0. Preparation

I need to install the following packages:

# install CRAN packages
pkg_install =   c("ggplot2", "tibble", "tidyr", "forcats", "purrr", "prismatic", "corrr", 
    "cowplot", "ggforce", "ggrepel", "ggridges", "ggsci", "ggtext", "ggthemes", 
    "grid", "gridExtra", "patchwork", "rcartocolor", "scico", "showtext", 
    "shiny", "plotly", "highcharter", "echarts4r")
install.packages(pkg_install)

I was facing the error of installing devtools:

# install from GitHub since not on CRAN
install.packages('devtools')
devtools::install_github("JohnCoene/charter")

I tried to update R to the latest version (commented the code as it would be run once):

update.packages(repos='http://cran.rstudio.com/', ask=FALSE, checkBuilt=TRUE)

–> Not worked.

Oops I was should be using “‘devtools’” instead of “devtools”!!! Problem solved

1. The Dataset

I was using the dataset: “National Morbidity and Mortality Air Pollution Study (NMMAPS)”

Install the readr first:

install.packages('readr')
# install.packages("quarto")

Import data

“::” here call the namespace and can be used to access a function without loading the package.

chic <- readr::read_csv("https://cedricscherer.com/data/chicago-nmmaps-custom.csv")

Rows: 1461 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): city, season, month
dbl  (6): temp, o3, dewpoint, pm10, yday, year
date (1): date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

view some data:

tibble::glimpse(chic)

Rows: 1,461
Columns: 10
$ city     <chr> "chic", "chic", "chic", "chic", "chic", "chic", "chic", "chic…
$ date     <date> 1997-01-01, 1997-01-02, 1997-01-03, 1997-01-04, 1997-01-05, …
$ temp     <dbl> 36.0, 45.0, 40.0, 51.5, 27.0, 17.0, 16.0, 19.0, 26.0, 16.0, 1…
$ o3       <dbl> 5.659256, 5.525417, 6.288548, 7.537758, 20.760798, 14.940874,…
$ dewpoint <dbl> 37.500, 47.250, 38.000, 45.500, 11.250, 5.750, 7.000, 17.750,…
$ pm10     <dbl> 13.052268, 41.948600, 27.041751, 25.072573, 15.343121, 9.3646…
$ season   <chr> "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "…
$ yday     <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ month    <chr> "Jan", "Jan", "Jan", "Jan", "Jan", "Jan", "Jan", "Jan", "Jan"…
$ year     <dbl> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1…

head(chic,10)

# A tibble: 10 × 10
   city  date        temp    o3 dewpoint  pm10 season  yday month  year
   <chr> <date>     <dbl> <dbl>    <dbl> <dbl> <chr>  <dbl> <chr> <dbl>
 1 chic  1997-01-01  36    5.66    37.5  13.1  Winter     1 Jan    1997
 2 chic  1997-01-02  45    5.53    47.2  41.9  Winter     2 Jan    1997
 3 chic  1997-01-03  40    6.29    38    27.0  Winter     3 Jan    1997
 4 chic  1997-01-04  51.5  7.54    45.5  25.1  Winter     4 Jan    1997
 5 chic  1997-01-05  27   20.8     11.2  15.3  Winter     5 Jan    1997
 6 chic  1997-01-06  17   14.9      5.75  9.36 Winter     6 Jan    1997
 7 chic  1997-01-07  16   11.9      7    20.2  Winter     7 Jan    1997
 8 chic  1997-01-08  19    8.68    17.8  33.1  Winter     8 Jan    1997
 9 chic  1997-01-09  26   13.4     24    12.1  Winter     9 Jan    1997
10 chic  1997-01-10  16   10.4      5.38 24.8  Winter    10 Jan    1997

3. The `{ggplot2}` Package

A ggplot is built up from a few basic elements:

Data;
Geometries geom_: the geometric shape (hình học) that will represent the data;
Aesthetics aes_: aesthetics (tính thẩm mỹ) of the geometric or statistical objects, such as postition, color, size, shape, and transparency;
Scales scale_: map between the data and the aesthetics dimensions (ánh xạ từ dữ liệu đến đồ thị), such as data range to plot width or factor values to colors;
Statistical transformations stat_: statistical summaries (thống kê) of data, such as quantitles, fitted curves, and sums;
Coordinate system coord_: the transformation used for mapping data coordinates into the plane of the data rectangles (hệ tọa độ);
Facets facet_: the arrangement of the data into a grid of plots;
Visual themes theme(): the overall visual defaults of a plot, such as background, grids, axes, default typeface, sizes and colors (tông).

