ggplot2

td_plot <- gps_data %>% 
  filter(Date == '2018-02-17') %>% 
  mutate(
    Name = forcats::fct_reorder( Name, dist_total, desc=T)
  ) %>%
  ggplot(aes(Name, dist_total, fill = `Position Specific`, 
             label = paste0(round(dist_total, 0),"m"))) +
  geom_col() +
  coord_flip() +
  geom_text(hjust=1) +
  bbplot::bbc_style() +
  labs(y = "Total Distance", x = 'Player Name', 
       title = 'Total Distance Covered', type='Garamond') +
  theme(
    axis.text.x = element_blank(),
    axis.text.y = element_text(size=14),
    axis.ticks.x = element_blank(),
    legend.justification=c(1,0), 
    legend.position=c(1,0),
    legend.text = ggplot2::element_text(size=8,
                                        color="#222222"))

hsr_plot <- gps_data %>% 
  filter(Date == '2018-02-17') %>% 
  mutate(
    Name = forcats::fct_reorder( Name, dist_total, desc=T)
  ) %>%
  ggplot(aes(Name, hsr_total, fill=`Position Specific`, label=
               paste0(round(hsr_total, 0),"m"))) +
  geom_col() +
  coord_flip() +
  geom_text(hjust=1) +
  bbplot::bbc_style() +
  labs(y = "High Speed Distance", x = 'Player Name', 
       title = 'High Speed Distance Covered', type='Garamond') +
  theme(
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "none"
  )


cowplot::plot_grid(td_plot, hsr_plot)

ggiraph

You can also embed plots, for example:

td_plot <- gps_data %>% 
  filter(Date == '2018-02-17') %>% 
  mutate(
    Name = forcats::fct_reorder( Name, dist_total, desc=T),
    tooltip = paste0(Name, "\n",`Position Specific`, "\n", round(hsr_total, 0),"m")
  ) %>%
  ggplot(aes(Name, dist_total, fill = `Position Specific`, 
             label = paste0(round(dist_total, 0),"m"))) +
  ggiraph::geom_bar_interactive(aes(y=dist_total, fill=`Position Specific`, 
                                    tooltip=tooltip),stat = 'identity') +
  coord_flip() +
  geom_text(hjust=1, size=2) +
  bbplot::bbc_style() +
  labs(y = "Total Distance", x = 'Player Name', 
       fill='Position', title = 'Total Distance Covered', type='Garamond') +
  theme(
    axis.text.x = element_blank(),
    axis.text.y = element_text(size=14),
    axis.ticks.x = element_blank(),
    legend.text = ggplot2::element_text(size=4,
                                        color="#222222"),
    legend.justification=c(1,0), 
    legend.position=c(1,0),
    legend.spacing.x = unit(0.06, 'cm'),
    plot.title = element_text(size=10))

hsr_plot <- gps_data %>% 
  filter(Date == '2018-02-17') %>% 
  mutate(
    Name = forcats::fct_reorder( Name, dist_total, desc=T),
    tooltip = paste0(Name, "\n",`Position Specific`, "\n", 
                     round(hsr_total, 0),"m")
  ) %>%
  ggplot(aes(Name, hsr_total, , label=
               paste0(round(hsr_total, 0),"m"))) +
  ggiraph::geom_bar_interactive(aes(y=hsr_total, fill=`Position Specific`,
                                    tooltip=tooltip),stat = 'identity') +
  coord_flip() +
  geom_text(hjust=1, size=2) +
  bbplot::bbc_style() +
  labs(y = "High Speed Distance", x = 'Player Name', 
       fill='Position', title = 'High Speed Distance Covered', type='Garamond') +
  theme(
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "none",
    plot.title = element_text(size=10)
  )

ggiraph_plots <- cowplot::plot_grid(td_plot, hsr_plot)

ggiraph::girafe(code=print(ggiraph_plots))

Plotly

td_plot <- gps_data %>% 
  filter(Date == '2018-02-17') %>% 
  mutate(
    Name = forcats::fct_reorder( Name, dist_total, desc=T)
  ) %>%
  ggplot(aes(Name, dist_total, fill = `Position Specific`, 
             label = paste0(round(dist_total, 0),"m")))+
  geom_col() +
  coord_flip() +
  geom_text(hjust=1) +
  bbplot::bbc_style() +
  theme(
    axis.text.x = element_blank(),
    axis.text.y = element_text(size=14),
    axis.ticks.x = element_blank(),
    legend.justification=c(1,0), 
    legend.position=c(1,0))

td_plot <- plotly::ggplotly(td_plot)

hsr_plot <- gps_data %>% 
  filter(Date == '2018-02-17') %>% 
  mutate(
    Name = forcats::fct_reorder(Name, dist_total, desc=T)
  ) %>%
  ggplot(aes(Name, hsr_total, fill=`Position Specific`,
             label = paste0(round(hsr_total, 0),"m")))+
  geom_col() +
  coord_flip() +
  geom_text(hjust=1) +
  bbplot::bbc_style() +
  theme(
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.x = element_blank())

hsr_plot <- plotly::ggplotly(hsr_plot)

subplot(style(td_plot,hoverinfo=c('y+x')), 
        style(hsr_plot, hoverinfo=c('y+x'))) %>%
  layout(title="Comparison of Total Distance to High Speed Running", 
         titlefont=10, showlegend=TRUE, legend = list(font = list(size = 8)))

