R Markdown

  • Produce high quality documents, reports, presentations and dashboards to share analyses
  • Fully reproducbile
  • Can include multiple languages, R, Python, SQL
  • Variety of output types: https://rmarkdown.rstudio.com/gallery.html
  • Interactive or static

This page 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.

To install from CRAN:

install.packages('rmarkdown')
install.packages('knitr')

library(rmarkdown)
library(knitr)

Resources

This guide is a compilation of material from the following sources:
Cheatsheet: https://www.rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf
Reference Guide: https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf

Knit a document

Open ‘R Markdown’ under ‘File’, ‘New File’ in the main menu. Select ‘Document’ and ‘HTML’ output. To generate the output from an Rmd you will click the Knit button. This will produce an HTML document that includes both content as well as the output of any embedded R code chunks.

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

Including Plots

You can also embed plots, for example:

Code chunks

Note that the echo = FALSE parameter was added to the previous code chunk header to prevent printing of the R code that generated the plot. Output can be customized with various knitr options set in the {} or a chunk header.

Options:
- echo = TRUE/FALSE; display code in output document
- error = TRUE/FALSE; whether or not to continue rendering when an error occurs
- eval = TRUE/FALSE; run code in chunk
- include = TRUE/FALSE; include code in output
- fig.align = ‘left’,‘right’,‘or ’center’
- fig.height or fig.width = plot dimensions in inches - warning = TRUE/FALSE; display code warnings in document
- message = TRUE/FALSE; include package messages etc in document
- results = TRUE/FALSE; ‘asis’, ‘hide’,‘or’hold’
- tidy = TRUE/FALSE - tidy code for display

More at: https://yihui.name/knitr/options/

Global options

Chunk options can also be set for the entire document. To do so, include in a chunk:

knitr::opts_chunk$set(error = TRUE, eval = TRUE, message = FALSE, warning = FALSE)

Shortcut

To quickly insert a chunk:
- press keyboard shortcut Cmd + Option = I (windows: Ctrl + Alt + I)

Sharing analyses

Rmd is an very functional way to share results and statistical methods. Code chunks are easily annotated and used to compile analyses with visualizations. How much or how little code is in the resulting output can be configured using chunk options.

Write a chunk that loads the ggplot2 package & set chunk options to ‘include = FALSE’

Read in dust dataset

library(readxl)
list.files('Mia/RdataDust')
##  [1] "DustFungi.guilds.txt"             "DustFungi.otu_table.taxonomy.txt"
##  [3] "DustFungi.otu_table.txt"          "filtered_table_w_metadata.csv"   
##  [5] "filtered_table_w_metadata.txt"    "filtered_table_w_metadata.xlsx"  
##  [7] "RDust_ScriptsEarly20180704.R"     "SierraMap5 copy.txt"             
##  [9] "SierraMap5.txt"                   "SierraMap6.csv"                  
## [11] "SierraMap6.txt"                   "SierraMap6.xlsx"                 
## [13] "SierraMap6.xltx"                  "unweighted_unifrac_dm.txt"       
## [15] "weighted_unifrac_dm.txt"
#data<-read.csv()

