omission of write?

Suggested edit: we could write a FizzBuzz function

I agree with the statement and used that as a basis for my answer.

sent_with_words_end_s <- str_subset(sentences, "\b[A-Za-z]{3,}s\b") # Focusing on only those sentences that meets the specified criteria

words_end_s <- str_extract(sent_with_words_end_s, "\b[A-Za-z]{3,}s\b") # Words ending in s (contains both plural words like "planks" and non plural words like "Sickness"

str_view(words_end_s, "\b[A-Za-z]+[^s]s$") #Extracts only words that end in s but not in ss.

Typo: it omitted.

Edit suggestion: Since (it) is always good practice to have clear

Can you explain this code to me? I've looked up the if_else function but I do not understand this code.

Why did you use filter arr_delay <= 0 and not arr_delay > 0 when we are looking for the plane with the worst on-time record? This sounds counterintuitive to me, what am I misunderstanding? Thank you.

For better clarity, change "this delay will not have those affects plans nor does it affect the total time spent traveling." to this delay will not affect those plans nor would it affect the total time spent traveling.

1. Please can you explain to me why you included the argument, width = 1 for geom_bar? Without it, the pie doesn't look different. I believe you must have specified for a reason?

2. This was my attempt to answer the question. I'm not totally if the resulting plot makes much sense. Please take a look and let me know what you think. Thank you.

ggplot(diamonds, mapping = aes(x = cut, fill = color)) + geom_bar() + coord_polar()

Typo. I think should read, such as, not such.

Please, can you explain why you have dots in the code? Thanks.

The way this is written could be somewhat confusing to a reader, in my opinion, although the code makes the order of the functions clearer..

Suggestion:

If we wanted a numeric vector, we could combine the map() followed with the flatten_dbl(),

Edit suggestion.

like shown:

Minor mistake: a not at

"Not" repeated. Delete?

Edit suggestion,

Neither NaN nor Inf is a number

Or

Both NaN and Inf are not even numbers.

If accepted, then "and so they aren’t even numbers" may be deleted as it becomes superfluous.

Could you please check the sentence? There seems to be some ambiguity.

redundant. delete?

Hi,

Please can you explain to me how to read this line of code and what it means. I'm having difficulty understanding it. Individually I do understand what each symbol means but put together as it is, I'm unable to. Moreover, it looks more efficient than my effort, as shown below.

Thank you.

fizzbuzz <- function(n) {

x <- n

if(( x %% 3 == 0 ) && ( x %% 5 == 0 )) {

print("fizzbuzz")

} else {

if(x %% 3 == 0) {

print("fizz")

} else {

if(x %% 5 == 0) {

print("buzz")

} else { print(x)

} } }

}

Change word order perhaps? that is

which has

Minor mistake. Should read if it does not

This seems to be a tad long so I apologise in advance. This is a whole new field for me and I would really like to understand.

Could it be AA and MQ use different values to represent tailnum?

filter( planes, tailnum == 0 )

A tibble: 0 x 9

length(is.na( planes$tailnum ))

[1] 3322

nrow(planes)

[1] 3322

filter( flights, tailnum == 0 )

A tibble: 0 x 19

length(is.na( flights$tailnum ) )

[1] 336776

nrow( flights )

[1] 336776

Yet , the anti_join () as shown in your code shows clearly that there are some talinum values in flights that are not represented in the planes datasets. How could that be? The one explanation I could come up with is that the two datasets used different talinum values, so I tried to investigate for AA and MQ.

tailnum_flights <- flights %>% filter( carrier == 'AA'| carrier == 'MQ' ) %>% select ( carrier, tailnum )

tailnum_planes <- planes %>% select( tailnum )

tailnum_planes %in% tailnum_flights

[1] FALSE

So, it looks like the tailnum values are not missing for the ten airlines but are represented with values different in the two datasets (flights and planes).

What are your thoughts? Thank you.

Erm, full stop omitted after no.

And thanks, for the link. It is an interesting read.

