Data Management
1 Import Data
Importing data takes syntax of the following form for
.csv files:
data <- read.csv("filepath/filename.csv")Note: it is important to use forward slashes (“/”) rather than
backslashes (“\”) when specifying filepaths in R.
Below, I import a .csv file and save it into an object
called mydata (you could call this object whatever you
want):
mydata <- read.csv("https://osf.io/s6wrm/download")Importing data takes syntax of the following form for
.RData files:
load("filepath/filename.RData")1.1 Import Multiple Data Files
dataNames <- paste("data", 1:100, sep = "")
dataFilenames <- paste(dataNames, ".csv", sep = "")
dataFilepaths <- paste("C:/users/username/", dataFilenames, sep = "")
data_list <- lapply(dataFilepaths, read.csv) # lapply(dataFilepaths, data.table::fread) is even faster
names(data_list) <- basename(dataFilepaths)Alternatively, if you want to load all .csv files in a
directory, you can identify the filenames programmatically:
dataFilenames <- list.files(
path = "C:/users/username/",
pattern = "\\.csv$")
dataFilepaths <- list.files(
path = "C:/users/username/",
pattern = "\\.csv$",
full.names = TRUE)
data_list <- lapply(dataFilepaths, read.csv) # lapply(dataFilepaths, data.table::fread) is even faster
names(data_list) <- basename(dataFilepaths)2 Save Data
Saving data takes syntax of the following form for .csv
files:
write.csv(object, file = "filepath/filename.csv")For example:
write.csv(mydata, file = "mydata.csv")Saving data takes syntax of the following form for
.RData files:
save(object, file = "filepath/filename.RData")3 Use Lab Functions
To use lab functions, first install the petersenlab
package. The petersenlab
package is here: https://devpsylab.github.io/petersenlab. You can install
it using the following commands:
install.packages("remotes")
remotes::install_github("DevPsyLab/petersenlab")Once you have the petersenlab
package installed, load the package:
library("petersenlab")To run scripts on the lab drive, set the path to the lab drive
(//lc-rs-store24.hpc.uiowa.edu/lss_itpetersen/Lab/) using
the following code:
petersenLabPath <- setLabPath()4 Load/Install Packages
To install a single package that is on the CRAN repository, use the following syntax:
install.packages("name_of_package")To install multiple packages that are on the CRAN repository, use the following syntax:
install.packages(c("name_of_package1","name_of_package2","name_of_package3"))To install a package that is on a GitHub repository, use
the following syntax:
install.packages("remotes")
remotes::install_github("username_of_GitHub_author/name_of_package")For instance:
remotes::install_github("DevPsyLab/petersenlab")The default way to load a package in R is:
library("packageName1")
library("packageName2")
library("packageName3")However, when sourcing (i.e., running) other R scripts,
it is possible that you will run scripts that use packages that you do
not have installed, resulting in an error that prevents the script from
running. Thus, it can be safer to load packages using the lab function,
load_or_install(), rather than using
library(). The load_or_install() function
checks whether a package is installed. If the package is not installed,
the function installs and loads the package. If the package is
installed, the function loads the package. To use this function, you
must have the petersenlab
package loaded.
library("petersenlab")
load_or_install(c("packageName1","packageName2","packageName3"))For example:
library("petersenlab")
load_or_install(c("tidyverse","psych"))── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Attaching package: 'psych'
The following objects are masked from 'package:ggplot2':
%+%, alpha5 Set a Seed
Set a seed (any number) to reproduce the results of analyses that involve random number generation.
set.seed(52242)6 Run an R
Script
To run an R script, use the following syntax:
source("filepath/filename.R")7 Render an
R Markdown (.Rmd) File
To render a .Rmd file, use the following syntax:
render("filepath/filename.Rmd")8 Variable Names
To look at the names of variables in a data frame, use the following syntax:
names(mydata)[1] "survived" "pclass" "sex" "age" "sibsp"
[6] "parch" "prediction"9 Logical Operators
Logical operators evaluate a condition for each value and yield
values of TRUE and FALSE, corresponding to
whether the evaluation for a given value met the condition.
9.1 Is Equal To:
==
mydata$survived == 1 [1] TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE
[13] TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[25] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
[37] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
[49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE
[61] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
[73] FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[85] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[97] FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
[109] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
[121] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
[133] TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE
[145] TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[157] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE
[169] FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE
[181] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
[193] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE
[205] TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE
[217] FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[229] TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
[241] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
[253] FALSE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE
[265] FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
[277] FALSE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
[289] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE
[301] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
[313] FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[325] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
[337] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
[349] FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
[361] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[373] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
[397] FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
[409] FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[421] FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[433] TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
[445] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[469] FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE
[481] FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE
[493] FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
[505] FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
[517] FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
[529] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[541] TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[553] TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
[577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[589] TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[601] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE
[613] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[625] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[649] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
[661] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[673] TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
[685] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[697] TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
[709] FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
[721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE
[745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[757] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[769] TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE
[781] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[793] FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
[805] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
[817] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
[829] FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[841] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
[853] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[865] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
[877] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE
[889] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
[901] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[925] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
[937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[961] FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
[973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[985] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE
[997] FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[1009] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[1033] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[1045] FALSE FALSE9.2 Is Not Equal To:
!=
mydata$survived != 1 [1] FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
[13] FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[25] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
[37] TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
[49] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
[61] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[73] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[85] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[97] TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
[121] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
[133] FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
[145] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[157] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
[169] TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE
[181] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE
[193] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
[205] FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE
[217] TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[229] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
[241] FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
[253] TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
[265] TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE
[277] TRUE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE
[289] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE
[301] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
[313] TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[325] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE
[337] FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE
[349] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[361] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[373] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[385] FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
[397] TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
[409] TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
[421] TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[433] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
[445] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
[457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
[469] TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE
[481] TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
[493] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE
[505] TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
[517] TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
[529] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[541] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[553] FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[565] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
[577] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[589] FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[601] TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE
[613] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[625] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[637] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[649] FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
[661] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[673] FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
[685] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[697] FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE
[709] TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE
[721] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[733] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE
[745] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[757] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[769] FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE FALSE
[781] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
[793] TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE
[805] TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE
[817] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
[829] TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[841] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
[853] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[865] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE
[877] TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
[889] TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE
[901] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[913] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[925] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE
[937] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[949] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[961] TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
[973] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[985] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE
[997] TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[1009] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1021] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[1033] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[1045] TRUE TRUE9.2.1 Greater Than:
>
mydata$parch > 1 [1] FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[73] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[85] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[97] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[109] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[277] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[301] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[337] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
[397] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[433] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[445] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[493] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[505] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[541] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[577] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[589] TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[601] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[673] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[685] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[697] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[721] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[733] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[745] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[829] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[877] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[913] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[925] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[949] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[961] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[973] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[1009] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1045] FALSE FALSE9.3 Less Than:
<
mydata$parch < 1 [1] TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[13] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
[25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
[49] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
[73] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[85] FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[97] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[109] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
[121] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[133] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE
[145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[157] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
[169] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
[193] TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[205] TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[217] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[229] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[241] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[253] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
[265] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[277] FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[301] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[313] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
[325] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE
[337] TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
[349] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[385] FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[397] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
[409] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
[433] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[445] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
[457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[469] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[481] TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
[493] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[505] TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[517] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[529] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[541] FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
[553] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[565] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[577] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[589] FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[601] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
[613] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
[625] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[637] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[649] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[661] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[673] FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[685] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[697] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[709] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[721] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[733] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE
[745] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[769] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
[781] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[793] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE
[805] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[817] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[829] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[853] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[865] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
[877] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[889] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE
[901] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[925] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[937] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[949] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[961] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[973] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[985] TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[997] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[1009] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[1021] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1033] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1045] TRUE TRUE9.4 Greater Than or Equal
To: >=
mydata$parch >= 1 [1] FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
[49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[73] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[85] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[97] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[109] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[121] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
[169] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
[193] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[205] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
[265] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[277] TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[301] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[313] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[325] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
[337] FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[349] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
[361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[397] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
[409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[433] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[445] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[469] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[481] FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[493] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[505] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[529] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[541] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
[553] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[577] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[589] TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[601] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
[613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[625] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[673] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[685] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[697] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[721] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[733] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
[745] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[793] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
[805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[817] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[829] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[877] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
[901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[913] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
[925] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[937] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
[949] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[961] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[973] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[985] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[997] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[1009] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1045] FALSE FALSE9.5 Less Than or Equal
To: <=
mydata$parch <= 1 [1] TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[49] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[73] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[85] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[97] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[109] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[133] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[157] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
[193] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[217] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[229] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[241] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[253] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[277] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[301] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[313] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[325] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[337] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[349] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[385] TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
