Objetives

 

  • Understand the logic used by ggplot2 to structure graphs

  • Get familiar with its basic functions and types layer

 

Before importing data

There are a few habits for data entry that will make it easier to import data correctly into R:

  • Reserve the first row for the header

  • Reserve the first column to the sampling unit

  • Use “_“,”.” or “-” instead of empty spaces (e.g. “Cornell_University”)

  • Use short names

  • Avoid using uncommon symbols such as ?, $,%, ^, &, *, (, ),-,#, ?,,,<,>, /, |, , [ ,] ,{, and }

  • Be consistent when referring to the same things (i.e. write them always in the same way)

  • Delete any extra comments outside the data set columns

  • Indicate missing values with NA (or at least empty spaces)

  • Don’t even think about color-coding your data in excel

 

Importing data

Importing data into R is a crucial and apparently simple step. However, given the diversity of data formats and operating systems, as well as the many possible sources of error, inputting data in R is not always that straightforward. Most issues are related with:

  • Telling R in which directory the file is found

  • Telling R how the data is encoded (e.g. comma-separated, fixed width, etc)

  • Handling empty cells and uncommon characters

 

Setting the working directory

To read data in R you need to specify the working directory. It can be set with the function setwd(). The way you do this depends on the operating system (windows, mac, Linux). The folder directory syntax follows the nested structure of the folders. For instance:

setwd("/home/m/Desktop/")

… sets the working directory in the folder “Desktop”, which is found within “m”, which is found within “home”.

 

Some basic tips for setting the working directory:

  • Make sure the location is quoted

  • Make sure you have forward-slash (/) between folder names (although double backward-slashes seem to work in windows)

  • Do not include any file name in the folder directory name

  • To find the location you can look at the properties of a file in that folder and copy it

  • The path to the folder should be quoted (““)

  • The name matches exactly (better to copy/paste)

  • Use list.files() to check which files are in the working directory

  • R can suggest and auto-complete the folder names by pressing “tab” when within quotes:

 

Color spectro

 

Setting the working directory in windows

In windows it should be something like this:

setwd("C:/location")

You can also do this (only on windows!):

setwd(choose.dir())

  That should pop-up a window where you can choose the location. However, this should only be used to figure out the correct way to write the directory location, not as part of the script itself.

 

Setting the working directory in OSX (mac)

For mac setting the working directory should be something like this:

setwd("/Users/yourname/..")

do not include whatever you have before “users” (like macintosh… )

 

Setting the working directory in Linux

Similar to the code used in OSX:

setwd("/home/m/Desktop/")

 

The “~” (tilde) can also be used to skip the “home” and “user” folder in Linux:

setwd("~/Desktop/")

 

The current working directory can be checked as follows:

getwd()
## [1] "/home/m/Dropbox/courses_and_workshops/OTS_TB_2023"

 

Reading data

Any file can be read in R. It’s just a matter of letting R know in which format is the file encoded (e.g. what conventions were followed when generating the file). The most common formats to store/exchange data sets as the ones we usually handle in biological sciences are txt, csv and xls/xlsx.

 

The most commonly used function to import data in R is read.table. The documentation of this function actually includes all the default functions for inputting data:

?read.table

 

Color spectro

 

Reading .txt files

.txt files can be read using read.table. Let’s first download a freely available data set in .txt format:

# set working directory
setwd("PUT THE FOLDER LOCATION WHERE YOU WANT TO SAVE THE FILE HERE")

download.file("http://esapubs.org/archive/ecol/E090/184/PanTHERIA_1-0_WR93_Aug2008.txt", destfile = "pantheria_mammals_data.txt")

You can also manually download the file from here

 

The file can be input into R as follows:

# read file
pntr_dt <- read.table("pantheria_mammals_data.txt", stringsAsFactors = FALSE, sep = "\t", header = TRUE)
# check structure
head(pntr_dt)
MSW93_Order MSW93_Family MSW93_Genus MSW93_Species MSW93_Binomial X1.1_ActivityCycle
Rodentia Muridae Abditomys latidens Abditomys latidens -999
Rodentia Muridae Abrawayaomys ruschii Abrawayaomys ruschii -999
Rodentia Abrocomidae Abrocoma bennettii Abrocoma bennettii 1
Rodentia Abrocomidae Abrocoma boliviensis Abrocoma boliviensis -999
Rodentia Abrocomidae Abrocoma cinerea Abrocoma cinerea -999
Chiroptera Pteropodidae Acerodon celebensis Acerodon celebensis -999
X5.1_AdultBodyMass_g X8.1_AdultForearmLen_mm X13.1_AdultHeadBodyLen_mm X2.1_AgeatEyeOpening_d
268 -999.00 223.99 -999
63 -999.00 -999.00 -999
251 -999.00 -999.00 -999
158 -999.00 -999.00 -999
194 -999.00 -999.00 -999
382 133.49 201.55 -999

 

The file name is quoted and contains the file extension.

 

Note that the value -999 is used to define empty cells. We can read this values as NAs while importing the data using the ‘na.strings’ argument:

# read file
pntr_dt <- read.table("pantheria_mammals_data.txt", sep = "\t", header = TRUE, na.strings = "-999")

# check structure
head(pntr_dt)
MSW93_Order MSW93_Family MSW93_Genus MSW93_Species MSW93_Binomial X1.1_ActivityCycle
Rodentia Muridae Abditomys latidens Abditomys latidens NA
Rodentia Muridae Abrawayaomys ruschii Abrawayaomys ruschii NA
Rodentia Abrocomidae Abrocoma bennettii Abrocoma bennettii 1
Rodentia Abrocomidae Abrocoma boliviensis Abrocoma boliviensis NA
Rodentia Abrocomidae Abrocoma cinerea Abrocoma cinerea NA
Chiroptera Pteropodidae Acerodon celebensis Acerodon celebensis NA
X5.1_AdultBodyMass_g X8.1_AdultForearmLen_mm X13.1_AdultHeadBodyLen_mm X2.1_AgeatEyeOpening_d
268 NA 223.99 NA
63 NA NA NA
251 NA NA NA
158 NA NA NA
194 NA NA NA
382 133.49 201.55 NA

 

Reading .csv files

Again, we can download an example file online:

download.file("http://www.birds.cornell.edu/clementschecklist/wp-content/uploads/2013/03/eBird_Taxonomy_v2017_18Aug2017.csv", destfile = "clements_bird_list.csv")

clm_lst <- read.csv("clements_bird_list.csv", stringsAsFactors = FALSE)

head(clm_lst)
TAXON_ORDER CATEGORY SPECIES_CODE PRIMARY_COM_NAME SCI_NAME
3 species ostric2 Common Ostrich Struthio camelus
5 species ostric3 Somali Ostrich Struthio molybdophanes
6 slash y00934 Common/Somali Ostrich Struthio camelus/molybdophanes
7 species grerhe1 Greater Rhea Rhea americana
13 species lesrhe2 Lesser Rhea Rhea pennata
14 issf lesrhe4 Lesser Rhea (Puna) Rhea pennata tarapacensis/garleppi
ORDER1 FAMILY SPECIES_GROUP REPORT_AS
Struthioniformes Struthionidae (Ostriches) Ostriches
Struthioniformes Struthionidae (Ostriches)
Struthioniformes Struthionidae (Ostriches)
Rheiformes Rheidae (Rheas) Rheas
Rheiformes Rheidae (Rheas)
Rheiformes Rheidae (Rheas) lesrhe2

You can also manually download the file from here

 

As in the previous example, we can tell R how to identify empty cells using the ‘na.strings’ argument:

clm_lst <- read.csv("clements_bird_list.csv", stringsAsFactors = FALSE, na.strings = "")

head(clm_lst)
TAXON_ORDER CATEGORY SPECIES_CODE PRIMARY_COM_NAME SCI_NAME
3 species ostric2 Common Ostrich Struthio camelus
5 species ostric3 Somali Ostrich Struthio molybdophanes
6 slash y00934 Common/Somali Ostrich Struthio camelus/molybdophanes
7 species grerhe1 Greater Rhea Rhea americana
13 species lesrhe2 Lesser Rhea Rhea pennata
14 issf lesrhe4 Lesser Rhea (Puna) Rhea pennata tarapacensis/garleppi
ORDER1 FAMILY SPECIES_GROUP REPORT_AS
Struthioniformes Struthionidae (Ostriches) Ostriches NA
Struthioniformes Struthionidae (Ostriches) NA NA
Struthioniformes Struthionidae (Ostriches) NA NA
Rheiformes Rheidae (Rheas) Rheas NA
Rheiformes Rheidae (Rheas) NA NA
Rheiformes Rheidae (Rheas) NA lesrhe2