Dygraphs

ts_data <- gps_data %>%
  filter(Name == '04fa3')%>%
  select(Date, dist_total, hsr_total)

names <- c('Date', 'Total Distance', 'High Speed Distance')
colnames(ts_data) <- names

gps_ts <- xts(x = ts_data,
              order.by = ts_data$Date)
dygraph(gps_ts, main = 'Seasonal Total Distance and High Speed Distance Data') %>% 
  dyRangeSelector(dateWindow = c(max(ts_data$Date)-30, max(ts_data$Date))) %>%
  dyAxis("y", label = "Meters", valueRange = c(0, 10000)) %>%
  dyHighlight(highlightCircleSize = 4, 
              highlightSeriesBackgroundAlpha = 0.5,
              hideOnMouseOut = TRUE) %>%
  dyLegend(show = "follow") %>%
  dyOptions(drawPoints = TRUE, pointSize = 2) %>%
  dyAxis("x", drawGrid = FALSE) %>%
  dySeries("High Speed Distance", fillGraph = TRUE, color = "red")

source("https://raw.githubusercontent.com/iascchen/VisHealth/master/R/calendarHeat.R")

ts_data <- gps_data %>%
  filter(Name == '04fa3')%>%
  select(Date, dist_total, hsr_total)

calendarHeat(ts_data$Date, ts_data$dist_total, color = "g2r")

ts_data <- gps_data %>%
  filter(Name == '04fa3')%>%
  select(Date, dist_total, hsr_total)


xts_heatmap <- function(x = datetime, y = value){
  gg <-  data.frame(Date = ymd(x), y) %>%
    setNames(c("Date","Value")) %>%
    arrange(Date) %>%
    mutate(
      Year = year(Date),
      Month = month(Date),
      tooltips = paste0(Date, '\n',round(Value, 0),"m"),
      # I use factors here to get plot ordering in the right order
      # without worrying about locale
      MonthTag = factor(Month,levels = as.character(1:12),
                        labels = c("Jan","Feb","Mar","Apr","May","Jun",
                                   "Jul","Aug","Sep","Oct","Nov","Dec"),
                        ordered=TRUE),
      # week start on Monday in my world
      Wday = wday(Date,week_start=1),
      # the rev reverse here is just for the plotting order
      WdayTag = factor(Wday,levels = rev(1:7),
                       labels = 
                         rev(c("Mon","Tue","Wed","Thu","Fri","Sat","Sun")),
                       ordered=TRUE),
      Week = as.numeric(format(Date,"%W"))
    ) %>%
    # ok here we group by year and month and then calculate the week of the month 
    # we are currently in
    group_by(Year,Month) %>% 
    mutate(Wmonth = 1 + Week - min(Week)) %>% 
    ungroup() %>% 
    ggplot(aes(x = Wmonth, y= WdayTag, fill = Value)) + 
    geom_tile_interactive(colour = "white", aes(tooltip=tooltips)) + 
    facet_grid(Year ~ MonthTag) + 
    scale_fill_gradient2(low = "blue", mid = 'green', high = "red", 
                         midpoint = 5000) +
    labs(y = NULL) +
    geom_vline(xintercept = 0, colour = 'grey')+
    guides(fill = guide_colourbar(barwidth = 20, barheight = 0.5))+
    bbplot::bbc_style() +
    labs(title = "Heatmap of Total Distance") +
    theme(
      axis.text.y = element_text(size=14),
      axis.text.x = element_blank(),
      strip.text.x = element_text(size=13)
      
    )
  
  girafe(code=print(gg))
}


# lets see
xts_heatmap(ts_data$Date, ts_data$dist_total)

library(ggiraph)
library(lubridate)

ts_data <- gps_data %>%
  filter(Name == '04fa3') %>%
  select(Date,  percentVmax) %>%
  mutate(
    dow = wday(Date,label=TRUE, week_start = 1),
    dow = factor(dow, levels = rev(levels(dow))),
    week = week(Date),
    weeks = format(Date, "%W"),
    weeks = factor(weeks, levels = unique(weeks)),
    weekStart = Date - as.POSIXlt(Date)$wday,
    month = month(Date, label = TRUE),
    year = year(Date),
    tooltips = paste0(Date, "\n", 'Percent Max Velocity ', scales::percent(percentVmax))
  )

values <- colorRampPalette(c('green', 'lightgreen', 'orange', 'red')) (nlevels(
  factor(ts_data$percentVmax)))

x2 <- ggplot(ts_data,aes(weekStart,dow,fill=factor(percentVmax))) +
  geom_tile_interactive(colour = "white", 
                        aes(tooltip=tooltips,
                            data_id=as.character(Date)), size=.1) + 
  scale_fill_manual(values = rev(values)) +
  scale_x_date(date_breaks = "1 week",date_labels="%d-%b") +
  bbplot::bbc_style()+
  ggExtra::removeGrid() + 
  ggExtra::rotateTextX() +
  ggtitle("Percent Max Velocity per Day", 
          subtitle = 'Red ->> Low, Orange ->> OK, Green ->> Good') +
  labs(x="Week Beginning", y=NULL) +
  facet_wrap(year~month, scales = 'free_x') +
  theme(
    plot.title = element_text(hjust = 0, size=14), 
    plot.subtitle = element_text(size = 10),
    strip.text = element_text(size=5),
    axis.ticks = element_blank(), 
    axis.text.y = element_text(size = 4), 
    axis.text.x = element_text(size=5, angle = 70), 
    legend.position = "none")

x3 <- girafe(code=print(x2))

tooltip_css <- "padding:10px;border-radius:10px 20px 10px 20px;"

girafe_options(x3, 
               opts_tooltip(offx = -60, 
                            offy = -120, use_fill = TRUE,
                            css=tooltip_css),
               opts_hover(css = "fill:#228B22;"),
               opts_zoom(max = 5))