Skip to content

Latest commit

 

History

History
125 lines (90 loc) · 4.53 KB

Exercises.md

File metadata and controls

125 lines (90 loc) · 4.53 KB
Raw

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.

data(iris)
str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

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.

### 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)

## [1] "SHOULDER" "SHOULDER" "EYEBALL"  "KNEE"

### 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.

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?

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'
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?