问题
I have a dataframe structured like this:
birthwt tobacco01 pscore pscoreblocks blocknumber
3425 0 0.18 (0.177, 0.187] 1
3527 1 0.15 (0.158, 0.168] 2
1638 1 0.34 (0.335, 0.345] 3
Explaining the data: The birthwt column is a continuous variable measuring birth weight in grams. The tobacco01 column contains values of 0 or 1. The pscore column contains probability values between 0 and 1. The pscoreblocks takes the pscore column and breaks it down into 100 equally sized blocks. The block number provides a number for each block, so it goes from 1 to 100.
I am trying to do the following for each of the blocks in pscoreblocks.
apply_model <- function(data) {
one <- lm(birthwt ~ tobacco01, data)
two <- one$coefficients[[2]]
two_5 <- ((sum(data$tobacco01 == 1)) + (sum(data$tobacco01 == 0)))/ sum(data$tobacco)
three <- two*two_5
return(three)
}
Method 1: One way of doing this (inefficiently) would be to use filter to create a separate dataframe for each block.
data1 <- data %>% filter(blocknumber == 1)
I could then manually run the function above on each block.
Method 2: However, I would like to be able to run this efficiently for 100 blocks.
The following solution has been proposed here:
data %>% group_split(blocknumber) %>% map(apply_model)
I get the same results here as when I use this:
lapply(split(data, data$blocknumber), apply_model)
The Problem:
When I compare the values I get when using Method 1 to the value using Method 2, I was expecting to get the same results. If I filter out block number 1 and run the analysis vs. looking at the value labeled (1) in the 2nd method, I do not get the same values. Why am I not getting the same value here?
More generally, how do I split the data into chunks based on a column value and then iterate to run a function that involves a term that refers to the dataframe being used?
Reproducible example:
> small <- dput(dfcsmall[1:40,])
structure(list(birthwt = c(3629, 3005, 3459, 4520, 3095.17811313023,
3714, 3515, 3232, 3686, 4281, 2645.29691556227, 3714, 3232, 3374,
3856, 3997, 3515, 3714, 3459, 3232, 3884, 3235, 3008.94507753983,
3799, 2940, 3389.51332290472, 3090, 1701, 3363, 3033, 2325, 3941,
3657, 3600, 3005, 4054, 3856, 3402, 2694.09822203382, 3413.03869100037
), tobacco01 = c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1), pscore = c(0.00988756408875347, 0.183983728674846,
0.24538311074894, 0.170701594663405, 0.179337494008595, 0.0770304781540708,
0.164003166666384, 0.0773042518100593, 0.0804603038634144, 0.0611822720382283,
0.481204657069376, 0.166016137665693, 0.107882394783232, 0.149799473798458,
0.04130366288307, 0.0360272679038012, 0.476513676221723, 0.214910849480014,
0.0687582392973688, 0.317662260996216, 0.206183065905609, 0.336553699970873,
0.0559863953956171, 0.103064791185442, 0.0445362319933672, 0.17097032928289,
0.245898950803051, 0.146235179401833, 0.284345485401689, 0.152121397241563,
0.0395696572471225, 0.116669642645446, 0.0672219220193578, 0.297173652687617,
0.436771917147971, 0.0517299620576624, 0.140760280612358, 0.179726730598874,
0.0118610298424373, 0.162996197785343), pscoreblocks = structure(c(1L,
19L, 25L, 18L, 19L, 8L, 17L, 8L, 9L, 7L, 49L, 17L, 11L, 16L,
