Sur Herrera Paredes 2022-06-06
- Introduction
- Getting ready
- Read data
- Basic barplot
- Adding sample metadata
- Adding OTU taxonomy
- Combining sample metadata and OTU taxonomy
- Extra excercises
- Session info
In this extended example I go through every step to produce a relative abundance barplot that represents bacterial communities living in individual hosts.
Bacterial communities are everywhere, and when we characterize them it is important to describe they overall taxonomic structure as that gives us clues as to what types of functions might be performed by the community. At the same time, it is important to show the variability of these communities and thus it is useful to plot them at the lowest aggregation level possible.
In this example, I utilize data from a big experiment that was published here. In that experiment, we planted individual Arabidopsis thaliana plants in individual pots. The pots each had one of two types of natural soil, each from two different seasons. We planted eight different accessions in each of those soils, and plants were harvested at two developmental stages. Additionally we had unplanted soil only pots, these are the “soil” samples in the example. For each individual plant, we harvested two fractions (i.e. E & R), one which we called the Endophytic Compartment (E) and corresponds to the interior of the root after removing the outer cell wall, and another which we called Rhizosphere (R) which is the soil within 1mm of the plant root. So E samples contains bacteria inside the root, and R samples contain bacteria immediately surrounding the root.
Plant-bacteria interactions in the root are incredibly important because the root is both the gut and the brain of the plant. Microbes there can benefit of the products of the plant photosynthesis as sources of nutrition, and can also provide chemistry that the plant couldn’t perform by itself. However, the story is more complicated because microbial competition and the plant immune system also provide a fertile evolutionary environment for antagonistic interactions.
Ultimately understanding and being able to manipulate plant-bacteria interactions has a lot of implications as hunger is one of the most pressing problems of humanity with 800 million people living in hunger. Agriculture is our only sustainable tool against hunger, and even though it currently employs a quarter of the World population it has not been enough to tackle this challenge.
First you need to get the data. If you haven’t check the
README
file of the GitHub repository of the workshop. It is also recommended
that you watch the YouTube
video that runs through
the example. Finally, you will need to install the tidyverse package.
Once you have everything you need, start an R session and load the tidyverse package:
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
First read the OTU table. You may need to change the file path to wherever you downloaded the files in your machine.
Tab <- read_tsv("data/rhizo/otu_table.tsv")## Rows: 129 Columns: 70
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): otu_id
## dbl (69): D1, D2, D3, D10, D57, D58, D86, D194, D195, D196, D197, D198, D214...
##
## 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.
Tab## # A tibble: 129 × 70
## otu_id D1 D2 D3 D10 D57 D58 D86 D194 D195 D196 D197
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 OTU_14834 39 66 56 125 4 18 7 0 5 1 1
## 2 OTU_18567 0 1 0 0 4 8 14 16 34 19 15
## 3 OTU_14402 10 27 32 45 2 4 4 0 1 0 0
## 4 OTU_14822 0 0 0 0 0 0 0 9 7 11 11
## 5 OTU_16757 0 0 0 0 0 0 0 8 10 12 10
## 6 OTU_15154 1 3 2 3 3 3 3 8 18 16 4
## 7 OTU_7054 16 8 4 7 1 0 6 2 9 4 8
## 8 OTU_1196 3 1 0 0 5 2 12 11 16 18 8
## 9 OTU_18954 0 0 0 0 0 0 1 5 7 2 4
## 10 OTU_14839 0 1 0 0 0 1 3 9 16 16 6
## # … with 119 more rows, and 58 more variables: D198 <dbl>, D214 <dbl>,
## # D215 <dbl>, D216 <dbl>, D217 <dbl>, D218 <dbl>, D219 <dbl>, D220 <dbl>,
## # D221 <dbl>, D222 <dbl>, D223 <dbl>, D224 <dbl>, D258 <dbl>, D259 <dbl>,
## # D260 <dbl>, D272 <dbl>, D273 <dbl>, D274 <dbl>, D275 <dbl>, D276 <dbl>,
## # D410 <dbl>, D411 <dbl>, D412 <dbl>, D413 <dbl>, D414 <dbl>, D415 <dbl>,
## # D416 <dbl>, D417 <dbl>, D418 <dbl>, D419 <dbl>, D420 <dbl>, D421 <dbl>,
## # D422 <dbl>, D423 <dbl>, D424 <dbl>, D425 <dbl>, D426 <dbl>, D427 <dbl>, …
The code above reads the file into a tibble, which is a type of
data.frame that has some neat additional properties. You don’t need to
concern yourself too much with the differences.