Text syntax

Reference Guide: https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf

Tables

You can use familiar command so include the structure of your data

data(iris)
head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
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 ...
summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 
dput(iris)
## structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 
## 5, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 
## 5.4, 5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 
## 5.5, 4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1, 
## 4.6, 5.3, 5, 7, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 
## 5, 5.9, 6, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 
## 6.1, 6.4, 6.6, 6.8, 6.7, 6, 5.7, 5.5, 5.5, 5.8, 6, 5.4, 6, 6.7, 
## 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 
## 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 
## 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 
## 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6, 
## 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9), Sepal.Width = c(3.5, 
## 3, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3, 3, 4, 
## 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3, 3.4, 3.5, 
## 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3, 3.4, 3.5, 
## 2.3, 3.2, 3.5, 3.8, 3, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 
## 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2, 3, 2.2, 2.9, 2.9, 3.1, 3, 2.7, 
## 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3, 2.8, 3, 2.9, 2.6, 2.4, 
## 2.4, 2.7, 2.7, 3, 3.4, 3.1, 2.3, 3, 2.5, 2.6, 3, 2.6, 2.3, 2.7, 
## 3, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3, 2.9, 3, 3, 2.5, 2.9, 2.5, 
## 3.6, 3.2, 2.7, 3, 2.5, 2.8, 3.2, 3, 3.8, 2.6, 2.2, 3.2, 2.8, 
## 2.8, 2.7, 3.3, 3.2, 2.8, 3, 2.8, 3, 2.8, 3.8, 2.8, 2.8, 2.6, 
## 3, 3.4, 3.1, 3, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3, 2.5, 3, 3.4, 
## 3), Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 
## 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 
## 1.5, 1, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 
## 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 
## 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 
## 3.5, 4.2, 4, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4, 4.9, 
## 4.7, 4.3, 4.4, 4.8, 5, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 
## 4.7, 4.4, 4.1, 4, 4.4, 4.6, 4, 3.3, 4.2, 4.2, 4.2, 4.3, 3, 4.1, 
## 6, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 
## 5, 5.1, 5.3, 5.5, 6.7, 6.9, 5, 5.7, 4.9, 6.7, 4.9, 5.7, 6, 4.8, 
## 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 
## 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5, 5.2, 5.4, 5.1), Petal.Width = c(0.2, 
## 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 
## 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 
## 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 
## 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 
## 1.3, 1.5, 1.3, 1.6, 1, 1.3, 1.4, 1, 1.5, 1, 1.4, 1.3, 1.4, 1.5, 
## 1, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1, 
## 1.1, 1, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 
## 1, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 
## 1.7, 1.8, 1.8, 2.5, 2, 1.9, 2.1, 2, 2.4, 2.3, 1.8, 2.2, 2.3, 
## 1.5, 2.3, 2, 2, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2, 2.2, 
## 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 
## 1.9, 2, 2.3, 1.8), Species = structure(c(1L, 1L, 1L, 1L, 1L, 
## 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
## 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
## 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
## 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 
## 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
## 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
## 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
## 3L), .Label = c("setosa", "versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length", 
## "Sepal.Width", "Petal.Length", "Petal.Width", "Species"), row.names = c(NA, 
## -150L), class = "data.frame")

or using HTML widgets to make them interactive:
http://www.htmlwidgets.org/showcase_datatables.html

install.packages('DT')
library(DT)
datatable(iris)

See the DT gituhub page for more customizations
https://rstudio.github.io/DT/

Include description of data, show structure

Statistical results

fit<-lm(Sepal.Length~Species, data=iris)
fit
## 
## Call:
## lm(formula = Sepal.Length ~ Species, data = iris)
## 
## Coefficients:
##       (Intercept)  Speciesversicolor   Speciesvirginica  
##             5.006              0.930              1.582
summary(fit)
## 
## Call:
## lm(formula = Sepal.Length ~ Species, data = iris)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6880 -0.3285 -0.0060  0.3120  1.3120 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         5.0060     0.0728  68.762  < 2e-16 ***
## Speciesversicolor   0.9300     0.1030   9.033 8.77e-16 ***
## Speciesvirginica    1.5820     0.1030  15.366  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5148 on 147 degrees of freedom
## Multiple R-squared:  0.6187, Adjusted R-squared:  0.6135 
## F-statistic: 119.3 on 2 and 147 DF,  p-value: < 2.2e-16
summary(aov(fit))
##              Df Sum Sq Mean Sq F value Pr(>F)    
## Species       2  63.21  31.606   119.3 <2e-16 ***
## Residuals   147  38.96   0.265                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

broom - Tidy up messy outputs https://cran.r-project.org/web/packages/broom/vignettes/broom.html

install.packages('broom')
library(broom)

tidy - returns a dataframe with results, coefficients

tidy(fit)
##                term estimate  std.error statistic       p.value
## 1       (Intercept)    5.006 0.07280222 68.761639 1.134286e-113
## 2 Speciesversicolor    0.930 0.10295789  9.032819  8.770194e-16
## 3  Speciesvirginica    1.582 0.10295789 15.365506  2.214821e-32

augment - returns a dataframe with fitted values and residuals for original points in the regression

