R programming for beginners: Select, filter and fill functions within the tidyverse
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- Опубліковано 26 вер 2024
- Learning R couldn't be easier. In this video, Greg and Andrew walk you through some tips and tricks for R programming. This is an R programming for beginners videos. If you're wanting to do data wrangling or data manipulation using R then this is a good place to start.
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Get my FREE cheat sheets for R programming and statistics (including transcripts of these lessons) here: www.learnmore365.com/courses/rprogramming-resource-library
Thank you Greg and Andrew. Great content!
Greg and Andrew, you've just taught me a new thing today. Thank so much, guys!!
These videos are just wonderful. Rich with information and practical.
Nice presentation. Thanks to both of you. I noticed that the location values are in a not-so-friendly format. There is a lat column which I presume is latitude and a longitude column. However, it looks like the values have been scrambled somehow since there is a longitude value (N) in the latitude column column and a latitude value (W) in the longitude column in row 9. Moreover, typically when working with latitude and longitude we use plus and minus rather than the usual cardinal directions (so latitude from -90 to +90 and longitude from -180 to +180).
Excellent video!! Thank you both!
Loving R more and more!
Greg ....💯💯.
So great!!! Thank you
This is great!!! One thing I’ve noticed is that the native pipe doesn’t support the. operator like the maggritr pipe does.
I think the native pipe uses underscore (_) instead of dot (.)
First, an excellent presentation by both of you.
Andrew, regarding your data set. It would appear that the latitudes and longitudes have been reversed in your data set (set 1a Lat 87.82, long 42.15, set 2a lat 42.15, long 87.49).
Data entry errors happen. Would there be a way of correcting this in R, or would you have to go back to the original data (*.csv, *.xlsx, etc) to correct this?
. . . I wonder what the shortcut to the native pipe is on Mac?
Tx sir
Wow🏆
Thank you cheers!
Hi, Gred and Andrew, Many thanks for your great tutorials. I got a question for you. How come {starwars %>%
filter(eye_color == c("blue","yellow","red"))} works but gets different results compared to {starwars %>%
filter(eye_color %in% c("blue","yellow","red"))}; while {msleep %>%
filter(name == c("Cow","Dog","Horse")) } does not work at all, only {msleep %>%
filter(name %in% c("Cow","Dog","Horse"))} works? I am a little confused.
Didn't know about the new pipe operator, it be looking sexy.
♥️
Hey Greg, what if I wanted to group the data first, and then fill the NAs with the mean or median, or mode of each group?
@Brittny, the way I understood, the data capturer or recorder, collected the data and filled in the first rows and left the subsequent rows that have the same records or data points for efficient reasons. That is where the Fill() function becomes handy to fill down programmatically those constant records. The values were not entirely "missing". To be precise, the fill() function is complementing the field data collection work.
So it would be inappropriate to go for imputation (applicable in data cleaning), unless I missed something out.
Boom chaka laka, easy just like taking breakfast in the morning!
There is no CSV file Did not understand any thing