exercise12.Rmd

## Exercise 12: ggplot2

Create the script "exercise12.R" and save it to the "Rcourse/Module3" directory: you will save all the commands of exercise 12 in that script.
<br>Remember you can comment the code using #.


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```{r, eval=F}	
getwd()
setwd("~/Rcourse/Module3")
```

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### Exercise 12a- Scatter plot

**1- Load ggplot2 package**

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```{r}	
library(ggplot2)
```

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**2- Download the data we will use for plotting:**

```{r}
download.file("https://raw.githubusercontent.com/sarahbonnin/CRG_RIntroduction/master/ex12_normalized_intensities.csv", "ex12_normalized_intensities.csv", method="curl")
```

**3- Read file into object "project1"**

About this file:
* It is comma separated (csv format)
* The first row is the header
* Take the row names from the first column

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```{r}
project1 <- read.table("ex12_normalized_intensities.csv", 
  sep=",", 
  header=TRUE, 
  row.names = 1)
```

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**4- Using ggplot, create a simple scatter plot representing gene expression of "sampleB" on the x-axis and "sampleH" on the y-axis.**

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```{r}
ggplot(data=project1, mapping=aes(x=sampleB, y=sampleH)) + 
  geom_point()
```

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**5- Create an extra column to the data frame "project1" (you can call this column "expr_limits"): if the expression of a gene is > 13 in both sampleB and sampleH, set to "high"; if the expression of a gene is < 6 in both sampleB and sampleH, set to "low"; if different, set to "normal".**

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```{r}
# Initialize all values to "normal"
project1$expr_limits <- "normal"
# "high" if project1$sampleB > 13 and project1$sampleH > 13
project1$expr_limits[project1$sampleB > 13 & project1$sampleH > 13] <- "high"
# "low" if project1$sampleB < 6 and project1$sampleH < 6
project1$expr_limits[project1$sampleB < 6 & project1$sampleH < 6] <- "low"
```

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**6- Color the points of your scatter plot according to the newly created column "expr_limits". Save that plot in the object "p"**

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```{r}
p <- ggplot(data=project1, mapping=aes(x=sampleB, y=sampleH, color=expr_limits)) + 
  geom_point()
```

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**7- Add a layer to "p" in order to change the points colors to blue (for low), grey (for normal) and red (for high). Save this plot in the object "p2".**

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```{r}
p2 <- p + scale_color_manual(values=c("red", "blue", "grey"))
```

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**8- Save p2 in a jpeg file.**
a. Try with RStudio Plots window (Export)<br>
b. Try  in the console:<br>

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```{r}
jpeg("myscatterggplot.jpg")
  p2
dev.off()
```

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### Exercise 12b- Box plot

**1- Convert "project1" from a wide format to a long format: save in the object "project_long"**
*Note: remember melt function from reshape2 package.*

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```{r}
library(reshape2)
project_long <- melt(data=project1)
```

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**2- Produce a boxplot of the expression of all samples (each sample should be represented by a box)**

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```{r}
ggplot(data=project_long, mapping=aes(x=variable, y=value)) + 
  geom_boxplot()
```

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**3- Modify the previous boxplot so as to obtain 3 "sub-boxplots"" per sample, each representing the expression of either "low", "normal" or "high" genes.**

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```{r}
ggplot(data=project_long, mapping=aes(x=variable, y=value, color=expr_limits)) + 
  geom_boxplot()
```

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**4- Rotate the x-axis labels (90 degrees angle).**
<br>
This is new ! Google it !!

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```{r}
ggplot(data=project_long, mapping=aes(x=variable, y=value, color=expr_limits)) + 
  geom_boxplot() + 
  theme(axis.text.x = element_text(angle = 90))
```

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**5- Finally, add a title to the plot.**

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```{r}
ggplot(data=project_long, mapping=aes(x=variable, y=value, color=expr_limits)) + 
  geom_boxplot() + 
  theme(axis.text.x = element_text(angle = 90)) +
  ggtitle("My boxplots")
```

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### Exercise 12c- Bar plot

**1- Produce a bar plot of how many low/normal/high genes are in the column "expr_limits" of "project1".**

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```{r}
ggplot(data=project1, mapping=aes(x=expr_limits)) + 
  geom_bar()
```

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**2- Add an horizontal line at counts 250 (y-axis). Save the plot in the object "bar"**

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```{r}
bar <- ggplot(data=project1, mapping=aes(x=expr_limits)) + 
  geom_bar() + 
  geom_hline(yintercept=250)
```

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**3- Swap the x and y axis. Save in bar2.**

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```{r}
bar2 <- bar + coord_flip()
```

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**4- Save "bar" and "bar2" plots in a "png" file, using the **png()** function: use grid.arrange (from the gridExtra package) to organize both plots in one page !**

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```{r}
png("mybarplots.png", width=1000)
grid.arrange(bar, bar2, nrow=1, ncol=2)
dev.off()
```

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### Exercise 12d- Histogram

**1- Create a simple histogram using project_long (column "value").**

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```{r}
ggplot(data=project_long, mapping=aes(x=value)) + 
  geom_histogram()
```

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**2- Notice that you get the following warning message" *stat_bin() using `bins = 30`. Pick better value with `binwidth`.***<br>
Set "bins"" parameter of geom_histogram() to 50.

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```{r}
ggplot(data=project_long, mapping=aes(x=value)) + 
  geom_histogram(bins=50)
```

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**3- This histogram plots expression values for **All samples**.**
<br>
Change the plot so as to obtain one histograms per sample.

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```{r}
ggplot(data=project_long, mapping=aes(x=value, fill=variable)) + 
  geom_histogram(bins=50)
```

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**4- By default, geom_histogram produces a stacked histogram.**
<br>
Change the "position" argument to "dodge".

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```{r}
hist1 <- ggplot(data=project_long, mapping=aes(x=value, fill=variable)) + 
  geom_histogram(position="dodge")
```
  
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**5- A bit messy ?? Run the following:**

```{r}
hist2 <- ggplot(data=project_long, mapping=aes(x=value, fill=variable)) + 
  geom_histogram(bins=50) + 
  facet_grid(~variable)
```

**facet_grid()** is another easy way to split the views!

**6- Change the default colors with scale_fill_manual().**
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You can try the rainbow() function for coloring.

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```{r}
hist3 <- hist2 + scale_fill_manual(values=rainbow(8))
```

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**7- Zoom in the plots: set the x-axis limits from from 6 to 13.**
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Add the **xlim()** layer.

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```{r}
hist4 <- hist3 + xlim(6, 13)
```

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**8- Change the default theme to theme_minimal()**

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```{r}
hist5 <- hist4 + theme_minimal()
```

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**9- Save that last plot to a file (format of your choice) with ggsave()**

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```{r}
ggsave(filename="myhistograms.png", plot=hist5, device="png", width=20)
```

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