Show the Right Numbers ggplots FLOW OF ACTION Will be handled - - PowerPoint PPT Presentation

Show the Right Numbers

ggplot’s FLOW OF ACTION

Data Aesthetic Mappings Geoms and/or Stats Coordinates and Scales Guides Themes

We always have to specify these to draw a plot Will be handled automatically unless we say

• therwise

Grouped Data and the group aesthetic

p + geom_line(color = "gray70", mapping = aes(group = country)) + geom_smooth(size = 1.1, method = "loess", se = FALSE) + scale_y_log10(labels=scales::dollar) + facet_wrap(~ continent, ncol = 5) + labs(x = "Year", y = "GDP per capita", title = "GDP per capita on Five Continents")

The labs() function lets you name labels, title, subtitle, etc.

geoms CAN TRANSFORM DATA

gss_sm

A subset of General Social Survey Questions from 2016

with(gss_sm, table(religion)) ## ## Protestant Catholic Jewish None Other ## 1371 649 51 619 159

Just the one aesthetic mapping, to x.

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion)) p + geom_bar()

The y-axis variable, count, is not in the

• data. Instead, ggplot has calculated it

for us. It does this using the default

stat_ function associated with geom_bar(), stat_count(). This

function can compute two new variables, count, and prop (short for proportion). The count statistic is the default one used.

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion)) p + geom_bar(mapping = aes(y = ..prop..))

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion)) p + geom_bar(mapping = aes(y = ..prop.., group = 1))

p + geom_bar() p + stat_count()

geom_ functions call their default stat_ functions behind the scenes. (And vice versa)

p <- ggplot(data = gss_sm, mapping = aes(x = religion)) p + geom_bar() p <- ggplot(data = gss_sm, mapping = aes(x = religion, fill = religion)) p + geom_bar() p <- ggplot(data = gss_sm, mapping = aes(x = religion, fill = religion)) p + geom_bar() + guides(fill = FALSE) p <- ggplot(data = gss_sm, mapping = aes(x = religion, color = religion)) p + geom_bar()

p <- ggplot(data = gss_sm, mapping = aes(x = religion, color = religion)) p + geom_bar() p <- ggplot(data = gss_sm, mapping = aes(x = religion, fill = religion)) p + geom_bar() + guides(fill = FALSE)

HISTOGRAMS & KERNEL DENSITIES

midwest

County-Level Census Data for Midwestern States

p <- ggplot(data = midwest, mapping = aes(x = area)) p + geom_histogram()

## `stat_bin()` using `bins = 30`. ## Pick better value with `binwidth`.

The default stat for this geom has to make a choice, and is letting us know we might want to override it.

p <- ggplot(data = midwest, mapping = aes(x = area)) p + geom_histogram(bins = 10)

p <- ggplot(data = subset(midwest, state %in% oh_wi), mapping = aes(x = percollege, fill = state)) p + geom_histogram(position = "identity", alpha = 0.4, bins = 20)

subset our data

• n the fly

a convenient, built-in operator Just plot x by its values on the scale, don’t stack

• r dodge
• h_wi <- c("OH", "WI")

p <- ggplot(data = midwest, mapping = aes(x = area)) p + geom_density()

geom_hist()’s continuous counterpart, geom_density()

p <- ggplot(data = midwest, mapping = aes(x = area, fill = state, color = state)) p + geom_density(alpha = 0.3)

AVOIDING TRANSFORMATIONS WHEN NECESSARY

## fate gender n percent ## 1 perished male 1364 62.0 ## 2 perished female 126 5.7 ## 3 survived male 367 16.7 ## 4 survived female 344 15.6

No counting up required? Then stat = identity

> titanic

p <- ggplot(data = titanic, mapping = aes(x = fate, y = percent, fill = sex)) p + geom_bar(stat = "identity", position = "dodge") + theme(legend.position = "top")

The theme() function controls parts of the plot that don’t belong to its “grammatical” structure

p <- ggplot(data = titanic, mapping = aes(x = fate, y = percent, fill = sex)) p + geom_col(position = "dodge") + theme(legend.position = "top")

Even better: for convenience when not counting up, just use geom_col()

• ecd_sum

## # A tibble: 57 x 5 ## # Groups: year [57] ## year other usa diff hi_lo ## <int> <dbl> <dbl> <dbl> <chr> ## 1 1960 68.6 69.9 1.30 Below ## 2 1961 69.2 70.4 1.20 Below ## 3 1962 68.9 70.2 1.30 Below ## 4 1963 69.1 70.0 0.900 Below ## 5 1964 69.5 70.3 0.800 Below ## 6 1965 69.6 70.3 0.700 Below ## 7 1966 69.9 70.3 0.400 Below ## 8 1967 70.1 70.7 0.600 Below ## 9 1968 70.1 70.4 0.300 Below ## 10 1969 70.1 70.6 0.500 Below ## # ... with 47 more rows

p <- ggplot(data = oecd_sum, mapping = aes(x = year, y = diff, fill = hi_lo)) p + geom_col() + guides(fill = FALSE) + labs(x = NULL, y = "Difference in Years", title = "The US Life Expectancy Gap", subtitle = "Difference between US and OECD average life expectancies, 1960-2015", caption = "Data: OECD. After a chart by Christopher Ingraham, Washington Post, December 27th 2017.")

