How I Scraped Jumia, Konga and Jiji to Compare Piano Prices

Hi! How’s it going? I’m doing well too, thanks for asking! It’s been two weeks since my last post. Why? Well, let’s just thank God for love and life.

This week, I decided to finally stop procrastinating on my quest to learn web scraping using R. An hour into my research (essentially looking for the minimal-effort-maximum-output way to properly scrape some useful data), I discovered something I’m super excited to share with you! I’ll be sharing the easiest possible ways (I have come up with thus far) to scrape data from Jumia, Konga and Jiji for price comparison.

First of…


R Vs Python — Let’s make love, not war.

Hello again! It’s the holidays and boy, am I glad to be home for a couple of days. I’ve missed the tranquility and clean-er (than Lagos) air. When last were you home? Go home! And if you’re home, happy holidays!

Last week, I released my first R Shiny App — Twitter Word Cloud App, the support was amazing and I have you to thank for that. Analytics of the app usage will be presented to my prospective employers in due time, so thanks once again, you’re awesome! Here’s a link to the post talking all about it.

This week, I…


A Lazy Approach to Tweet Scraping and Some Mild Stalking — Part 2

twitter_word_cloud

Hello again! How’d your week go? Mine was busy too! Between Coursera, Data Engineering projects and work, I may need to subscribe for that 30-hour day plan you mentioned earlier. Hook me up with a link in the comments, tenk you.

Last week, we introduced generating word clouds from Twitter with R using the Twitter API. You can check out how this was done on Medium and/or RPubs for the word cloud interactivity.

Also last week, I promised we’d build a Shiny App for some mild stalking. It’d interest you to know that as at the time I wrote last…


A Lazy Approach to Tweet Scraping and Some Mild Stalking — Part 1

twitter_word_cloud

Last week was super exciting for me after rolling out my first post. I’m grateful for the super positive feedback, especially the encouragement to push out more technical content. Okay, before you scream, I already promised easier-to-read code, so let’s proceed.

I was recently messing around with what I could do with my Twitter Developer access for tweet scraping (albeit controlled) and I also recently became fascinated with word clouds.So I decided to marry them into something simple yet useful.

This article will be a two-part series where the endgame is to build an R Shiny App for generating word…


Hi! What’s your name?

names()

The names function in R is a simple yet powerful function for accessing and even replacing “names” of attributes of vectors and objects.

To illustrate this, we’ll be working with some air quality data from kaggle, it covers air quality in India from 2015 to 2020. Here’s a link to the dataset.

You can get the code for this on my github

Downloaded the data? Let’s load it.

wkdir <- "C:/Users/koli0/Downloads/Compressed/archive/"

csvs <- list.files(path = wkdir, pattern = "*.csv")

csvs
## [1] "city_day.csv" "city_hour.csv" "station_day.csv" "station_hour.csv" "stations.csv"

We want granular data, at least time-wise, so let’s go with the hourly…

Kolapo Obajuluwa

Just a lad passionate about data and all the cool stuff you can do with it!

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