andrewheiss

library(tidyverse)
library(scales)
library(patchwork)

births_1994_1999 <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/births/US_births_1994-2003_CDC_NCHS.csv") %>% 
  # Ignore anything after 2000
  filter(year < 2000)

births_2000_2014 <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/births/US_births_2000-2014_SSA.csv")

andrewheiss

library(tidyverse)
library(scales)

births_1994_1999 <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/births/US_births_1994-2003_CDC_NCHS.csv") %>% 
  # Ignore anything after 2000
  filter(year < 2000)

births_2000_2014 <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/births/US_births_2000-2014_SSA.csv")

births_combined <- bind_rows(births_1994_1999, births_2000_2014)

andrewheiss

library(tidyverse)

example_df <- tribble(
  ~Year, ~Category, ~Name,
  2015,  "A",       "1",
  2015,  "A",       "3",
  2015,  "A",       "5",
  2015,  "C",       "2",
  2015,  "C",       "4",

andrewheiss

library(tidyverse)

example <- tribble(
  ~Country, ~speed_100m, ~speed_200m, ~speed_400m, ~speed_800m, ~speed_1500m, ~speed_3000m, ~speed_marathon,
  "Argentina", 11.57, 22.94, 52.50, 2.05, 4.25, 9.19, 150.32,
  "Australia", 11.12, 22.23, 48.63, 1.98, 4.02, 8.63, 143.51,
  "Brazil", 11.17, 22.60, 50.62, 1.97, 4.17, 9.04, 147.41
)

andrewheiss

library(tidyverse)

set.seed(1234)

# Make a fake version of your dataset
possible_V4529s <- c("Completed rape", "Attempted rape", "Sex aslt w m aslt", 
                     "Sex aslt w s aslt", "At rob inj m asl", "Rob w inj maslt",
                     "Simp aslt w inj", "At mtr veh theft")

andrewheiss

library(tidyverse)
library(broom)
library(latex2exp)
library(patchwork)
set.seed(1234)

logit_df <- tibble(x = seq(-5, 5, length.out = 100)) %>% 
    mutate(p = 1/(1 + exp(-x))) %>% 
    mutate(y = rbinom(n(), size = 1, prob = p))

andrewheiss

library(tidyverse)
library(palmerpenguins)

avg_flipper <- penguins %>% 
  group_by(species) %>% 
  summarize(avg_flipper_length = mean(flipper_length_mm, na.rm = TRUE))
#> `summarise()` ungrouping output (override with `.groups` argument)

# Native horizontal geoms, but legend pointranges are still vertical

andrewheiss

# Colors via http://clrs.cc/
clrs <- list(
  navy = "#001F3F",
  blue = "#0074D9",
  aqua = "#7FDBFF",
  teal = "#39CCCC",
  olive = "#3D9970",
  green = "#2ECC40",
  lime = "#01FF70",

andrewheiss

library(tidyverse)
library(tidygeocoder)
library(sf)
library(albersusa)

places <- tribble(
  ~name, ~address,
  "My empty GSU office", "14 Marietta Street NW, Atlanta, GA 30303",
  "My old BYU office", "155 East 1230 North, Provo, UT 84604",
  "My old Duke office", "201 Science Dr, Durham, NC 27708"

andrewheiss

library(tidyverse)
library(broom)

cars <- cars %>% 
  mutate(speed.c = scale(speed, center = TRUE, scale = FALSE))

mod1 <- lm(dist ~ speed.c, data = cars)

tidy(mod1)