Expressiveness and Effectiveness

Code

library(readxl)
Weevils <- read_excel("~/Mi unidad/Uidaho/Clases/PhD/Spring 2024/BCB 520/Data/Weevils.xlsx")
head(Weevils)

# A tibble: 6 × 9
  Treatment Replicate `Insecticide Name` Before `7 dpa` `14 dpa` `21 dpa`
      <dbl>     <dbl> <chr>               <dbl>   <dbl>    <dbl>    <dbl>
1         0         1 Control                11       3        5        2
2         0         2 Control                 4       4        4        3
3         0         3 Control                16       6        7        3
4         0         4 Control                 5       3        1        3
5         0         5 Control                10       6        7        4
6         0         6 Control                 2       5        2        4
# ℹ 2 more variables: `28 dpa` <dbl>, `35 dpa` <dbl>

Code

library(tidyverse)

Warning: package 'readr' was built under R version 4.2.3

Warning: package 'dplyr' was built under R version 4.2.3

Warning: package 'stringr' was built under R version 4.2.3

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✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Code

Bmeans <-Weevils %>%
  group_by(`Insecticide Name`) %>%
  summarise(AVG = mean(Before),
            AVG1 = mean(`7 dpa`, na.rm = TRUE), AVG2 = mean(`14 dpa`, na.rm = TRUE), AVG3 = mean(`21 dpa`, na.rm = TRUE), AVG4 = mean(`28 dpa`, na.rm = TRUE), AVG5 = mean(`35 dpa`, na.rm = TRUE) )
library(ggplot2)

Figure 1

This figure uses different colors to make clear the difference between treatments. As the concentration of the experimental sample increases, the hue of the blue color increases as well making sense (to me).

Code

library(tidyverse)
Bmeans <-Weevils %>%
  group_by(`Insecticide Name`) %>%
  summarise(AVG = mean(Before),
            AVG1 = mean(`7 dpa`, na.rm = TRUE), AVG2 = mean(`14 dpa`, na.rm = TRUE), AVG3 = mean(`21 dpa`, na.rm = TRUE), AVG4 = mean(`28 dpa`, na.rm = TRUE), AVG5 = mean(`35 dpa`, na.rm = TRUE) )
library(ggplot2)
my_colors <- c("green", "cyan", "blue", "darkblue", "gold", "black")

ggplot(Bmeans, aes(x = `Insecticide Name`, y = AVG, fill = `Insecticide Name`)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = my_colors) +
  labs(title = "Mean weevils per plant before treatments",
       x = "Insecticides",
       y = "Mean weevils/Plant") +
  theme(axis.text.x = element_text(angle = 10, vjust = 0.5, hjust = 1))

Figure 2

This figure violates the channel correct use, since it uses the same color for every treatment, still is possible to understand what is going on but it takes significantly more time.

Code

ggplot(Bmeans, aes(x = `Insecticide Name`, y = AVG)) +
  geom_bar(stat = "identity", fill = "black") +
  labs(title = "Mean weevils per plant before treatments",
       x = "Insecticides",
       y = "Mean weevils/Plant") +
  theme(axis.text.x = element_text(angle = 10, vjust = 0.5, hjust = 1))

Discriminability

Figure 3

This figure uses different colors and marks (dots) to make clear the difference between treatments.

Code

ggplot(Bmeans, aes(x = `Insecticide Name`, y = AVG5, group = 1, color = `Insecticide Name`)) +
  geom_line() +
  geom_point() + # Adds points to the line graph for clarity
  scale_color_manual(values = my_colors) +
  labs(title = "Mean weevils per plant 35 DPA",
       x = "Insecticides",
       y = "Mean weevils/Plant") +
  theme(axis.text.x = element_text(angle = 10, vjust = 0.5, hjust = 1))

Figure 4

A thick line, no color difference, no marks in between treatments, makes difficult to separate them. Same channel for everything.

Code

ggplot(Bmeans, aes(x = `Insecticide Name`, y = AVG5, group = 1)) +
  geom_line(size = 30, color = "black")

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Code

  labs(title = "Mean weevils per plant 35 DPA",
       x = "Insecticides",
       y = "Mean weevils/Plant") +
  theme(axis.text.x = element_text(angle = 10, vjust = 0.5, hjust = 1))

NULL

Separability

Figure 5

Standard Error of the Mean (SEM), and bars for each treatment are presented using different channels. They are easily distinguishable between each other.

