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32 changes: 16 additions & 16 deletions _pages/labs/lab_7_1_R_intro.md
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ R is a language and environment for statistical computing and graphics. R and it
* Excellent community support: mailing list, blogs, tutorials
* Easy to extend by writing new functions

### Lab Setup
**Lab Setup**

1. Option: Use **RStudio** (free and open-source integrated development environment for R)

Expand Down Expand Up @@ -55,19 +55,19 @@ q()
* Execute commands.
* Try tab completion.

#### Add 3 plus 3 in R.
**Add 3 plus 3 in R.**

```R
3 + 3
```

#### Calculate the squre root of 7.
**Calculate the squre root of 7.**

```R
sqrt(7)
```

#### Install and load the package ggplot2.
**Install and load the package ggplot2.**

```R
install.packages("ggplot2")
Expand All @@ -78,7 +78,7 @@ Alternative: In Rstudio, go to the "Packages" tab and click the "Istall" button.
Search in the pop-up window and click "Install".


#### Using R help.
**Using R help.**

```R
help(help)
Expand All @@ -91,7 +91,7 @@ help(sqrt)

### Excercise 1: R basics.

#### Variable assignment
**Variable assignment**

Values can be assigned names and used in subsequent operations.

Expand All @@ -103,7 +103,7 @@ sqrt(7) #calculate square root of 7; result is not stored anywhere
x <- sqrt(7) #assign result to a variable named x
```

#### Calling R functions and reading data
**Calling R functions and reading data**

We will use an example project of the most popular baby names in the United States and the United Kingdom. A cleaned and merged version of the data file is available at ***http://tutorials.iq.harvard.edu/R/Rintro/dataSets/babyNames.csv***.

Expand Down Expand Up @@ -177,7 +177,7 @@ baby.names[baby.names$Name == "jill",]
```


#### Relational and logical operators
**Relational and logical operators**

Operator | Meaning
--- | --- | ---
Expand All @@ -197,7 +197,7 @@ How many babies were born after 2003? Save the subset in a new dataframe.



#### Adding columns
**Adding columns**

Add a new column specifying the country.

Expand Down Expand Up @@ -271,7 +271,7 @@ Output:
table(baby.names$Country)
```

#### Replacing data entries
**Replacing data entries**

```R
table(baby.names$Sex)
Expand All @@ -292,7 +292,7 @@ Check the output table again.

Now that we have made some changes to our data set, we might want to save those changes to a file.

#### Save the output as a csv file
**Save the output as a csv file**

```R
getwd() # Check current working directory. Is this where you want to save your file?
Expand All @@ -306,7 +306,7 @@ How would you save other file formats?
Locate and open the file outside of R.


#### Save the output as an R object
**Save the output as an R object**

```R
save(baby.names, file="babyNames.Rdata")
Expand Down Expand Up @@ -342,7 +342,7 @@ Which are the longest names?

Which are the shortest names?

#### Summary of the whole data.frame
**Summary of the whole data.frame**

```R
summary(baby.names)
Expand All @@ -353,7 +353,7 @@ summary(baby.names)
### Excercise 6: Simple graphs.


#### Boxplots
**Boxplots**

Compare the length of baby names for boys and girls using a boxplot.

Expand All @@ -375,7 +375,7 @@ Change the layout of the plot:
* http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3


### Save plot as a pdf
**Save plot as a pdf**

```R
pdf(file="boxplot.pdf")
Expand All @@ -390,7 +390,7 @@ What about other file formats?



#### Histograms
**Histograms**

How many names were recorded for each year?

Expand Down
38 changes: 19 additions & 19 deletions _pages/labs/lab_7_metagenomic_viz.md
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Expand Up @@ -42,17 +42,17 @@ load(file="Data/metaphlan_merged_MGX_species_relAb.Rdata")



#### Load the packages that we will need for the tutorial. (Install them if necessary.)
**Load the packages that we will need for the tutorial. (Install them if necessary.)**

```R
library(vegan)
library(ggplot2)
library(grid)
```

## 1. Visualization techniques: PCoAs and Biplots of microbial species data
### 1. Visualization techniques: PCoAs and Biplots of microbial species data

### Prepare the data
**Prepare the data**

* Extract the metadata

Expand All @@ -73,7 +73,7 @@ species[1:4,1:4] # check the output
```


### Ordination: PCoA with Bray-Curtis distance
#### Ordination: PCoA with Bray-Curtis distance
```R
data.bray=vegdist(species)
data.b.pcoa=cmdscale(data.bray,k=(nrow(species)-1),eig=TRUE)
Expand All @@ -94,7 +94,7 @@ p = ggplot(pcoa, aes(x=PC1, y=PC2)) + geom_point(size=4) + theme_bw()
p
```

#### Adding additional information to the plot: colours and shapes
**Adding additional information to the plot: colours and shapes**

Which metadata would be interesting to include in the plot?

Expand All @@ -111,7 +111,7 @@ pcoa$time_point = gsub("C","",metadata$Time_point)
head(pcoa) # check the data.frame
```

