Explore your data using R programming
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- Опубліковано 26 чер 2024
- When doing data analysis, you need to start with a good understanding of you data. To explore your data, R has some fantastic and easy to use functions. In this video I take you through the process of exploring dataset and understanding its various characteristics and dimensions using data that you can access on your computer. This is an R programming for beginners video. It is for people interested in data science, statistics, r programming, quantitative analysis and research in general. I use the tidyverse to do my data exploration. That includes ggplot, dplyr and other packages. I also work in R-Studio.
If you're interested in doing a course in R - go to www.learnmore365.com
Get my FREE cheat sheets for R programming and statistics (including transcripts of these lessons) here: www.learnmore365.com/courses/rprogramming-resource-library
Just started Learning R Programming and your videos are some of the best and easiest to follow. Keep up the great work!
Thanks for the encouragement!! Will do :)
Thank you for these tutorials! So far, your channel is the best one I've found to start learning R.
Keep up the good work! We appreciate it!
Please, please keep making these videos. It is crucial for us data analyst who are just getting started and would like to continue expanding our knowledge. I would even donate if you had a patreon
Honestly, I've never done anything in R before, just started randomly watching this video. I understood EVERYTHING! Just amazing, definitely subscribed.
Great content, Greg. Thanks much for taking the time to put it all together.
Great work! It re-ignited my passion for R. Thanks a lot!
Thank you, Greg. You are the best!
Your videos are simply great! I feel I've become a bit more proficient with R also thanks to you. I recently started to do some PCA on my datasets and I'm wondering if you could give an introduction to multivariate analysis with R in your next videos. Keep up the good work 👍
Thanks !!! :)
Thank you for making these videos, it must take a lot of your time in preparation and editing. Cheers from Scotland.
Great tutorials and huge thank you for your content! I always use a split screen with my data set on the left and your videos on the right. Please keep providing! Greetings from Berlin
Your videos are amazing and so helpful for half beginner to look like a pro!
Well done for the fantastic lectures that make life with R easier
I love these videos. They are so incredibly helpful. Thanks a million!
Thank YOU so much! I've learned a lot about R functions. I will be watching the rest of your video tutorial. Thank YOU, Professor!
Wow thank you so much for this! You have taken away my fear of using this program, with this video I broke through a WALL that was in between me and my data analysis. Very clear way of explaining things as compared to other videos.
Wow - what a nice thing to say (thanks!!)
Great to see the level of detail to learn R here. You are so right about cut and paste 🙂
the way of your teaching is "THE BEST "
Thanks for giving the best information
You are most welcome. Thanks for the comment!
Learned different methods of performing same tasks. Great work ser!
Thank you for these kind of content, someone who is learning via internet… I salute you 😊
Thank you!
I needed to catch up to all things tidyverse and your videos have been an absolute life saver. It's like you know exactly how my brain works.
Are you going to get to spatial data and the 'sf' package at some point?
Good work right here. Consider doing another on data imputation especially using MICE, I know it'll be🔥🔥. Waiting, thanks in advance
Wow, but also feel bad that why I didn't discover your videos earlier. Great experience!
This is so helpful, thank you so much! I am on the capstone project for Coursera analytics 8 part course and I am SO LOST lol. I know exactly what I want to see, but throughout the course there was only 1 module on "R" and many bookmarks were noted. But the commands are not organized so it takes hours to see what code is necessary to clean something or narrow the data that you are looking for. I think that I chose a project that has an overwhelming amount of data... and it has my Kaggle freaking out lol... I WAS going to use RStudio but I was having trouble importing the datasets that I needed so I figured that if I could just code in Kaggle; and that was the platform that I was going to showcase the project on (AND that is where I found the datasets that I am using) I might as well just run the code there.. but it is taking months lol.
Your videos are simply great!
So incredibly helpful! THANK YOU
Glad it was helpful! Thanks!
Thank you for the great videos, so well explained, it's like you knew what I needed!
You are so welcome!
Great video for someone about to move into a position scoping data visualization using the R Suite of applications.
Thank you for the feedback.
Best R teacher, thanks a lot, it was a great video!
