Getting Started with R Markdown (optional)

Last updated on 2026-04-28 | Edit this page

Overview

Questions

What is R Markdown?
How can I integrate my R code with text and plots?
How can I convert .Rmd files to .html, .pdf, or .docx?

Objectives

Create a .Rmd document containing R code, text, and plots
Create a YAML header to control output
Understand basic syntax of R Markdown
Customize code chunks to control formatting
Use code chunks and in-line code to create dynamic, reproducible documents

R Markdown

R Markdown is a flexible type of document that allows you to seamlessly combine executable R code (and its output) with text and images in a single document. These documents can be readily converted to multiple static and dynamic output formats, including PDF (.pdf), Word (.docx), and HTML (.html).

The benefit of a well-prepared R Markdown document is full reproducibility! This also means that, if you notice a data transcription error or you are able to add more data to your analysis, you will be able to recompile the report without making any changes in the actual document.

The rmarkdown package comes pre-installed with RStudio, so no action is necessary to begin using R Markdown documents.

R Markdown wizard monsters creating a R Markdown document from a recipe. Art by Allison Horst — Image credit: Allison Horst

Creating an R Markdown File

To create a new R Markdown document in RStudio, click File -> New File -> R Markdown:

Screenshot of the New R Markdown file dialogue box in RStudio

Then, click on ‘Create Empty Document’ to generate your R Markdown file.

In practice, you can enter the title of your document, your name (Author), and select the type of output. However, in this lesson, we will be learning how to start from a blank document.

Basic Components of R Markdown

To control the output, a YAML header is needed. YAML (which stands for YAML Ain’t Markup Language) is a human-readable serialization language that helps with the configuration of files!

An example of a YAML header can be seen below:

---
title: "My Awesome Report"
author: "Emmet Brickowski"
date: ""
output: html_document
---

In R Markdown, the header is defined by the three hyphens at the beginning (---) and the three hyphens at the end (---).

Within this header, the only required field is the output, which specifies the type of output you want. This can be an html_document, a pdf_document, or a word_document. We will start with an HTML document and discuss the other options later.

Since the other fields are not required, you can delete them if they are unneeded!

To begin the body of your document, start typing after the end of the YAML header (i.e. after the second ---).

Markdown Syntax

Markdown is a popular markup language that allows you to add formatting elements to text, such as bold, italics, and code. However, the formatting will not be immediately visible in your markdown (.md) document, like you would see in a Word document. Rather, Markdown syntax applied to text within your file is converted into formatted elements upon output. Markdown is useful because it is lightweight, flexible, and platform independent.

Some platforms provide a real time preview of the formatting, like RStudio’s visual markdown editor (available from version 1.4).

First, let’s create a heading! A # in front of text indicates to Markdown that this text is a heading. Adding more #s make the heading smaller, i.e. one # is a first level heading, two ##s is a second level heading, etc. This can be repeated up until the 6th level heading.

# Title
## Section
### Sub-section
#### Sub-sub section
##### Sub-sub-sub section
###### Sub-sub-sub-sub section

Please note that you should only use a level if the one above it is also in use! For example, you should not create a header using #### unless headers at ### and all higher levels are present earlier in the document.

Since we have already defined our title in the YAML header, we will use a section heading to create an Introduction section.

## Introduction

You can make things bold by surrounding the word with double asterisks, **bold**, or double underscores, __bold__. Italics can be applied using single asterisks, *italics*, or single underscores, _italics_.

You can also combine bold and italics to write something really important with triple-asterisks, ***really***, or underscores, ___really___. If you’re feeling bold (pun intended), you can also use a combination of asterisks and underscores, **_really_**, *__really__*.

To create code-type font, surround the word with back-ticks, `code-type`.

Now, let’s apply everything we’ve learned about markdown syntax thus far:

## Introduction

This report uses the **tidyverse** package along with the *Check-In* Dataset,
which has columns that include:

Then we can create a list for the variables using -, +, or * keys.

## Introduction

This report uses the **tidyverse** package along with the *Check-In* Dataset,
which has columns that include:

- checkin\_id
- checkin\_length
- checkin\_time
- location
- precinct
- device

You can also create an ordered list using numbers:

1. checkin\_id
2 checkin\_length
3. checkin\_time
4. location
5. precinct
6. device

And nested items by tab-indenting:

- checkin\_id
  + Unique key/ID for each ballot instance
- checkin\_length
  + Number of seconds it took for the person submitting the ballot to check-in
- checkin\_time
  + Arrival time of the person submitting the ballot
- location
  + Anonymized ID for the location of the ballot box
- precinct
  + Anonymized ID for the precinct that the ballot box belongs to
- device
  + Anonymized ID for each ballot box

For more Markdown syntax see the following reference guide.

