pr130

# nested error handling
third_level <- function(c, d){
  print(paste0("c ", c, " d ", d))
  
  if (c > 2){
    stop("Invalid value for c. Exiting third level.")
  }
  else {
    return(c + d)
  }

pr130

# written as part of the sealr package: github.com/jandix/sealr

library(purrr)
#'
#' This function checks that all claims passed in the \code{claims} argument of the jwt function are
#' correct.
#' @param token JWT extracted with jose::jwt_decode_hmac.
#' @param claims named list of claims to check in the JWT. Claims can be nested.
#' @return TRUE if the all claims are present in the JWT, FALSE if not.

pr130

Keybase proof

I hereby claim:

• I am friep on github.
• I am friep (https://keybase.io/friep) on keybase.
• I have a public key ASAIEdvRrwkKfZoqisFOZfo4I4-QtL3lA8Kd2pE0vDAFLwo

To claim this, I am signing this object:

pr130

library(stringr)
library(dplyr)
library(stringdist)

df <- tibble(word = c("Agrilus pilosivittatus",
                      "Agrilus pilosovittatus", 
                      "foo", 
                      "bar", 
                      "baz", 
                      "something else", 

pr130

# Using version 3 to provide play-with-docker badge
# You can change to version 2 without breaking.
#version: '2'
version: '3'
services:
  database:
    # Don't upgrade PostgreSQL by simply changing the version number
    # You need to migrate the Database to the new PostgreSQL version
    image: postgres:9.6-alpine
    #mem_limit: 256mb         # version 2 only

pr130

library(dplyr)
library(tibble)
library(stringr)
library(tidyr)
py_list_vec <- c('["foo", "bar", "baz"]', '["test1", "tst2"]', '["a", "b", "c", "d", "e"]')
df <- tibble::tibble(id = c(1, 2, 3), x = py_list_vec)

df %>% 
  dplyr::mutate(x = stringr::str_replace(x, '\\[', "")) %>% 
  dplyr::mutate(x = stringr::str_replace(x, '\\]', "")) %>% 

pr130

---
title: "DKB Export Cleanup"
author: "Frie"
date: "10/10/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(purrr)

pr130

library(tidyverse)
data <- readr::read_csv("https://raw.githubusercontent.com/friep/correlaid-utils/main/correlaid-analytics/data/all_daily.csv")

# clean out facebook outliers /errors
data <- data %>% 
  filter(!(platform == "facebook" & date > "2020-01-01" & n < 10))

# geom line connects the missing pieces but i don't want that!!!
data %>% 
  ggplot(aes(x = date, y = n, color = platform))+