Related
Is there a way to include an absolute panel like in this example (https://shiny.rstudio.com/gallery/superzip-example.html) to a flexdashboard (on a leaflet) ?
The idea would be to have a mobile panel dedicated to the leaflet output instead of a sidebar panel.
The absolute panel example here based on a shiny example (with ui and server parts)
library(shiny)
ui <- shinyUI(bootstrapPage(
absolutePanel(
id = "controls", class = "panel panel-default", fixed = TRUE,
draggable = TRUE, top = 60, left = "auto", right = 20, bottom = "auto",
width = 330, height = "auto",
HTML('<button data-toggle="collapse" data-target="#demo">Collapsible</button>'),
tags$div(id = 'demo',
checkboxInput('input_draw_point', 'Draw point', FALSE ),
verbatimTextOutput('summary')))
))
server <- shinyServer(function(input, output, session) {
output$summary <- renderPrint(print(cars))
})
shinyApp(ui = ui, server = server)
a code example below for the Flexdashboard part :
---
title: "Waste Lands - America's forgotten nuclear legacy"
author: Philipp Ottolinger
output:
flexdashboard::flex_dashboard:
theme: journal
social: menu
source_code: embed
---
```{r setup, include = FALSE}
library(flexdashboard)
library(shiny)
library(jsonlite)
library(maptools)
library(ggplot2)
library(tidyr)
library(dplyr)
library(purrr)
library(leaflet)
library(plotly)
sites <- fromJSON(flatten=TRUE,
"https://raw.githubusercontent.com/ottlngr/2016-15/ottlngr/ottlngr/sites.json")
sites$locations <- map(sites$locations, function(x) {
if (nrow(x) == 0) {
data_frame(latitude=NA, longitude=NA, postal_code=NA, name=NA, street_address=NA)
} else {
x
}
})
sites <- unnest(sites)
sites <- sites[complete.cases(sites[,c("longitude", "latitude")]),]
sites$ratingcol <- ifelse(sites$site.rating == 0, "orange",
ifelse(sites$site.rating == 1, "green",
ifelse(sites$site.rating == 2, "red", "black")))
sites$ratingf <- factor(sites$site.rating,
levels=c(3:0),
labels=c("Remote or no potential for radioactive contamination.",
"No authority to clean up or status unclear.",
"Cleanup declared complete.",
"Cleanup in progress."))
sites$campus <- ifelse(grepl("University", sites$site.name) |
grepl("University", pattern = sites$street_address) |
grepl("Campus", sites$street_address), 1, 0)
sites$campuscol <- ifelse(sites$campus == 1, "red", "black")
```
Column {data-width=650}
-----------------------------------------------------------------------
### All sites and their current status
```{r}
leaflet() %>%
addTiles() %>%
fitBounds(-127.44,24.05,-65.30,50.35) %>%
addCircleMarkers(sites$longitude,
sites$latitude,
color = sites$ratingcol,
radius = 6,
fill = T,
fillOpacity = 0.2,
opacity = 0.6,
popup = paste(sites$site.city,
sites$site.name,
sep = "")) %>%
addLegend("bottomleft",
colors = c("orange","green", "red", "black"),
labels = c("Cleanup in progress.",
"Cleanup complete.",
"Status unclear.",
"No potential for radioactive contamination."),
opacity = 0.8)
```
Column {data-width=350}
-----------------------------------------------------------------------
### Number of sites
```{r}
sites %>%
count(ratingf) %>%
plot_ly(type = "bar",
x = ratingf,
y = n,
color = ratingf,
text = paste(n,ratingf,sep=""),
hoverinfo = "text") %>%
layout(xaxis = list(showline = F,
showticklabels = F,
fixedrange = T,
title = ""),
yaxis = list(fixedrange = T,
title = ""))
```
### Sites on campus
```{r}
leaflet() %>%
addTiles() %>%
fitBounds(-127.44,24.05,-65.30,50.35) %>%
addCircleMarkers(sites[sites$campus == 1, ]$longitude,
sites[sites$campus == 1, ]$latitude,
color = sites[sites$campus == 1, ]$campuscol,
radius = 6,
fill = T,
fillOpacity = 0.2,
opacity = 0.6,
popup = paste(sites[sites$campus == 1, ]$site.city,
sites[sites$campus == 1, ]$site.name,
sep = ""))
```
Thanks
Try this.
