diff --git a/docs/lab02.html b/docs/lab02.html index d2685d8..5cfe9e7 100644 --- a/docs/lab02.html +++ b/docs/lab02.html @@ -473,7 +473,7 @@
Then, before we start, we need to fetch some data to work on.
r_for_bio_data_science.Rproj
file.Then, without further ado, run each of the following lines separately in your console:
+
Failed to open file...
+target_url
, which defines “a location on the internet”. From here we want to download some data. We need to tell R
, where to put this data, so we also define the output_file
-variable. Then we call the function curl_download()
from the curl
-package, hence the notation curl::curl_download()
. As arguments to the function-parameters url
and destfile
, we pass the aforementioned variables. So far so good. Now, look at the target_url
- and the output_file
-variables, which one of those are we responsible for? If you think about it - We cannot change the target_url
, we can only change the output_file
, so there is a good chance, that the error you are getting pertains to this variable. But what does the variable contain? It contains the path to where you want to place the file, that is the data/
-part and then it contains the filename you want to use, that is the gravier
-part and then finally, it contains the filetype you want to use, that is the .RData
-part. The filename and filetype is up to you to choose, but the path… Well, R
can only find that if it exists… So… Did you remember to create a folder data
in the same location as your r_for_bio_data_science.Rproj
-file? If this small intermezzo has left things even more unclear, you should go over the primer on paths and projects. Remember, if at first you don’t succeed - Try and try again!
+files
pane, check the folder you created to see if you managed to retrieve the file.# A tibble: 10 × 30
Experiment Subject `Cell Type` `Target Type` Cohort Age Gender Race
<chr> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
- 1 eAV100 1995 PBMC C19_cII COVID-19-Con… 29 F <NA>
- 2 ePD86 92 PBMC C19_cI COVID-19-Con… 58 M White
- 3 eTH332 2685 B-CD8-_PBMC C19_cII COVID-19-Con… NA <NA> <NA>
- 4 eMR13 2059 PBMC C19_cI COVID-19-Con… NA <NA> <NA>
- 5 eQD119 5314 PBMC C19_cI COVID-19-Con… 51 M <NA>
- 6 ePD81 2922 PBMC C19_cI COVID-19-Con… 64 M <NA>
- 7 eHO131 3282 PBMC C19_cI COVID-19-Con… 58 F <NA>
- 8 eQD134 2903 PBMC C19_cI COVID-19-Con… NA <NA> <NA>
- 9 eLH46 359 PBMC C19_cI COVID-19-Con… 57 F White
-10 eHO124 3819 PBMC C19_cI Healthy (No … 62 M <NA>
+ 1 eJL148 3211 PBMC C19_cI COVID-19-Con… 41 F <NA>
+ 2 ePD91 169 PBMC C19_cI COVID-19-Con… 52 M White
+ 3 eQD129 2283 PBMC C19_cI COVID-19-Con… 60 F White
+ 4 eLH43 3565 PBMC C19_cI COVID-19-Con… 57 M <NA>
+ 5 eMR14 2845 PBMC C19_cI COVID-19-Con… NA <NA> <NA>
+ 6 eQD135 6359 PBMC C19_cII COVID-19-Con… 74 M <NA>
+ 7 eQD115 2513 PBMC C19_cI COVID-19-Con… 48 M <NA>
+ 8 ePD84 19931 naive_CD8 C19_cI Healthy (No … 29 F Asian
+ 9 eJL152 7842 PBMC C19_cI COVID-19-Con… 41 F <NA>
+10 eJL162 6890 PBMC C19_cI COVID-19-Con… 61 M <NA>
# ℹ 22 more variables: `HLA-A...9` <chr>, `HLA-A...10` <chr>,
# `HLA-B...11` <chr>, `HLA-B...12` <chr>, `HLA-C...13` <chr>,
# `HLA-C...14` <chr>, DPA1...15 <chr>, DPA1...16 <chr>, DPB1...17 <chr>,
@@ -643,16 +643,16 @@ The Peptide Detai
# A tibble: 10 × 7
`TCR BioIdentity` TCR Nucleotide Seque…¹ Experiment `ORF Coverage`
<chr> <chr> <chr> <chr>
- 1 CASSLMGNQPQHF+TCRBV12-03/12… AAGATCCAGCCCTCAGAACCC… eOX56 envelope,ORF1…
- 2 CASSPGQGWDNEQFF+TCRBV07-07+… CAGCGCACAGAGCAGCGGGAC… eXL27 ORF3a
- 3 CASSWDSSYEQYF+TCRBV07-02+TC… ACGATCCAGCGCACACAGCAG… ePD82 surface glyco…
- 4 VSVGEDQPQHF+TCRBV30-01+TCRB… ATCCTGAGTTCTAAGAAGCTC… eOX52 ORF1ab
