2024-05-06
The dataframe read in here was generated using a python script which can be found in the github. This script was used to parse ologies episodes and extract meaningful information - duration of the episode, type of ology, and date.
Data was then manually curated to add in broad classifications and subcategories. These were based on Wikipedia designations of branches of science: Formal, Natural, and Social sciences; with Applied or Foundational designations. This was further broken down into additional branches. Since many episodes are interdisciplinary, we gave primary and secondary designations.
df <- read.csv("Ologies_Data.csv")
View(df)
df <- df[-c(12:14)] #trimming out extraneous columns
ologies <- df[!is.na(df$ology),] #selecting just the episodes with ologies
ologies <- ologies[!is.na(ologies$branch),] #excluding any NAs for branch - this also excludes smologies
View(ologies)
#we want to exclude encores so they aren't counted twice
ologies <- subset(ologies, encore == 0)
#same with part 2 episodes
ologies <- subset(ologies, part_2 == 0)
#final dataframe
dim(ologies)## [1] 246 11
write.csv(ologies, file = "trimmed_ologies.csv")
ologies <- read.csv(file = "trimmed_ologies.csv")Final analysis includes 246 unique episodes of Ologies by Alie Ward.
#good but we can do better. let's reorder the dataframe so we can order by occurence
primary2 <- ggplot(data = ologies, aes(primary, fill = primary)) +
geom_bar(stat = "count", col = "#d397fa", alpha = 0.2) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
ggtitle("Ologies episode by primary topic")
primary2
## By broad category
branch <- ggplot(data = ologies, aes(branch, fill = branch)) +
geom_bar(stat = "count", col = "#d397fa", alpha = 0.2) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
ggtitle("Ologies episode by branch of science")
branch
Transform frequency into a count variable using dplyr
ologies_count <- ologies %>%
group_by(primary) %>%
count(primary)Re-order
ologies_count <- as.data.frame(ologies_count)
colnames(ologies_count) <- c("Primary", "Count")
ologies_order <- ologies_count[order(ologies_count$Count),]
ologies_order$Primary <- factor(ologies_order$Primary, levels = ologies_order$Primary)
ggplot(ologies_order, aes(x = Primary, y = Count, fill = Primary)) +
geom_bar(stat = "identity", col = "#d397fa", alpha = 0.2) +
theme_minimal() +
theme(axis.text.x = element_text(hjust = 1, vjust = 0.5)) +
ggtitle("Ologies episode by primary scientific category")+
coord_flip()
head(ologies)## X title
## 1 1 Theoretical & Creative Ecology (SCIENCE & ECOPOETRY) with Madhur Anand
## 2 3 Evolutionary Anthropology (METABOLISM) with Herman Pontzer
## 3 6 Carcinology (CRABS) Part 1 with Adam Wall
## 4 7 Ethnoecology (ETHNOBOTANY/NATIVE PLANTS) with Leigh Joseph
## 5 11 Lemurology (LEMURS) with Lydia Greene
## 6 12 Quasithanatology (NEAR-DEATH EXPERIENCES) with Bruce Greyson
## release_date duration_ms duration_min ology smologies encore
## 1 2/7/2024 3423791 57.06 Ecology 0 0
## 2 1/31/2024 5058324 84.31 Anthropology 0 0
## 3 1/16/2024 4087431 68.12 Carcinology 0 0
## 4 1/9/2024 4767425 79.46 Ethnoecology 0 0
## 5 12/20/2023 4563173 76.05 Lemurology 0 0
## 6 12/13/2023 5287340 88.12 Quasithanatology 0 0
## part_2 branch primary secondary
## 1 0 FNS Art Biology
## 2 0 FSS Anthropology Biology
## 3 0 FNS Biology <NA>
## 4 0 FSS Anthropology Biology
## 5 0 FNS Biology <NA>
## 6 0 FSS Psychology Biology
ologies_ms_order <- ologies[order(ologies$duration_ms),]
ologies_ms_order$duration_ms <- factor(ologies_ms_order$duration_ms, levels = ologies_ms_order$duration_ms)
ologies_ms_order <- subset(ologies_ms_order, X != 158)
ologies_ms_order <- subset(ologies_ms_order, X != 323)
durationplot <- ggplot(data = ologies_ms_order, aes(x = reorder(primary, -duration_min), y = duration_min, fill = primary)) +
geom_boxplot(col = "#d397fa", alpha = 0.2) +
geom_point(col = "#d397fa", alpha = 0.6)+
theme_minimal() +
xlab("Primary category") +
ylab("Episode duration (minutes)") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
theme(legend.position = "none") +
ggtitle("Duration of Ologies episodes")
durationplot
ggsave("duration_plot.jpg", plot = durationplot)## Saving 7 x 5 in image
ggplot(ologies_ms_order, aes(x = reorder(primary, -duration_min), y = duration_min, fill = primary)) +
geom_bar(stat="summary", fun.y="mean", col = "#d397fa", alpha = 0.2) +
geom_errorbar(stat="summary", colour = "#d397fa", width = 0.2,
fun.ymin=function(x) {mean(x)-sd(x)/sqrt(length(x))},
fun.ymax=function(x) {mean(x)+sd(x)/sqrt(length(x))}) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
ggtitle("Mean duration of ologies episodes (with error)")## Warning in geom_bar(stat = "summary", fun.y = "mean", col = "#d397fa", alpha =
## 0.2): Ignoring unknown parameters: `fun.y`
## Warning in geom_errorbar(stat = "summary", colour = "#d397fa", width = 0.2, :
## Ignoring unknown parameters: `fun.ymin` and `fun.ymax`
## No summary function supplied, defaulting to `mean_se()`
## No summary function supplied, defaulting to `mean_se()`
#split the release date column so we can look by month, year
ologies[c('Month', 'Day', 'Year')] <- str_split_fixed(ologies$release_date, '/*/', 3)
year <- ggplot(data = ologies, aes(Year, fill = Year)) +
geom_bar(stat = "count", col = "#d397fa", alpha = 0.2) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
ggtitle("How many episodes released per year?")
year
###
Month
#create month labels
monthNames2 <- c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")
month <- ggplot(data = ologies, aes(Month, fill = Month)) +
geom_bar(stat = "count", col = "#d397fa", alpha = 0.2) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
theme(legend.position = "none")+
scale_x_discrete(limits = c('1', '2', '3','4','5','6','7','8','9','10','11','12'), labels = monthNames2)+
ggtitle("Frequency by month (cumulative)")
month
#split by year
monthNames2 <- c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December")
monthyear <- ggplot(data = ologies, aes(Month, fill = Month)) +
geom_bar(stat = "count", col = "#d397fa", alpha = 0.2) +
theme_minimal() +
facet_wrap(~Year) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
theme(legend.position = "none")+
scale_x_discrete(limits = c('1', '2', '3','4','5','6','7','8','9','10','11','12'), labels = monthNames2)+
ggtitle("Frequency by month and year")
monthyear