Table Vehicles:
Unit
Type_vehicle
Vehicle_ID
1
car
12332
1
car
656
2
bike
534
2
car
98
2
truck
778
3
car
098
3
car
345
3
car
1114
4
bike
234
4
bike
66
4
truck
4
I want to know which unit has most cars. I need an output that looks like this:
Unit
count(cars)
1
2
2
1
3
3
4
0
In this example, the unit 3 has the most cars. The previous table can be of course sorted and limited to just the unit 3. I want to know how to obtain this output, since I don't know how to count only for cars.
Could use:
SELECT
Unit,
count(Type_vehicle)
FROM
Vehicles
WHERE
Type_vehicle = 'car'
GROUP BY
Unit
;
Or if 0 -count id's should be included:
SELECT
Unit,
count(Type_vehicle = 'car')
FROM
Vehicles
GROUP BY
Unit
;
Related
There is a problem in DataCamp about computing the probability of winning an NBA series. Cavs and the Warriors are playing a seven game championship series. The first to win four games wins the series. They each have a 50-50 chance of winning each game. If the Cavs lose the first game, what is the probability that they win the series?
Here is how DataCamp computed the probability using Monte Carlo simulation:
B <- 10000
set.seed(1)
results<-replicate(B,{x<-sample(0:1,6,replace=T) # 0 when game is lost and 1 when won.
sum(x)>=4})
mean(results)
Here is a different way they computed the probability using simple code:
# Assign a variable 'n' as the number of remaining games.
n<-6
# Assign a variable `outcomes` as a vector of possible game outcomes: 0 indicates a loss and 1 a win for the Cavs.
outcomes<-c(0,1)
# Assign a variable `l` to a list of all possible outcomes in all remaining games. Use the `rep` function on `list(outcomes)` to create list of length `n`.
l<-rep(list(outcomes),n)
# Create a data frame named 'possibilities' that contains all combinations of possible outcomes for the remaining games.
possibilities<-expand.grid(l) # My comment: note how this produces 64 combinations.
# Create a vector named 'results' that indicates whether each row in the data frame 'possibilities' contains enough wins for the Cavs to win the series.
rowSums(possibilities)
results<-rowSums(possibilities)>=4
# Calculate the proportion of 'results' in which the Cavs win the series.
mean(results)
Question/Problem:
They both produce approximately the same probability of winning the series ~ 0.34. However, there seems to be a flaw in the the concept and the code design. For example, the code (sampling six times) allows for combinations such as the following:
G2 G3 G4 G5 G6 G7 rowSums
0 0 0 0 0 0 0 # Series over after G4 (Cavs lose). No need for game G5-G7.
0 0 0 0 1 0 1 # Series over after G4 (Cavs lose). Double counting!
0 0 0 0 0 1 1 # Double counting!
...
1 1 1 1 0 0 4 # No need for game G6 and G7.
1 1 1 1 0 1 5 # Double counting! This is the same as 1,1,1,1,0,0.
0 1 1 1 1 1 5 # No need for game G7.
1 1 1 1 1 1 6 # Series over after G5 (Cavs win). Double counting!
> rowSums(possibilities)
[1] 0 1 1 2 1 2 2 3 1 2 2 3 2 3 3 4 1 2 2 3 2 3 3 4 2 3 3 4 3 4 4 5 1 2 2 3 2 3 3 4 2 3 3 4 3 4 4 5 2 3 3 4 3 4 4 5 3 4 4 5 4 5 5 6
As you can see, these are never possible. After winning the first four of the remaining six games, no more games should be played. Similarly, after losing the first three games of the remaining six games, no more games should be played. So these combinations shouldn't be included in the computation of the probability of winning the series. There is double counting for some of the combinations.
Here is what I did to omit some of the combinations that are not possible in real life.
outcomes<-c(0,1)
l<-rep(list(outcomes),6)
possibilities<-expand.grid(l)
possibilities<-possibilities %>% mutate(rowsums=rowSums(possibilities)) %>% filter(rowsums<=4)
But then I am not able to omit the other unnecessary combinations. For example, I want to remove two of these three: (a) 1,0,0,0,0,0 (b) 1,0,0,0,0,1 (c) 1,0,0,0,1,1. This is because no more games will be played after losing three times in a row. And they are basically double counting.
There are too many conditions for me to be able to filter them individually. There has to be a more efficient and intuitive way to do this. Can someone provide me with some hints on how to solve this whole mess?
Here is a way:
library(dplyr)
outcomes<-c(0,1)
l<-rep(list(outcomes),6)
possibilities<-expand.grid(l)
possibilities %>%
mutate(rowsums=rowSums(cur_data()),
anti_sum = rowSums(!cur_data())) %>%
filter(rowsums<=4, anti_sum <= 3)
We use the fact that r can coerce into a logical where 0 will be false. See sum(!0) as a short example.