CROSSTABULATION THE AWKWARD WAY

WARNING!

There’s nothing wrong with the code on the next few

• slides. If you go searching online for how to make a

proportional bar chart with ggplot you’ll see answers like this. But, doing it this way is confusing and I find it is much easier to work a slightly different way. So, I won’t cover this approach in class. I’m including it here so you can see why it’s awkward.

p <- ggplot(data = gss_sm, mapping = aes(x = religion, color = religion)) p + geom_bar() p <- ggplot(data = gss_sm, mapping = aes(x = religion, fill = religion)) p + geom_bar() + guides(fill = FALSE)

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion, fill = religion)) p + geom_bar()

Counts are easy

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion, fill = religion)) p + geom_bar(position = "fill")

Position adjustments don’t give us the view we want

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion, fill = religion)) p + geom_bar(position = "dodge", mapping = aes(y = ..prop..))

Nope

p <- ggplot(data = gss_sm, mapping = aes(x = bigregion, fill = religion)) p + geom_bar(position = "dodge", mapping = aes(y = ..prop.., group = religion))

Still not right!

Also: hard to read

p <- ggplot(data = gss_sm, mapping = aes(x = religion)) p + geom_bar(position = "dodge", mapping = aes(y = ..prop.., group = bigregion)) + facet_wrap(~ bigregion, ncol = 2)

Time to take a step back

SURELY THINGS CAN BE EASIER THAN THIS?

TRANSFORM AND SUMMARIZE FIRST THEN SEND CLEAN TABLES TO ggplot

CROSSTABULATION

Column percents / Column Marginals

Row percents / Row Marginals

Total percents

dplyr lets you manipulate tables in a series of steps, or pipeline

dplyr draws on the logic of database queries, where the focus is managing and summarizing tables

group_by()

Group the data at the level we want, such as "Religion by Region"

• r "Authors by Publications by Year".

filter() rows select() columns

Filter or Select pieces of the data. This gets us the subset of the table we want to work on.

mutate()

Mutate the data by creating new variables at the current level of

• grouping. Mutating adds new columns to the table.

summarize()

Summarize the grouped data. This creates new variables at a higher level of grouping. For example we might calculate means with mean() or counts with n(). This results in a smaller, summary table, which we might do more things with if we want.

%>%

Create a pipeline of tabular transformations with the pipe operator

REORGANIZING TABLES WITH dplyr

rel_by_region <- gss_sm %>% group_by(bigregion, religion) %>% summarize(n = n()) %>% mutate(freq = n / sum(n), pct = round((freq*100), 1))

rel_by_region <- gss_sm

rel_by_region <- gss_sm %>% group_by(bigregion, religion)

rel_by_region <- gss_sm %>% group_by(bigregion, religion) %>% summarize(n = n())

A function to count how many items there are in the current group

The result of the calculation

rel_by_region <- gss_sm %>% group_by(bigregion, religion) %>% summarize(n = n()) %>% mutate(freq = n / sum(n), pct = round((freq*100), 1))

mutate() operations add columns to existing tables

Objects in a pipeline carry forward some assumptions about context

rel_by_region <- gss_sm %>% group_by(bigregion, religion) %>% summarize(n = n()) %>% mutate(freq = n / sum(n), pct = round((freq*100), 1))

Grouping with group_by() carries forward; summary calculations are applied to the innermost group

rel_by_region <- gss_sm %>% group_by(bigregion, religion) %>% summarize(n = n()) %>% mutate(freq = n / sum(n), pct = round((freq*100), 1))

mutate() adds or modifies columns, it doesn’t change the grouping level or the number of rows in the table

rel_by_region <- gss_sm %>% group_by(bigregion, religion) %>% summarize(n = n()) %>% mutate(freq = n / sum(n), pct = round((freq*100), 1))

Notice how we can create variables

• n the fly and use them immediately

rel_by_region <- gss_sm %>% group_by(bigregion, religion) %>% summarize(n = n()) %>% mutate(freq = n / sum(n), pct = round((freq*100), 1))

rel_by_region ## Source: local data frame [24 x 5] ## Groups: bigregion [4] ## ## # A tibble: 24 x 5 ## bigregion religion n freq pct ## <fctr> <fctr> <int> <dbl> <dbl> ## 1 Northeast Protestant 158 0.32377049 32.4 ## 2 Northeast Catholic 162 0.33196721 33.2 ## 3 Northeast Jewish 27 0.05532787 5.5 ## 4 Northeast None 112 0.22950820 23.0 ## 5 Northeast Other 28 0.05737705 5.7 ## 6 Northeast NA 1 0.00204918 0.2 ## 7 Midwest Protestant 325 0.46762590 46.8 ## 8 Midwest Catholic 172 0.24748201 24.7 ## 9 Midwest Jewish 3 0.00431655 0.4 ## 10 Midwest None 157 0.22589928 22.6 ## # ... with 14 more rows