I am working on a project for school in R that is looking at swimming data compiled up of 8 different teams looking at each of the 13 events, over 6 years. I have over 8700 rows of data that I have appended and am trying to find out how to draw the specific means that I am looking for. For example, I would like to look at the progression of mean times for team 1 for event 3 for men. Thanks!
You can subset your data-frame to only include those variables, e.g.
ss = subset(df, team == 1 & event == 3)
mean(ss$times)
Related
Hello i have a data frame with more than 3632200+ obs, and I'm trying to find some useful information out of it. I have cleaned it a bit so now this is what the data looks like
Order Lane Days
18852324 796005 - Ahmedabad 2
232313 796008 - Delhi 5
63963231 796005 - Ahmedabad 5
23501231 788152 - Chennai 1
2498732 796008 - Delhi 2
231413 796005 - Ahmedabad 3
75876876 796012 - Chennai 4
14598676 796008 - Delhi 4
Order are different Order Id's, they all are unique, Lane are different paths on which the order was delivered(Lanes can repeat for various orders) & Days is calculated using difftime function in R by differentiating Order delivered and created date.
Now What I'm trying to achieve is something like this
Now I can calculate 98.% order achieved date by using quantile function in R across various lane.
But how do I achieve % of orders fulfilled by day 1 to 5 across various lanes?
Any help would be highly appreciated.
Thank You
Hard to tell without the data, but maybe something like this:
library(purrr)
#df = your data
max_days = max(df$days)
aggregate_fun = function(x){
days = factor(x$days,levels=c(1:max_days))
prop.table(table(days))
}
df = split(df,df$lane)
results = reduce(lapply(df,aggregate_fun),rbind)
Okay, let me be as clear as I can in my problem. I'm new to R, so your patience is appreciated.
I want to create a histogram using two different vectors. The first vector contains a list of models (products). These models are listed as either integers, strings, or NA. I'm not exactly sure how R is storing them (I assume they're kept as strings), or if that is a relevant issue. I also have a vector containing a list of incidents pertaining to that model. So for example, one row in the dataframe might be:
Model Incidents
XXX1991 7
How can I create a histogram where the number of incidents for each model is shown? So the histogram will look like
| =
| =
Frequency of | =
Incidents | = =
| = = =
| = = = = =
- - - - - -
Each different Model
Just to give a general idea.
I also need to be able to map everything out with standard deviation lines, so that it's easy to see which models are the least reliable. But that's not the main question here. I just don't want to do anything that will make me unable to use standard deviation in the future.
So far, all I really understand is how to make a histogram with the frequency marked, but for some reason, the x-axis is marked with numbers, not the models' names.
I don't really care if I have to download new packages to make this work, but I suspect that this already exists in basic R or ggplot2 and I'm just too dumb to figure it out.
Feel free to ask clarfying questions. Thanks.
EDIT: I forgot to mention, there are multiple rows of incidents listed under each model. So to add to my example earlier:
Model Incidents
XXX1991 7
XXX1991 1
XXX1991 19
3
5
XXX1002 9
XXX1002 4
etc . . .
I want to add up all the incidents for a model under one label.
I am assuming that you did not mean to leave the model blank in your example, so I filled in some values.
You can add up the number of incidents by model using aggregate then make the relevant plot using barplot.
## Example Data
data = read.table(text="Model Incidents
XXX1991 7
XXX1991 1
XXX1991 19
XXX1992 3
XXX1992 5
XXX1002 9
XXX1002 4",
header=TRUE)
TAB = aggregate(data$Incidents, list(data$Model), sum)
TAB
Group.1 x
1 XXX1002 13
2 XXX1991 27
3 XXX1992 8
barplot(TAB$x, names.arg=TAB$Group.1 )
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')]]$w == country, na.rm = TRUE));
wc_dataframes}
)
I was wondering how I could view certain lines of data based on specific data i.e. good for viewing anomalies in results.
E.g. I have the following results from the command table(df$A)
2 3 4 5 6 19
143914 52194 30856 10662 2901 1
I'm surprised by the 1 observation where df$A=19. How can I see this observation easily in the console without having to make a subset (x<-subset(df, df$A==19)) ?
Thanks in advance
If your goal is to just view the output in an interactive session, and you have no interest in storing that value, you can use [ to "interactively" subset and view the result:
df[df$A == 19, ]
I have a rather large dataframe with 13 variables. Here is the first line just to give an idea:
prov_code nuts1 nuts1name nuts2 nuts2name prov_geoorder prov_name NUTS_ID EDAD year ORDER graphs value prov_geo
1. 15 1 NW 11 Galicia 1 La Corunna ES111 11 1975 1 1 0.000000000 La Corunna
I would like to obtain the maximum for a certain set of variables according to a combination of variables year ORDER and prov_code (ie, f_all being my data.frame: f_all[(f_all$year==1975)&(f_all$ORDER==1)&(f_all$prov_code=="1"),] ). The goal is to repeat the operation in order to obtain a new data frame containing all the maximum values for each year, ORDER, prov_code.
Is there a simple and quick way to do this?
Thanks for any suggestion on the matter,
There are several way of doing this, for example the one #James mentions. I want to suggest using plyr:
library(ply)
ddply(f_all, .(year, ORDER, prov_code), summarise, mx_value = max(value))
Alternatively, if you have a lot of data, data.table provides similar functionality, but is much much faster in that case.