615 R LABS
HW: homework for that week
IC: in class exercises
1| HW
1.1| File organization
1.2| Cottonwood
cottonwood <- read.csv("Data/cottonwood.csv")
1.3| Tree_height
Tree_height <- 15
Tree_circ <- pi*Tree_height
Tree_circ## [1] 47.12389
little doodle, the end.
1| IC
See in class handout for specifics. I did not create a workflow for this day.
little doodle, the end.
2| HW
[coming soon]
little doodle while you wait.
2| IC
Importing Data | 9.2.26
Exercise 1
plants <- c("Orange", "Plum", "Lemon", "Peach", "Apple")
height <- as.numeric(c(5, 10, 4, 3, 2))
presence <- c(T, F, T, T, F)
width <- as.numeric(c(0.2, 0.5, 0.6, 0.2, 0.1))
df <- data.frame(plants, height, width, presence)
write.csv(df, "homemade_df_class4.csv")
cats <- read.csv("homemade_df_class4.csv", row.names = 1)
half <- cats$height*0.5
cats <- cbind(cats, half)
Exercise 2
new <- c(32, 14, "100")
str(new)## chr [1:3] "32" "14" "100"coerced <- as.numeric(new)
str(coerced)## num [1:3] 32 14 100dec <- c(5.2, 3.4, 4.4, 5.5, 3.4)
str(dec)## num [1:5] 5.2 3.4 4.4 5.5 3.4coerced_2 <- as.integer(dec)
str(coerced_2)## int [1:5] 5 3 4 5 3num <- c(3, 9, 11, 15)
coerced_3 <- as.character(num)
str(coerced_3)## chr [1:4] "3" "9" "11" "15"#if you want to create categories with your data..
Exercise 3
logical <- ifelse(cats$half >= 2, "Pass", "Fail")
cats <- cbind(cats, logical)
cats$logical## [1] "Pass" "Pass" "Pass" "Fail" "Fail"new <- factor(cats$logical, levels=c("Pass", "Fail"))
str(new)## Factor w/ 2 levels "Pass","Fail": 1 1 1 2 2little doodle, the end.
3| HW
[coming soon]
little seagull while you wait.
3| IC
[coming soon]
little doodle while you wait.
Videos
2
Importing Data & Data Types: Video 4
Code
# Creating objects
# we assign values to objects
# object_name <- value
weight_kg <- 55
weight_kg## [1] 55# math with objects
weight_lb <- 2.2 * weight_kg
weight_lb## [1] 121# Functions
#getwd()
#print()
# Getting help
#?print
name <- "Bronco"
print(name, quote = TRUE)## [1] "Bronco"print(name, quote = FALSE)## [1] Broncoprint(name, quote = TRUE)## [1] "Bronco"# Data Structures
# student heights: 1.7, 1.8, 1.6, 1.75
# vectors are groups of values
# create a vector with the c() function
# Vector with numbers
length_cm <- c(10, 15, 50)
length_cm## [1] 10 15 50# Character vector
animals <- c("mouse", "rat", "dog")
animals## [1] "mouse" "rat" "dog"# quotes are important!
#THE FOLLOWING DOESNT WORK
#animals <- c(mouse, rat, dog)
# Vector from objects
weights <- c(weight_kg, weight_lb)
weights## [1] 55 121weight_bad <- c(40, 88)
# Data frames
# data frames have rows and columns
animal_length <- data.frame(animals, length_cm)
head(animal_length)## animals length_cm
## 1 mouse 10
## 2 rat 15
## 3 dog 50# install.packages("stringr")
# Load the stringr package
library(stringr)
#?str_replace_all
# Replace spaces with underscores
sentence <- "I can write code"
sentence_under <- str_replace_all(string = sentence,
pattern = " ",
replacement = "_")
sentence_under## [1] "I_can_write_code"# tidyverse
# install.packages("tidyverse")
# Load the tidyverse package
library(tidyverse)
#?tidyverse
citation("tidyverse")## To cite package 'tidyverse' in publications use:
