Social_network

Quarto

library(tidyr)
library(broom)
library(lme4)

Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(lme4)
library(lmerTest)

Attaching package: 'lmerTest'

The following object is masked from 'package:lme4':

    lmer

The following object is masked from 'package:stats':

    step

library(emmeans)
library(car)

Loading required package: carData

Attaching package: 'car'

The following object is masked from 'package:dplyr':

    recode

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ readr     2.1.5
✔ ggplot2   3.5.0     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(corrplot)

corrplot 0.92 loaded

library(RColorBrewer)
library(ggplot2)
library(MASS)

Attaching package: 'MASS'

The following object is masked from 'package:dplyr':

    select

library(agricolae)
library(vegan)

Loading required package: permute
Loading required package: lattice
This is vegan 2.6-4

library(dplyr)
library(readr)
library(DT)
library(ggplot2)
library(quantreg)

Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

library(broom.mixed)
library(pedigree)
library(pedigreemm)
library(pedtools)

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Reading data

When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:

rm(list = ls())

setwd("C:\\Users\\anunez\\OneDrive - Iowa State University\\Desktop\\PIC_DataAnalysis_files")

data_PIC <- read.csv("FINAL_FIRE_2019_2023_AN.csv")

summary(data_PIC)

       ID                 LINE    PED_IDENT_SIRE     PED_IDENT_DAM     
 Min.   : 80406526   Min.   :65   Min.   :72321895   Min.   :70838758  
 1st Qu.: 84007778   1st Qu.:65   1st Qu.:79257130   1st Qu.:79153747  
 Median : 88690351   Median :65   Median :83589571   Median :84025461  
 Mean   : 89253749   Mean   :65   Mean   :83724294   Mean   :83988626  
 3rd Qu.: 93986447   3rd Qu.:65   3rd Qu.:87591160   3rd Qu.:88191389  
 Max.   :100603759   Max.   :65   Max.   :95485445   Max.   :96751344  
 LIT_LITTER_ID          PEN              TEST_FARM    ENTRY_TIME       
 Min.   :68967039   Length:1048575     Min.   :774   Length:1048575    
 1st Qu.:71920248   Class :character   1st Qu.:774   Class :character  
 Median :74483118   Mode  :character   Median :774   Mode  :character  
 Mean   :74566106                      Mean   :774                     
 3rd Qu.:77074566                      3rd Qu.:774                     
 Max.   :80351636                      Max.   :774                     
  EXIT_TIME            STAY_IN            FEED_INTK       FEEDER_ENTRY_WT
 Length:1048575     Min.   :        2   Min.   :-3892.0   Min.   :-993   
 Class :character   1st Qu.:      513   1st Qu.:  231.0   1st Qu.: 786   
 Mode  :character   Median :     1103   Median :  521.0   Median :1051   
                    Mean   :     3582   Mean   :  575.4   Mean   :1106   
                    3rd Qu.:     1757   3rd Qu.:  848.0   3rd Qu.:1399   
                    Max.   :665125948   Max.   : 9280.0   Max.   :9282   
 FEEDER_EXIT_WT     FEEDER_NO      START_DAY           END_DAY         
 Min.   :-993.0   Min.   : 1.00   Length:1048575     Length:1048575    
 1st Qu.: 414.0   1st Qu.:14.00   Class :character   Class :character  
 Median : 525.0   Median :31.00   Mode  :character   Mode  :character  
 Mean   : 530.8   Mean   :30.19                                        
 3rd Qu.: 647.0   3rd Qu.:46.00                                        
 Max.   :8182.0   Max.   :66.00                                        

data_PIC <- mutate(data_PIC, 

                   ENTRY_DATE = as_date(mdy_hm(ENTRY_TIME, tz = "UTC")),

                   ENTRY = mdy_hm(ENTRY_TIME, tz = "UTC"),
                   
                   
                   EXIT_DATE = as_date(mdy_hm(EXIT_TIME, tz = "UTC")),

                   EXIT = mdy_hm(EXIT_TIME, tz = "UTC"), 
                   
                   START_DAY = format(as.Date(START_DAY, format = "%d-%b-%y"), "%d%m%y"),

                   OFFTEST_DAY = format(as.Date(END_DAY, format = "%d-%b-%y"), "%d%m%y"),
                   

                   )

summary(data_PIC$ENTRY_TIME)

