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Lab 2 2023-02-06 ### This lab will prepare you for PS4! ### This is a _graded lab_, you’ll get 3 ### points if you solve and submit it correctly ### Squrrel census data is downloaded from NY Open Data Portal ### See the readme ### https://bitbucket.org/otoomet/data/src/master/nature/ ### for more information and better layout of the ### variable names ### ### Variables: ### ### **X**: Longitude coordinate for squirrel sighting point ### **Y**: Latitude coordinate for squirrel sighting point ### **Unique Squirrel ID**: Identification tag for each squirrel ### sightings. The tag is comprised of “Hectare ID” + “Shift” + “Date” + ### “Hectare Squirrel Number.” ### **Hectare**: ID tag, which is derived from the hectare grid used to ### divide and count the park area. One axis that runs predominantly ### north-to-south is numerical (1-42), and the axis that runs ### predominantly east-to-west is roman characters (A-I). ### **Shift**: Value is either “AM” or “PM,” to communicate whether or ### not the sighting session occurred in the morning or late afternoon. ### **Date**: Concatenation of the sighting session day and month. ### **Hectare Squirrel Number**: Number within the chronological ### sequence of squirrel sightings for a discrete sighting session. ### **Age**: Value is either “Adult” or “Juvenile.” ### **Primary Fur Color**: Value is either “Gray,” “Cinnamon” or ### “Black.” ### **Highlight Fur Color**: Discrete value or string values comprised ### of “Gray,” “Cinnamon” or “Black.” ### **Combination of Primary and Highlight Color**: A combination of the ### previous two columns; this column gives the total permutations of ### primary and highlight colors observed. ### **Color notes**: Sighters occasionally added commentary on the ### squirrel fur conditions. These notes are provided here. ### **Location**: Value is either “Ground Plane” or “Above Ground.” ### Sighters were instructed to indicate the location of where the ### squirrel was when first sighted. ### **Above Ground Sighter Measurement**: For squirrel sightings on the ### ground plane, fields were populated with a value of “FALSE.” ### **Specific Location**: Sighters occasionally added commentary on the 1 ### squirrel location. These notes are provided here. ### **Running**: Squirrel was seen running. ### **Chasing**: Squirrel was seen chasing another squirrel. ### **Climbing**: Squirrel was seen climbing a tree or other ### environmental landmark. ### **Eating**: Squirrel was seen eating. ### **Foraging**: Squirrel was seen foraging for food. ### **Other Activities**: ### **Kuks**: Squirrel was heard kukking, a chirpy vocal communication ### used for a variety of reasons. ### **Quaas**: Squirrel was heard quaaing, an elongated vocal ### communication which can indicate the presence of a ground predator ### such as a dog. ### **Moans**: Squirrel was heard moaning, a high-pitched vocal ### communication which can indicate the presence of an air predator ### such as a hawk. ### **Tail flags**: Squirrel was seen flagging its tail. Flagging is a ### whipping motion used to exaggerate squirrel’s size and confuse ### rivals or predators. Looks as if the squirrel is scribbling with ### tail into the air. ### **Tail twitches**: Squirrel was seen twitching its tail. Looks like ### a wave running through the tail, like a breakdancer doing the arm ### wave. Often used to communicate interest, curiosity. ### **Approaches**: Squirrel was seen approaching human, seeking food. ### **Indifferent**: Squirrel was indifferent to human presence. ### **Runs from**: Squirrel was seen running from humans, seeing them as ### a threat. ### **Other Interactions**: Sighter notes on other types of interactions ### between squirrels and humans. ### **Lat/Long**: Latitude and longitude ## Load tidyverse (or dplyr) library library(tidyverse) ## — Attaching packages ————————————— tidyverse 1.3.2 — ## v ggplot2 3.4.0 v purrr 0.3.4 ## v tibble 3.1.6 v dplyr 1.0.10 ## v tidyr 1.2.0 v stringr 1.5.0 ## v readr 2.1.2 v forcats 0.5.1 ## — Conflicts —————————————— tidyverse_conflicts() — ## x dplyr::filter() masks stats::filter() ## x dplyr::lag() masks stats::lag() ## Load the dataset df <- read.csv(‘nyc-central-park-squirrel-census-2019.csv.bz2′) ## How many rows and columns does it contain? nrow(df) ## [1] 3023 2 ncol(df) ## [1] 31 ## What are the variable names? colnames(df) ## [1] “X” ## [2] “Y” ## [3] “Unique.Squirrel.ID” ## [4] “Hectare” ## [5] “Shift” ## [6] “Date” ## [7] “Hectare.Squirrel.Number” ## [8] “Age” ## [9] “Primary.Fur.Color” ## [10] “Highlight.Fur.Color” ## [11] “Combination.of.Primary.and.Highlight.Color” ## [12] “Color.notes” ## [13] “Location” ## [14] “Above.Ground.Sighter.Measurement” ## [15] “Specific.Location” ## [16] “Running” ## [17] “Chasing” ## [18] “Climbing” ## [19] “Eating” ## [20] “Foraging” ## [21] “Other.Activities” ## [22] “Kuks” ## [23] “Quaas” ## [24] “Moans” ## [25] “Tail.flags” ## [26] “Tail.twitches” ## [27] “Approaches” ## [28] “Indifferent” ## [29] “Runs.from” ## [30] “Other.Interactions” ## [31] “Lat.Long” ## Show