- 5 CASSRRNEKLFF+TCRBV06-06+TCR… CTCAGGCTGGAGTTGGCTGCT… eAV93 surface glyco…
- 6 CASSLGYEQFF+TCRBV07-08+TCRB… ACTCTGAAGATCCAGCGCACA… eLH47 surface glyco…
- 7 CASSQDWRVANTGELFF+TCRBV03-0… CTGGAGCTTGGTGACTCTGCT… eXL32 ORF7b
- 8 CSVEAGTGFMQFF+TCRBV29-01+TC… ACTGTGAGCAACATGAGCCCT… eEE240 ORF10
- 9 CASSPPVGGSYEQYF+TCRBV18-01+… CAGCAGGTAGTGCGAGGAGAT… eAM13 ORF3a
-10 CASSPTDRVIGSAEQFF+TCRBV05-0… TTGGAGCTGGGGGACTCGGCC… eQD137 ORF1ab
+ 1 CASSPQDRGASYEQYF+TCRBV18-01… CAGGTAGTGCGAGGAGATTCG… eOX52 ORF7b
+ 2 unproductive+TCRBV11-02+TCR… TGCAAAGCTTGAGGACTCGGC… eHO141 nucleocapsid …
+ 3 CATVPDQNTGELFF+TCRBV10-02+T… CTGGAGTCAGCTACCCGCTCC… eHO141 surface glyco…
+ 4 CSARPGSRETQYF+TCRBV29-01+TC… ACTGTGAGCAACATGAGCCCT… eXL27 ORF3a
+ 5 CASSPGQGAYEQYF+TCRBV04-02+T… CTACACACCCTGCAGCCAGAA… eXL27 ORF1ab
+ 6 CASSLGFSVRTENTEAFF+TCRBV11-… AAGCTTGAGGACTCGGCCGTG… eOX56 ORF1ab
+ 7 CSSPGPGYNEQFF+TCRBV29-01+TC… ACTGTGAGCAACATGAGCCCT… eOX43 ORF7a
+ 8 CSAREEVKNYEQYF+TCRBV20-X+TC… GTGACCAGTGCCCATCCTGAA… eOX52 membrane glyc…
+ 9 CASSLTGETQYF+TCRBV07-03+TCR… CTGAAGATCCAGCGCACAGAG… eOX46 surface glyco…
+10 CASSLVLDVGIQYF+TCRBV07-09+T… ATCCAGCGCACAGAGCAGGGG… eAV93 ORF10
# ℹ abbreviated name: ¹`TCR Nucleotide Sequence`
# ℹ 3 more variables: `Amino Acids` <chr>, `Start Index in Genome` <dbl>,
# `End Index in Genome` <dbl>
@@ -937,18 +937,18 @@ The Peptide Detai
sample_n(10)
# A tibble: 10 × 3
- Experiment `TCR BioIdentity` `Amino Acids`
- <chr> <chr> <chr>
- 1 eXL31 CASSHYRGSYNEQFF+TCRBV03-01/03-02+TCRBJ02-01 FLQSINFVR,FLQSINFVRI,…
- 2 eOX43 CSASSLAEPKETQYF+TCRBV20-X+TCRBJ02-05 AFLLFLVLI,FLAFLLFLV,F…
- 3 eOX43 CASARLAGDTDTQYF+TCRBV16-01+TCRBJ02-03 FLNGSCGSV
- 4 eOX52 CASSDLAGGLNEQFF+TCRBV09-01+TCRBJ02-01 AFPFTIYSL,GYINVFAFPF,…
- 5 eAV93 CASSTGQGWTEAFF+TCRBV05-06+TCRBJ01-01 CMTSCCSCLK,MTSCCSCLK
- 6 eEE226 CASSPLQSNTEAFF+TCRBV05-01+TCRBJ01-01 AFLLFLVLI,FLAFLLFLV,F…
- 7 eOX54 CSVVLFGNEQFF+TCRBV29-01+TCRBJ02-01 FVDGVPFVV
- 8 eEE240 CATYPLGGPPRDTEAFF+TCRBV15-01+TCRBJ01-01 FLNGSCGSV
- 9 eOX56 CASSLGGATADEQFF+TCRBV07-03+TCRBJ02-01 KLPDDFTGCV
-10 eXL31 CSARDLGRGSNEQFF+TCRBV20-X+TCRBJ02-01 AFLLFLVLI,FLAFLLFLV,F…
+ Experiment `TCR BioIdentity` `Amino Acids`
+ <chr> <chr> <chr>
+ 1 eEE226 CASSPWGAEDGYTF+TCRBV27-01+TCRBJ01-02 FLWLLWPVT,FLWLLWPVTL,LWLL…
+ 2 eEE224 CASSESGTPYNEQFF+TCRBV06-01+TCRBJ02-01 FVDGVPFVV
+ 3 eOX52 CASSQAGQPQHF+TCRBV07-06+TCRBJ01-05 FLCLFLLPSL,FLLPSLATV
+ 4 eOX52 CASSQDRAMLNEQFF+TCRBV04-03+TCRBJ02-01 SELVIGAVI,SELVIGAVIL
+ 5 eXL30 CASSQDSAGGSYNEQFF+TCRBV04-02+TCRBJ02-01 KLPDDFTGCV
+ 6 eAM23 CASSLVGRGTDTQYF+TCRBV05-05+TCRBJ02-03 DGVYFASTEK,GVYFASTEK,LPFN…
+ 7 eEE228 CASSPYPGLVVVDEQFF+TCRBV07-02+TCRBJ02-01 FVCNLLLLFV,LLFVTVYSHL,TVY…
+ 8 eXL30 CASSIGAGGTDTQYF+TCRBV19-01+TCRBJ02-03 AFLLFLVLI,FLAFLLFLV,FYLCF…
+ 9 eEE228 CASRGFTVYNEQFF+TCRBV11-02+TCRBJ02-01 AFLLFLVLI,FLAFLLFLV,FYLCF…
+10 ePD83 CASSSGQGVSYEQYF+TCRBV19-01+TCRBJ02-07 SEHDYQIGGYTEKW,YQIGGYTEK,…
# A tibble: 10 × 5
- Experiment CDR3b V_gene J_gene `Amino Acids`
- <chr> <chr> <chr> <chr> <chr>
- 1 eAV91 CASSEGISGSFKDTQYF TCRBV02-01 TCRBJ02-03 LSPRWYFYY,SPRWYFYYL
- 2 eOX52 CASSLGGPPSYTQYF TCRBV03-01/03-02 TCRBJ02-03 GMEVTPSGTWL,MEVTPS…
- 3 eEE228 CASSRGLDTEAFF TCRBV19-01 TCRBJ01-01 FPNITNLCPF,QPTESIV…
- 4 eOX49 CASAWTSGAYEQYF TCRBV12-05 TCRBJ02-07 AFLLFLVLI,FLAFLLFL…
- 5 eMR18 CASSQDKETQYF TCRBV14-01 TCRBJ02-05 ALSKGVHFV
- 6 eMR13 unproductive TCRBV29-01 TCRBJ02-03 HTTDPSFLGRY
- 7 eXL27 CASSLLAESYNEQFF TCRBV07-03 TCRBJ02-01 FLWLLWPVT,FLWLLWPV…
- 8 eLH48 CASSKYAGGNTGELFF TCRBV19-01 TCRBJ02-02 AYKTFPPTEPK,KTFPPT…
- 9 eEE226 CASSSTGTGSYEQYF TCRBV07-09 TCRBJ02-07 LLDDFVEII,LLLDDFVEI
-10 eEE240 CASSQEPFRVAGDNEQFF TCRBV04-03 TCRBJ02-01 ILGLPTQTV
+ Experiment CDR3b V_gene J_gene `Amino Acids`
+ <chr> <chr> <chr> <chr> <chr>