   Length     Class      Mode 
  1048575 character character 

class(data_PIC)

[1] "data.frame"

data_PIC$PEN <- as.factor(data_PIC$PEN)

data_PIC$Social_Group <- paste(data_PIC$PEN, data_PIC$START_DAY, data_PIC$OFFTEST_DAY, sep = "")

head(data_PIC$Social_Group)

[1] "B0315181023181223" "B0315181023181223" "B0315181023181223"
[4] "B0315181023181223" "B0315181023181223" "B0315181023181223"

data_PIC <- group_by(data_PIC, Social_Group)

Raw data arrange

data_PIC.arrange <- arrange(data_PIC, Social_Group, ENTRY, EXIT, by_group = TRUE)%>%
  mutate(line = row_number())


data_PIC.arrange <- data_PIC.arrange %>%
  dplyr::select(ID, ENTRY, EXIT, Social_Group)

head(data_PIC.arrange)

# A tibble: 6 × 4
# Groups:   Social_Group [1]
        ID ENTRY               EXIT                Social_Group     
     <int> <dttm>              <dttm>              <chr>            
1 98782150 2023-05-24 05:40:00 2023-05-24 05:40:00 B0102240523240723
2 98782154 2023-05-24 05:48:00 2023-05-24 05:49:00 B0102240523240723
3 98782330 2023-05-24 05:58:00 2023-05-24 06:00:00 B0102240523240723
4 98782150 2023-05-24 06:03:00 2023-05-24 06:03:00 B0102240523240723
5 98753116 2023-05-24 06:05:00 2023-05-24 06:07:00 B0102240523240723
6 98782152 2023-05-24 07:13:00 2023-05-24 07:26:00 B0102240523240723

animal_counts1 <- data_PIC.arrange %>%
  group_by(Social_Group) %>%
  summarise(Unique_Animal_Count = n_distinct(ID))

animal_counts1

# A tibble: 309 × 2
   Social_Group      Unique_Animal_Count
   <chr>                           <int>
 1 B0102240523240723                  16
 2 B0104090519160719                  15
 3 B0104180719250919                  15
 4 B0104201218260219                  14
 5 B0104240523240723                  16
 6 B0104240621300821                  14
 7 B0104280219070519                  15
 8 B0106090519160719                   2
 9 B0106090720140920                  16
10 B0106130220130420                  16
# ℹ 299 more rows

social_group_distribution1 <- data_PIC.arrange %>%
  group_by(Social_Group) %>%
  summarise(Animal_Count = n())

social_group_distribution1

# A tibble: 309 × 2
   Social_Group      Animal_Count
   <chr>                    <int>
 1 B0102240523240723         3541
 2 B0104090519160719         4818
 3 B0104180719250919         5113
 4 B0104201218260219         3949
 5 B0104240523240723         3290
 6 B0104240621300821         2961
 7 B0104280219070519         3988
 8 B0106090519160719          632
 9 B0106090720140920         4051
10 B0106130220130420         3488
# ℹ 299 more rows

write.csv(animal_counts1, "animal_counts_per_social_group1.csv", row.names = FALSE)

write.csv(social_group_distribution1, "social_group_distribution1.csv", row.names = FALSE)



social_group_data1 <- data_PIC.arrange %>%
  group_by(Social_Group) %>%
  summarise(Unique_Animal_Count = n_distinct(ID),
            Animal_Count = n())

social_group_data1

# A tibble: 309 × 3
   Social_Group      Unique_Animal_Count Animal_Count
   <chr>                           <int>        <int>
 1 B0102240523240723                  16         3541
 2 B0104090519160719                  15         4818
 3 B0104180719250919                  15         5113
 4 B0104201218260219                  14         3949
 5 B0104240523240723                  16         3290
 6 B0104240621300821                  14         2961
 7 B0104280219070519                  15         3988
 8 B0106090519160719                   2          632
 9 B0106090720140920                  16         4051
10 B0106130220130420                  16         3488
# ℹ 299 more rows

write.csv(social_group_data1, "social_group_data1.csv", row.names = FALSE)