[397] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[409] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[433] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[445] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[469] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[493] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[505] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[517] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[529] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[541] FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[553] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[565] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[577] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[589] FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[601] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[613] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[625] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[637] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[649] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[661] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[673] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[685] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[697] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[709] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[721] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[733] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[745] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[769] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[781] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[793] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[805] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[817] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[829] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[853] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[865] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[877] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[889] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[901] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[913] TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[925] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[937] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[949] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[961] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[973] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[985] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[997] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[1009] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[1021] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1033] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1045] TRUE TRUE9.6 Is in a Value of
Another Vector: %in%
anotherVector <- c(0,1)
mydata$parch %in% anotherVector [1] TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[13] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[49] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[73] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[85] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[97] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[109] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[133] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[157] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
[193] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[205] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[217] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[229] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[241] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[253] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[277] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[301] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[313] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[325] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[337] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[349] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[385] TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
[397] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[409] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[433] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[445] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[457] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[469] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
[493] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[505] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[517] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[529] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[541] FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[553] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[565] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[577] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[589] FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[601] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[613] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[625] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[637] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[649] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[661] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[673] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[685] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[697] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[709] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[721] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[733] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
[745] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[769] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[781] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[793] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[805] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[817] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[829] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[853] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[865] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[877] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[889] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[901] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[913] TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[925] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[937] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[949] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
[961] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[973] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[985] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[997] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[1009] TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[1021] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1033] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1045] TRUE TRUE9.7 Is Not in a Value of
Another Vector: %ni%
Note: this function is part of the petersenlab
package and is not available in base R.
mydata$parch %ni% anotherVector [1] FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[73] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[85] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[97] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[109] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[277] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[301] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[337] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
[397] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[433] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[445] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[493] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[505] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[541] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
[553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[577] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[589] TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[601] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[673] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[685] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[697] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[721] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[733] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
[745] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[829] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[877] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[913] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[925] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[949] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[961] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[973] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[1009] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1045] FALSE FALSE9.8 Is Missing:
is.na()
is.na(mydata$prediction) [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1009] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1045] FALSE FALSE9.9 Is Not Missing:
!is.na()
!is.na(mydata$prediction) [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[99] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[127] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[141] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[197] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[225] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[267] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[281] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[295] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[309] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[323] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[351] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[365] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[379] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[393] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[407] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[435] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[449] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[463] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[477] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[491] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[505] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[519] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[533] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[547] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[561] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[575] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[589] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[603] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[617] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[631] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[645] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[659] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[673] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[687] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[701] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[715] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[743] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[757] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[771] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[785] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[799] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[813] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[827] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[841] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[855] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[869] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[883] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[897] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[911] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[925] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[939] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[953] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[967] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[981] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[995] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1009] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1023] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[1037] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE9.10 And:
&
!is.na(mydata$prediction) & mydata$parch >= 1 [1] FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
[49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[73] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[85] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[97] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[109] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[121] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
