# load packages
if(!require(pacman))
install.packages("pacman")
pacman::p_load(countdown,
tidyverse,
gt,
scales,
colorspace,
ggthemes)
# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))
# set width of code output
options(width = 65)
# set figure parameters for knitr
knitr::opts_chunk$set(
fig.width = 7, # 7" width
fig.asp = 0.618, # the golden ratio
fig.retina = 3, # dpi multiplier for displaying HTML output on retina
fig.align = "center", # center align figures
dpi = 300 # higher dpi, sharper image
)Tables
Lecture 22
# A tibble: 33 × 2
field perc
<chr> <dbl>
1 Agriculture and natural resources 1.91e-2
2 Architecture and related services 4.80e-3
3 Area, ethnic, cultural, gender, and group studies 4.11e-3
4 Biological and biomedical sciences 5.80e-2
5 Business 1.92e-1
6 Communication, journalism, and related programs 4.78e-2
7 Communications technologies 2.71e-3
8 Computer and information sciences 3.14e-2
9 Education 4.84e-2
10 Engineering 5.16e-2
11 Engineering technologies 9.10e-3
12 English language and literature/letters 2.42e-2
13 Family and consumer sciences/human sciences 1.30e-2
14 Foreign languages, literatures, and linguistics 1.03e-2
15 Health professions and related programs 1.14e-1
16 Homeland security, law enforcement, and firefighting 3.31e-2
17 Legal professions and studies 2.33e-3
18 Liberal arts and sciences, general studies, and human… 2.30e-2
19 Library science 5.22e-5
20 Mathematics and statistics 1.15e-2
21 Military technologies and applied sciences 1.46e-4
22 Multi/interdisciplinary studies 2.51e-2
23 Parks, recreation, leisure, and fitness studies 2.59e-2
24 Philosophy and religious studies 5.84e-3
25 Physical sciences and science technologies 1.59e-2
26 Precision production 2.53e-5
27 Psychology 6.20e-2
28 Public administration and social services 1.81e-2
29 Social sciences and history 8.81e-2
30 Theology and religious vocations 5.12e-3
31 Transportation and materials moving 2.49e-3
32 Visual and performing arts 5.06e-2
33 Not classified by field of study 0 
# A tibble: 33 × 2
field perc
<chr> <dbl>
1 Business 1.92e-1
2 Health professions and related programs 1.14e-1
3 Social sciences and history 8.81e-2
4 Psychology 6.20e-2
5 Biological and biomedical sciences 5.80e-2
6 Engineering 5.16e-2
7 Visual and performing arts 5.06e-2
8 Education 4.84e-2
9 Communication, journalism, and related programs 4.78e-2
10 Homeland security, law enforcement, and firefighting 3.31e-2
11 Computer and information sciences 3.14e-2
12 Parks, recreation, leisure, and fitness studies 2.59e-2
13 Multi/interdisciplinary studies 2.51e-2
14 English language and literature/letters 2.42e-2
15 Liberal arts and sciences, general studies, and human… 2.30e-2
16 Agriculture and natural resources 1.91e-2
17 Public administration and social services 1.81e-2
18 Physical sciences and science technologies 1.59e-2
19 Family and consumer sciences/human sciences 1.30e-2
20 Mathematics and statistics 1.15e-2
21 Foreign languages, literatures, and linguistics 1.03e-2
22 Engineering technologies 9.10e-3
23 Philosophy and religious studies 5.84e-3
24 Theology and religious vocations 5.12e-3
25 Architecture and related services 4.80e-3
26 Area, ethnic, cultural, gender, and group studies 4.11e-3
27 Communications technologies 2.71e-3
28 Transportation and materials moving 2.49e-3
29 Legal professions and studies 2.33e-3
30 Military technologies and applied sciences 1.46e-4
31 Library science 5.22e-5
32 Precision production 2.53e-5
33 Not classified by field of study 0 
| Field | Percentage |
|---|---|
| Business | 19.2% |
| Health professions and related programs | 11.4% |
| Social sciences and history | 8.8% |
| Psychology | 6.2% |
| Biological and biomedical sciences | 5.8% |
| Engineering | 5.2% |
| Visual and performing arts | 5.1% |
| Education | 4.8% |
| Communication, journalism, and related programs | 4.8% |
| Homeland security, law enforcement, and firefighting | 3.3% |
| Computer and information sciences | 3.1% |
| Parks, recreation, leisure, and fitness studies | 2.6% |
| Multi/interdisciplinary studies | 2.5% |
| English language and literature/letters | 2.4% |
| Liberal arts and sciences, general studies, and humanities | 2.3% |
| Agriculture and natural resources | 1.9% |
| Public administration and social services | 1.8% |
| Physical sciences and science technologies | 1.6% |
| Family and consumer sciences/human sciences | 1.3% |
| Mathematics and statistics | 1.2% |
| Foreign languages, literatures, and linguistics | 1.0% |
| Engineering technologies | 0.9% |
| Philosophy and religious studies | 0.6% |
| Theology and religious vocations | 0.5% |
| Architecture and related services | 0.5% |
| Area, ethnic, cultural, gender, and group studies | 0.4% |
| Communications technologies | 0.3% |
| Transportation and materials moving | 0.2% |
| Legal professions and studies | 0.2% |
| Military technologies and applied sciences | 0.0% |
| Library science | 0.0% |
| Precision production | 0.0% |
| Not classified by field of study | 0.0% |
Or in a plot?