Hello, I have been using R for 10 years and the Tidyverse for about a year or two. This channel is fantastic. What software do you use to do the coloured, highlighting, annotations ? Thanks
Glad that u are back! Thank you
More to come! Thank you!
I am a beginner in R and I found your videos are the best. Very helpful, thank you so much.
I'm so glad! Thank you for your kind feedback
Omg love your videos just finished a r training and feel i have learnt so much from jus 3 or ur videos.. 💛💛💛💛💛
I'm so glad! Thank you for the feedback.
Simply the Best -> I love it
Thanks for the kind comment :)
This is nice, can you do CCA ANALYSIS
Thank you for this great lesson!
Glad you liked it!
Thank you for this great tutorial!
Glad it was helpful!
really very cool videos i hope you explain more about survival analysis and regression
Brilliant job... Thanks a lot
this was sooo helpful! thank you
Thank you this was so helpful
Masterful instruction.
thanks for the kind comment (much appreciated)
Excellente~! Thanks -
Super helpful video, thanks!
You're welcome!
It was a great start.
Thank you very much. Very helpful
Glad it was helpful! Thanks for your feedback
Thank you sir, you explain it well ..appreaciate
Greg you are an amazing teacher! can you make videos on PCA and ANOVA using R. Thanks!
Great suggestion! will do
thank you for making it so understandable....
So nice of you to say, Sania - thanks for the great feedback!!
Thanks very much for the great teaching way you do. I would highly appreciate if you can do a video about cluster using R.
Great suggestion! You are most welcome :)
@@RProgramming101 Thank you very much
Thanks a lot.
Most welcome!
many thanks!
You are most welcome.
Fantastic teaching!
I'm glad you enjoyed the video! Your positive feedback means a lot to me.
Thank you!
You're welcome!
vary Tx sir,
Oh wow! Nice😊
Thanks! 😊
This is so useful!
I'm so glad! Thank you for the feedback.
Great video!
I'm glad you enjoyed the video! Your positive feedback means a lot to me.
It would be amazing if you do a full on tutorial on the R package called caret and machine learning. :)
Great suggestion! Thank you!
You make R interesting.
Thank you!
thanks to you!
Thank you too! Glad you liked it!
Thanks!!
Welcome! Thanks for watching
Great Tutor.
Thanks!
Very helpful for teaching students
Glad you think so!
Thanks
Amazing :)
Thank you! Cheers!
I humbly request you to do a video on Bayesian mixing models using R statistics software.
Noted on this one, thank you! :)
this is so helpful
Glad you found it helpful!
where have you been?? keep it up!!
haha - day job has been busy - but want to do more of this (I love this stuff).
Gender and sex is now updated in the dataset. (gender in this video was what sex is now; female/male)
ah - interesting. Thanks for pointing that out.
I've checked but can't find it. Where is the playlist for explore-clean-manipulate you mention in these videos? There are four playlists but none on that cycle exactly :)
You can try mine too. The playlist for Python and R provide most of the fundamentals. Source files downloadable.
Hi, just a quick question when I use this line of codes sort(table(starwars$hair_color)) it is now showing the NA values , is it because of R does not know what the NA values are and can not sort it ? Do I think right ?
Tidyverse
Hi, I cannot find any library tidyverse on R studio these days ??
How to solve Partial Differential Equations in R?
Where can I find the code?
Please HELP! Why are the functions "arrange" and "filter" not working on my data even after I have made sure and confirmed that the columns are numeric types of data?
Did anybody by any chance in the very beginning instead of having as the format 87 rows and 13 variables they have A tibble with 87 rows and 14 variables?? If so how could I change the variable count to match his? Any help is appreciated thank you!
I have just tried Dim(starwars) and the output was 87 14
lifeline!
> select(hair_color)
Error in UseMethod("select") :
no applicable method for 'select' applied to an object of class "character"
This is the error system is throwing.
I have installed the package dplyr, still the problem persists.
I am still not able to use the code -
> starwars %>%
+ select(hair_color) %>%
+ arrange(desc(n)) %>%
+ View()
Why can't I install tidyverse?