+ 1 eMR18 CSVSGDHNTGELFF TCRBV29-01 TCRBJ02-02 YLQPRTFL,YLQPRTFLL,YYVGYLQ…
+ 2 ePD83 CASSKGQGLSYEQYF TCRBV19-01 TCRBJ02-07 SEHDYQIGGYTEKW,YQIGGYTEK,Y…
+ 3 eLH48 CASSVGGKTFNYGYTF TCRBV09-01 TCRBJ01-02 AYKTFPPTEPK,KTFPPTEPK
+ 4 eOX43 CASSGLLKSYEQYF TCRBV27-01 TCRBJ02-07 EEHVQIHTI
+ 5 eEE228 CASSVRQQYF TCRBV18-01 TCRBJ02-07 IMLIIFWFSL,MLIIFWFSL
+ 6 eHO141 CSVRTGHEQFF TCRBV29-01 TCRBJ02-01 LLYDANYFL,LLYDANYFLC,LYDAN…
+ 7 eMR15 CASSLAGSYEQYF TCRBV05-01 TCRBJ02-07 LSPRWYFYY,SPRWYFYYL
+ 8 eXL30 CASSVTGGSYEQYF TCRBV09-01 TCRBJ02-07 ITDVFYKENSY,SEYKGPITDVFY
+ 9 eXL30 CASSVDAYSNQPQHF TCRBV06-05 TCRBJ01-05 FIAGLIAIV
+10 eOX43 CASSPSGSSADTQYF TCRBV12-X TCRBJ02-03 FVCNLLLLFV,LLFVTVYSHL,TVYS…
# A tibble: 10 × 6
Experiment CDR3b V_gene J_gene `Amino Acids` n_peptides
<chr> <chr> <chr> <chr> <chr> <dbl>
- 1 eEE226 CASSFVASFTDTQYF TCRBV27-01 TCRBJ02-03 FPPTSFGPL 1
- 2 eQD128 CASSRGGLGYGYTF TCRBV28-01 TCRBJ01-02 ITDVFYKENSY,S… 2
- 3 eQD114 CASSYWTGLPYEQYF TCRBV05-01 TCRBJ02-07 SYFTSDYYQL,VL… 3
- 4 eJL158 CASSSDIEQYF TCRBV07-09 TCRBJ02-07 YLQPRTFL,YLQP… 3
- 5 eLH41 CASSSMEGGSGAYNEQFF TCRBV07-09 TCRBJ02-01 AYKTFPPTEPK,K… 2
- 6 eAV91 CSVGTGGDEQYF TCRBV29-01 TCRBJ02-07 CMTSCCSCLK,MT… 2
- 7 eEE240 CASSRNQPQHF TCRBV28-01 TCRBJ01-05 AFLLFLVLI,FLA… 11
- 8 eOX46 CASSISGPNTGELFF TCRBV27-01 TCRBJ02-02 HPLADNKFAL,SP… 2
- 9 eHO133 CASSFGGGPNEQFF TCRBV07-03 TCRBJ02-01 KAYNVTQAF 1
-10 eMR17 CASRIGKGQYNEKLFF TCRBV19-01 TCRBJ01-04 SYFTSDYYQL,VL… 3
+ 1 eEE226 CASSFYGGLTDTQYF TCRBV07-03 TCRBJ02-03 FVDGVPFVV 1
+ 2 eOX52 CASSLWGRSNQPQHF TCRBV28-01 TCRBJ01-05 FVDGVPFVV 1
+ 3 eXL30 CASSPVGGLDEQYF TCRBV07-03 TCRBJ02-07 AFPFTIYSL,GYI… 7
+ 4 eEE226 CASSQVGSLAQKYEQYF TCRBV04-02 TCRBJ02-07 KLPDDFTGCV 1
+ 5 eHO141 CASSLAGSLLSGANVLTF TCRBV12-X TCRBJ02-06 ALNTPKDHI,ATE… 2
+ 6 eHH175 CASSYSRNYNEQFF TCRBV06-05 TCRBJ02-01 GRLQSLQTY,LIT… 3
+ 7 eXL36 CASSPGGRNEKLFF TCRBV13-01 TCRBJ01-04 FLNGSCGSV 1
+ 8 eQD121 CASSDRGTTDTQYF TCRBV27-01 TCRBJ02-03 HTTDPSFLGRY 1
+ 9 eEE240 CASSSQGETQYF TCRBV28-01 TCRBJ02-05 FLWLLWPVT,FLW… 7
+10 eOX43 CASRNIAGIYNEQFF TCRBV19-01 TCRBJ02-01 APKEIIFL,KEII… 2
# A tibble: 10 × 18
Experiment CDR3b V_gene J_gene peptide_1 peptide_2 peptide_3 peptide_4
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
- 1 eEE228 CASSLSDPNQP… TCRBV… TCRBJ… FPNITNLC… QPTESIVRF RFPNITNL… TESIVRFP…
- 2 eDH96 CASSLLGGSNQ… TCRBV… TCRBJ… FLWLLWPVT FLWLLWPV… LWLLWPVTL LWPVTLACF
- 3 eOX49 CASSGGTGDNI… TCRBV… TCRBJ… ILHCANFNV <NA> <NA> <NA>
- 4 eAV93 CAWSERGGNEQ… TCRBV… TCRBJ… DGVYFAST… GVYFASTEK LPFNDGVYF LPFNDGVY…
- 5 eEE240 CASSQGQGSGE… TCRBV… TCRBJ… SLVKPSFYV <NA> <NA> <NA>
- 6 eOX54 CASSVWTQETQ… TCRBV… TCRBJ… NLNESLIDL <NA> <NA> <NA>
- 7 eOX43 CASSFEGAGAQ… TCRBV… TCRBJ… SELVIGAVI SELVIGAV… <NA> <NA>
- 8 eHO134 CASSSRTSGTT… TCRBV… TCRBJ… SYFTSDYY… VLHSYFTS… YFTSDYYQ… <NA>
- 9 eOX46 CASSPGAGAGS… TCRBV… TCRBJ… LLDDFVEII LLLDDFVEI <NA> <NA>
-10 ePD83 CASSIGQGAIY… TCRBV… TCRBJ… SEHDYQIG… YQIGGYTEK YQIGGYTE… <NA>
+ 1 ePD83 CASSIGAGMSY… TCRBV… TCRBJ… SEHDYQIG… YQIGGYTEK YQIGGYTE… <NA>
+ 2 eXL30 CASSLGVGEIG… TCRBV… TCRBJ… AEAELAKN… AELAKNVS… <NA> <NA>
+ 3 eOX52 CSASKGLADQE… TCRBV… TCRBJ… GMEVTPSG… MEVTPSGT… TPSGTWLTY VTPSGTWL…
+ 4 eHO133 CASSQTSGTLY… TCRBV… TCRBJ… KAYNVTQAF <NA> <NA> <NA>
+ 5 eLH46 CASSLAQVNTE… TCRBV… TCRBJ… STQDLFLP… TQDLFLPFF <NA> <NA>
+ 6 eEE226 CASSFPGQAYN… TCRBV… TCRBJ… DGVYFAST… GVYFASTEK LPFNDGVYF LPFNDGVY…
+ 7 eAM23 CASSLTGASTD… TCRBV… TCRBJ… AFPFTIYSL GYINVFAF… INVFAFPF… MGYINVFAF
+ 8 eOX54 CASSFGDRYEQ… TCRBV… TCRBJ… ILGLPTQTV <NA> <NA> <NA>
+ 9 eEE226 CASSPEGQGGT… TCRBV… TCRBJ… DFLEYHDVR EDFLEYHD… LEYHDVRVV LEYHDVRV…
+10 eQD123 CASSAPQGLVT… TCRBV… TCRBJ… LSPRWYFYY SPRWYFYYL <NA> <NA>
