Unit 1
R and RStudio
What is R?
- R is a free software environment originally designed for statistical computing and graphics. It has grown considerably in capability and usability over the years. Now it is an excellent “jackknife” for anything that you might want to do with data.
- R is open-source and open-development, which means that thousands of users contribute improvements and enhancements, without changing the basic functionality.
Why use R?
- It’s free!
- Statistics plus data management/representation/analysis
- Great graphics
- Ever-expanding capabilities (just about any statistics under the sun)
- Can easily import data from most other major programs (including SPSS, SAS, Stata, Excel)
- Beautifully-formatted output (R notebooks, R bookdown)
- Not too hard to learn (especially after this workshop!)
- Comprehensive help resources (internal help;r-bloggers.com Google search)
- Excellent video tutorials: MarinStatsLectures
Check out the following opinions: Opinion 1. Opinion 2. Opinion 3. Opinion 4.
Capabilities of R:
R’s capabilities are simply amazing. Check out:
What is Rstudio?
RStudio is a graphical user interface for R which includes a set of integrated tools designed to help you be more productive with R. It includes:
- Console, which displays executed R commands
- An editor (with syntax highlighting). Code may be executed directly from the editor.
- History viewer (record of past commands)
- Environment viewer (displays all variables in your current workspace)
- Package installer (adds new capabilities to R)
- Plot viewer / exporter
- Help window
Downloading and installing R and Rstudio (windows)
Exercises:
- Install R (either from USB or from the Internet)
- Install Rstudio (either from USB or from the Internet)
Note: Once R and Rstudio are installed, it is not necessary to start R, because Rstudio will start it
1.3 Measures of Central Tendency
1.3.1 Introduction
A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. They are also classed as summary statistics. The mean (often called the average) is most likely the measure of central tendency that you are most familiar with, but there are others, such as the median and the mode.
The mean, median and mode are all valid measures of central tendency, but under different conditions, some measures of central tendency become more appropriate to use than others. In the following sections, we will look at the mean, mode and median, and learn how to calculate them and under what conditions they are most appropriate to be used.
1.3.2 Mean:
Mean (Arithmetic) The mean (or average) is the most popular and well known measure of central tendency. It can be used with both discrete and continuous data, although its use is most often with continuous data. The mean is equal to the sum of all the values in the data set divided by the number of values in the data set.
The mean is essentially a model of your data set. It is the value that is most common. You will notice, however, that the mean is not often one of the actual values that you have observed in your data set. However, one of its important properties is that it minimises error in the prediction of any one value in your data set. That is, it is the value that produces the lowest amount of error from all other values in the data set.
An important property of the mean is that it includes every value in your data set as part of the calculation. In addition, the mean is the only measure of central tendency where the sum of the deviations of each value from the mean is always zero.
The median is the middle score for a set of data that has been arranged in order of magnitude. The median is less affected by outliers and skewed data.
Week 2
Import files into R
Consider the following data file on the body temperatures of ten US males.
https://www.amazon.com/clouddrive/share/RJLhFeGmPR8j4b4dQDUzjuxbnhDLhIKqabQvJCKDnER
Watch the following video on how to import a .csv file into R. https://www.amazon.com/clouddrive/share/bcK8ZluX3i45PvJaQ5Omwc0ii53iVzRJx1jcrYIAbp9
This week we cover the following topics:
Histograms and numerical summaries
A histogram is a visual representation of the distribution of a dataset. The shape of a histogram allows you to easily see where most of the data is situated. In particular, you can see where the middle of distribution is located, how closely the data lie around the middle, and where possible outliers are to be found. As shown in the figures below, a histogram consists of an x-axis, a y-axis and bars of different heights. The x-axis is divided into intervals (called “bins”), and on each bin a vertical bar is constructed whose height represents the number of data values within that bin. Note that histograms (unlike bar charts) don’t have gaps between the bars (if it looks like there’s a gap, that’s because that particular bin has no data in it).
Example: Suppose you are interested in the distribution of ages for employees working in a certain office. The following data is available: 36, 25, 38, 46, 55, 68, 72, 55, 36, 38, 67, 45, 22, 48, 91, 46, 52, 61, 58, 55. We use R to construct a histogram to represent the distribution of the data.
age<-c(36, 25, 38, 46, 55, 68, 72, 55, 36, 38, 67, 45, 22, 48, 91, 46, 52, 61, 58, 55)
hist(age)
The output appears under the ‘Plots’ tab, and looks like this:
[Histogram of age] The ‘hist’ command has many options that enable the user to change the display. For example, the user can control the number of bins by using the ‘breaks’ option. The title of the histogram by using the ‘main’ option, and the x- and y-axis labels using the ‘xlab’ and ‘ylab’ options.
