Biostats Lab 1 Assignment
Andrew Hand
09-09-2025
Question 1
# odd and prime vectors
odd <- c(1,3,5,7,9,11)
print(odd)## [1] 1 3 5 7 9 11prime <- c(2,3,5,7,11,13)
print(prime)## [1] 2 3 5 7 11 13Question 2
# moving vectors into the same data frame
my_numbers <- c(odd, prime)
my_numbers## [1] 1 3 5 7 9 11 2 3 5 7 11 13my_data <- data.frame(odd, prime)
my_data## odd prime
## 1 1 2
## 2 3 3
## 3 5 5
## 4 7 7
## 5 9 11
## 6 11 13summary(my_data)## odd prime
## Min. : 1.0 Min. : 2.000
## 1st Qu.: 3.5 1st Qu.: 3.500
## Median : 6.0 Median : 6.000
## Mean : 6.0 Mean : 6.833
## 3rd Qu.: 8.5 3rd Qu.:10.000
## Max. :11.0 Max. :13.000Question 3
my_data## odd prime
## 1 1 2
## 2 3 3
## 3 5 5
## 4 7 7
## 5 9 11
## 6 11 13diff <- prime - odd
diff## [1] 1 0 0 0 2 2my_data$odd <- as.factor(my_data$odd)
my_data$prime <- as.factor(my_data$prime)
my_data$new_column <- diff # adding new column 'diff' to my data frame
my_data$new_column <- as.factor(my_data$new_column)
my_data## odd prime new_column
## 1 1 2 1
## 2 3 3 0
## 3 5 5 0
## 4 7 7 0
## 5 9 11 2
## 6 11 13 2Question 4 mean and variance of resistance
library(readr)
daphnia_resistance <- read_csv("daphnia_resistance.csv")
View(daphnia_resistance)
# load daphnia csv file
dr <- daphnia_resistance # re-naming
summary(dr) # summary statistics ## cyandensity resistance
## Length:32 Min. :0.5600
## Class :character 1st Qu.:0.6775
## Mode :character Median :0.7600
## Mean :0.7509
## 3rd Qu.:0.8225
## Max. :0.9000mean_resistance <- mean(dr$resistance)
var_resistance <- var(dr$resistance)
mean_resistance## [1] 0.7509375var_resistance## [1] 0.009111996Question 5
# plot developement rate over temp
library(readr)
aedes_development <- read_csv("aedes_development.csv")
View(aedes_development)
ad <- aedes_development
head(ad)## # A tibble: 6 × 9
## start.date end.date temp sex jar dev.time.days dev.rate weight.g
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2/21/2008 3/5/2008 22 F 2 13 0.0769 0.0037
## 2 2/21/2008 3/6/2008 22 F 2 14 0.0714 NA
## 3 2/21/2008 3/3/2008 22 M 1 11 0.0909 0.0026
## 4 2/21/2008 3/3/2008 22 M 1 11 0.0909 0.002
## 5 2/21/2008 3/3/2008 22 M 1 11 0.0909 0.0025
## 6 2/21/2008 3/3/2008 22 M 1 11 0.0909 0.0023
## # ℹ 1 more variable: weight.mg <dbl>summary(ad) # summary statistics ## start.date end.date temp sex
## Length:243 Length:243 Min. : 5.0 Length:243
## Class :character Class :character 1st Qu.: 9.0 Class :character
## Mode :character Mode :character Median :12.0 Mode :character
## Mean :12.9
## 3rd Qu.:16.0
## Max. :22.0
##
## jar dev.time.days dev.rate weight.g
## Min. :1.000 Min. : 11.00 Min. :0.006897 Min. :0.001700
## 1st Qu.:1.000 1st Qu.: 21.00 1st Qu.:0.017241 1st Qu.:0.002500
## Median :1.000 Median : 29.00 Median :0.034483 Median :0.002900
## Mean :1.428 Mean : 46.01 Mean :0.035088 Mean :0.003602
## 3rd Qu.:2.000 3rd Qu.: 58.00 3rd Qu.:0.047619 3rd Qu.:0.004800
## Max. :2.000 Max. :145.00 Max. :0.090909 Max. :0.005900
## NA's :20
## weight.mg
## Min. :1.700
## 1st Qu.:2.500
## Median :2.900
## Mean :3.602
## 3rd Qu.:4.800
## Max. :5.900
## NA's :20library(ggplot2) # plotting dev.rate over temp as p1
p1 <- ggplot(ad, aes(x = dev.rate, y = temp)) +
geom_point(aes(color = dev.rate)) +
geom_smooth(method = lm, color = "black", fill = "red", alpha = 0.3) +
labs(title = "Development Rate Over Temperature", x = "Development Rate", y = "Temperature") +