#| warning: true
#| echo: true

animal_counts1 <- data_PIC.arrange %>%
  group_by(Social_Group) %>%
  summarise(Unique_Animal_Count = n_distinct(ID))

print(animal_counts1)

# A tibble: 309 × 2
   Social_Group      Unique_Animal_Count
   <chr>                           <int>
 1 B0102240523240723                  16
 2 B0104090519160719                  15
 3 B0104180719250919                  15
 4 B0104201218260219                  14
 5 B0104240523240723                  16
 6 B0104240621300821                  14
 7 B0104280219070519                  15
 8 B0106090519160719                   2
 9 B0106090720140920                  16
10 B0106130220130420                  16
# ℹ 299 more rows

social_group_distribution1 <- data_PIC.arrange %>%
  group_by(Social_Group) %>%
  summarise(Animal_Count = n())

print(social_group_distribution1)

# A tibble: 309 × 2
   Social_Group      Animal_Count
   <chr>                    <int>
 1 B0102240523240723         3541
 2 B0104090519160719         4818
 3 B0104180719250919         5113
 4 B0104201218260219         3949
 5 B0104240523240723         3290
 6 B0104240621300821         2961
 7 B0104280219070519         3988
 8 B0106090519160719          632
 9 B0106090720140920         4051
10 B0106130220130420         3488
# ℹ 299 more rows

write.csv(animal_counts1, "animal_counts_per_social_group.csv", row.names = FALSE)

write.csv(social_group_distribution1, "social_group_distribution.csv", row.names = FALSE)



social_group_data1 <- data_PIC.arrange %>%
  group_by(Social_Group) %>%
  summarise(Unique_Animal_Count = n_distinct(ID),
            Animal_Count = n())


print(social_group_data1)

# A tibble: 309 × 3
   Social_Group      Unique_Animal_Count Animal_Count
   <chr>                           <int>        <int>
 1 B0102240523240723                  16         3541
 2 B0104090519160719                  15         4818
 3 B0104180719250919                  15         5113
 4 B0104201218260219                  14         3949
 5 B0104240523240723                  16         3290
 6 B0104240621300821                  14         2961
 7 B0104280219070519                  15         3988
 8 B0106090519160719                   2          632
 9 B0106090720140920                  16         4051
10 B0106130220130420                  16         3488
# ℹ 299 more rows

Graphics

melted_data1 <- social_group_data1 %>%
  pivot_longer(cols = c(Unique_Animal_Count, Animal_Count), 
               names_to = "Variable", 
               values_to = "Count")

density1 <- ggplot(social_group_data1, aes(x = Animal_Count)) +
  geom_density(fill = "skyblue", alpha = 0.5) +
  theme_minimal() +
  labs(title = "Density Plot of Animal Counts per Social Group",
       x = "Animal Count",
       y = "Density")
density1

histogram1 <- ggplot(social_group_data1, aes(x = Unique_Animal_Count, y = Animal_Count)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  theme_minimal() +
  labs(title = "Distribution of Animal Counts per Social Group",
       x = "Social Group",
       y = "Animal Count") +
  theme(axis.text.x = element_blank())

histogram1 

ggsave("C:\\Users\\anunez\\OneDrive - Iowa State University\\Desktop\\PIC_DataAnalysis_files\\density1.png", plot = density1, width = 10, height = 6, dpi = 300)

ggsave("C:\\Users\\anunez\\OneDrive - Iowa State University\\Desktop\\PIC_DataAnalysis_files\\histogram1.png", plot = histogram1, width = 10, height = 6, dpi = 300)

Creating variable time between feeders and separating records

You can add options to executable code like this

data_PIC <- data_PIC %>%
  arrange(Social_Group, ENTRY) %>%
  group_by(Social_Group) %>%
  mutate(Follower_ID = lead(ID),
         Follower_Time = lead(ENTRY),
         Follower_Social_Group = lead(Social_Group),
         line= row_number(),
         Hour_ENTRY = hour(ENTRY),
         time_between= as.numeric(Follower_Time - EXIT, unit="secs"))%>%
  filter(time_between < 36000,time_between>=0)

data_PIC%>%
  mutate(time_between= as.numeric(Follower_Time - ENTRY, unit="secs"),
         lapse_Time = seconds(Follower_Time - ENTRY))%>%
  dplyr::select(time_between, lapse_Time)