5L, 4L, 49L, 22L, 7L, 33L, 21L, 35L, 6L, 11L, 5L, 18L, 25L, 15L,
29L, 16L, 5L, 12L, 7L, 31L, 45L, 6L, 15L, 19L, 2L, 17L), .Label = c(" [3.88e-05,0.0099]",
"(0.0099,0.0198]", "(0.0198,0.0296]", "(0.0296,0.0395]", " (0.0395,0.0493]",
"(0.0493,0.0592]", "(0.0592,0.069]", "(0.069,0.0789]", "(0.0789,0.0888]",
"(0.0888,0.0986]", "(0.0986,0.108]", "(0.108,0.118]", "(0.118,0.128]",
"(0.128,0.138]", "(0.138,0.148]", "(0.148,0.158]", "(0.158,0.168]",
"(0.168,0.177]", "(0.177,0.187]", "(0.187,0.197]", "(0.197,0.207]",
"(0.207,0.217]", "(0.217,0.227]", "(0.227,0.237]", "(0.237,0.246]",
"(0.246,0.256]", "(0.256,0.266]", "(0.266,0.276]", "(0.276,0.286]",
"(0.286,0.296]", "(0.296,0.306]", "(0.306,0.315]", "(0.315,0.325]",
"(0.325,0.335]", "(0.335,0.345]", "(0.345,0.355]", "(0.355,0.365]",
"(0.365,0.375]", "(0.375,0.384]", "(0.384,0.394]", "(0.394,0.404]",
"(0.404,0.414]", "(0.414,0.424]", "(0.424,0.434]", "(0.434,0.444]",
"(0.444,0.453]", "(0.453,0.463]", "(0.463,0.473]", "(0.473,0.483]",
"(0.483,0.493]", "(0.493,0.503]", "(0.503,0.513]", "(0.513,0.522]",
"(0.522,0.532]", "(0.532,0.542]", "(0.542,0.552]", "(0.552,0.562]",
"(0.562,0.572]", "(0.572,0.582]", "(0.582,0.591]", "(0.591,0.601]",
"(0.601,0.611]", "(0.611,0.621]", "(0.621,0.631]", "(0.631,0.641]",
"(0.641,0.651]", "(0.651,0.66]", "(0.66,0.67]", "(0.67,0.68]",
"(0.68,0.69]", "(0.69,0.7]", "(0.7,0.71]", "(0.71,0.72]", "(0.72,0.73]",
"(0.73,0.739]", "(0.739,0.749]", "(0.749,0.759]", "(0.759,0.769]",
"(0.769,0.779]", "(0.779,0.789]", "(0.789,0.799]", "(0.799,0.808]",
"(0.808,0.818]", "(0.818,0.828]", "(0.828,0.838]", "(0.838,0.848]",
"(0.848,0.858]", "(0.858,0.868]", "(0.868,0.877]", "(0.877,0.887]",
"(0.887,0.897]", "(0.897,0.907]", "(0.907,0.917]", "(0.917,0.927]",
"(0.927,0.937]", "(0.937,0.946]", "(0.946,0.956]", "(0.956,0.966]",
"(0.966,0.976]", "(0.976,0.986]"), class = "factor"), blocknumber = c(1L,
19L, 25L, 18L, 19L, 8L, 17L, 8L, 9L, 7L, 49L, 17L, 11L, 16L,
5L, 4L, 49L, 22L, 7L, 33L, 21L, 35L, 6L, 11L, 5L, 18L, 25L, 15L,
29L, 16L, 5L, 12L, 7L, 31L, 45L, 6L, 15L, 19L, 2L, 17L)), row.names = c(NA,
-40L), class = c("tbl_df", "tbl", "data.frame"))
回答1:
They are giving equivalent results, but I believe you're indexing based on position instead of name:
data %>% filter(blocknumber == 6) %>% apply_model()
# [1] -2090
If we then try to index model_list with position 6, it's not equal:
data_split <- data %>% group_split(blocknumber)
models <- data_split %>% map(apply_model)
models[[6]]
# [1] NA
But that's because data_split[[6]] isn't the same as data %>% filter(blocknumber == 6):
data_split[[6]]
# # A tibble: 3 x 5
# birthwt tobacco01 pscore pscoreblocks blocknumber
# <dbl> <dbl> <dbl> <fct> <int>
# 1 4281 0 0.0612 (0.0592,0.069] 7
# 2 3459 0 0.0688 (0.0592,0.069] 7
# 3 3657 0 0.0672 (0.0592,0.069] 7
You can fix this by assigning names and then indexing by name:
names(models) <- data_split %>% map("blocknumber") %>% map_chr(unique)
models[["6"]]
# [1] -2090
base::split also preserves names by default so I generally prefer to use it:
models <- data %>% split(.$blocknumber) %>% map(apply_model)
models[["6"]]
# [1] -2090
来源：https://stackoverflow.com/questions/61741207/splitting-data-into-chunks-and-iterating-over-each-chunk-in-r