---
title: "haha"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
editor_options:
chunk_output_type: console
runtime: shiny
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(flexdashboard)
library(shiny)
library(leaflet)
```
# without container-fluid
### Sites on campus
```{r}
df <- data.frame(NY = c(-74.418997, 43.257408), CA = c(-120.765285, 35.604380))
renderLeaflet(mapfunction())
```
```{r}
absolutePanel(
draggable = TRUE, top = "15%", left = "auto", right = "5%", bottom = "auto",
width = '30%', height = 'auto',
style = "background: orange; opacity: 0.9",
p(strong("some text")),
selectInput("someinput", label = "location", choices = c("NY", "CA"))
)
```
### server
```{r}
mapfunction <- reactive({
leaflet() %>%
addTiles() %>%
fitBounds(-127.44,24.05,-65.30,50.35) %>%
addMarkers(lng = df[[input$someinput]][1], lat = df[[input$someinput]][2])
})
```
# with container-fluid
```{r}
shinyApp(
fluidPage(
leafletOutput(outputId = "somemap"),
absolutePanel(
draggable = TRUE, top = "15%", left = "auto", right = "5%", bottom = "auto",
width = '30%', height = 'auto', fixed = TRUE,
style = "background: orange; opacity: 0.9",
p(strong("some text")),
selectInput("someinput", label = "location", choices = c("NY", "CA"))
)
),
server = function(input, output, session){
df <- data.frame(NY = c(-74.418997, 43.257408), CA = c(-120.765285, 35.604380))
output$somemap <- renderLeaflet({
leaflet() %>%
addTiles() %>%
fitBounds(-127.44,24.05,-65.30,50.35) %>%
addMarkers(lng = df[[input$someinput]][1], lat = df[[input$someinput]][2])
})
}
)
```
If you need to use interactive components from shiny, like XXinput, you need to specify runtime: shiny on the top, otherwise, you can delete this line.
I use reactive as the simplest server part. If you want to use a more complex server (logic), e.g. several components interact together, you need to write the actual server function. I would suggest just write a shiny app instead of a flexdashboard.
unfortunately, components in flexdash are not inside container-fluid class which with this can allow you to drag the panel. There may be a way to work around, you can search for it. Look at the last chunck, I inserted a actual shiny app and the panel is draggable. You should see two tabs when you run the doc, watch the difference. So, if you really want to drag this panel, you should write a "real" shiny app.
I'm trying to filter a data frame and then do some simple ggplots off of the data. I've tried to leverage the R studio example on Shiny documents along with the following SO post on the subject:
Reactively filtering/subsetting a data frame in shiny
Here is my code.
---
title: "Shiny Filter Test"
author: "Novice"
date: "12/13/2019"
output: html_document
runtime: shiny
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(tidyverse)
library(shiny)
inputPanel(
selectInput("n_break", label = "Number of bins:",
choices = c(10, 20, 35, 50), selected = 10)
)
cdat <- reactive({
data <- tibble(x = c(10,20,35), y = c("a","b","c"))
data %>%
filter(x %in% input$n_break)
output$table <- DT::renderDT({
cdat()
}, options = list(scrollX = TRUE))
})
```
Can anyone point out where I'm going wrong? When I run the code I get my dropdown box, but that is all. No errors. Just no filtered datatable.
Thanks.
The closing brackets of your reactive are at the wrong place. They should close once you have filtered the data.