Adding missing grouping variables: `Social_Group`

# A tibble: 1,038,897 × 3
# Groups:   Social_Group [309]
   Social_Group      time_between lapse_Time
   <chr>                    <dbl> <Period>  
 1 B0102240523240723          480 480S      
 2 B0102240523240723          600 600S      
 3 B0102240523240723          300 300S      
 4 B0102240523240723          120 120S      
 5 B0102240523240723         4080 4080S     
 6 B0102240523240723          900 900S      
 7 B0102240523240723         2280 2280S     
 8 B0102240523240723          240 240S      
 9 B0102240523240723         1020 1020S     
10 B0102240523240723          300 300S      
# ℹ 1,038,887 more rows

data_PIC$time_between <- as.numeric(data_PIC$time_between)

data_PIC_pvalues60 <- filter (data_PIC, time_between <= 60) %>%
  mutate(TIME_FEEDER = as.numeric(STAY_IN))

summary(data_PIC_pvalues60$TIME_FEEDER)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      4     556    1170    1266    1822   11206 

head(data_PIC_pvalues60)

# A tibble: 6 × 29
# Groups:   Social_Group [1]
        ID  LINE PED_IDENT_SIRE PED_IDENT_DAM LIT_LITTER_ID PEN   TEST_FARM
     <int> <int>          <int>         <int>         <int> <fct>     <int>
1 98782187    65       93704219      94286020      79385516 B0102       774
2 98793248    65       93679672      91289790      79408788 B0102       774
3 98782187    65       93704219      94286020      79385516 B0102       774
4 98793431    65       93543632      91128767      79408787 B0102       774
5 98782150    65       93543534      94380138      79399025 B0102       774
6 98782150    65       93543534      94380138      79399025 B0102       774
# ℹ 22 more variables: ENTRY_TIME <chr>, EXIT_TIME <chr>, STAY_IN <int>,
#   FEED_INTK <int>, FEEDER_ENTRY_WT <int>, FEEDER_EXIT_WT <int>,
#   FEEDER_NO <int>, START_DAY <chr>, END_DAY <chr>, ENTRY_DATE <date>,
#   ENTRY <dttm>, EXIT_DATE <date>, EXIT <dttm>, OFFTEST_DAY <chr>,
#   Social_Group <chr>, Follower_ID <int>, Follower_Time <dttm>,
#   Follower_Social_Group <chr>, line <int>, Hour_ENTRY <int>,
#   time_between <dbl>, TIME_FEEDER <dbl>

Converting to Logarithm of time

data_PIC_pvalues60 <- data_PIC_pvalues60 %>%
  mutate(L_time = log(TIME_FEEDER))


dim(data_PIC_pvalues60)

[1] 707223     30

head(data_PIC_pvalues60)

# A tibble: 6 × 30
# Groups:   Social_Group [1]
        ID  LINE PED_IDENT_SIRE PED_IDENT_DAM LIT_LITTER_ID PEN   TEST_FARM
     <int> <int>          <int>         <int>         <int> <fct>     <int>
1 98782187    65       93704219      94286020      79385516 B0102       774
2 98793248    65       93679672      91289790      79408788 B0102       774
3 98782187    65       93704219      94286020      79385516 B0102       774
4 98793431    65       93543632      91128767      79408787 B0102       774
5 98782150    65       93543534      94380138      79399025 B0102       774
6 98782150    65       93543534      94380138      79399025 B0102       774
# ℹ 23 more variables: ENTRY_TIME <chr>, EXIT_TIME <chr>, STAY_IN <int>,
#   FEED_INTK <int>, FEEDER_ENTRY_WT <int>, FEEDER_EXIT_WT <int>,
#   FEEDER_NO <int>, START_DAY <chr>, END_DAY <chr>, ENTRY_DATE <date>,
#   ENTRY <dttm>, EXIT_DATE <date>, EXIT <dttm>, OFFTEST_DAY <chr>,
#   Social_Group <chr>, Follower_ID <int>, Follower_Time <dttm>,
#   Follower_Social_Group <chr>, line <int>, Hour_ENTRY <int>,
#   time_between <dbl>, TIME_FEEDER <dbl>, L_time <dbl>