The code above also produces a warning, indicating that read_tsv tried
to guess the types of data in each column of the table. It guessed
correctly but you should always specify the expected columns with the
option col_types (use ?read_tsv for additional details).
Tab <- read_tsv("data/rhizo/otu_table.tsv",
col_types = cols(otu_id = col_character(),
.default = col_number()))
Tab## # A tibble: 129 × 70
## otu_id D1 D2 D3 D10 D57 D58 D86 D194 D195 D196 D197
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 OTU_14834 39 66 56 125 4 18 7 0 5 1 1
## 2 OTU_18567 0 1 0 0 4 8 14 16 34 19 15
## 3 OTU_14402 10 27 32 45 2 4 4 0 1 0 0
## 4 OTU_14822 0 0 0 0 0 0 0 9 7 11 11
## 5 OTU_16757 0 0 0 0 0 0 0 8 10 12 10
## 6 OTU_15154 1 3 2 3 3 3 3 8 18 16 4
## 7 OTU_7054 16 8 4 7 1 0 6 2 9 4 8
## 8 OTU_1196 3 1 0 0 5 2 12 11 16 18 8
## 9 OTU_18954 0 0 0 0 0 0 1 5 7 2 4
## 10 OTU_14839 0 1 0 0 0 1 3 9 16 16 6
## # … with 119 more rows, and 58 more variables: D198 <dbl>, D214 <dbl>,
## # D215 <dbl>, D216 <dbl>, D217 <dbl>, D218 <dbl>, D219 <dbl>, D220 <dbl>,
## # D221 <dbl>, D222 <dbl>, D223 <dbl>, D224 <dbl>, D258 <dbl>, D259 <dbl>,
## # D260 <dbl>, D272 <dbl>, D273 <dbl>, D274 <dbl>, D275 <dbl>, D276 <dbl>,
## # D410 <dbl>, D411 <dbl>, D412 <dbl>, D413 <dbl>, D414 <dbl>, D415 <dbl>,
## # D416 <dbl>, D417 <dbl>, D418 <dbl>, D419 <dbl>, D420 <dbl>, D421 <dbl>,
## # D422 <dbl>, D423 <dbl>, D424 <dbl>, D425 <dbl>, D426 <dbl>, D427 <dbl>, …
We need to think back to the original figure and reformat our data to
have one column for the x-axis and another for the y-axis. This is a
requirement for ggplot2. We can to that with pivot_longer, a
function of the tidyverse.
Tab %>%
pivot_longer(-otu_id, names_to = "sample_id", values_to = "count")## # A tibble: 8,901 × 3
## otu_id sample_id count
## <chr> <chr> <dbl>
## 1 OTU_14834 D1 39
## 2 OTU_14834 D2 66
## 3 OTU_14834 D3 56
## 4 OTU_14834 D10 125
## 5 OTU_14834 D57 4
## 6 OTU_14834 D58 18
## 7 OTU_14834 D86 7
## 8 OTU_14834 D194 0
## 9 OTU_14834 D195 5
## 10 OTU_14834 D196 1
## # … with 8,891 more rows
In the code above the options samples_to and names_to indicate the
names of the new columns in the new tibble.