augment(fit)
##     Sepal.Length    Species .fitted    .se.fit .resid .hat    .sigma
## 1            5.1     setosa   5.006 0.07280222  0.094 0.02 0.5164896
## 2            4.9     setosa   5.006 0.07280222 -0.106 0.02 0.5164734
## 3            4.7     setosa   5.006 0.07280222 -0.306 0.02 0.5159156
## 4            4.6     setosa   5.006 0.07280222 -0.406 0.02 0.5154331
## 5            5.0     setosa   5.006 0.07280222 -0.006 0.02 0.5165492
## 6            5.4     setosa   5.006 0.07280222  0.394 0.02 0.5154981
## 7            4.6     setosa   5.006 0.07280222 -0.406 0.02 0.5154331
## 8            5.0     setosa   5.006 0.07280222 -0.006 0.02 0.5165492
## 9            4.4     setosa   5.006 0.07280222 -0.606 0.02 0.5140590
## 10           4.9     setosa   5.006 0.07280222 -0.106 0.02 0.5164734
## 11           5.4     setosa   5.006 0.07280222  0.394 0.02 0.5154981
## 12           4.8     setosa   5.006 0.07280222 -0.206 0.02 0.5162622
## 13           4.8     setosa   5.006 0.07280222 -0.206 0.02 0.5162622
## 14           4.3     setosa   5.006 0.07280222 -0.706 0.02 0.5131663
## 15           5.8     setosa   5.006 0.07280222  0.794 0.02 0.5122666
## 16           5.7     setosa   5.006 0.07280222  0.694 0.02 0.5132807
## 17           5.4     setosa   5.006 0.07280222  0.394 0.02 0.5154981
## 18           5.1     setosa   5.006 0.07280222  0.094 0.02 0.5164896
## 19           5.7     setosa   5.006 0.07280222  0.694 0.02 0.5132807
## 20           5.1     setosa   5.006 0.07280222  0.094 0.02 0.5164896
## 21           5.4     setosa   5.006 0.07280222  0.394 0.02 0.5154981
## 22           5.1     setosa   5.006 0.07280222  0.094 0.02 0.5164896
## 23           4.6     setosa   5.006 0.07280222 -0.406 0.02 0.5154331
## 24           5.1     setosa   5.006 0.07280222  0.094 0.02 0.5164896
## 25           4.8     setosa   5.006 0.07280222 -0.206 0.02 0.5162622
## 26           5.0     setosa   5.006 0.07280222 -0.006 0.02 0.5165492
## 27           5.0     setosa   5.006 0.07280222 -0.006 0.02 0.5165492
## 28           5.2     setosa   5.006 0.07280222  0.194 0.02 0.5162947
## 29           5.2     setosa   5.006 0.07280222  0.194 0.02 0.5162947
## 30           4.7     setosa   5.006 0.07280222 -0.306 0.02 0.5159156
## 31           4.8     setosa   5.006 0.07280222 -0.206 0.02 0.5162622
## 32           5.4     setosa   5.006 0.07280222  0.394 0.02 0.5154981
## 33           5.2     setosa   5.006 0.07280222  0.194 0.02 0.5162947
## 34           5.5     setosa   5.006 0.07280222  0.494 0.02 0.5148958
## 35           4.9     setosa   5.006 0.07280222 -0.106 0.02 0.5164734
## 36           5.0     setosa   5.006 0.07280222 -0.006 0.02 0.5165492
## 37           5.5     setosa   5.006 0.07280222  0.494 0.02 0.5148958
## 38           4.9     setosa   5.006 0.07280222 -0.106 0.02 0.5164734
## 39           4.4     setosa   5.006 0.07280222 -0.606 0.02 0.5140590
## 40           5.1     setosa   5.006 0.07280222  0.094 0.02 0.5164896
## 41           5.0     setosa   5.006 0.07280222 -0.006 0.02 0.5165492
## 42           4.5     setosa   5.006 0.07280222 -0.506 0.02 0.5148144
## 43           4.4     setosa   5.006 0.07280222 -0.606 0.02 0.5140590
## 44           5.0     setosa   5.006 0.07280222 -0.006 0.02 