🚀 Không nhất thiết một phần tử được gọi, và chúng cũng có thể được gọi nhiều lần.

4. A default `ggplot`

Load the package for ability to use the functionality:

library(ggplot2)
# library(tidyverse) # can also be imported from the tidy-universe!

A default ggplot needs three things that you have to specify: the data, aesthetics, and a geometry.

starting define a plot by using ggplot(data = df);
if we want to plot (in most cases) 2 variables, we must add positional aesthetics aes(x = var1, y = var2);

🚀 Data được đề cập bên ngoài aes(), trong khi đó biến/variables được đề cập bên trong aes().

Ví dụ:

(g <- ggplot(chic, aes(x = date, y = temp)))

Just a blank panel, because ggplot2 does not know how we plot data ~ we still need to provide geometry.

🚀 ggplot2 cho phép chúng ta lưu ggobject thành một biến, trong trường hợp này là g . Chúng ta có thể mở rộng** g bằng cách thêm cách layers về sau.**

🚀 Bằng cách dùng dấu (), chúng ta có thể in ngay object được gán ra.

Many different geometries to use (called geoms because each function usually starts with geom_). For e.g., if we want to plot a scatter plot.

g + geom_point()

also a lineplot which our managers always like:

g + geom_line()

cool but the plot does not look optimal, we can also using mutiple layers of geometry, where the magic and fun start.

g + geom_point() + geom_line()

# it's the same if we write g + geom_line() + geom_point()

Change properties of geometries

Turn all points to large fire-red diamonds:

g + geom_point(color = 'firebrick', shape = 'diamond', size = 2)

🚀 ggplot2 hiểu khi chúng ta dùng color, colour, cũng như col.

🚀 Có thể dùng màu mặc định hoặc màu hex, hoặc thậm chí là màu RGB/RGBA với hàm rgb(). Ví dụ:

g + geom_point(color = "#b22222", shape = "diamond", size = 2)

g + geom_point(color = rgb(178, 34, 34, maxColorValue = 255), shape = "diamond", size = 2)

Replacing the default `ggplot2` theme

Calling eg theme_bw() using theme_set(), all following plots will have same blank’n’white theme.

theme_set(theme_bw())

g + geom_point(color = 'firebrick')

🚀 theme() is also a useful function to modify all kinds of theme elements (texts, rectangles, and lines).

5. Axes

Change Axis Titles

Use labs() to assign character string for each lable.

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = 'firebrick') +
    labs(x = 'Year', y = 'Temperature (°F)')

Can also using xlab() and ylab():

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  xlab("Year") +
  ylab("Temperature (°F)")

Not only the degree symbol before F, but also the supper script:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = expression(paste("Temperature (", degree ~ F, ")"^"(Hey, why should we use metric units?!)")))

Increase space between Axis and Axis Titles.

Overwrite the default element_text() within the theme() call:

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = 'firebrick') +
    labs(x = 'Year', y = 'Temperature (°F)') +
    theme(axis.title.x = element_text(vjust = 0, size = 30),
         axis.title.y = element_text(vjust = 2, size = 30))

vjust refer to vertical alignment. We can also change the distance by specifying the margin of both text elements.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title.x = element_text(margin = margin(t = 10), size = 15),
        axis.title.y = element_text(margin = margin(r = 10), size = 15))

r and t in the margin are top and right. Margin has 4 arguments: margin(t, r, b, l). A good way to remember the order of the margin sides is “t-r-ou-b-l-e”.