Lets create a smaller subset of the data to make some basic plots
dat <- Tab %>%
pivot_longer(-otu_id, names_to = "sample_id", values_to = "count") %>%
filter(otu_id %in% c("OTU_14834", "OTU_18567", "OTU_14402", "OTU_14822"))
print(dat)## # A tibble: 276 × 3
## otu_id sample_id count
## <chr> <chr> <dbl>
## 1 OTU_14834 D1 39
## 2 OTU_14834 D2 66
## 3 OTU_14834 D3 56
## 4 OTU_14834 D10 125
## 5 OTU_14834 D57 4
## 6 OTU_14834 D58 18
## 7 OTU_14834 D86 7
## 8 OTU_14834 D194 0
## 9 OTU_14834 D195 5
## 10 OTU_14834 D196 1
## # … with 266 more rows
Now we create a basic barplot that shows relative abundances of the 4 selected OTUs
dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = otu_id), stat = "identity", position = "fill")
Now we re-make the plot with some beautification
dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = otu_id), stat = "identity", position = "fill") +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90))
Read sample metadata
Meta <- read_tsv("data/rhizo/sample_metadata.tsv",
col_types = cols(.default = col_character()))
Meta## # A tibble: 69 × 4
## sample_id soil accession fraction
## <chr> <chr> <chr> <chr>
## 1 D1 M21 Col-0 E
## 2 D2 M21 Col-0 E
## 3 D3 M21 Col-0 E
## 4 D10 M21 Ler-0 E
## 5 D57 C21 Col-0 E
## 6 D58 C21 Col-0 E
## 7 D86 C21 Ler-0 E
## 8 D194 C21 Col-0 R
## 9 D195 C21 Col-0 R
## 10 D196 C21 Col-0 R
## # … with 59 more rows
We join our long format dataset with the sample metadata table
print(dat)## # A tibble: 276 × 3
## otu_id sample_id count
## <chr> <chr> <dbl>
## 1 OTU_14834 D1 39
## 2 OTU_14834 D2 66
## 3 OTU_14834 D3 56
## 4 OTU_14834 D10 125
## 5 OTU_14834 D57 4
## 6 OTU_14834 D58 18
## 7 OTU_14834 D86 7
## 8 OTU_14834 D194 0
## 9 OTU_14834 D195 5
## 10 OTU_14834 D196 1
## # … with 266 more rows
dat <- dat %>%
left_join(Meta, by = "sample_id")
dat## # A tibble: 276 × 6
## otu_id sample_id count soil accession fraction
## <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 OTU_14834 D1 39 M21 Col-0 E
## 2 OTU_14834 D2 66 M21 Col-0 E
## 3 OTU_14834 D3 56 M21 Col-0 E
## 4 OTU_14834 D10 125 M21 Ler-0 E
## 5 OTU_14834 D57 4 C21 Col-0 E
## 6 OTU_14834 D58 18 C21 Col-0 E
## 7 OTU_14834 D86 7 C21 Ler-0 E
## 8 OTU_14834 D194 0 C21 Col-0 R
## 9 OTU_14834 D195 5 C21 Col-0 R
## 10 OTU_14834 D196 1 C21 Col-0 R
## # … with 266 more rows
We add facts with facet_grid
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = otu_id), stat = "identity", position = "fill") +
facet_grid(~ fraction) +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90),
axis.text.y = element_text(color = "black"))
p1
We
set scales="free_x" to plot a different set of samples on each facet.
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = otu_id), stat = "identity", position = "fill") +
facet_grid(~ fraction, scales = "free_x") +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90),
axis.text.y = element_text(color = "black"))
p1
We repeat the plot with some beautification
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = otu_id), stat = "identity", position = "fill", width = 1) +
facet_grid(~ fraction, scales = "free_x") +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90),
axis.text.y = element_text(color = "black"),
strip.text = element_text(face = "bold"),
strip.background = element_blank())
p1
We
can add an extra variable to the facet
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = otu_id), stat = "identity", position = "fill", width = 1) +
facet_grid(~ fraction + soil, scales = "free_x") +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90),
axis.text.y = element_text(color = "black"),
strip.text = element_text(face = "bold"),
strip.background = element_blank())
p1
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = otu_id), stat = "identity", position = "fill", width = 1) +
facet_grid(~ fraction + soil, scales = "free_x", space = "free_x") +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90),
axis.text.y = element_text(color = "black"),
strip.text = element_text(face = "bold"),
strip.background = element_blank())
p1
Re-make the plot above but using accession instead of soil type in the facets
Re-make the plot above but show all three variables (fraction, accession and soil) in the facets. What happens if you change the order of the terms in the facet formulas? is any of the orders better?