0.5165492
## 45           5.1     setosa   5.006 0.07280222  0.094 0.02 0.5164896
## 46           4.8     setosa   5.006 0.07280222 -0.206 0.02 0.5162622
## 47           5.1     setosa   5.006 0.07280222  0.094 0.02 0.5164896
## 48           4.6     setosa   5.006 0.07280222 -0.406 0.02 0.5154331
## 49           5.3     setosa   5.006 0.07280222  0.294 0.02 0.5159643
## 50           5.0     setosa   5.006 0.07280222 -0.006 0.02 0.5165492
## 51           7.0 versicolor   5.936 0.07280222  1.064 0.02 0.5088329
## 52           6.4 versicolor   5.936 0.07280222  0.464 0.02 0.5150908
## 53           6.9 versicolor   5.936 0.07280222  0.964 0.02 0.5102238
## 54           5.5 versicolor   5.936 0.07280222 -0.436 0.02 0.5152618
## 55           6.5 versicolor   5.936 0.07280222  0.564 0.02 0.5143929
## 56           5.7 versicolor   5.936 0.07280222 -0.236 0.02 0.5161725
## 57           6.3 versicolor   5.936 0.07280222  0.364 0.02 0.5156523
## 58           4.9 versicolor   5.936 0.07280222 -1.036 0.02 0.5092366
## 59           6.6 versicolor   5.936 0.07280222  0.664 0.02 0.5135580
## 60           5.2 versicolor   5.936 0.07280222 -0.736 0.02 0.5128716
## 61           5.0 versicolor   5.936 0.07280222 -0.936 0.02 0.5105881
## 62           5.9 versicolor   5.936 0.07280222 -0.036 0.02 0.5165406
## 63           6.0 versicolor   5.936 0.07280222  0.064 0.02 0.5165217
## 64           6.1 versicolor   5.936 0.07280222  0.164 0.02 0.5163674
## 65           5.6 versicolor   5.936 0.07280222 -0.336 0.02 0.5157851
## 66           6.7 versicolor   5.936 0.07280222  0.764 0.02 0.5125854
## 67           5.6 versicolor   5.936 0.07280222 -0.336 0.02 0.5157851
## 68           5.8 versicolor   5.936 0.07280222 -0.136 0.02 0.5164243
## 69           6.2 versicolor   5.936 0.07280222  0.264 0.02 0.5160777
## 70           5.6 versicolor   5.936 0.07280222 -0.336 0.02 0.5157851
## 71           5.9 versicolor   5.936 0.07280222 -0.036 0.02 0.5165406
## 72           6.1 versicolor   5.936 0.07280222  0.164 0.02 0.5163674
## 73           6.3 versicolor   5.936 0.07280222  0.364 0.02 0.5156523
## 74           6.1 versicolor   5.936 0.07280222  0.164 0.02 0.5163674
## 75           6.4 versicolor   5.936 0.07280222  0.464 0.02 0.5150908
## 76           6.6 versicolor   5.936 0.07280222  0.664 0.02 0.5135580
## 77           6.8 versicolor   5.936 0.07280222  0.864 0.02 0.5114743
## 78           6.7 versicolor   5.936 0.07280222  0.764 0.02 0.5125854
## 79           6.0 versicolor   5.936 0.07280222  0.064 0.02 0.5165217
## 80           5.7 versicolor   5.936 0.07280222 -0.236 0.02 0.5161725
## 81           5.5 versicolor   5.936 0.07280222 -0.436 0.02 0.5152618
## 82           5.5 versicolor   5.936 0.07280222 -0.436 0.02 0.5152618
## 83           5.8 versicolor   5.936 0.07280222 -0.136 0.02 0.5164243
## 84           6.0 versicolor   5.936 0.07280222  0.064 0.02 0.5165217
## 85           5.4 versicolor   5.936 0.07280222 -0.536 0.02 0.5146021
## 86           6.0 versicolor   5.936 0.07280222  0.064 0.02 0.5165217
## 87           6.7 versicolor   5.936 0.07280222  0.764 0.02 0.5125854
## 88           6.3 versicolor   5.936 0.07280222  0.364 0.02 0.5156523
## 89           5.6 versicolor   5.936 0.07280222 -0.336 0.02 0.5157851
