## Homework 4

Enter your name and EID here

This homework is due on Feb. 13, 2018 at 7:00pm. Please submit as a PDF file on Canvas.

Problem 1: (4 pts) The Titanic data set contains the information about the passengers of the ocean liner Titanic.

Titanic
## , , Age = Child, Survived = No
##
##       Sex
## Class  Male Female
##   1st     0      0
##   2nd     0      0
##   3rd    35     17
##   Crew    0      0
##
## , , Age = Adult, Survived = No
##
##       Sex
## Class  Male Female
##   1st   118      4
##   2nd   154     13
##   3rd   387     89
##   Crew  670      3
##
## , , Age = Child, Survived = Yes
##
##       Sex
## Class  Male Female
##   1st     5      1
##   2nd    11     13
##   3rd    13     14
##   Crew    0      0
##
## , , Age = Adult, Survived = Yes
##
##       Sex
## Class  Male Female
##   1st    57    140
##   2nd    14     80
##   3rd    75     76
##   Crew  192     20

We have extracted data from Titanic about the passengers that survived. We placed that information into two data-frames, one Adult and one Child. Using the dplyr and tidyr packages, make these data-frames tidy and then combine them into a single data-frame. Make sure that your final data-frame has an Age column indicating which data-frame the observations originally came from. HINT: You can use the bind_rows function to add rows from one data-frame onto another as long as both data-frames have identical column names.

Adult <- read.table(text="
Class  Male Female
1st    57    140
2nd    14     80
3rd    75     76
Crew  192     20

Class  Male Female
1st     5      1
2nd    11     13
3rd    13     14
Crew    0      0

# R code goes here

Using the data-frame you created above, compute the total counts for each age group (i.e., the sum of the counts for each age).

# R code goes here

Problem 2: (3 pts) Recall that the Alfalfa data set contains the height of alfalfa sprouts after four days (Ht4) grown indoors in different acidic environments (Acid). The column Row contains information on the amount of daylight the plants received. The rows were indicated with values a through e, where a indicates the row farthest from the window and e indicates the row closest to the window. We have created a new data-frame (Row_numbers), that contains row information as numbers. Using one of the dplyr join functions, combine the data-frames Alfalfa and Row_numbers so that there is an additional Row_num column and all of the original columns and rows in Alfalfa are retained. Which join function is most appropriate to use and why?

Alfalfa <- read.csv("http://wilkelab.org/classes/SDS348/data_sets/Alfalfa.csv")
head(Alfalfa)
##    Ht4   Acid Row
## 1 1.45  water   a
## 2 2.79  water   b
## 3 1.93  water   c
## 4 2.33  water   d
## 5 4.85  water   e
## 6 1.00 1.5HCl   a
Row_numbers <- data.frame(Row=c("a","b","c","d","e","f","g"),
Row_num=c(1:7))
Row_numbers
##   Row Row_num
## 1   a       1
## 2   b       2
## 3   c       3
## 4   d       4
## 5   e       5
## 6   f       6
## 7   g       7
# R code goes here

Problem 3: (3 pts) Make up your own data set which is not tidy and input it into R via the text option of read.table(). First, explain why it is not tidy. Then, using dplyr and/or tidyr convert it into a tidy data set.
# R code goes here
# R code goes here