Tables with gt
We will use the gt (Grammar of Tables) package to create tables in R.
The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts.
Source: gt.rstudio.com
Should these data be displayed in a table or a plot?
| Popular Bachelor's degrees over the years | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Field | 1971 | 1976 | 1981 | 1986 | 1991 | 1996 | 2001 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
| Business | 14% | 15% | 21% | 24% | 23% | 19% | 21% | 22% | 21% | 21% | 21% | 22% | 22% | 21% | 20% | 20% | 19% | 19% |
| Health professions | 3% | 6% | 7% | 7% | 5% | 7% | 6% | 6% | 6% | 7% | 7% | 8% | 8% | 8% | 9% | 10% | 11% | 11% |
| Social sciences and history | 18% | 14% | 11% | 9% | 11% | 11% | 10% | 11% | 11% | 11% | 11% | 11% | 10% | 10% | 10% | 10% | 9% | 9% |
| Other | 65% | 65% | 61% | 60% | 60% | 62% | 62% | 62% | 62% | 61% | 61% | 60% | 60% | 60% | 60% | 61% | 61% | 61% |
Add visualizations to your table
Example: Add sparklines to display trend alongside raw data
| Popular Bachelor's degrees over the years | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Field | Trend | 1971 | 1976 | 1981 | 1986 | 1991 | 1996 | 2001 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
| Business |  |
14% | 15% | 21% | 24% | 23% | 19% | 21% | 22% | 21% | 21% | 21% | 22% | 22% | 21% | 20% | 20% | 19% | 19% |
| Health professions |  |
3% | 6% | 7% | 7% | 5% | 7% | 6% | 6% | 6% | 7% | 7% | 8% | 8% | 8% | 9% | 10% | 11% | 11% |
| Social sciences and history |  |
18% | 14% | 11% | 9% | 11% | 11% | 10% | 11% | 11% | 11% | 11% | 11% | 10% | 10% | 10% | 10% | 9% | 9% |
| Other |  |
65% | 65% | 61% | 60% | 60% | 62% | 62% | 62% | 62% | 61% | 61% | 60% | 60% | 60% | 60% | 61% | 61% | 61% |
Livecoding: Recreate this table of popular Bachelor’s degrees awarded over time.