# ℹ 10 more variables: peptide_5 <chr>, peptide_6 <chr>, peptide_7 <chr>,
# peptide_8 <chr>, peptide_9 <chr>, peptide_10 <chr>, peptide_11 <chr>,
# peptide_12 <chr>, peptide_13 <chr>, n_peptides <dbl>
@@ -1112,18 +1112,18 @@ # A tibble: 10 × 7
- Experiment CDR3b V_gene J_gene n_peptides peptide_n peptide
- <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
- 1 eMR25 CASSLTGGRYGYNEQFF TCRBV… TCRBJ… 2 peptide_4 <NA>
- 2 eLH50 CSARDGPQQNTGELFF TCRBV… TCRBJ… 3 peptide_… <NA>
- 3 eOX54 CALRKDYEQYF TCRBV… TCRBJ… 2 peptide_4 <NA>
- 4 eEE226 CASSLGLNTEAFF TCRBV… TCRBJ… 4 peptide_… <NA>
- 5 eAV93 CSVEDSSVNEQFF TCRBV… TCRBJ… 2 peptide_1 GEIPVA…
- 6 eEE228 CASSPPGLAGETQYF TCRBV… TCRBJ… 1 peptide_… <NA>
- 7 eAV93 CASSPRMGSGELFF TCRBV… TCRBJ… 1 peptide_1 FVDGVP…
- 8 eOX46 CASPNGSGYSGANVLTF TCRBV… TCRBJ… 7 peptide_9 <NA>
- 9 eHH175 CSVDGQTDAYGYTF TCRBV… TCRBJ… 11 peptide_1 AFLLFL…
-10 eOX52 CASSTWDGQHLIVFRSTPDIQYF TCRBV… TCRBJ… 4 peptide_… <NA>
+ Experiment CDR3b V_gene J_gene n_peptides peptide_n peptide
+ <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
+ 1 eEE228 CASSLWGQQPQHF TCRBV05-06 TCRBJ… 1 peptide_1 FVDGVP…
+ 2 eEE228 CASSKGNFSNQPQHF TCRBV21-01 TCRBJ… 1 peptide_8 <NA>
+ 3 eQD131 CASSYEGVDGTTF TCRBV06-05 TCRBJ… 4 peptide_7 <NA>
+ 4 eXL30 CASSLATGAETQYF TCRBV05-01 TCRBJ… 1 peptide_… <NA>
+ 5 eOX43 CASSISPLAETYNEQFF TCRBV27-01 TCRBJ… 1 peptide_… <NA>
+ 6 eAV88 CASSQGPSGSWEQYF TCRBV03-01/… TCRBJ… 1 peptide_… <NA>
+ 7 eEE228 CASSFGGQQETQYF TCRBV27-01 TCRBJ… 6 peptide_5 VQPTES…
+ 8 eJL161 CASSLGDPASDTQYF TCRBV27-01 TCRBJ… 1 peptide_… <NA>
+ 9 eXL31 CASSLGLTEAFF TCRBV11-03 TCRBJ… 3 peptide_… <NA>
+10 eEE224 CASSVEGTEIYEQYF TCRBV09-01 TCRBJ… 1 peptide_9 <NA>
# A tibble: 10 × 5
- Experiment CDR3b V_gene J_gene peptide
- <chr> <chr> <chr> <chr> <chr>
- 1 eOX54 CASSQESPTSGRANEQFF TCRBV18-01 TCRBJ02-01 LITLATCELY
- 2 eXL37 CSASTGTGRVETQYF TCRBV20-X TCRBJ02-05 FLAFLLFLV
- 3 eQD136 CASSPVLTF TCRBV12-03/12-04 TCRBJ02-06 NVFAFPFTIY
- 4 eXL30 CASSFRDRNDYEQYF TCRBV05-04 TCRBJ02-07 AFLLFLVLI
- 5 eXL27 CSAFEEPSYEQYF TCRBV20-X TCRBJ02-07 NVFAFPFTI
- 6 eEE240 CSAQGLADSYEQYF TCRBV20-X TCRBJ02-07 FYLCFLAFLL
- 7 eEE228 CASSSDRETQYF TCRBV05-04 TCRBJ02-05 YVVDDPCPI
- 8 eXL31 CASSQLVPTGVYFTDTQYF TCRBV14-01 TCRBJ02-03 SYFIASFRLF
- 9 eEE243 CASSLDGGTYEQYF TCRBV05-04 TCRBJ02-07 YDANYFLCW
-10 eHH175 CATSDHRTDAADTQYF TCRBV24-01 TCRBJ02-03 IDFYLCFLAF
+ Experiment CDR3b V_gene J_gene peptide
+ <chr> <chr> <chr> <chr> <chr>
+ 1 eAV88 CASRGLAGGHEQFF TCRBV05-08 TCRBJ02-01 SELVIGAVIL
+ 2 eEE226 CSVVGPSGGYEQYF TCRBV29-01 TCRBJ02-07 DGVYFASTEK
+ 3 eOX54 CASSSGAGGGTDTQYF TCRBV07-07 TCRBJ02-03 QYIKWPWYI
+ 4 eXL30 CASSLDRGGSPLHF TCRBV07-06 TCRBJ01-06 VLWAHGFEL
+ 5 eEE240 CASSQIVGGLYGYTF TCRBV04-03 TCRBJ01-02 LWPVTLACF
+ 6 eEE226 CASSEWGQEGNTEAFF TCRBV06-01 TCRBJ01-01 IDFYLCFLAF
+ 7 ePD76 CASRGSFTDTQYF TCRBV05-01 TCRBJ02-03 GMEVTPSGTWL
+ 8 ePD85 CASSIGVGTSYEQYF TCRBV19-01 TCRBJ02-07 YQIGGYTEKW
+ 9 eAV88 CASSAGPRNEQFF TCRBV07-09 TCRBJ02-01 QELYSPIFL
+10 eEE226 CASSLTGGGGPDTQYF TCRBV07-03 TCRBJ02-03 AFPFTIYSL
# A tibble: 10 × 7
- Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
- <chr> <chr> <chr> <chr> <chr> <int> <int>
- 1 eOX54 CASSFGTGNTGELFF TCRBV11-03 TCRBJ… KLSYGI… 15 9
- 2 eEE228 CASSHPGLAARGRYNEQFF TCRBV04-02 TCRBJ… YLCFLA… 19 9
- 3 eEE226 CASSVGRTGDYEQYF TCRBV09-01 TCRBJ… SLIDFY… 15 10
- 4 eQD129 CASSQQGALSTEAFF TCRBV14-01 TCRBJ… INFVRI… 15 9
- 5 eXL30 CASPPAGNIGELFF TCRBV07-02 TCRBJ… FVDGVP… 14 9
- 6 eEE228 CSARWEGPEQFF TCRBV20-X TCRBJ… GYINVF… 12 10
- 7 eLH43 CASSIGGVGYTF TCRBV19-01 TCRBJ… SNEKQE… 12 13
- 8 eEE240 CASSWDLNEQFF TCRBV07-06 TCRBJ… MIELSL… 12 10
- 9 eHO133 CASSFSSGEAHEQYF TCRBV28-01 TCRBJ… KAYNVT… 15 9
-10 eEE226 CASSFADTQYF TCRBV12-03/1… TCRBJ… GYINVF… 11 10
+ Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
+ <chr> <chr> <chr> <chr> <chr> <int> <int>
+ 1 eOX52 CSAPERTGIAYEQYF TCRBV20-X TCRBJ02-… FLAFLL… 15 9
+ 2 eAV93 CASSLGQSTEAFF TCRBV27-01 TCRBJ01-… SIIAYT… 13 9
+ 3 eOX52 CASSYEGGRDTQYF TCRBV27-01 TCRBJ02-… SELVIG… 14 9
+ 4 eQD109 CASSSMTSGGALQETQYF TCRBV07-03 TCRBJ02-… LSPRWY… 18 9
+ 5 eJL161 CSVEFRGDSSYEQYF TCRBV29-01 TCRBJ02-… HTTDPS… 15 11
+ 6 eQD128 CASSPRPGLAGLSYNEQFF TCRBV06-X TCRBJ02-… VLPFND… 19 10
+ 7 eQD137 CASTRTQIRDRVDTEAFF TCRBV19-01 TCRBJ01-… EILDIT… 18 10
+ 8 eOX52 CSETGPLETQYF TCRBV20-X TCRBJ02-… FYLCFL… 12 10
+ 9 eLH54 CASSLVGYNEQFF TCRBV05-08 TCRBJ02-… FLQSIN… 13 9
+10 eXL30 CASSGPTSGGARDTQYF TCRBV25-01 TCRBJ02-… FYLCFL… 17 10
# A tibble: 10 × 7
- Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
- <chr> <chr> <chr> <chr> <chr> <int> <int>
- 1 eHO130 CASSFRDNITDTQYF TCRBV07-09 TCRBJ0… INVFAF… 15 10
- 2 eOX46 CASSVGTGGHQPQHF TCRBV19-01 TCRBJ0… FLWLLW… 15 10
- 3 eEE228 CASSIMGLGNTEAFF TCRBV19-01 TCRBJ0… WLLWPV… 15 9
- 4 eHO130 CASSVQGATNNEQFF TCRBV02-01 TCRBJ0… FLQSIN… 15 10
- 5 eXL30 CASSFYGFVTEQYF TCRBV12-X TCRBJ0… NVFAFP… 14 10
- 6 eXL27 CASSLLGNQPQHF TCRBV12-03/12-04 TCRBJ0… IPTNFT… 13 9
- 7 eOX54 CASSLVSSGNNEQFF TCRBV11-02 TCRBJ0… AFPFTI… 15 9
- 8 eEE224 CASIGLAGGNEQFF TCRBV28-01 TCRBJ0… VQELYS… 14 9
- 9 eOX52 CASSLSVKYEQYF TCRBV28-01 TCRBJ0… QPTESI… 13 9
-10 eEE224 CARTGHTEAFF TCRBV30-01 TCRBJ0… KLNVGD… 11 9
+ Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
+ <chr> <chr> <chr> <chr> <chr> <int> <int>
+ 1 eEE228 CAISEWDRGGKNTEAFF TCRBV10-03 TCRBJ01-01 YLDAYNM… 17 9
+ 2 eEE226 CASMGPGQGKETQYF TCRBV27-01 TCRBJ02-05 NPLLYDA… 15 9
+ 3 eQD110 CASSSWTGSYNEQFF TCRBV05-01 TCRBJ02-01 VLHSYFT… 15 10
+ 4 eOX52 CASSEEGREGGYEQYF TCRBV06-01 TCRBJ02-07 MEVTPSG… 16 10
+ 5 eXL30 CATSSPRSSSFYEQYF TCRBV15-01 TCRBJ02-07 FLWLLWP… 16 9
+ 6 eEE228 CASRRTENYEQYF TCRBV02-01 TCRBJ02-07 IDFYLCF… 13 10
+ 7 eEE240 CASSVVGVNTGELFF TCRBV09-01 TCRBJ02-02 GMEVTPS… 15 11
+ 8 eEE226 CASSLAGGLPGDTQYF TCRBV07-03 TCRBJ02-03 IDFYLCF… 16 10
+ 9 eEE240 CASSQRTEWNTEAFF TCRBV04-03 TCRBJ01-01 VQELYSP… 15 9
+10 eXL30 CASSLAPGNWGAGELFF TCRBV13-01 TCRBJ02-02 YIIKLIF… 17 10
# A tibble: 10 × 11
Experiment Cohort Age Gender Race A1 A2 B1 B2 C1 C2
<chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
- 1 eQD123 COVID-19-B… 49 F White "A*0… "A*0… "B*0… "B*1… "C*0… "C*0…
- 2 eQD131 COVID-19-E… NA <NA> <NA> "A*0… "A*3… "B*1… "B*5… "C*0… "C*0…
- 3 eHO138 COVID-19-B… NA <NA> <NA> "" "" "" "" "" ""
- 4 ePD87 COVID-19-C… 47 M White "A*0… "A*2… "B*0… "B*0… "C*0… "C*0…
- 5 eLH45 COVID-19-C… 53 M <NA> "A*0… "A*0… "B*0… "B*5… "C*0… "C*1…
- 6 eLH53 COVID-19-B… 42 M White "A*0… "A*1… "B*5… "B*5… "C*0… "C*0…
- 7 eJL153 COVID-19-C… 36 M <NA> "A*0… "A*1… "B*0… "B*1… "C*0… "C*0…
- 8 eAV100 COVID-19-C… 29 F <NA> "A*0… "A*6… "B*0… "B*4… "C*0… "C*0…
- 9 eQD114 COVID-19-C… 73 M <NA> "A*0… "A*2… "B*0… "B*4… "C*0… "C*1…
-10 eOX49 Healthy (N… 21 M White "A*0… "A*2… "B*4… "B*5… "C*0… "C*0…
+ 1 ePD90 COVID-19-C… 29 M <NA> "" "" "" "" "" ""
+ 2 eLH54 COVID-19-C… NA <NA> <NA> "A*0… "A*0… "B*0… "B*4… "C*0… "C*0…
+ 3 eQD123 COVID-19-B… 49 F White "A*0… "A*0… "B*0… "B*1… "C*0… "C*0…
+ 4 ePD100 COVID-19-C… 66 M <NA> "" "" "" "" "" ""
+ 5 eGK111 COVID-19-C… 50 F <NA> "A*0… "A*0… "B*0… "B*1… "C*0… "C*0…
+ 6 ePD76 Healthy (N… 33 M White "A*0… "A*0… "B*3… "B*4… "C*0… "C*0…
+ 7 eQD108 COVID-19-C… NA <NA> <NA> "A*1… "A*6… "B*0… "B*5… "C*0… "C*1…
+ 8 eEE224 Healthy (N… 24 M White "A*0… "A*0… "B*2… "B*4… "C*0… "C*0…
+ 9 eOX56 Healthy (N… 30 M Blac… "A*0… "A*3… "B*5… "B*5… "C*0… "C*0…
+10 eGK120 COVID-19-C… 46 F White "A*1… "A*6… "B*0… "B*5… "C*0… "C*1…
Remember you can scroll in the data.
