I have a data frame and I want to generate a new column that has the result of an calculation based on the row before. Additionally the calculation has some conditions.
The data frame consist of energy production = p, energy consumption = c, energy grid = g, energy safe = s
My goal is to calculate the usage of a battery in a PV-System. When the modules produces more then needed the battery gets loaded and otherwise unloaded. When the batterie don't have enough energy the grid delivers the remainig energy.
So in the first line the batterie gets loaded because I produce more than I need. In the 5 line I need more energy than I produce, so the batterie gets unloaded and so on.
One row is one hour. So n+1 is based on the energy demand and supply of n.
### Old:
n p c g
1 2 1 0
2 3 1 0
3 4 3 0
4 3 5 2
5 5 8 3
6 2 1 0
### New:
n p c g s
1 2 1 0 1
2 3 1 0 3
3 4 3 0 4
4 3 5 0 2
5 5 8 1 0
6 2 1 0 1
When i use your code the result is like this:
First column - c
Second Colum - p
Third colum - g
Fourth colum - s
The battery gets loaded but the unload process does not fit from what is expected. The battery has 2.3801 energy and the demand in n+1 is 0.875.
So the result should be 2.3801 - 0.875 = 1.5015
This process should end when s = 0
I dont understand why your codes works for the rest of data.
I found a solution here which works very well for my problem.
My battery is floored at 0 and limited to 16 kWh, so I added just the pmin function.
mutate(result = accumulate(production-consumw1, ~ pmin(16,pmax(0, .x + .y)), .init = 0)[-1])
Thanks for your help!
Related
I have a dataset with four variables (df)
household
group
income
post
1
0
20'000
0
1
0
22'000
1
2
1
10'000
0
2
1
20'000
1
3
0
20'000
0
3
0
21'000
1
4
1
9'000
0
4
1
16'000
1
5
1
8'000
0
5
1
18'000
1
6
0
22'000
0
6
0
26'000
1
7
1
12'000
0
7
1
24'000
1
8
0
24'000
0
8
0
27'000
1
Group is a binary variable and is 1, when household got support from state. and post variable is also binary and is 1, when it is after some household got support from state.
Now I would like to run a before vs after regression that estimates the group effect by comparing post-period and before period for the supported group. I would like to put the dependent variable in logs, to have the effect in percentage, so the impact of state support on income.
I used that code, but I don't know if it is right to get the answer?
library("fixest")
feols(log(income) ~ group + post,data=df) %>% etable()
Is there another way?
If you are looking for the classic 2x2 design your code was almost correct. Change '+' with '*'. This tell us that the supported group increased the income with 7 250 more than the group which not received support.
comparing = feols(income ~ group * post,data)
comparing_log = feols(log(income) ~ group * post,data)
etable(comparing,comparing_log)
PS: The interpretation of the coefficient as percentage change is a good approximation for small numbers. The correct formula for % change is: exp(beta)-1. In this case it is exp(0.5829)-1 = 0.7912.
So the change here is 79,12%.
Volume
f
Explanation
10
0
no volume before 10
7
0
no smaller volume before 7
13
2
Both 10 and 7 are smaller than 13
6
0
13 is larger than 6
4
0
6 is larger than 4
8
2
Both 6 and 4 are smaller than 8
7
0
8 is larger than 7
3
0
7 is larger than 3
4
1
3 is smaller than 4
As shown in the above table, I’d like to obtain the f column based on volume in DolphinDB.
Suppose the current volume is t, the desired output f is the count of volumes that meet the following conditions:
There are consecutive elements in volume column that are less than t
The last volume of the consecutive elements is the preceding volume
before t;
The calculation principle in detail is illustrated in the explanation column.
I tried for-loop but it didn't work. Does DolphinDB support any other functions to obtain the result?
t = table(1..10 as volume) tmp = select volume, iif(deltas(volume)>0, rowNo(volume), NULL) as flag from t tmp.bfill!() select volume, cumrank(volume) from tmp context by flag
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.
Looking to fill a matrix with a reverse cumsum. There are multiple breaks that must be maintained.
I have provided a sample matrix for what I want to accomplish. The first column is the data, the second column is what I want. You will see that column 2 is updated to reflect the number of items that are left. When there are 0's the previous number must be carried through.
update <- matrix(c(rep(0,4),rep(1,2),2,rep(0,2),1,3,
rep(10,4), 9,8,6, rep(6,2), 5, 2),ncol=2)
I have tried multiple ways to create a sequence, loop using numerous packages (i.e. zoo). What is difficult is that the numbers in column 1 can be between 0,1,..,X but less than column 2.
Any help or tips would be appreciated
EDIT: Column 2 starts with a given value which can represent any starting value (i.e. inventory at the beginning of a month). Column 1 would then represent "purchases" made which; thus, column 2 should reflect the total number of remaining items available.