Change Aesthetics of the Axis Titles

Again, we use theme() function and modify the axis.tile and/or the subordinated elements axis.tile.x and axis.tile.y . Within element_text() we can modify the default of size, color, and face.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title = element_text(size = 15, color = "firebrick",
                                  face = "italic"))

the face argument can be used to make the font bold, italic, or even bold.italic.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title.x = element_text(color = "sienna", size = 15, face = 'bold'),
        axis.title.y = element_text(color = "orangered", size = 15, face = 'bold.italic'))

You could also use a combination of axis.title and axis.title.y, since axis.title.x inherits the values from axis.title. Eg:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title = element_text(color = "sienna", size = 15),
        axis.title.y = element_text(color = "orangered", size = 15))

One can modify some properties for both axis titles and other only for one or properties for each on its own:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.title = element_text(color = "sienna", size = 15, face = "bold"),
        axis.title.y = element_text(face = "bold.italic"))

Change Aesthetics of Axis Text

Similar to the title, we can change the appearance of the axis text (number indeed) by using axis.text and/or the subordinated elements axis.text.x and axis.text.y.

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = 'firebrick') +
    labs(x= "Year", y = expression(paste("Temperature(",degree ~ F, ")"))) +
    theme(axis.text = element_text(color = "dodgerblue", size = 13),
         axis.text.x = element_text(face = 'italic'))

Rotate Axis Text

Specifying an angle help us to rotate any text elements. With hjust and vjust we can adjust the position of text afterwards horizontally (0 = left, 1 = right), and vertically (0 = top, 1 = bottom).

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = 'firebrick') +
    labs(x= "Year", y = expression(paste("Temperature(",degree ~ F, ")"))) +
    theme(axis.text.x = element_text(angle = 50, vjust = 1, hjust = 1, size = 13))

# 50 means 50 degrees, not % =)))

Remove Axis Text & Ticks

Rarely a reason to do this but this is how it works.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  theme(axis.ticks.y = element_blank(),
        axis.text.y = element_blank())

🚀If you want to get rid of a theme element, the element is always element_blank.

Remove Axis Titles

We could again use element_blank() but it is way simpler to just remove the label in the labs() (or xlab()) call:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = NULL, y = "")

Note that NULL removes the element (similarly to element_blank()) while empty quotes "" will keep the spacing for the axis title and simply print nothing.

Limit Axis Range

Some time you want to take a closer look at some range of you data. You can do this without subsetting your data:

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = "Temperature (°F)") +
  ylim(c(0, 50))

Warning: Removed 777 rows containing missing values or values outside the scale range
(`geom_point()`).

Alternatively you can use scale_y_continuous(limits = c(0, 50)) (subset) or coord_cartesian(ylim = c(0, 50)). The former removes all data points outside the range while the second adjusts the visible area (zooming) and is similar to ylim(c(0, 50)) (subset).

Force Plot to start at the origin

chic_high <- dplyr::filter(chic, temp > 25, o3 > 20)

ggplot(chic_high, aes(x = temp, y = o3)) +
  geom_point(color = "darkcyan") +
  labs(x = "Temperature higher than 25°F",
       y = "Ozone higher than 20 ppb") +
  expand_limits(x = 0, y = 0)

Using coord_cartesian(xlim = c(0,NA), ylim = c(0,NA)) will lead to the same result.

chic_high <- dplyr::filter(chic, temp > 25, o3 > 20)

ggplot(chic_high, aes(x = temp, y = o3)) +
  geom_point(color = "darkcyan") +
  labs(x = "Temperature higher than 25°F",
       y = "Ozone higher than 20 ppb") +
  coord_cartesian(xlim = c(0, NA), ylim = c(0, NA))

But we can also force it to literally start at the origin!

ggplot(chic_high, aes(x = temp, y = o3)) +
  geom_point(color = "darkcyan") +
  labs(x = "Temperature higher than 25°F",
       y = "Ozone higher than 20 ppb") +
  expand_limits(x = 0, y = 0) +
  coord_cartesian(expand = FALSE, clip = "off")

🚀 The argument clip = "off" in any coordinate system, always starting with coord_*, allows to draw outside of the panel area. Call it here to make sure that the tick marks at c(0, 0) are not cut.