To render your document into HTML, click the Knit button at the top of the Source panel (top left), or use the keyboard shortcut Ctrl+Shift+K for Windows and Linux or Cmd+Shift+K for Mac. If you haven’t saved the document yet, you will be prompted to do so when you Knit for the first time.

The 'knitting' process: First, R Markdown is converted to Markdown, which is then converted (via pandoc) to .html, .pdf, .docx, etc.

Writing an R Markdown Report

Next, we will add some R code from our previous data wrangling and visualization, which means we need to make sure tidyverse is loaded. However, it is no longer enough to just load tidyverse from the console – when working with R Markdown, you must ensure any necessary packages are loaded within the document itself. The same applies to our data. To do so, we will need to create a ‘code chunk’ at the top of our document (below the YAML header).

A code chunk can be inserted by clicking Code -> Insert Chunk, or by using the keyboard shortcuts Ctrl+Alt+I for Windows and Linux or Cmd+Option+I for Mac.

The syntax of a code chunk is:

MARKDOWN

```{r chunk-name}
"This is where you would place your R code!"
```

An R Markdown document knows that this text is not part of the report from the ``` that begins and ends the chunk. It also knows that the code inside of the chunk is written in R from the r inside of the curly braces ({}). After the r, you can add a name for the code chunk. Naming a chunk is optional, but recommended for organizational purposes. When naming chunks, each chunk name must be unique, and only contain alphanumeric characters and -.

To load tidyverse and our checkin_data.csv file, we will insert a chunk and call it ‘setup’. Since we don’t want this code or the output to show in our knitted HTML document, we add an include = FALSE option after the code chunk name ({r setup, include = FALSE}).

MARKDOWN

```{r setup, include = FALSE}
#loads in the tidyverse and here packages
library(tidyverse)
library(here)

#reads in data and assigns it to the 'data' variable using 'here'
data <- read_csv(here("data", "checkin_data.csv"))
```

Callout

Important Note!

The file paths you give in a .Rmd document, e.g. to load a .csv file, are relative to the .Rmd document, not the project root.

As suggested in the Starting with Data episode, we highly recommend the use of the here() function to keep the file paths consistent within your project.

Insert Table

Next, we will re-create a table from the Data Wrangling episode which shows the total number of check-ins grouped by precinct. We can do this by creating a new code chunk and calling it ‘anon-tbl’. Alternatively, you can come up with something more creative (just remember to stick to the naming rules).

When writing code chunks, unlike text, it isn’t necessary to Knit your document every time you want to see the output. Instead you can run the code chunk with the green triangle in the top right corner of the the chunk, or by using the keyboard shortcuts Ctrl+Alt+C for Windows and Linux or Cmd+Option+C for Mac.

To make sure the table is formatted nicely in our output document, we will need to use the kable() function from the knitr package. The kable() function takes the output of your R code and knits it into a nice looking HTML table. You can also specify different aspects of the table (i.e., the column names or the caption).

Run the code chunk below to ensure you get the desired output:

R

data %>%
  group_by(precinct) %>%
  summarize(total_checkins = n()) %>%
  arrange(desc(total_checkins)) %>%
  knitr::kable(caption = "We can also add a caption.", 
               col.names = c("Precinct", 
                             "Total Check-Ins"))