#### Colour by diagnosis
**Colour by diagnosis**

```R
p = ggplot(pcoa, aes(x=PC1, y=PC2, color=diagnosis)) + geom_point(size=4) + theme_bw()
Expand All @@ -131,7 +131,7 @@ p = p + guides(col=guide_legend(title="Diagnosis"))
p
```

#### Exercise: Display diagnosis as a shape instead!
**Exercise: Display diagnosis as a shape instead!**

<!-- p = ggplot(pcoa, aes(x=PC1, y=PC2, shape=diagnosis)) + geom_point(size=4) + theme_bw()
p = p + guides(shape=guide_legend(title="Diagnosis"))
Expand All @@ -145,7 +145,7 @@ p = p + scale_shape_manual(values=c(23,22,19))
p
```

#### Highlight inter-individual variation: Colour by individual
**Highlight inter-individual variation: Colour by individual**

How many participants are there and how many time points per participant?

Expand Down Expand Up @@ -188,7 +188,7 @@ For loops are not very effective in R. A better way to do this is by using sappl
?sapply
```

#### Excercise: Create the colour varialbe using sapply. Check that the resulting variable is identical to the one we just created with the for loop.
**Excercise: Create the colour varialbe using sapply. Check that the resulting variable is identical to the one we just created with the for loop.**

<!---
longitudinal_colour_v2=NA
Expand Down Expand Up @@ -240,7 +240,7 @@ dev.off()
```


#### Adding time course information for some individuals
**Adding time course information for some individuals**

Next we will connect all sample from a particular patient and add time point numbers to the plot.

Expand All @@ -265,7 +265,7 @@ p
```


#### Excercise: Add time courses for 3 more participants!
**Excercise: Add time courses for 3 more participants!**

<!---
patient2="C3001"
Expand Down Expand Up @@ -347,7 +347,7 @@ p_biplot
```


#### Adding specific species to the plot
**Adding specific species to the plot**

```R
list=c("s__Collinsella_intestinalis",
Expand Down Expand Up @@ -435,9 +435,9 @@ head(MGX_pwys[,1:2], n=30)

What does the stratification "unclassified" mean?

#### Create a list of all pathways that were detected in the metagenomic and metatranscriptomic data, respectively. How many are there? ```grep``` the pathway totals
**Create a list of all pathways that were detected in the metagenomic and metatranscriptomic data, respectively. How many are there? ```grep``` the pathway totals**

#### Metagenomic data
**Metagenomic data**

```R
MGX_pwy_lst_all = unique(gsub("\\|.*", "", MGX_pwys$Pathway))
Expand All @@ -447,7 +447,7 @@ MGX_pwy_lst = MGX_pwy_lst_all[intersect(grep("UNMAPPED", MGX_pwy_lst_all, invert
length(MGX_pwy_lst)
```

#### Metatranscriptomic data
**Metatranscriptomic data**

```R
MTX_pwy_lst_all = unique(gsub("\\|.*", "", MTX_pwys$Pathway))
Expand All @@ -458,7 +458,7 @@ length(MTX_pwy_lst)
```


### Compute alpha diversity for each pathway in each sample
**Compute alpha diversity for each pathway in each sample**

We will define a function that we can apply to the metagenomic and metatranscriptomic data:

Expand Down Expand Up @@ -561,7 +561,7 @@ rownames(MTX_a_div)= sapply(seq(1,length(MTX_pwy_lst)), function(p) as.character
nrow(MTX_a_div)
```

#Remove rows that are all "-1" and are all contributed by "unclassified"
Remove rows that are all "-1" and are all contributed by "unclassified".

```R
MTX_a_div = MTX_a_div[apply(MTX_a_div, 1, function(x) sum(x==-1)!=ncol(MTX_a_div)),]
Expand Down Expand Up @@ -602,7 +602,7 @@ summary_MTX_a_div = apply(MTX_a_div_sub, 1, function(x) mean(x[x>=0]))
```


### Alpha diversity plots
**Alpha diversity plots**

We will compare the mean alpha-diversity for each pathway on the RNA level (y-axis) and DNA level (x-axis). Each point will represents one pathway and the color will indicates the mean RNA/DNA ratio across all samples on a log scale.

Expand Down Expand Up @@ -643,7 +643,7 @@ g
dev.off()
```

### Now we want to colour the points by the log DNA/RNA ratio of each pathway.
**Now we want to colour the points by the log DNA/RNA ratio of each pathway.**

Create variable that reflects the dna/rna ratio

Expand Down