##
## Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R,
## Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller
## E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V,
## Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). "Welcome to
## the tidyverse." _Journal of Open Source Software_, *4*(43), 1686.
## doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {Welcome to the {tidyverse}},
## author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
## year = {2019},
## journal = {Journal of Open Source Software},
## volume = {4},
## number = {43},
## pages = {1686},
## doi = {10.21105/joss.01686},
## }# Importing data with read.csv
pikas <- read.csv("Data/data_pika.csv")
# check View window
View(pikas)
# see first few lines of data frame
head(pikas)## Date days Station UTM_Easting UTM_Northing Notes Vial
## 1 8/2/2018 10 LL3 449888 4435612 Heard and saw pika here 172
## 2 8/20/2018 12 WK7 449224 4434233 191
## 3 8/2/2018 10 LL2 449888 4435516 Really fresh! 171
## 4 8/20/2018 12 WK8 449242 4434295 192
## 5 8/20/2018 12 WK12 449076 4434440 196
## 6 8/20/2018 12 LL1 449915 4435575 202
## Concentration_pg_g Plate Site Biweek Sex elev
## 1 834.0471 3 LL 4 U 3278.479
## 2 1328.0385 4 WK 6 U 3616.446
## 3 1332.2918 3 LL 4 U 3305.235
## 4 1626.2902 4 WK 6 M 3606.109
## 5 1711.8800 4 WK 6 U 3580.776
## 6 1804.8702 5 LL 6 U 3295.538# structure of data
str(pikas)## 'data.frame': 109 obs. of 13 variables:
## $ Date : chr "8/2/2018" "8/20/2018" "8/2/2018" "8/20/2018" ...
## $ days : int 10 12 10 12 12 12 11 9 9 12 ...
## $ Station : chr "LL3" "WK7" "LL2" "WK8" ...
## $ UTM_Easting : int 449888 449224 449888 449242 449076 449915 449076 451462 451411 449012 ...
## $ UTM_Northing : int 4435612 4434233 4435516 4434295 4434440 4435575 4434440 4432989 4432986 4434384 ...
## $ Notes : chr "Heard and saw pika here" "" "Really fresh!" "" ...
## $ Vial : int 172 191 171 192 196 202 184 168 167 195 ...
## $ Concentration_pg_g: num 834 1328 1332 1626 1712 ...
## $ Plate : int 3 4 3 4 4 5 4 3 3 4 ...
## $ Site : chr "LL" "WK" "LL" "WK" ...
## $ Biweek : int 4 6 4 6 6 6 5 4 4 6 ...
## $ Sex : chr "U" "U" "U" "M" ...
## $ elev : num 3278 3616 3305 3606 3581 ...# access a single column of data
head(pikas$Date)## [1] "8/2/2018" "8/20/2018" "8/2/2018" "8/20/2018" "8/20/2018" "8/20/2018"# convert character data to date data
pikas$Date <- as.Date(pikas$Date, format = "%m/%d/%Y")
head(pikas)## Date days Station UTM_Easting UTM_Northing Notes Vial
## 1 2018-08-02 10 LL3 449888 4435612 Heard and saw pika here 172
## 2 2018-08-20 12 WK7 449224 4434233 191
## 3 2018-08-02 10 LL2 449888 4435516 Really fresh! 171
## 4 2018-08-20 12 WK8 449242 4434295 192
## 5 2018-08-20 12 WK12 449076 4434440 196
## 6 2018-08-20 12 LL1 449915 4435575 202
## Concentration_pg_g Plate Site Biweek Sex elev
## 1 834.0471 3 LL 4 U 3278.479
## 2 1328.0385 4 WK 6 U 3616.446
## 3 1332.2918 3 LL 4 U 3305.235
## 4 1626.2902 4 WK 6 M 3606.109
## 5 1711.8800 4 WK 6 U 3580.776
## 6 1804.8702 5 LL 6 U 3295.538# summary statistics
summary(pikas)## Date days Station UTM_Easting
## Min. :2018-06-08 Min. : 1.00 Length:109 Min. :448884
## 1st Qu.:2018-07-11 1st Qu.: 6.00 Class :character 1st Qu.:449132
## Median :2018-07-31 Median : 8.00 Mode :character Median :449299
## Mean :2018-07-29 Mean : 8.56 Mean :449667
## 3rd Qu.:2018-08-20 3rd Qu.:12.00 3rd Qu.:449888
## Max. :2018-09-04 Max. :13.00 Max. :451617
## UTM_Northing Notes Vial Concentration_pg_g
## Min. :4432963 Length:109 Min. : 98.0 Min. : 834
## 1st Qu.:4434091 Class :character 1st Qu.:133.0 1st Qu.: 2668
## Median :4434204 Mode :character Median :164.0 Median : 4261
## Mean :4434135 Mean :161.8 Mean : 5176
## 3rd Qu.:4434383 3rd Qu.:192.0 3rd Qu.: 7169
## Max. :4435616 Max. :219.0 Max. :13531
## Plate Site Biweek Sex
## Min. :1.000 Length:109 Min. :1.000 Length:109
## 1st Qu.:2.000 Class :character 1st Qu.:3.000 Class :character
## Median :3.000 Mode :character Median :4.000 Mode :character
## Mean :3.055 Mean :4.202
## 3rd Qu.:4.000 3rd Qu.:6.000
## Max. :5.000 Max. :7.000
## elev
## Min. :3278
## 1st Qu.:3407
## Median :3584
## Mean :3526
## 3rd Qu.:3602
## Max. :3616little doodle, the end.