---
title: "Shiny Filter Test"
author: "Novice"
date: "12/13/2019"
output: html_document
runtime: shiny
---
```{r setup}
knitr::opts_chunk$set(
echo = FALSE
)
```
```{r}
library(tidyverse)
library(shiny)
inputPanel(
selectInput("n_break", label = "Number of bins:",
choices = c(10, 20, 35, 50), selected = 10)
)
cdat <- reactive({
data <- tibble(x = c(10,20,35), y = c("a","b","c"))
data %>% filter(x %in% input$n_break)
})
DT::renderDT({
cdat()
}, options = list(scrollX = is ))
```
A remark on the reactive: if you plan to extend this futher, such that the filtered data is used elsewhere, it makes sense to do the filtering in a reactive function. However, if this is not the case I would just do the filtering inside the renderDT:
---
title: "Shiny Filter Test"
author: "Novice"
date: "12/13/2019"
output: html_document
runtime: shiny
---
```{r setup}
knitr::opts_chunk$set(
echo = FALSE
)
```
```{r}
library(tidyverse)
library(shiny)
data <- tibble(x = c(10,20,35), y = c("a","b","c"))
inputPanel(
selectInput("n_break", label = "Number of bins:",
choices = c(10, 20, 35, 50), selected = 10)
)
DT::renderDT({
data %>% filter(x %in% input$n_break)
}, options = list(scrollX = TRUE))
```
I am new to Rmarkdown and trying to generate a table that has some conditional text formatting. The table comes out as what I want except for the color. It displays \textcolor{red}{50} for example instead of 50 in red. Any idea what I'm doing wrong?
In my R script:
table2 <- data %>% filter(student == 1) %>%
dplyr::mutate(grade = cell_spec(grade, "latex", color = ifelse(grade > 75, "blue", "red"))
In my Rmarkdown file:
```{r, results = "asis"}
kable(table2, format = "latex")
```
Your question isn't reproducible, but it appears the issue is that you didn't include escape = FALSE in the call to kable:
library(dplyr)
library(kableExtra)
data <- data.frame(student = rep(1:5, 2), grade = sample(100, 10))
table2 <- data %>% filter(student == 1) %>%
dplyr::mutate(grade = cell_spec(grade, "latex",
color = ifelse(grade > 75, "blue", "red")))
kable(table2, format = "latex", escape = FALSE)
I have seen that this problem has happened to other people, but their solutions have not worked for me. I have my app.R file and a .RData file with the required inputs in the same ECWA_Strategic_Planning_Tool directory. When I run:
library(rsconnect)
rsconnect::deployApp('C:/Users/mikialynn/Documents/Duke/Spring2017/MP/GISTool/Final/ECWA_Strategic_Planning_Tool')
I get the following error on the web page that opens up:
ERROR: An error has occurred. Check your logs or contact the app author for clarification.
However, I cannot find anything wrong. I install all of my packages, I use relative pathways etc. I am pasting all of the code from my app below. If anyone can spot what I'm doing wrong, I would greatly appreciate it!
library(shiny)
library(leaflet)
library(sp)
library(rgdal)
library(rstudioapi) # For working directory
library(raster)
library(RColorBrewer)
library(rgeos) #Maybe use gSimplify to simplify polygon
library(DT) #To make interactive DataTable
library(plotly) #For pie chart
library(ggplot2) # for layout
# Set Working Directory
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# Load R Workspace
load('Shiny.Strategies.RData')
# UI variables
neigh.names <- levels(merge.proj$View)
neigh.default <- c("Urban7")
dt.names <- c('PARCEL_ID', 'PIN', 'OWNER_NAME', 'SITE_ADDRE', 'OWNER_ADDR',
'SUM_ACRE', 'LANDUSE_DE', 'LAND_VALUE', 'TOTAL_VALU', 'SALE_PRICE',
'Pluvial_WtScore', 'Rest_WtScore', 'GI_WtScore', 'SC_WtScore',
'UNCWI_WtScore', 'Total_Score', 'View')
dt.default <- c('PARCEL_ID', 'Pluvial_WtScore', 'Rest_WtScore',
'GI_WtScore', 'SC_WtScore', 'UNCWI_WtScore', 'Total_Score', 'View')
# Build UI
ui <- fluidPage(
titlePanel("ECWA Strategic Planning Tool"),
HTML('<br>'),
column(2,
HTML("<strong>Instructions:</strong><br/><br/>"),
HTML("<p>1) Select weights for parameters and click 'Run' to
initiate tool.<br/><br/>
2) Use rightside panel to adjust Table and Map Settings.<br/>
<br/>
3) Use search/sort functions of Table to identify parcels.
