using dplyr to organize temperature heatwave data. call that piping hot
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using dplyr to organize temperature heatwave data. call that piping hot
feb 18 2021 // day 43 of 100 days of productivity
today:
draft frequency tables on verb and wh_type
research syntax for anova_test()
review consent and send compensations
psycholing readings
draft responses to whorfianism questions
it’s been such slow going with data analysis and this week I’ve already put more than ten hours into data wrangling and reading documentation and tutorials on different functions. the good news is that I’m not feeling nearly as frustrated as I did yesterday or the day before, and only part of that is thanks to the huge fluffy snowflakes outside.
some part of me was convinced that my value as a student and researcher was directly related to how few days it would take for me to turn around the analysis on this project and get it to my advisor. I don’t know where I got that notion since 1) stats definitely isn’t my strong suit 2) I had no idea how to do an actual anova in R before and 3) my advisor has decades of experience so of course she’ll be faster at this than I am. in the end, I realized that I’m learning and accomplishing one new and important thing a day in RStudio and that’s more than enough. for instance, learning about tidy data yesterday meant I could reorganize my data in a better way. then today I learned to make frequency tables with dplyr that I could finally use with anova_test(). the time between coding sessions really helped me see things more clearly and kept me from tearing my hair out.
anyway, it’s time to get back to my homework...
CARA MUDAH MEMBACA CHEATSHEET DPLYR | Algoritma | 2022
Kenalan dengan Library dplyr, Yuk! Untuk kamu yang sering memanipulasi, mengurutkan, meringkas, dan menggabungkan … source
i made this for ME because I PERSONALLY am amused
OICA production output statistics for automotive industry (R analysis)
OICA production output statistics for automotive industry (R analysis)
In this post I want to show another public data source related to automotive industry. OICA, the International Organization of Motor Vehicle Manufacturers, provides a series of statistics on its website, including sales and production statistics. The comprises all manufacturers world-wide, and considers passenger as well as commercial vehicles.
OICA production statistics can be accessed here:
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Kaggle second hand car data for German automotive market, analyzed in R
Kaggle second hand car data for German automotive market, analyzed in R
In this post I read in a data set in R containing data on used car sale postings on German ebay. The data has been shared on kaggle and can be found here: https://www.kaggle.com/orgesleka/used-cars-database/data
# read in the data data_df = read.csv("autos.csv",header=TRUE,sep = ",") # print a summary summary(data_df)
## dateCrawled name seller ## 2016-03-08 15:50:29: 5 Ford_Fiesta : 336…
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Initiation à la manipulation de données avec le package dplyr
#rstats #rstatsFR #biostats #rstatsnewbie 💥 Nouvel article💥 : Initiation à la manipulation de données avec le package dplyr ==>
La semaine dernière, je discutais avec un étudiant qui débute dans l’analyse de données avec R. Il devait manipuler un tableau de données (on appelle cela un data frame en R), plus précisément il avait besoin de sélectionner certaines lignes, créer de nouvelles variables, calculer des moyennes, etc…
Il avait réussi à faire la plupart de ces tâches, mais avec beaucoup de difficultés, parce qu’il…
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