 

Reading excel files

Most researchers enter data into excel spreadsheets. So it would be pretty handy to read the data directly from there. To read xls and xlsx files we need to install the package “readxl” (there are other packages that can be used but they all work similarly):

install.packages(pkgs = "readxl")

 

And load it:

library(readxl)

 

As we did above, download an example file from an online repository. In this case is the same Clements bird taxonomy list in xlsx format:

download.file("http://www.birds.cornell.edu/clementschecklist/wp-content/uploads/2017/08/eBird_Taxonomy_v2017_18Aug2017.xlsx", destfile = "clements_bird_list.xlsx")

You can also manually download the file from here

 

Now we can use the function read_excel() to read the file:

# read file
clm_lst2 <- read_excel("clements_bird_list.xlsx", sheet = 1)

head(clm_lst2)
TAXON_ORDER CATEGORY SPECIES_CODE PRIMARY_COM_NAME SCI_NAME
3 species ostric2 Common Ostrich Struthio camelus
5 species ostric3 Somali Ostrich Struthio molybdophanes
6 slash y00934 Common/Somali Ostrich Struthio camelus/molybdophanes
7 species grerhe1 Greater Rhea Rhea americana
13 species lesrhe2 Lesser Rhea Rhea pennata
14 issf lesrhe4 Lesser Rhea (Puna) Rhea pennata tarapacensis/garleppi
ORDER1 FAMILY SPECIES_GROUP REPORT_AS
Struthioniformes Struthionidae (Ostriches) Ostriches NA
Struthioniformes Struthionidae (Ostriches) NA NA
Struthioniformes Struthionidae (Ostriches) NA NA
Rheiformes Rheidae (Rheas) Rheas NA
Rheiformes Rheidae (Rheas) NA NA
Rheiformes Rheidae (Rheas) NA lesrhe2

 

You need to specify the file name (including extension) and the excel sheet (tab) name. read_excel() auto detects the format from the file extension. The functions read_xls() and read_xlsx() can be used to read files without extension.

 

Exercise 1

All default functions to input data into R have a counterpart to export the same type of data. The names of these other functions are similar to the ones for reading data, although they typically start with “write” or “save”.


1.1 What are the names of the default functions for exporting the data formats we used above? (hint: try apropos to check which functions are available)


1.2 Export the mammals data as a .csv file


1.3 Export the mammals data again, this time excluding the row names


1.4 Read the .csv file using read.table


1.5 What other packages can import excel files into R?


1.6 Can you export an excel file or add data to an existing excel file from R?


1.7 Using the “clements_bird_list.csv” file, how would you tell R to read both “Rheiformes” and “Ostriches” as empty cells (while still reading empty cells as empty cells)?

Formatting data

This section deals with organizing your data in a way that simplify its handling, exploration and analysis. As you can probably guess, the more consistent things are done, the more predictable they become. This also applies to data. If data is organized with the same logic, you can expect the same type of manipulations and analyses to be applicable on different data sets. Organizing the data is a key (but usually neglected) aspect of the data analysis workflow. When data is properly organized you will spend much less time on formatting and more time on the actual analytic questions.

When possible, we will run the data formatting examples using both the ‘tidyr’ package and the base R functions.

