diff --git a/00-introduction.md b/00-introduction.md
index b90c9b86..de76a2c9 100644
--- a/00-introduction.md
+++ b/00-introduction.md
@@ -428,7 +428,7 @@ with a decimal:
 round(3.14)
 ```
 
-```{.output}
+```output
 [1] 3
 ```
 
@@ -455,7 +455,7 @@ You can also see a function's argument using the `args()` function:
 args(round)
 ```
 
-```{.output}
+```output
 function (x, digits = 0) 
 NULL
 ```
@@ -473,7 +473,7 @@ function:
 round(3.14159, digits = 2)
 ```
 
-```{.output}
+```output
 [1] 3.14
 ```
 
@@ -487,7 +487,7 @@ digits is 2.
 round(3.14159, 2)
 ```
 
-```{.output}
+```output
 [1] 3.14
 ```
 
diff --git a/01-r-basics.md b/01-r-basics.md
index 45c17b2d..9f02c60a 100644
--- a/01-r-basics.md
+++ b/01-r-basics.md
@@ -338,7 +338,7 @@ their modes. Try to guess what the mode will be before you look at the solution
 mode(chromosome_name)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -346,7 +346,7 @@ mode(chromosome_name)
 mode(od_600_value)
 ```
 
-```{.output}
+```output
 [1] "numeric"
 ```
 
@@ -354,7 +354,7 @@ mode(od_600_value)
 mode(chr_position)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -362,7 +362,7 @@ mode(chr_position)
 mode(spock)
 ```
 
-```{.output}
+```output
 [1] "logical"
 ```
 
@@ -371,7 +371,7 @@ mode(spock)
 mode(pilot)
 ```
 
-```{.error}
+```error
 Error in eval(expr, envir, enclos): object 'pilot' not found
 ```
 
@@ -399,7 +399,7 @@ to check their classes.
 class(chromosome_name)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -407,7 +407,7 @@ class(chromosome_name)
 class(od_600_value)
 ```
 
-```{.output}
+```output
 [1] "numeric"
 ```
 
@@ -415,7 +415,7 @@ class(od_600_value)
 class(chr_position)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -423,7 +423,7 @@ class(chr_position)
 class(spock)
 ```
 
-```{.output}
+```output
 [1] "logical"
 ```
 
@@ -432,7 +432,7 @@ class(spock)
 class(pilot)
 ```
 
-```{.error}
+```error
 Error in eval(expr, envir, enclos): object 'pilot' not found
 ```
 
@@ -458,7 +458,7 @@ pilot <- "Earhart"
 mode(pilot)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -468,7 +468,7 @@ pilot <- "Earhart"
 typeof(pilot)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -496,7 +496,7 @@ These can be used with literal numbers:
 (1 + (5 ** 0.5))/2
 ```
 
-```{.output}
+```output
 [1] 1.618034
 ```
 
@@ -512,7 +512,7 @@ by R) a numeric object:
 human_chr_number * 2
 ```
 
-```{.output}
+```output
 [1] 46
 ```
 
@@ -534,7 +534,7 @@ functions. Hint: remember the `round()` function can take 2 arguments.
 round((1 + sqrt(5))/2, digits = 3)
 ```
 
-```{.output}
+```output
 [1] 1.618
 ```
 
@@ -574,7 +574,7 @@ Another useful function that gives both of these pieces of information is the
 mode(snp_genes)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -582,7 +582,7 @@ mode(snp_genes)
 length(snp_genes)
 ```
 
-```{.output}
+```output
 [1] 4
 ```
 
@@ -590,7 +590,7 @@ length(snp_genes)
 str(snp_genes)
 ```
 
-```{.output}
+```output
  chr [1:4] "OXTR" "ACTN3" "AR" "OPRM1"
 ```
 
@@ -624,7 +624,7 @@ we place the index (e.g. a number) in that bracket as follows:
 snps[3]
 ```
 
-```{.output}
+```output
 [1] "rs6152"
 ```
 
@@ -639,7 +639,7 @@ range of numbers:
 snps[1:3]
 ```
 
-```{.output}
+```output
 [1] "rs53576"   "rs1815739" "rs6152"   
 ```
 
