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12.ggplot.qmd
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---
title: "12. ggplot"
author: "Patrick Spauster"
format:
html:
code-copy: true
toc: true
toc-location: right
editor: visual
---
## Video Tutorial
```{=html}
<div style="max-width:608px"><div style="position:relative;padding-bottom:66.118421052632%"><iframe id="kaltura_player" src="https://cdnapisec.kaltura.com/p/1674401/sp/167440100/embedIframeJs/uiconf_id/23435151/partner_id/1674401?iframeembed=true&playerId=kaltura_player&entry_id=1_1z2obv8k&flashvars[streamerType]=auto&flashvars[localizationCode]=en&flashvars[sideBarContainer.plugin]=true&flashvars[sideBarContainer.position]=left&flashvars[sideBarContainer.clickToClose]=true&flashvars[chapters.plugin]=true&flashvars[chapters.layout]=vertical&flashvars[chapters.thumbnailRotator]=false&flashvars[streamSelector.plugin]=true&flashvars[EmbedPlayer.SpinnerTarget]=videoHolder&flashvars[dualScreen.plugin]=true&flashvars[LeadWithHLSOnFlash]=true&flashvars[Kaltura.addCrossoriginToIframe]=true&&wid=1_peanbvbw" width="608" height="402" allowfullscreen webkitallowfullscreen mozAllowFullScreen allow="autoplay *; fullscreen *; encrypted-media *" sandbox="allow-downloads allow-forms allow-same-origin allow-scripts allow-top-navigation allow-pointer-lock allow-popups allow-modals allow-orientation-lock allow-popups-to-escape-sandbox allow-presentation allow-top-navigation-by-user-activation" frameborder="0" title="14. ggplot" style="position:absolute;top:0;left:0;width:100%;height:100%"></iframe></div></div>
```
## Preparing Data for ggplot
Data for ggplot needs to be in "long" format, meaning that all the **values** need to be in the same column and all the variables need to be in another column.
Let's use that rent stabilization data to create a plot. In this case the data is wide by year, meaning there is a column for each year with different values. We want a dataset where each year has its own row - where each row is a year-building combination - that we can plot.
```{r warning=F, message=F}
library(tidyverse)
library(janitor)
rent_stab_long <- read_csv("https://taxbillsnyc.s3.amazonaws.com/joined.csv") %>%
select(borough, ucbbl, ends_with("uc")) %>%
pivot_longer(
ends_with("uc"), # The multiple column names we want to mush into one column
names_to = "year", # The title for the new column of names we're generating
values_to = "units" # The title for the new column of values we're generating
)
```
## ggplot syntax
ggplots use similar syntax to regular R operations - they are groups of functions beginning with `ggplot()`. Instead of the pipe, ggplot uses `+` between different functions to build layers on top of each other.
Typically, ggplot will start with `ggplot(dataframe) +` a geometric function like `geom_col()` and an aesthetics or `aes` argument that indicates which variables to plot.
After that, ggplot has a ton of options to specify the labels, scales, axes, themes, legends,and more. It's best shown through examples
## Sample Line Chart
Line charts are often a good fit for time-series data. Let's summarize the long data by year and borough and count the number of units.
```{r}
rs_long_manhattan_summary <- rent_stab_long %>%
filter(borough %in% c("MN","BK") # Filter only Manhattan and Brooklyn values
& !is.na(units)) %>% # Filter out null unit count values
mutate(year = as.numeric(gsub("uc","", year))) %>% # Remove "uc" from year values
select(year, borough, units) %>%
# Grouping by 2 columns means each row will have a unique pair of the two columns' values.
# Our rows will look like: 2007 MN, 2007 BK, 2008 MN...
group_by(year, borough) %>%
summarise(total_units = sum(units))
```
Now let's plot it. We start with the data, then add aesthetics, our chart type (line), axis specifications, and labels.
```{r}
rs_over_time_graph <- ggplot(rs_long_manhattan_summary) +
# Note these arguments inside 'geom_line' :
geom_line(aes(x=year, y=total_units, color=borough)) +
# Restyle the Y-axis labels:
scale_y_continuous(
limits = c(0,300000),
labels = scales::unit_format(scale = 1/1000, unit="K")) +
scale_x_continuous(breaks = seq(2007, 2017, by = 1))+
# Restyle the Legend:
scale_fill_discrete(
name="Borough",
breaks=c("BK", "MN"),
labels=c("Brooklyn", "Manhattan")) +
labs(
title = "Total Rent Stabilized Units over Time",
subtitle = "Manhattan and Brooklyn, 2007 to 2017",
x = "Year",
y = "Total Rent Stabilized Units",
caption = "Source: taxbills.nyc"
)+
theme_minimal()
rs_over_time_graph
```
## Sample Bar Chart + Faceting
Let's make some bar charts out of the table of \# of children by the languages they speak at home by borough.
We'll learn how we pulled this data in an upcoming lesson, but for now just download [this csv](https://github.com/pspauster/learning_R/blob/master/langs_by_boro.csv). Or run `read_csv("https://raw.githubusercontent.com/pspauster/learning_R/master/langs_by_boro.csv")`
```{r message=F, warning=F}
langs_by_boro_for_graphing <- read_csv("langs_by_boro.csv") %>%
mutate(
labels = # Add a new column with neat category labels
case_when(
variable == 'englishkids' ~ 'English',
variable == 'spanishkids' ~ 'Spanish',
variable == 'indoeurkids' ~ 'Indo-European',
variable == 'apikids' ~ 'Asian & Pacific Islander',
variable == 'otherkids' ~ 'Other Languages'
),
labels = fct_reorder(labels, estimate)
) %>% # Overwrite the variable column of langs_by_boro with a function that tells R to order the variable column by the estimate column when it gets plotted (like a sort). If I wanted it ordered in the other direction I would put estimate inside of desc().
filter(variable != 'totalkids')
```
Produce a horizontal bar chart
```{r}
# Note - this builds progressively; you can run all the code before any + sign as a step toward the final result.
barchart <- ggplot(langs_by_boro_for_graphing) +
aes(x = estimate, y = labels) +
geom_col() +
scale_x_continuous(labels = scales::comma) + # format count labels with commas and thousands
theme_minimal() +
theme(panel.grid.major.y = element_blank()) +
labs(
title = 'What languages do NYC kids speak at home?',
subtitle = 'Number of children ages 5-17 by the language spoken at home',
x = NULL,
y = NULL,
caption = "Source: Census American Community Survey 2020 5-Year Estimates, Table B16007"
)
barchart
```
Now let's facet it by borough
```{r fig.height=6}
barchart +
facet_wrap( ~ name, ncol = 1)
```
## Additional Resources
This [guide from the Urban Institute](https://urbaninstitute.github.io/r-at-urban/graphics-guide.html) has a number of helpful tutorials for how to create a wide number of graphs with ggplot.