Axes with Same Scaling

Use coord_equal() with default ratio = 1 to ensure the units are equally scaled on the x-axis and on the y-axis. We can set the aspect ratio of a plot with coord_fixed() or coord_equal(). Both use aspect = 1 (1:1) as a default.

ggplot(chic, aes(x = temp, y = temp + rnorm(nrow(chic), sd = 20))) +
  geom_point(color = "sienna") +
  labs(x = "Temperature (°F)", y = "Temperature (°F) + random noise") +
  xlim(c(0, 100)) + ylim(c(0, 150)) +
  coord_fixed()

Warning: Removed 60 rows containing missing values or values outside the scale range
(`geom_point()`).

Ratios higher than one make units on the y axis longer than units on the x-axis, and vice versa:

ggplot(chic, aes(x = temp, y = temp + rnorm(nrow(chic), sd = 20))) +
  geom_point(color = "sienna") +
  labs(x = "Temperature (°F)", y = "Temperature (°F) + random noise") +
  xlim(c(0, 100)) + ylim(c(0, 150)) +
  coord_fixed(ratio = 1/5)

Warning: Removed 55 rows containing missing values or values outside the scale range
(`geom_point()`).

Use a Function to Alter Labels

In case you want to format (eg adding % sign) without change the data.

ggplot(chic, aes(x = date, y = temp)) +
  geom_point(color = "firebrick") +
  labs(x = "Year", y = NULL) +
  scale_y_continuous(label = function(x) {return(paste(x, "Degrees Fahrenheit"))})

6. Titles

Add a Title

We can add a title via ggtitle() function.

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = "firebrick") +
    labs(x = "Year", y = "Temperature (°F)") +
    ggtitle("Temperatures in Chicago")

Alternatively, we can use labs(), where we can add serveral arguments ~ metadata of the plot (a sub-title, a caption, and a tag):

ggplot(chic, aes(x = date, y = temp)) +
    geom_point(color = "firebrick") +
    labs(x = "Year", y = "Temperature (°F)",
        title = "Temperatures in Chicago",
        subtitle = "Seasonal pattern of daily temperatures from 1997 to 2001",
        caption = "Data: NMMAPS",
        tag = "Fig 1")

Make title bold & add a space at the baseline

7. Legends

8. Backgrounds & Grid Lines

9. Margins

10. Multi-panel Plots

11. Colors

12. Themes

13. Lines

14. Text

15. Coordinates

16. Chart Types

17. Ribbons (AUC, CI, etc.)

18. Smoothings

19. Interactive Plots

20. Remarks, Tipps & Resources

References

Source: https://www.cedricscherer.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/