I have some data extracted via HIVE. In the end we are talking of csv with around 500 000 rows. I want to plot them after grouping them in intervals.
Beside the grouping it's not clear how to visualize the data. Since we are talking about low spends and sometimes a high frequency I'm not sure how to handle this problem.
Here is just an overview via head(data)
userid64 spend freq
575033023245123 0.00924205 489
12588968125440467 0.00037 2
13830962861053825 0.00168 1
18983461971805285 0.001500366 333
25159368164208149 0.00215 1
32284253673482883 0.001721303 222
33221593608613197 0.00298 709
39590145306822865 0.001785281 11
45831636009567401 0.00397 654
71526649454205197 0.000949978 1
78782620614743930 0.00552 5
I want to group the data in intervals. So I want an extra columns indicating the groups. The first group should contain all data with an frequency (called freq) between 1 and 100. The second group should contain all rows where there entries have a frequency between 101 and 200... and so on.
The result should look like
userid64 spend freq group
575033023245123 0.00924205 489 5
12588968125440467 0.00037 2 1
13830962861053825 0.00168 1 1
18983461971805285 0.001500366 333 3
25159368164208149 0.00215 1 1
32284253673482883 0.001721303 222 2
33221593608613197 0.00298 709 8
39590145306822865 0.001785281 11 1
45831636009567401 0.00397 654 7
71526649454205197 0.000949978 1 1
78782620614743930 0.00552 5 1
Is there a nice and gentle art to get this? I need this grouping for upcoming plots. I want to do visualization for all intervals to get an overview regarding the spend. If you have any ideas for the visualization please let me know. I thought I should work with boxplots.
If you want to group freq for every 100 units, you can try ceiling function in base R
ceiling(df$freq / 100)
#[1] 5 1 1 4 1 3 8 1 7 1 1
where df is your dataframe.
I´m obviously a novice in writing R-code.
I have tried multiple solutions to my problem from stackoverflow but I'm still stuck.
My dataset is carcinoid, patients with a small bowel cancer, with multiple variables.
i would like to know how different variables are distributed
carcinoid$met_any - with metastatic disease 1=yes, 2=no(computed variable)
carcinoid$liver_mets_y_n - liver metastases 1=yes, 2=no
carcinoid$regional_lymph_nodes_y_n - regional lymph nodes 1=yes, 2=no
peritoneal_carcinosis_y_n - peritoneal carcinosis 1=yes, 2=no
i have tried this solution which is close to my wanted result
ddply(carcinoid, .(carcinoid$met_any), summarize,
livermetastases=sum(carcinoid$liver_mets_y_n=="1"),
regionalmets=sum(carcinoid$regional_lymph_nodes_y_n=="1"),
pc=sum(carcinoid$peritoneal_carcinosis_y_n=="1"))
with the result being:
carcinoid$met_any livermetastases regionalmets pc
1 1 21 46 7
2 2 21 46 7
Now, i expected the row with 2(=no metastases), to be empty. i would also like the rows in the column carcinoid$met_any to give the number of patients.
If someone could help me it would be very much appreciated!
John
Edit
My dataset, although the column numbers are: 1, 43,28,31,33
1=yes2=no
case_nr met_any liver_mets_y_n regional_lymph_nodes_y_n pc
1 1 1 1 2
2 1 2 1 2
3 2 2 2 2
4 1 2 1 1
5 1 2 1 1
desired output - I want to count the numbers of 1:s and 2:s, if it works, all 1:s should end up in the met_any=1 row
nr liver_mets regional_lymph_nodes pc
met_any=1 4 1 4 2
met_any=2 1 4 1 3
EDIT
Although i probably was very unclear in my question, with your help i could make the table i needed!
setDT(carcinoid)[,lapply(.SD,table),.SDcols=c(43,28,31,33,17)]
gives
met_any lymph_nod liver_met paraortal extrahep
1: 50 46 21 6 15
2: 111 115 140 151 146
i am very grateful! #mtoto provided the solution
John
Based on your example data, this data.table approach works:
library(data.table)
setDT(df)[,lapply(.SD,table),.SDcols=c(2:5)]
# met_any liver_mets_y_n regional_lymph_nodes_y_n pc
# 1: 4 1 4 2
# 2: 1 4 1 3
I have a table with values
KId sales_month quantity_sold
100 1 0
100 2 0
100 3 0
496 2 6
511 2 10
846 1 4
846 2 6
846 3 1
338 1 6
338 2 0
now i require output as
KId sales_month quantity_sold result
100 1 0 1
100 2 0 1
100 3 0 1
496 2 6 1
511 2 10 1
846 1 4 1
846 2 6 1
846 3 1 0
338 1 6 1
338 2 0 1
Here, the calculation has to go as such if quantity sold for the month of march(3) is less than 60% of two months January(1) and February(2) quantity sold then the result should be 1 or else it should display 0. Require solution to perform this.