Checking if immediate or distant

umbral <- 300

total_counts1 <- data_PIC_pvalues60 %>%
  mutate(time_between_group = case_when(
    time_between <= umbral ~ "immediate",
    time_between > umbral ~ "distant"
  )) %>%
  group_by(time_between_group) %>%
  summarise(total = n())  

print(total_counts1)

# A tibble: 1 × 2
  time_between_group  total
  <chr>               <int>
1 immediate          707223

Checking social groups with filter

head(data_PIC_pvalues60)

# A tibble: 6 × 30
# Groups:   Social_Group [1]
        ID  LINE PED_IDENT_SIRE PED_IDENT_DAM LIT_LITTER_ID PEN   TEST_FARM
     <int> <int>          <int>         <int>         <int> <fct>     <int>
1 98782187    65       93704219      94286020      79385516 B0102       774
2 98793248    65       93679672      91289790      79408788 B0102       774
3 98782187    65       93704219      94286020      79385516 B0102       774
4 98793431    65       93543632      91128767      79408787 B0102       774
5 98782150    65       93543534      94380138      79399025 B0102       774
6 98782150    65       93543534      94380138      79399025 B0102       774
# ℹ 23 more variables: ENTRY_TIME <chr>, EXIT_TIME <chr>, STAY_IN <int>,
#   FEED_INTK <int>, FEEDER_ENTRY_WT <int>, FEEDER_EXIT_WT <int>,
#   FEEDER_NO <int>, START_DAY <chr>, END_DAY <chr>, ENTRY_DATE <date>,
#   ENTRY <dttm>, EXIT_DATE <date>, EXIT <dttm>, OFFTEST_DAY <chr>,
#   Social_Group <chr>, Follower_ID <int>, Follower_Time <dttm>,
#   Follower_Social_Group <chr>, line <int>, Hour_ENTRY <int>,
#   time_between <dbl>, TIME_FEEDER <dbl>, L_time <dbl>

data_PIC.arrange1 <- arrange(data_PIC_pvalues60, Social_Group, ENTRY, EXIT, by_group = TRUE)%>%
  mutate(line= row_number())


data_PIC.arrange1 <- data_PIC.arrange1 %>%
  dplyr::select(ID, Follower_ID, ENTRY, EXIT, Social_Group)

print(data_PIC.arrange1)

# A tibble: 707,223 × 5
# Groups:   Social_Group [308]
         ID Follower_ID ENTRY               EXIT                Social_Group    
      <int>       <int> <dttm>              <dttm>              <chr>           
 1 98782187    98791463 2023-05-24 08:36:00 2023-05-24 08:48:00 B01022405232407…
 2 98793248    98782188 2023-05-24 09:51:00 2023-05-24 09:51:00 B01022405232407…
 3 98782187    98793248 2023-05-24 11:33:00 2023-05-24 11:42:00 B01022405232407…
 4 98793431    98782154 2023-05-24 13:26:00 2023-05-24 13:50:00 B01022405232407…
 5 98782150    98793249 2023-05-24 13:59:00 2023-05-24 14:03:00 B01022405232407…
 6 98782150    98782187 2023-05-24 14:07:00 2023-05-24 14:12:00 B01022405232407…
 7 98782187    98791460 2023-05-24 14:13:00 2023-05-24 14:36:00 B01022405232407…
 8 98791463    98782154 2023-05-24 15:56:00 2023-05-24 16:24:00 B01022405232407…
 9 98782154    98791463 2023-05-24 16:24:00 2023-05-24 16:24:00 B01022405232407…
10 98791463    98782154 2023-05-24 16:24:00 2023-05-24 16:25:00 B01022405232407…
# ℹ 707,213 more rows

head(data_PIC.arrange1)

