YAY GRAPHS! Using the airquality dataset from R, that has info about air quality measurements in New York City.

ggplot template (for my brain)

p1 <- ggplot(data= <DATA>) +
      aes(<MAPPINGS>) + 
      <GEOM_FUNCTION>(aes(<MAPPINGS>),
                      stat=<STAT>,
                      position=<POSITION>) +
                      <COORDINATE_FUNCTION> +
                      <FACET_FUNCTION>
# loading things area
library(ggplot2)
library(beeswarm)
library(ggbeeswarm)
## Warning: package 'ggbeeswarm' was built under R version 4.1.2
library(RColorBrewer)
## Warning: package 'RColorBrewer' was built under R version 4.1.2
library(tidyr)
## Warning: package 'tidyr' was built under R version 4.1.2
data(airquality)
head(airquality)
##   Ozone Solar.R Wind Temp Month Day
## 1    41     190  7.4   67     5   1
## 2    36     118  8.0   72     5   2
## 3    12     149 12.6   74     5   3
## 4    18     313 11.5   62     5   4
## 5    NA      NA 14.3   56     5   5
## 6    28      NA 14.9   66     5   6

Beeswarm Plot! bzzzzzzzzz

# had a lot of trouble since month is technically a numeric value, but here is how to turn it into a factor
airquality$Month <- factor(airquality$Month)

ggplot(airquality,aes(x=Month, y=Wind, color=Month)) +
  geom_beeswarm()+
  scale_color_manual(values = c("olivedrab3","gold","orange2","red1","darkred"))+
  ggtitle("Windspeed Variation in New York City Based on Month")

Making a plot with some of my very initial data

data <- read.csv("NLPsheet2.csv")
ggplot(data=data,aes(x=Date, y=Total_NLPs)) +
  geom_beeswarm()

I think this is kinda the wrong graph type for this data right now though!

Bar Plot of NLPs

ggplot(data, aes(x = Behavior, y = Total_NLPs, fill = Behavior)) + 
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("#ffffcc", "#a1dab4", "#41b6c4","#2c7fb8", "#225ea8")) +
  ggtitle("Presence of NLPs Dependent on Dolphin Behavior")

other plots that I made for my committee meeting, with updated data

# --------- BEHAVIOR BAR PLOT ----------
ggplot(data, aes(x = Behavior, y = Total_NLPs, fill = Behavior)) + 
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("#ffffcc", "#c7e9b4", "#a1dab4", "#41b6c4","#2c7fb8", "#225ea8"))

# -------------------- NLP Bar Plot------
# Reshape the data from wide to long format
Collect_NLP <- gather(data, key = "category", value = "value", BP, DC, SB, SH)

# Create the bar plot
ggplot(Collect_NLP, aes(x = category, y = value, fill = category)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values=c("#c7e9b4","#a1dab4","#41b6c4","#225ea8"))+
  labs(title = "Total Presence of NLPs: June 2023",
       x = "NLP Type",
       y = "Total Appearances") +
  theme_minimal()

# ------------- Group Size v Whistle Numbers Comparison ------------
# Scatter plot
scatter <- ggplot(data, aes(x = Group_Size, y = Total_NLPs)) +
  geom_point() +
  geom_smooth(method="lm", color="red", se=FALSE) +
  labs(title = "Group Size v. NLP Totals",
       x = "Group Size",
       y = "Number of NLPs") +
  theme_minimal()
scatter
## `geom_smooth()` using formula = 'y ~ x'