@@ -654,7 +654,7 @@ positions you wish to retrieve.
 snps[c(1, 3, 4)]
 ```
 
-```{.output}
+```output
 [1] "rs53576"   "rs6152"    "rs1799971"
 ```
 
@@ -670,7 +670,7 @@ Also, several of these subsetting expressions can be combined:
 snps[c(1:3,4)]
 ```
 
-```{.output}
+```output
 [1] "rs53576"   "rs1815739" "rs6152"    "rs1799971"
 ```
 
@@ -693,7 +693,7 @@ We can verify that "snp\_genes" contains the new gene entry
 snp_genes
 ```
 
-```{.output}
+```output
 [1] "OXTR"   "ACTN3"  "AR"     "OPRM1"  "CYP1A1" "APOA5" 
 ```
 
@@ -705,7 +705,7 @@ value removed:
 snp_genes[-6]
 ```
 
-```{.output}
+```output
 [1] "OXTR"   "ACTN3"  "AR"     "OPRM1"  "CYP1A1"
 ```
 
@@ -717,7 +717,7 @@ snp_genes <- snp_genes[-6]
 snp_genes
 ```
 
-```{.output}
+```output
 [1] "OXTR"   "ACTN3"  "AR"     "OPRM1"  "CYP1A1"
 ```
 
@@ -729,7 +729,7 @@ snp_genes[6]<- "APOA5"
 snp_genes
 ```
 
-```{.output}
+```output
 [1] "OXTR"   "ACTN3"  "AR"     "OPRM1"  "CYP1A1" "APOA5" 
 ```
 
@@ -774,7 +774,7 @@ There is one last set of cool subsetting capabilities we want to introduce. It i
 snp_positions[snp_positions > 100000000]
 ```
 
-```{.output}
+```output
 [1] 154039662
 ```
 
@@ -805,7 +805,7 @@ evaluates to:
 snp_positions > 100000000
 ```
 
-```{.output}
+```output
 [1] FALSE FALSE FALSE  TRUE
 ```
 
@@ -817,7 +817,7 @@ you pass a logical vector as an index, R will return the true values:
 snp_positions[c(FALSE, FALSE, FALSE, TRUE)]
 ```
 
-```{.output}
+```output
 [1] 154039662
 ```
 
@@ -840,7 +840,7 @@ evaluates as TRUE in our comparison:
 which(snp_positions > 100000000)
 ```
 
-```{.output}
+```output
 [1] 4
 ```
 
@@ -857,7 +857,7 @@ snp_marker_cutoff <- 100000000
 snp_positions[snp_positions > snp_marker_cutoff]
 ```
 
-```{.output}
+```output
 [1] 154039662
 ```
 
@@ -883,7 +883,7 @@ value:
 is.na(snp_genes)
 ```
 
-```{.output}
+```output
 [1] FALSE FALSE FALSE FALSE FALSE FALSE
 ```
 
@@ -903,7 +903,7 @@ the vector you are searching:
 c("ACTN3","APOA5") %in% snp_genes
 ```
 
-```{.output}
+```output
 [1] TRUE TRUE
 ```
 
@@ -942,7 +942,7 @@ c. `snp_positions`
 mode(snps)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -950,7 +950,7 @@ mode(snps)
 mode(snp_chromosomes)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -958,7 +958,7 @@ mode(snp_chromosomes)
 mode(snp_positions)
 ```
 
-```{.output}
+```output
 [1] "numeric"
 ```
 
@@ -985,7 +985,7 @@ snps <- c(snps, "rs662799")
 snps
 ```
 
-```{.output}
+```output
 [1] "rs53576"   "rs1815739" "rs6152"    "rs1799971" "rs662799" 
 ```
 
@@ -994,7 +994,7 @@ snp_chromosomes <- c(snp_chromosomes, "11") # did you use quotes?
 snp_chromosomes
 ```
 
-```{.output}
+```output
 [1] "3"  "11" "X"  "6"  "11"
 ```
 
@@ -1003,7 +1003,7 @@ snp_positions <- c(snp_positions, 116792991)
 snp_positions
 ```
 
-```{.output}
+```output
 [1]   8762685  66560624  67545785 154039662 116792991
 ```
 
