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hist_box_bar.Rmd
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hist_box_bar.Rmd
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---
title: "R Notebook"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*.
```{r}
#Load the ggplot2 library
```
```{r}
#read in the .csv file of GC% for your 26 scaffolds data set.
gc_26_scaffolds <-
#print the dataframe
gc_26_scaffolds
```
```{r}
#Plot the histogram for the GC% of your 26 scaffolds dataset. What do you notice about the shape of your data?
#Add a line to your plot showing the mean for your dataset
#Add a title, x labels, and y labels to your plot
```
```{r}
#load in data for cymbomonas genome as a whole
cymbo_gc <-
#Print your dataframe
cymbo_gc
```
```{r}
#Plot a histogram for the GC% of the entire Cymbomonas tetramitiformis genome.
#Add a line to your plot showing the mean for your dataset
#Add a title, x labels, and y labels to your plot
```
```{r}
#This block of code will allow you to create a boxplot showing the interquartile range for the GC% of the 26 suspected viral scaffolds and the whole Cymbomonas genome.
ggplot() +
geom_boxplot(data=cymbo_gc,
aes(x="Cymbomonas tetramitiformis Genome",
y=cymbo_gc$V4),
fill="blue",
alpha=0.5) +
geom_boxplot(data=gc_26_scaffolds,
aes(x="26 Suspected Viral Scaffolds",
y=gc_26_scaffolds$V2),
fill="green",
alpha=0.5)
# Add code for a title, x-axis label, and y-axis label.
```
```{r}
#Calculate the mean and standard deviation for the 26 Scaffolds dataframe
#Calculate the mean and standard deviation for the Cymbomonas tetramitiformis genome
```
```{r}
#The following block of code will create a new dataframe of summary statistics that you can use to create a bar plot with error bars.
#Please include comments identifying whether each of the following lines creates a variable, a vector, or a dataframe.
mean_cymbo <- mean(cymbo_gc$V4)
mean_26 <- mean(gc_26_scaffolds$V2)
means <- c(mean_cymbo,mean_26)
sd_cymbo <- sd(cymbo_gc$V4)
sd_26 <- sd(gc_26_scaffolds$V2)
stdevs <- c(sd_cymbo, sd_26)
names <- c("Cymbomonas tetramitiformis Genome", "26 Suspected Viral Sequences")
summary_df <- data.frame(means, stdevs, names)
summary_df
```
```{r}
#This code creates a barplot with error bars showing the differences between the means of the 26 scaffolds dataset and the Cymbomonas tetramitiformis dataset.
#Can you figure out from previous plot types how to adjust the colors and axis titles of this plot?
ggplot(data=summary_df, aes(x=names, y=means)) +
geom_bar(stat="identity") +
#Can you figure out what this line of code is doing?
geom_errorbar(aes(ymax = means + stdevs, ymin = means-stdevs))
```
Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.