[169] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
[193] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[205] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
[265] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[277] TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[301] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[313] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[325] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
[337] FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[349] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
[361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[397] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
[409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[433] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[445] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[469] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[481] FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[493] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[505] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[529] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[541] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
[553] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[577] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[589] TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[601] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
[613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[625] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[673] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[685] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[697] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[721] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[733] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
[745] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[793] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
[805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[817] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[829] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[877] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
[901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[913] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
[925] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[937] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
[949] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[961] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[973] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[985] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[997] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[1009] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1045] FALSE FALSE9.11 Or:
|
is.na(mydata$prediction) | mydata$parch >= 1 [1] FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
[49] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[73] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[85] TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[97] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[109] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[121] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[133] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
[169] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
[193] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[205] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[217] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253] FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
[265] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
[277] TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[301] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[313] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[325] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
[337] FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
[349] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
[361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[385] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[397] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
[409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[433] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[445] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
[457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[469] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
[481] FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
[493] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[505] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[529] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[541] TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
[553] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[565] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[577] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
[589] TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[601] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
[613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[625] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[673] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[685] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[697] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[721] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[733] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
[745] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE
[757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[793] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
[805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[817] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
[829] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
[877] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
[901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[913] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
[925] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[937] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
[949] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
[961] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[973] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[985] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
[997] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[1009] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[1045] FALSE FALSE10 Subset
To subset a data frame, use brackets to specify the subset of rows and columns to keep, where the value/vector before the comma specifies the rows to keep, and the value/vector after the comma specifies the columns to keep:
dataframe[rowsToKeep, columnsToKeep]You can subset by using any of the following:
- numeric indices of the rows/columns to keep (or drop)
- names of the rows/columns to keep (or drop)
- values of
TRUEandFALSEcorresponding to which rows/columns to keep
10.1 One Variable
To subset one variable, use the following syntax:
mydata$age [1] 29.00 0.92 2.00 30.00 25.00 48.00 63.00 39.00 53.00 71.00 47.00 18.00
[13] 24.00 26.00 80.00 24.00 50.00 32.00 36.00 37.00 47.00 26.00 42.00 29.00
[25] 25.00 25.00 19.00 35.00 28.00 45.00 40.00 30.00 58.00 42.00 45.00 22.00
[37] 41.00 48.00 44.00 59.00 60.00 41.00 45.00 42.00 53.00 36.00 58.00 33.00
[49] 28.00 17.00 11.00 14.00 36.00 36.00 49.00 36.00 76.00 46.00 47.00 27.00
[61] 33.00 36.00 30.00 45.00 27.00 26.00 22.00 47.00 39.00 37.00 64.00 55.00
[73] 70.00 36.00 64.00 39.00 38.00 51.00 27.00 33.00 31.00 27.00 31.00 17.00
[85] 53.00 4.00 54.00 50.00 27.00 48.00 48.00 49.00 39.00 23.00 38.00 54.00
[97] 36.00 36.00 30.00 24.00 28.00 23.00 19.00 64.00 60.00 30.00 50.00 43.00
[109] 22.00 60.00 48.00 37.00 35.00 47.00 35.00 22.00 45.00 24.00 49.00 71.00
[121] 53.00 19.00 38.00 58.00 23.00 45.00 46.00 25.00 25.00 48.00 49.00 45.00
[133] 35.00 40.00 27.00 24.00 55.00 52.00 42.00 55.00 16.00 44.00 51.00 42.00
[145] 35.00 35.00 38.00 35.00 38.00 50.00 49.00 46.00 50.00 32.50 58.00 41.00
[157] 42.00 45.00 39.00 49.00 30.00 35.00 42.00 55.00 16.00 51.00 29.00 21.00
[169] 30.00 58.00 15.00 30.00 16.00 19.00 18.00 24.00 46.00 54.00 36.00 28.00
[181] 65.00 44.00 33.00 37.00 30.00 55.00 47.00 37.00 31.00 23.00 58.00 19.00
[193] 64.00 39.00 22.00 65.00 28.50 45.50 23.00 29.00 22.00 18.00 17.00 30.00
[205] 52.00 47.00 56.00 38.00 22.00 43.00 31.00 45.00 33.00 46.00 36.00 33.00
[217] 55.00 54.00 33.00 13.00 18.00 21.00 61.00 48.00 24.00 35.00 30.00 34.00
[229] 40.00 35.00 50.00 39.00 56.00 28.00 56.00 56.00 24.00 18.00 24.00 23.00
[241] 6.00 45.00 40.00 57.00 32.00 62.00 54.00 43.00 52.00 62.00 67.00 63.00
[253] 61.00 48.00 18.00 52.00 39.00 48.00 49.00 17.00 39.00 31.00 40.00 61.00
[265] 47.00 35.00 64.00 60.00 60.00 54.00 21.00 55.00 31.00 57.00 45.00 50.00
[277] 27.00 50.00 21.00 51.00 21.00 31.00 62.00 36.00 30.00 28.00 30.00 18.00
[289] 25.00 34.00 36.00 57.00 18.00 23.00 36.00 28.00 51.00 32.00 19.00 28.00
[301] 1.00 4.00 12.00 36.00 34.00 19.00 23.00 26.00 42.00 27.00 24.00 15.00
[313] 60.00 40.00 20.00 25.00 36.00 25.00 42.00 42.00 0.83 26.00 22.00 35.00
[325] 19.00 44.00 54.00 52.00 37.00 29.00 25.00 45.00 29.00 28.00 29.00 28.00
[337] 24.00 8.00 31.00 31.00 22.00 30.00 21.00 8.00 18.00 48.00 28.00 32.00
[349] 17.00 29.00 24.00 25.00 18.00 18.00 34.00 54.00 8.00 42.00 34.00 27.00
[361] 30.00 23.00 21.00 18.00 40.00 29.00 18.00 36.00 38.00 35.00 38.00 34.00
[373] 34.00 16.00 26.00 47.00 21.00 21.00 24.00 24.00 34.00 30.00 52.00 30.00
[385] 0.67 24.00 44.00 6.00 28.00 62.00 30.00 7.00 43.00 45.00 24.00 24.00
[397] 49.00 48.00 55.00 24.00 32.00 21.00 18.00 20.00 23.00 36.00 54.00 50.00
[409] 44.00 29.00 21.00 42.00 63.00 60.00 33.00 17.00 42.00 24.00 47.00 24.00
[421] 22.00 32.00 23.00 34.00 24.00 22.00 35.00 45.00 57.00 31.00 26.00 30.00
[433] 1.00 3.00 25.00 22.00 17.00 34.00 36.00 24.00 61.00 50.00 42.00 57.00
[445] 1.00 31.00 24.00 30.00 40.00 32.00 30.00 46.00 13.00 41.00 19.00 39.00
[457] 48.00 70.00 27.00 54.00 39.00 16.00 62.00 32.50 14.00 2.00 3.00 36.50
[469] 26.00 19.00 28.00 20.00 29.00 39.00 22.00 23.00 29.00 28.00 50.00 19.00
[481] 41.00 21.00 19.00 43.00 32.00 34.00 30.00 27.00 2.00 8.00 33.00 36.00
[493] 34.00 30.00 28.00 23.00 0.83 3.00 24.00 50.00 19.00 21.00 26.00 25.00
[505] 27.00 25.00 18.00 20.00 30.00 59.00 30.00 35.00 40.00 25.00 41.00 25.00
[517] 18.50 14.00 50.00 23.00 28.00 27.00 29.00 27.00 40.00 31.00 30.00 23.00
[529] 31.00 12.00 40.00 32.50 27.00 29.00 2.00 4.00 29.00 0.92 5.00 36.00
[541] 33.00 66.00 31.00 26.00 24.00 42.00 13.00 16.00 35.00 16.00 25.00 20.00
[553] 18.00 30.00 26.00 40.00 0.83 18.00 26.00 26.00 20.00 24.00 25.00 35.00
[565] 18.00 32.00 19.00 4.00 6.00 2.00 17.00 38.00 9.00 11.00 39.00 27.00
[577] 26.00 39.00 20.00 26.00 25.00 18.00 24.00 35.00 5.00 9.00 3.00 13.00
[589] 5.00 40.00 23.00 38.00 45.00 21.00 23.00 17.00 30.00 23.00 13.00 20.00
[601] 32.00 33.00 0.75 0.75 5.00 24.00 18.00 40.00 26.00 20.00 18.00 45.00
[613] 27.00 22.00 19.00 26.00 22.00 20.00 32.00 21.00 18.00 26.00 6.00 9.00
[625] 40.00 32.00 21.00 22.00 20.00 29.00 22.00 22.00 35.00 18.50 21.00 19.00
[637] 18.00 21.00 30.00 18.00 38.00 17.00 17.00 21.00 21.00 21.00 28.00 24.00
[649] 16.00 37.00 28.00 24.00 21.00 32.00 29.00 26.00 18.00 20.00 18.00 24.00
[661] 36.00 24.00 31.00 31.00 22.00 30.00 70.50 43.00 35.00 27.00 19.00 30.00
[673] 9.00 3.00 36.00 59.00 19.00 17.00 44.00 17.00 22.50 45.00 22.00 19.00
[685] 30.00 29.00 0.33 34.00 28.00 27.00 25.00 24.00 22.00 21.00 17.00 36.50
[697] 36.00 30.00 16.00 1.00 0.17 26.00 33.00 25.00 22.00 36.00 19.00 17.00
[709] 42.00 43.00 32.00 19.00 30.00 24.00 23.00 33.00 65.00 24.00 23.00 22.00
[721] 18.00 16.00 45.00 39.00 17.00 15.00 47.00 5.00 40.50 40.50 18.00 26.00
[733] 21.00 9.00 18.00 16.00 48.00 25.00 22.00 16.00 9.00 33.00 41.00 31.00
[745] 38.00 9.00 1.00 11.00 10.00 16.00 14.00 40.00 43.00 51.00 32.00 20.00
[757] 37.00 28.00 19.00 24.00 17.00 28.00 24.00 20.00 23.50 41.00 26.00 21.00
[769] 45.00 25.00 11.00 27.00 18.00 26.00 23.00 22.00 28.00 28.00 2.00 22.00
[781] 43.00 28.00 27.00 42.00 30.00 27.00 25.00 29.00 21.00 20.00 48.00 17.00
[793] 34.00 26.00 22.00 33.00 31.00 29.00 4.00 1.00 49.00 33.00 19.00 27.00
[805] 23.00 32.00 27.00 20.00 21.00 32.00 17.00 21.00 30.00 21.00 33.00 22.00
[817] 4.00 39.00 18.50 34.50 44.00 22.00 26.00 4.00 29.00 26.00 1.00 18.00
[829] 36.00 25.00 37.00 22.00 26.00 29.00 29.00 22.00 22.00 32.00 34.50 36.00
[841] 39.00 24.00 25.00 45.00 36.00 30.00 20.00 28.00 30.00 26.00 20.50 27.00
[853] 51.00 23.00 32.00 24.00 22.00 29.00 30.50 35.00 33.00 15.00 35.00 24.00
[865] 19.00 55.50 21.00 24.00 21.00 28.00 25.00 6.00 27.00 34.00 24.00 18.00
[877] 22.00 15.00 1.00 20.00 19.00 33.00 12.00 14.00 29.00 28.00 18.00 26.00
