Brtdku

First, I'm trying to subsample a large dataset with many individuals, but each individual requires a different subsample size. I'm comparing across two time periods, so I want to subsample each individual by the minimum data points each has across the two periods. Second, I have multiple metrics (mostly various means) to calculate per individual, per time period (I've provided one example below). Third, I want to bootstrap 1,000 reps for those metrics. I also want to do this for the population (by averaging across individuals). I have an example of what I've tried below, but that may be way off. I'm open to functions or for loops - I can't conceptualize which is better for this question. (I apologize ahead of time if my code is not efficient - I'm self taught from googling.)

# Example dataset

Data <- data.frame(

ID = sample(c("A", "B", "C", "D"), 50, replace = TRUE),

Act = sample(c("eat", "sleep", "play"), 50, replace = TRUE),

Period = sample(c("pre", "post"), 50, replace = TRUE)

)



# Separate my data by period

DataPre <- as.data.frame(Data[ which(Data $Period == "pre"), ])

DataPost <- as.data.frame(Data[ which(Data $Period == "post"), ])



# Get the minimum # observations for each ID across both periods

Num <- Data %>% 

group_by(ID, Period) %>% 

summarise(number=n()) %>%

group_by(ID) %>%

summarise(min=min(number)) 



# Function to get the mean proportion per ID

meanAct <- function(x){

x %>%

group_by(ID, Act) %>%

summarise (n = n()) %>%

mutate(freq = n / sum(n))

}

Below is how I would subsample if just ONE ID (not many different with different subsampling requirements). I don't know how to specify to subsample different amounts by ID and then replicate each.

# See "8888" Here I want to subsample the Num$Min for each ID

DataResults <- function(x, rep){

reps <- replicate(rep, meanAct(x[sample(1:nrow(x), 8888, replace=FALSE),]))

meanfreq <- apply(simplify2array(reps[3, 1:2]), 1, mean)

sd <- apply(simplify2array(reps[3, 1:2]), 1, sd)

lower <- meanfreq - 1.96*(sd/sqrt(8888))

upper <- meanfreq + 1.96*(sd/sqrt(8888))

meanAct <- as.vector(reps[[1]])

output <- data.frame(meanAct, meanfreq, sd, lower, upper)

print(output)

}



# Print results

DataResults(DataPre, 1000)

DataResults(DataPost, 1000)



# Somehow I get the mean for the population by averaging across all IDs

DataMeanGroup <- DataMean %>%

group_by(Period) %>%

summarise (mean = mean(prop))

The results I'm looking for are the means for each activity for each individual based on subsampling (by minimum datapoints PER INDIVIDUAL) and bootstrapping 1000 reps. Also, if possible, the overall mean for the population by averaging across individuals (again from subsampling and bootstrapping).

EDIT: Additional information:
The ultimate result should allow me to compare the proportion of time that each ID does each activity across the two time periods (e.g. compare % time that A spends eating in the pre vs post, etc). But, subsampled for the period with too much data so that we're comparing an equal number of observations. The way the code would run in my head would be (1) subsample the observations so that we’re comparing an equal number of observations for each ID across the two periods, (2) calculate the proportion of each activity for each ID in each time period, (3) repeat that subsample calculation 1,000 times so that the proportion we end up with is representative of the total observations.

Consider generalizing your subsampling function to receive subsets of data frame passed in by which can slice data frame by each unique pairing of ID and Period. But first calculate MinNum by each ID and Period using ave (inline aggregation). All code below uses base R (i.e., no other package):

Data and Functions

# Example dataset (WITH MORE ROWS)

set.seed(11919)

Data <- data.frame(

  ID = sample(c("A", "B", "C", "D"), 500, replace = TRUE),

  Act = sample(c("eat", "sleep", "play"), 500, replace = TRUE),

  Period = sample(c("pre", "post"), 500, replace = TRUE)

)



