r - Evaluate dnorm for multiple parameter values and the same argument -


i trying accomplish same in this post, namely overlaying multiple histrograms densities. solution in referred post works, made me wonder if calculation of dfn can done newer packages purrr/purrrlyr:

set.seed(1) df <- data.frame(bsa=rnorm(200, mean=rep(c(1,4),each=100)),                   group=rep(c("test","control"),each=100))  stats <- df %>% group_by(group) %>% summarise(m = mean(bsa), sd = sd(bsa)) x <- with(df, seq(min(bsa), max(bsa), len=100))  dfn <- do.call(rbind,lapply(1:nrow(stats),                              function(i) with(stats[i,],data.frame(group, x, y=dnorm(x,mean=m,sd=sd)))))

to perform inner lapply part, have been trying stuff along lines of

stats %>%     dplyr::group_by(group) %>%     purrr::map(x, dnorm, m, sd)

that is, passing on m , sd stats , using same x. unfortunately, doesn't work. (once inner part accomplished, can pass on result do.call, not problem).

if go dplyr, think don't need compute stats nor x separately. i'd do:

dfn_2 <-   df %>%    mutate_at(vars(bsa), funs(min, max)) %>%    arrange(group) %>%    group_by(group) %>%    transmute(     x = seq(first(min), first(max), length.out = n()),      y = dnorm(x, mean(bsa), sd(bsa))   ) %>%    as.data.frame()  all.equal(dfn, dfn_2) # [1] true

alternatively, here 2 approaches not recommend. demonstrate possible, , how have done trying:

dfn_3 <-   stats %>%    split(.$group) %>%    map2_df(names(.), ~ tibble(group = .y, x, y = dnorm(x, .x$m, .x$sd)))  # # tibble: 200 x 3 #      group         x            y #      <chr>     <dbl>        <dbl> #  1 control -1.888921 6.490182e-09 #  2 control -1.809524 1.045097e-08 #  3 control -1.730128 1.672139e-08 #  4 control -1.650731 2.658301e-08 #  5 control -1.571334 4.199062e-08 #  6 control -1.491938 6.590471e-08 #  7 control -1.412541 1.027772e-07 #  8 control -1.333145 1.592550e-07 #  9 control -1.253748 2.451917e-07 # 10 control -1.174352 3.750891e-07 # # ... 190 more rows  all.equal(dfn, as.data.frame(mutate_at(dfn_3, vars(group), as.factor))) # [1] true   dfn_4 <-   stats %>%    group_by(group) %>%    transmute(x = list(x), y = map(x, dnorm, m, sd)) %>%    ungroup() %>%    tidyr::unnest()  # # tibble: 200 x 3 #      group         x            y #     <fctr>     <dbl>        <dbl> #  1 control -1.888921 6.490182e-09 #  2 control -1.809524 1.045097e-08 #  3 control -1.730128 1.672139e-08 #  4 control -1.650731 2.658301e-08 #  5 control -1.571334 4.199062e-08 #  6 control -1.491938 6.590471e-08 #  7 control -1.412541 1.027772e-07 #  8 control -1.333145 1.592550e-07 #  9 control -1.253748 2.451917e-07 # 10 control -1.174352 3.750891e-07 # # ... 190 more rows  all.equal(dfn, as.data.frame(dfn_4)) # [1] true

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