[889] 21.00 41.00 39.00 21.00 28.50 22.00 61.00 23.00 22.00 9.00 28.00 42.00
[901] 31.00 28.00 32.00 20.00 23.00 20.00 20.00 16.00 31.00 2.00 6.00 3.00
[913] 8.00 29.00 1.00 7.00 2.00 16.00 14.00 41.00 21.00 19.00 32.00 0.75
[925] 3.00 26.00 21.00 25.00 22.00 25.00 24.00 28.00 19.00 25.00 18.00 32.00
[937] 17.00 24.00 38.00 21.00 10.00 4.00 7.00 2.00 8.00 39.00 22.00 35.00
[949] 50.00 47.00 2.00 18.00 41.00 50.00 16.00 25.00 38.50 14.50 24.00 21.00
[961] 39.00 1.00 24.00 4.00 25.00 20.00 24.50 29.00 22.00 40.00 21.00 18.00
[973] 4.00 10.00 9.00 2.00 40.00 45.00 19.00 30.00 32.00 33.00 23.00 21.00
[985] 60.50 19.00 22.00 31.00 27.00 2.00 29.00 16.00 44.00 25.00 74.00 14.00
[997] 24.00 25.00 34.00 0.42 16.00 32.00 30.50 44.00 25.00 7.00 9.00 29.00
[1009] 36.00 18.00 63.00 11.50 40.50 10.00 36.00 30.00 33.00 28.00 28.00 47.00
[1021] 18.00 31.00 16.00 31.00 22.00 20.00 14.00 22.00 22.00 32.50 38.00 51.00
[1033] 18.00 21.00 47.00 28.50 21.00 27.00 36.00 27.00 15.00 45.50 14.50 26.50
[1045] 27.00 29.00or:
mydata[,"age"] [1] 29.00 0.92 2.00 30.00 25.00 48.00 63.00 39.00 53.00 71.00 47.00 18.00
[13] 24.00 26.00 80.00 24.00 50.00 32.00 36.00 37.00 47.00 26.00 42.00 29.00
[25] 25.00 25.00 19.00 35.00 28.00 45.00 40.00 30.00 58.00 42.00 45.00 22.00
[37] 41.00 48.00 44.00 59.00 60.00 41.00 45.00 42.00 53.00 36.00 58.00 33.00
[49] 28.00 17.00 11.00 14.00 36.00 36.00 49.00 36.00 76.00 46.00 47.00 27.00
[61] 33.00 36.00 30.00 45.00 27.00 26.00 22.00 47.00 39.00 37.00 64.00 55.00
[73] 70.00 36.00 64.00 39.00 38.00 51.00 27.00 33.00 31.00 27.00 31.00 17.00
[85] 53.00 4.00 54.00 50.00 27.00 48.00 48.00 49.00 39.00 23.00 38.00 54.00
[97] 36.00 36.00 30.00 24.00 28.00 23.00 19.00 64.00 60.00 30.00 50.00 43.00
[109] 22.00 60.00 48.00 37.00 35.00 47.00 35.00 22.00 45.00 24.00 49.00 71.00
[121] 53.00 19.00 38.00 58.00 23.00 45.00 46.00 25.00 25.00 48.00 49.00 45.00
[133] 35.00 40.00 27.00 24.00 55.00 52.00 42.00 55.00 16.00 44.00 51.00 42.00
[145] 35.00 35.00 38.00 35.00 38.00 50.00 49.00 46.00 50.00 32.50 58.00 41.00
[157] 42.00 45.00 39.00 49.00 30.00 35.00 42.00 55.00 16.00 51.00 29.00 21.00
[169] 30.00 58.00 15.00 30.00 16.00 19.00 18.00 24.00 46.00 54.00 36.00 28.00
[181] 65.00 44.00 33.00 37.00 30.00 55.00 47.00 37.00 31.00 23.00 58.00 19.00
[193] 64.00 39.00 22.00 65.00 28.50 45.50 23.00 29.00 22.00 18.00 17.00 30.00
[205] 52.00 47.00 56.00 38.00 22.00 43.00 31.00 45.00 33.00 46.00 36.00 33.00
[217] 55.00 54.00 33.00 13.00 18.00 21.00 61.00 48.00 24.00 35.00 30.00 34.00
[229] 40.00 35.00 50.00 39.00 56.00 28.00 56.00 56.00 24.00 18.00 24.00 23.00
[241] 6.00 45.00 40.00 57.00 32.00 62.00 54.00 43.00 52.00 62.00 67.00 63.00
[253] 61.00 48.00 18.00 52.00 39.00 48.00 49.00 17.00 39.00 31.00 40.00 61.00
[265] 47.00 35.00 64.00 60.00 60.00 54.00 21.00 55.00 31.00 57.00 45.00 50.00
[277] 27.00 50.00 21.00 51.00 21.00 31.00 62.00 36.00 30.00 28.00 30.00 18.00
[289] 25.00 34.00 36.00 57.00 18.00 23.00 36.00 28.00 51.00 32.00 19.00 28.00
[301] 1.00 4.00 12.00 36.00 34.00 19.00 23.00 26.00 42.00 27.00 24.00 15.00
[313] 60.00 40.00 20.00 25.00 36.00 25.00 42.00 42.00 0.83 26.00 22.00 35.00
[325] 19.00 44.00 54.00 52.00 37.00 29.00 25.00 45.00 29.00 28.00 29.00 28.00
[337] 24.00 8.00 31.00 31.00 22.00 30.00 21.00 8.00 18.00 48.00 28.00 32.00
[349] 17.00 29.00 24.00 25.00 18.00 18.00 34.00 54.00 8.00 42.00 34.00 27.00
[361] 30.00 23.00 21.00 18.00 40.00 29.00 18.00 36.00 38.00 35.00 38.00 34.00
[373] 34.00 16.00 26.00 47.00 21.00 21.00 24.00 24.00 34.00 30.00 52.00 30.00
[385] 0.67 24.00 44.00 6.00 28.00 62.00 30.00 7.00 43.00 45.00 24.00 24.00
[397] 49.00 48.00 55.00 24.00 32.00 21.00 18.00 20.00 23.00 36.00 54.00 50.00
[409] 44.00 29.00 21.00 42.00 63.00 60.00 33.00 17.00 42.00 24.00 47.00 24.00
[421] 22.00 32.00 23.00 34.00 24.00 22.00 35.00 45.00 57.00 31.00 26.00 30.00
[433] 1.00 3.00 25.00 22.00 17.00 34.00 36.00 24.00 61.00 50.00 42.00 57.00
[445] 1.00 31.00 24.00 30.00 40.00 32.00 30.00 46.00 13.00 41.00 19.00 39.00
[457] 48.00 70.00 27.00 54.00 39.00 16.00 62.00 32.50 14.00 2.00 3.00 36.50
[469] 26.00 19.00 28.00 20.00 29.00 39.00 22.00 23.00 29.00 28.00 50.00 19.00
[481] 41.00 21.00 19.00 43.00 32.00 34.00 30.00 27.00 2.00 8.00 33.00 36.00
[493] 34.00 30.00 28.00 23.00 0.83 3.00 24.00 50.00 19.00 21.00 26.00 25.00
[505] 27.00 25.00 18.00 20.00 30.00 59.00 30.00 35.00 40.00 25.00 41.00 25.00
[517] 18.50 14.00 50.00 23.00 28.00 27.00 29.00 27.00 40.00 31.00 30.00 23.00
[529] 31.00 12.00 40.00 32.50 27.00 29.00 2.00 4.00 29.00 0.92 5.00 36.00
[541] 33.00 66.00 31.00 26.00 24.00 42.00 13.00 16.00 35.00 16.00 25.00 20.00
[553] 18.00 30.00 26.00 40.00 0.83 18.00 26.00 26.00 20.00 24.00 25.00 35.00
[565] 18.00 32.00 19.00 4.00 6.00 2.00 17.00 38.00 9.00 11.00 39.00 27.00
[577] 26.00 39.00 20.00 26.00 25.00 18.00 24.00 35.00 5.00 9.00 3.00 13.00
[589] 5.00 40.00 23.00 38.00 45.00 21.00 23.00 17.00 30.00 23.00 13.00 20.00
[601] 32.00 33.00 0.75 0.75 5.00 24.00 18.00 40.00 26.00 20.00 18.00 45.00
[613] 27.00 22.00 19.00 26.00 22.00 20.00 32.00 21.00 18.00 26.00 6.00 9.00
[625] 40.00 32.00 21.00 22.00 20.00 29.00 22.00 22.00 35.00 18.50 21.00 19.00
[637] 18.00 21.00 30.00 18.00 38.00 17.00 17.00 21.00 21.00 21.00 28.00 24.00
[649] 16.00 37.00 28.00 24.00 21.00 32.00 29.00 26.00 18.00 20.00 18.00 24.00
[661] 36.00 24.00 31.00 31.00 22.00 30.00 70.50 43.00 35.00 27.00 19.00 30.00
[673] 9.00 3.00 36.00 59.00 19.00 17.00 44.00 17.00 22.50 45.00 22.00 19.00
[685] 30.00 29.00 0.33 34.00 28.00 27.00 25.00 24.00 22.00 21.00 17.00 36.50
[697] 36.00 30.00 16.00 1.00 0.17 26.00 33.00 25.00 22.00 36.00 19.00 17.00
[709] 42.00 43.00 32.00 19.00 30.00 24.00 23.00 33.00 65.00 24.00 23.00 22.00
[721] 18.00 16.00 45.00 39.00 17.00 15.00 47.00 5.00 40.50 40.50 18.00 26.00
[733] 21.00 9.00 18.00 16.00 48.00 25.00 22.00 16.00 9.00 33.00 41.00 31.00
[745] 38.00 9.00 1.00 11.00 10.00 16.00 14.00 40.00 43.00 51.00 32.00 20.00
[757] 37.00 28.00 19.00 24.00 17.00 28.00 24.00 20.00 23.50 41.00 26.00 21.00
[769] 45.00 25.00 11.00 27.00 18.00 26.00 23.00 22.00 28.00 28.00 2.00 22.00
[781] 43.00 28.00 27.00 42.00 30.00 27.00 25.00 29.00 21.00 20.00 48.00 17.00
[793] 34.00 26.00 22.00 33.00 31.00 29.00 4.00 1.00 49.00 33.00 19.00 27.00
[805] 23.00 32.00 27.00 20.00 21.00 32.00 17.00 21.00 30.00 21.00 33.00 22.00
[817] 4.00 39.00 18.50 34.50 44.00 22.00 26.00 4.00 29.00 26.00 1.00 18.00
[829] 36.00 25.00 37.00 22.00 26.00 29.00 29.00 22.00 22.00 32.00 34.50 36.00
[841] 39.00 24.00 25.00 45.00 36.00 30.00 20.00 28.00 30.00 26.00 20.50 27.00
[853] 51.00 23.00 32.00 24.00 22.00 29.00 30.50 35.00 33.00 15.00 35.00 24.00
[865] 19.00 55.50 21.00 24.00 21.00 28.00 25.00 6.00 27.00 34.00 24.00 18.00
[877] 22.00 15.00 1.00 20.00 19.00 33.00 12.00 14.00 29.00 28.00 18.00 26.00
[889] 21.00 41.00 39.00 21.00 28.50 22.00 61.00 23.00 22.00 9.00 28.00 42.00
[901] 31.00 28.00 32.00 20.00 23.00 20.00 20.00 16.00 31.00 2.00 6.00 3.00
[913] 8.00 29.00 1.00 7.00 2.00 16.00 14.00 41.00 21.00 19.00 32.00 0.75
[925] 3.00 26.00 21.00 25.00 22.00 25.00 24.00 28.00 19.00 25.00 18.00 32.00
[937] 17.00 24.00 38.00 21.00 10.00 4.00 7.00 2.00 8.00 39.00 22.00 35.00
[949] 50.00 47.00 2.00 18.00 41.00 50.00 16.00 25.00 38.50 14.50 24.00 21.00
[961] 39.00 1.00 24.00 4.00 25.00 20.00 24.50 29.00 22.00 40.00 21.00 18.00
[973] 4.00 10.00 9.00 2.00 40.00 45.00 19.00 30.00 32.00 33.00 23.00 21.00
[985] 60.50 19.00 22.00 31.00 27.00 2.00 29.00 16.00 44.00 25.00 74.00 14.00
[997] 24.00 25.00 34.00 0.42 16.00 32.00 30.50 44.00 25.00 7.00 9.00 29.00
[1009] 36.00 18.00 63.00 11.50 40.50 10.00 36.00 30.00 33.00 28.00 28.00 47.00
[1021] 18.00 31.00 16.00 31.00 22.00 20.00 14.00 22.00 22.00 32.50 38.00 51.00
[1033] 18.00 21.00 47.00 28.50 21.00 27.00 36.00 27.00 15.00 45.50 14.50 26.50
[1045] 27.00 29.0010.2 Particular Rows of One Variable
To subset one variable, use the following syntax:
mydata$age[which(mydata$survived == 1)] [1] 29.00 0.92 48.00 63.00 53.00 18.00 24.00 26.00 80.00 50.00 32.00 37.00
[13] 47.00 26.00 42.00 29.00 25.00 19.00 35.00 28.00 40.00 30.00 58.00 45.00
[25] 22.00 44.00 59.00 60.00 41.00 42.00 53.00 36.00 58.00 11.00 14.00 36.00
[37] 36.00 76.00 47.00 27.00 33.00 36.00 30.00 45.00 26.00 22.00 39.00 64.00
[49] 55.00 36.00 64.00 38.00 51.00 27.00 33.00 27.00 31.00 17.00 53.00 4.00
[61] 54.00 27.00 48.00 48.00 49.00 23.00 38.00 54.00 36.00 24.00 28.00 23.00
[73] 60.00 30.00 50.00 43.00 22.00 60.00 48.00 35.00 35.00 22.00 45.00 49.00
[85] 53.00 19.00 58.00 23.00 45.00 25.00 25.00 48.00 49.00 35.00 27.00 24.00
[97] 52.00 16.00 44.00 51.00 35.00 35.00 38.00 35.00 38.00 49.00 42.00 45.00
[109] 39.00 49.00 30.00 35.00 55.00 16.00 51.00 21.00 58.00 15.00 16.00 18.00
[121] 24.00 36.00 33.00 37.00 30.00 31.00 23.00 19.00 39.00 22.00 22.00 17.00
[133] 30.00 52.00 56.00 43.00 45.00 33.00 33.00 54.00 13.00 18.00 21.00 48.00
[145] 24.00 35.00 30.00 34.00 40.00 35.00 39.00 56.00 28.00 18.00 24.00 23.00
[157] 6.00 45.00 40.00 32.00 54.00 43.00 52.00 62.00 48.00 18.00 39.00 48.00
[169] 17.00 39.00 31.00 35.00 60.00 55.00 31.00 45.00 50.00 21.00 21.00 31.00
[181] 36.00 28.00 36.00 36.00 32.00 19.00 1.00 4.00 12.00 36.00 34.00 19.00
[193] 24.00 15.00 40.00 20.00 36.00 42.00 0.83 26.00 22.00 35.00 25.00 45.00
[205] 28.00 24.00 8.00 31.00 22.00 8.00 48.00 28.00 24.00 18.00 34.00 8.00
[217] 34.00 27.00 30.00 29.00 34.00 0.67 24.00 6.00 62.00 7.00 45.00 24.00
[229] 24.00 48.00 55.00 20.00 54.00 29.00 42.00 17.00 24.00 23.00 24.00 45.00
[241] 1.00 3.00 22.00 17.00 34.00 42.00 1.00 24.00 13.00 41.00 19.00 14.00
[253] 2.00 3.00 20.00 29.00 22.00 29.00 50.00 21.00 19.00 32.00 30.00 2.00
[265] 8.00 33.00 30.00 28.00 0.83 3.00 24.00 50.00 21.00 25.00 18.00 20.00
[277] 30.00 30.00 40.00 50.00 28.00 27.00 31.00 31.00 12.00 40.00 32.50 29.00
[289] 2.00 4.00 29.00 0.92 5.00 33.00 31.00 26.00 35.00 16.00 25.00 20.00
[301] 18.00 0.83 18.00 26.00 19.00 17.00 27.00 3.00 5.00 23.00 38.00 45.00
[313] 13.00 33.00 0.75 0.75 5.00 24.00 18.00 20.00 32.00 22.00 21.00 16.00
[325] 32.00 18.00 22.00 9.00 3.00 36.00 17.00 45.00 30.00 29.00 36.50 36.00
[337] 30.00 1.00 0.17 33.00 19.00 19.00 30.00 23.00 24.00 22.00 5.00 16.00
[349] 9.00 31.00 24.00 45.00 27.00 26.00 22.00 2.00 22.00 27.00 29.00 21.00
[361] 26.00 4.00 1.00 27.00 32.00 32.00 21.00 4.00 39.00 4.00 29.00 26.00
[373] 25.00 22.00 26.00 22.00 20.00 27.00 23.00 32.00 24.00 15.00 21.00 6.00
[385] 27.00 24.00 15.00 1.00 20.00 19.00 12.00 14.00 18.00 26.00 39.00 22.00
[397] 22.00 9.00 32.00 31.00 25.00 32.00 21.00 1.00 24.00 4.00 25.00 29.00
[409] 18.00 23.00 31.00 16.00 44.00 14.00 25.00 0.42 16.00 25.00 7.00 9.00
[421] 29.00 18.00 63.00 22.00 38.00 47.00 15.00or:
mydata[which(mydata$survived == 1), "age"] [1] 29.00 0.92 48.00 63.00 53.00 18.00 24.00 26.00 80.00 50.00 32.00 37.00
[13] 47.00 26.00 42.00 29.00 25.00 19.00 35.00 28.00 40.00 30.00 58.00 45.00
[25] 22.00 44.00 59.00 60.00 41.00 42.00 53.00 36.00 58.00 11.00 14.00 36.00
[37] 36.00 76.00 47.00 27.00 33.00 36.00 30.00 45.00 26.00 22.00 39.00 64.00
[49] 55.00 36.00 64.00 38.00 51.00 27.00 33.00 27.00 31.00 17.00 53.00 4.00
[61] 54.00 27.00 48.00 48.00 49.00 23.00 38.00 54.00 36.00 24.00 28.00 23.00
[73] 60.00 30.00 50.00 43.00 22.00 60.00 48.00 35.00 35.00 22.00 45.00 49.00
[85] 53.00 19.00 58.00 23.00 45.00 25.00 25.00 48.00 49.00 35.00 27.00 24.00
[97] 52.00 16.00 44.00 51.00 35.00 35.00 38.00 35.00 38.00 49.00 42.00 45.00
[109] 39.00 49.00 30.00 35.00 55.00 16.00 51.00 21.00 58.00 15.00 16.00 18.00
[121] 24.00 36.00 33.00 37.00 30.00 31.00 23.00 19.00 39.00 22.00 22.00 17.00
[133] 30.00 52.00 56.00 43.00 45.00 33.00 33.00 54.00 13.00 18.00 21.00 48.00
[145] 24.00 35.00 30.00 34.00 40.00 35.00 39.00 56.00 28.00 18.00 24.00 23.00
[157] 6.00 45.00 40.00 32.00 54.00 43.00 52.00 62.00 48.00 18.00 39.00 48.00
[169] 17.00 39.00 31.00 35.00 60.00 55.00 31.00 45.00 50.00 21.00 21.00 31.00
[181] 36.00 28.00 36.00 36.00 32.00 19.00 1.00 4.00 12.00 36.00 34.00 19.00
[193] 24.00 15.00 40.00 20.00 36.00 42.00 0.83 26.00 22.00 35.00 25.00 45.00
[205] 28.00 24.00 8.00 31.00 22.00 8.00 48.00 28.00 24.00 18.00 34.00 8.00
[217] 34.00 27.00 30.00 29.00 34.00 0.67 24.00 6.00 62.00 7.00 45.00 24.00
[229] 24.00 48.00 55.00 20.00 54.00 29.00 42.00 17.00 24.00 23.00 24.00 45.00
[241] 1.00 3.00 22.00 17.00 34.00 42.00 1.00 24.00 13.00 41.00 19.00 14.00
[253] 2.00 3.00 20.00 29.00 22.00 29.00 50.00 21.00 19.00 32.00 30.00 2.00
[265] 8.00 33.00 30.00 28.00 0.83 3.00 24.00 50.00 21.00 25.00 18.00 20.00
[277] 30.00 30.00 40.00 50.00 28.00 27.00 31.00 31.00 12.00 40.00 32.50 29.00
[289] 2.00 4.00 29.00 0.92 5.00 33.00 31.00 26.00 35.00 16.00 25.00 20.00
[301] 18.00 0.83 18.00 26.00 19.00 17.00 27.00 3.00 5.00 23.00 38.00 45.00
[313] 13.00 33.00 0.75 0.75 5.00 24.00 18.00 20.00 32.00 22.00 21.00 16.00
[325] 32.00 18.00 22.00 9.00 3.00 36.00 17.00 45.00 30.00 29.00 36.50 36.00
[337] 30.00 1.00 0.17 33.00 19.00 19.00 30.00 23.00 24.00 22.00 5.00 16.00