Select row to display Total Score Chart.<br/><br/>
4) Input View and Parcel ID from Table to Map settings to
identify parcel in Map.<br/><br/>
5) When satisfied with weights, click 'Export Shapefile' to
save shapefile of all parcels.<p/><br/>"),
HTML("<strong>Calculate Parcel Scores: </strong><br/>"),
helpText('The sum of the weights must equal to 1.'),
sliderInput(inputId = "weightPluvial", label = "Weight for Pluvial
Flooding",
value = 0.20, min = 0, max = 1),
sliderInput(inputId = "weightRest", label = "Weight for
Restoration",
value = 0.20, min = 0, max = 1),
sliderInput(inputId = "weightGI", label = "Weight for Green
Infrastructure",
value = 0.20, min = 0, max = 1),
sliderInput(inputId = "weightSC", label = "Weight for City
Stormwater Controls",
value = 0.20, min = 0, max = 1),
sliderInput(inputId = "weightUNCWI", label = "Weight for UNCWI",
value = 0.20, min = 0, max = 1),
actionButton("run", "Run"),
actionButton("export", "Export Shapefile")),
column(8,
HTML("<h3><strong>Table Summary</strong></h3>"),
HTML("<br>"),
dataTableOutput("table")),
column(2,
HTML("<p><br><br></p>"),
HTML("<h4>Table Settings:</h4>"),
checkboxGroupInput(inputId = 'show_vars', label = 'Select column(s)
to display in Table:', choices = dt.names, selected = dt.default),
HTML("<strong>Total Score Chart:</strong>"),
helpText("Please select Table row to display pie chart."),
plotlyOutput("pie")
),
fluidRow(
column(8, offset = 2,
HTML("<br>"),
HTML("<h3><strong>Map Display</strong></h3>"),
leafletOutput("map", height = 800),
HTML("<br><br>")),
column(2,
HTML("<p><br><br><br></p>"),
HTML("<h4>Map Settings:</h4>"),
checkboxGroupInput(inputId = 'show_neigh', label = 'Select
View(s) to display in Map:', choices = neigh.names,
selected = neigh.default),
HTML("<br>"),
sliderInput("range", "Select score range to display in Map:", min
= 0.0, max= 10.0, value = as.numeric(c("0.0", "10.0")), step = 0.1),
HTML("<br>"),
HTML("<strong>Parcel Zoom:</strong>"),
helpText("The View and Score Range must contain the parcel of
interest to execute zoom."),
numericInput('parcel','Enter Parcel ID',0)
)
))
# SERVER
server <- function(input, output) {
defaultData <-
eventReactive(input$run, {
# Multiply by Weights
merge.proj#data$Pluvial_WtScore <-
round(merge.proj#data$Pluvial_Score*input$weightPluvial, digits = 1)
merge.proj#data$Rest_WtScore <-
round(merge.proj#data$Rest_Score*input$weightRest, digits = 1)
merge.proj#data$GI_WtScore <-
round(merge.proj#data$GI_Score*input$weightGI, digits = 1)
merge.proj#data$SC_WtScore <-
round(merge.proj#data$SC_Score*input$weightSC, digits = 1)
merge.proj#data$UNCWI_WtScore <-
round(merge.proj#data$UNCWI_Score*input$weightUNCWI, digits = 1)
# Find Total Score
merge.proj#data$Total_Score <- merge.proj#data$Pluvial_WtScore +
merge.proj#data$Rest_WtScore + merge.proj#data$GI_WtScore +
merge.proj#data$SC_WtScore + merge.proj#data$UNCWI_WtScore
return(merge.proj)
})
# Subset by neighborhood
neighData <- reactive ({
merge.proj <- defaultData()
merge.proj[merge.proj$View%in%input$show_neigh,]
})
# Plot with leaflet
# Palette for map
colorpal <- reactive({
merge.proj <- neighData()
colorNumeric(palette = "YlOrRd",
domain = merge.proj$Total_Score)
})
# Pop Up Option for map
# popup <- paste0("<strong>Parcel ID: </strong>",
# merge.proj#data$PARCEL_ID,
# "<br><strong>Total Score: </strong>",
# merge.proj#data$Total_Score)
# Label Option for map
labels <- reactive({
merge.proj <- neighData()
sprintf("<strong>Parcel ID: </strong>%s<br/><strong>Total Score:
</strong>%g",
merge.proj$PARCEL_ID,
merge.proj$Total_Score) %>% lapply(htmltools::HTML)
})
# Render Default Map
output$map <- renderLeaflet ({
merge.proj <- neighData()
pal <- colorpal()
lab <- labels()
leaflet() %>%
