tmasjc

library(shiny)

ui <- fluidPage(
    column(12,
           # toggle between normal or uniform distribution
           actionButton("rnorm", "Normal"),
           actionButton("runif", "Uniform")
    ),
    column(12,
           plotOutput("plot")

tmasjc

#-- BASED ON https://cloud.google.com/vision/docs/request#json_request_format --#

# image url on Google Cloud Storage
imgURI <- "gs://vacation_room/dublin-001.jpg"

# Requests are POST requests to 
endpoint <- "https://vision.googleapis.com/v1/images:annotate"

#### remember to set api token in ``options``
# options(gcv_api_token = "YOUR_API_TOKEN")

tmasjc

#!/bin/bash

# Header
echo `date`

if [ -d .git ];then
    echo -e "\n\033[0;42m **** Current Status ****\033[0m\n"
    git status -s 

    echo -e "\n\033[0;43m **** Pending Issue ****\033[0m\n"

tmasjc

#!/bin/bash

function _awsSwitchProfile() {

   # Check if argument exists
   if [ -z $1 ]; then  echo "Usage: _awsSwitchProfile <profilename>"; return; fi

   # Check if profile exists
   exists="$(aws configure get aws_access_key_id --profile $1)"

tmasjc

# China Outbound Tourism
library(readxl)
library(tidyr)
library(ggplot2)
library(broom)

# read data
cot <- read_excel("~/Downloads/outbound_tourism_china.xlsx", skip = 5)
# mod columns name
colnames(cot) <- c("country", "series", 1995:2016, "pct_chg_16_15")

tmasjc

#!/usr/bin/env Rscript --vanilla

args <- commandArgs(trailingOnly = TRUE)

# List all files
items <- list.files(path = ".")

# See which matches argument (regex)
old <- items[grep(items, pattern = args[1])]

tmasjc

library(readxl)
library(tidyr)
library(ggplot2)
library(broom)
library(rvest)
library(plotly)


# Import data -------------------------------------------------------------

tmasjc

library(httr)
library(dplyr)

# Find out which cities are being covered in China
china <- "https://api.openaq.org/v1/cities?country=CN"
china_df <- GET(china) %>% content("parsed") %>% `[[`('results') %>% bind_rows()

# Main interest
cities <- c("Beijing", "Shanghai", "Guangzhou")
china_df %>% filter(city %in% cities)

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# Mon Feb 12 16:48:40 2018 ------------------------------

library(tidyverse)
library(wbstats)
set.seed(1234)

# GDP per capita
gdp <- wb(indicator = "NY.GDP.PCAP.CD") %>% as.tibble()
skimr::skim(gdp)

tmasjc

Checklist

-	Does the project contain R script?
-	Does the project contain data?
-	Does the project contain a test script?
-	Do the scripts contain proper comments?
-	Is the project organised in proper structure? 
-	Do the scripts follow R style guide?
		http://style.tidyverse.org/
-	Does the README display the project structure?