Tax <- read_tsv("data/rhizo/otu_taxonomy.tsv",
col_types = cols(otu_id = col_character(),
Phylum = col_character()))
Tax## # A tibble: 129 × 2
## otu_id Phylum
## <chr> <chr>
## 1 OTU_14834 Actinobacteria
## 2 OTU_18567 Proteobacteria
## 3 OTU_14402 Actinobacteria
## 4 OTU_14822 Proteobacteria
## 5 OTU_16757 Bacteroidetes
## 6 OTU_15154 Proteobacteria
## 7 OTU_7054 Firmicutes
## 8 OTU_1196 Proteobacteria
## 9 OTU_18954 Bacteroidetes
## 10 OTU_14839 Proteobacteria
## # … with 119 more rows
Let’s include all OTUs in our data table now
dat <-Tab %>%
pivot_longer(-otu_id, names_to = "sample_id",
values_to = "count")
print(dat)## # A tibble: 8,901 × 3
## otu_id sample_id count
## <chr> <chr> <dbl>
## 1 OTU_14834 D1 39
## 2 OTU_14834 D2 66
## 3 OTU_14834 D3 56
## 4 OTU_14834 D10 125
## 5 OTU_14834 D57 4
## 6 OTU_14834 D58 18
## 7 OTU_14834 D86 7
## 8 OTU_14834 D194 0
## 9 OTU_14834 D195 5
## 10 OTU_14834 D196 1
## # … with 8,891 more rows
As we did before with sample metadata, we use left_join to add OTU
taxonomic information to our data table
dat <- dat %>%
left_join(Tax, by = "otu_id")
print(dat)## # A tibble: 8,901 × 4
## otu_id sample_id count Phylum
## <chr> <chr> <dbl> <chr>
## 1 OTU_14834 D1 39 Actinobacteria
## 2 OTU_14834 D2 66 Actinobacteria
## 3 OTU_14834 D3 56 Actinobacteria
## 4 OTU_14834 D10 125 Actinobacteria
## 5 OTU_14834 D57 4 Actinobacteria
## 6 OTU_14834 D58 18 Actinobacteria
## 7 OTU_14834 D86 7 Actinobacteria
## 8 OTU_14834 D194 0 Actinobacteria
## 9 OTU_14834 D195 5 Actinobacteria
## 10 OTU_14834 D196 1 Actinobacteria
## # … with 8,891 more rows
Let’s make our basic plot, but using Phylum instead of otu_id
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = Phylum), stat = "identity", position = "fill", width = 1) +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90),
axis.text.y = element_text(face = "bold"))
p1
Can we find better colors
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = Phylum), stat = "identity", position = "fill", width = 1) +
scale_fill_brewer(palette = "Paired") +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90),
axis.text.y = element_text(face = "bold"))
p1
I
want phyla to be ordered by abundance and “unclassified” to be at the
end
Code to get order not shown but results are in mean_freqs
## # A tibble: 11 × 2
## Phylum mean
## <chr> <dbl>
## 1 Proteobacteria 0.354
## 2 Actinobacteria 0.232
## 3 Bacteroidetes 0.179
## 4 Acidobacteria 0.101
## 5 Firmicutes 0.0707
## 6 Cyanobacteria 0.0197
## 7 Verrucomicrobia 0.0172
## 8 Gemmatimonadetes 0.0130
## 9 unclassified 0.00547
## 10 Armatimonadetes 0.00438
## 11 Chloroflexi 0.00423
Create a vector with the desired order of phyla and use it to convert phylum column to a factor.
phyla_order <- c("Proteobacteria",
"Actinobacteria",
"Bacteroidetes",
"Acidobacteria",
"Firmicutes",
"Cyanobacteria",
"Verrucomicrobia",
"Gemmatimonadetes",
"Armatimonadetes",
"Chloroflexi",
"unclassified")
dat <- dat %>%
mutate(Phylum = factor(Phylum, levels = phyla_order))
dat## # A tibble: 8,901 × 4
## otu_id sample_id count Phylum
## <chr> <chr> <dbl> <fct>
## 1 OTU_14834 D1 39 Actinobacteria
## 2 OTU_14834 D2 66 Actinobacteria
## 3 OTU_14834 D3 56 Actinobacteria
## 4 OTU_14834 D10 125 Actinobacteria
## 5 OTU_14834 D57 4 Actinobacteria
## 6 OTU_14834 D58 18 Actinobacteria
## 7 OTU_14834 D86 7 Actinobacteria
## 8 OTU_14834 D194 0 Actinobacteria
## 9 OTU_14834 D195 5 Actinobacteria
## 10 OTU_14834 D196 1 Actinobacteria
## # … with 8,891 more rows
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
geom_bar(aes(fill = Phylum), stat = "identity", position = "fill", width = 1) +
scale_fill_brewer(palette = "Paired") +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
theme(axis.text.x = element_text(angle = 90),
axis.text.y = element_text(face = "bold"))
p1
## Excercise
Use scale_color_manual to manually select a good set of colors for
this plot