--- title: "A tuturial of ggplot2" description: "This is a again a lesson on ggplot2" author: - name: "Tuan Le Khac" url: https://lktuan.github.io/ date: 12-24-2023 date-modified: 05-28-2024 categories: [R, ggplot2, Data Visualization] image: ggplot2.png draft: false code-tools: true code-fold: false --- # 0. Preparation ------------------------------------------------------------------------ I need to install the following packages: ```{r} #| eval: false # install CRAN packages pkg_install = c("ggplot2", "tibble", "tidyr", "forcats", "purrr", "prismatic", "corrr", "cowplot", "ggforce", "ggrepel", "ggridges", "ggsci", "ggtext", "ggthemes", "grid", "gridExtra", "patchwork", "rcartocolor", "scico", "showtext", "shiny", "plotly", "highcharter", "echarts4r") install.packages(pkg_install) ``` I was facing the error of installing devtools: ```{r} #| eval: false # install from GitHub since not on CRAN install.packages('devtools') devtools::install_github("JohnCoene/charter") ``` I tried to update R to the latest version (commented the code as it would be run once): ```{r} #| eval: false update.packages(repos='http://cran.rstudio.com/', ask=FALSE, checkBuilt=TRUE) ``` --\> Not worked. **Oops I was should be using "'devtools'" instead of "devtools"!!! Problem solved** # 1. The Dataset ------------------------------------------------------------------------ I was using the dataset: "*National Morbidity and Mortality Air Pollution Study* (NMMAPS)" Install the readr first: ```{r} #| eval: false install.packages('readr') # install.packages("quarto") ``` ### Import data "::" here call the namespace and can be used to access a function without loading the package. ```{r} chic <- readr::read_csv("https://cedricscherer.com/data/chicago-nmmaps-custom.csv") ``` view some data: ```{r} tibble::glimpse(chic) ``` ```{r} head(chic,10) ``` # 3. The `{ggplot2}` Package ------------------------------------------------------------------------ A ggplot is built up from a few basic elements: 1. Data; 2. Geometries `geom_`: the geometric shape (hình học) that will represent the data; 3. Aesthetics `aes_`: aesthetics (tính thẩm mỹ) of the geometric or statistical objects, such as postition, color, size, shape, and transparency; 4. Scales `scale_`: map between the data and the aesthetics dimensions (ánh xạ từ dữ liệu đến đồ thị), such as data range to plot width or factor values to colors; 5. Statistical transformations `stat_`: statistical summaries (thống kê) of data, such as quantitles, fitted curves, and sums; 6. Coordinate system `coord_`: the transformation used for mapping data coordinates into the plane of the data rectangles (hệ tọa độ); 7. Facets `facet_`: the arrangement of the data into a grid of plots; 8. Visual themes `theme()`: the overall visual defaults of a plot, such as background, grids, axes, default typeface, sizes and colors (tông). 🚀 **Không nhất thiết một phần tử được gọi, và chúng cũng có thể được gọi nhiều lần.** # 4. A default `ggplot` ------------------------------------------------------------------------ Load the package for ability to use the functionality: ```{r} library(ggplot2) # library(tidyverse) # can also be imported from the tidy-universe! ``` A default ggplot needs three things that you have to specify: the *data*, *aesthetics*, and a *geometry.* - starting define a plot by using `ggplot(data = df)`; - if we want to plot (in most cases) 2 variables, we must add *positional aesthetics* `aes(x = var1, y = var2);` 🚀 **Data được đề cập *bên ngoài* `aes()`, trong khi đó biến/variables được đề cập *bên trong* `aes()`.** Ví dụ: ```{r} (g <- ggplot(chic, aes(x = date, y = temp))) ``` Just a blank panel, because `{ggplot2}` *does not know* how we plot data \~ we still need to provide geometry. 🚀 **`ggplot2` cho phép chúng ta lưu `ggobject` thành một biến, trong trường hợp này là `g` . Chúng ta có thể **mở rộng** `g` bằng cách thêm cách layers về sau.** 🚀 **Bằng cách dùng dấu `()`, chúng ta có thể *in* ngay object được gán ra.** Many different geometries to use (called `geoms` because each function usually starts with `geom_`). For e.g., if we want to plot a scatter plot. ```{r} g + geom_point() ``` also a lineplot which our managers always like: ```{r} g + geom_line() ``` cool but the plot does not look optimal, we can also using mutiple layers of geometry, where the magic and fun start. ```{r} g + geom_point() + geom_line() # it's the same if we write g + geom_line() + geom_point() ``` ### Change properties of geometries Turn all points to large fire-red diamonds: ```{r} g + geom_point(color = 'firebrick', shape = 'diamond', size = 2) ``` 🚀 **`ggplot2` hiểu khi chúng ta dùng `color`, `colour`, cũng như `col`.