@@ -1036,7 +1036,7 @@ snp_genes <- c(snp_genes, NA, NA)
 snp_genes
 ```
 
-```{.output}
+```output
 [1] "OXTR"  "ACTN3" "AR"    "OPRM1" "APOA5" NA      NA     
 ```
 
@@ -1065,7 +1065,7 @@ combined <- c(snp_genes[1], snps[1], snp_chromosomes[1], snp_positions[1])
 combined
 ```
 
-```{.output}
+```output
 [1] "OXTR"    "rs53576" "3"       "8762685"
 ```
 
@@ -1088,7 +1088,7 @@ What type of data is `combined`?
 typeof(combined)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -1125,7 +1125,7 @@ snp_data <- list(genes = snp_genes,
 str(snp_data)
 ```
 
-```{.output}
+```output
 List of 4
  $ genes         : chr [1:7] "OXTR" "ACTN3" "AR" "OPRM1" ...
  $ refference_snp: chr [1:5] "rs53576" "rs1815739" "rs6152" "rs1799971" ...
@@ -1142,7 +1142,7 @@ To get all the values for the `position` object in the list, we use the `$` nota
 snp_data$position
 ```
 
-```{.output}
+```output
 [1]   8762685  66560624  67545785 154039662 116792991
 ```
 
@@ -1155,7 +1155,7 @@ To get the first value in the `position` object, use the `[]` notation to index:
 snp_data$position[1]
 ```
 
-```{.output}
+```output
 [1] 8762685
 ```
 :::::::::::::::::::::::::::::::::::::::::
diff --git a/03-basics-factors-dataframes.md b/03-basics-factors-dataframes.md
index fde632e4..70b9c568 100644
--- a/03-basics-factors-dataframes.md
+++ b/03-basics-factors-dataframes.md
@@ -198,7 +198,7 @@ frame. Let's examine what each of these functions can tell us:
 summary(variants)
 ```
 
-```{.output}
+```output
   sample_id            CHROM                POS             ID         
  Length:801         Length:801         Min.   :   1521   Mode:logical  
  Class :character   Class :character   1st Qu.:1115970   NA's:801      
@@ -284,7 +284,7 @@ at how data frames work:
 str(subset)
 ```
 
-```{.output}
+```output
 'data.frame':	801 obs. of  4 variables:
  $ sample_id: chr  "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863" ...
  $ CHROM    : chr  "CP000819.1" "CP000819.1" "CP000819.1" "CP000819.1" ...
@@ -323,7 +323,7 @@ Ok, thats a lot up unpack! Some things to notice.
   mode(variants)
   ```
   
-  ```{.output}
+  ```output
   [1] "list"
   ```
 
@@ -334,7 +334,7 @@ Ok, thats a lot up unpack! Some things to notice.
   class(variants)
   ```
   
-  ```{.output}
+  ```output
   [1] "data.frame"
   ```
 
@@ -374,7 +374,7 @@ Let's look at the first few items in our factor using `head()`:
 head(alt_alleles)
 ```
 
-```{.output}
+```output
 [1] "G"         "T"         "T"         "CTTTTTTTT" "CCGCGC"    "T"        
 ```
 
@@ -401,19 +401,19 @@ now:
 plot(snps)
 ```
 
-```{.warning}
+```warning
 Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion
 ```
 
-```{.warning}
+```warning
 Warning in min(x): no non-missing arguments to min; returning Inf
 ```
 
-```{.warning}
+```warning
 Warning in max(x): no non-missing arguments to max; returning -Inf
 ```
 
-```{.error}
+```error
 Error in plot.window(...): need finite 'ylim' values
 ```
 
@@ -434,7 +434,7 @@ Let's learn a little more about this new type of vector:
 str(factor_snps)
 ```
 
-```{.output}
+```output
  Factor w/ 4 levels "A","C","G","T": 1 1 1 1 1 1 1 1 1 1 ...
 ```
 