We can also add a caption.
Precinct	Total Check-Ins
PRECINCT_219	1968
PRECINCT_016	1807
PRECINCT_271	1798
PRECINCT_317	1731
PRECINCT_358	1717
PRECINCT_239	1705
PRECINCT_199	1700
PRECINCT_323	1695
PRECINCT_106	1680
PRECINCT_045	1671
PRECINCT_008	1652
PRECINCT_051	1652
PRECINCT_046	1640
PRECINCT_133	1640
PRECINCT_408	1636
PRECINCT_119	1633
PRECINCT_254	1630
PRECINCT_242	1628
PRECINCT_047	1621
PRECINCT_386	1617
PRECINCT_315	1611
PRECINCT_367	1607
PRECINCT_307	1600
PRECINCT_215	1598
PRECINCT_134	1592
PRECINCT_294	1585
PRECINCT_136	1584
PRECINCT_340	1584
PRECINCT_376	1583
PRECINCT_387	1580
PRECINCT_309	1568
PRECINCT_246	1565
PRECINCT_319	1564
PRECINCT_105	1561
PRECINCT_395	1554
PRECINCT_306	1550
PRECINCT_027	1539
PRECINCT_251	1527
PRECINCT_210	1519
PRECINCT_211	1507
PRECINCT_308	1507
PRECINCT_146	1500
PRECINCT_039	1489
PRECINCT_161	1483
PRECINCT_266	1479
PRECINCT_262	1478
PRECINCT_258	1475
PRECINCT_297	1470
PRECINCT_324	1466
PRECINCT_263	1464
PRECINCT_179	1459
PRECINCT_200	1459
PRECINCT_035	1448
PRECINCT_022	1436
PRECINCT_235	1432
PRECINCT_335	1427
PRECINCT_256	1417
PRECINCT_177	1415
PRECINCT_121	1402
PRECINCT_398	1402
PRECINCT_217	1392
PRECINCT_018	1380
PRECINCT_193	1380
PRECINCT_084	1370
PRECINCT_158	1360
PRECINCT_196	1358
PRECINCT_204	1352
PRECINCT_007	1347
PRECINCT_225	1344
PRECINCT_150	1336
PRECINCT_066	1334
PRECINCT_044	1332
PRECINCT_128	1328
PRECINCT_070	1317
PRECINCT_320	1317
PRECINCT_282	1314
PRECINCT_303	1314
PRECINCT_237	1313
PRECINCT_336	1306
PRECINCT_399	1306
PRECINCT_036	1291
PRECINCT_117	1278
PRECINCT_178	1278
PRECINCT_236	1268
PRECINCT_412	1268
PRECINCT_331	1265
PRECINCT_050	1262
PRECINCT_124	1251
PRECINCT_096	1246
PRECINCT_109	1246
PRECINCT_037	1241
PRECINCT_280	1236
PRECINCT_157	1232
PRECINCT_371	1232
PRECINCT_290	1225
PRECINCT_375	1220
PRECINCT_404	1219
PRECINCT_216	1212
PRECINCT_054	1211
PRECINCT_356	1203
PRECINCT_041	1201
PRECINCT_126	1199
PRECINCT_328	1198
PRECINCT_332	1198
PRECINCT_351	1188
PRECINCT_065	1187
PRECINCT_195	1187
PRECINCT_125	1185
PRECINCT_406	1183
PRECINCT_055	1179
PRECINCT_098	1179
PRECINCT_048	1174
PRECINCT_339	1173
PRECINCT_038	1171
PRECINCT_139	1170
PRECINCT_191	1168
PRECINCT_011	1167
PRECINCT_014	1161
PRECINCT_270	1154
PRECINCT_110	1149
PRECINCT_118	1130
PRECINCT_153	1127
PRECINCT_015	1125
PRECINCT_097	1122
PRECINCT_341	1122
PRECINCT_257	1119
PRECINCT_281	1114
PRECINCT_052	1109
PRECINCT_318	1109
PRECINCT_255	1105
PRECINCT_159	1102
PRECINCT_396	1101
PRECINCT_333	1096
PRECINCT_174	1092
PRECINCT_312	1091
PRECINCT_079	1090
PRECINCT_353	1089
PRECINCT_269	1082
PRECINCT_220	1079
PRECINCT_067	1074
PRECINCT_230	1063
PRECINCT_137	1062
PRECINCT_160	1056
PRECINCT_033	1054
PRECINCT_313	1050
PRECINCT_260	1047
PRECINCT_187	1042
PRECINCT_206	1040
PRECINCT_129	1036
PRECINCT_203	1028
PRECINCT_296	1028
PRECINCT_029	1026
PRECINCT_377	1023
PRECINCT_081	1022
PRECINCT_080	1021
PRECINCT_221	1006
PRECINCT_154	1002
PRECINCT_415	998
PRECINCT_394	995
PRECINCT_325	992
PRECINCT_115	991
PRECINCT_321	988
PRECINCT_085	987
PRECINCT_184	986
PRECINCT_064	982
PRECINCT_370	981
PRECINCT_202	979
PRECINCT_299	977
PRECINCT_310	976
PRECINCT_201	974
PRECINCT_420	963
PRECINCT_021	956
PRECINCT_114	954
PRECINCT_241	954
PRECINCT_194	944
PRECINCT_316	943
PRECINCT_397	943
PRECINCT_059	942
PRECINCT_053	941
PRECINCT_049	939
PRECINCT_143	934
PRECINCT_075	932
PRECINCT_168	926
PRECINCT_298	925
PRECINCT_349	915
PRECINCT_381	910
PRECINCT_197	908
PRECINCT_166	904
PRECINCT_372	904
PRECINCT_123	890
PRECINCT_083	889
PRECINCT_288	885
PRECINCT_010	882
PRECINCT_068	882
PRECINCT_017	875
PRECINCT_207	873
PRECINCT_127	872
PRECINCT_337	868
PRECINCT_283	866
PRECINCT_327	861
PRECINCT_393	858
PRECINCT_107	856
PRECINCT_140	854
