juliasilge

library(tidymodels)

car_rec <- recipe(~ ., data = mtcars) %>%
  step_normalize(disp, qsec)

car_prep <- prep(car_rec)

juice(car_prep)
#> # A tibble: 32 x 11

juliasilge

library(rsample)
library(tidyverse)
library(palmerpenguins)
data("penguins")

penguins %>%
  count(species)
#> # A tibble: 3 x 2
#>   species       n

juliasilge

## https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-06-23/readme.md
library(tidyverse)
library(bcmaps)
locations <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-23/locations.csv')

locations_sf <- locations %>%
    select(animal_id, longitude, latitude) %>%
    st_as_sf(coords = c("longitude", "latitude")) %>%
    st_set_crs(4326) %>%
    transform_bc_albers()

juliasilge

library(tidyverse)
library(tidylo)
library(babynames)

top_names <- babynames %>%
  filter(year >= 1950,
         year < 1990) %>%
  mutate(decade = (year %/% 10) * 10,
         decade = paste0(decade, "s")) %>%
  group_by(decade) %>%

juliasilge

library(tidyverse)
library(rtweet)

potus <- get_timeline("realDonaldTrump", n = 3200)

potus %>%
    transmute(creation_date = as.Date(created_at),
              text = str_remove_all(text, "https://t.co/[A-Za-z\\d]+"),
              exclamations = str_extract_all(text, "\\!"),
              exclamations = map_int(exclamations, length),

juliasilge

library(tidyverse)
library(tidytext)

dat <- tibble(
  a = c("row1", "row1", "row2", "row2", "row2"),
  b = c("col1", "col2", "col1", "col3", "col4"),
  val = 1:5
)

d <- cast_dtm(dat, a, b, val)

juliasilge

library(tidytext)
library(widyr)
library(igraph)
library(ggraph)

words_cooccur <- tidy_words %>%
    group_by(word) %>%
    filter(n() > 250) %>%
    ungroup() %>%
    pairwise_cor(word, Respondent, sort = TRUE, upper = FALSE) %>%

juliasilge

Keybase proof

I hereby claim:

• I am juliasilge on github.
• I am juliasilge (https://keybase.io/juliasilge) on keybase.
• I have a public key ASDmuxcJrM55BGKWGxCENIRSHvzgkeXqhw61aiiVwcqMPQo

To claim this, I am signing this object:

juliasilge

## this analysis assumes a dataframe `post_views` with columns:
##     PostId
##     CreationDate
##     Tag
##     AnswerCount
##     ViewCount

library(tidyverse)

post_views %>%

juliasilge

library(tidyverse)
library(scales)
theme_set(silgelib::theme_plex())
library(geniusR)
library(tidytext)

albums <-  tibble(artist = rep("The Beatles", 13),
                  album = c("Please Please Me",
                            "With the Beatles",
                            "A Hard Day's Night",