GGPlot summary plots

Objective

How to read in excel file and plot the data using GGPlot.

Data for the exercise

This page has a link to all of the data files

We will use a mock data file that uses M&M’s

M&M CSV file and also the M&M Excel file

Load Libraries

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

##Read files

# Note you can read in excel files just as easy
  mm.df <- read_excel("data/mms.xlsx")
  
  head(mm.df)

# A tibble: 6 × 4
  center        color  diameter  mass
  <chr>         <chr>     <dbl> <dbl>
1 peanut butter blue       16.2  2.18
2 peanut butter brown      16.5  2.01
3 peanut butter orange     15.5  1.78
4 peanut butter brown      16.3  1.98
5 peanut butter yellow     15.6  1.62
6 peanut butter brown      17.4  2.59

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	complete_rate	min	max	n_unique
color	peanut	1	3	6	6
color	peanut butter	1	3	6	6
color	plain	1	3	6	6

Variable type: numeric

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

Finally you can get a summary manually but is a bit longer. These can also be saved into a new dataframe.

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 × 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 mean 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)")

# 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,
               position= position_dodge(width = 0.2)) + 
  stat_summary(fun.data = mean_se, na.rm = TRUE,
               geom = "errorbar",
               width = 0.2,
               position= position_dodge(width = 0.2)) +
  labs(x = "color", y = "Diamter (units)")