This worked for me as well:

str_view(stringr::words, ".*[^e]ed$", match = TRUE)

Please, could explain why you included this in the code? I replicated the answer without it. Thanks.

Please, could you explain to me why if I pipe this directly to ggplot the colour aesthetics is not applied. See code below, it's basically a replication of yours but with ggplot directly piped to avg_dest_delays:

avg_dest_delays <- flights %>% group_by( dest ) %>% summarise( delay = mean( arr_delay, na.rm = TRUE )) %>% inner_join(airports, by = c( dest = "faa" ) ) %>% ggplot( aes(lon, lat, colour = delay ) ) + borders("state" ) + geom_point( ) + coord_quickmap( )

Thanks

repetition?

This also seems to be the case where the by = argument is not used in a code. In that case, it seems, the semi_join() will give outputs only where the rows for both datasets correctly match, for example:

fueleconomy::vehicles %>% semi_join(fueleconomy::common)

produces the same output as:

fueleconomy::vehicles %>% semi_join(fueleconomy::common, by = c("make", "model"))

Or is that a coincident?

But, fueleconomy::vehicles %>% semi_join(fueleconomy::common, by = "make"

will produce a different output as R will match only by "make" in this example.

The code doesn't affect the output but I thought you might mean, sum( !is.na( dep_delay ))

The code in the solution book totally dropped the data for planes age > 25. How might we combine them into a single row? I didn’t think it could be done without first defining a second tibble that contains all the planes age >25, then merge it with the first tibble that contains the data for planes age <= 25, before carrying out the summarise actions on them after applying the group_by = age argument. older_plane_cohorts <- inner_join( flights, select( planes, tailnum, plane_year = year ), by = "tailnum" ) %>% mutate(age = year - plane_year) %>% filter(!is.na(age)) %>% mutate(age = pmin(46, age) - pmin( 25, age ) ) %>% filter( age != 0 )

Then I got stuck. I can’t figure out how to proceed after that. And frankly, I can say with any certainty if my argument is sensible. And I’m not even sure it’s possible to combine the 17 rows into a single row. Your help will be appreciated.

Thank you in advance and for your help so far. Truly appreciated.

I think you used this line of code to limit the selection to planes not older than 25 years. But in the text above, you stated, "There are few planes older than 30 years, so we combine them into a single category." So, I was expecting the selection to be age <= 30, or using your notation pmin(30, age) and not pmin(25, age). Perhaps an edit of the text may be required unless I'm wrong in my supposition?

Edit suggestion: However, this default...

Hi, I hope this doesn't come across as nitpicking:

If we needed a unique identifier for our analysis, we could add a surrogate key, perhaps?

"It looks like it is possible for certain variables to missing for (country, years)." Edit suggestion: It looks like it is possible that certain variables are missing for (country, years).

delete?

run:

Using $,

Please, can you explain to me what I am getting wrong in the below code and especially the error message, which I have tried goggling to no success.

I tried to see if instead of count to show, in my practice exercise, the average price per carat group using the code below:

group_by( diamonds, carat ) %>% summarise( avg_price = mean(price ) ) %>% ggplot( ) + mapping = aes( color = cut_width(carat, 5 ), x = avg_price ) + geom_freqpoly( )

The code returns the following error:

Error in group_by(diamonds, carat) %>% summarise(avg_price = mean(price)) %>% : could not find function "+<-"

I've googled the error without success. So my confusion is in two parts:

1. What is the right code to show the average price per carat type
2. What does the above error mean?

Thanks in advance

Does anyone else notice that the R4DS uses British spelling for visualisation, but the Solution textbook uses the American spelling. It's a bit weird when one switches directly from the text book to the solution. I'm sure not many people notice the difference. I probably do because I generally write Brit English. By the way, I am not censuring, just expressing my thought. I am really grateful to the author for making this available.

Omission of are? Perhaps, you meant there are?

Typo. Either the or these, preferably these in my opinion.

Can anyone please explain the n in this code? Thanks in advance.