Here's a free R bootcamp that introduces the tidyverse set of packages that makes data science easier for beginners: https://r-bootcamp.netlify.com/

Trouble remembering? You can always refer to cheatsheets: https://www.rstudio.com/resources/cheatsheets/

I recommend using rstudio. It shows the list of functions as you start typing, so it becomes easier to code if you remember just part of a function name.

You're welcome.

Here's the cheat sheet for the dplyr package, it will help a ton.

Good luck with that professor, you'll need it.

You might want to check out the University of Washington's Introduction to Computational Finance and Financial Econometrics course. R is very popular in quantitative finance, and that might be right up your alley. R skills in finance will almost certainly greatly increase your value to an employer. LinkedIn's The 25 Skills That Can Get You Hired in 2016 list R under their #2 skill, iirc it was #1 in previous years.

You can do this just adding a second term in summarize, as u/RookWV already said. In cases where you really need to combine different data based in a specific variable, you should use: inner_join(first_dataframe, second_dataframe, by = common_Variable). There are other possibilities too, like left_join, right_join and full_join. Take a look: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

It took me a month of practicing every day (5 hours a day, as part of my internship). After this month and after reading many stackoverflow posts, I was able to automate the production of an specific graph (an age pyramid) for any city in my country. In addition to the books already recommended, I found very useful the cheat sheets that you can download here: https://www.rstudio.com/resources/cheatsheets/

Good luck!

You can set the table dimnames right from within the table function by namimg the arguments. See example here: https://tio.run/##K/r/vyQxKSdVIzHRNrMoszhaxzBWRyEpSQHKM4rV/P8fAA

Also, table used in calculation is different from what you see in print. Check View(f) to see the actual layout.

You're welcome :)

https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

The + sign is unique to ggplot. But it also makes sense in that each component added to the graph is additive and layered in the order things are added (i.e. bottom up, like a cake).

install dplyr iris %>% select(Sepal.Length, Sepal.Width)

should get you your two columns :)

Check out the dplyr package, it's fairly commonly used for data analysis / wrangling. This cheat sheet is also a good guide: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

Using the "tidyverse" package of functions:

data %>% group_by(Governorate) %>% count() %>%

ggplot(aes(x = ??, y = ??) + geom_????

That will group all your observations by their Governorate and count how many in each. I don't want to do all of it for you because I want you to learn. Here is the "cheat sheet" for ggplot. Try to figure it out from there.

The ggplot2 cheat sheet is a pretty nice reference for helping to determine what plot will be useful given the characteristics of variables you want to plot.

If you create a histogram, the x-axis would be sentiment score and the y-axis would be number of retweets. That would conflate the number of retweets with the number of tweets assigned a given sentiment score. A histogram also wouldn't show correlation. Correlation could be represented by a single number:

> df <- data.frame(score = rnorm(1000)), retweets = sample(1:10000, 1000)) > cor(df$score, df$retweets) [1] 0.01223942

If you want to show a relationship between score and retweets graphically you could do a scatterplot with a smoothing parameter, something like this:

# giving a relationship to df$retweets > df$retweets <- (round(df$score, 2) * 100 + sample(1000))

> cor(df$score, df$retweets) [1] 0.349281

> ggplot(aes(x = score, y = retweets), data = df) + geom_point() + geom_smooth()

That would show the score on the x-axis, the number of retweets on the y-axis, and overlay a smoothing parameter with a confidence interval over it so you could see the strength of the relationship for different values of score and retweets. The code above made this plot. (Forgive the negative number of retweets and the broad sentiment score values, it's just for illustrative purposes.)

This has a strong biology focus and is written for the beginner https://www.amazon.com/Introductory-Beginners-Visualisation-Statistical-Programming-ebook/dp/B00BU34QTM/ref=mp_s_a_1_1?dchild=1&amp;keywords=introductory+r+knell&amp;qid=1604345682&amp;sr=8-1

For sync'ing settings, newer versions of RStudio (> 1.3) do have a central file that's similar to settings.json. It's called rstudio-prefs.json. Read more about it here: https://www.rstudio.com/blog/rstudio-1-3-preview-configuration/

For syncing your packages between systems, the industry standard is renv: https://rstudio.github.io/renv/articles/renv.html

The following worked fine for me, using RSelenium to load the page, the rvest to work with it once it was completely rendered.

library(RSelenium)
library(rvest)

rD <- rsDriver()
remDr <- rD[["client"]]
remDr$navigate('https://www.wunderground.com/history/daily/KSFO/date/2015-1-1')

wunderground <- read_html(remDr$getPageSource()[[1]])
wunderground %>% 
  html_nodes(xpath='//*[@id="history-observation-table"]') %>%
  html_table()

Also, for a solution based on tidyverse (dplyr and stringr) consider the code at the end.

