So I don't think this has been asked before, but SO search might just be getting confused by combinations of 'ratio' and 'faceting'. I'm trying to calculate a productivity ratio; number of widgets produced for number of workers on a given day or period. I've got my data structured in a single data frame, with each widget produced each day by each worker in it's own record, and other workers that worked that day but didn't produce a widget also in their own record, along with various metadata.
Something like this:
widget_ind
employee_active_ind
employee_id
day
product_type
employee_bu
1
1
123
6/1/2021
pc
americas
0
1
234
6/1/2021
mac
emea
0
1
345
6/1/2021
mac
apac
1
1
444
6/1/2021
mac
americas
1
1
333
6/1/2021
pc
emea
0
1
356
6/1/2021
pc
americas
I'm trying to find the ratio of widget_inds to employee_active_inds, over time, while retaining the metadata, so that i can filter or facet within the ggplot2 code, something like:
plot <- ggplot(data = df[df$employee_bu == 'americas',],aes(y = (widget_ind/employee_active_ind), x = day)) +
geom_bar(stat = 'identity', position = 'stack') +
facet_wrap(product_type ~ ., scales = 'fixed') + #change these to look at different cuts of metadata
print(plot)
Retaining the metadata is appealing rather than making individual dataframes summarizing by the various combinations, but the results with no faceting aren't even correct (e.g. the ggplot is showing a barchart with a height of ~18 widgets per person; creating a summarized dataframe with no faceting is showing a ratio of less than 1 widget per person).
I'm currently getting this error when I run the ggplot code:
Warning message:
Removed 9865 rows containing missing values (geom_bar).
Which doesn't make sense since in my data frame both widget_ind and employee_active_ind have no NA values, so calculating the ratio of the two should always work?
Edit 1: Clarifying employee_active_ind: I should not have any employee_active_ind = 0, but my current joins produce them (and it passes the reality sniff test; the process we are trying to model allows you to do work on day 1 that results in a widget on day 2, where you may not do any work, so wouldn't be counted as active on that day). I think I need to re-think my data structure. Even so, I'm assuming here that ggplot2 is acting like it would for a given bar chart; it's taking the number in each widget_ind record, for a given day (along with any facets and filters), and is then summing that set and displaying the result. The wrinkle I'm adding is dividing by the number of active employees on that day, and while you can have some one out on a given day, you'd never have everyone out. But that isn't what ggplot is doing is it?
I agree with MrFlick - especially the question concerning employee_active_ind of 0. If you have them, this could create NA values where something is divided by 0.
I am currently working on the so-called "Moneyball" problem. I am basically trying to select the best combination of three baseball players (based on certain baseball-relevant statistics) for the least amount of money.
I have the following dataset (OBP, SLG, and AB are statistics that describe the performance of a player):
# the table has about 100 observations;
# the data frame is called "batting.2001"
playerID OBP SLG AB salary
giambja01 0.3569001 0.6096154 20 410333
heltoto01 0.4316547 0.4948382 57 4950000
berkmla01 0.2102326 0.6204506 277 305000
gonzalu01 0.4285714 0.3880131 409 9200000
martied01 0.4234079 0.5425532 100 5500000
My goal is to pick three players who in combination have the highest possible sum of OBP, SLG, and AB, but at the same time do not exceed a total salary of 15.000.000 dollar.
My approach so far has been rather simple... I just tried to arrange (in descending order) the columns OBP, SLG, and AB and simply picking the three players on the top that in combination do not exceed the salary restriction of 15 Million dollar:
batting.2001 %>%
arrange(desc(OPB), desc(SLG), desc(AB))
Can anyone of you think of a better solution? Also, what if I would like to get the best combination of three players for the least amount of money? What approach would you use in that scenario?
Thanks in advance, and looking forward to reading your solutions.
just earlier today I received a very helpful answer for a problem I was running into that allowed me to move onto the next step of one of my projects. However, I got stuck again later on in the project, and I'm wondering if any of you can help me move forward.
Context
Currently, I have a list of data frames that are full of soccer matches called wc_match_dataframes. Here is what one of the data frames looks like:
type_id tourn_id day month year team_A score_A score_B team_B win loss
f wc_1934 27 5 1934 Germany 5 2 Belgium Germany Belgium
I wasn't able to fit the data for the final three columns, draw, drawA, and drawB but basically the draw column is TRUE if the match is a draw, if not, it is FALSE. In the case of a draw, the win and loss columns are just filled by Draw. The drawA column is filled by team_A if the match was a draw, and likewise, the drawB column is filled by team_B.
The type_id is either f or q depending on if the match was a World Cup qualifier or a World Cup finals match. The tourn_id refers to the tournament the match was for, whether it was a qualifier or finals.