## 90           5.5 versicolor   5.936 0.07280222 -0.436 0.02 0.5152618
## 91           5.5 versicolor   5.936 0.07280222 -0.436 0.02 0.5152618
## 92           6.1 versicolor   5.936 0.07280222  0.164 0.02 0.5163674
## 93           5.8 versicolor   5.936 0.07280222 -0.136 0.02 0.5164243
## 94           5.0 versicolor   5.936 0.07280222 -0.936 0.02 0.5105881
## 95           5.6 versicolor   5.936 0.07280222 -0.336 0.02 0.5157851
## 96           5.7 versicolor   5.936 0.07280222 -0.236 0.02 0.5161725
## 97           5.7 versicolor   5.936 0.07280222 -0.236 0.02 0.5161725
## 98           6.2 versicolor   5.936 0.07280222  0.264 0.02 0.5160777
## 99           5.1 versicolor   5.936 0.07280222 -0.836 0.02 0.5117994
## 100          5.7 versicolor   5.936 0.07280222 -0.236 0.02 0.5161725
## 101          6.3  virginica   6.588 0.07280222 -0.288 0.02 0.5159880
## 102          5.8  virginica   6.588 0.07280222 -0.788 0.02 0.5123314
## 103          7.1  virginica   6.588 0.07280222  0.512 0.02 0.5147729
## 104          6.3  virginica   6.588 0.07280222 -0.288 0.02 0.5159880
## 105          6.5  virginica   6.588 0.07280222 -0.088 0.02 0.5164970
## 106          7.6  virginica   6.588 0.07280222  1.012 0.02 0.5095738
## 107          4.9  virginica   6.588 0.07280222 -1.688 0.02 0.4968993
## 108          7.3  virginica   6.588 0.07280222  0.712 0.02 0.5131084
## 109          6.7  virginica   6.588 0.07280222  0.112 0.02 0.5164645
## 110          7.2  virginica   6.588 0.07280222  0.612 0.02 0.5140093
## 111          6.5  virginica   6.588 0.07280222 -0.088 0.02 0.5164970
## 112          6.4  virginica   6.588 0.07280222 -0.188 0.02 0.5163102
## 113          6.8  virginica   6.588 0.07280222  0.212 0.02 0.5162453
## 114          5.7  virginica   6.588 0.07280222 -0.888 0.02 0.5111869
## 115          5.8  virginica   6.588 0.07280222 -0.788 0.02 0.5123314
## 116          6.4  virginica   6.588 0.07280222 -0.188 0.02 0.5163102
## 117          6.5  virginica   6.588 0.07280222 -0.088 0.02 0.5164970
## 118          7.7  virginica   6.588 0.07280222  1.112 0.02 0.5081151
## 119          7.7  virginica   6.588 0.07280222  1.112 0.02 0.5081151
## 120          6.0  virginica   6.588 0.07280222 -0.588 0.02 0.5142051
## 121          6.9  virginica   6.588 0.07280222  0.312 0.02 0.5158904
## 122          5.6  virginica   6.588 0.07280222 -0.988 0.02 0.5099029
## 123          7.7  virginica   6.588 0.07280222  1.112 0.02 0.5081151
## 124          6.3  virginica   6.588 0.07280222 -0.288 0.02 0.5159880
## 125          6.7  virginica   6.588 0.07280222  0.112 0.02 0.5164645
## 126          7.2  virginica   6.588 0.07280222  0.612 0.02 0.5140093
## 127          6.2  virginica   6.588 0.07280222 -0.388 0.02 0.5155299
## 128          6.1  virginica   6.588 0.07280222 -0.488 0.02 0.5149358
## 129          6.4  virginica   6.588 0.07280222 -0.188 0.02 0.5163102
## 130          7.2  virginica   6.588 0.07280222  0.612 0.02 0.5140093
## 131          7.4  virginica   6.588 0.07280222  0.812 0.02 0.5120694
## 132          7.9  virginica   6.588 0.07280222  1.312 0.02 0.5047699
## 133          6.4  virginica   6.588 0.07280222 -0.188 0.02 0.5163102
## 134          6.3  virginica   6.588 0.07280222 -0.288 0.02 0.5159880
## 