PRECINCT_116	853
PRECINCT_390	853
PRECINCT_131	851
PRECINCT_348	849
PRECINCT_132	845
PRECINCT_354	845
PRECINCT_164	844
PRECINCT_095	843
PRECINCT_209	838
PRECINCT_359	831
PRECINCT_248	820
PRECINCT_169	819
PRECINCT_058	816
PRECINCT_076	816
PRECINCT_198	815
PRECINCT_181	810
PRECINCT_378	810
PRECINCT_003	806
PRECINCT_023	797
PRECINCT_025	796
PRECINCT_069	796
PRECINCT_234	795
PRECINCT_267	791
PRECINCT_144	785
PRECINCT_322	783
PRECINCT_130	776
PRECINCT_224	766
PRECINCT_416	766
PRECINCT_329	764
PRECINCT_005	762
PRECINCT_352	762
PRECINCT_142	761
PRECINCT_012	759
PRECINCT_120	757
PRECINCT_314	748
PRECINCT_102	743
PRECINCT_009	742
PRECINCT_250	738
PRECINCT_013	737
PRECINCT_024	734
PRECINCT_108	734
PRECINCT_057	733
PRECINCT_113	732
PRECINCT_228	731
PRECINCT_149	728
PRECINCT_391	727
PRECINCT_073	724
PRECINCT_071	708
PRECINCT_231	701
PRECINCT_185	691
PRECINCT_034	682
PRECINCT_138	682
PRECINCT_145	682
PRECINCT_304	680
PRECINCT_006	676
PRECINCT_369	669
PRECINCT_172	663
PRECINCT_030	662
PRECINCT_183	660
PRECINCT_155	652
PRECINCT_001	648
PRECINCT_233	648
PRECINCT_243	643
PRECINCT_188	639
PRECINCT_364	638
PRECINCT_028	633
PRECINCT_111	621
PRECINCT_212	621
PRECINCT_213	614
PRECINCT_026	604
PRECINCT_060	601
PRECINCT_094	592
PRECINCT_170	585
PRECINCT_208	581
PRECINCT_223	581
PRECINCT_344	580
PRECINCT_141	578
PRECINCT_350	573
PRECINCT_063	571
PRECINCT_182	571
PRECINCT_122	570
PRECINCT_086	565
PRECINCT_273	562
PRECINCT_252	560
PRECINCT_388	556
PRECINCT_278	555
PRECINCT_151	553
PRECINCT_368	552
PRECINCT_384	547
PRECINCT_343	546
PRECINCT_186	543
PRECINCT_409	540
PRECINCT_087	536
PRECINCT_259	530
PRECINCT_249	528
PRECINCT_240	527
PRECINCT_289	520
PRECINCT_287	513
PRECINCT_347	511
PRECINCT_311	504
PRECINCT_072	498
PRECINCT_407	493
PRECINCT_192	490
PRECINCT_104	489
PRECINCT_295	482
PRECINCT_214	479
PRECINCT_245	478
PRECINCT_305	477
PRECINCT_247	473
PRECINCT_103	469
PRECINCT_004	466
PRECINCT_366	463
PRECINCT_226	462
PRECINCT_147	459
PRECINCT_402	459
PRECINCT_162	457
PRECINCT_284	454
PRECINCT_019	444
PRECINCT_293	443
PRECINCT_156	441
PRECINCT_152	439
PRECINCT_077	429
PRECINCT_100	415
PRECINCT_279	412
PRECINCT_135	406
PRECINCT_165	404
PRECINCT_099	403
PRECINCT_090	397
PRECINCT_264	397
PRECINCT_218	396
PRECINCT_276	393
PRECINCT_413	386
PRECINCT_383	385
PRECINCT_338	373
PRECINCT_361	371
PRECINCT_362	367
PRECINCT_405	363
PRECINCT_190	362
PRECINCT_418	362
PRECINCT_373	359
PRECINCT_040	356
PRECINCT_093	348
PRECINCT_392	342
PRECINCT_400	339
PRECINCT_173	333
PRECINCT_379	324
PRECINCT_082	321
PRECINCT_163	320
PRECINCT_285	320
PRECINCT_232	313
PRECINCT_286	296
PRECINCT_277	295
PRECINCT_222	288
PRECINCT_301	284
PRECINCT_275	280
PRECINCT_291	279
PRECINCT_238	274
PRECINCT_385	265
PRECINCT_389	259
PRECINCT_002	257
PRECINCT_357	248
PRECINCT_148	244
PRECINCT_380	243
PRECINCT_302	241
PRECINCT_342	234
PRECINCT_330	232
PRECINCT_417	232
PRECINCT_032	227
PRECINCT_268	224
PRECINCT_374	220
PRECINCT_363	218
PRECINCT_346	213
PRECINCT_300	212
PRECINCT_265	207
PRECINCT_334	206
PRECINCT_074	190
PRECINCT_043	189
PRECINCT_167	187
PRECINCT_205	184
PRECINCT_410	184
PRECINCT_401	180
PRECINCT_229	179
PRECINCT_089	178
PRECINCT_112	171
PRECINCT_365	171
PRECINCT_274	169
PRECINCT_326	167
PRECINCT_078	150
PRECINCT_244	148
PRECINCT_056	143
PRECINCT_061	142
PRECINCT_088	140
PRECINCT_171	124
PRECINCT_176	124
PRECINCT_292	111
PRECINCT_020	109
PRECINCT_091	102
PRECINCT_180	101
PRECINCT_261	101
PRECINCT_382	101
PRECINCT_272	98
PRECINCT_419	89
PRECINCT_042	78
PRECINCT_062	75
PRECINCT_189	70
PRECINCT_227	70
PRECINCT_403	68
PRECINCT_414	68
PRECINCT_031	66
PRECINCT_175	64
PRECINCT_355	60
PRECINCT_253	58
PRECINCT_101	43
PRECINCT_345	42
PRECINCT_411	37
PRECINCT_360	11
PRECINCT_092	2

