Discover and read the best of Twitter Threads about #dplyr

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If you're starting out in data science (or if your wondering what you need to learn), don't believe everything you read. 🧵

Spot BS and focus on these 4 steps to grow your career.

#datascience #rstats #career Image
My friend Rafael Nicolas Fermin Cota (Nico) pointed me to this modified graphic from a Harvard Business Review Article on "Prioritizing Which Data Science Skills Your Company Needs".
With ChatGPT, AI, and the "trendiness" of buzzwords, this graphic becomes even more dangerous. hbr.org/2018/10/priori…
Read 13 tweets
As the #rstats course material is not public (yet?) or available as online training (yet?), I thought I am sharing some slides from the deck.

The course covers all steps of the #DataScience workflow as featured in @hadleywickham's fantastic #R4DS 📕 r4ds.had.co.nz/index.html The title slide of the workshop "Reproducible Data AnalThree avatars (customised versions of the lovely Open Peeps A chapter slide entitled "What is This Course About?&quA conceptional representation of the data science workflow:t
Let's start with session 1:
"Introduction to #rstats and #rstudio" ®️ The title slide for the first session of the "ReproduciA slide explaining what the R programming language is:  &quoA comparison of R and Rstudio, taken from ModernDive: R is tA screenshot of Rstudio with the default panes: Script (uppe
The fundamentals of R includes:

* values
* assignments and objects
* functions
* data types
* unknown values
* vectors
* factors
* packages
* tabular data
* data generation
* data import A colorful overview of how functions work: the function nameA function might return a value, which is printed on a new lA colorful representation of objects and assignments in R: tA colorful explanation of vectors that start with the vector
Read 16 tweets
Embarrassed by your #R code?

Here are 4 mistakes beginner R coders make AND how to avoid them.

#rstats #datascience
The reality is you aren't going to become a master R programmer over night.

But I see beginners making the same mistakes time and time again.

And they are easy to correct.

Here are the 4 most common mistakes and how to easily correct them.
1. Not using comments

This is a huge no-no.

Why?

Because comments help others understand your code INCLUDING future you.
Read 13 tweets
Starting out in #R can be tough.

Here are the 7 packages that have helped me tremendously.

#rstats
1. #dplyr for data wrangling

github.com/tidyverse/dplyr
2. #tidyr for tidying, wrangling and pivoting

github.com/tidyverse/tidyr
Read 10 tweets
Va 🧵 actualizando el análisis para el #BIADN40 sobre el #Covid19 en México, América y el mundo.

Datos a las 19 hrs. del 10/04 (CDMX).

Comienzo con las gráficas de México.

Anoche la cifra de casos *confirmados* llegó a 3,844...
Como he explicado antes, el dato de casos *confirmados* debe ser interpretado con cautela por cuando menos dos motivos:

1) casos confirmados ≠ casos totales. La 1era. cifra suele ser mucho menor que la 2nda.

2) El núm. de casos confirmados depende del núm. de pruebas hechas.
El miércoles 08/04 la @SSalud_mx presentó la primera y -hasta ahora- única cifra de casos *estimados*.

De acuerdo con la tabla presentada en la conferencia de esa noche, a la semana epidemiológica 13 había 26,519 casos *estimados* acumulados y 1,039 casos *confirmados*.
Read 49 tweets
Va 🧵actualizando el análisis para el #BIADN40 sobre el #Covid19 en México, América y el mundo.

Datos a las 19 hrs. del 28/03 (CDMX).

Primero, las gráficas de México.

Hoy se reportaron 848 casos...
Read 29 tweets
Va un nuevo hilo 🧵con muchas gráficas actualizando el análisis del #Covid19 para el #BIADN40 con datos a las 19 hrs. del 25/03 (CDMX).

Comienzo con gráficas de México.
Hoy se registró el mayor incremento diario hasta la fecha: 70 nuevos casos
Ya hay casos en las 32 entidades. Tlaxcala fue la última
Read 32 tweets
Va un largo 🧵con muchas gráficas y poco texto, actualizando el análisis del #Covid19 para el #BIADN40.

Comienzo con los datos globales.
Read 23 tweets
1) Hasta las 18:30 hrs. de ayer, 03/03, se habían confirmado 101,800 casos de #COVID19; un incremento diario de 3.5% y el mayor desde el 14/02.

🧵con actualización del análisis para el #BIADN40.
2) De los 3,867 nuevos casos reportados ayer, 65% se registraron en tres países:🇮🇷(31.9%, 1,234 casos), 🇮🇹 (20.1%, 778) y 🇰🇷 (13.1%, 505).

Solamente el 3.9% (151) en 🇨🇳.
3) Como ilustran la gráfica anterior y la de este tuit, en los últimos días se ha acelerado notablemente el ritmo de contagio en 🇮🇷, 🇮🇹 y 🇰🇷, así como en 🇫🇷 y 🇩🇪.

Del total de nuevos casos registrados ayer, 7.14% y 4.86% ocurrieron en los dos últimos países, respectivamente.
Read 17 tweets

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