# A tibble: 6 × 5
# Groups:   Social_Group [1]
        ID Follower_ID ENTRY               EXIT                Social_Group     
     <int>       <int> <dttm>              <dttm>              <chr>            
1 98782187    98791463 2023-05-24 08:36:00 2023-05-24 08:48:00 B0102240523240723
2 98793248    98782188 2023-05-24 09:51:00 2023-05-24 09:51:00 B0102240523240723
3 98782187    98793248 2023-05-24 11:33:00 2023-05-24 11:42:00 B0102240523240723
4 98793431    98782154 2023-05-24 13:26:00 2023-05-24 13:50:00 B0102240523240723
5 98782150    98793249 2023-05-24 13:59:00 2023-05-24 14:03:00 B0102240523240723
6 98782150    98782187 2023-05-24 14:07:00 2023-05-24 14:12:00 B0102240523240723

animal_counts2 <- data_PIC.arrange1 %>%
  group_by(Social_Group) %>%
  summarise(Unique_Animal_Count2 = n_distinct(ID))

print(animal_counts2)

# A tibble: 308 × 2
   Social_Group      Unique_Animal_Count2
   <chr>                            <int>
 1 B0102240523240723                   16
 2 B0104090519160719                   15
 3 B0104180719250919                   15
 4 B0104201218260219                   14
 5 B0104240523240723                   16
 6 B0104240621300821                   14
 7 B0104280219070519                   15
 8 B0106090519160719                    2
 9 B0106090720140920                   16
10 B0106130220130420                   16
# ℹ 298 more rows

social_group_distribution <- data_PIC.arrange1 %>%
  group_by(Social_Group) %>%
  summarise(Animal_Count2 = n())

print(social_group_distribution)

# A tibble: 308 × 2
   Social_Group      Animal_Count2
   <chr>                     <int>
 1 B0102240523240723          2775
 2 B0104090519160719          3303
 3 B0104180719250919          3010
 4 B0104201218260219          2527
 5 B0104240523240723          2164
 6 B0104240621300821          1851
 7 B0104280219070519          2513
 8 B0106090519160719            60
 9 B0106090720140920          2725
10 B0106130220130420          2753
# ℹ 298 more rows

write.csv(animal_counts2, "animal_counts_per_social_group.csv", row.names = FALSE)

write.csv(social_group_distribution, "social_group_distribution.csv", row.names = FALSE)



social_group_data <- data_PIC.arrange1 %>%
  group_by(Social_Group) %>%
  summarise(Unique_Animal_Count2 = n_distinct(ID),
            Animal_Count2 = n())


print(social_group_data)

# A tibble: 308 × 3
   Social_Group      Unique_Animal_Count2 Animal_Count2
   <chr>                            <int>         <int>
 1 B0102240523240723                   16          2775
 2 B0104090519160719                   15          3303
 3 B0104180719250919                   15          3010
 4 B0104201218260219                   14          2527
 5 B0104240523240723                   16          2164
 6 B0104240621300821                   14          1851
 7 B0104280219070519                   15          2513
 8 B0106090519160719                    2            60
 9 B0106090720140920                   16          2725
10 B0106130220130420                   16          2753
# ℹ 298 more rows

write.csv(social_group_data, "social_group_data.csv", row.names = FALSE)

Graphics distribution

melted_data <- social_group_data %>%
  pivot_longer(cols = c(Unique_Animal_Count2, Animal_Count2), 
               names_to = "Variable", 
               values_to = "Count")

density <- ggplot(social_group_data, aes(x = Animal_Count2)) +
  geom_density(fill = "skyblue", alpha = 0.5) +
  theme_minimal() +
  labs(title = "Density Plot of Animal Counts per Social Group",
       x = "Animal Count",
       y = "Density")
density

histogram <- ggplot(social_group_data, aes(x = Unique_Animal_Count2, y = Animal_Count2)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  theme_minimal() +
  labs(title = "Distribution of Animal Counts per Social Group",
       x = "Social Group",
       y = "Animal Count") +
  theme(axis.text.x = element_blank())

histogram 

ggsave("C:\\Users\\anunez\\OneDrive - Iowa State University\\Desktop\\PIC_DataAnalysis_files\\density.png", plot = density, width = 10, height = 6, dpi = 300)

ggsave("C:\\Users\\anunez\\OneDrive - Iowa State University\\Desktop\\PIC_DataAnalysis_files\\histogram.png", plot = histogram, width = 10, height = 6, dpi = 300)



write.csv(data_PIC.arrange1, "Social_Network_PIC.csv", row.names = FALSE)

The echo: false option disables the printing of code (only output is displayed).