@@ -1285,16 +1285,16 @@# A tibble: 10 × 7
Experiment Cohort Age Gender Race Gene Allele
<chr> <chr> <dbl> <chr> <chr> <chr> <chr>
- 1 eHO130 Healthy (No known exposure) 28 F White C1 "C*07…
- 2 eJL160 COVID-19-Acute 52 F African Ame… B2 "B*81…
- 3 ePD76 Healthy (No known exposure) 33 M White B2 "B*40…
- 4 eGK120 COVID-19-Convalescent 46 F White B2 "B*52…
- 5 eDH113 Healthy (No known exposure) 56 <NA> <NA> A2 "A*29…
- 6 ePD82 COVID-19-Convalescent 60 F <NA> B2 "B*44…
- 7 eJL154 COVID-19-Exposed 35 F Native Hawa… A1 "A*02…
- 8 eMR25 COVID-19-Convalescent 21 F <NA> A2 ""
- 9 eLH59 COVID-19-Convalescent NA <NA> <NA> A1 "A*01…
-10 eJL148 COVID-19-Convalescent 41 F <NA> A2 "A*02…
+ 1 eXL30 Healthy (No known exposure) 21 F White C2 C*07:…
+ 2 eQD132 COVID-19-Convalescent NA <NA> <NA> B2 B*40:…
+ 3 eHO134 COVID-19-Convalescent 36 M White A1 A*01:…
+ 4 eEE217 Healthy (No known exposure) 32 F White A1 A*02:…
+ 5 eJL162 COVID-19-Convalescent 61 M <NA> B1 B*40:…
+ 6 eLH43 COVID-19-Convalescent 57 M <NA> B2 B*44:…
+ 7 eXL37 Healthy (No known exposure) 33 M White B1 B*40:…
+ 8 eQD110 COVID-19-Convalescent 55 M <NA> C1 C*05:…
+ 9 eHH170 Healthy (No known exposure) 24 F Black or Af… B1 B*35:…
+10 eEE224 Healthy (No known exposure) 24 M White B1 B*27:…
Remember, what we are aiming for here, is to create one data set from two. So:
@@ -1312,16 +1312,16 @@# A tibble: 10 × 2
Experiment Allele
<chr> <chr>
- 1 eJL162 "A*01:01:01"
- 2 eLH47 "B*08:01:01"
- 3 eJL152 "A*24:02:01"
- 4 eHO131 "B*51:01:01"
- 5 eLH51 "A*24:07:01"
- 6 eMR23 ""
- 7 ePD81 "C*15:02:01"
- 8 eHH174 "A*01:01"
- 9 eTH332 ""
-10 eMR20 "A*26:01:01"
+ 1 eQD135 "A*02:01:01"
+ 2 eMR23 ""
+ 3 eMR14 "C*07:02:01"
+ 4 eJL158 "C*15:02:01"
+ 5 eLH51 "C*12:04:02"
+ 6 eQD120 "C*03:04:01"
+ 7 eJL158 "B*15:01:01"
+ 8 ePD73 "C*03:04"
+ 9 eQD110 "C*06:02:01"
+10 ePD79 "B*07:02:01"
Use the View()
function again, to look at the meta_data
. Notice something? Some alleles are e.g. A*11:01
, whereas others are B*51:01:02
. You can find information on why, by visiting Nomenclature for Factors of the HLA System.
# A tibble: 10 × 3
Experiment Allele Allele_F_1_2
<chr> <chr> <chr>
- 1 eHO125 A*02:01:01 A*02:01
- 2 ePD81 B*46:01:01 B*46:01
- 3 eAV93 A*11:01 A*11:01
- 4 eJL164 B*40:02:01 B*40:02
- 5 eQD138 C*06:02:01 C*06:02
- 6 eJL151 A*68:01:01 A*68:01
- 7 eMR21 C*07:02:01 C*07:02
- 8 eAM23 A*24:02:01 A*24:02
- 9 ePD86 C*14:02:01 C*14:02
-10 eJL157 B*18:01:01 B*18:01
+ 1 eQD109 A*03:01:01 A*03:01
+ 2 eQD120 A*01:01:01 A*01:01
+ 3 eQD138 A*01:01:01 A*01:01
+ 4 eQD129 A*02:01:01 A*02:01
+ 5 eHO126 B*07:02:01 B*07:02
+ 6 ePD83 A*02:01 A*02:01
+ 7 ePD79 C*07:02:01 C*07:02
+ 8 eLH49 C*16:01:01 C*16:01
+ 9 eXL36 B*15:01 B*15:01
+10 eMR18 C*07:01:01 C*07:01
The asterisk, i.e. *
is a rather annoying character because of ambiguity, so:
# A tibble: 10 × 2
Experiment Allele
<chr> <chr>
- 1 eOX49 C05:01
- 2 eQD128 A02:10
- 3 eEE224 C07:04
- 4 eJL160 C05:01
- 5 eOX56 B53:01
- 6 eQD139 A01:01
- 7 eMR26 A02:01
- 8 eQD111 B08:01
- 9 eOX52 A02:01
-10 ePD76 A03:01
+ 1 eHO125 C07:02
+ 2 ePD73 B15:01
+ 3 eDH113 A29:02
+ 4 eOX49 B52:01
+ 5 eQD115 A03:01
+ 6 eLH41 B13:02
+ 7 eQD113 B55:01
+ 8 eLH54 C02:02
+ 9 eLH48 A24:02
+10 eHH175 B07:02
# A tibble: 10 × 7
- Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
- <chr> <chr> <chr> <chr> <chr> <int> <int>
- 1 eHH175 CASSLGPAGGMNTEAFF TCRBV07-09 TCRBJ01… IELSLI… 17 10
- 2 eOX52 CASQTSGSTDTQYF TCRBV27-01 TCRBJ02… FLAFLL… 14 9
- 3 eOX46 CAISVSYEQYF TCRBV28-01 TCRBJ02… FYLCFL… 11 9
- 4 ePD82 CASSKRAGSGQGAYGRGYTF TCRBV21-01 TCRBJ01… HTTDPS… 20 11
- 5 eMR17 CASSPPLQETQYF TCRBV27-01 TCRBJ02… LQSINF… 13 10
- 6 eMR13 CASSLGEGAFTDTQYF TCRBV05-05 TCRBJ02… ALRKVP… 16 14
- 7 ePD100 CASTPGGGTGNTIYF TCRBV07-08 TCRBJ01… FLQSIN… 15 9
- 8 eQD127 CSAKRRDNQPQHF TCRBV20-X TCRBJ01… VYFLQS… 13 10
- 9 eOX49 CASSLTDLGYGYTF TCRBV05-04 TCRBJ01… WPVTLA… 14 10
-10 eMR18 CASSFGTGIGGYGYTF TCRBV27-01 TCRBJ01… QSINFV… 16 9
+ Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide
+ <chr> <chr> <chr> <chr> <chr> <int> <int>
+ 1 eOX43 CSATSSGETQYF TCRBV20-X TCRBJ… IELSLI… 12 10
+ 2 ePD83 CASSMGQGVYNEQFF TCRBV19-01 TCRBJ… YQIGGY… 15 10
+ 3 eOX46 CASSPYSNQPQHF TCRBV27-01 TCRBJ… IELSLI… 13 10
+ 4 eAV88 CASSFIFPDRIETQYF TCRBV27-01 TCRBJ… TVLSFC… 16 9
+ 5 eMR12 CASSTSHSTDTQYF TCRBV27-01 TCRBJ… HTTDPS… 14 11
+ 6 eDH113 CASSQLAPDTQYF TCRBV14-01 TCRBJ… FLAFLL… 13 9
+ 7 eXL32 CASSVTANYGYTF TCRBV27-01 TCRBJ… AFLLFL… 13 9
+ 8 ePD76 CASRSGANVLTF TCRBV19-01 TCRBJ… QPTESI… 12 9
+ 9 eHO134 CASSQTSLGEQFF TCRBV03-01/03-02 TCRBJ… KVPTDN… 13 11
+10 eQD125 CASSWTEGAYEQYF TCRBV28-01 TCRBJ… HTTDPS… 14 11
# A tibble: 10 × 8
- Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide Allele
- <chr> <chr> <chr> <chr> <chr> <int> <int> <chr>
- 1 eAV88 CASSPGEGQPTEAFF TCRBV0… TCRBJ… MLIIFW… 15 9 A03:01
- 2 eQD123 CSARAGQGGWSYNSPLHF TCRBV2… TCRBJ… YLYALV… 18 9 A03:01
- 3 eOX52 CASSQEGLAVWAGELFF TCRBV0… TCRBJ… KLSYGI… 17 9 C07:01
- 4 eOX54 CASSFSNTQYF TCRBV1… TCRBJ… INVFAF… 11 10 B15:03
- 5 eMR12 CASSQEATASSPLHF TCRBV0… TCRBJ… YANRNR… 15 9 B40:01
- 6 eXL30 CASSFTGPNTEAFF TCRBV2… TCRBJ… MIELSL… 14 10 B39:01
