ezplot

Package for generating common plots.

https://github.com/wkostelecki/ezplot

Repository

Package for generating common plots.

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  fig.width = 6,
  fig.height = 3,
  fig.align = "center"
)
```
# ezplot


[![Coverage status](https://codecov.io/gh/wkostelecki/ezplot/branch/master/graph/badge.svg)](https://app.codecov.io/github/wkostelecki/ezplot?branch=master)
[![CRAN status](https://www.r-pkg.org/badges/version/ezplot)](https://cran.r-project.org/package=ezplot)
[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/ezplot)](https://www.r-pkg.org:443/pkg/ezplot)
[![Total CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/ezplot)](https://www.r-pkg.org:443/pkg/ezplot)
[![lifecycle](https://img.shields.io/badge/lifecycle-stable-green.svg)](https://lifecycle.r-lib.org/articles/stages.html#maturing)
[![R build status](https://github.com/wkostelecki/ezplot/workflows/R-CMD-check/badge.svg)](https://github.com/wkostelecki/ezplot/actions)
[![R-CMD-check](https://github.com/wkostelecki/ezplot/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/wkostelecki/ezplot/actions/workflows/R-CMD-check.yaml)


## 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](https://CRAN.R-project.org) with:

``` r
install.packages("ezplot")
```

And the development version from [GitHub](https://github.com/wkostelecki/ezplot) with:

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

## Usage

```{r load packages}
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:
```{r line_plot-1}
line_plot(ansett, x = "Week", y = "Passengers")
```

Add grouping:
```{r line_plot-2}
line_plot(ansett, x = "Week", y = "Passengers", group = "Class")
```

Add faceting:
```{r line_plot-3, fig.width = 8, fig.height = 5}
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:
```{r line_plot-4, fig.width = 8, fig.height = 4}
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:
```{r line_plot-5, fig.width = 7, fig.height = 4}
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:
```{r area_plot-1}
area_plot(ansett, x = "as.Date(Week)", y = "Passengers")
```

Add grouping:
```{r area_plot-2}
area_plot(ansett, x = "as.Date(Week)",
          y = c("Weekly Passengers" = "Passengers"),
          "Class")
```

Add faceting:
```{r area_plot-3, fig.width = 8, fig.height = 4}
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:
```{r bar_plot-1}
bar_plot(subset(aus_retail, year(Month) >= 2010),
         x = "year(Month)",
         y = "Turnover")
```

With grouping:
```{r bar_plot-2, fig.width = 7, fig.height = 4}
bar_plot(subset(aus_retail, year(Month) >= 2010), 
         x = "year(Month)",
         y = "Turnover",
         group = "State")
```
Share of turnover:
```{r bar_plot-3, fig.width = 7, fig.height = 4}
bar_plot(subset(aus_retail, year(Month) >= 2010), 
         x = "year(Month)",
         y = "Turnover",
         group = "State",
         position = "fill")
```

### tile_plot
```{r tile_plot-1}
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"))
```

```{r tile_plot-2, fig.width = 7}
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
```{r waterfall_plot-1, fig.height = 4}
waterfall_plot(aus_retail,
               "lubridate::year(Month)",
               "Turnover", 
               "sub(' Territory', '\nTerritory', State)", 
               rotate_xlabel = TRUE)
```

### side_plot
```{r side_plot-1, fig.width = 8}
side_plot(PBS, 
          "paste(Concession, Type, sep = ' - ')", 
          c("Scripts", "Cost", "Average Cost" = "~ Cost / Scripts"))
```

### secondary_plot
Plot with secondary y-axis.
```{r secondary_plot-1}
secondary_plot(pelt, "Year",
               c("Hare Population" = "Hare"), c("Lynx Population" = "Lynx"),
               ylim1 = c(0, 160e3),
               ylim2 = c(0, 80e3))
```

### density_plot
```{r density_plot-1}
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
```{r histogram_plot-1}
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
```{r classification data}
data(ROCR.simple)
df = data.frame(pred = ROCR.simple$predictions,
                lab = ROCR.simple$labels)
set.seed(4)
```

### roc_plot
```{r roc_plot-1}
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
```{r pr_plot-1}
set.seed(4)
pr_plot(df, "pred", "lab") +
  pr_plot(df, "pred", "lab", group = "sample(c(0, 1), n(), replace = TRUE)")
```

### lift_plot

```{r lift_plot-1}
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")
```

Owner

GitHub Events

Packages