a few lines of data! head(df, 3) ## X Y Unique.Squirrel.ID Hectare Shift Date ## 1 -73.95613 40.79408 37F-PM-1014-03 37F PM 10142018 ## 2 -73.96886 40.78378 21B-AM-1019-04 21B AM 10192018 ## 3 -73.97428 40.77553 11B-PM-1014-08 11B PM 10142018 ## Hectare.Squirrel.Number Age Primary.Fur.Color Highlight.Fur.Color ## 1 3 ## 2 4 ## 3 8 Gray ## Combination.of.Primary.and.Highlight.Color Color.notes Location ## 1 + 3 ## 2 + ## 3 Gray+ Above Ground ## Above.Ground.Sighter.Measurement Specific.Location Running Chasing Climbing ## 1 false false false ## 2 false false false ## 3 10 false true false ## Eating Foraging Other.Activities Kuks Quaas Moans Tail.flags Tail.twitches ## 1 false false false false false false false ## 2 false false false false false false false ## 3 false false false false false false false ## Approaches Indifferent Runs.from Other.Interactions ## 1 false false false ## 2 false false false ## 3 false false false ## Lat.Long ## 1 POINT (-73.9561344937861 40.7940823884086) ## 2 POINT (-73.9688574691102 40.7837825208444) ## 3 POINT (-73.97428114848522 40.775533619083) ## How many different unique squirrels are there? n_distinct(df$Unique.Squirrel.ID) ## [1] 3018 ## How many squirrels were Approaching humans? num.approach <- nrow(df[df$Approaches==’true’,]) num.approach ## [1] 178 ## How many squirrels were indifferent, and how many ## were running from humans? ## use a single ‘summarize()’ to compute it! df %>% summarize( indifferent = sum(ifelse(Indifferent==’true’, 1, 0)), runs.from = sum(ifelse(Runs.from==’true’, 1, 0)) ) ## indifferent runs.from ## 1 1454 678 ## Compute percentage of squirrels who are approaching ## humans perc.approach <- num.approach / nrow(df) perc.approach ## [1] 0.05888191 ## Show 10 randomly selected ‘Other Activities’ what squirrels do ## But only if those are not NA df %>% filter(Other.Activities != ”) %>% sample_n(10) %>% select(Other.Activities) 4 ## Other.Activities ## 1 sitting ## 2 stole (found?) an entire sandwich ## 3 made a back-door escape from dog off-leash ## 4 watching ## 5 carrying food in mouth ## 6 digging ## 7 playing ## 8 chasing (#7),playing? ## 9 burying the food on ground ## 10 stood still & watched me then jumped on a fence and ran away ## Are there any squirrels who are climbing to ## approach humans? df %>% filter(Climbing==’true’ & Approaches==’true’) %>% count() ## n ## 1 27 # Yes, there are 27 squirrels who are climbing to approach humans. ## What kind of values are there for squirrel age? unique(df$Age) ## [1] “” “Adult” “Juvenile” “?” ## Explain what you see. What does it tell about data quality? # For squirrel age, We have missings, coded in two ways (NA and ‘?’). # Otherwise looks good but not particularly precise with only two age categories (Adult, Juvenile). ## How many squirrels of different age group were observed? df %>% group_by(Age) %>% count() ## # A tibble: 4 x 2 ## # Groups: Age [4] ## Age n ## ## 1 “” 121 ## 2 “?” 4 ## 3 “Adult” 2568 ## 4 “Juvenile” 330 ## Compute the percentage of adult and juveline squirrels ## who were approaching humans df %>% filter(Age %in% c(‘Adult’,’Juvenile’) & Approaches==’true’) %>% group_by(Age) %>% summarise(n = n()) %>% mutate(freq = n / sum(n)) ## # A tibble: 2 x 3 ## Age n freq 5 ## ## 1 Adult 151 0.873 ## 2 Juvenile 22 0.127 ## Make a plot showing the squirrel location in the park ## (use ‘X’, ‘Y’, ‘Lat/Long’ is complex to use) plot(df$X, df$Y) −73.980 −73.975 −73.970 −73.965 −73.960 −73.955 −73.950 40.765 40.775 40.785 40.795 df$X df$Y ## Make a plot that shows only squirrels that approach humans df %>% filter(Approaches==’true’) %>% with(plot(X, Y)) 6 −73.980 −73.975 −73.970 −73.965 −73.960 −73.955 40.765 40.775 40.785 40.795 X Y ## Split date into 3 variables: month (2 first digits), ## day (2 subsequent digits), and year (4 last digits) ## demonstrate that it work correctly by printing a random ## sample of `Date` and your 3 date variables ## (but no other variables) ## ## hint: check out str_sub() function df$month <- str_sub(df$Date,1,2) df$day <- str_sub(df$Date,3,4) df$year <- str_sub(df$Date,5,-1) sighted <- df %>% sample_n(1) %>% select(Date, year, month, day) sighted ## Date year month day ## 1 10142018 2018 10 14 ## Compute the percentage of juvenile squirrels among those sighted df %>% filter(Age %in% c(‘Adult’,’Juvenile’) & Date==sighted$Date) %>% group_by(Age) %>% summarise(n = n()) %>% mutate(freq = n / sum(n)) ## # A tibble: 2 x 3 ## Age n freq ## 7 ## 1 Adult 311 0.896 ## 2 Juvenile 36 0.104 ## Make a line plot where you show the number of sightings by day df$ISOdate <- as.Date(ISOdate(year = df$year, month = df$month, day = df$day)) # Convert to Date object df_new <- df[order(df$ISOdate), ] %>% group_by(ISOdate) %>% count() # Order data plot(df_new$ISOdate, # Draw plot without x-axis df_new$n, type = “l”, xaxt = “n”, xlab = ‘Date’, ylab = ‘# Sightings’) axis(1, # Add dates to x-axis df_new$ISOdate, format(df_new$ISOdate, “%Y-%m-%d”)) 100 200 300 400 Date # Sightings 2018−10−06 2018−10−10 2018−10−14 2018−10−18 8
