t-test and interpreting p values using R Programming 

I will like you to seperate the statistics analysis from business analysis

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You're very welcome!

Awesome, thank you!

Thanks again!

Thanks, will do!

@@RProgramming101 Yes!

Of course, more to come, Roman. Thank you for the amazing feedback!

Glad you like them! You are so welcome!

It's my pleasure

Thanks 👍🏻

I was able to figure out how to do the first graphic at 5:26 ggplot(data = ) + geom_density(mapping = aes(lifeExp), fill = "red", alpha = 0.2) + geom_vline(xintercept = mean($lifeExp), color = "red", alpha = 0.4, linetype = "dashed", size = 1.5)

I was able to figure out how to recreate the 2nd graph too! (not perfect, but pretty close) ggplot(data = ) + geom_density(aes(lifeExp, color = continent, fill = continent), alpha = 0.3) + geom_vline(xintercept = 48.9, color = "red", alpha = 0.4, linetype = "dashed", size = 1.2) + geom_vline(xintercept = 71.9, color = "cyan4", alpha = 0.4, linetype = "dashed", size = 1.2) + labs( title = "Density Plot of Life Expectency in Africa and Europe", y = "", x = "Age in yrs") + annotate( "text", x = 43, y = 0.075, label = "48.9 yrs", color = "red", alpha = 0.8, size = 4.5) + annotate( "text", x = 80, y = 0.075, label = "48.9 yrs", color = "cyan4", alpha = 0.8, size = 4.5) **NOTE** The data frame I had loaded was gapminder filtered(continent %in% c("Africa", "Europe"))

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Glad it was helpful!

@@RProgramming101 Please remember to do a tutorial on difference in differences between a control and experimental group. Thanks

Wow, thanks! So nice of you, Javier 😁

You're very welcome! Glad it was helpful!

My pleasure 😊

I'm so glad! Thanks for the feedback, Hernan. Much appreciated.

You are too sweet Amit - thanks! More R videos to come of course! ☺

So nice of you, Janine. Thank you for your feedback!

You are so welcome! Glad you enjoyed it!

Wow, thanks! So nice of you.

You are welcome!

Very kind of you to say.

Will do!! I have a video on missing data using R on my Global Health channel (go to global health with greg martin and you'll find it there)

Great to hear!

Wow thank you. Cheers

My pleasure

Thank you for the feedback!

Very interesting observation Mauro. I hadn't thought of that. I think however that in this case, what is being sampled are countries (not individuals). It is the countries that are matched. But its an interested thought and I'll muse over your comment a little.

Great suggestion!

Great suggestion! Thank you for the feedback.

I'm thrilled that my video helped you or provided you with useful information. Thanks for letting me know!

Thanks for the suggestion!

Great suggestion!

Most welcome

will do :)

Glad you think so!

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You're welcome!

What village are you in? Kikikikiki

@@mashfintech Yamumbi

@@mugomuiruri2313 nice one brother. I am in Pretoria South Africa hiding from dudula.

They are density plots, easy to make, I just havent figured out the vertical line yet. gapminder%>% filter(continent %in% c("Africa", "Europe"))%>% ggplot(aes(x=lifeExp, color=continent, fill=continent))+ geom_density(alpha = 0.2) gives you the basic graph

@@sciencefliestothemoon2305 gapminder%>% filter(continent %in% c("Africa", "Europe"))%>% ggplot(aes(x=lifeExp, color=continent, fill=continent))+ geom_density(alpha = 0.2)+ geom_vline(xintercept = 50, linetype="dashed", color = "red", size=0.8) #50 or mu is the median of population in your test

Hi Francois - great question. In the fist example, we're comparing our sample data mean to some hypothesised mean (which may be because of previous studies or assumptions). We may, for example, all believe that Irish men are on average 6 foot tall. We can take a sample of men, measure the mean and ask if it is different from that asumptions (the 6 feet)

ah - thanks for the questions (good ones). Hard to answer in the comments but I will make a video that explains. Watch this space. Happy day. Greg

@@RProgramming101 Wonderful, thank you! All the best:)

will try to make a video that addresses this.

@@RProgramming101 Looking forward to that! Thank you very much

To answer your question 1, use (geom_vline) at the end of your ggplot code, this is an example using the GAPMINDER dataset: gapminder %>% select(continent, lifeExp) %>% filter(continent == 'Europe') %>% ggplot(aes(x = lifeExp))+ geom_density(fill ='orange', alpha = 0.5)+ geom_vline(aes(xintercept = mean(lifeExp)),linetype = 'dashed')

@@souhaibsebbane5623 Thank you very much for your helpful answer! I'm going to try this out right away.

I had that issue too and I downloaded the following packages for the function. Maaybe they work for you install.packages("magritter") install.packages("dplyr")

dplyr::select(lifeExp) %>% #MASS and dplyr package Select function clashes, so we tell R to use dplyr

First you need to load the data: install.packages("patchwork") library(patchwork) and if want, you cant attach the data so you dont need to call the data all the time: attach(gapminder) and if you want to create a object to work with it: data("gapminder") name_your_data

I'll make a video about that (hard to address in commments)

@@RProgramming101 thanks. 👍

Will find a way to get the script for you.

Amazing, Jamie! Thanks for your feedback.

Good question. The truth is that there are so many ways that people p hack (even without knowing it). It’s a big problem. The best way forward is to make people aware of it.

Thanks for the suggestion!

Boom...