The following will report the purchase and inventory balance as described:
starting_inventory <- 100
df <- data.frame(purchases=c(rep(0,4),rep(1,2),2,rep(0,2),1,3))
df$cum_purchases <- cumsum(df$purchases)
df$remaining_inventory <- starting_inventory - df$cum_purchases
Result:
purchases cum_purchases remaining_inventory
1 0 0 100
2 0 0 100
3 0 0 100
4 0 0 100
5 1 1 99
6 1 2 98
7 2 4 96
8 0 4 96
9 0 4 96
10 1 5 95
11 3 8 92
I will present my question in two ways. First, requesting a solution for a task; and second, as a description of my overall objective (in case I am overthinking this and there is an easier solution).
1) Task Solution
Data context: each row contains four price variables (columns) representing (a) the price at which the respondent feels the product is too cheap; (b) the price that is perceived as a bargain; (c) the price that is perceived as expensive; (d) the price that is too expensive to purchase.
## mock data set
a<-c(1,5,3,4,5)
b<-c(6,6,5,6,8)
c<-c(7,8,8,10,9)
d<-c(8,10,9,11,12)
df<-as.data.frame(cbind(a,b,c,d))
## result
# a b c d
#1 1 6 7 8
#2 5 6 8 10
#3 3 5 8 9
#4 4 6 10 11
#5 5 8 9 12
Task Objective: The goal is to create a single column in a new data frame that lists all of the unique values contained in a, b, c, and d.
price
#1 1
#2 3
#3 4
#4 5
#5 6
...
#12 12
My initial thought was to use rbind() and unique()...
price<-rbind(df$a,df$b,df$c,df$d)
price<-unique(price)
...expecting that a, b, c and d would stack vertically.
[Pseudo illustration]
a[1]
a[2]
a[...]
a[n]
b[1]
b[2]
b[...]
b[n]
etc.
Instead, the "columns" are treated as rows and stacked horizontally.
V1 V2 V3 V4 V5
1 1 5 3 4 5
2 6 6 5 6 8
3 7 8 8 10 9
4 8 10 9 11 12
How may I stack a, b, c and d such that price consists of only one column ("V1") that contains all twenty responses? (The unique part I can handle separately afterwards).
2) Overall Objective: The Bigger Picture
Ultimately, I want to create a cumulative share of population for each price (too cheap, bargain, expensive, too expensive) at each price point (defined by the unique values described above). For example, what percentage of respondents felt $1 was too cheap, what percentage felt $3 or less was too cheap, etc.
The cumulative shares for bargain and expensive are later inverted to become not.bargain and not.expensive and the four vectors reside in a data frame like this:
buckets too.cheap not.bargain not.expensive too.expensive
1 0.01 to 0.50 0.000000000 1 1 0
2 0.51 to 1.00 0.000000000 1 1 0
3 1.01 to 1.50 0.000000000 1 1 0
4 1.51 to 2.00 0.000000000 1 1 0
5 2.01 to 2.50 0.001041667 1 1 0
6 2.51 to 3.00 0.001041667 1 1 0
...
from which I may plot something that looks like this:
Above, I accomplished my plotting objective using defined price buckets ($0.50 ranges) and the hist() function.
However, the intersections of these lines have meanings and I want to calculate the exact price at which any of the lines cross. This is difficult when the x-axis is defined by price range buckets instead of a specific value; hence the desire to switch to exact values and the need to generate the unique price variable.
[Postscript: This analysis is based on Peter Van Westendorp's Price Sensitivity Meter (https://en.wikipedia.org/wiki/Van_Westendorp%27s_Price_Sensitivity_Meter) which has known practical limitations but is relevant in the context of my research which will explore consumer perceptions of value under different treatments rather than defining an actual real-world price. I mention this for two reasons 1) to provide greater insight into my objective in case another approach comes to mind, and 2) to keep the thread focused on the mechanics rather than whether or not the Price Sensitivity Meter should be used.]
We can unlist the data.frame to a vector and get the sorted unique elements
sort(unique(unlist(df)))
When we do an rbind, it creates a matrix and unique of matrix calls the unique.matrix
methods('unique')
#[1] unique.array unique.bibentry* unique.data.frame unique.data.table* unique.default unique.IDate* unique.ITime*
#[8] unique.matrix unique.numeric_version unique.POSIXlt unique.warnings
which loops through the rows as the default MARGIN is 1 and then looks for unique elements. Instead, if we use the 'price', either as.vector or c(price) converts into vector
sort(unique(c(price)))
#[1] 1 3 4 5 6 7 8 9 10 11 12
If we use unique.default
sort(unique.default(price))
#[1] 1 3 4 5 6 7 8 9 10 11 12