added dplyr code from day2

day2.R

@@ -0,0 +1,123 @@
+
+# in R, what's the difference between a data frame, and a list?
+# a: a data frame is a special kind of list!
+
+x <- list(NA, 3, "L")
+
+y <- c(NA, 3, "L")
+
+my_dog <- list(
+  name = "Thor",
+  sex = "male",
+  favorite_foods = list("peanut butter", "kibble", "paper"),
+  weigh_ins = c(3.4, 3.5, 3.2, 3.3)
+)
+
+
+cats <- data.frame(
+  name = c("Loki", "Tickles", "Bjork", "Cinnabun"),
+  color = c("black", "tabby", "white", "ginger"),
+  num_lives = c(2L, 8L, 1L, 9L),
+  likes_string = c(TRUE, FALSE, FALSE, NA)
+)
+
+
+catliss <- list(
+  name = c("Loki", "Tickles", "Bjork", "Cinnabun"),
+  color = c("black", "tabby", "white", "ginger"),
+  num_lives = c(2L, 8L, 1L, 9L),
+  likes_string = c(TRUE, FALSE, FALSE, NA)
+)
+
+cats$name[1] <- "Lokii"
+
+cats_test <- cats
+
+cats_test$name[1] <- catliss
+
+
+# how to add stuff onto a data frame?
+
+cats$age <- c(5, 8, 3, 7)
+
+nrow(cats)
+ncol(cats)
+dim(cats)
+
+new_cat <- list(color = "gray", name = "Jelly Bean", cholesterol = "high")
+
+rbind(cats, new_cat)
+
+library(dplyr)
+
+cats_plus <- bind_rows(cats, new_cat)
+
+
+# dplyr munging!
+
+gapminder <- read.csv("data/gapminder_data.csv")
+
+str(gapminder)
+
+sum(gapminder$pop[which(gapminder$continent == "Asia" & gapminder$year == 1952)])
+
+gapminder %>%
+  filter(continent == "Asia") %>% 
+  filter(year == 1952) %>% 
+  summarise(
+    popSum = sum(pop),
+    nCountries = n(),
+    pop_per_country_avg = popSum / nCountries,
+    nCountries2 = length(country), # same as n()
+    nCountries3 = length(unique(country)) # same as n()
+  )
+
+
+gapminder %>%
+  filter(continent == "Asia") %>% 
+  filter(year == 1952) -> gm_asia_1952
+
+str(gm_asia_1952)
+
+# say you wanted these calculations on EVERY continent for 1952?
+
+gapminder %>%
+  group_by(continent) %>% 
+  filter(year == 1952) %>% 
+  summarise(
+    popSum = sum(pop),
+    nCountries = n(),
+    pop_per_country_avg = popSum / nCountries,
+  )
+
+# say you wanted these calculations on EVERY YEAR for ASIA?
+
+gapminder %>%
+  filter(continent == "Asia") %>% 
+  group_by(year) %>% 
+  summarise(
+    popSum = sum(pop),
+    nCountries = n(),
+    pop_per_country_avg = popSum / nCountries,
+  ) %>% 
+  as.data.frame()
+
+# say you wanted these calculations for EVERY YEAR for EVERY CONTINENT?
+
+gapminder %>%
+  group_by(continent, year) %>% 
+  summarise(
+    countries = toString(country),
+    popSum = sum(pop),
+    nCountries = n(),
+    pop_per_country_avg = popSum / nCountries,
+  ) %>% 
+  as.data.frame()
+
+gapminder <- read.csv("data/gapminder_data.csv")
+
+library(ggplot2)
+
+ggplot(data = gapminder,
+       mapping = aes(x = gdpPercap, y = lifeExp))
+