I think the recent update has changed this dataset starwars
savage vdo. thankssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss!
my version of the starwars data renames gender as sex, and gender becomes "masculine",and "feminine"
Excuse me, Sir, why not You make it/them more simplified? For example, gender, height and weight or fresh and dry weight (plant) to draw histogramme
Can you do a difference in differences analysis in R. Thanks
The StarWars dataset is no more available.
daughterset
Starwars
Amazing! Thx a lot, it really helped me to get my first steps in this new language, if you dont mind, I typed all the codes you showed and put some explanations on portuguese for those who might find it useful...
```{r}
# Explorar
# Limpar
# Manipular
# Descrever
# Visualizar
# Analisar
install.packages("tidyverse")
library(tidyverse)
# Para ver quais sao as bases de dados que estao dentro do tidyverse
data()
# Qunado colocamos uma interrogacao antes da base de dados, o sistema retorna algumas informacoes sobre importantes sobre a base
?starwars
# O comando abaixo retorna o tamanho da base de dados, ou seja, quantas observacoes e quantas variaveis. Esta e a dimensao da base de dados, linhas e colunas respectivamente.
dim(starwars)
# A funcao abaixo mostra a estrutura dos dados. Nela nos vemos o nome das variaveis, o tipo dos dados e o valor de algumas primeiras observacoes no formato de lista, pode ficar um pouco confuso...
str(starwars)
# O comando abaixo, glimpse, faz a mesma coisa, no entanto e um comando especifico do tidyverse. Ele e menos poluido visualmente
glimpse(starwars)
# O comando View, com V maiusculo, abaixo os traz um visao muito mais moderna da base de dados, como se tivessemos vendo no excel mesmo. No entanto esta visualizacao e feita em uma nova aba.
View(starwars)
# Usado para visualizar as primeiras observacoes da base, como padrao 6.
head(starwars)
# Mesma coisa do comando head, so que para as ultimas observacoes
tail(starwars)
# Se voce quiser ver especificamente uma unica variavel
starwars$name
# O comando attach faz com que o R entenda que estamos trabalhando somente com essa base, e, entao nao precisamos mais indicar que se trata da base de dados starwars por exemplo
attach(starwars)
detach(starwars)
# O comando names mostra os nomes das variaveis
names(starwars)
# O comando length traz pra gente o numero de colunas dessa base
length(starwars)
# O comando class retorna o tipo de dados de que se trata a variavel em questao
class(hair_color)
# Quando colocamos length no nome de uma variavel o que obtemos e o numero de observacoes dessa variavel
length(hair_color)
# O comando unique nos traz os valores unicos presentes nessa variavel
unique(hair_color)
# Traz a frequencia com que a variavel assumiu determinado valor
table(hair_color)
# Sort ira sortir os valores abaixo do comando table do menor para o maior
sort(table(hair_color))
sort(table(hair_color), decreasing = TRUE) # Do maior para o menor
# Abre uma nova aba onde podemos ver o resultado do comando sort dentro do layout do View
View(sort(table(hair_color), decreasing = TRUE))
# O comando abaixo apresenta um grafico de barras sobre as observacoes sortidas em ordem decrescente
barplot(sort(table(hair_color), decreasing = TRUE))
# O comando %>% significa para o R executar a partir de %>% ate antes do proximo %>%
starwars %>%
select(hair_color) %>% # Apresenta a variavel com o valor de suas observacoes
count(hair_color) %>% # Conta quantas vezes a variavel assumiu determinado valor
arrange(desc(n)) %>% # Mostra os primeiros valores num layout tipo excel
# Vamos agora ver como isolar os valores NA, aqueles que nao foram coletados, para olhar mais de perto
# Seleciona todas as linhas e colunas da base
starwars[ , ]
# Responde se cada observacao e ou nao e um NA
is.na(hair_color)
# Seleciona somenta as que estao com NA com todas colunas
View(starwars[is.na(hair_color) , ])
# O comando abaixo serve para vermos o tipo da variavel height, lembre-se tem que ter rodado o comando attach antes para funcionar
class(height)
length(height) # numero de observacoes para cada varaivel
summary(height) # resumo estatistico
boxplot(height) # diagrama de caixa
hist(height) # histograma
```
Thank you!
You're welcome!