Data visualization with ggplot2

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Last updated: 2019-09-25

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Setup

library(tidyverse)

── Attaching packages ──────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.2.1     ✔ purrr   0.3.2
✔ tibble  2.1.3     ✔ dplyr   0.8.3
✔ tidyr   1.0.0     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0

── Conflicts ─────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(maps)

Attaching package: 'maps'

The following object is masked from 'package:purrr':

    map

library(mapproj)
data(mpg)
head(mpg)

# A tibble: 6 x 11
  manufacturer model displ  year   cyl trans  drv     cty   hwy fl    class
  <chr>        <chr> <dbl> <int> <int> <chr>  <chr> <int> <int> <chr> <chr>
1 audi         a4      1.8  1999     4 auto(… f        18    29 p     comp…
2 audi         a4      1.8  1999     4 manua… f        21    29 p     comp…
3 audi         a4      2    2008     4 manua… f        20    31 p     comp…
4 audi         a4      2    2008     4 auto(… f        21    30 p     comp…
5 audi         a4      2.8  1999     6 auto(… f        16    26 p     comp…
6 audi         a4      2.8  1999     6 manua… f        18    26 p     comp…

data(diamonds)
head(diamonds)

# A tibble: 6 x 10
  carat cut       color clarity depth table price     x     y     z
  <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23  Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
2 0.21  Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
3 0.23  Good      E     VS1      56.9    65   327  4.05  4.07  2.31
4 0.290 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
5 0.31  Good      J     SI2      63.3    58   335  4.34  4.35  2.75
6 0.24  Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

First steps

p. 6

Run ggplot(data = mpg). What do you see?

A blank plot

ggplot(data = mpg)

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

How many rows are in mpg? How many columns?

nrow(mpg)

[1] 234

ncol(mpg)

[1] 11

What does the drv variable describe? Read the help for ?mpg to find out.

?mpg

drv: f = front-wheel drive, r = rear wheel drive, 4 = 4wd

Make a scatterplot of hwy vs cyl.

ggplot(data = mpg) +
  geom_point(aes(x = cyl, y = hwy))

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

ggplot(data = mpg) +
  geom_point(aes(x = class, y = drv))

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

Because the points are overplotted, it doesn’t provide any information on the number of cars with each combination.

table(mpg$class, mpg$drv)

            
              4  f  r
  2seater     0  0  5
  compact    12 35  0
  midsize     3 38  0
  minivan     0 11  0
  pickup     33  0  0
  subcompact  4 22  9
  suv        51  0 11

Aesthetic mappings

p. 12

What’s gone wrong with this code? Why are the points not blue?

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

Defining color inside of aes() maps the color variable to the character vector c("blue"). Since it only has one unique value, there is only one entry in the legend. Furthermore it is defined the first default color (more on this below).

To turn all the points uniformly to blue, define color outside of aes().

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

The default colors are determined by running scale_color_hue():

scales::show_col(scale_color_hue()$palette(1))

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

p <- ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))
unique(ggplot_build(p)$data[[1]]$colour)

[1] "#F8766D"

https://stackoverflow.com/a/8197706 https://stackoverflow.com/a/25211125

Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

str(mpg)

Classes 'tbl_df', 'tbl' and 'data.frame':   234 obs. of  11 variables:
 $ manufacturer: chr  "audi" "audi" "audi" "audi" ...
 $ model       : chr  "a4" "a4" "a4" "a4" ...
 $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
 $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
 $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
 $ trans       : chr  "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
 $ drv         : chr  "f" "f" "f" "f" ...
 $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
 $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
 $ fl          : chr  "p" "p" "p" "p" ...
 $ class       : chr  "compact" "compact" "compact" "compact" ...

Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

p <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy))
p + geom_point(aes(color = class))

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

p + geom_point(aes(color = cty))

Past versions of unnamed-chunk-13-2.png

Version	Author	Date
7213610	John Blischak	2019-09-25

p + geom_point(aes(size = class))

Warning: Using size for a discrete variable is not advised.

Past versions of unnamed-chunk-13-3.png

Version	Author	Date
7213610	John Blischak	2019-09-25

p + geom_point(aes(size = cty))

Past versions of unnamed-chunk-13-4.png

Version	Author	Date
7213610	John Blischak	2019-09-25

p + geom_point(aes(shape = class))

Warning: The shape palette can deal with a maximum of 6 discrete values
because more than 6 becomes difficult to discriminate; you have 7.
Consider specifying shapes manually if you must have them.

