Exercises.Rmd

---
title: "Exercises"
author: "Drs. Sarangan Ravichandran and Randall Johnson"
output: md_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(lubridate)
```

## <a name="tibbleEx"></a>Tibble Exercises

### Create a new tibble

Create a new tibble called `tbl1` with 5 observations and the following variables:

* name
* age
* height
* weight
* smoker (TRUE or FALSE)
* Male (TRUE or FALSE)

Once you have created `tbl1`, add a new dummary variable, `agecat`, that categorizes age by decade (i.e. `agecat = 0` when `age < 10`, `agecat = 1` when `10 <= age < 20`, ...)

### Conversion of a data.frame to a data_frame

Convert the iris data set to a tibble.

```{r tibble conversion}
data(iris)
str(iris)
```

How can you verify that your modified `iris` data set is formatted as a tibble?

### NEISS data 

The datset that we will be using for this section comes from the  
National Electronic Injury Surveillance System (NEISS)
https://www.cpsc.gov/research--statistics/neiss-injury-data 
Here is a short description of the data file from the NEISS. 

*"Each record (case) is separated by a carriage return/line feed, and the fields (parameters and narrative) are separated by a tab character, which you can specify as the delimiter when importing into a spreadsheet or database."*

Read the data file, **nss15.tsv** file from the sub-folder/directory, **Data** and call the data as **nss15** variable.

**Hint:** You could use the File --> Import --> Data DataSet option to read the file. The data is in a tab-separated format.

**Hint:** Watch out for the data type choices that are suggested to you and be sure to select the appropriate ones

**Hint:** Please watch out for any warnings or issues while R/Rstudio  is reading the files. If you spot any errors, think of how to fix it. After fixing the problems (if any), go back and read the file. 

```{r load NEISS data}
### Setup
# this will create look-up variables so you can make better sense of the codes in the data set
source('Data/NEISSlabels.R')

# for example, if you want to know what body parts these codes indicate...
codes <- c(30, 30, 77, 35)
get_label(codes, body_part_lab)


### Import data code goes here


### mutate code converting codes into human readable labels goes here


### further exploration to answer questions goes here

```
 
Answer the following questions.

- How many cases are reported in this dataset?
- How many covariates this dataset has? 
- Access CPSC Case # 150620565 and report the following things:
- What is the age of the patient? 
- What is the Race, weight, Stratum, Sex, Race and Diagnosis
- How many are more than 100 years old?  
- From the reported cases, get the CPSC case number and age for the 20th entry.


## <a name="timeEx"></a>Date/Time Exercises

Answer the following questions using the NEISS data set that you imported above.

- Report the number of cases for the month of May? 
    - Tell us how many cases were reported for May 13 - May 16, 2015? 
    - Use this information to answer the following questions. 
- How many were children ( < 5 years)? 
- provide the proportion of male/female? 
- What was the race distribution? 

## <a name="regEx"></a>Regular Expressions Exercises

* `regexpr()` returns the position in the string of the pattern. It will also return the length of 
the pattern matched. Use `regexpr()` to locate all species names with the pattern 'sa'.
* Identify all species that end with the letter 's'

```{r regex1}
species <- c("Arabidopsis_thaliana", "Bos_taurus", "Caenorhabditis_elegans", "Danio_rerio", 
             "Dictyostelium_discoideum", "Drosophila_melanogaster", "Escherichia_coli",
             "Homo_sapiens", "Mus_musculus", "Mycoplasma_pneumoniae",
             "Oryza_sativa","Plasmodium_falciparum","Pneumocystis_carinii","Rattus_norvegicus",
             "Saccharmomyces_cerevisiae","Schizosaccharomyces_pombe","Takifugu_rubripes","Xenopus_laevis",
             "Zea_mays", "Hepatitis_C_Virus")

# your regexpr code goes here
```

* Find out what `regexec()` and  `gregexpr()` do. How are they different from the other regular expression functions we have covered?