#addProviderTiles(provider='Esri.WorldImagery') %>%
# setView(zoom =) %>%
addTiles() %>%
addPolygons(
#data = merge.proj[input$show_neigh,, drop = FALSE],
data=merge.proj,
fillColor = ~pal(Total_Score),
weight = 1,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlight = highlightOptions(
weight = 3,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
# popup= popup) %>%
label = lab,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
addLegend(position = "bottomleft",pal = pal, opacity = 0.7, values =
merge.proj$Total_Score, title = "<strong>Total Score</strong>")
})
# Build Data Table
output$table <- renderDataTable({
merge.proj <- defaultData()
table.dat <- merge.proj[, c('PARCEL_ID', 'PIN', 'OWNER_NAME',
'SITE_ADDRE', 'OWNER_ADDR', 'SUM_ACRE', 'LANDUSE_DE', 'LAND_VALUE',
'TOTAL_VALU', 'SALE_PRICE', 'Pluvial_WtScore', 'Rest_WtScore', 'GI_WtScore',
'SC_WtScore', 'UNCWI_WtScore', 'Total_Score', 'View')]
datatable(data = table.dat#data[, input$show_vars, drop = FALSE],
options = list(lengthMenu = c(5, 10, 20, 30), pageLength = 20), rownames =
FALSE)
})
# Plot-ly
output$pie <- renderPlotly({
merge.proj <- defaultData()
names <- c('Pluvial', 'Rest', 'GI', 'SC', 'UNCWI')
colors <- c('rgb(128,133,133)', 'rgb(211,94,96)', 'rgb(144,103,167)',
'rgb(114,147,203)', 'rgb(171,104,87)')
selectedrowindex <-
input$table_rows_selected[length(input$table_rows_selected)]
selectedrowindex <- as.numeric(selectedrowindex)
df <- data.frame(merge.proj[selectedrowindex, c('Pluvial_WtScore',
'Rest_WtScore', 'GI_WtScore', 'SC_WtScore', 'UNCWI_WtScore')])
vector <- unname(unlist(df[1,]))
if (!is.null(input$table_rows_selected)) {
par(mar = c(4, 4, 1, .1))
plot_ly(labels = names, values = vector, type = 'pie',
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF'),
hoverinfo = 'text',
text = ~paste('Score:', vector),
marker = list(colors = colors,
line = list(color = '#FFFFFF', width = 1)),
#The 'pull' attribute can also be used to create space between the sectors
showlegend = FALSE) %>%
layout(#title = '% Total Score',
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
}
else {return(NULL)}
})
# Update map to parcel score slider
# Subset data
filteredData <- reactive({
merge.proj <- neighData()
merge.proj[merge.proj#data$Total_Score >= input$range[1] &
merge.proj#data$Total_Score <= input$range[2],]
})
# New Palette
colorpal2 <- reactive({
merge.proj <- filteredData()
colorNumeric(palette = "YlOrRd",
domain = merge.proj$Total_Score)
})
# Pop Up Option
# popup <- paste0("<strong>Parcel ID: </strong>",
# merge.proj#data$PARCEL_ID,
# "<br><strong>Total Score: </strong>",
# merge.proj#data$Total_Score)
# Label Option
labels2 <- reactive({
merge.proj <- filteredData()
sprintf("<strong>Parcel ID: </strong>%s<br/><strong>Total Score:
</strong>%g",
merge.proj$PARCEL_ID,
merge.proj$Total_Score) %>% lapply(htmltools::HTML)
})
#Leaflet Proxy
observe({
merge.proj <- filteredData()
pal2 <- colorpal2()
lab2 <- labels2()
leaf <- leafletProxy("map", data = filteredData()) %>%
clearShapes() %>%
addPolygons(
fillColor = ~pal2(Total_Score),
weight = 1,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlight = highlightOptions(
weight = 3,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
# popup= popup) %>%
label = lab2,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))
if(input$parcel>0){
sub.dat <- merge.proj[merge.proj$PARCEL_ID==input$parcel,]
zx <- mean(extent(sub.dat)[1:2])
zy <- mean(extent(sub.dat)[3:4])
leaf <- leaf %>%
setView(lng=zx,lat=zy,zoom=16)
}
leaf
})
#Update Legend
observe({
proxy <- leafletProxy("map", data = filteredData())
pal2 <- colorpal2()
proxy %>% clearControls()
proxy %>% addLegend(position = "bottomleft",pal = pal2, opacity = 0.7,
values = ~Total_Score, title = "<strong>Total Score</strong>")