# MIN NUM PER ID AND PERIOD GROUPING (NESTED ave FOR COUNT AND MIN AGGREGATIONS)

Data$Min_Num <- with(Data, ave(ave(1:nrow(Data), ID, Period, FUN=length), ID, FUN=min))



# Function to get the mean proportion per ID

meanAct <- function(x){

  within(x, { 

        n <- ave(1:nrow(x), ID, Act, FUN=length)

        freq <- n / sum(n)

  })

}



DataResults <- function(df, rep){

  reps <- replicate(rep, meanAct(df[sample(1:nrow(df), df$Min_Num[1], replace=FALSE),]))

  mean_freq <- apply(simplify2array(reps["freq", ]), 1, mean)    # ADJUSTED  INDEXING

  sd <- apply(simplify2array(reps["freq", ]), 1, sd)             # ADJUSTED  INDEXING

  lower <- mean_freq - 1.96*(sd/sqrt(df$Min_Num[1]))

  upper <- mean_freq + 1.96*(sd/sqrt(df$Min_Num[1]))

  mean_act <- as.vector(reps[[2]])                               # ADJUSTED [[#]] NUMBER 

  id <- df$ID[1]                                                 # ADD GROUP INDICATOR

  period <- df$Period[1]                                         # ADD GROUP INDICATOR



  output <- data.frame(id, period, mean_act, mean_freq, sd, lower, upper)

  return(output)

}

Processing

# BY CALL

df_list <- by(Data, Data[c("ID", "Period")], function(sub) DataResults(sub, 1000))



# BIND ALL DFs INTO ONE DF

final_df <- do.call(rbind, df_list)

head(final_df, 10)

#    id period mean_act  mean_freq          sd      lower      upper

# 1   A   post    sleep 0.02157354 0.005704140 0.01992512 0.02322196

# 2   A   post      eat 0.02151701 0.005720058 0.01986399 0.02317003

# 3   A   post    sleep 0.02171393 0.005808156 0.02003546 0.02339241

# 4   A   post      eat 0.02164184 0.005716603 0.01998982 0.02329386

# 5   A   post     play 0.02174095 0.005678416 0.02009996 0.02338193

# 6   A   post      eat 0.02181380 0.005716590 0.02016178 0.02346581

# 7   A   post    sleep 0.02172458 0.005691051 0.02007995 0.02336922

# 8   A   post    sleep 0.02174288 0.005666839 0.02010524 0.02338052

# 9   A   post     play 0.02166234 0.005673047 0.02002291 0.02330177

# 10  A   post     play 0.02185057 0.005813680 0.02017050 0.02353065

Summarization

# SUMMARIZE FINAL DF (MEAN PROP BY ID AND ACT)

agg_df <- aggregate(mean_freq ~ id + mean_act, final_df, mean)

agg_df

#    id mean_act  mean_freq

# 1   A      eat 0.02172782

# 2   B      eat 0.01469706

# 3   C      eat 0.01814771

# 4   D      eat 0.01696995

# 5   A     play 0.02178283

# 6   B     play 0.01471497

# 7   C     play 0.01819898

# 8   D     play 0.01688828

# 9   A    sleep 0.02169912

# 10  B    sleep 0.01470978

# 11  C    sleep 0.01818944

# 12  D    sleep 0.01697438



# SUMMARIZE FINAL DF (MEAN PROP BY ID AND PERIOD)

agg_df <- aggregate(mean_freq ~ id + period, final_df, mean)

agg_df

#   id period  mean_freq

# 1  A   post 0.02173913

# 2  B   post 0.01470588

# 3  C   post 0.01818182

# 4  D   post 0.01694915

# 5  A    pre 0.02173913

# 6  B    pre 0.01470588

# 7  C    pre 0.01818182

# 8  D    pre 0.01694915



# SUMMARIZE FINAL DF (MEAN PROP BY ID)

agg_df <- aggregate(mean_freq ~ id, final_df, mean)

agg_df

#   id  mean_freq

# 1  A 0.02173913

# 2  B 0.01470588

# 3  C 0.01818182

# 4  D 0.01694915