Thanks in advance.
If I understand well, your requirement is to compare sold quantity in month t with the sum of quantity sold in months t-1 and t-2. If so, I can suggest using dplyr package that offer the nice feature of grouping rows and mutating columns in your data frame.
resultData <- group_by(data, KId) %>%
arrange(sales_month) %>%
mutate(monthMinus1Qty = lag(quantity_sold,1), monthMinus2Qty = lag(quantity_sold, 2)) %>%
group_by(KId, sales_month) %>%
mutate(previous2MonthsQty = sum(monthMinus1Qty, monthMinus2Qty, na.rm = TRUE)) %>%
mutate(result = ifelse(quantity_sold/previous2MonthsQty >= 0.6,0,1)) %>%
select(KId,sales_month, quantity_sold, result)
The result is as below:
Adding
select(KId,sales_month, quantity_sold, result)
at the end let us display only columns we care about (and not all these intermediate steps).
I believe this should satisfy your requirement. NA is the result column are due to 0/0 division or no data at all for the previous months.
Should you need to expand your calculation beyond one calendar year, you can add year column and adjust group_by() arguments appropriately.
For more information on dplyr package, follow this link
I hava this table in data base
http://i.stack.imgur.com/r7ECj.jpg
I want to group data in this form
------------ Group 1 -------------
1.FoodGroupName : Milk
a.FoodSubGroup: Milk(type1)
1.food 1
2.food 2
3.food 3
4.food 4
b.FoodSubGroup: Milk(type2)
1.food 1
2.food 2
3.food 3
4.food 4
c.FoodSubGroup: Milk(type3)
1.food 1
2.food 2
3.food 3
4.food 4
--------- Group 2 ------------
2.FoodGroupName : Meat
a.FoodSubGroup: Meat(type1)
1.food 1
2.food 2
3.food 3
4.food 4
b.FoodSubGroup: Meat(type2)
1.food 1
2.food 2
3.food 3
4.food 4
c.FoodSubGroup: Meat(type3)
1.food 1
2.food 2
3.food 3
4.food 4
You could use a query like this:
SELECT fg.FoodGroupName, fsg.FoodSubGroupName, f.FoodName
FROM FoodGroups fg
INNER JOIN FoodSubGroups fsg
ON fg.FoodGroupId = fsg.FoodGroupId
INNER JOIN Foods f
ON fsg.FoodSubGroupId = f.FoodSubGroupId
ORDER BY fg.FoodGroupName, fsg.FoodSubGroupName, f.FoodName
to retrieve data and then output results as you please...
There is several ways to do that kind of thing :
1) you write a single query that will combines the 3 table (using joins) and return a single dataset (like Marco suggested).
eg :
Group SubGroup Food
--------------------------------
Meat Meat1 Food 1
Meat Meat1 Food 2
Meat Meat1 Food 3
Meat Meat2 Food 1
Milk Milk1 Food 1
Milk Milk2 Food 1
Then by using C# code, you group them.
You can do this using very few lines of code (and efficiently) by using GroupBy() linq lambda expression. Have also a look at ToLookUp() extension.
If you are not familiar with them, check this page :
http://code.msdn.microsoft.com/LINQ-to-DataSets-Grouping-c62703ea
In the end you should get a collection of objects that allows you to do the following:
var groups = ...
foreach(var group in groups)
{
//do something with group
foreach(var subGroup in group.SubGroups)
{
//do something with subGroup
foreach(var food in subGroup.Foods)
{
//do something with food
}
}
}
Then it is very easy to fill a treeview or present data using nested repeaters.
2) you write independent queries that will query each table separately. In the end, you get 3 datasets.
Group
-------
1 Meat
2 Milk
SubGroup
---------
1 1 Meat1
2 1 Meat2
3 2 Milk1
4 2 Milk2
Food
----------
1 1 Food 1
2 1 Food 2
3 1 Food 3
4 2 Food 1
5 3 Food 1
6 4 Food 1
Then you use can Group Join linq operator to group them.
In that case, C# will do the joins for you, not sql.
Check here : http://code.msdn.microsoft.com/LINQ-Join-Operators-dabef4e9/description#groupjoin
In the end you will get same result at 1). Both have advantages / disavantages.