Sparklines
plot_spark <- function(df){
ggplot(df, aes(x = year, y = perc)) +
geom_line(linewidth = 20) +
theme_void()
}
BA_degrees_other_plots <- BA_degrees_other |>
nest(field_df = c(year, perc)) |>
mutate(plot = map(field_df, plot_spark))
BA_degrees_other |>
pivot_wider(names_from = year, values_from = perc) |>
mutate(ggplot = NA, .after = field) |>
gt() |>
text_transform(
locations = cells_body(columns = ggplot),
fn = function(x){
map(BA_degrees_other_plots$plot, ggplot_image, height = px(15), aspect_ratio = 4)
}
) |>
cols_width(
ggplot ~ px(1000)
) |>
cols_align(
align = "left",
columns = field
) |>
fmt_percent(
columns = where(is.numeric),
decimals = 0
) |>
cols_label(
field = "Field",
ggplot = "Trend"
) |>
tab_spanner(
label = "Popular Bachelor's degrees over the years",
columns = everything()
) |>
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_spanners()
)| Popular Bachelor's degrees over the years | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Field | Trend | 1971 | 1976 | 1981 | 1986 | 1991 | 1996 | 2001 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
| Business |  |
14% | 15% | 21% | 24% | 23% | 19% | 21% | 22% | 21% | 21% | 21% | 22% | 22% | 21% | 20% | 20% | 19% | 19% |
| Health professions |  |
3% | 6% | 7% | 7% | 5% | 7% | 6% | 6% | 6% | 7% | 7% | 8% | 8% | 8% | 9% | 10% | 11% | 11% |
| Social sciences and history |  |
18% | 14% | 11% | 9% | 11% | 11% | 10% | 11% | 11% | 11% | 11% | 11% | 10% | 10% | 10% | 10% | 9% | 9% |
| Other |  |
65% | 65% | 61% | 60% | 60% | 62% | 62% | 62% | 62% | 61% | 61% | 60% | 60% | 60% | 60% | 61% | 61% | 61% |
Your turn: Add color to the previous table.
Colored Sparklines
plot_spark_color <- function(df){
ggplot(df, aes(x = year, y = perc, color = line_color)) +
geom_line(linewidth = 20) +
theme_void() +
scale_color_identity()
}
BA_degrees_other_plots_color <- BA_degrees_other |>
mutate(line_color = case_when(
field == "Business" ~ "#9D6C06",
field == "Health professions" ~ "#077DAA",
field == "Social sciences and history" ~ "#026D4E",
field == "Other" ~ "#A39A09"
)) |>
nest(field_df = c(year, perc, line_color)) |>
mutate(plot = map(field_df, plot_spark_color))
BA_degrees_other |>
pivot_wider(names_from = year, values_from = perc) |>
mutate(ggplot = NA, .after = field) |>
gt() |>
text_transform(
locations = cells_body(columns = ggplot),
fn = function(x){
map(BA_degrees_other_plots_color$plot, ggplot_image, height = px(15), aspect_ratio = 4)
}
) |>
cols_width(
ggplot ~ px(1000)
) |>
cols_align(
align = "left",
columns = field
) |>
fmt_percent(
columns = where(is.numeric),
decimals = 0
) |>
tab_style(
style = cell_text(color = "#9D6C06"),
locations = cells_body(rows = 1, columns = field)
) |>
tab_style(
style = cell_text(color = "#077DAA"),
locations = cells_body(rows = 2, columns = field)
) |>
tab_style(
style = cell_text(color = "#026D4E"),
locations = cells_body(rows = 3, columns = field)
) |>
tab_style(
style = cell_text(color = "#A39A09"),
locations = cells_body(rows = 4, columns = field)
) |>
cols_label(
field = "Field",
ggplot = "Trend"
) |>
tab_spanner(
label = "Popular Bachelor's degrees over the years",
columns = everything()
) |>
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_spanners()
)| Popular Bachelor's degrees over the years | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Field | Trend | 1971 | 1976 | 1981 | 1986 | 1991 | 1996 | 2001 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
| Business | |
14% | 15% | 21% | 24% | 23% | 19% | 21% | 22% | 21% | 21% | 21% | 22% | 22% | 21% | 20% | 20% | 19% | 19% |
| Health professions | |
3% | 6% | 7% | 7% | 5% | 7% | 6% | 6% | 6% | 7% | 7% | 8% | 8% | 8% | 9% | 10% | 11% | 11% |
| Social sciences and history | |
18% | 14% | 11% | 9% | 11% | 11% | 10% | 11% | 11% | 11% | 11% | 11% | 10% | 10% | 10% | 10% | 9% | 9% |
| Other | |
65% | 65% | 61% | 60% | 60% | 62% | 62% | 62% | 62% | 61% | 61% | 60% | 60% | 60% | 60% | 61% | 61% | 61% |
10:00
Table inspiration
Storytelling with data: storytellingwithdata.com/blog/2020/9/1/swdchallenge-build-a-table - #23SWDchallenge on Twitter
-
2022 Posit table contest: Winners of the 2022 Table Contest
- Look out for 2023 table contest anouncement!
