Load libraries

# install.packages("devtools")
# devtools::install_github("thomasp85/patchwork")

# load the libraries each time you restart R
library("readxl") # read in excel files
library("tidyverse") # dplyr and piping and ggplot etc
library("lubridate") # dates and times
library("scales") # scales on ggplot ases
library("skimr") # quick summary stats
library("janitor") # clean up excel imports
library("patchwork") # multipanel graphs
library(skimr) # great way to do summary stats

##Read files

# lets read in a new file to add some complexity for fun
mm.df <- read_csv("data/mms.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   center = col_character(),
##   color = col_character(),
##   diameter = col_double(),
##   mass = col_double()
## )

Summary Stats

Lets look at a few ways to get summary statistics The first is the simplist and uses base R

summary(mm.df)
##     center             color              diameter          mass     
##  Length:816         Length:816         Min.   :11.23   Min.   :0.72  
##  Class :character   Class :character   1st Qu.:13.22   1st Qu.:0.86  
##  Mode  :character   Mode  :character   Median :13.60   Median :0.92  
##                                        Mean   :14.17   Mean   :1.42  
##                                        3rd Qu.:15.30   3rd Qu.:1.93  
##                                        Max.   :17.88   Max.   :3.62

A better way is using Skimr

mm.df %>% 
  skim()
Data summary
Name Piped data
Number of rows 816
Number of columns 4
_______________________
Column type frequency:
character 2
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
center 0 1 5 13 0 3 0
color 0 1 3 6 0 6 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
diameter 0 1 14.17 1.22 11.23 13.22 13.60 15.30 17.88 ▁▇▂▃▁
mass 0 1 1.42 0.71 0.72 0.86 0.92 1.93 3.62 ▇▂▂▂▁

The cool part of skimr is that you can do groups

mm.df %>% 
  group_by(center) %>% 
  skim()
Data summary
Name Piped data
Number of rows 816
Number of columns 4
_______________________
Column type frequency:
character 1
numeric 2
________________________
Group variables center

Variable type: character

skim_variable center n_missing complete_rate min max empty n_unique whitespace
color peanut 0 1 3 6 0 6 0
color peanut butter 0 1 3 6 0 6 0
color plain 0 1 3 6 0 6 0

Variable type: numeric

skim_variable center n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
diameter peanut 0 1 14.77 0.98 12.45 14.13 14.69 15.47 17.88 ▂▇▇▃▁
diameter peanut butter 0 1 15.77 0.63 13.91 15.32 15.72 16.19 17.61 ▁▅▇▃▁
diameter plain 0 1 13.28 0.34 11.23 13.08 13.28 13.48 14.38 ▁▁▃▇▁
mass peanut 0 1 2.60 0.34 1.93 2.36 2.58 2.81 3.62 ▃▇▆▃▁
mass peanut butter 0 1 1.80 0.27 1.19 1.62 1.77 1.94 2.63 ▂▇▇▂▁
mass plain 0 1 0.86 0.05 0.72 0.83 0.87 0.89 1.01 ▁▃▇▃▁

Finally you can get a summary a differnt way but is a bit longer

mm.df %>% 
  group_by(center, color) %>% 
  summarize(mean_diamter = mean(diameter, na.rm=TRUE),
            mean_mass = mean(mass, na.rm=TRUE))
## `summarise()` has grouped output by 'center'. You can override using the `.groups` argument.
## # A tibble: 18 x 4
## # Groups:   center [3]
##    center        color  mean_diamter mean_mass
##    <chr>         <chr>         <dbl>     <dbl>
##  1 peanut        blue           14.8     2.58 
##  2 peanut        brown          14.7     2.57 
##  3 peanut        green          15.0     2.68 
##  4 peanut        orange         14.6     2.57 
##  5 peanut        red            15.0     2.63 
##  6 peanut        yellow         14.5     2.57 
##  7 peanut butter blue           15.9     1.85 
##  8 peanut butter brown          15.7     1.80 
##  9 peanut butter green          16.0     1.92 
## 10 peanut butter orange         15.7     1.73 
## 11 peanut butter red            15.8     1.74 
## 12 peanut butter yellow         15.7     1.74 
## 13 plain         blue           13.2     0.860
## 14 plain         brown          13.3     0.871
## 15 plain         green          13.3     0.870
## 16 plain         orange         13.3     0.865
## 17 plain         red            13.3     0.854
## 18 plain         yellow         13.4     0.865

Graphing mena and SE

These are all well and good but looking at a graph is cool.

# now for the plot
ggplot(mm.df, aes(color, diameter, color=color)) + 
  stat_summary(fun = mean, na.rm = TRUE,
               geom = "point",
               size = 3) + 
  stat_summary(fun.data = mean_se, na.rm = TRUE,
               geom = "errorbar",
               width = 0.2) +
  labs(x = "color", y = "Diamter (units")

We can also add in shape as a grouping varaible for the center of the m&m’s

# now for the plot
ggplot(mm.df, aes(color, diameter, group=center, shape=center, color=color)) + 
  stat_summary(fun = mean, na.rm = TRUE,
               geom = "point",
               size = 3) + 
  stat_summary(fun.data = mean_se, na.rm = TRUE,
               geom = "errorbar",
               width = 0.2) +
  labs(x = "color", y = "Diamter (units)")