@@ -458,7 +458,7 @@ We can see how many items in our vector fall into each category:
 summary(factor_snps)
 ```
 
-```{.output}
+```output
   A   C   G   T 
 211 139 154 203 
 ```
@@ -552,9 +552,9 @@ it will look for a CRAN repository to install from. So, for example, to install
 install.packages("ggplot2")
 ```
 
-```{.output}
+```output
 The following package(s) will be installed:
-- ggplot2 [3.5.0]
+- ggplot2 [3.5.1]
 These packages will be installed into "~/work/genomics-r-intro/genomics-r-intro/renv/profiles/lesson-requirements/renv/library/R-4.3/x86_64-pc-linux-gnu".
 
 # Installing packages --------------------------------------------------------
@@ -614,7 +614,7 @@ a.
 variants[1, 1]
 ```
 
-```{.output}
+```output
 [1] "SRR2584863"
 ```
 
@@ -625,7 +625,7 @@ b.
 variants[2, 4]
 ```
 
-```{.output}
+```output
 [1] NA
 ```
 
@@ -636,7 +636,7 @@ c.
 variants[801, 29]
 ```
 
-```{.output}
+```output
 [1] "T"
 ```
 
@@ -647,7 +647,7 @@ d.
 variants[2, ]
 ```
 
-```{.output}
+```output
    sample_id      CHROM    POS ID REF ALT QUAL FILTER INDEL IDV IMF DP      VDB
 2 SRR2584863 CP000819.1 263235 NA   G   T   85     NA FALSE  NA  NA  6 0.096133
   RPB MQB BQB MQSB       SGB     MQ0F ICB HOB AC AN     DP4 MQ
@@ -666,7 +666,7 @@ variants[-1, ]
 ```
 
 
-```{.output}
+```output
    sample_id      CHROM     POS ID      REF       ALT QUAL FILTER INDEL IDV IMF
 2 SRR2584863 CP000819.1  263235 NA        G         T   85     NA FALSE  NA  NA
 3 SRR2584863 CP000819.1  281923 NA        G         T  217     NA FALSE  NA  NA
@@ -704,7 +704,7 @@ f.
 variants[1:4, 1]
 ```
 
-```{.output}
+```output
 [1] "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863"
 ```
 
@@ -715,7 +715,7 @@ g.
 variants[1:10, c("REF", "ALT")]
 ```
 
-```{.output}
+```output
                                 REF
 1                                 T
 2                                 G
@@ -748,7 +748,7 @@ variants[, c("sample_id")]
 ```
 
 
-```{.output}
+```output
 [1] "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863"
 [6] "SRR2584863"
 ```
@@ -760,7 +760,7 @@ i.
 head(variants)
 ```
 
-```{.output}
+```output
    sample_id      CHROM    POS ID      REF       ALT QUAL FILTER INDEL IDV IMF
 1 SRR2584863 CP000819.1   9972 NA        T         G   91     NA FALSE  NA  NA
 2 SRR2584863 CP000819.1 263235 NA        G         T   85     NA FALSE  NA  NA
@@ -798,7 +798,7 @@ j.
 tail(variants)
 ```
 
-```{.output}
+```output
      sample_id      CHROM     POS ID REF ALT QUAL FILTER INDEL IDV IMF DP
 796 SRR2589044 CP000819.1 3444175 NA   G   T  184     NA FALSE  NA  NA  9
 797 SRR2589044 CP000819.1 3481820 NA   A   G  225     NA FALSE  NA  NA 12
@@ -837,7 +837,7 @@ variants$sample_id
 ```
 
 
-```{.output}
+```output
 [1] "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863" "SRR2584863"
 [6] "SRR2584863"
 ```
@@ -850,7 +850,7 @@ variants[variants$REF == "A", ]
 ```
 
 
-```{.output}
+```output
     sample_id      CHROM     POS ID REF ALT QUAL FILTER INDEL IDV IMF DP
 11 SRR2584863 CP000819.1 2407766 NA   A   C  104     NA FALSE  NA  NA  9
 12 SRR2584863 CP000819.1 2446984 NA   A   C  225     NA FALSE  NA  NA 20
@@ -916,7 +916,7 @@ SRR2584863_variants <- variants[variants$sample_id == "SRR2584863", ]
 dim(SRR2584863_variants)
 ```
 
-```{.output}
+```output
 [1] 25 29
 ```
 
@@ -926,7 +926,7 @@ dim(SRR2584863_variants)
 summary(SRR2584863_variants)
 ```
 