** 🚀 **Có thể dùng [màu mặc định](http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf) hoặc [màu hex](https://www.techopedia.com/definition/29788/color-hex-code), hoặc thậm chí là màu RGB/RGBA với hàm `rgb()`. Ví dụ:** ```{r} g + geom_point(color = "#b22222", shape = "diamond", size = 2) g + geom_point(color = rgb(178, 34, 34, maxColorValue = 255), shape = "diamond", size = 2) ``` #### Replacing the default `ggplot2` theme Calling eg `theme_bw()` using `theme_set()`, all following plots will have same blank'n'white theme. ```{r} theme_set(theme_bw()) g + geom_point(color = 'firebrick') ``` 🚀 **`theme()` is also a useful function to modify all kinds of theme elements (texts, rectangles, and lines).** # 5. Axes ------------------------------------------------------------------------ ### Change Axis Titles Use `labs()` to assign character string for each lable. ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = 'firebrick') + labs(x = 'Year', y = 'Temperature (°F)') ``` Can also using `xlab()` and `ylab()`: ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + xlab("Year") + ylab("Temperature (°F)") ``` Not only the degree symbol before F, but also the supper script: ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = expression(paste("Temperature (", degree ~ F, ")"^"(Hey, why should we use metric units?!)"))) ``` ### Increase space between Axis and Axis Titles. Overwrite the default `element_text()` within the `theme()` call: ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = 'firebrick') + labs(x = 'Year', y = 'Temperature (°F)') + theme(axis.title.x = element_text(vjust = 0, size = 30), axis.title.y = element_text(vjust = 2, size = 30)) ``` `vjust` refer to vertical alignment. We can also change the distance by specifying the margin of both text elements. ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)") + theme(axis.title.x = element_text(margin = margin(t = 10), size = 15), axis.title.y = element_text(margin = margin(r = 10), size = 15)) ``` `r` and `t` in the `margin` are **top** and **right**. Margin has 4 arguments: `margin(t, r, b, l)`. A good way to remember the order of the margin sides is “***t-r***-ou-***b-l***-e”. ### Change Aesthetics of the Axis Titles Again, we use `theme()` function and modify the `axis.tile` and/or the subordinated elements `axis.tile.x` and `axis.tile.y` . Within `element_text()` we can modify the default of `size`, `color`, and `face`. ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)") + theme(axis.title = element_text(size = 15, color = "firebrick", face = "italic")) ``` the `face` argument can be used to make the font `bold`, `italic`, or even `bold.italic`. ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)") + theme(axis.title.x = element_text(color = "sienna", size = 15, face = 'bold'), axis.title.y = element_text(color = "orangered", size = 15, face = 'bold.italic')) ``` You could also use a combination of `axis.title` and `axis.title.y`, since `axis.title.x` inherits the values from `axis.title`. Eg: ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)") + theme(axis.title = element_text(color = "sienna", size = 15), axis.title.y = element_text(color = "orangered", size = 15)) ``` One can modify some properties for both axis titles and other only for one or properties for each on its own: ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)") + theme(axis.title = element_text(color = "sienna", size = 15, face = "bold"), axis.title.y = element_text(face = "bold.italic")) ``` ### Change Aesthetics of Axis Text Similar to the `title`, we can change the appearance of the axis text (number indeed) by using `axis.text` and/or the subordinated elements `axis.text.x` and `axis.text.y`. ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = 'firebrick') + labs(x= "Year", y = expression(paste("Temperature(",degree ~ F, ")"))) + theme(axis.text = element_text(color = "dodgerblue", size = 13), axis.text.x = element_text(face = 'italic')) ``` ### Rotate Axis Text Specifying an `angle` help us to rotate any text elements. With `hjust` and `vjust` we can adjust the position of text afterwards horizontally (0 = left, 1 = right), and vertically (0 = top, 1 = bottom). ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = 'firebrick') + labs(x= "Year", y = expression(paste("Temperature(",degree ~ F, ")"))) + theme(axis.text.x = element_text(angle = 50, vjust = 1, hjust = 1, size = 13)) # 50 means 50 degrees, not % =))) ``` ### Remove Axis Text & Ticks Rarely a reason to do this but this is how it works. ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)") + theme(axis.ticks.y = element_blank(), axis.text.y = element_blank()) ``` **🚀If you want to get rid of a theme element, the element is always `element_blank`.