Code

library(tidyverse)
Bmeans <- Weevils %>%
  group_by(`Insecticide Name`) %>%
  summarise(AVG = mean(Before),
            AVG1 = mean(`7 dpa`, na.rm = TRUE),
            AVG2 = mean(`14 dpa`, na.rm = TRUE),
            AVG3 = mean(`21 dpa`, na.rm = TRUE),
            AVG4 = mean(`28 dpa`, na.rm = TRUE),
            AVG5 = mean(`35 dpa`, na.rm = TRUE),
            SD_14dpa = sd(`14 dpa`, na.rm = TRUE), 
            N_14dpa = sum(!is.na(`14 dpa`))) %>% 
  mutate(SEM_14dpa = SD_14dpa / sqrt(N_14dpa)) 
ggplot(Bmeans, aes(x = `Insecticide Name`, y = AVG2, fill = `Insecticide Name`)) +
  geom_bar(stat = "identity") +
  geom_errorbar(aes(ymin = AVG2 - SEM_14dpa, ymax = AVG2 + SEM_14dpa), width = 0.2) +
  scale_fill_manual(values = my_colors) +
  labs(title = "Mean weevils per plant at 14 dpa",
       x = "Insecticides",
       y = "Mean weevils/Plant") +
  theme(axis.text.x = element_text(angle = 10, vjust = 0.5, hjust = 1))

Figure 6

Standard Error of the Mean (SEM), and bars for each treatment shares the same channels. They are hardly distinguishable between each other.

Code

library(tidyverse)
ggplot(Bmeans, aes(x = `Insecticide Name`, y = AVG2, fill = `Insecticide Name`)) +
  geom_bar(stat = "identity") +
  geom_errorbar(aes(ymin = AVG2 - SEM_14dpa, ymax = AVG2 + SEM_14dpa, color = `Insecticide Name`), 
                width = 1, size = 3) +
  scale_fill_manual(values = my_colors) +
  scale_color_manual(values = my_colors) + 
  labs(title = "Mean weevils per plant at 14 dpa",
       x = "Insecticides",
       y = "Mean weevils/Plant") +
  theme(axis.text.x = element_text(angle = 10, vjust = 0.5, hjust = 1))

Figure 7

As we compare the insecticide treatments with the control, this figure clearly depicts the differences between them and the control, using different channels,

Code

library(tidyverse)

Bmeans <- Weevils %>%
  group_by(`Insecticide Name`) %>%
  summarise(AVG = mean(Before),
            AVG1 = mean(`7 dpa`, na.rm = TRUE),
            AVG2 = mean(`14 dpa`, na.rm = TRUE),
            AVG3 = mean(`21 dpa`, na.rm = TRUE),
            AVG4 = mean(`28 dpa`, na.rm = TRUE),
            AVG5 = mean(`35 dpa`, na.rm = TRUE),
            SD_35dpa = sd(`35 dpa`, na.rm = TRUE),  # Calculate SD for 35 dpa
            N_35dpa = sum(!is.na(`35 dpa`))) %>%  # Count non-NA values for 35 dpa
  mutate(SEM_35dpa = SD_35dpa / sqrt(N_35dpa))  # Calculate SEM for 35 dpa

# Assuming my_colors is already defined
ggplot(Bmeans, aes(x = `Insecticide Name`, y = AVG5, fill = `Insecticide Name`)) +
  geom_bar(stat = "identity") +
  geom_errorbar(aes(ymin = AVG5 - SEM_35dpa, ymax = AVG5 + SEM_35dpa), width = 0.2) +
  scale_fill_manual(values = my_colors) +
  labs(title = "Mean weevils per plant at 35 dpa",
       x = "Insecticides",
       y = "Mean weevils/Plant") +
  theme(axis.text.x = element_text(angle = 10, vjust = 0.5, hjust = 1))

Figure 8

Here, I’m using the same data at 35 dpa, but the scale of the Y-axis is incorrect. The same channels are used for everything without any markers for separability, resulting in an awful graph.

Code

ggplot(Bmeans, aes(x = `Insecticide Name`, y = AVG5, group = 1)) +
  geom_line(size = 15, color = "black") +  # Adjust line size to a reasonable value
  scale_y_continuous(limits = c(NA, 50)) + # Set the upper limit of y-axis to 50
  labs(title = "Mean weevils per plant 35 DPA",
       x = "Insecticides",
       y = "Mean weevils/Plant") +
  theme(axis.text.x = element_text(angle = 10, vjust = 0.5, hjust = 1))