Warning: Removed 62 rows containing missing values (geom_point).

Past versions of unnamed-chunk-13-5.png

Version	Author	Date
7213610	John Blischak	2019-09-25

tools::assertError(print(p + geom_point(aes(shape = cty))), verbose = TRUE)

Asserted error: A continuous variable can not be mapped to shape

Past versions of unnamed-chunk-13-6.png

Version	Author	Date
7213610	John Blischak	2019-09-25

Note: Have to use print() to catch error because the error is generated by print.ggplot().

https://stackoverflow.com/a/11782043

What happens if you map the same variable to multiple aesthetics?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class, size = class))

Warning: Using size for a discrete variable is not advised.

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

Both aesthetics are applied.

What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)

?geom_point

stroke controls the width of the border (for shapes that have a border).

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy),
             shape = 21, fill = "white", color = "red", stroke = 3)

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)? Note, you’ll also need to specify x and y.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, colour = displ < 5))

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

This is analogous to the color = "blue" question above. Since a logical vector has two unique values, TRUE and FALSE, the points are colored using the first two default colors. Note that since the x-axis is also displ, the color is providing redundant information in this plot.

Facets

p. 15

What happens if you facet on a continuous variable?

It creates a separate facet for each unique value of the continuous variable:

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_wrap(~ cty)

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_wrap(~ displ)

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they relate to this plot?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = drv, y = cyl))

Past versions of unnamed-chunk-20-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

The data set does not include any cars with rear wheel drive and 4 or 5 cylinders.

What plots does the following code make? What does . do?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ .)

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ cyl)

Past versions of unnamed-chunk-21-2.png

Version	Author	Date
7213610	John Blischak	2019-09-25

The . is a place holder for the rows or columns.

Take the first faceted plot in this section:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Past versions of unnamed-chunk-23-1.png

Version	Author	Date
7213610	John Blischak	2019-09-25

The facets make it easier to see the full shape of the distribution for each class. The disadvantage is that it makes it more difficult to directly compare across each class. With more cars, there would be an increased possibility of overplotting, makeing the faceting more advantageous.

Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol arguments?

?facet_wrap

nrow controls the number of rows and ncol controls the number of columns. as.table determines the order of the facets. drop removes empty facets. dir controls horizontal or vertical ordering.

The number of rows and columns for facet_grid() are determined by the number of unique levels in the variables used for facetting.

When using facet_grid() you should usually put the variable with more unique levels in the columns. Why?

I don’t feel that this is an obvious question. This answer states it is because screens tend to be wider than longer. But that makes a lot of assumptions. First, what if the variable has so many unique levels that it doesn’t fit well horizontally at a readable size on a smaller laptop screen. I personally find it easier to scroll vertically rather than horizontally. Also, I feel like this should be informed by the importance of the x versus y axis. Having more columns means it will be easier to compare the y-axis values. And having more rows means it will be easier to compare the x-axis values.

Geometric objects

p. 20

What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

• line chart - geom_line()
• boxplot - geom_boxplot()
• histogram - geom_histogram()
• area chart - geom_area()

Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

Plots a separate smoothing line for each unique value of drv because it inherits the mapping from ggplot().

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(se = FALSE)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Moving color = drv to geom_point() results in the points still being colored, but only one smoothing line.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(aes(color = drv)) + 
  geom_smooth(se = FALSE)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

What does show.legend = FALSE do? What happens if you remove it?
Why do you think I used it earlier in the chapter?

show.legend = FALSE omits the figure legend. It is useful for saving space, especially when showing multiple variations of a particular visualization.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
  geom_smooth(se = FALSE, show.legend = FALSE)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

What does the se argument to geom_smooth() do?

Displays the confidence interval associated with the smoothing method applied.

Will these two graphs look different? Why/why not?

No. The first uses inheritance and the second uses duplication, but they both result in the same data and mapping in both layers.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot() + 
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Recreate the R code necessary to generate the following graphs.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(se = FALSE)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(mapping = aes(group = drv), se = FALSE)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = drv)) +
  geom_smooth(se = FALSE)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(mapping = aes(color = drv)) +
  geom_smooth(mapping = aes(linetype = drv), se = FALSE)

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
  geom_point()

Statistical transformations

p. 26

What is the default geom associated with stat_summary()? How could you rewrite the previous plot to use that geom function instead of the stat function?