-```{.output}
+```output
   sample_id            CHROM                POS             ID         
  Length:25          Length:25          Min.   :   9972   Mode:logical  
  Class :character   Class :character   1st Qu.:1331794   NA's:25       
@@ -1017,7 +1017,7 @@ snp_chromosomes <- c('3', '11', 'X', '6')
 typeof(snp_chromosomes)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -1031,7 +1031,7 @@ snp_chromosomes_2 <- c(3, 11, 'X', 6)
 typeof(snp_chromosomes_2)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -1039,7 +1039,7 @@ typeof(snp_chromosomes_2)
 snp_chromosomes_2[1]
 ```
 
-```{.output}
+```output
 [1] "3"
 ```
 
@@ -1053,7 +1053,7 @@ snp_positions_2 <- c("8762685", "66560624", "67545785", "154039662")
 typeof(snp_positions_2)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -1061,7 +1061,7 @@ typeof(snp_positions_2)
 snp_positions_2[1]
 ```
 
-```{.output}
+```output
 [1] "8762685"
 ```
 
@@ -1073,7 +1073,7 @@ snp_positions_2 <- as.numeric(snp_positions_2)
 typeof(snp_positions_2)
 ```
 
-```{.output}
+```output
 [1] "double"
 ```
 
@@ -1081,7 +1081,7 @@ typeof(snp_positions_2)
 snp_positions_2[1]
 ```
 
-```{.output}
+```output
 [1] 8762685
 ```
 
@@ -1093,7 +1093,7 @@ using `as.numeric()` on `snp_chromosomes_2`
 snp_chromosomes_2 <- as.numeric(snp_chromosomes_2)
 ```
 
-```{.warning}
+```warning
 Warning: NAs introduced by coercion
 ```
 
@@ -1105,7 +1105,7 @@ data) has been introduced.
 snp_chromosomes_2
 ```
 
-```{.output}
+```output
 [1]  3 11 NA  6
 ```
 
@@ -1118,7 +1118,7 @@ look at the result:
 as.numeric(factor_snps)
 ```
 
-```{.output}
+```output
   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
@@ -1160,7 +1160,7 @@ variants$REF <- as.character(variants$REF)
 typeof(variants$REF)
 ```
 
-```{.output}
+```output
 [1] "character"
 ```
 
@@ -1213,7 +1213,7 @@ reads that support each of the reported variants.
 max(variants$DP)
 ```
 
-```{.output}
+```output
 [1] 79
 ```
 
@@ -1225,7 +1225,7 @@ sorted_by_DP <- variants[order(variants$DP), ]
 head(sorted_by_DP$DP)
 ```
 
-```{.output}
+```output
 [1] 2 2 2 2 2 2
 ```
 
@@ -1247,7 +1247,7 @@ variants with the greatest filtered depth ("DP").
    head(sorted_by_DP$DP)
 ```
 
-```{.output}
+```output
 [1] 79 46 41 29 29 27
 ```
 
@@ -1265,7 +1265,7 @@ colnames(variants)[colnames(variants) == "sample_id"] <- "strain"
 colnames(variants)
 ```
 
-```{.output}
+```output
  [1] "strain"        "CHROM"         "POS"           "ID"           
  [5] "REF"           "ALT"           "QUAL"          "FILTER"       
  [9] "INDEL"         "IDV"           "IMF"           "DP"           
@@ -1339,7 +1339,7 @@ frame:
 head(Ecoli_metadata)
 ```
 
-```{.output}
+```output
 # A tibble: 6 × 7
   sample   generation clade   strain cit     run       genome_size
   <chr>         <dbl> <chr>   <chr>  <chr>   <chr>           <dbl>
@@ -1386,7 +1386,7 @@ H) Save the edited Ecoli\_metadata data frame as "exercise\_solution.csv" in you
 dim(Ecoli_metadata)
 ```
 
-```{.output}
+```output
 [1] 30  7
 ```
 
@@ -1394,7 +1394,7 @@ dim(Ecoli_metadata)
 levels(as.factor(Ecoli_metadata$cit))
 ```
 