- 7 eXL30 CASSSTGLAATYEQYF TCRBV0… TCRBJ… RFPNIT… 16 11 B35:02
- 8 eOX52 CAEGHSYNEQFF TCRBV1… TCRBJ… VASQSI… 12 9 C04:82
- 9 eQD108 CASGWGTGELFF TCRBV0… TCRBJ… FPQSAP… 12 11 B08:01
-10 eOX52 CATVSGRTAEQFF TCRBV0… TCRBJ… YINVFA… 13 9 A02:01
+ Experiment CDR3b V_gene J_gene peptide k_CDR3b k_peptide Allele
+ <chr> <chr> <chr> <chr> <chr> <int> <int> <chr>
+ 1 eOX52 CASSQDIDVYWGYTF TCRBV04-… TCRBJ… FNATRF… 15 10 B40:01
+ 2 eOX43 CSGVGTSTYEQYF TCRBV20-X TCRBJ… NVFAFP… 13 10 B27:05
+ 3 eEE228 CASSQETGVDEQFF TCRBV14-… TCRBJ… LLFVTV… 14 10 C04:01
+ 4 eLH54 CASNLGHYNSPLHF TCRBV28-… TCRBJ… PLLYDA… 14 10 C07:02
+ 5 eXL31 CSASFLAGGPQETQYF TCRBV20-X TCRBJ… AFLLFL… 16 9 B07:02
+ 6 eJL148 CASSDTTGAGNTIYF TCRBV06-… TCRBJ… GLEAPF… 15 10 B15:01
+ 7 eOX52 CSAVSSGGPYEQFF TCRBV20-X TCRBJ… NVFAFP… 14 10 A02:01
+ 8 eAV93 CASSFFAAGLAGELFF TCRBV12-X TCRBJ… FLQSIN… 16 10 C04:01
+ 9 eXL30 CASSQGTLNTGELFF TCRBV04-… TCRBJ… GEIPVA… 15 12 B35:02
+10 eHO134 CASSSGTEYQETQYF TCRBV28-… TCRBJ… VYFLQS… 15 9 A01:01
run_simulation(temp = c(15, 20, 25, 30, 35))
[1] 34.80057 43.96642 53.15623 65.65284 73.43373
+[1] 30.74661 42.83833 50.49116 66.31663 68.62529
Let’s just go ahead and create some data, we can work with. For this example, we take samples starting at 5 degree celsius and then in increments of 1 up to 50 degrees:
diff --git a/docs/search.json b/docs/search.json index 6ea2274..acfcc87 100644 --- a/docs/search.json +++ b/docs/search.json @@ -151,7 +151,7 @@ "href": "lab02.html#getting-started", "title": "Lab 2: Data Visualisation I", "section": "Getting Started", - "text": "Getting Started\nFirst of all, make sure to read every line in these exercises carefully!\nIf you get stuck with Quarto, revisit R4DS2e: Chapter 28 Quarto or take a look at the Comprehensive guide to using Quarto\nIf you get stuck working with paths and projects, remember there is a primer to help you, see the preparations materials for this session\n\nGo to the R for Bio Data Science RStudio Cloud Server session from last time and log in and choose the project you created.\nMake sure you are working in the right project, check in the upper right corner, it should say r_for_bio_data_science\nCreate a NEW Quarto Document for today’s exercises, e.g. lab02_exercises.qmd and remember to SAVE it in the same location as your .Rproj-file\n\nRecall the layout of the IDE (Integrated Development Environment)\n\n\n\n\n\nThen, before we start, we need to fetch some data to work on.\n\nSee if you can figure out how to create a new folder called “data”, make sure to place it the same place as your my_project_name.Rproj file.\n\nThen, without further ado, run each of the following lines separately in your console:\n\ntarget_url <- \"https://github.com/ramhiser/datamicroarray/raw/master/data/gravier.RData\"\noutput_file <- \"data/gravier.RData\"\ncurl::curl_download(url = target_url, destfile = output_file)\n\n\nUsing the files pane, check the folder you created to see if you managed to retrieve the file.\n\nRecall the syntax for a new code chunk:\n ```{r}\n #| echo: true\n #| eval: true\n # Here goes the code... Note how this part does not get executed because of the initial hashtag, this is called a code-comment\n 1 + 1\n my_vector <- c(1, 2, 3)\n my_mean <- mean(my_vector)\n print(my_mean)\n ```\nIMPORTANT! You are mixing code and text in a Quarto Document! Anything within a “chunk” as defined above will be evaluated as code, whereas anything outside the chunks is markdown. You can use shortcuts to insert new code chunks:\n\nMac: CMD + OPTION + i\nWindows: CTRL + ALT + i\n\nNote, this might not work, depending on your browser. In that case you can insert a new code chunk using or You can change the shortcuts via “Tools” > “Modify Keyboard shortcuts…” > Filter for “Insert Chunk” and then choose the desired shortcut. E.g. change the shortcut for code chunks to Shift+Cmd+i or similar.\n\nAdd a new code chunk and use the load() function to load the data you retrieved.\n\n\n\n\nClick here for a hint\n\n\nRemember, you can use ?load to get help on how the function works and remember your project root path is defined by the location of your .Rproj file, i.e. the path. A path is simply where R can find your file, e.g. /home/projects/r_for_bio_data_science/ or similar depending on your particular setup.\n\nNow, in the console, run the ls() command and confirm, that you did indeed load the gravier data.\n\nRead the information about the gravier data here\n\nNow, in your Quarto Document, add a new code chunk like so\n\nlibrary(\"tidyverse\")\n\nThis will load our data science toolbox, including ggplot." + "text": "Getting Started\nFirst of all, make sure to read every line in these exercises carefully!\nIf you get stuck with Quarto, revisit R4DS2e: Chapter 28 Quarto or take a look at the Comprehensive guide to using Quarto\nIf you get stuck working with paths and projects, remember there is a primer to help you, see the preparations materials for this session\n\nGo to the R for Bio Data Science RStudio Cloud Server session from last time and log in and choose the project you created.