theme_minimal() + theme(plot.title = element_text(face = "bold"),
axis.title.x = element_text(face = "bold"),
axis.title.y = element_text(face = "bold"))
p1
# note: calculating mean and variance of dev.rate and weight.mg (use 'na,rm = TRUE')
summary(ad)## start.date end.date temp sex
## Length:243 Length:243 Min. : 5.0 Length:243
## Class :character Class :character 1st Qu.: 9.0 Class :character
## Mode :character Mode :character Median :12.0 Mode :character
## Mean :12.9
## 3rd Qu.:16.0
## Max. :22.0
##
## jar dev.time.days dev.rate weight.g
## Min. :1.000 Min. : 11.00 Min. :0.006897 Min. :0.001700
## 1st Qu.:1.000 1st Qu.: 21.00 1st Qu.:0.017241 1st Qu.:0.002500
## Median :1.000 Median : 29.00 Median :0.034483 Median :0.002900
## Mean :1.428 Mean : 46.01 Mean :0.035088 Mean :0.003602
## 3rd Qu.:2.000 3rd Qu.: 58.00 3rd Qu.:0.047619 3rd Qu.:0.004800
## Max. :2.000 Max. :145.00 Max. :0.090909 Max. :0.005900
## NA's :20
## weight.mg
## Min. :1.700
## 1st Qu.:2.500
## Median :2.900
## Mean :3.602
## 3rd Qu.:4.800
## Max. :5.900
## NA's :20mean_dev.rate <- mean(ad$dev.rate, na.rm = TRUE)
var_dev.rate <- mean(ad$dev.rate, na.rm = TRUE)
mean_weight.mg <- mean(ad$weight.mg, na.rm = TRUE)
var_weight.mg <- mean(ad$weight.mg, na.rm = TRUE)
# plot pupal rate in mg over temp
mean_dev.rate## [1] 0.03508771var_dev.rate## [1] 0.03508771mean_weight.mg## [1] 3.602242var_weight.mg## [1] 3.602242 # plotting pupal weight in mg over temp
head(ad)## # A tibble: 6 × 9
## start.date end.date temp sex jar dev.time.days dev.rate weight.g
## <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2/21/2008 3/5/2008 22 F 2 13 0.0769 0.0037
## 2 2/21/2008 3/6/2008 22 F 2 14 0.0714 NA
## 3 2/21/2008 3/3/2008 22 M 1 11 0.0909 0.0026
## 4 2/21/2008 3/3/2008 22 M 1 11 0.0909 0.002
## 5 2/21/2008 3/3/2008 22 M 1 11 0.0909 0.0025
## 6 2/21/2008 3/3/2008 22 M 1 11 0.0909 0.0023
## # ℹ 1 more variable: weight.mg <dbl>p2 <- ggplot(ad, aes(x = weight.mg, y = temp, fill = weight.mg)) +
geom_col() +
labs(title = "Pupal Weight by Temperature",
x = "Weight (mg)",
y = "Temperature") +
theme_minimal() + theme(plot.title = element_text(face = "bold"),
axis.title.x = element_text(face = "bold"),
axis.title.y = element_text(face = "bold"))
p2
library(cowplot) #plot_grid()
mp <- plot_grid(
p1 + theme(plot.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()),
p2 + theme(plot.title = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()))
mp
Interpretation of The Two Figures of Question 5
What do the figures show you about the biology of development in these mosquitoes?
the mosquitoes which are exposed to higher temperatures have a greater development rate compared to the mosquitoes exposed to lower temperatures.
Does anything look ‘weird’?
in terms of weight, there seems to be a drop off around 3-4mg. There seems to be a correlation between greater mass and lower temperature and vice-versa.
Does the data ‘make sense’?
Figure 1 shows that there is a correlation between higher temperatures and faster development rates of mosquitoes. Knowing that mosquitoes thrive and are more active in hotter environments this would be expected and does make sense. Figure 2 shows more mosquitoes as a lighter weight in high temperatures than heavier mosquitoes. Maybe heavier mosquitoes have a harder time regulating body functions in harsher environments? Or once a mosquito matures, they move to lower temperatures.