[349] 9.00 31.00 24.00 45.00 27.00 26.00 22.00 2.00 22.00 27.00 29.00 21.00
[361] 26.00 4.00 1.00 27.00 32.00 32.00 21.00 4.00 39.00 4.00 29.00 26.00
[373] 25.00 22.00 26.00 22.00 20.00 27.00 23.00 32.00 24.00 15.00 21.00 6.00
[385] 27.00 24.00 15.00 1.00 20.00 19.00 12.00 14.00 18.00 26.00 39.00 22.00
[397] 22.00 9.00 32.00 31.00 25.00 32.00 21.00 1.00 24.00 4.00 25.00 29.00
[409] 18.00 23.00 31.00 16.00 44.00 14.00 25.00 0.42 16.00 25.00 7.00 9.00
[421] 29.00 18.00 63.00 22.00 38.00 47.00 15.0010.3 Particular Columns (Variables)
To subset particular columns/variables, use the following syntax:
10.3.1 Base
R
subsetVars <- c("survived","age","prediction")
mydata[,c(1,2,3)]mydata[,c("survived","age","prediction")]mydata[,subsetVars]Or, to drop columns:
dropVars <- c("sibsp","parch")
mydata[,-c(5,6)]mydata[,names(mydata) %ni% c("sibsp","parch")]mydata[,names(mydata) %ni% dropVars]10.3.2 Tidyverse
mydata %>%
select(survived, age, prediction)mydata %>%
select(survived:prediction)mydata %>%
select(all_of(subsetVars))Or, to drop columns:
mydata %>%
select(-sibsp, -parch)mydata %>%
select(-c(sibsp:parch))mydata %>%
select(-all_of(dropVars))10.4 Particular Rows
To subset particular rows, use the following syntax:
10.4.1 Base
R
subsetRows <- c(1,3,5)
mydata[c(1,3,5),]mydata[subsetRows,]mydata[which(mydata$survived == 1),]10.4.2 Tidyverse
mydata %>%
filter(survived == 1)mydata %>%
filter(survived == 1, parch <= 1)mydata %>%
filter(survived == 1 | parch <= 1)10.5 Particular Rows and Columns
To subset particular rows and columns, use the following syntax:
10.5.1 Base
R
mydata[c(1,3,5), c(1,2,3)]mydata[subsetRows, subsetVars]mydata[which(mydata$survived == 1), subsetVars]10.5.2 Tidyverse
mydata %>%
filter(survived == 1) %>%
select(all_of(subsetVars))11 View Data
11.1 All Data
To view data, use the following syntax:
View(mydata)11.2 First 6 Rows/Elements
To view only the first six rows (if a data frame) or elements (if a vector), use the following syntax:
head(mydata)head(mydata$age)[1] 29.00 0.92 2.00 30.00 25.00 48.0012 Data Characteristics
12.1 Data Structure
str(mydata)'data.frame': 1046 obs. of 7 variables:
$ survived : int 1 1 0 0 0 1 1 0 1 0 ...
$ pclass : int 1 1 1 1 1 1 1 1 1 1 ...
$ sex : chr "female" "male" "female" "male" ...
$ age : num 29 0.92 2 30 25 48 63 39 53 71 ...
$ sibsp : int 0 1 1 1 1 0 1 0 2 0 ...
$ parch : int 0 2 2 2 2 0 0 0 0 0 ...
$ prediction: num 0.945 0.784 0.979 0.516 0.946 ...12.2 Data Dimensions
Number of rows and columns:
dim(mydata)[1] 1046 712.3 Number of Elements
length(mydata$age)[1] 104612.4 Number of Missing Elements
length(mydata$age[which(is.na(mydata$age))])[1] 012.5 Number of Non-Missing Elements
length(mydata$age[which(!is.na(mydata$age))])[1] 1046length(na.omit(mydata$age))[1] 104613 Create New Variables
To create a new variable, use the following syntax:
mydata$newVar <- NAHere is an example of creating a new variable:
mydata$ID <- 1:nrow(mydata)14 Create a Data Frame
Here is an example of creating a data frame:
mydata2 <- data.frame(
ID = c(1:5, 1047:1051),
cat = sample(0:1, 10, replace = TRUE)
)
mydata215 Recode Variables
Here is an example of recoding a variable:
mydata$oldVar1[which(mydata$sex == "male")] <- 0
mydata$oldVar1[which(mydata$sex == "female")] <- 1
mydata$oldVar2[which(mydata$sex == "male")] <- 1
mydata$oldVar2[which(mydata$sex == "female")] <- 0Recode multiple variables:
mydata %>%
mutate(across(c(
survived:pclass),
~ case_match(
.,
0 ~ "No",
1 ~ "Yes")))mydata %>%
mutate(across(c(
survived:pclass),
~ case_match(
.,
c(0,1) ~ 1,
c(2,3) ~ 2)))16 Rename Variables
mydata <- mydata %>%
rename(
newVar1 = oldVar1,
newVar2 = oldVar2)Using a vector of variable names:
varNamesFrom <- c("oldVar1","oldVar2")
varNamesTo <- c("newVar1","newVar2")
mydata <- mydata %>%
rename_with(~ varNamesTo, all_of(varNamesFrom))17 Convert the Types of Variables
One variable:
mydata$factorVar <- factor(mydata$sex)
mydata$numericVar <- as.numeric(mydata$prediction)
mydata$integerVar <- as.integer(mydata$parch)
mydata$characterVar <- as.character(mydata$sex)Multiple variables:
mydata %>%
mutate(across(c(
age,
parch,
prediction),
as.numeric))mydata %>%
mutate(across(
age:parch,
as.numeric))mydata %>%
mutate(across(where(is.factor), as.character))18 Merging/Joins
18.1 Overview
Merging (also called joining) merges two data objects using a shared
set of variables called “keys.” The keys are the variable(s) that
uniquely identify each row (i.e., they account for the levels of
nesting). In some data objects, the key might be the participant’s ID
(e.g., participantID). However, some data objects have
multiple keys. For instance, in long form data objects, each participant
may have multiple rows corresponding to multiple timepoints. In this
case, the keys are participantID and
timepoint. If a participant has multiple rows corresponding
to timepoints and measures, the keys are participantID,
timepoint, and measure. In general, each row
should have a value on each of the keys; there should be no missingness
in the keys.
To merge two objects, the keys must be present in both objects. The
keys are used to merge the variables in object 1 (x) with
the variables in object 2 (y). Different merge types select
different rows to merge.
Note: if the two objects include variables with the same name (apart
from the keys), R will not know how you want each to appear
in the merged object. So, it will add a suffix (e.g., .x,
.y) to each common variable to indicate which object (i.e.,
object x or object y) the variable came from,
where object x is the first object—i.e., the object to
which object y (the second object) is merged. In general,
apart from the keys, you should not include variables with the same name
in two objects to be merged. To prevent this, either remove or rename
the shared variable in one of the objects, or include the shared
variable as a key. However, as described above, you should include it as
a key only if it uniquely identifies each row
in terms of levels of nesting.
18.2 Data Before Merging
Here are the data in the mydata object:
mydatadim(mydata)[1] 1046 14Here are the data in the mydata2 object:
mydata2dim(mydata2)[1] 10 218.3 Types of Joins
18.3.1 Visual Overview of Join Types
Below is a visual that depicts various types of merges/joins. Object
x is the circle labeled as A. Object
y is the circle labeled as B. The area of
overlap in the Venn diagram indicates the rows on the keys that are
shared between the two objects (e.g., participantID values
1, 2, and 3). The non-overlapping area indicates the rows on the keys
that are unique to each object (e.g., participantID values
4, 5, and 6 in Object x and values 7, 8, and 9 in Object
y). The shaded yellow area indicates which rows (on the
keys) are kept in the merged object from each of the two objects, when
using each of the merge types. For instance, a left outer join keeps the
shared rows and the rows that are unique to object x, but
it drops the rows that are unique to object y.
Types of merges/joins
Image source: Predictive Hacks (archived at: https://perma.cc/WV7U-BS68)
18.3.2 Full Outer Join
A full outer join includes all rows in \(x\) or \(y\). It returns columns from \(x\) and \(y\). Here is how to merge two data frames using a full outer join (i.e., “full join”):
fullJoinData <- merge(mydata, mydata2, by = "ID", all = TRUE)
fullJoinDatadim(fullJoinData)[1] 1051 15Or, alternatively, using tidyverse:
full_join(mydata, mydata2, by = "ID")18.3.3 Left Outer Join
A left outer join includes all rows in \(x\). It returns columns from \(x\) and \(y\). Here is how to merge two data frames using a left outer join (“left join”):
leftJoinData <- merge(mydata, mydata2, by = "ID", all.x = TRUE)
leftJoinDatadim(leftJoinData)[1] 1046 15Or, alternatively, using tidyverse:
left_join(mydata, mydata2, by = "ID")18.3.4 Right Outer Join
A right outer join includes all rows in \(y\). It returns columns from \(x\) and \(y\). Here is how to merge two data frames using a right outer join (“right join”):
rightJoinData <- merge(mydata, mydata2, by = "ID", all.y = TRUE)
rightJoinDatadim(rightJoinData)[1] 10 15Or, alternatively, using tidyverse:
right_join(mydata, mydata2, by = "ID")18.3.5 Inner Join
An inner join includes all rows that are in both \(x\) and \(y\). An inner join will return one row of \(x\) for each matching row of \(y\), and can duplicate values of records on either side (left or right) if \(x\) and \(y\) have more than one matching record. It returns columns from \(x\) and \(y\). Here is how to merge two data frames using an inner join:
innerJoinData <- merge(mydata, mydata2, by = "ID", all.x = FALSE, all.y = FALSE)
innerJoinDatadim(innerJoinData)[1] 5 15Or, alternatively, using tidyverse:
inner_join(mydata, mydata2, by = "ID")18.3.6 Semi Join
A semi join is a filter. A left semi join returns all rows from \(x\) with a match in \(y\). That is, it filters out records from \(x\) that are not in \(y\). Unlike an inner join, a left semi join will never duplicate rows of \(x\), and it includes columns from only \(x\) (not from \(y\)). Here is how to merge two data frames using a left semi join:
semiJoinData <- semi_join(mydata, mydata2, by = "ID")
semiJoinDatadim(semiJoinData)[1] 5 1418.3.7 Anti Join
An anti join is a filter. A left anti join returns all rows from \(x\) without a match in \(y\). That is, it filters out records from \(x\) that are in \(y\). It returns columns from only \(x\) (not from \(y\)). Here is how to merge two data frames using a left anti join:
antiJoinData <- anti_join(mydata, mydata2, by = "ID")
antiJoinDatadim(antiJoinData)[1] 1041 1418.3.8 Cross Join
A cross join combines each row in \(x\) with each row in \(y\).
crossJoinData <- cross_join(
data.frame(rater = c("Mother","Father","Teacher")),
data.frame(timepoint = 1:3))
crossJoinDatadim(crossJoinData)[1] 9 219 Long to Wide
Original data:
fish_encountersData widened by a variable (station), using
tidyverse:
fish_encounters %>%
pivot_wider(
names_from = station,
values_from = seen)20 Wide to Long
Original data:
mtcarsData in long form, transformed from wide form using
tidyverse:
mtcars %>%
pivot_longer(
cols = everything(),
names_to = "variable",
values_to = "value")21 Average Ratings Across Coders
Create data with multiple coders:
idWaveCoder <-
expand.grid(
id = 1:100,
wave = 1:3,
coder = 1:3,
positiveAffect = NA,
negativeAffect = NA
)
idWaveCoder$positiveAffect <- rnorm(nrow(idWaveCoder))
idWaveCoder$negativeAffect <- rnorm(nrow(idWaveCoder))
idWaveCoder %>%
arrange(id, wave, coder)Average data across coders:
idWave <- idWaveCoder %>%
group_by(id, wave) %>%
summarise(
across(everything(),
~ mean(.x, na.rm = TRUE)),
.groups = "drop") %>%
select(-coder)
idWave22 Loops
If you want to perform the same computation multiple times, it can be
faster to do it in a loop compared to writing out the same computation
many times. For instance, here is a loop that prints each element of a
vector and the loop index (i) that indicates where the loop
is in terms of its iterations:
fruits <- c("apple", "banana", "cherry")
for(i in 1:length(fruits)){
print(paste("The loop is at index:", i, sep = " "))
print(fruits[i])
}[1] "The loop is at index: 1"
[1] "apple"
[1] "The loop is at index: 2"
[1] "banana"
[1] "The loop is at index: 3"
[1] "cherry"23 Session Info
sessionInfo()R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
time zone: UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] psych_2.4.6.26 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 petersenlab_1.1.0
loaded via a namespace (and not attached):
[1] gtable_0.3.6 xfun_0.49 bslib_0.8.0 htmlwidgets_1.6.4
[5] lattice_0.22-6 tzdb_0.4.0 quadprog_1.5-8 vctrs_0.6.5
[9] tools_4.4.2 generics_0.1.3 stats4_4.4.2 parallel_4.4.2
[13] fansi_1.0.6 cluster_2.1.6 pkgconfig_2.0.3 data.table_1.16.2
[17] checkmate_2.3.2 RColorBrewer_1.1-3 lifecycle_1.0.4 compiler_4.4.2
[21] munsell_0.5.1 mnormt_2.1.1 mitools_2.4 htmltools_0.5.8.1
[25] sass_0.4.9 yaml_2.3.10 htmlTable_2.4.3 Formula_1.2-5
[29] pillar_1.9.0 jquerylib_0.1.4 cachem_1.1.0 Hmisc_5.2-0
[33] rpart_4.1.23 nlme_3.1-166 lavaan_0.6-19 tidyselect_1.2.1
[37] digest_0.6.37 mvtnorm_1.3-2 stringi_1.8.4 reshape2_1.4.4
[41] fastmap_1.2.0 grid_4.4.2 colorspace_2.1-1 cli_3.6.3
[45] magrittr_2.0.3 base64enc_0.1-3 utf8_1.2.4 pbivnorm_0.6.0
[49] withr_3.0.2 foreign_0.8-87 scales_1.3.0 backports_1.5.0
[53] timechange_0.3.0 rmarkdown_2.29 nnet_7.3-19 gridExtra_2.3
[57] hms_1.1.3 evaluate_1.0.1 knitr_1.49 mix_1.0-12
[61] viridisLite_0.4.2 rlang_1.1.4 Rcpp_1.0.13-1 xtable_1.8-4
[65] glue_1.8.0 DBI_1.2.3 rstudioapi_0.17.1 jsonlite_1.8.9
[69] R6_2.5.1 plyr_1.8.9 ---
title: "Data Management"
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  echo = TRUE,
  error = TRUE,
  comment = "",
  class.source = "fold-show")
```

# Import Data {#importData}

Importing data takes syntax of the following form for `.csv` files:

```{r, eval = FALSE}
data <- read.csv("filepath/filename.csv")
```

Note: it is important to use forward slashes ("/") rather than backslashes ("\\") when specifying filepaths in `R`.

Below, I import a `.csv` file and save it into an object called `mydata` (you could call this object whatever you want):

```{r, eval = FALSE}
mydata <- read.csv("https://osf.io/s6wrm/download")
```

```{r, include = FALSE}
mydata <- read.csv("./data/titanic.csv") #https://osf.io/s6wrm/download
```

Importing data takes syntax of the following form for `.RData` files:

```{r, eval = FALSE}
load("filepath/filename.RData")
```

## Import Multiple Data Files {#importMultipleDataFiles}

```{r, eval = FALSE}
dataNames <- paste("data", 1:100, sep = "")
dataFilenames <- paste(dataNames, ".csv", sep = "")
dataFilepaths <- paste("C:/users/username/", dataFilenames, sep = "")

data_list <- lapply(dataFilepaths, read.csv) # lapply(dataFilepaths, data.table::fread) is even faster
names(data_list) <- basename(dataFilepaths)
```

Alternatively, if you want to load all `.csv` files in a directory, you can identify the filenames programmatically:

```{r, eval = FALSE}
dataFilenames <- list.files(
  path = "C:/users/username/",
  pattern = "\\.csv$")

dataFilepaths <- list.files(
  path = "C:/users/username/",
  pattern = "\\.csv$",
  full.names = TRUE)

data_list <- lapply(dataFilepaths, read.csv) # lapply(dataFilepaths, data.table::fread) is even faster
names(data_list) <- basename(dataFilepaths)
```

# Save Data {#saveData}

Saving data takes syntax of the following form for `.csv` files:

```{r, eval = FALSE}
write.csv(object, file = "filepath/filename.csv")
```

For example:

```{r, eval = FALSE}
write.csv(mydata, file = "mydata.csv")
```

Saving data takes syntax of the following form for `.RData` files:

```{r, eval = FALSE}
save(object, file = "filepath/filename.RData")
```

# Use Lab Functions {#labFunctions}

To use lab functions, first install the [`petersenlab` package](https://devpsylab.github.io/petersenlab).
The [`petersenlab` package](https://devpsylab.github.io/petersenlab) is here: https://devpsylab.github.io/petersenlab.
You can install it using the following commands:

```{r, eval = FALSE}
install.packages("remotes")
remotes::install_github("DevPsyLab/petersenlab")
```

Once you have the [`petersenlab` package](https://devpsylab.github.io/petersenlab) installed, load the package:

```{r}
library("petersenlab")
```

To run scripts on the lab drive, set the path to the lab drive (`//lc-rs-store24.hpc.uiowa.edu/lss_itpetersen/Lab/`) using the following code:

```{r, eval = FALSE}
petersenLabPath <- setLabPath()
```

# Load/Install Packages {#loadInstallPackages}

To install a single package that is on the CRAN repository, use the following syntax:

```{r, eval = FALSE}
install.packages("name_of_package")
```

To install multiple packages that are on the CRAN repository, use the following syntax:

```{r, eval = FALSE}
install.packages(c("name_of_package1","name_of_package2","name_of_package3"))
```

To install a package that is on a `GitHub` repository, use the following syntax:

```{r, eval = FALSE}
install.packages("remotes")
remotes::install_github("username_of_GitHub_author/name_of_package")
```

For instance:

```{r, eval = FALSE}
remotes::install_github("DevPsyLab/petersenlab")
```

The default way to load a package in `R` is:

```{r, eval = FALSE}
library("packageName1")
library("packageName2")
library("packageName3")
```

However, when sourcing (i.e., running) other `R` scripts, it is possible that you will run scripts that use packages that you do not have installed, resulting in an error that prevents the script from running.
Thus, it can be safer to load packages using the lab function, `load_or_install(),` rather than using `library()`.
The `load_or_install()` function checks whether a package is installed.
If the package is not installed, the function installs and loads the package.
If the package is installed, the function loads the package.
To use this function, you must have the [`petersenlab` package](https://devpsylab.github.io/petersenlab) loaded.

```{r, eval = FALSE}
library("petersenlab")
load_or_install(c("packageName1","packageName2","packageName3"))
```

For example:

```{r}
library("petersenlab")
load_or_install(c("tidyverse","psych"))
```

# Set a Seed {#seed}

Set a seed (any number) to reproduce the results of analyses that involve random number generation.

```{r}
set.seed(52242)
```

# Run an `R` Script {#source}

To run an `R` script, use the following syntax:

```{r, eval = FALSE}
source("filepath/filename.R")
```

# Render an `R` Markdown (`.Rmd`) File {#renderRmd}

To render a `.Rmd` file, use the following syntax:

```{r, eval = FALSE}
render("filepath/filename.Rmd")
```

# Variable Names {#varNames}

To look at the names of variables in a data frame, use the following syntax:

```{r}
names(mydata)
```

# Logical Operators {#logicalOperators}

Logical operators evaluate a condition for each value and yield values of `TRUE` and `FALSE`, corresponding to whether the evaluation for a given value met the condition.

## Is Equal To: `==`

```{r}
mydata$survived == 1
```

## Is Not Equal To: `!=`

```{r}
mydata$survived != 1
```

### Greater Than: `>`

```{r}
mydata$parch > 1
```

## Less Than: `<`

```{r}
mydata$parch < 1
```

## Greater Than or Equal To: `>=`

```{r}
mydata$parch >= 1
```

## Less Than or Equal To: `<=`

```{r}
mydata$parch <= 1
```
## Is in a Value of Another Vector: `%in%`

```{r}
anotherVector <- c(0,1)
mydata$parch %in% anotherVector
```

## Is Not in a Value of Another Vector: `%ni%`

Note: this function is part of the [`petersenlab`](https://github.com/DevPsyLab/petersenlab) package and is not available in base `R`.

```{r}
mydata$parch %ni% anotherVector
```

## Is Missing: `is.na()`

```{r}
is.na(mydata$prediction)
```

## Is Not Missing: `!is.na()`

```{r}
!is.na(mydata$prediction)
```

## And: `&`

```{r}
!is.na(mydata$prediction) & mydata$parch >= 1
```

## Or: `|`

```{r}
is.na(mydata$prediction) | mydata$parch >= 1
```

# Subset {#subset}

To subset a data frame, use brackets to specify the subset of rows and columns to keep, where the value/vector before the comma specifies the rows to keep, and the value/vector after the comma specifies the columns to keep:

```{r, eval = FALSE}
dataframe[rowsToKeep, columnsToKeep]
```

You can subset by using any of the following:

- numeric indices of the rows/columns to keep (or drop)
- names of the rows/columns to keep (or drop)
- values of `TRUE` and `FALSE` corresponding to which rows/columns to keep

## One Variable

To subset one variable, use the following syntax:

```{r}
mydata$age
```

or:

```{r}
mydata[,"age"]
```

## Particular Rows of One Variable

To subset one variable, use the following syntax:

```{r}
mydata$age[which(mydata$survived == 1)]
```

or:

```{r}
mydata[which(mydata$survived == 1), "age"]
```

## Particular Columns (Variables)

To subset particular columns/variables, use the following syntax:

### Base `R`

```{r}
subsetVars <- c("survived","age","prediction")

mydata[,c(1,2,3)]
mydata[,c("survived","age","prediction")]
mydata[,subsetVars]
```

Or, to drop columns:

```{r}
dropVars <- c("sibsp","parch")

mydata[,-c(5,6)]
mydata[,names(mydata) %ni% c("sibsp","parch")]
mydata[,names(mydata) %ni% dropVars]
```

### Tidyverse

```{r}
mydata %>%
  select(survived, age, prediction)

mydata %>%
  select(survived:prediction)

mydata %>%
  select(all_of(subsetVars))
```

Or, to drop columns:

```{r}
mydata %>%
  select(-sibsp, -parch)

mydata %>%
  select(-c(sibsp:parch))

mydata %>%
  select(-all_of(dropVars))
```

## Particular Rows

To subset particular rows, use the following syntax:

### Base `R`

```{r}
subsetRows <- c(1,3,5)

mydata[c(1,3,5),]
mydata[subsetRows,]
mydata[which(mydata$survived == 1),]
```

### Tidyverse

```{r}
mydata %>%
  filter(survived == 1)

mydata %>%
  filter(survived == 1, parch <= 1)

mydata %>%
  filter(survived == 1 | parch <= 1)
```

## Particular Rows and Columns

To subset particular rows and columns, use the following syntax:

### Base `R`

```{r}
mydata[c(1,3,5), c(1,2,3)]
mydata[subsetRows, subsetVars]
mydata[which(mydata$survived == 1), subsetVars]
```

### Tidyverse

```{r}
mydata %>%
  filter(survived == 1) %>%
  select(all_of(subsetVars))
```

# View Data {#viewData}

## All Data

To view data, use the following syntax:

```{r, eval = FALSE}
View(mydata)
```

## First 6 Rows/Elements

To view only the first six rows (if a data frame) or elements (if a vector), use the following syntax:

```{r}
head(mydata)
head(mydata$age)
```

# Data Characteristics {#dataCharacteristics}

## Data Structure

```{r}
str(mydata)
```

## Data Dimensions

Number of rows and columns:

```{r}
dim(mydata)
```

## Number of Elements

```{r}
length(mydata$age)
```

## Number of Missing Elements

```{r}
length(mydata$age[which(is.na(mydata$age))])
```

## Number of Non-Missing Elements

```{r}
length(mydata$age[which(!is.na(mydata$age))])
length(na.omit(mydata$age))
```

# Create New Variables {#createNewVars}

To create a new variable, use the following syntax:

```{r}
mydata$newVar <- NA
```

```{r, include = FALSE}
mydata$newVar <- NULL
```

Here is an example of creating a new variable:

```{r}
mydata$ID <- 1:nrow(mydata)
```

# Create a Data Frame {#createDF}

Here is an example of creating a data frame:

```{r}
mydata2 <- data.frame(
  ID = c(1:5, 1047:1051),
  cat = sample(0:1, 10, replace = TRUE)
)

mydata2
```

# Recode Variables {#recodeVars}

Here is an example of recoding a variable:

```{r}
mydata$oldVar1[which(mydata$sex == "male")] <- 0
mydata$oldVar1[which(mydata$sex == "female")] <- 1

mydata$oldVar2[which(mydata$sex == "male")] <- 1
mydata$oldVar2[which(mydata$sex == "female")] <- 0
```

Recode multiple variables:

```{r}
mydata %>%
  mutate(across(c(
    survived:pclass),
    ~ case_match(
      .,
      0 ~ "No",
      1 ~ "Yes")))

mydata %>%
  mutate(across(c(
    survived:pclass),
    ~ case_match(
      .,
      c(0,1) ~ 1,
      c(2,3) ~ 2)))
```

# Rename Variables {#renameVars}

```{r}
mydata <- mydata %>% 
  rename(
    newVar1 = oldVar1,
    newVar2 = oldVar2)
```

Using a vector of variable names:

```{r, eval = FALSE}
varNamesFrom <- c("oldVar1","oldVar2")
varNamesTo <- c("newVar1","newVar2")

mydata <- mydata %>% 
  rename_with(~ varNamesTo, all_of(varNamesFrom))
```

# Convert the Types of Variables {#convertVarTypes}

One variable:

```{r}
mydata$factorVar <- factor(mydata$sex)
mydata$numericVar <- as.numeric(mydata$prediction)
mydata$integerVar <- as.integer(mydata$parch)
mydata$characterVar <- as.character(mydata$sex)
```

Multiple variables:

```{r}
mydata %>%
  mutate(across(c(
    age,
    parch,
    prediction),
    as.numeric))

mydata %>%
  mutate(across(
    age:parch,
    as.numeric))

mydata %>%
  mutate(across(where(is.factor), as.character))
```

# Merging/Joins {#merging}

## Overview

Merging (also called joining) merges two data objects using a shared set of variables called "keys."
The keys are the variable(s) that uniquely identify each row (i.e., they account for the levels of nesting).
In some data objects, the key might be the participant's ID (e.g., `participantID`).
However, some data objects have multiple keys.
For instance, in long form data objects, each participant may have multiple rows corresponding to multiple timepoints.
In this case, the keys are `participantID` and `timepoint`.
If a participant has multiple rows corresponding to timepoints and measures, the keys are `participantID`, `timepoint`, and `measure`.
In general, each row should have a value on each of the keys; there should be no missingness in the keys.

To merge two objects, the keys must be present in both objects.
The keys are used to merge the variables in object 1 (`x`) with the variables in object 2 (`y`).
Different merge types select different rows to merge.

Note: if the two objects include variables with the same name (apart from the keys), `R` will not know how you want each to appear in the merged object.
So, it will add a suffix (e.g., `.x`, `.y`) to each common variable to indicate which object (i.e., object `x` or object `y`) the variable came from, where object `x` is the first object—i.e., the object to which object `y` (the second object) is merged.
In general, apart from the keys, you should not include variables with the same name in two objects to be merged.
To prevent this, either remove or rename the shared variable in one of the objects, or include the shared variable as a key.
However, as described above, you should include it as a key ***only*** if it uniquely identifies each row in terms of levels of nesting.

## Data Before Merging

Here are the data in the `mydata` object:

```{r}
mydata

dim(mydata)
```

Here are the data in the `mydata2` object:

```{r}
mydata2

dim(mydata2)
```

## Types of Joins {#mergeTypes}

### Visual Overview of Join Types

Below is a visual that depicts various types of merges/joins.
Object `x` is the circle labeled as `A`.
Object `y` is the circle labeled as `B`.
The area of overlap in the Venn diagram indicates the rows on the keys that are shared between the two objects (e.g., `participantID` values 1, 2, and 3).
The non-overlapping area indicates the rows on the keys that are unique to each object (e.g., `participantID` values 4, 5, and 6 in Object `x` and values 7, 8, and 9 in Object `y`).
The shaded yellow area indicates which rows (on the keys) are kept in the merged object from each of the two objects, when using each of the merge types.
For instance, a left outer join keeps the shared rows and the rows that are unique to object `x`, but it drops the rows that are unique to object `y`.

![Types of merges/joins](images/joins.png)

Image source: [Predictive Hacks](https://predictivehacks.com/?all-tips=anti-joins-with-pandas) (archived at: https://perma.cc/WV7U-BS68)

### Full Outer Join {#fullJoin}

A full outer join includes all rows in $x$ **or** $y$.
It returns columns from $x$ and $y$.
Here is how to merge two data frames using a full outer join (i.e., "full join"):

```{r}
fullJoinData <- merge(mydata, mydata2, by = "ID", all = TRUE)

fullJoinData
dim(fullJoinData)
```

Or, alternatively, using `tidyverse`:

```{r}
full_join(mydata, mydata2, by = "ID")
```

### Left Outer Join {#leftJoin}

A left outer join includes all rows in $x$.
It returns columns from $x$ and $y$.
Here is how to merge two data frames using a left outer join ("left join"):

```{r}
leftJoinData <- merge(mydata, mydata2, by = "ID", all.x = TRUE)

leftJoinData
dim(leftJoinData)
```

Or, alternatively, using `tidyverse`:

```{r}
left_join(mydata, mydata2, by = "ID")
```

### Right Outer Join {#rightJoin}

A right outer join includes all rows in $y$.
It returns columns from $x$ and $y$.
Here is how to merge two data frames using a right outer join ("right join"):

```{r}
rightJoinData <- merge(mydata, mydata2, by = "ID", all.y = TRUE)

rightJoinData
dim(rightJoinData)
```

Or, alternatively, using `tidyverse`:

```{r}
right_join(mydata, mydata2, by = "ID")
```

### Inner Join {#innerJoin}

An inner join includes all rows that are in **both** $x$ **and** $y$.
An inner join will return one row of $x$ for each matching row of $y$, and can duplicate values of records on either side (left or right) if $x$ and $y$ have more than one matching record.
It returns columns from $x$ and $y$.
Here is how to merge two data frames using an inner join:

```{r}
innerJoinData <- merge(mydata, mydata2, by = "ID", all.x = FALSE, all.y = FALSE)

innerJoinData
dim(innerJoinData)
```

Or, alternatively, using `tidyverse`:

```{r}
inner_join(mydata, mydata2, by = "ID")
```

### Semi Join {#semiJoin}

A semi join is a filter.
A left semi join returns all rows from $x$ **with** a match in $y$.
That is, it filters out records from $x$ that are not in $y$.
Unlike an [inner join](#innerJoin), a left semi join will never duplicate rows of $x$, and it includes columns from only $x$ (not from $y$). 
Here is how to merge two data frames using a left semi join:

```{r}
semiJoinData <- semi_join(mydata, mydata2, by = "ID")

semiJoinData
dim(semiJoinData)
```

### Anti Join {#antiJoin}

An anti join is a filter.
A left anti join returns all rows from $x$ **without** a match in $y$.
That is, it filters out records from $x$ that are in $y$.
It returns columns from only $x$ (not from $y$).
Here is how to merge two data frames using a left anti join:

```{r}
antiJoinData <- anti_join(mydata, mydata2, by = "ID")

antiJoinData
dim(antiJoinData)
```

### Cross Join {#crossJoin}

A cross join combines each row in $x$ with each row in $y$.

```{r}
crossJoinData <- cross_join(
  data.frame(rater = c("Mother","Father","Teacher")),
  data.frame(timepoint = 1:3))

crossJoinData
dim(crossJoinData)
```

# Long to Wide {#longToWide}

Original data:

```{r}
fish_encounters
```

Data widened by a variable (`station`), using `tidyverse`: 

```{r}
fish_encounters %>% 
  pivot_wider(
    names_from = station,
    values_from = seen)
```

# Wide to Long {#wideToLong}

Original data:

```{r}
mtcars
```

Data in long form, transformed from wide form using `tidyverse`:

```{r}
mtcars %>% 
  pivot_longer(
    cols = everything(),
    names_to = "variable",
    values_to = "value")
```

# Average Ratings Across Coders {#avgAcrossCoders}

Create data with multiple coders:

```{r, class.source = "fold-hide"}
idWaveCoder <- 
  expand.grid(
    id = 1:100,
    wave = 1:3,
    coder = 1:3,
    positiveAffect = NA,
    negativeAffect = NA
  )

idWaveCoder$positiveAffect <- rnorm(nrow(idWaveCoder))
idWaveCoder$negativeAffect <- rnorm(nrow(idWaveCoder))

idWaveCoder %>% 
  arrange(id, wave, coder)
```

Average data across coders:

```{r}
idWave <- idWaveCoder %>% 
  group_by(id, wave) %>% 
  summarise(
    across(everything(),
      ~ mean(.x, na.rm = TRUE)),
    .groups = "drop") %>% 
  select(-coder)

idWave
```

# Loops {#loops}

If you want to perform the same computation multiple times, it can be faster to do it in a loop compared to writing out the same computation many times.
For instance, here is a loop that prints each element of a vector and the loop index (`i`) that indicates where the loop is in terms of its iterations:

```{r}
fruits <- c("apple", "banana", "cherry")

for(i in 1:length(fruits)){
  print(paste("The loop is at index:", i, sep = " "))
  print(fruits[i])
}
```

# Session Info

```{r, class.source = "fold-hide"}
sessionInfo()
```