Tidy data

“Tidy data” is a logic for organizing data sets in a consistent and intuitive way. To run some of the code below you will need the ‘tidyr’ and ‘dplyr’ packages, which can be installed/loaded as follows:

install.packages(pkgs = "tidyr")

install.packages(pkgs = "dplyr")

library(tidyr)

library(dplyr)

 

The same data can be represented in many ways. In the example below each data set shows exactly the same values of four variables country, year, population, and cases, but in each data set values are organized in a different way. The data shows the number of tuberculosis cases in Afghanistan, Brazil, and China between 1999 and 2000:

as.data.frame(table1)
country year cases population
Afghanistan 1999 745 19987071
Afghanistan 2000 2666 20595360
Brazil 1999 37737 172006362
Brazil 2000 80488 174504898
China 1999 212258 1272915272
China 2000 213766 1280428583 
as.data.frame(table2)
country year type count
Afghanistan 1999 cases 745
Afghanistan 1999 population 19987071
Afghanistan 2000 cases 2666
Afghanistan 2000 population 20595360
Brazil 1999 cases 37737
Brazil 1999 population 172006362
Brazil 2000 cases 80488
Brazil 2000 population 174504898
China 1999 cases 212258
China 1999 population 1272915272
China 2000 cases 213766
China 2000 population 1280428583 
as.data.frame(table3)
country year rate
Afghanistan 1999 745/19987071
Afghanistan 2000 2666/20595360
Brazil 1999 37737/172006362
Brazil 2000 80488/174504898
China 1999 212258/1272915272
China 2000 213766/1280428583

Or even spread across 2 different data sets:

as.data.frame(table4a)
country 1999 2000
Afghanistan 745 2666
Brazil 37737 80488
China 212258 213766 
as.data.frame(table4b)
country 1999 2000
Afghanistan 19987071 20595360
Brazil 172006362 174504898
China 1272915272 1280428583

 

All these data sets contained the same underlying data. However, they are not equally easy to use.

There are three interrelated rules to make a data set tidy:

  1. Each variable must have its own column

  2. Each observation must have its own row

  3. Each value must have its own cell

This figure shows the rules visually:

tidy data * Modified from R for Data Science  

These three rules are interrelated because it’s impossible to only satisfy two of the three. That interrelationship leads to an even simpler set of practical instructions:

  1. Put each data set in a data frame

  2. Put each variable in a column

 

In the example above, only table1 is tidy. It’s the only representation where each column is a variable. There are two main advantages of formatting the data in this way:

  1. If you have a consistent data structure, it’s easier to learn the tools that work with it because they have an underlying uniformity

  2. Placing variables in columns fits well R’s vectorized nature. As we have seen, built-in R functions work with vectors of values. That makes transforming tidy data feel particularly natural

 

Exercise 2


2.1 Describe how the variables and observations are organized in each of the sample data frames


2.2 Calculate the rate of cases per 10000 people for “table1”, “table2” and “table4a”/“table4b”

Gathering data

A common problem is a data set where some of the column names are not names of variables, but values of a variable. Take “table4a”: the column names 1999 and 2000 represent values of the year variable, and each row represents two observations, not one:

as.data.frame(table4a)
country 1999 2000
Afghanistan 745 2666
Brazil 37737 80488
China 212258 213766

 

To tidy a data set like this, we need to gather those columns into a new pair of variables. To do this we need three parameters:

  • The set of columns that represent values, not variables. In this example, those are the columns 1999 and 2000

  • The name of the variable whose values form the column names. In the ‘tidyr’ syntax that is called the key, which in this case is year

  • The name of the variable whose values are spread over the cells. In the ‘tidyr’ syntax that is called that value, which in this case is the number of cases

These parameters can be used to create a tidy data set using the function gather():

gather(table4a, key = "year", value = "cases", `1999`, `2000`)
country year cases
Afghanistan 1999 745
Brazil 1999 37737
China 1999 212258
Afghanistan 2000 2666
Brazil 2000 80488
China 2000 213766

We can visualize this formatting as follows:

gather data * Modified from R for Data Science  

gather() can also be used to tidy table4b. The only difference is the variable stored in the cell values:

gather(data = table4b, key = "year", value = "population", `1999`, `2000`)
country year population
Afghanistan 1999 19987071
Brazil 1999 172006362
China 1999 1272915272
Afghanistan 2000 20595360
Brazil 2000 174504898
China 2000 1280428583