-```{.output}
+```output
 [1] "minus"   "plus"    "unknown"
 ```
 
@@ -1402,7 +1402,7 @@ levels(as.factor(Ecoli_metadata$cit))
 table(as.factor(Ecoli_metadata$cit))
 ```
 
-```{.output}
+```output
 
   minus    plus unknown 
       9       9      12 
@@ -1412,7 +1412,7 @@ table(as.factor(Ecoli_metadata$cit))
 Ecoli_metadata[7, 7]
 ```
 
-```{.output}
+```output
 # A tibble: 1 × 1
   genome_size
         <dbl>
@@ -1423,7 +1423,7 @@ Ecoli_metadata[7, 7]
 median(Ecoli_metadata$genome_size)
 ```
 
-```{.output}
+```output
 [1] 4.625
 ```
 
diff --git a/05-dplyr.md b/05-dplyr.md
index 571c6982..2a04388b 100644
--- a/05-dplyr.md
+++ b/05-dplyr.md
@@ -114,7 +114,7 @@ Now let's load our vcf .csv file using `read_csv()`:
 Similar to `str()`, which comes built into R, `glimpse()` is a `dplyr` function that (as the name suggests) gives a glimpse of the data frame.
 
 
-```{.output}
+```output
 Rows: 801
 Columns: 29
 $ sample_id     <chr> "SRR2584863", "SRR2584863", "SRR2584863", "SRR2584863", …
@@ -159,7 +159,7 @@ To select columns of a data frame, use `select()`. The first argument to this fu
 select(variants, sample_id, REF, ALT, DP)
 ```
 
-```{.output}
+```output
 # A tibble: 801 × 4
    sample_id  REF                              ALT                            DP
    <chr>      <chr>                            <chr>                       <dbl>
@@ -184,7 +184,7 @@ the variable to exclude it.
 select(variants, -CHROM)
 ```
 
-```{.output}
+```output
 # A tibble: 801 × 28
    sample_id      POS ID    REF      ALT    QUAL FILTER INDEL   IDV    IMF    DP
    <chr>        <dbl> <lgl> <chr>    <chr> <dbl> <lgl>  <lgl> <dbl>  <dbl> <dbl>
@@ -211,7 +211,7 @@ select(variants, -CHROM)
 select(variants, ends_with("B"))
 ```
 
-```{.output}
+```output
 # A tibble: 801 × 8
       VDB   RPB   MQB   BQB   MQSB    SGB ICB   HOB  
     <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> <lgl> <lgl>
@@ -249,7 +249,7 @@ variants_result <- select(variants_subset, -Indiv, -FILTER)
 variants_result
 ```
 
-```{.output}
+```output
 # A tibble: 801 × 7
        POS sample_id  ID    INDEL   IDV    IMF ICB  
      <dbl> <chr>      <lgl> <lgl> <dbl>  <dbl> <lgl>
@@ -280,7 +280,7 @@ variants_result <- select(variants, POS, contains("i"), -Indiv, -FILTER)
 variants_result
 ```
 
-```{.output}
+```output
 # A tibble: 801 × 7
        POS sample_id  ID    INDEL   IDV    IMF ICB  
      <dbl> <chr>      <lgl> <lgl> <dbl>  <dbl> <lgl>
@@ -308,7 +308,7 @@ To choose rows, use `filter()`:
 filter(variants, sample_id == "SRR2584863")
 ```
 
-```{.output}
+```output
 # A tibble: 25 × 29
    sample_id  CHROM        POS ID    REF   ALT    QUAL FILTER INDEL   IDV    IMF
    <chr>      <chr>      <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl>  <dbl>
@@ -338,7 +338,7 @@ Here are a few examples:
 filter(variants, REF %in% c("T", "G"))
 ```
 
-```{.output}
+```output
 # A tibble: 340 × 29
    sample_id CHROM    POS ID    REF   ALT    QUAL FILTER INDEL   IDV   IMF    DP
    <chr>     <chr>  <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl> <dbl> <dbl>
@@ -363,7 +363,7 @@ filter(variants, REF %in% c("T", "G"))
 filter(variants, INDEL)
 ```
 
-```{.output}
+```output
 # A tibble: 101 × 29
    sample_id CHROM    POS ID    REF   ALT    QUAL FILTER INDEL   IDV   IMF    DP
    <chr>     <chr>  <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl> <dbl> <dbl>
@@ -388,7 +388,7 @@ filter(variants, INDEL)
 filter(variants, !is.na(IDV))
 ```
 
