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Pivoting data can be a pain point in bioinformatics workflows. Lots of bioinformatics software are tied to the wide format with data spread out among multiple columns while the whole tidyverse/ggplot system requires long data with as few columns as possible. Becoming proficient at switching your data to long format has several benefits. (1) It provides a unified format for any required data manipulations and summarizations making them faster to write and easier to read and (2) it is the required input format for the ggplot system. In R the tidyverse provides the tools to interchange wide and long data.
The Problem:
Typically subjects in bioinformatics datasets (columns) will have associated metadata like treatments and indicators of groups or replicates. Any metadata that corresponds to rows can be easily added to the data.frame to be pivoted (eg. with cbind). But if there are column metadata they have to be added manually after the pivot.
The Solution:
There are a couple of ways to do this. The way I've settled on is to have a table of target meta-data and use a join after the pivot to connect it to the data. I find myself doing this repeatedly in almost all of my analyses but it's a solution I stumbled on by trial and error. I have never seen it spelled out explicitly anywhere so here it is.
head(relig_income) ## # A tibble: 6 x 12 ## religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k` `$75-100k` `$100-150k` ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 Agnostic 27 34 60 81 76 137 122 109 ## 2 Atheist 12 27 37 52 35 70 73 59 ## 3 Buddhist 27 21 30 34 33 58 62 39 ## 4 Catholic 418 617 732 670 638 1116 949 792 ## 5 Don’t k~ 15 14 15 11 10 35 21 17 ## 6 Evangel~ 575 869 1064 982 881 1486 949 723 ## # ... with 3 more variables: `>150k` <dbl>, `Don't know/refused` <dbl>, religionClass <chr>
First, create the metadata.
I'll use the relig_income dataset as an example. I will demonstrate how to add both row metadata (easy) and column metadata (bit tricky). For row metadata I will add a new column for religion class that will be defined randomly and for column metadata I will group income levels into low, medium, high and unknown listed in a separate data.frame. Note that this method relies on linking data column names to metadata so check the metadata table carefully!
To add the row metadata I simply add a new column to the relig_income table with my random values. For the column metadata I will make a new data.frame.
## Row metadata
set.seed(10)
relig_income$religionClass <-
sample(c("A", "B", "C"), nrow(relig_income), replace = TRUE)
## Column metadata
columnMetadata <- data.frame(
income = c(colnames(relig_income)[
grepl("0", colnames(relig_income))],
"Don't know/refused"),
incomeGroup = c(rep("low", 3), rep("medium", 3),
rep("high", 3), "Don't know/refused"))
columnMetadata
## income incomeGroup
## 1 <$10k low
## 2 $10-20k low
## 3 $20-30k low
## 4 $30-40k medium
## 5 $40-50k medium
## 6 $50-75k medium
## 7 $75-100k high
## 8 $100-150k high
## 9 >150k high
## 10 Don't know/refused Don't know/refused
Step 1: pivot_longer as usual
Don't forget to exclude the new religionClass column from the pivot.
relig_income %>% pivot_longer(-c(religion, religionClass), names_to = "income", values_to = "count") ## # A tibble: 180 x 4 ## religion religionClass income count ## <chr> <chr> <chr> <dbl> ## 1 Agnostic C <$10k 27 ## 2 Agnostic C $10-20k 34 ## 3 Agnostic C $20-30k 60 ## 4 Agnostic C $30-40k 81 ## 5 Agnostic C $40-50k 76 ## 6 Agnostic C $50-75k 137 ## 7 Agnostic C $75-100k 122 ## 8 Agnostic C $100-150k 109 ## 9 Agnostic C >150k 84 ## 10 Agnostic C Don't know/refused 96 ## # ... with 170 more rows
Step 2: join the column metadata
All metadata columns will be added automatically with this step.
relig_income %>% pivot_longer(-c(religion, religionClass), names_to = "income", values_to = "count") %>% inner_join(columnMetadata, by = "income") ## # A tibble: 180 x 5 ## religion religionClass income count incomeGroup ## <chr> <chr> <chr> <dbl> <chr> ## 1 Agnostic C <$10k 27 low ## 2 Agnostic C $10-20k 34 low ## 3 Agnostic C $20-30k 60 low ## 4 Agnostic C $30-40k 81 medium ## 5 Agnostic C $40-50k 76 medium ## 6 Agnostic C $50-75k 137 medium ## 7 Agnostic C $75-100k 122 high ## 8 Agnostic C $100-150k 109 high ## 9 Agnostic C >150k 84 high ## 10 Agnostic C Don't know/refused 96 Don't know/refused ## # ... with 170 more rows
Step 3 (optional): Convert character data to ordered factors to control plotting order
relig_income %>% pivot_longer(-c(religion, religionClass), names_to = "income", values_to = "count") %>% inner_join(columnMetadata, by = "income") %>% mutate(income = ordered(income, levels = columnMetadata$income)) ## # A tibble: 180 x 5 ## religion religionClass income count incomeGroup ## <chr> <chr> <ord> <dbl> <chr> ## 1 Agnostic C <$10k 27 low ## 2 Agnostic C $10-20k 34 low ## 3 Agnostic C $20-30k 60 low ## 4 Agnostic C $30-40k 81 medium ## 5 Agnostic C $40-50k 76 medium ## 6 Agnostic C $50-75k 137 medium ## 7 Agnostic C $75-100k 122 high ## 8 Agnostic C $100-150k 109 high ## 9 Agnostic C >150k 84 high ## 10 Agnostic C Don't know/refused 96 Don't know/refused ## # ... with 170 more rows
Finally look at the mapping to ensure it worked. Unfortunately table doesn't play well with the %>% operator so this step is a bit inelegant.
test <- relig_income %>% pivot_longer(-c(religion, religionClass), names_to = "income", values_to = "count") %>% inner_join(columnMetadata, by = "income") %>% mutate(income = ordered(income, levels = columnMetadata$income)) table(test$income, test$incomeGroup) ## ## Don't know/refused high low medium ## <$10k 0 0 18 0 ## $10-20k 0 0 18 0 ## $20-30k 0 0 18 0 ## $30-40k 0 0 0 18 ## $40-50k 0 0 0 18 ## $50-75k 0 0 0 18 ## $75-100k 0 18 0 0 ## $100-150k 0 18 0 0 ## >150k 0 18 0 0 ## Don't know/refused 18 0 0 0
The metadata columns are now available
We can plot the data summarized by our arbitrary grouping of religions and colored by our grouped income levels. Order the income classes to make a sensible presentation
relig_income %>%
pivot_longer(-c(religion, religionClass), names_to = "income", values_to = "count") %>%
inner_join(columnMetadata, by = "income") %>%
mutate(income = ordered(income, levels = columnMetadata$income)) %>%
mutate(incomeGroup = ordered(incomeGroup, levels = c("low", "medium", "high", "Don't know/refused"))) %>%
group_by(religionClass, income, incomeGroup) %>%
summarize(meanCount = mean(count), .groups = "drop_last") %>%
ggplot(aes(x = income, y = meanCount, fill = incomeGroup)) +
geom_col() +
facet_wrap(vars(religionClass)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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