to figures/adipo.png +ggsave("figures/adipo.png", + plot = fig1, + width = 3.5, + height = 3.5, + units = "in", + dpi = 300) + +``` + +## Grouse Parasites + +Grouse livers were dissected and the number of individuals of a parasitic nematode were counted for two estates 'Gordon' and 'Moss'. We want to know if the two estates have different infection rates. The data are in [grouse.csv](data-raw/grouse.csv) + + +![](images/do_on_your_computer.png) Save a copy of [grouse.csv](data-raw/grouse.csv) to `data-raw` + +![](images/do_in_R.png) Read in the data and check the structure. I used the name `grouse` for the dataframe/tibble. + +```{r} +#| include: false + +#| #---CODING ANSWER--- +grouse <- read_csv("data-raw/grouse.csv") +str(grouse) +``` + + +### Selecting + +![](images/answer.png) Using your common sense, do these data look normally distributed? + + + + + + + +![](images/answer.png) What test do you suggest? + + + + + + +### Applying, interpreting and reporting + +![](images/do_in_R.png) Summarise the data by finding the median of each group: +```{r} +#| include: false + +# ---CODING ANSWER--- +grouse %>% + group_by(estate) %>% + summarise(median(nematodes)) +``` + + +![](images/do_in_R.png) Carry out a two-sample Wilcoxon test (also known as a Mann-Whitney): +```{r} +wilcox.test(data = grouse, nematodes ~ estate) +``` +![](images/answer.png) What do you conclude from the test? Write your conclusion in a form suitable for a report. + + + + + + + + +### Illustrating + +A box plot is a usually good choice for illustrating a two-sample Wilcoxon test because it shows the median and interquartile range. + +![](images/do_in_R.png) We can create a simple boxplot with: +```{r} +ggplot(data = grouse, aes(x = estate, y = nematodes) ) + + geom_boxplot() +``` + +![](images/do_in_R.png) Annotate and format the figure so it is more suitable for a report and save it to your figures folder. +```{r} +#| include: false + +# ---CODING ANSWER--- + +fig2 <- ggplot(data = grouse, aes(x = estate, y = nematodes) ) + + geom_boxplot() + + scale_x_discrete(name = "Estate", + labels = c("Gordon", "Moss")) + + scale_y_continuous(name = "Number of nematodes", + expand = c(0, 0), + limits = c(0, 75)) + + annotate("segment", x = 1, xend = 2, + y = 67, yend = 67, + colour = "black") + + annotate("text", x = 1.5, y = 69, + label = expression(italic(p)~"= 0.035")) + + theme_classic() + +# save figure to figures/grouse.png +ggsave("figures/grouse.png", + plot = fig2, + width = 3, + height = 3, + units = "in", + dpi = 300) +``` + +## Gene Expression + +Bambara groundnut (*Vigna subterranea*) is an African legume with good nutritional value which can be influenced by low temperature stress. Researchers are interested in the expression levels of a particular set of 35 genes (`probe_id`) in response to temperature stress. They measure the expression of the genes at 23 and 18 degrees C (high and low temperature). These samples are **not** independent because we have two measure from one gene. The data are in [expr.xlxs](data-raw/expr.xlsx). + +### Selecting +![](images/answer.png) What is the null hypothesis? + + + + + + + +![](images/do_in_R.png) Save a copy of [expr.xlxs](data-raw/expr.xlsx) and import the data. I named the dataframe `bambara` + +```{r} +#| include: false + +#---CODING ANSWER--- + +# we need the readxl package +library(readxl) +bambara <- read_excel("data-raw/expr.xlsx") +``` + +![](images/answer.png) What is the appropriate parametric test? + + + + + + +### Applying, interpreting and reporting + +A paired test requires us to test whether the difference in expression between high and low temperatures is zero on average. One handy way to achieve this is to organise our groups into two columns. The `pivot_wider()` function will do this for us. We need to tell it what column gives the identifiers (i.e., matches the the pairs) - the probe_ids in this case. We also need to say which variable contains what will become the column names and which contains the values. + +![](images/do_in_R.png) Pivot the data so there is a column for each temperature: +```{r} +bambara <- bambara |> + pivot_wider(names_from = temperature, + values_from = expression, + id_cols = probe_id) +``` + +![](images/do_in_R.png) Click on the `bambara` dataframe in the environment to open a view of it so that you understand what `pivot_wider()` has done. + + +![](images/do_in_R.png) Create a paired-sample model like this: + +```{r} +mod <- lm(data = bambara, + highert - lowert ~ 1) +``` + +Since we have done `highert - lowert`, the "(Intercept) Estimate" will be the average of the higher temperature expression minus the lower temperature expression for each gene. + +![](images/do_in_R.png) Examine the model with: + +```{r} +summary(mod) +``` + + +![](images/answer.png) State your conclusion from the test in a form suitable for including in a report. Make sure you give the direction of any significant effect. + + + + + + + + +## Look after future you! + +The code required to summarise, test, and plot data for any two-sample test AND for any for any one-way ANOVA is **exactly the same** except for the names of the dataframe, variables and the axis labels and limits. Take some time to comment it your code so that you can make use of it next week. + +![](images/future_you.png){fig-alt="xxx" width="300"} + + +You're finished! + +# 🥳 Well Done! 🎉 + + + +# Independent study following the workshop + +[Consolidate](study_after_workshop.qmd) + +# The Code file + +These contain all the code needed in the workshop even where it is not visible on the webpage. + +The [workshop.qmd](workshop.qmd) file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interweaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Rmd file in RStudio. Alternatively, [View in Browser](https://github.com/3mmaRand/R4BABS/blob/main/r4babs2/week-3/workshop.qmd).Coding and thinking answers are marked with `#---CODING ANSWER---` and `#---THINKING ANSWER---` + +Pages made with R [@R-core], Quarto [@allaire2022], `knitr` [@knitr], `kableExtra` [@kableExtra] + +# References diff --git a/r4babs4/week-4/data-raw/ECOLIGreen_ISOTYPE.fcs b/r4babs4/week-4/data-raw/ECOLIGreen_ISOTYPE.fcs new file mode 100644 index 0000000000000000000000000000000000000000..30d214e18646b27f12220385a25780adaa0a0684 GIT binary patch literal 2207966 zcmce8bzGEL)c<*AhVBOGZkVFGd+6>IkWfHT96Lc2Fu(%4yStknh8D1UbzR+6cXgNF zd4};__x1hz=jY=vJoBA<&U5a$-xK#f`1?e&Z0LI5|FIa3wUBQj<3ybgz_4A2| z3g-m*M043TEIYx)@Q4`lcb1J4gBzMzo?FK7%`M8D#bq*W940O>CoZrZY*^fAuds+v zzaU@kTfd)*WKEmIA)Q2h1NAF)D5zL?TVNU47{NMUSu%ftj z4AL4bp;#7&8!^48JXbHiAg3aq{2=;&U-(ft>nGu?pMvMEePe z_CHB<_*bHXP@)5e8~xp=4$=4?WjOqJiw-{tbogl&9VP@ikOJ-K$dN*qj&S@}prcTr zqd=h0sE$I3jz3Ct{7ItYPZAv`Bs%^i(dl1_PC|)J6SpWZs*_Nl(|?TW^pixVpCmd> 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