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global.R
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global.R
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library("shiny")
library("GGally")
library("data.table")
library("reshape2")
library("plyr")
library("dplyr")
library("dtplyr")
library("plotly")
library("DT")
library("lubridate")
library("shinyjs")
library("Formula")
# Data Load ---------------------------------------------------------------
athlete_performance <- readRDS("data/athlete_performance.rds")
# Functions ---------------------------------------------------------------
capit <- function(x) {
if (all(sapply(x, is.na))) return(NA)
paste0(toupper(substring(x, 1, 1)), substring(x, 2, nchar(x)))
}
camel <- function(x)
# convert text string to camel case
{
y <- tolower(x)
sapply(strsplit(y, " "), function(x) {
if (all(sapply(x, is.na))) return(NA)
paste(capit(x), collapse = " ")
})
}
get_weather_data <- function(x) {
extract <- function(x) {
set <- x$history$observations
set$date %>%
as.data.table %>%
transform(
temp = set$tempi
, humidity = set$hum) %>%
dplyr::select(year, mon, mday, hour, min, temp, humidity)
}
dta <- format(x, "%Y%m%d") %>%
paste0("http://api.wunderground.com/api/1cf032104d646cd6/history_", ., "/q/IL/Chicago.json") %>%
httr::GET(.) %>%
httr::content(., "text", encoding = "UTF-8") %>%
jsonlite::fromJSON() %>%
extract
saveRDS(dta, paste0("data/chicago_weather_", gsub("-", "_", x), ".rds"))
}
RegressionPlots <- function(fit, type){
# Extract fitted values from lm() object
Fitted.Values <- fitted(fit)
# Extract residuals from lm() object
Residuals <- resid(fit)
# Extract standardized residuals from lm() object
Standardized.Residuals <- MASS::stdres(fit)
# Extract fitted values for lm() object
Theoretical.Quantiles <- qqnorm(Residuals, plot.it = F)$x
# Square root of abs(residuals)
Root.Residuals <- sqrt(abs(Standardized.Residuals))
# Calculate Leverage
Leverage <- lm.influence(fit)$hat
# Create data frame
# Will be used as input to plot_ly
regMat <- data.frame(Fitted.Values,
Residuals,
Standardized.Residuals,
Theoretical.Quantiles,
Root.Residuals,
Leverage)
# Plot using Plotly
# Fitted vs Residuals
# For scatter plot smoother
if (type == "fitted_vs_residuals") {
LOESS1 <- loess.smooth(Fitted.Values, Residuals)
plt <- regMat %>%
plot_ly(x = Fitted.Values, y = Residuals,
type = "scatter", mode = "markers", hoverinfo = "x+y", name = "Data",
marker = list(size = 10, opacity = 0.5), showlegend = F) %>%
add_trace(x = LOESS1$x, y = LOESS1$y, type = "scatter", mode = "line", name = "Smooth",
line = list(width = 2)) %>%
layout(title = "Residuals vs Fitted Values", plot_bgcolor = "#e6e6e6", width = 1000, showlegend = FALSE)
} else if (type == "qq") {
# QQ Pot
plt <- regMat %>%
plot_ly(x = Theoretical.Quantiles, y = Standardized.Residuals,
type = "scatter", mode = "markers", hoverinfo = "x+y", name = "Data",
marker = list(size = 10, opacity = 0.5), showlegend = F) %>%
add_trace(x = Theoretical.Quantiles, y = Theoretical.Quantiles, type = "scatter", mode = "line", name = "",
line = list(width = 2)) %>%
layout(title = "Q-Q Plot", plot_bgcolor = "#e6e6e6", showlegend = FALSE)
} else if (type == "scale_location") {
# Scale Location
# For scatter plot smoother
# LOESS2 <- loess.smooth(Fitted.Values, Root.Residuals)
#
# plt3 <- regMat %>%
# plot_ly(x = Fitted.Values, y = Root.Residuals,
# type = "scatter", mode = "markers", hoverinfo = "x+y", name = "Data",
# marker = list(size = 10, opacity = 0.5), showlegend = F) %>%
#
# add_trace(x = LOESS2$x, y = LOESS2$y, type = "scatter", mode = "line", name = "Smooth",
# line = list(width = 2)) %>%
#
# layout(title = "Scale Location", plot_bgcolor = "#e6e6e6", width = 1000)
} else if (type == "resid_vs_leverage") {
# Residuals vs Leverage
# For scatter plot smoother
LOESS3 <- loess.smooth(Leverage, Residuals)
plt <- regMat %>%
plot_ly(x = Leverage, y = Residuals,
type = "scatter", mode = "markers", hoverinfo = "x+y", name = "Data",
marker = list(size = 10, opacity = 0.5), showlegend = F) %>%
add_trace(x = LOESS3$x, y = LOESS3$y, type = "scatter", mode = "line", name = "Smooth",
line = list(width = 2)) %>%
layout(title = "Leverage vs Residuals", plot_bgcolor = "#e6e6e6", showlegend = FALSE)
}
return(plt)
}