There are a total of 39 of these data frames, with a "finals" data frame for each of the 20 World Cup tournaments, and a "qualifiers" data frame for 19 tournaments (the first World Cup did not have qualifying).
What I Want To Do
I'm trying to populate a different list of data frames wc_dataframes with data for each of the 20 World Cups at the country level as opposed to the match level. Each of these twenty data frames will have the countries that made it to the finals of said tournament and their data like so:
Country
Wins in qualifying
Wins in finals
Losses in qualifying
Losses in finals
... and so on.
I have been able to populate the first country column for every World Cup no problem, but I'm running into issues for the rest of the columns.
Here is what I'm doing
This is the unlooped (only works for one World Cup) version of my code that works successfully:
wc_dataframes$wc_1930$fw <- apply(wc_dataframes$wc_1930, MARGIN = 1, function(country)
sum(wc_match_dataframes$`wc_1930 f`$w == country, na.rm = TRUE))
This is successfully populating the finals win column in the wc_dataframes$wc_1930 data frame by counting the number of wins.
Now, when I try and nest this under lapply to do it across all World Cup years like so:
lapply(names(wc_dataframes), function(year)
wc_dataframes$year$fw <- apply(wc_dataframes$year, MARGIN = 1, function(country)
sum(wc_match_dataframes$`year f`$w == country, na.rm = TRUE)))
It does not work for me. I suspect that the issue has to do with defining the year function and running into issues in the sum portion of my code. I come from a background in STATA so I am more used to running for loops and what not. I'm still getting used to R and lists and everything so I really appreciate the help.
Thank you!
Thank you so much in advance for the help, and happy holidays! :)
What you need is to output whatever you have replaced:
lapply(names(wc_dataframes), function(year){
wc_dataframes[[year]]$fw <- apply(wc_dataframes[[year]], MARGIN = 1, function(country)
sum(wc_match_dataframes[[paste(year,'f')]]$w == country, na.rm = TRUE));
wc_dataframes}
)
I have an example data set that looks like this:
Ho<-c(12,12,12,24,12,11,12,12,14,12,11,13,25,25,12,11,13,12,11,11,12,14,12,2,2,2,11,12,13,14,12,11,12,3,2,2,2,3,2,2,1,14,12,11,13,11,12,13,12,11,12,12,12,2,2,2,12,12,12,12,15)
This data set has both positive and negative spikes in it that I would like to use as markers to calculate means on within the data. I would define the start of a spike as any number that is 40% greater or lessor than the number preceding it. A spike ends when it jumps back by more than 40%. So ideally I would like to locate each spike in the data set, and take the mean of the 5 data points immediately following the last number of the spike.
As can be seen, a spike can last for up to 5 data points long. The rule for averaging I would like to follow are:
Start averaging after the last recorded spike data point, not after the first spike data point. So if a spike lasts for three data points, begin averaging after the third spiked data point.
So the ideal output would look something like this:
1= 12.2
2= 11.8
3= 12.4
4= 12.2
5= 12.6
With the first spike being Ho(4)- followed by the following 5 numbers (12,11,12,12,14) for a mean of 12.1
The next spike in the data is data points Ho(13,14) (25,25) followed by the set of 5 numbers (12,11,13,12,11) for an average of 11.8.
And so on for the rest of the sequence.
It kind of seems like you're actually defining a spike to mean differing from the "medium" values in the dataset, as opposed to differing from the previous value. I've operationalized this by defining a spike as being any data more than 40% above or below the median value (which is 12 for the sample data posted). Then you can use the nifty rle function to get at your averages:
r <- rle(Ho >= mean(Ho)*0.6 & Ho <= median(Ho)*1.4)
run.begin <- cumsum(r$lengths)[r$values] - r$lengths[r$values] + 1
run.end <- run.begin + pmin(4, r$lengths[r$values]-1)
apply(cbind(run.begin, run.end), 1, function(x) mean(Ho[x[1]:x[2]]))
# [1] 12.2 11.8 12.4 12.2 12.6
So here is come code that seems to get the same result as you.
#Data
Ho<-c(12,12,12,24,12,11,12,12,14,12,11,13,25,25,12,11,13,12,11,11,12,14,12,2,2,2,11,12,13,14,12,11,12,3,2,2,2,3,2,2,1,14,12,11,13,11,12,13,12,11,12,12,12,2,2,2,12,12,12,12,15)
#plot(seq_along(Ho), Ho)
#find changes
diffs<-tail(Ho,-1)/head(Ho,-1)
idxs<-which(diffs>1.4 | diffs<.6)+1
starts<-idxs[seq(2, length(idxs), by=2)]
ends<-ifelse(starts+4<=length(Ho), starts+4, length(Ho))
#find means
mapply(function(a,b) mean(Ho[a:b]), starts, ends)