-```{.output}
+```output
 # A tibble: 101 × 29
    sample_id CHROM    POS ID    REF   ALT    QUAL FILTER INDEL   IDV   IMF    DP
    <chr>     <chr>  <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl> <dbl> <dbl>
@@ -420,7 +420,7 @@ have a QUAL score above a certain threshold:
 filter(variants, QUAL >= 100)
 ```
 
-```{.output}
+```output
 # A tibble: 666 × 29
    sample_id CHROM    POS ID    REF   ALT    QUAL FILTER INDEL   IDV   IMF    DP
    <chr>     <chr>  <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl> <dbl> <dbl>
@@ -449,7 +449,7 @@ filter(variants, QUAL >= 100)
 filter(variants, sample_id == "SRR2584863", QUAL >= 100)
 ```
 
-```{.output}
+```output
 # A tibble: 19 × 29
    sample_id CHROM    POS ID    REF   ALT    QUAL FILTER INDEL   IDV   IMF    DP
    <chr>     <chr>  <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl> <dbl> <dbl>
@@ -482,7 +482,7 @@ filter(variants, sample_id == "SRR2584863", QUAL >= 100)
 filter(variants, sample_id == "SRR2584863", (MQ >= 50 | QUAL >= 100))
 ```
 
-```{.output}
+```output
 # A tibble: 23 × 29
    sample_id  CHROM        POS ID    REF   ALT    QUAL FILTER INDEL   IDV    IMF
    <chr>      <chr>      <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl>  <dbl>
@@ -521,7 +521,7 @@ Hint: to flip logical values such as TRUE to a FALSE, we can use to negation sym
 filter(variants, POS >= 1e6 & POS <= 2e6, QUAL > 200, !INDEL)
 ```
 
-```{.output}
+```output
 # A tibble: 77 × 29
    sample_id CHROM    POS ID    REF   ALT    QUAL FILTER INDEL   IDV   IMF    DP
    <chr>     <chr>  <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl> <dbl> <dbl>
@@ -564,7 +564,7 @@ variants %>%
   select(REF, ALT, DP)
 ```
 
-```{.output}
+```output
 # A tibble: 25 × 3
    REF                              ALT                                       DP
    <chr>                            <chr>                                  <dbl>
@@ -613,7 +613,7 @@ the first six rows to confirm it's what we want:
 SRR2584863_variants
 ```
 
-```{.output}
+```output
 # A tibble: 25 × 3
    REF                              ALT                                       DP
    <chr>                            <chr>                                  <dbl>
@@ -637,7 +637,7 @@ Similar to `head()` and `tail()` functions, we can also look at the first or las
 SRR2584863_variants %>% slice(1:6)
 ```
 
-```{.output}
+```output
 # A tibble: 6 × 3
   REF      ALT          DP
   <chr>    <chr>     <dbl>
@@ -654,7 +654,7 @@ SRR2584863_variants %>% slice(1:6)
 SRR2584863_variants %>% slice(10:25)
 ```
 
-```{.output}
+```output
 # A tibble: 16 × 3
    REF   ALT      DP
    <chr> <chr> <dbl>
@@ -697,7 +697,7 @@ Showing only 5th through 11th rows of columns `REF`, `ALT`, and `POS`.
  select(sample_id, DP, REF, ALT, POS)
 ```
 
-```{.output}
+```output
 # A tibble: 7 × 5
   sample_id     DP REF   ALT       POS
   <chr>      <dbl> <chr> <chr>   <dbl>
@@ -734,7 +734,7 @@ variants %>%
   mutate(POLPROB = 1 - (10 ^ -(QUAL/10)))
 ```
 
