library(plotly)

airquality_sept <- airquality[which(airquality$Month == 9),] airquality_sept$Date <- as.Date(paste(airquality_sept$Month, airquality_sept$Day, 1973, sep = "."), format = "%m.%d.%Y") View(airquality_sept) ggplot(airquality_sept, aes(x = Date, y = Wind)) + geom_bar(stat = "identity") + geom_smooth(method = "lm")+ theme(axis.text.x = element_text(angle = 45, vjust = 0.5))

############## showing all x axis labels ###############

ID <- 1:50 A <- runif(50,1,100) df <- data.frame(ID,A) ggplot(df, aes(x = ID, y = A)) + geom_point() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) + scale_x_continuous("ID", labels = as.character(ID), breaks = ID)

library(plotly)

airquality_sept <- airquality[which(airquality$Month == 9),] airquality_sept$Date <- as.Date(paste(airquality_sept$Month, airquality_sept$Day, 1973, sep = "."), format = "%m.%d.%Y") View(airquality_sept) plot_ly(airquality_sept) %>%
add_trace(x = ~Date, y = ~Wind, type = ‘bar’, name = ‘Wind’,
marker = list(color = ‘#C9EFF9’),
hoverinfo = “text”,
text = ~paste(Wind, ‘ mph’)) %>%
add_trace(x = ~Date, y = ~Temp, type = ‘scatter’, mode = ‘lines’, name = ‘Temperature’, yaxis = ‘y2’,
line = list(color = ‘#45171D’),
hoverinfo = “text”,
text = ~paste(Temp, ‘°F’)) %>%
layout(title = ‘New York Wind and Temperature Measurements for September 1973’,
xaxis = list(title = “”),
yaxis = list(side = ‘left’, title = ‘Wind in mph’, showgrid = FALSE, zeroline = FALSE),
yaxis2 = list(side = ‘right’, overlaying = “y”, title = ‘Temperature in degrees F’, showgrid = FALSE, zeroline = FALSE))

plot_ly(airquality_sept) %>%
add_trace(x = ~Date, y = ~Wind, type = ‘bar’, name = ‘Wind’,
marker = list(color = ‘#C9EFF9’),
hoverinfo = “text”,
text = ~paste(Wind, ‘ mph’)) %>%
layout(title = ‘New York Wind and Temperature Measurements for September 1973’,
xaxis = list(title = “”),
yaxis = list(side = ‘left’, title = ‘Wind in mph’, showgrid = TRUE, zeroline = FALSE),
yaxis2 = list(side = ‘right’, overlaying = “y”, title = ‘Temperature in degrees F’, showgrid = FALSE, zeroline = FALSE))

# Create a shareable link to your chart
# Set up API credentials: https://plot.ly/r/getting-started
chart_link = api_create(p, filename=”multiple-bar_line”)

#adding smooth trend linear-trend-line-to-a-scatter-plot-in-r

https://stats.stackexchange.com/questions/30975/how-to-add-non-linear-trend-line-to-a-scatter-plot-in-r

## add moving average in

https://www.quantmod.com/documentation/addMA.html

#running average
https://stackoverflow.com/questions/40857319/plot-running-average-in-ggplot2

## different chart type

https://plot.ly/r/graphing-multiple-chart-types/

## integrate with sql server

Using R in SQL Server Reporting Services (SSRS)

http://www.sthda.com/english/wiki/a-complete-guide-to-3d-visualization-device-system-in-r-r-software-and-data-visualization

https://rpubs.com/FelipeRego/MultipleLinearRegressionInRFirstSteps

https://www.r-bloggers.com/r-tutorial-series-multiple-linear-regression/

https://www.statmethods.net/stats/regression.html

https://rcompanion.org/rcompanion/e_05.html

https://docs.microsoft.com/en-us/sql/advanced-analytics/tutorials/rtsql-create-a-predictive-model-r

library(XML)
theurl <- "http://finance.yahoo.com" tables <- readHTMLTable(theurl) n.rows <- unlist(lapply(tables, function(t) dim(t)[1])) the picked table is the longest one on the page lstRes <- tables[[which.max(n.rows)]] as.data.frame(lstRes)

DFcars[-(1:2),]
mtcars[-(1:2)]
use “-” negate the rows

First install the MySQL packcages

now, run the connection command to connect MySQL db on localhost

show the tables and show selected table fields