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plot(cars)

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#Install
install.packages("tidyverse")

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The downloaded binary packages are in
    C:\Users\abiga\AppData\Local\Temp\RtmpM9GGeb\downloaded_packages

install.packages("lme4")

#Setup
library(readxl)
library(lme4)
library(ggplot2)

RStudio.Version()

$citation
To cite RStudio in publications
use:

  Posit team (2025). RStudio:
  Integrated Development
  Environment for R. Posit
  Software, PBC, Boston, MA.
  URL http://www.posit.co/.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {RStudio: Integrated Development Environment for R},
    author = {{Posit team}},
    organization = {Posit Software, PBC},
    address = {Boston, MA},
    year = {2025},
    url = {http://www.posit.co/},
  }

$mode
[1] "desktop"

$version
[1] ‘2025.9.0.387’

$long_version
[1] "2025.09.0+387"

$release_name
[1] "Cucumberleaf Sunflower"

#Remove Missing Values
na.omit(LMM_Spreadsheet_AMS$Spot_Count)

  [1] 49 40 16 17 40 52 12 17 39 42 21 28 27 14  8 30 10 17 28 31 10 12 30 46  6 11 32
 [28] 25 11 24 33 46 15 11 38 39  9 20 41 32 10 16 29 27  6 40 27  7  8 13 41 28 12 13
 [55] 25 40 12 20 22 26 12 11 40 31 14 12 33 40 20  8 41 41 16 20 41 41 16 20 33 40 20
 [82]  8 22 36 21 29 25 30 10 14 47 30  9 13 37 30 14 11 31 22 12 20 30 40 12 19 43 30
[109] 12  8 27 42  9  8 38 32 12 12 27 31 15  8
attr(,"na.action")
[1] 12 17 45
attr(,"class")
[1] "omit"

#Practice Plot
point_graph <- ggplot(LMM_Spreadsheet_AMS, aes(x = Treatment, y = Spot_Count, color = Sex)) + geom_point(na.rm = TRUE)

point_graph

#Boxplot!
box_plot <- ggplot(LMM_Spreadsheet_AMS, aes(x = Treatment, y = Spot_Count, main = "Average PNN Count per Slice", ylab = "PNN Count")) + geom_boxplot(na.rm = TRUE)

box_plot

#simple linear regression
model <- lm(LMM_Spreadsheet_AMS$Spot_Count ~ LMM_Spreadsheet_AMS$Treatment + LMM_Spreadsheet_AMS$`Brain Region`, data = train)

Error in eval(mf, parent.frame()) : object 'train' not found

#Summary of LM
summary(model)

Call:
lm(formula = LMM_Spreadsheet_AMS$Spot_Count ~ LMM_Spreadsheet_AMS$Treatment + 
    LMM_Spreadsheet_AMS$`Brain Region`, data = LMM_Spreadsheet_AMS)

Residuals:
    Min      1Q  Median      3Q 
-13.522  -4.516  -1.237   4.797 
    Max 
 16.194 

Coefficients:
                                       Estimate
(Intercept)                             33.3780
LMM_Spreadsheet_AMS$TreatmentSaline      2.4284
LMM_Spreadsheet_AMS$`Brain Region`ACCR  -0.2843
LMM_Spreadsheet_AMS$`Brain Region`ICL  -20.7097
LMM_Spreadsheet_AMS$`Brain Region`ICR  -21.2903
                                       Std. Error
(Intercept)                                1.2729
LMM_Spreadsheet_AMS$TreatmentSaline        1.1292
LMM_Spreadsheet_AMS$`Brain Region`ACCR     1.6002
LMM_Spreadsheet_AMS$`Brain Region`ICL      1.5728
LMM_Spreadsheet_AMS$`Brain Region`ICR      1.5728
                                       t value
(Intercept)                             26.222
LMM_Spreadsheet_AMS$TreatmentSaline      2.150
LMM_Spreadsheet_AMS$`Brain Region`ACCR  -0.178
LMM_Spreadsheet_AMS$`Brain Region`ICL  -13.168
LMM_Spreadsheet_AMS$`Brain Region`ICR  -13.537
                                       Pr(>|t|)
(Intercept)                              <2e-16
LMM_Spreadsheet_AMS$TreatmentSaline      0.0336
LMM_Spreadsheet_AMS$`Brain Region`ACCR   0.8593
LMM_Spreadsheet_AMS$`Brain Region`ICL    <2e-16
LMM_Spreadsheet_AMS$`Brain Region`ICR    <2e-16
                                          
(Intercept)                            ***
LMM_Spreadsheet_AMS$TreatmentSaline    *  
LMM_Spreadsheet_AMS$`Brain Region`ACCR    
LMM_Spreadsheet_AMS$`Brain Region`ICL  ***
LMM_Spreadsheet_AMS$`Brain Region`ICR  ***
---
Signif. codes:  
  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’
  0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.192 on 117 degrees of freedom
  (3 observations deleted due to missingness)
Multiple R-squared:  0.7507,    Adjusted R-squared:  0.7422 
F-statistic:  88.1 on 4 and 117 DF,  p-value: < 2.2e-16

#Plot LM
ggplotRegression(lm(LMM_Spreadsheet_AMS$Treatment ~ LMM_Spreadsheet_AMS$Spot_Count))