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ezplot

Overview

ezplot provides high-level wrapper functions for common chart types with reduced typing and easy faceting. e.g.:

• line_plot()
• area_plot()
• bar_plot()
• tile_plot()
• waterfall_plot()
• side_plot()
• secondary_plot()

Installation

You can install the released version of ezplot from CRAN with:

install.packages("ezplot")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("wkostelecki/ezplot")

Usage

library(ezplot)
suppressPackageStartupMessages(library(tsibble))
library(tsibbledata)
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(lubridate))
suppressPackageStartupMessages(library(ggplot2))
library(patchwork)
suppressPackageStartupMessages(library(ROCR, warn.conflicts = FALSE))

line_plot

Weekly aggregation:

line_plot(ansett, x = "Week", y = "Passengers")

Add grouping:

line_plot(ansett, x = "Week", y = "Passengers", group = "Class")

Add faceting:

line_plot(ansett, x = "Week", y = "Passengers",
          group = "Class", facet_x = "Airports",
          facet_scales = "free_y") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.38, hjust = 1))

Plot YOY comparisons:

line_plot(gafa_stock, "Date", c("Closing Stock Price" = "Close"),
          facet_y = "Symbol",
          facet_scales = "free_y",
          reorder = NULL,
          yoy = TRUE,
          labels = function(x) ez_labels(x, prepend = "$"))

Plot multiple numeric columns:

line_plot(hh_budget,
          "Year",
          c("DI", "Expenditure", "Savings"),
          facet_x = "Country") +
  theme(panel.spacing.x = unit(1, "lines")) +
  ylab(NULL)

area_plot

Weekly aggregation:

area_plot(ansett, x = "as.Date(Week)", y = "Passengers")

Add grouping:

area_plot(ansett, x = "as.Date(Week)",
          y = c("Weekly Passengers" = "Passengers"),
          "Class")

Add faceting:

area_plot(ansett,
          "year(Week) + (month(Week) - 1) / 12",
          y = c("Monthly Passengers" = "Passengers"),
          group = "substr(Airports, 5, 7)",
          facet_x = "substr(Airports, 1, 3)", facet_y = "Class",
          facet_scales = "free_y") +
          theme(axis.text.x = element_text(angle = 90, vjust = 0.38, hjust = 1))

bar_plot

Yearly aggregation:

bar_plot(subset(aus_retail, year(Month) >= 2010),
         x = "year(Month)",
         y = "Turnover")

With grouping:

bar_plot(subset(aus_retail, year(Month) >= 2010), 
         x = "year(Month)",
         y = "Turnover",
         group = "State")

Share of turnover:

bar_plot(subset(aus_retail, year(Month) >= 2010), 
         x = "year(Month)",
         y = "Turnover",
         group = "State",
         position = "fill")

tile_plot

nyc_bikes %>%
  mutate(duration = as.numeric(stop_time - start_time)) %>%
  filter(between(duration, 0, 16)) %>%
  tile_plot(c("Trip Start (Hour of Day)" = "lubridate::hour(start_time) + 0.5"),
            c("Ride Duration (min)" = "duration - duration %% 2 + 1"))

tile_plot(vic_elec,
          c("Temperature (C)" = "round(Temperature)"), 
          c("Half-Hourly Demand (MW)" = "round(Demand, -2)"),
          labels_y = ez_labels, facet_x = "year(Time)")

waterfall_plot

waterfall_plot(aus_retail,
               "lubridate::year(Month)",
               "Turnover", 
               "sub(' Territory', '\nTerritory', State)", 
               rotate_xlabel = TRUE)

side_plot

side_plot(PBS, 
          "paste(Concession, Type, sep = ' - ')", 
          c("Scripts", "Cost", "Average Cost" = "~ Cost / Scripts"))

secondary_plot

Plot with secondary y-axis.

secondary_plot(pelt, "Year",
               c("Hare Population" = "Hare"), c("Lynx Population" = "Lynx"),
               ylim1 = c(0, 160e3),
               ylim2 = c(0, 80e3))

density_plot

nyc_bikes %>% 
  mutate(duration = as.numeric(stop_time - start_time)) %>%
  density_plot(c("time of day" = "as.numeric(start_time) %% 86400 / 60 / 60"),
               group = "ifelse(wday(start_time) %in% c(1, 7), 'week end', 'week day')")

histogram_plot

nyc_bikes %>% 
  mutate(duration = as.numeric(stop_time - start_time)) %>%
  histogram_plot(c("time of day" = "as.numeric(start_time) %% 86400 / 60 / 60"),
                 "density",
                 group = "ifelse(wday(start_time) %in% c(1, 7), 'week end', 'week day')",
                 position = "identity",
                 bins = 48)

Classification data

data(ROCR.simple)
df = data.frame(pred = ROCR.simple$predictions,
                lab = ROCR.simple$labels)
set.seed(4)

roc_plot

set.seed(4)
roc_plot(df, "pred", "lab") + 
  roc_plot(df, "pred", "lab", group = "sample(c(0, 1), n(), replace = TRUE)")

pr_plot

Precision-Recall plot

set.seed(4)
pr_plot(df, "pred", "lab") +
  pr_plot(df, "pred", "lab", group = "sample(c(0, 1), n(), replace = TRUE)")

lift_plot

set.seed(4)
performance_plot(df, "pred", "lab", x = "rpp", y = "lift") +
  performance_plot(df, "pred", "lab", group = "sample(c(0, 1), n(), replace = TRUE)", x = "rpp", y = "lift")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.