For the data set below I want to plot histograms overlaid with the normal distribution curve using the dnorm function inside stat_function().
MWE
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-1.07648025754191, -1.08683366758667, -1.00806118810575, -1.96103491804365,
-4.00654026954256, 0.0213791245613211, 2.52869026577227, -0.604681702818922,
0.142711931394348, -1.88501035846813, 0.790036414060718, -4.00654026954256,
-0.104275169168235, -0.685768260039533, 0.380328419010124, -0.945871357510961,
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1.72477747423386, -0.646847099824719, -4.00654026954256, -2.19041294177219,
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-0.223750301006649, -0.425476061402947, -0.236234048814492, 1.71988538759175,
2.78897727385525, -1.1063009370049, -1.28238725888469, -0.0604231015948721,
-0.971107771695997, 0.229371740028129, -1.01332291672405, -0.0661293467445485,
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-4.00654026954256, -1.08705046472179, -0.934984680113084, -0.796697155239635,
1.30050102424142, -0.546206094377298, -0.386648415040902)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -218L))
Code scenario 1: worked
When data was gathered using pivot_longer()separately from ggplot code, it worked. Both histograms and curves were drawn. However, as @G.Grothendieck suggested dnorm in stat_function() does not see the passed mean and sd arguments as it should, we always see the same curve in all food items without any variation.
p1 <- MWE %>%
dplyr::select(Carrot, Nuts, Potato, Cabbage) %>%
tidyr::pivot_longer(cols = colnames(.), names_to = "Food", values_to = "logAmt")
p2 <- ggplot(p1, aes(logAmt)) +
geom_histogram(aes(y = ..density..), fill= "white", colour = "black", binwidth = 1) +
labs(x = "log Amt", y = "Density") +
stat_function(fun=dnorm, args=list(mean= mean(p$logAmt, na.rm = TRUE), sd = sd(p$logAmt, na.rm = TRUE)), colour = "black",size=1) +
facet_wrap(vars(Food)) +
theme_linedraw()
p2
Histogram + Distribution Curve
Code scenario 2: worked partially
When both code snippets for data gathering and ggplot were combined into one and the tilde ~ was used, it resulted into a plot with histograms, but without the curves.
p1 <- MWE %>%
dplyr::select(Carrot, Nuts, Potato, Cabbage) %>%
tidyr::pivot_longer(cols = colnames(.), names_to = "Food", values_to = "logAmt") %>%
ggplot(aes(logAmt)) +
geom_histogram(aes(y = ..density..), fill= "white", colour = "black", binwidth = 1) +
labs(x = "log Amt", y = "Density") +
stat_function(fun=dnorm, args=list(mean= ~ mean(logAmt, na.rm = TRUE), sd = ~ sd(logAmt, na.rm = TRUE)), colour = "black",size=1) +
facet_wrap(vars(Food)) +
theme_linedraw()
p1
Output
Code scenario 3: worked partially too
Curves didn't show up even when tilde was used and p1$logAmt was passed explicitly into the stat_function().
p1 <- MWE %>%
dplyr::select(Carrot, Nuts, Potato, Cabbage) %>%
tidyr::pivot_longer(cols = colnames(.), names_to = "Food", values_to = "logAmt") %>%
ggplot(aes(logAmt)) +
geom_histogram(aes(y = ..density..), fill= "white", colour = "black", binwidth = 1) +
labs(x = "log Amt", y = "Density") +
stat_function(fun=dnorm, args=list(mean= ~mean(p1$logAmt, na.rm = TRUE), sd = ~sd(p1$logAmt, na.rm = TRUE)), colour = "black",size=1) +
facet_wrap(vars(Food)) +
theme_linedraw()
p1
Question:
How to make stat_function() accept arguments from the pipe-lined data above?