 

To combine the tidied versions of table4a and table4b into a single data frame (or ‘tibble’), we can either use dplyr::left_join() or merge() from base R:

tidy4a <- gather(table4a, key = "year", value = "cases", `1999`, `2000`)

tidy4b <- gather(table4b, key = "year", value = "population", `1999`, `2000`)

left_join(x = tidy4a, y = tidy4b, by = c("country", "year"))
country year cases population
Afghanistan 1999 745 19987071
Brazil 1999 37737 172006362
China 1999 212258 1272915272
Afghanistan 2000 2666 20595360
Brazil 2000 80488 174504898
China 2000 213766 1280428583

 

merge(x = tidy4a, y =  tidy4b, by = c("country", "year"))
country year cases population
Afghanistan 1999 745 19987071
Afghanistan 2000 2666 20595360
Brazil 1999 37737 172006362
Brazil 2000 80488 174504898
China 1999 212258 1272915272
China 2000 213766 1280428583

 

Spreading

Spreading is the opposite of gathering. You use it when an observation is scattered across multiple rows. For example, in table2 an observation is a country in a year, but each observation is spread across two rows:

table2
country year type count
Afghanistan 1999 cases 745
Afghanistan 1999 population 19987071
Afghanistan 2000 cases 2666
Afghanistan 2000 population 20595360
Brazil 1999 cases 37737
Brazil 1999 population 172006362
Brazil 2000 cases 80488
Brazil 2000 population 174504898
China 1999 cases 212258
China 1999 population 1272915272
China 2000 cases 213766
China 2000 population 1280428583

 

To tidy this data set up, we only need two parameters:

  • The column that contains variable names, the key column. Here, it’s type.

  • The column that contains values forms multiple variables, the value column. Here it’s count.

 

To do this we can use spread():

spread(table2, key = "type", value = "count")
country year cases population
Afghanistan 1999 745 19987071
Afghanistan 2000 2666 20595360
Brazil 1999 37737 172006362
Brazil 2000 80488 174504898
China 1999 212258 1272915272
China 2000 213766 1280428583

 

which can be visualized as follows:

spreading data * Modified from R for Data Science  

spread() and gather() are complementary functions. gather() makes wide tables narrower and longer; spread() makes long tables shorter and wider.

 

Exercise 3


3.1 Tidy up the following data set on tree height for 2 species:

plnt_sz <- data.frame(forest = c("old_growth", "disturbed"), 
                      Species_1 = c(154, 160), 
                      Species_2 = c(120, 113))

 

Separating and uniting

So far we have fixed “table2” and “table4”, but not “table3”. “table3” has a different problem: we have one column (rate) that contains two variables (cases and population*). This can be fixed using the separate() function . We will also look at its complementunite(), which is used when a single variable is spread across multiple columns.

 

Separate

separate() pulls apart one column into multiple columns, by splitting wherever a separator character appears. Take table3:

as.data.frame(table3)
country year rate
Afghanistan 1999 745/19987071
Afghanistan 2000 2666/20595360
Brazil 1999 37737/172006362
Brazil 2000 80488/174504898
China 1999 212258/1272915272
China 2000 213766/1280428583

Visually it does something like this:

separate data * Modified from R for Data Science

 

The rate column contains both cases and population variables, and we need to split it into two variables. separate() takes the name of the column to separate, and the names of the new columns to be created:

separate(data = table3, col =  rate, into = c("cases", "population"))
country year cases population
Afghanistan 1999 745 19987071
Afghanistan 2000 2666 20595360
Brazil 1999 37737 172006362
Brazil 2000 80488 174504898
China 1999 212258 1272915272
China 2000 213766 1280428583

By default, separate() will split based on any non-alphanumeric character (i.e. a character that isn’t a number or letter). In the code above, separate() split the values of rate at the forward slash characters. This can be explicitly stated (to avoid any errors):

tb3 <- separate(data = table3, col = rate,  into = c("cases", "population"), sep = "/")

tb3
country year cases population
Afghanistan 1999 745 19987071
Afghanistan 2000 2666 20595360
Brazil 1999 37737 172006362
Brazil 2000 80488 174504898
China 1999 212258 1272915272
China 2000 213766 1280428583 
## tibble [6 × 4] (S3: tbl_df/tbl/data.frame)
##  $ country   : chr [1:6] "Afghanistan" "Afghanistan" "Brazil" "Brazil" ...
##  $ year      : num [1:6] 1999 2000 1999 2000 1999 ...
##  $ cases     : chr [1:6] "745" "2666" "37737" "80488" ...
##  $ population: chr [1:6] "19987071" "20595360" "172006362" "174504898" ...