\nMake sure you are working in the right project, check in the upper right corner, it should say r_for_bio_data_science\nCreate a NEW Quarto Document for today’s exercises, e.g. lab02_exercises.qmd and remember to SAVE it in the same location as your .Rproj-file\n\nRecall the layout of the IDE (Integrated Development Environment)\n\n\n\n\n\nThen, before we start, we need to fetch some data to work on.\n\nSee if you can figure out how to create a new folder called “data”, make sure to place it the same place as your r_for_bio_data_science.Rproj file.\n\nThen, without further ado, run each of the following lines separately in your console:\n\ntarget_url <- \"https://github.com/ramhiser/datamicroarray/raw/master/data/gravier.RData\"\noutput_file <- \"data/gravier.RData\"\ncurl::curl_download(url = target_url, destfile = output_file)\n\n\n\n\nClick here for a hint if you get an error along the lines of Failed to open file...\n\n\nThink about what we are doing here. If you look at the code chunk above, then you can see that we are setting a variable target_url, which defines “a location on the internet”. From here we want to download some data. We need to tell R, where to put this data, so we also define the output_file-variable. Then we call the function curl_download() from the curl-package, hence the notation curl::curl_download(). As arguments to the function-parameters url and destfile, we pass the aforementioned variables. So far so good. Now, look at the target_url- and the output_file-variables, which one of those are we responsible for? If you think about it - We cannot change the target_url, we can only change the output_file, so there is a good chance, that the error you are getting pertains to this variable. But what does the variable contain? It contains the path to where you want to place the file, that is the data/-part and then it contains the filename you want to use, that is the gravier-part and then finally, it contains the filetype you want to use, that is the .RData-part. The filename and filetype is up to you to choose, but the path… Well, R can only find that if it exists… So… Did you remember to create a folder data in the same location as your r_for_bio_data_science.Rproj-file? If this small intermezzo has left things even more unclear, you should go over the primer on paths and projects. Remember, if at first you don’t succeed - Try and try again!\n\n\nUsing the files pane, check the folder you created to see if you managed to retrieve the file.\n\nRecall the syntax for a new code chunk:\n ```{r}\n #| echo: true\n #| eval: true\n # Here goes the code... Note how this part does not get executed because of the initial hashtag, this is called a code-comment\n 1 + 1\n my_vector <- c(1, 2, 3)\n my_mean <- mean(my_vector)\n print(my_mean)\n ```\nIMPORTANT! You are mixing code and text in a Quarto Document! Anything within a “chunk” as defined above will be evaluated as code, whereas anything outside the chunks is markdown. You can use shortcuts to insert new code chunks:\n\nMac: CMD + OPTION + i\nWindows: CTRL + ALT + i\n\nNote, this might not work, depending on your browser. In that case you can insert a new code chunk using or You can change the shortcuts via “Tools” > “Modify Keyboard shortcuts…” > Filter for “Insert Chunk” and then choose the desired shortcut. E.g. change the shortcut for code chunks to Shift+Cmd+i or similar.\n\nAdd a new code chunk and use the load() function to load the data you retrieved.\n\n\n\n\nClick here for a hint\n\n\nRemember, you can use ?load to get help on how the function works and remember your project root path is defined by the location of your .Rproj file, i.e. the path. A path is simply where R can find your file, e.g. /home/projects/r_for_bio_data_science/ or similar depending on your particular setup.\n\nNow, in the console, run the ls() command and confirm, that you did indeed load the gravier data.\n\nRead the information about the gravier data here\n\nNow, in your Quarto Document, add a new code chunk like so\n\nlibrary(\"tidyverse\")\n\nThis will load our data science toolbox, including ggplot." }, { "objectID": "lab02.html#create-data", @@ -340,7 +340,7 @@ "href": "lab05.html#creating-the-micro-report", "title": "Lab 5: Data Wrangling II", "section": "Creating the Micro-Report", - "text": "Creating the Micro-Report\n\nBackground\nFeel free to copy paste the one stated in the background-section above\n\n\nAim\nState the aim of the micro-report, i.e. what are the questions you are addressing?\n\n\nLoad Libraries\n\n\n\nLoad the libraries needed\n\n\nLoad Data\nRead the two data sets into variables peptide_data and meta_data.\n\n\n\nClick here for hint\n\n\nThink about which Tidyverse package deals with reading data and what are the file types we want to read here?\n\n\n\n\n\n\nData Description\nIt is customary to include a description of the data, helping the reader if the report, i.e. your stakeholder, to get an easy overview\n\nThe Subject Meta Data\nLet’s take a look at the meta data:\n\nmeta_data |> \n sample_n(10)\n\n# A tibble: 10 × 30\n Experiment Subject `Cell Type` `Target Type` Cohort Age Gender Race \n