The default geom associated with stat_summary() is geom_pointrange():

formals(stat_summary)$geom

[1] "pointrange"

Original using stat_summary() with the default of geom_pointrange():

ggplot(data = diamonds) + 
  stat_summary(
    mapping = aes(x = cut, y = depth),
    fun.ymin = min,
    fun.ymax = max,
    fun.y = median
  )

Alternative using geom_pointrange() and specifying stat = "summary":

ggplot(data = diamonds) +
  geom_pointrange(mapping = aes(x = cut, y = depth), stat = "summary",
                  fun.ymin = min, fun.ymax = max, fun.y = median)

What does geom_col() do? How is it different to geom_bar()?

geom_col() uses stat = "identity", so the height of the bar is equal to the exact value of y. geom_bar() uses stat = "count", so the height of the bar is equal to the number of observations of x.

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut))

diamonds %>%
  group_by(cut) %>%
  summarize(mean_price = mean(price)) %>%
  ggplot() +
  geom_col(mapping = aes(x = cut, y = mean_price))

Most geoms and stats come in pairs that are almost always used in concert. Read through the documentation and make a list of all the pairs. What do they have in common?

What variables does stat_smooth() compute? What parameters control its behaviour?

stat_smooth() computes the predicted value (y), lower CI bound to plot (ymin), upper CI bound to plot (ymax), and standard error se.

Its behavior is controlled by method, formula, and se. Furthermore, the arguments n, span, fullrange, level, and method.args can be passed to stat_smooth()

p <- ggplot(data = diamonds) +
  geom_smooth(mapping = aes(x = carat, y = price), level = 0.99) # default is 0.95
y <- ggplot_build(p)$data[[1]]

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

unique((y$ymax - y$ymin) / y$se)

 [1] 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659
 [8] 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659
[15] 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659
[22] 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659
[29] 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659
[36] 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659 5.151659
[43] 5.151659 5.151659

In our proportion bar chart, we need to set group = 1. Why? In other words what is the problem with these two graphs?

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop..))

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = color, y = ..prop..))

This is strange behavior. I’m unclear why it is the default¹. The proportion is calculated for each group, and thus is always equal to 1.

p1 <- ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop..))
y1 <- ggplot_build(p1)$data[[1]]
y1

  y count prop x PANEL group ymin ymax xmin xmax colour   fill size
1 1  1610    1 1     1     1    0    1 0.55 1.45     NA grey35  0.5
2 1  4906    1 2     1     2    0    1 1.55 2.45     NA grey35  0.5
3 1 12082    1 3     1     3    0    1 2.55 3.45     NA grey35  0.5
4 1 13791    1 4     1     4    0    1 3.55 4.45     NA grey35  0.5
5 1 21551    1 5     1     5    0    1 4.55 5.45     NA grey35  0.5
  linetype alpha
1        1    NA
2        1    NA
3        1    NA
4        1    NA
5        1    NA

By hardcoding group = 1, all the counts are treated aas part of the same group, and thus the proportions are from the total.

p2 <- ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))
y2 <- ggplot_build(p2)$data[[1]]
y2

           y count       prop x group PANEL ymin       ymax xmin xmax
1 0.02984798  1610 0.02984798 1     1     1    0 0.02984798 0.55 1.45
2 0.09095291  4906 0.09095291 2     1     1    0 0.09095291 1.55 2.45
3 0.22398962 12082 0.22398962 3     1     1    0 0.22398962 2.55 3.45
4 0.25567297 13791 0.25567297 4     1     1    0 0.25567297 3.55 4.45
5 0.39953652 21551 0.39953652 5     1     1    0 0.39953652 4.55 5.45
  colour   fill size linetype alpha
1     NA grey35  0.5        1    NA
2     NA grey35  0.5        1    NA
3     NA grey35  0.5        1    NA
4     NA grey35  0.5        1    NA
5     NA grey35  0.5        1    NA

Position adjustments

p. 31

What is the problem with this plot? How could you improve it?

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_point()

Overplotting. Improve by jitterring the points.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_point(position = "jitter")

What parameters to geom_jitter() control the amount of jittering?

width and height, which are passed to position_jitter().