Many different R packages can be used to generate tables. Some of the more commonly used options are listed in the table below:

Name	Creator(s)	Description
condformat	Oller Moreno (2022)	Allows for the application and visualization of conditional formatting to data frames using defined criteria.
DT	Xie et al. (2023)	By using the JavaScript library ‘DataTables’ (included within the library), data objects can be rendered as HTML tables via R Markdown or Shiny.
formattable	Ren and Russell (2021)	Provides functions that create “formattable” vectors and data frames. Formattable vectors are displayed with text formatting, while formattable data frames use HTML to enhance the readability when rendered on web pages.
flextable	Gohel and Skintzos (2023)	Assists in the creation and customization of tables for reporting and publication purposes. The following formats are supported: ‘HTML’, ‘PDF’, ‘RTF’, ‘Microsoft Word’, ‘Microsoft PowerPoint’ and R ‘Grid Graphics’. ‘R Markdown’, ‘Quarto’, and the package ‘officer’ can be used to produce files with results.
gt	Iannone et al. (2022)	Builds display tables from tabular data. Within this package, tables are constructed using a set of cohesive table parts. Table values can be formatted using any of the included formatting functions.
huxtable	Hugh-Jones (2022)	Creates styled tables for data presentation. These tables can be exported to HTML, LaTeX, RTF, ‘Word’, ‘Excel’, and ‘PowerPoint’. Using this package, you can manipulate borders, size, position, captions, colors, text styles and number formatting.
pander	Daróczi and Tsegelskyi (2022)	Includes functions that catch all messages, ‘stdout’ and other useful information while evaluating R code. It also provides helpers to return user-specified text elements (e.g., header, paragraph, table, image, lists, etc.), or several types of R objects similarly automatically transformed to markdown format, in ‘pandoc’ markdown.
pixiedust	Nutter and Kretch (2021)	Provides tidy data frames with a programming interface intended to be similar to ’ggplot2’s system of layers, allowing fine-tuned control over each cell of the table.
reactable	Lin et al. (2023)	Creates interactive data tables for R based on the ‘React Table’ JavaScript library. Provides an HTML widget that can be used in ‘R Markdown’ or ‘Quarto’ documents, ‘Shiny’ applications, or viewed from an R console.
rhandsontable	Owen et al. (2021)	Provides an R interface to the ‘Handsontable’ JavaScript library (a minimalist Excel-like data grid editor).
stargazer	Hlavac (2022)	Generates LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that display regression analysis results from multiple models side-by-side, along with summary statistics.
tables	Murdoch (2022)	Computes and displays complex tables of summary statistics. Output can be in LaTeX, HTML, plain text, or an R matrix for further processing.
tangram	Garbett et al. (2023)	Provides a flexible formula system to create production quality tables quickly and easily. The processing steps include a formula parser, statistical content generation from data defined by a formula, and table rendering.
xtable	Dahl et al. (2019)	Coerces data to LaTeX and HTML tables.
ztable	Moon (2021)	Makes zebra-striped tables (tables with alternating row colors) in LaTeX and HTML formats using data.frame, matrix, lm, aov, anova, glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.