135          6.1  virginica   6.588 0.07280222 -0.488 0.02 0.5149358
## 136          7.7  virginica   6.588 0.07280222  1.112 0.02 0.5081151
## 137          6.3  virginica   6.588 0.07280222 -0.288 0.02 0.5159880
## 138          6.4  virginica   6.588 0.07280222 -0.188 0.02 0.5163102
## 139          6.0  virginica   6.588 0.07280222 -0.588 0.02 0.5142051
## 140          6.9  virginica   6.588 0.07280222  0.312 0.02 0.5158904
## 141          6.7  virginica   6.588 0.07280222  0.112 0.02 0.5164645
## 142          6.9  virginica   6.588 0.07280222  0.312 0.02 0.5158904
## 143          5.8  virginica   6.588 0.07280222 -0.788 0.02 0.5123314
## 144          6.8  virginica   6.588 0.07280222  0.212 0.02 0.5162453
## 145          6.7  virginica   6.588 0.07280222  0.112 0.02 0.5164645
## 146          6.7  virginica   6.588 0.07280222  0.112 0.02 0.5164645
## 147          6.3  virginica   6.588 0.07280222 -0.288 0.02 0.5159880
## 148          6.5  virginica   6.588 0.07280222 -0.088 0.02 0.5164970
## 149          6.2  virginica   6.588 0.07280222 -0.388 0.02 0.5155299
## 150          5.9  virginica   6.588 0.07280222 -0.688 0.02 0.5133372
##          .cooksd  .std.resid
## 1   2.314478e-04  0.18445277
## 2   2.943127e-04 -0.20799994
## 3   2.452676e-03 -0.60045265
## 4   4.317670e-03 -0.79667900
## 5   9.429743e-07 -0.01177358
## 6   4.066210e-03  0.77313184
## 7   4.317670e-03 -0.79667900
## 8   9.429743e-07 -0.01177358
## 9   9.619280e-03 -1.18913171
## 10  2.943127e-04 -0.20799994
## 11  4.066210e-03  0.77313184
## 12  1.111557e-03 -0.40422629
## 13  1.111557e-03 -0.40422629
## 14  1.305590e-02 -1.38535806
## 15  1.651347e-02  1.55803726
## 16  1.261584e-02  1.36181090
## 17  4.066210e-03  0.77313184
## 18  2.314478e-04  0.18445277
## 19  1.261584e-02  1.36181090
## 20  2.314478e-04  0.18445277
## 21  4.066210e-03  0.77313184
## 22  2.314478e-04  0.18445277
## 23  4.317670e-03 -0.79667900
## 24  2.314478e-04  0.18445277
## 25  1.111557e-03 -0.40422629
## 26  9.429743e-07 -0.01177358
## 27  9.429743e-07 -0.01177358
## 28  9.858272e-04  0.38067913
## 29  9.858272e-04  0.38067913
## 30  2.452676e-03 -0.60045265
## 31  1.111557e-03 -0.40422629
## 32  4.066210e-03  0.77313184
## 33  9.858272e-04  0.38067913
## 34  6.392213e-03  0.96935819
## 35  2.943127e-04 -0.20799994
## 36  9.429743e-07 -0.01177358
## 37  6.392213e-03  0.96935819
## 38  2.943127e-04 -0.20799994
## 39  9.619280e-03 -1.18913171
## 40  2.314478e-04  0.18445277
## 41  9.429743e-07 -0.01177358
## 42  6.706538e-03 -0.99290535
## 43  9.619280e-03 -1.18913171
## 44  9.429743e-07 -0.01177358
## 45  2.314478e-04  0.18445277
## 46  1.111557e-03 -0.40422629
## 47  2.314478e-04  0.18445277
## 48  4.317670e-03 -0.79667900
## 49  2.264081e-03  0.57690548
## 50  9.429743e-07 -0.01177358
## 51  2.965382e-02  2.08784841
## 52  5.639405e-03  0.91049029
## 53  2.434173e-02  1.89162206
## 54  4.979323e-03 -0.85554691
## 55  8.332121e-03  1.10671664
## 56  1.458886e-03 -0.46309420
## 57  3.470564e-03  0.71426393
## 58  2.811363e-02 -2.03290504
## 59  1.154871e-02  1.30294300
## 60  1.418904e-02 -1.44422597
## 61  2.294822e-02 -1.83667868
## 62  3.394707e-05 -0.07064149
## 63  1.072895e-04  0.12558487
## 64  7.045065e-04  0.32181122
## 65  2.957167e-03 -0.65932055
## 66  1.528918e-02  1.49916935
## 67  2.957167e-03 -0.65932055
## 68  4.844792e-04 -0.26686784
## 69  1.825598e-03  0.51803758
## 70  2.957167e-03 -0.65932055
## 71  3.394707e-05 -0.07064149
## 72  7.045065e-04  0.32181122
## 73  3.470564e-03  0.71426393
## 74  7.045065e-04  0.32181122
## 75  5.639405e-03  0.91049029
## 76  1.154871e-02  1.30294300
## 77  1.955351e-02  1.69539570
## 78  1.528918e-02  1.49916935
## 79  1.072895e-04  0.12558487
## 80  1.458886e-03 -0.46309420
## 81  4.979323e-03 -0.85554691
## 82  4.979323e-03 -0.85554691
## 83  4.844792e-04 -0.26686784
## 84  1.072895e-04  0.12558487
## 85  7.525354e-03 -1.05177326
## 86  1.072895e-04  0.12558487
## 87  1.528918e-02  1.49916935
## 88  3.470564e-03  0.71426393
## 89  2.957167e-03 -0.65932055
## 90  4.979323e-03 -0.85554691
## 91  4.979323e-03 -0.85554691
## 92  7.045065e-04  0.32181122
## 93  4.844792e-04 -0.26686784
## 94  2.294822e-02 -1.83667868
## 95  2.957167e-03 -0.65932055
## 96  1.458886e-03 -0.46309420
## 97  1.458886e-03 -0.46309420
## 98  1.825598e-03  0.51803758
## 99  1.830669e-02 -1.64045233
## 100 1.458886e-03 -0.46309420
## 101 2.172613e-03 -0.56513190
## 102 1.626484e-02 -1.54626368
## 103 6.866529e-03  1.00467894
## 104 2.172613e-03 -0.56513190
## 105 2.028442e-04 -0.17267919
## 106 2.682615e-02  1.98581071
## 107 7.463495e-02 -3.31230087
## 108 1.327875e-02  1.39713165
## 109 3.285741e-04  0.21977352
## 110 9.810704e-03  1.20090529
## 111 2.028442e-04 -0.17267919
## 112 9.257912e-04 -0.36890555
## 113 1.177251e-03  0.41599987
## 114 2.065491e-02 -1.74249003
## 115 1.626484e-02 -1.54626368
## 116 9.257912e-04 -0.36890555
## 117 2.028442e-04 -0.17267919
## 118 3.238970e-02  2.18203706
## 119 3.238970e-02  2.18203706
## 120 9.056325e-03 -1.15381097
## 121 2.549802e-03  0.61222623
## 122 2.556885e-02 -1.93871638
## 123 3.238970e-02  2.18203706
## 124 2.172613e-03 -0.56513190
## 125 3.285741e-04  0.21977352
## 126 9.810704e-03  1.20090529
## 127 3.943309e-03 -0.76135826
## 128 6.237880e-03 -0.95758461
## 129 9.257912e-04 -0.36890555
## 130 9.810704e-03  1.20090529
## 131 1.727068e-02  1.59335800
## 132 4.508842e-02  2.57448977
## 133 9.257912e-04 -0.36890555
## 134 2.172613e-03 -0.56513190
## 135 6.237880e-03 -0.95758461
## 136 3.238970e-02  2.18203706
## 137 2.172613e-03 -0.56513190
## 138 9.257912e-04 -0.36890555
## 139 9.056325e-03 -1.15381097
## 140 2.549802e-03  0.61222623
## 141 3.285741e-04  0.21977352
## 142 2.549802e-03  0.61222623
## 143 1.626484e-02 -1.54626368
## 144 1.177251e-03  0.41599987
## 145 3.285741e-04  0.21977352
## 146 3.285741e-04  0.21977352
## 147 2.172613e-03 -0.56513190
## 148 2.028442e-04 -0.17267919
## 149 3.943309e-03 -0.76135826
## 150 1.239864e-02 -1.35003732

glance - returns a dataframe with summary statistics R2 and F-stat

glance(fit)
##   r.squared adj.r.squared     sigma statistic      p.value df   logLik
## 1 0.6187057     0.6135181 0.5147894  119.2645 1.669669e-31  3 -111.726
##       AIC      BIC deviance df.residual
## 1 231.452 243.4945  38.9562         147

Add results of statistical test to rmd

Figures

Produce and annotate a plot using the dust data

plotly

You can make figures interactive and very web friendly using plotly.
https://plot.ly/r/