Compare and contrast geom_jitter() with geom_count().

Alternative for overplotting. geom_count() counts the number of points at each position and then maps the count to the size of the point.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_count()

Thus mapping it in combination with the size aesthetic is a disaster.

table(mpg$cyl)

 4  5  6  8 
81  4 79 70 

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + 
  geom_count(mapping = aes(size = cyl))

What’s the default position adjustment for geom_boxplot()? Create a visualisation of the mpg dataset that demonstrates it.

The default is to use position_dodge2(), which is designed specifically for boxplots.

formals(geom_boxplot)$position

[1] "dodge2"

At least for this example, even when allowing variable width via varwidth, "dodge"is able to display the bboxplots just like "dodge2".

ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = class, y = hwy))

ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = class, y = hwy),  varwidth = TRUE)

ggplot(data = mpg) +
  geom_boxplot(mapping = aes(x = class, y = hwy),  varwidth = TRUE,
               position = "dodge")

Coordinate systems

Turn a stacked bar chart into a pie chart using coord_polar().

stacked <- ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = clarity))
stacked

stacked +  coord_polar()

What does labs() do? Read the documentation.

Adds labels like the title, subtitle, x-axis label, and y-axis label.

What’s the difference between coord_quickmap() and coord_map()?

coord_map() requires the package mapproj and projects the spherical earth onto a 2D plane. coord_quickmap() is a quicker approximation, and thus works best for areas near the equator.

nz <- map_data("nz")
ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black")

ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black") +
  coord_quickmap()

ggplot(nz, aes(long, lat, group = group)) +
  geom_polygon(fill = "white", colour = "black") +
  coord_map()

What does the plot below tell you about the relationship between city and highway mpg? Why is coord_fixed() important? What does geom_abline() do?

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() + 
  geom_abline() +
  coord_fixed()

For each car, highway mpg is always better than city mpg. geom_abline() adds a line with a given slope and intercept. The default is the y=x line with intercept = 0 and slope = 1.

coord_fixed() maintains consistent spacing on both axes. Doing so ensures that the line is at a 45 degree angle, which is critical for the interpretation above.

Removing coord_fixed() results in the y=x line not being at a 45 degree angle.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() + 
  geom_abline()

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] mapproj_1.2.6   maps_3.3.0      forcats_0.4.0   stringr_1.4.0  
 [5] dplyr_0.8.3     purrr_0.3.2     readr_1.3.1     tidyr_1.0.0    
 [9] tibble_2.1.3    ggplot2_3.2.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5  xfun_0.9          reshape2_1.4.3   
 [4] splines_3.6.1     haven_2.1.1       lattice_0.20-38  
 [7] colorspace_1.4-1  vctrs_0.2.0       generics_0.0.2   
[10] viridisLite_0.3.0 htmltools_0.3.6   mgcv_1.8-29      
[13] yaml_2.2.0        utf8_1.1.4        rlang_0.4.0      
[16] pillar_1.4.2      glue_1.3.1        withr_2.1.2      
[19] modelr_0.1.5      readxl_1.3.1      plyr_1.8.4       
[22] lifecycle_0.1.0   munsell_0.5.0     gtable_0.3.0     
[25] workflowr_1.4.0   cellranger_1.1.0  rvest_0.3.4      
[28] evaluate_0.14     labeling_0.3      knitr_1.25       
[31] fansi_0.4.0       broom_0.5.2       Rcpp_1.0.2       
[34] scales_1.0.0      backports_1.1.4   jsonlite_1.6     
[37] fs_1.3.1          hms_0.5.1         digest_0.6.21    
[40] stringi_1.4.3     grid_3.6.1        rprojroot_1.2    
[43] cli_1.1.0         tools_3.6.1       magrittr_1.5     
[46] lazyeval_0.2.2    crayon_1.3.4      whisker_0.4      
[49] pkgconfig_2.0.2   zeallot_0.1.0     Matrix_1.2-17    
[52] xml2_1.2.2        lubridate_1.7.4   assertthat_0.2.1 
[55] rmarkdown_1.15    httr_1.4.1        rstudioapi_0.10  
[58] R6_2.4.0          nlme_3.1-141      git2r_0.26.1     
[61] compiler_3.6.1   

---

1. Apparently it is for consistent behavior↩