-```{.output}
+```output
 # A tibble: 801 × 30
    sample_id  CHROM        POS ID    REF   ALT    QUAL FILTER INDEL   IDV    IMF
    <chr>      <chr>      <dbl> <lgl> <chr> <chr> <dbl> <lgl>  <lgl> <dbl>  <dbl>
@@ -774,7 +774,7 @@ variants %>%
  select(sample_id, POS, QUAL, POLPROB)
 ```
 
-```{.output}
+```output
 # A tibble: 801 × 4
    sample_id      POS  QUAL POLPROB
    <chr>        <dbl> <dbl>   <dbl>
@@ -812,7 +812,7 @@ variants %>%
   tally()
 ```
 
-```{.output}
+```output
 # A tibble: 3 × 2
   sample_id      n
   <chr>      <int>
@@ -829,7 +829,7 @@ variants %>%
   count(sample_id)
 ```
 
-```{.output}
+```output
 # A tibble: 3 × 2
   sample_id      n
   <chr>      <int>
@@ -854,7 +854,7 @@ variants %>%
   count(INDEL)
 ```
 
-```{.output}
+```output
 # A tibble: 2 × 2
   INDEL     n
   <lgl> <int>
@@ -901,7 +901,7 @@ variants %>%
     max_DP = max(DP))
 ```
 
-```{.output}
+```output
 # A tibble: 3 × 5
   sample_id  mean_DP median_DP min_DP max_DP
   <chr>        <dbl>     <dbl>  <dbl>  <dbl>
@@ -935,7 +935,7 @@ variants_wide <- variants %>%
   pivot_wider(names_from = sample_id, values_from = mean_DP)
 ```
 
-```{.output}
+```output
 `summarise()` has grouped output by 'sample_id'. You can override using the
 `.groups` argument.
 ```
@@ -944,7 +944,7 @@ variants_wide <- variants %>%
 variants_wide
 ```
 
-```{.output}
+```output
 # A tibble: 1 × 4
   CHROM      SRR2584863 SRR2584866 SRR2589044
   <chr>           <dbl>      <dbl>      <dbl>
@@ -959,7 +959,7 @@ variants_wide %>%
   pivot_longer(-CHROM, names_to = "sample_id", values_to = "mean_DP")
 ```
 
-```{.output}
+```output
 # A tibble: 3 × 3
   CHROM      sample_id  mean_DP
   <chr>      <chr>        <dbl>
diff --git a/06-data-visualization.md b/06-data-visualization.md
index 54987fd6..759c1133 100644
--- a/06-data-visualization.md
+++ b/06-data-visualization.md
@@ -70,18 +70,18 @@ library(readr)
 library(dplyr)
 ```
 
-```{.output}
+```output
 
 Attaching package: 'dplyr'
 ```
 
-```{.output}
+```output
 The following objects are masked from 'package:stats':
 
     filter, lag
 ```
 
-```{.output}
+```output
 The following objects are masked from 'package:base':
 
     intersect, setdiff, setequal, union
@@ -103,7 +103,7 @@ Explore the *structure* (types of columns and number of rows) of the dataset usi
 glimpse(variants) # Show a snapshot of the rows and columns
 ```
 
-```{.output}
+```output
 Rows: 801
 Columns: 29
 $ sample_id     <chr> "SRR2584863", "SRR2584863", "SRR2584863", "SRR2584863", …
diff --git a/07-r-help.md b/07-r-help.md
index db8c66fb..7e4b469d 100644
--- a/07-r-help.md
+++ b/07-r-help.md
@@ -155,7 +155,7 @@ they come up commonly:
 1:101 # generates the sequence of numbers from 1 to 101
 ```
 
-```{.output}
+```output
   [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
  [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
  [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
diff --git a/config.yaml b/config.yaml
deleted file mode 100644
index d35bbb68..00000000
--- a/config.yaml
+++ /dev/null
@@ -1,88 +0,0 @@
-#------------------------------------------------------------
-# Values for this lesson.
-#------------------------------------------------------------
-
-# Which carpentry is this (swc, dc, lc, or cp)?
-# swc: Software Carpentry
-# dc: Data Carpentry
-# lc: Library Carpentry
-# cp: Carpentries (to use for instructor training for instance)
-# incubator: The Carpentries Incubator
-carpentry: 'dc'
-
-# Overall title for pages.
-title: 'Intro to R and RStudio for Genomics'
-
-# Date the lesson was created (YYYY-MM-DD, this is empty by default)
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-
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-
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-
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-    }
-  }
-}