R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

The examples in this document are based on a workshop that I took at JMM, on teaching Statistics with R and RStudio

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

Text

Text can be decorated with bold or italics. It is also possible to

create links
include mathematics like $e=mc^2$ or \[y = \beta_0 + \beta_1 x_1 + \beta_2 x_2\]

Be sure to put a space after the * when you are creating bullets and a space after # when creating section headers, but not between $ and the mathematical formulas.

We can create vector using c() or concatenate command also.

a <- c(1.8, 4.5)   #numeric
b <- c(1 + 2i, 3 - 6i) #complex
d <- c(23, 44)   #integer
e <- vector("logical", length = 5)
class(qt) # to check the class of a vector

## [1] "function"

my_list <- list(22, "ab", TRUE, 1 + 2i) #  Vector with elements of differenttypes
my_matrix <- matrix(1:6, nrow=3, ncol=2) #matrix
dim(my_matrix)

## [1] 3 2

bar <- 0:5
class(bar)

## [1] "integer"

as.numeric(bar)

## [1] 0 1 2 3 4 5

class(bar)

## [1] "integer"

You can embed an R code chunk like this:

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

You can also embed plots, for example:

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.

head(cars)

##   speed dist
## 1     4    2
## 2     4   10
## 3     7    4
## 4     7   22
## 5     8   16
## 6     9   10

knitr settings to control how R chunks work.

require(knitr) opts_chunk$set( tidy=FALSE, # display code as typed size=“small” # slightly smaller font for code )

The most important template is

goal(y~x|z,data=mydata,groups=…)
What you want R to do is Goal : This determines the function to use(favstat, mean, SD, lm)
What must R know to do that: This determines the inputs to the function, must identify variables and data frame
it produces single and multiple variable graphical summaries
it produces single and multiple variable numerical summaries
linear models
y: is y-axis
x: is x-axis variable
z: is conditioning variable (seperate panels)
groups: conditioning variable (overlaid graphs)

Numerical Summaries one variable

favstats(~Sepal.Length, data=iris)

##  min  Q1 median  Q3 max     mean        sd   n missing
##  4.3 5.1    5.8 6.4 7.9 5.843333 0.8280661 150       0

Numerical summaries two variables

favstats(age~substance|sex,data=HELPrct)

##              sex min Q1 median   Q3 max     mean       sd   n missing
## 1 alcohol.female  23 33   37.0 45.0  58 39.16667 7.980333  36       0
## 2 cocaine.female  24 31   34.0 38.0  49 34.85366 6.195002  41       0
## 3  heroin.female  21 29   34.0 39.0  55 34.66667 8.035839  30       0
## 4   alcohol.male  20 32   38.0 42.0  58 37.95035 7.575644 141       0
## 5   cocaine.male  23 30   33.0 37.0  60 34.36036 6.889772 111       0
## 6    heroin.male  19 27   32.5 39.0  53 33.05319 7.973568  94       0
## 7         female  21 31   35.0 40.5  58 36.25234 7.584858 107       0
## 8           male  19 30   35.0 40.0  60 35.46821 7.750110 346       0

mean(age~substance|sex,data=HELPrct,.format="table") #tabular form

##   substance    sex     mean
## 1   alcohol female 39.16667
## 2   alcohol   male 37.95035
## 3   cocaine female 34.85366
## 4   cocaine   male 34.36036
## 5    heroin female 34.66667
## 6    heroin   male 33.05319

Numerical summaries: Tables

tally(sex~substance,data=HELPrct)

##         substance
## sex      alcohol cocaine heroin
##   female      36      41     30
##   male       141     111     94

summary(sex~substance,data=HELPrct)

##  Length   Class    Mode 
##       3 formula    call

Next replace summary name by plot name

bwplot(age~substance|sex,data=HELPrct, .format="table")

# Graphical Summaries one variable

histogram(~age,data=HELPrct)

densityplot(~age,data=HELPrct)

bwplot(~age,data=HELPrct)

qqmath(~age,data=HELPrct)

freqpolygon(~age,data=HELPrct)

bargraph(~age,data=HELPrct)

# graphical summaries two variables * i1 average numebr of drinks consumed per day in past 30 days

xyplot(i1~age,data=HELPrct)

bwplot(age~substance,data=HELPrct)

# add groups = group to overlay * use y~x|z to create multipanel plots

densityplot(~age|sex,data=HELPrct,groups=substance, auto.key=TRUE)

Your turn

Create a fancy plot using kidsFeet data or faculty salary data

names(KidsFeet) # 4th graders feet

## [1] "name"       "birthmonth" "birthyear"  "length"     "width"     
## [6] "sex"        "biggerfoot" "domhand"

#?KidsFeet

some other things

Mosaic package includes datasets, xtras: xchisq.test(data name),xpnorm(),mplot(),xqqmath

xpnorm( 700, mean=500, sd=100)

## 
## If X ~ N(500, 100), then 
## 
##  P(X <= 700) = P(Z <= 2) = 0.9772499
##  P(X >  700) = P(Z >  2) = 0.02275013

## [1] 0.9772499

xpnorm( c(300, 700), mean=500, sd=100)

## 
## If X ~ N(500, 100), then 
## 
##  P(X <= 300) = P(Z <= -2) = 0.02275013
##      P(X <= 700) = P(Z <=  2) = 0.97724987
##  P(X >  300) = P(Z >  -2) = 0.97724987
##      P(X >  700) = P(Z >   2) = 0.02275013

## [1] 0.02275013 0.97724987

Modelling

linear models lm(),glm() # linear models

a<-lm(age~substance*sex, data=HELPrct)
plot(a)

Bare_Basics2R_viaRmarkdown

TZaihra

February 27, 2017

R Markdown

The examples in this document are based on a workshop that I took at JMM, on teaching Statistics with R and RStudio

Including Plots

Text

We can create vector using c() or concatenate command also.

You can embed an R code chunk like this:

You can also embed plots, for example:

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.

knitr settings to control how R chunks work.

The most important template is

Numerical Summaries one variable

Numerical summaries two variables

Numerical summaries: Tables

Next replace summary name by plot name

Your turn

some other things

Modelling

Bare_Basics2R_viaRmarkdown

TZaihra

February 27, 2017

R Markdown

The examples in this document are based on a workshop that I took at JMM, on teaching Statistics with R and RStudio

Including Plots

Text

We can create vector using c() or concatenate command also.

You can embed an R code chunk like this:

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

knitr settings to control how R chunks work.

The most important template is

Numerical Summaries one variable

Numerical summaries two variables

Numerical summaries: Tables

Next replace summary name by plot name

Your turn

some other things

Modelling

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.