Customizing Chunk Output

Earlier, we mentioned using include = FALSE in a code chunk to prevent the code and output from printing in the knitted document. There are additional options available to customize how the code-chunks are presented in the output document. The options are entered in the code chunk after chunk-name and separated by commas, e.g. {r chunk-name, eval = FALSE, echo = TRUE}.

Option	Options	Output
`eval`	`TRUE` or `FALSE`	Whether or not the code within the code chunk should be run.
`echo`	`TRUE` or `FALSE`	Choose if you want to show your code chunk in the output document. `echo = TRUE` will show the code chunk.
`include`	`TRUE` or `FALSE`	Choose if the output of a code chunk should be included in the document. `FALSE` means that your code will run, but will not show up in the document.
`warning`	`TRUE` or `FALSE`	Whether or not you want your output document to display potential warning messages produced by your code.
`message`	`TRUE` or `FALSE`	Whether or not you want your output document to display potential messages produced by your code.
`fig.align`	`default`, `left`, `right`, `center`	Where the figure from your R code chunk should be output on the page

Callout

Tip

The default settings for the above chunk options are all TRUE.
The default settings can be modified per chunk, or with knitr::opts_chunk$set() (i.e., entering knitr::opts_chunk$set(echo = FALSE) will change the default of value of echo to FALSE for every code chunk in the document).

Challenge

Exercise

Play around with the different options in the chunk with the code for the table, and re-Knit to see what each option does to the output.

What happens if you use eval = FALSE and echo = FALSE? What is the difference between this and include = FALSE?

Solution to Exercise

Chunk 1:

MARKDOWN

```{r eval = FALSE, echo = FALSE}
data %>%
  group_by(precinct) %>%
  summarize(total_checkins = n()) %>%
  arrange(desc(total_checkins)) %>%
  knitr::kable(caption = "We can also add a caption.", 
               col.names = c("Precinct", 
                             "Total Check-Ins"))
```

Chunk 2:

MARKDOWN

```{r include = FALSE}
data %>%
  group_by(precinct) %>%
  summarize(total_checkins = n()) %>%
  arrange(desc(total_checkins)) %>%
  knitr::kable(caption = "We can also add a caption.", 
               col.names = c("Precinct", 
                             "Total Check-Ins"))
```

eval = FALSE and echo = FALSE will neither run the code in the chunk, nor show the code in the knitted document. The code chunk essentially doesn’t exist in the knitted document!
include = FALSE will not display the code nor the output, but it will be ran, with the output stored for later use!

In-Line R Code

Now we will use some in-line R code to present some descriptive statistics. To use in-line R-code, we use the same back-ticks that we used in the Markdown section, with an r to specify that we are generating R-code. The difference between in-line code and a code chunk is the number of back-ticks. In-line R code uses one back-tick (`r`), whereas code chunks use three back-ticks (```r```).

For example, today’s date is `r Sys.Date()`, will be rendered as: today’s date is 2026-04-28. The code will display today’s date in the output document (or, technically, the date the document was last knitted).

The best way to use in-line R code is by preparing the output in code chunks, minimizing the code needed to produce the output. For example, let’s say we’re interested in presenting the total check-ins for a specific precinct.

