Untitled

Get data

library(tidyverse)

## ── Attaching packages ── tidyverse 1.2.1 ──

## ✔ ggplot2 3.2.1     ✔ purrr   0.3.2
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.0     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0

## ── Conflicts ───── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(cowplot)

## 
## ********************************************************

## Note: As of version 1.0.0, cowplot does not change the

##   default ggplot2 theme anymore. To recover the previous

##   behavior, execute:
##   theme_set(theme_cowplot())

## ********************************************************

library(calecopal)

heights<-read.table("~/Desktop/heights.txt",header=T)
heights

##    height rep sex
## 1      52   1  XX
## 2      56   1  XX
## 3      58   2  XX
## 4      59   2  XX
## 5      60   5  XX
## 6      61   1  XX
## 7      62  11  XX
## 8      63  12  XX
## 9      64   6  XX
## 10     65   6  XX
## 11     66  13  XX
## 12     67   6  XX
## 13     68   7  XX
## 14     69   3  XX
## 15     70   1  XX
## 16     71   1  XX
## 17     72   1  XX
## 18     73   1  XX
## 19     59   2  XY
## 20     60   2  XY
## 21     64   1  XY
## 22     65   1  XY
## 23     66   2  XY
## 24     67   7  XY
## 25     68   5  XY
## 26     69   2  XY
## 27     70   2  XY
## 28     71   1  XY
## 29     72   3  XY
## 30     73   3  XY
## 31     74   1  XY
## 32     75   1  XY

Replicate heights, make new data frame

xx<-filter(heights,sex =="XX") %>% select(height)
xxrep<-filter(heights,sex =="XX") %>% select(rep)
xx<-rep(xx$height,xxrep$rep)

xy<-filter(heights,sex =="XY") %>% select(height)
xyrep<-filter(heights,sex =="XY") %>% select(rep)
xy<-rep(xy$height,xyrep$rep)

new_heights<-data.frame(c(xx,xy),c(rep("XX",length(xx)),rep("XY",length(xy)))) 
colnames(new_heights)=c("height","sex")
head(new_heights)

##   height sex
## 1     52  XX
## 2     56  XX
## 3     58  XX
## 4     58  XX
## 5     59  XX
## 6     59  XX

Get normals

group_by(heights,sex) %>%
  summarize(mean(height),sd(height))

## # A tibble: 2 x 3
##   sex   `mean(height)` `sd(height)`
##   <fct>          <dbl>        <dbl>
## 1 XX              64.2         5.86
## 2 XY              68.1         4.92

Plot data

ggplot(new_heights,aes(x=height,fill=sex))+theme_bw()+
  geom_histogram(position="identity",color="black", aes(y=..density..),alpha=0.5,breaks=50:76)+
  scale_fill_manual(values = cal_palette("superbloom3"))+  
  stat_function(fun = dnorm, args = list(mean = 64.22, sd = 5.86),col=cal_palette("superbloom3")[1])+
  stat_function(fun = dnorm, args = list(mean = 68.07, sd = 4.92),col=cal_palette("superbloom3")[2])

xx

##  [1] 52 56 58 58 59 59 60 60 60 60 60 61 62 62 62 62 62 62 62 62 62 62 62
## [24] 63 63 63 63 63 63 63 63 63 63 63 63 64 64 64 64 64 64 65 65 65 65 65
## [47] 65 66 66 66 66 66 66 66 66 66 66 66 66 66 67 67 67 67 67 67 68 68 68
## [70] 68 68 68 68 69 69 69 70 71 72 73