The key is the pattern to check, in my case: ^P\\d+[^S]$. Let's unpack this a bit: * ^P checks that the first character present is P. The start of the string is denoted with ^. * \\d+ is using the \d which is regex shorthand for all digits, followed by + which means 1 or more. The extract slash in front is an R peculiarity that has to do with the particular regex implementation. * [^S]$ simply means that the last character must not be S. The ^ symbolizes negation when within a set (i.e. the brackets []), grouped together with the character we don't want in [^S]. On the other hand, $ simply means end of string, the opposite of ^ that we used at first.

As u/jdnewmil notes, regex is really useful. A good playground with an integrated cheatsheet to get you start is available here.

Altogether, an implementation based on tidyverse is below:

# Create a test dataframe

df <- tibble(id = c("P1000", "P1021S", "H9920", "Z1234"))

# Solution

df %>%
  # remove any whitespace from beginning and end:
  transmute(id = str_trim(id)) %>%
  # create the desired column:
  mutate(
    # create new column in dataframe:
    idNew = if_else(
      # check string starts with P and does NOT end with S:
      str_detect(id, "^P\\d+[^S]$"),
      # if TRUE, append an S:
      str_c(id, "S"),
      # if false, return the original string: 
      id
    ) 
  )

# Check results
df

Which outputs: ```

A tibble: 4 x 2

id idNew <chr> <chr> 1 P1000 P1000S 2 P1021S P1021S 3 H9920 H9920 4 Z1234 Z1234 ```

Try uninstalling using purge to remove all the configuration files. Then go to your home directory and delete any packages you have downloaded. After that try reinstalling. You should make sure you have the most recent r version by doing the PPA installer I have had good success with adding CRAN to sources and installing and updating with apt-get (https://cran.r-project.org/bin/linux/ubuntu/README). You can also use an unsupported PPA for the RStudio install (https://launchpad.net/~opencpu/+archive/ubuntu/rstudio)

I don't think it's broken, they were taken from https://openweathermap.org/current#current_JSON

Use the same structure, the problem being weather being a list. Mostly been trying to turn that said list with unlist (which works on the console), but can't get it to add it into the data frame.

You should get aws to spin you up a Linux/Windows server instance and try to run it on there. You may even be able to take advantage of their cluster computing to get your script to run faster. At the very least you can get something that has more power and ram than a consumer machine.

I installed R and Rstudio on my M1 mini and it's fine. I installed using Homebrew (just install iTerm2 first so you get a good terminal app in rosetta mode) and it's working well. With Homebrew you have to install both pieces separately.

brew install r
brew install rstudio

By 2 files would presume you mean ui.R and server.R. The code of both these files can be combined into one file and the app would work as well.

On a general note, and since you have up to a month to your presentation, why don't your go through the basic Shiny tutorial at http://shiny.rstudio.com? It wont take more than 2 days to cover the material for the introductory section.

Several people have mentioned rest which is my R web scraping library of choice as well. To add to the conversation I want to also suggest using selector gadget. Selector gadget allows you to pull the tags off of html objects interactively which makes the task of web scraping far easier. Also, you may find rcurl useful for making http requests.

Good luck! Feel free to reach out with any questions. I have done quite a bit of web scraping and even taught a couple classes on the subject.

I think you first need to define, 'exactly what you need.' Then your definition will help guide your decision. In my personal experience, I find that Datacamp.com works best for me.

Another option is through Anaconda. Accessing R in it is straightforward. A con is the R version runs behind the latest in a stand alone. A benefit is that there is Python and some other features built in. https://www.anaconda.com/products/individual

Yeah then mutate is not really what you need here. For more information on tidyr and dplyr check out this cheatsheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

It’s super handy. I think you’d be fine with a simple if statement here, if value is bad, value =0. That make sense?

To the OP: if you get an error when running library(tidyverse) it likely means that you don't have the package installed on your computer.

Run this code: install.packages("tidyverse") and then run u/tylersvgs's code again.

This is a link to several "cheat sheets" that will make your life easier.

Yes, it is! Any version should work, but you might like our new preview release:

https://www.rstudio.com/products/rstudio/download/preview/

I suggest you take a look at "tidyverse". It is a collection of packages that help with plotting and manipulating data in R. There is even a nifty cheat sheet

There are several problems with what you have here. You are only looking for one character in between the braces and brackets, but more importantly you need to escape those braces and brackets. You should at a minimum check out the cheatsheet on working with strings (https://www.rstudio.com/resources/cheatsheets/). Try using the stringr package (which has functions as described in the cheatsheet).

I found reading R for Data Science by Hadley Wickham, repeating all the code in R Studio and doing all the excersises very helpful. I also use the cheatsheets from RStudio all the time.