We can run the below code to create the total_2866 object, making future in-line R code much easier to write:

R

#create a summary tibble with the total check-ins per precinct
df <- data %>%
      group_by(precinct) %>%
      summarize(total_checkins = n())

#select the precinct we want to use
total_2866 <- df %>%
              filter(precinct == "2866")

Now we can make an informative statement on the counts of each precinct, and include the total values as in-line R-code. For example:

The total check-ins at precinct 2866 is `r total_2866$total_checkins`

becomes…

The total check-ins at precinct 2866 is .

Because we are using in-line R code instead of the actual values, we have created a dynamic document that will automatically update if we make changes to the data set and/or code chunks.

Plots

Finally, our last addition to our document will be a plot from the Data Visualization lesson!

Challenge

Exercise

Create a new code chunk for the plot, and copy the code from any of the plots we created in the previous episode to produce a plot in the chunk.

If you are feeling adventurous, you can also create a new plot using the data tibble.

Solution to Exercise

R

#retrieve top devices
top_devices <- data %>%
  count(device) %>%
  top_n(10, n) %>%
  pull(device)

#create plot
data %>%
  filter(device %in% top_devices) %>%
  mutate(device = str_remove(device, "DEVICE_")) %>%
  ggplot(aes(x = device, fill = device)) +
  geom_bar() +
  labs(title = "Top 10 Devices by Number of Check-ins",
       x = "Device",
       y = "Count")+
  theme_classic() + 
  theme(legend.position = "none")

We can also create a caption with the chunk option fig.cap.

MARKDOWN

```{r chunk-name, fig.cap = "I made this plot while attending an
awesome workshop where I learned a ton of cool stuff!"}
"Insert the code for the plot here"
```

…or, ideally, something more informative.

R

#retrieve top devices
top_devices <- data %>%
  count(device) %>%
  top_n(10, n) %>%
  pull(device)

#create plot
data %>%
  filter(device %in% top_devices) %>%
  mutate(device = str_remove(device, "DEVICE_")) %>%
  ggplot(aes(x = device, fill = device)) +
  geom_bar() +
  labs(title = "Top 10 Devices by Number of Check-ins",
       x = "Device",
       y = "Count")+
  theme_classic() + 
  theme(legend.position = "none")

I made this plot while attending an awesome workshop where I learned a ton of cool stuff!

Other Output Options

To convert an R Markdown file to a PDF or Word Document, you can either click the little triangle next to the Knit button to get a drop-down menu or put pdf_document or word_document in the initial header of the file.

For example, to output to a word_document:

---
title: "My Awesome Report"
author: "Emmet Brickowski"
date: ""
output: word_document
---

Callout

Note: Creating PDF Documents

Creating .pdf documents may require installation of some extra software. The R package tinytex provides some tools to help make this process easier for R users. With tinytex installed, run tinytex::install_tinytex() to install the required software (you’ll only need to do this once) and then when you Knit to pdf tinytex will automatically detect and install any additional LaTeX packages that are needed to produce the pdf document. For more information, visit the tinytex website.

Callout

Note: Inserting Citations into an R Markdown File

It is possible to insert citations into an R Markdown file using the editor toolbar. The editor toolbar includes commonly seen formatting buttons generally seen in text editors (e.g., bold and italic buttons) and is accessible by using the settings drop-down menu (next to the ‘Knit’ drop-down menu) to select ‘Use Visual Editor’. You can also use the keyboard shortcuts Ctrl+Shift+F4 for Windows and Linux or Cmd+Shift+F4 for Mac. From here, clicking ‘Insert’ allows ‘Citation’ to be selected.

Using this menu, you can search various sources for citations and insert the appropriate citation necessary. For example, searching ‘10.1007/978-3-319-24277-4’ in ‘From DOI’ and inserting will provide the citation for ggplot2 [@wickham2016]. This will also save the citation(s) in ‘references.bib’ in the current working directory. Visit the R Studio website for more information.

Additionally, you can obtain citation information from relevant packages by using citation("package").

Resources

Knitr in a knutshell tutorial
Dynamic Documents with R and knitr (book)
R Markdown documentation
R Markdown cheat sheet
Getting started with R Markdown
Markdown tutorial
R Markdown: The Definitive Guide (book by Rstudio team)
Reproducible Reporting
Introducing Bookdown

Key Points

R Markdown is a useful language for creating reproducible documents combining text and executable R-code.
You can specify chunk options to control formatting of the output document.