Charts and Graphs
‘ggplot2’ is a easy to use plotting library in R. ‘ggplot2’ syntax
consists of creating a ggplot object with a dataframe and adding
subsequent arguments to that object. Aesthetics (aes()) in
ggplot represents the data mapping aspect of the plot. A simple example
using collections is shown below.
library(ggplot2)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
#creates a month column and translates numerics
sample_collections$month = as.factor(month(sample_collections$collection_date))
collections_sums = sample_collections %>%
filter(
species_display_name %in% c(
"Cx tarsalis",
"Cx pipiens",
"An freeborni",
"Cs incidens",
"Ae melanimon",
"Cs inornata",
"Cx stigmatosoma",
"Cx erythrothorax",
"Ae vexans",
"I pacificus"
)
) %>%
group_by(month, species_display_name) %>%
summarise(sum_count = sum(num_count, na.rm = T)) %>% arrange(desc(sum_count), .by_group = T)
#> `summarise()` has grouped output by 'month'. You can override using the
#> `.groups` argument.
#ggplot with dots a values for each species of interest
ggplot(data = collections_sums,
aes(x = month, y = sum_count, color = species_display_name)) +
geom_point()
#bar chart
ggplot(data = collections_sums,
aes(x = month, y = sum_count, fill = species_display_name)) +
geom_bar(stat = "identity")
#adding labels
ggplot(data = collections_sums,
aes(x = month, y = sum_count, fill = species_display_name)) +
geom_bar(stat = "identity") +
labs(title = "Mosquito Counts by Month and Species",
x = "Month",
y = "Sum of Mosquitoes",
fill = "Species")
When plotting with libraries in R, it is easiest when the data is prepared in long form. Most calculator outputs from our functions are in wide form. The following wrapper functions help process and plot this data.
processAbunAnom()
Description
processAbunAnom() processes the output returned from
getAbundanceAnomaly() into a long form suitable for
plotting in ‘ggplot’
Usage
processAbunAnom(AbAnomOutput)
Arguments
- AbAnomOutput: Output from returned
getAbundanceAnomaly()
AbAnOut = getAbundanceAnomaly(
sample_collections,
interval = "Biweek",
target_year = 2020,
species_list = c("Cx tarsalis", "Cx pipiens"),
species_separate = TRUE
)
AbAnOut_L = processAbunAnom(AbAnOut)
#> Warning in colnames(AbAnomOutput)[grep("Abundance", colnames(AbAnomOutput), :
#> number of items to replace is not a multiple of replacement lengthWe can take the output of processAbunAnom() and create a
plot comparing the target year abundance to the five year average.
AbAnOut_L %>% filter(Abundance_Type %in% c("2020_Abundance",
"Five_Year_Avg")) %>%
ggplot(aes(x = Biweek,
y = Abundance_Calculation,
color = Abundance_Type)) +
geom_point() +
geom_line() +
facet_wrap( ~ species_display_name) +
labs(title = "2020 Abundance Anomaly", y = "")
We can also create a plot which displays the percent change from the five year average.
AbAnOut_L %>%
filter(Abundance_Type == "Delta") %>%
mutate(Change = ifelse(Abundance_Calculation > 0, "Increase", "Decrease")) %>%
ggplot(aes(x = Biweek,
y = Abundance_Calculation,
fill = Change)) +
geom_bar(stat = "identity") +
facet_wrap( ~ species_display_name) +
labs(x = "Biweek",
y = "Percent Change",
title = "Relative Abundance 2023, % Change from 5-year average",
fill = "Relative Change")
plotInfectionRate()
Description
plotInfectionRate() plots the output returned from
getInfectionRate() with confidence intervals using
‘ggplot2’.
Usage
plotInfectionRate(InfRtOutput, year)
Arguments
- InfRtOutput: Output from returned
getInfectionRate() - year: Desired year of plot (year must be in data)
IR = getInfectionRate(
sample_pools,
interval = "Week",
target_disease = "WNV",
pt_estimate = "mle",
species_list = c("Cx pipiens"),
trap_list = c("CO2", "GRVD")
)
plotInfectionRate(InfRtOutput = IR, year = 2020)