** ### Remove Axis Titles We could again use `element_blank()` but it is way simpler to just remove the label in the `labs()` (or `xlab()`) call: ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = NULL, y = "") ``` Note that `NULL` removes the element (similarly to `element_blank()`) while empty quotes `""` will keep the spacing for the axis title and simply print nothing. ### Limit Axis Range Some time you want to take a closer look at some range of you data. You can do this without subsetting your data: ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)") + ylim(c(0, 50)) ``` Alternatively you can use `scale_y_continuous(limits = c(0, 50))` (subset) or `coord_cartesian(ylim = c(0, 50))`. The former removes all data points outside the range while the second adjusts the visible area (zooming) and is similar to `ylim(c(0, 50))` (subset). ### Force Plot to start at the origin ```{r} chic_high <- dplyr::filter(chic, temp > 25, o3 > 20) ggplot(chic_high, aes(x = temp, y = o3)) + geom_point(color = "darkcyan") + labs(x = "Temperature higher than 25°F", y = "Ozone higher than 20 ppb") + expand_limits(x = 0, y = 0) ``` Using `coord_cartesian(xlim = c(0,NA), ylim = c(0,NA))` will lead to the same result. ```{r} chic_high <- dplyr::filter(chic, temp > 25, o3 > 20) ggplot(chic_high, aes(x = temp, y = o3)) + geom_point(color = "darkcyan") + labs(x = "Temperature higher than 25°F", y = "Ozone higher than 20 ppb") + coord_cartesian(xlim = c(0, NA), ylim = c(0, NA)) ``` But we can also force it to *literally* start at the origin! ```{r} ggplot(chic_high, aes(x = temp, y = o3)) + geom_point(color = "darkcyan") + labs(x = "Temperature higher than 25°F", y = "Ozone higher than 20 ppb") + expand_limits(x = 0, y = 0) + coord_cartesian(expand = FALSE, clip = "off") ``` 🚀 **The argument `clip = "off"` in any coordinate system, always starting with `coord_*`, allows to draw outside of the panel area. Call it here to make sure that the tick marks at `c(0, 0)` are not cut.** ### Axes with Same Scaling Use `coord_equal()` with default `ratio = 1` to ensure the units are equally scaled on the x-axis and on the y-axis. We can set the aspect ratio of a plot with `coord_fixed()` or `coord_equal()`. Both use `aspect = 1` (1:1) as a default. ```{r} ggplot(chic, aes(x = temp, y = temp + rnorm(nrow(chic), sd = 20))) + geom_point(color = "sienna") + labs(x = "Temperature (°F)", y = "Temperature (°F) + random noise") + xlim(c(0, 100)) + ylim(c(0, 150)) + coord_fixed() ``` Ratios higher than one make units on the y axis longer than units on the x-axis, and vice versa: ```{r} ggplot(chic, aes(x = temp, y = temp + rnorm(nrow(chic), sd = 20))) + geom_point(color = "sienna") + labs(x = "Temperature (°F)", y = "Temperature (°F) + random noise") + xlim(c(0, 100)) + ylim(c(0, 150)) + coord_fixed(ratio = 1/5) ``` ### Use a Function to Alter Labels In case you want to format (eg adding % sign) without change the data. ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = NULL) + scale_y_continuous(label = function(x) {return(paste(x, "Degrees Fahrenheit"))}) ``` # 6. Titles ------------------------------------------------------------------------ ### Add a Title We can add a title via `ggtitle()` function. ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)") + ggtitle("Temperatures in Chicago") ``` Alternatively, we can use `labs()`, where we can add serveral arguments \~ metadata of the plot (a sub-title, a caption, and a tag): ```{r} ggplot(chic, aes(x = date, y = temp)) + geom_point(color = "firebrick") + labs(x = "Year", y = "Temperature (°F)", title = "Temperatures in Chicago", subtitle = "Seasonal pattern of daily temperatures from 1997 to 2001", caption = "Data: NMMAPS", tag = "Fig 1") ``` ### Make title bold & add a space at the baseline # 7. Legends ------------------------------------------------------------------------ # 8. Backgrounds & Grid Lines ------------------------------------------------------------------------ # 9. Margins ------------------------------------------------------------------------ # 10. Multi-panel Plots ------------------------------------------------------------------------ # 11. Colors ------------------------------------------------------------------------ # 12. Themes ------------------------------------------------------------------------ # 13. Lines ------------------------------------------------------------------------ # 14. Text ------------------------------------------------------------------------ # 15. Coordinates ------------------------------------------------------------------------ # 16. Chart Types ------------------------------------------------------------------------ # 17. Ribbons (AUC, CI, etc.) ------------------------------------------------------------------------ # 18. Smoothings ------------------------------------------------------------------------ # 19. Interactive Plots ------------------------------------------------------------------------ # 20. Remarks, Tipps & Resources ------------------------------------------------------------------------ # References ------------------------------------------------------------------------ Source: <https://www.cedricscherer.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/>