mine-cetinkaya-rundel

library(tidyverse)

# mutate -----------------------------------------------------------------------
iris %>%
  mutate(
    Sepal.Length_cat = ifelse(Sepal.Length < mean(Sepal.Length), "below mean", "at or above mean"),
    Sepal.Width_cat  = ifelse(Sepal.Width  < mean(Sepal.Width),  "below mean", "at or above mean"),
    Petal.Length_cat = ifelse(Petal.Length < mean(Petal.Length), "below mean", "at or above mean"),
    Petal.Width_cat  = ifelse(Petal.Width  < mean(Petal.Width),  "below mean", "at or above mean")
  ) %>%

mine-cetinkaya-rundel

# load packages ----------------------------------------------------------------
library(ggmap)     # for geocoding cities, need v2.7 from https://github.com/dkahle/ggmap
library(fields)    # for finding distance between two points
library(maps)      # for world map
library(geosphere) # for connecting dots

# geocode cities lived ---------------------------------------------------------
ist <- geocode("Istanbul, Turkey")
nyc <- geocode("New York, NY, USA")
lax <- geocode("Los Angeles, CA, USA")

mine-cetinkaya-rundel

# Load packages ----------------------------------------------------------------
library(shiny)
library(shinythemes)
library(ggplot2)
library(dplyr)
library(fiftystater)
library(tools)
library(mapproj)
library(fontawesome)

mine-cetinkaya-rundel

library(tidyverse)
library(rtweet)
library(glue)

tml <- get_timelines("CostcoRiceBag", n = 3200)

br <- tml %>% 
  filter(is.na(reply_to_screen_name)) %>%
  slice(
    which(str_detect(text, "IS IT JUST ME")):

mine-cetinkaya-rundel

library(tidyverse)

# list col for age --------------------------------------------------
df_listcol <- tibble(
  age = list(30, 40, 50, 55, "65+")
)

typeof(df_listcol$age)
#> [1] "list"

mine-cetinkaya-rundel

# Fit model with parallel lines (main effects only)
m_pr <- lm(log(price) ~ Surface + factor(artistliving), data = pp_Surf_lt_5000)

## Option 1

# Save regression coefficients in a tibble
m_pr_coefs <- m_pr %>%
  tidy() %>%
  select(term, estimate)

mine-cetinkaya-rundel

library(shiny)
library(ggplot2)

## Pre-app calculations

# Get the current day of the month
dom <- 25

# Define the target for salespeople in our organization.
salesTarget <- 15000

mine-cetinkaya-rundel

library(shiny)
library(ggplot2)

dataset <- diamonds

ui <- pageWithSidebar(
  
  headerPanel("Diamonds Explorer"),
  
  sidebarPanel(

mine-cetinkaya-rundel

# Could this be an alternative solution for the following?
# https://github.com/jcheng5/rstudio2017-shiny-workshop/blob/master/apps/reactivity/cranlogs.solution.R

library(cranlogs)
library(dplyr)
library(lubridate)
library(ggplot2)
library(shiny)

# Define UI for specifying package and plotting cranlogs ------------

mine-cetinkaya-rundel

# An example based on http://shiny.rstudio.com/articles/dynamic-ui.html

library(shiny)

ui = basicPage(
  fluidRow(
    actionButton(inputId = "add_buttons", label = "Add 5 Buttons")
  ),
  uiOutput("more_buttons") # this is where the dynamically added buttons will go.
)