Example: The following command creates a histogram with 7 nonempty bins, with title “Age of Employees” and x label “Employee ages”:
hist(age,breaks=7,main="Age of Employees",xlab="Employee ages")
The output appears under the ‘Plots’ tab, and looks like this:
[Histogram of age] ### XY plots {#xyplots} ###### Top of page
The command ‘xyplot’ can be used to plot one variable against another. The command uses the ‘lattice’ package, so before using it you must load the package.
Example: Load a new package called ‘lattice’.
If you get an error message, it probably means you haven’t installed ‘lattice’. In this case, go back to “R_RStudioWindows” and follow the instructions found in the section ‘Packages window’.
To demonstrate ‘xyplot’ we will be using data from the ‘mosaicData package’, so you must load this package as well.
Week 4
Install the package ‘mosaic’:
install.packages('mosaic')
Install the package ‘mosaicData’:
install.packages('mosaicData')
Load the package ‘mosaic’:
require(mosaic)
Load the package ‘mosaicData’:
require(mosaicData)
We set the default number of digits to 2:
options(digits =2)
Consider the HELPrct (Health Evaluation and Linkage to Primary Care) data set that can be found under the “mosaicData” package. The HELP study was a clinical trial for adult inpatients recruited from a detoxification unit. Patients with no primary care physician were randomized to receive a multidisciplinary assessment and a brief motivational intervention or usual care, with the goal of linking them to primary medical care.
This is a data frame with 453 observations on the following variables.
age subject age at baseline (in years)
anysub use of any substance post-detox: a factor with levels no yes
cesd Center for Epidemiologic Studies Depression measure at baseline (high scores indicate more depressive symptoms)
d1 lifetime number of hospitalizations for medical problems (measured at baseline)
daysanysub time (in days) to first use of any substance post-detox
dayslink time (in days) to linkage to primary care
drugrisk Risk Assessment Battery drug risk scale at baseline
e2b number of times in past 6 months entered a detox program (measured at baseline)
female 0 for male, 1 for female
sex a factor with levels male female
g1b experienced serious thoughts of suicide in last 30 days (measured at baseline): a factor with levels no yes
homeless housing status: a factor with levels housed homeless
i1 average number of drinks (standard units) consumed per day, in the past 30 days (measured at baseline)
i2 maximum number of drinks (standard units) consumed per day, in the past 30 days (measured at baseline)
id subject identifier
indtot Inventory of Drug Use Consequences (InDUC) total score (measured at baseline)
linkstatus post-detox linkage to primary care (0 = no, 1 = yes)
link post-detox linkage to primary care: no yes
mcs SF-36 Mental Component Score (measured at baseline, lower scores indicate worse status)
pcs SF-36 Physical Component Score (measured at baseline, lower scores indicate worse status)
pss_fr perceived social support by friends (measured at baseline, higher scores indicate more support)
racegrp race/ethnicity: levels black hispanic other white
satreat any BSAS substance abuse treatment at baseline: no yes
sexrisk Risk Assessment Battery sex risk score (measured at baseline)
substance primary substance of abuse: alcohol cocaine heroin
treat randomized to HELP clinic: no yes
We find the mean of the cesd (Center for Epidemiologic Studies Depression measure at baseline (high scores indicate more depressive symptoms)) variable:
mean(HELPrct$cesd)
Which is equal to 33.
The standard deviation is:
sd(HELPrct$cesd)
Which works out to be 13.
The variance is:
var(HELPrct$cesd)
157
We can also calculate the median:
median(HELPrct$cesd)
which is 34.
We can use the “summary” command to print out the min, max, mean, median, and quantiles:
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Loading required package: ggformula
## Loading required package: ggplot2
## Loading required package: ggstance
##
## Attaching package: 'ggstance'
## The following objects are masked from 'package:ggplot2':
##
## geom_errorbarh, GeomErrorbarh
##
## New to ggformula? Try the tutorials:
## learnr::run_tutorial("introduction", package = "ggformula")
## learnr::run_tutorial("refining", package = "ggformula")
## Loading required package: mosaicData
## Loading required package: Matrix
##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.
##
## Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.
##
## Attaching package: 'mosaic'
## The following object is masked from 'package:Matrix':
##
## mean
## The following object is masked from 'package:ggplot2':
##
## stat
## The following objects are masked from 'package:dplyr':
##
## count, do, tally
## The following objects are masked from 'package:stats':
##
## binom.test, cor, cor.test, cov, fivenum, IQR, median,
## prop.test, quantile, sd, t.test, var
## The following objects are masked from 'package:base':
##
## max, mean, min, prod, range, sample, sum
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 25.00 34.00 32.85 41.00 60.00
Min. 1st Qu. Median Mean 3rd Qu. Max.
1 25 34 33 41 60