})
# Export new shapefile
#make so that user can choose name and allow overwrite
observeEvent(input$export, {
merge.proj <- defaultData()
writeOGR(merge.proj, dsn = "Data", layer = "Strategies_Output", driver =
"ESRI Shapefile")
})
}
shinyApp(ui = ui, server = server)
Issue resolved! My initial suspicion was correct; it had to do with the .rdata file. It also relates to shinyapp.io's servers which run on a Linux based server. From my reading, Linux only handles lowercase file paths and extensions. The reason why it worked for the .csv file is because it's pretty common to have the file extension saved in all lowercase. This was not the case for the .RData file. Using the RStudio IDE and the physical "Save Workspace" button, the default file extension is .RData (case sensitive). I couldn't rename the file extension (for some reason, I'm not the most tech-savvy person). Similar to the load() function, there's the save() function. Previously, I used the save() file as follows (note the capitalized .RData at the end):
save(df_training_separated_with_models, file = "sample_data_with_models.RData")
However, using the same function in all lowercase fixes the issue:
save(df_training_separated_with_models, file = "sample_data_with_models.rdata")
Hope this helps any other poor soul with the same issue that is scouring the internet and other forums.
Cheers!
---
title: "Title"
author: ''
date: ''
output:
pdf_document:
template: default.tex
geometry: top=0.5cm, bottom=0.5cm, left=0.5cm, right=0.5cm
header-includes: null
fontsize: 4pt
classoption: portrait
sansfont: Calibri Light
---
#Name1: `r "Name1"`
#Name2: `r "Name2"`
```{r, echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
df <- mtcars
n = nrow(df)
hlines=c(-1,0,(n-1),n)
my_align = "c|c|c|ccccc|ccc|c|"
rws <- seq(1, (n-1), by = 2)
col <- rep("\\rowcolor[gray]{.90} ", length(rws))
xtable::print.xtable(xtable(df
, align = my_align)
, add.to.row = list(pos = as.list(rws), command = col)
, booktabs = F
, hline.after = hlines, type = "latex")
```
I am using an Rmarkdown to print a table which has a lot of formatting. When I add the add.to.rwo part to get grey and white alternate rows the vertical lines are removed in the grey rows.
How do I correct this? It is very difficult to create a reproducible example but hopefully the same problem will apply to any df (with the correct Latex packages behind it)
Thanks :)
Try comparing these two tables. The first is your table as you coded it, the second is done by pixiedust with the hhline option set to TRUE.
---
title: "Title"
author: ''
date: ''
output:
pdf_document:
geometry: top=0.5cm, bottom=0.5cm, left=0.5cm, right=0.5cm
header-includes:
- \usepackage{amssymb}
- \usepackage{arydshln}
- \usepackage{caption}
- \usepackage{graphicx}
- \usepackage{hhline}
- \usepackage{longtable}
- \usepackage{multirow}
- \usepackage[dvipsnames,table]{xcolor}
fontsize: 4pt
classoption: portrait
sansfont: Calibri Light
---
#Name1: `r "Name1"`
#Name2: `r "Name2"`
```{r, echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
library(xtable)
df <- mtcars
n = nrow(df)
hlines=c(-1,0,(n-1),n)
my_align = "c|c|c|ccccc|ccc|c|"
rws <- seq(1, (n-1), by = 2)
col <- rep("\\rowcolor[gray]{.90} ", length(rws))
xtable::print.xtable(xtable(df
, align = my_align)
, add.to.row = list(pos = as.list(rws), command = col)
, booktabs = F
, hline.after = hlines, type = "latex")
```
```{r}
library(pixiedust)
dust(df,
hhline = TRUE,
keep_rownames = TRUE) %>%
medley_bw() %>%
sprinkle_colnames(.rownames = "") %>%
sprinkle(cols = c(".rownames", "mpg", "cyl", "qsec", "gear", "carb"),
border = "right") %>%
sprinkle(rows = nrow(mtcars),
border = "top") %>%
sprinkle(bg_pattern_by = "rows")
```