We can use lef_join twice in a row to get a combinde data table with
all the information
dat <- Tab %>%
pivot_longer(-otu_id, names_to = "sample_id", values_to = "count") %>%
left_join(Meta, by = "sample_id") %>%
left_join(Tax, by = "otu_id")
print(dat)## # A tibble: 8,901 × 7
## otu_id sample_id count soil accession fraction Phylum
## <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 OTU_14834 D1 39 M21 Col-0 E Actinobacteria
## 2 OTU_14834 D2 66 M21 Col-0 E Actinobacteria
## 3 OTU_14834 D3 56 M21 Col-0 E Actinobacteria
## 4 OTU_14834 D10 125 M21 Ler-0 E Actinobacteria
## 5 OTU_14834 D57 4 C21 Col-0 E Actinobacteria
## 6 OTU_14834 D58 18 C21 Col-0 E Actinobacteria
## 7 OTU_14834 D86 7 C21 Ler-0 E Actinobacteria
## 8 OTU_14834 D194 0 C21 Col-0 R Actinobacteria
## 9 OTU_14834 D195 5 C21 Col-0 R Actinobacteria
## 10 OTU_14834 D196 1 C21 Col-0 R Actinobacteria
## # … with 8,891 more rows
To get the same order as above we convert the Phylum column to a factor
dat <- dat %>%
mutate(Phylum = factor(Phylum, levels = phyla_order))
dat## # A tibble: 8,901 × 7
## otu_id sample_id count soil accession fraction Phylum
## <chr> <chr> <dbl> <chr> <chr> <chr> <fct>
## 1 OTU_14834 D1 39 M21 Col-0 E Actinobacteria
## 2 OTU_14834 D2 66 M21 Col-0 E Actinobacteria
## 3 OTU_14834 D3 56 M21 Col-0 E Actinobacteria
## 4 OTU_14834 D10 125 M21 Ler-0 E Actinobacteria
## 5 OTU_14834 D57 4 C21 Col-0 E Actinobacteria
## 6 OTU_14834 D58 18 C21 Col-0 E Actinobacteria
## 7 OTU_14834 D86 7 C21 Ler-0 E Actinobacteria
## 8 OTU_14834 D194 0 C21 Col-0 R Actinobacteria
## 9 OTU_14834 D195 5 C21 Col-0 R Actinobacteria
## 10 OTU_14834 D196 1 C21 Col-0 R Actinobacteria
## # … with 8,891 more rows
Now we can combine facets and phylum color and ordering in just one plot
p1 <- dat %>%
ggplot(aes(x = sample_id, y = count)) +
facet_grid(~ fraction + soil, scales = "free_x", space = "free_x") +
geom_bar(aes(fill = Phylum), stat = "identity", position = "fill", width = 1) +
scale_y_continuous(name = "Relative abundance", labels = scales::percent) +
scale_fill_brewer(palette = "Paired") +
theme(axis.text.x = element_text(angle = 90, size = 6),
axis.text.y = element_text(color = "black"),
strip.text = element_text(face = "bold"),
strip.background = element_blank())
p1
We save the plot to a file
ggsave("rhizo_phylo_distribution.png", p1, width = 8, height = 4)Look at the files at data/hmp_v13 which contain much bigger data tables generated from the Human Microbiome Project (HMP).
Can you make similar plots illustrating the bacterial taxonomic distributions of the Stool and Saliva? Are there any differences by sex?
Can you order the samples by Proteobacteria relative abundance in the rhizosphere dataset? what about the Bacteroidetes abundance in the HMP dataset? (HINT: Use the same approach we used to sort taxonomic groups)
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
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## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4
## [5] readr_2.1.2 tidyr_1.2.0 tibble_3.1.6 ggplot2_3.3.5
## [9] tidyverse_1.3.1
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## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.8 lubridate_1.8.0 assertthat_0.2.1 digest_0.6.29
## [5] utf8_1.2.2 R6_2.5.1 cellranger_1.1.0 backports_1.4.1
## [9] reprex_2.0.1 evaluate_0.14 httr_1.4.2 highr_0.9
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## [21] broom_0.7.12 compiler_4.1.0 modelr_0.1.8 xfun_0.30
## [25] pkgconfig_2.0.3 htmltools_0.5.2 tidyselect_1.1.1 fansi_1.0.2
## [29] crayon_1.5.0 tzdb_0.2.0 dbplyr_2.1.1 withr_2.4.3
## [33] grid_4.1.0 jsonlite_1.7.3 gtable_0.3.0 lifecycle_1.0.1
## [37] DBI_1.1.2 magrittr_2.0.2 scales_1.1.1 cli_3.2.0
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## [49] RColorBrewer_1.1-2 tools_4.1.0 bit64_4.0.5 glue_1.6.1
## [53] hms_1.1.1 parallel_4.1.0 fastmap_1.1.0 yaml_2.3.4
## [57] colorspace_2.0-2 rvest_1.0.2 knitr_1.37 haven_2.4.3