wrangling_practical_ans.R

# Wrangling practical - Answer Key

## Reading in and inspecting data

# 2. Read the csv file into your environment and assign it to a variable called `animals`. Be sure to check that your row names are the different animals.
animals <- read.csv("data/animals.csv")

# 3. Check to make sure that `animals` is a dataframe.
class(animals)

# 4. How many rows are in the `animals` dataframe? How many columns?
nrow(animals)
ncol(animals)

## Data wrangling

# 1. Extract the `speed` value of 40 km/h from the `animals` dataframe.
animals[1,1]
animals[which(animals$speed == 40), 1]
animals[which(animals$speed == 40), "speed"]
animals$speed[which(animals$speed == 40)]

# 2. Return the rows with animals that are the `color` Tan.
animals[c(2,5),]
animals[which(animals$color == "Tan"),]

# 3. Return the rows with animals that have `speed` greater than 50 km/h and output only the `color` column. Keep the output as a data frame.  
animals[which(animals$speed > 50), "color", drop =F]

# 4. Change the color of "Grey" to "Gray". 
animals$color[which(animals$color == "Grey")] <- "Gray"
animals[which(animals$color == "Grey"), "color"] <- "Gray"

# 5. Create a list called `animals_list` in which the first element contains the speed column of the `animals` dataframe and the second element contains the color column of the `animals` dataframe. 
animals_list <- list(animals$speed, animals$color)

# 6. Give each element of your list the appropriate name (i.e speed and color).
names(animals_list) <- colnames(animals)

## The %in% operator, reordering and matching

# 1. Read in the project summary file ("project-summary.txt") to a variable called proj_summary; this file contains quality metric information for an RNA-seq dataset. Be sure to specify the row names are in column 1 and the separator is a tab.
proj_summary <- read.table("data/project-summary.txt", header = TRUE, row.names = 1)

# 2. We have obtained batch information for the control samples in this dataset. Copy and paste the code below to create a dataframe of control samples with the associated batch information:
ctrl_samples <- data.frame(row.names = c("sample3", "sample10", "sample8", "sample4", "sample15"), date = c("01/13/2018", "03/15/2018", "01/13/2018", "09/20/2018","03/15/2018"))

# 3. How many of the control samples are also in the `proj_summary` dataframe? Use the %in% operator to check.
length(which(rownames(ctrl_samples) %in% rownames(proj_summary)))

# 4. Keep only the rows in `proj_summary` which correspond to control samples. Do this with the %in% operator. Save it to a variable called `proj_summary_ctrl`.
proj_summary_ctrl <- proj_summary[which(rownames(proj_summary) %in% rownames(ctrl_samples)),]


# 5. We would like to add in the batch information for the samples in `proj_summary_ctrl`. Find the rows that match in `ctrl_samples`.
m <- match(rownames(proj_summary_ctrl), rownames(ctrl_samples))

# 6. Use `cbind()` to add a column called `batch` to the `proj_summary_ctrl` dataframe. Assign this new dataframe back to `proj_summary_ctrl`.
proj_summary_ctrl <- cbind(proj_summary_ctrl, batch=ctrl_samples[m,])

## BONUS: Using `map_lgl()` 

# 1. Subset `proj_summary` to keep only the "high" and "low" samples based on the treament column. Save the new dataframe to a variable called `proj_summary_noctl`
proj_summary_noctl <- proj_summary[which(proj_summary$treatment != "control"), ]

# 2. Further subset the dataframe to remove the non-numeric columns "Quality_format", and "treatment". Try to do this using the `map()` function in addition to `is.numeric()`. Save the new dataframe back to `proj_summary_noctl`

keep <- map_lgl(proj_summary_noctl, is.numeric)
proj_summary_noctl <- proj_summary_noctl[,keep]