 

Note that the case and population are character columns. By default separate() leaves the type of the new columns as in the original one. In this case this is not ideal as those really are numbers. We can ask separate() to try and convert to better types using convert = TRUE:

tb3 <- separate(data = table3, col = rate, into = c("cases", "population"), convert = TRUE)

str(tb3)
## tibble [6 × 4] (S3: tbl_df/tbl/data.frame)
##  $ country   : chr [1:6] "Afghanistan" "Afghanistan" "Brazil" "Brazil" ...
##  $ year      : num [1:6] 1999 2000 1999 2000 1999 ...
##  $ cases     : int [1:6] 745 2666 37737 80488 212258 213766
##  $ population: int [1:6] 19987071 20595360 172006362 174504898 1272915272 1280428583

 

You can also pass a vector of integers to sep, which will be interpreted as positions to split at. Positive values start at 1 on the far-left of the strings; negative value start at -1 on the far-right of the strings. When using integers to separate strings, the length of sep should be one less than the number of names in into. You can use this to separate the last two digits of each year:

separate(data = table3, col = year, into = c("century", "year"), 
         sep = 2)
country century year rate
Afghanistan 19 99 745/19987071
Afghanistan 20 00 2666/20595360
Brazil 19 99 37737/172006362
Brazil 20 00 80488/174504898
China 19 99 212258/1272915272
China 20 00 213766/1280428583

Separating columns can also be done with base R, although it takes a bit more coding:

table3$cases <- sapply(table3$rate, function(x) try(strsplit(x, "/")[[1]][1]), USE.NAMES = FALSE)

table3$population <- sapply(table3$rate, function(x) try(strsplit(x, "/")[[1]][2]), USE.NAMES = FALSE)

tb3
country year rate cases population
Afghanistan 1999 745/19987071 745 19987071
Afghanistan 2000 2666/20595360 2666 20595360
Brazil 1999 37737/172006362 37737 172006362
Brazil 2000 80488/174504898 80488 174504898
China 1999 212258/1272915272 212258 1272915272
China 2000 213766/1280428583 213766 1280428583 
## tibble [6 × 4] (S3: tbl_df/tbl/data.frame)
##  $ country   : chr [1:6] "Afghanistan" "Afghanistan" "Brazil" "Brazil" ...
##  $ year      : num [1:6] 1999 2000 1999 2000 1999 ...
##  $ cases     : chr [1:6] "745" "2666" "37737" "80488" ...
##  $ population: chr [1:6] "19987071" "20595360" "172006362" "174504898" ...

 

Unite

unite() is the inverse of separate(): combining multiple columns into a single column:

unite data * Modified from R for Data Science

 

However, you will need it much less frequently than separate().

We can use unite() to rejoin the century and year columns that we created above:

unite(data = table5, col = "new", "century", "year")
country new rate
Afghanistan 19_99 745/19987071
Afghanistan 20_00 2666/20595360
Brazil 19_99 37737/172006362
Brazil 20_00 80488/174504898
China 19_99 212258/1272915272
China 20_00 213766/1280428583

In this function we can also use the sep argument (although in this example it was not specified).

 

Exercise 3


3.1 Unite century and year in “table5” using base R (hint: paste())


References

  • Clements, J. F., T. S. Schulenberg, M. J. Iliff, D. Roberson, T. A. Fredericks, B. L. Sullivan, and C. L. Wood. 2017. The eBird/Clements checklist of birds of the world: v2016.

  • Jones, Jon Bielby, Marcel Cardillo, Susanne A. Fritz, Justin O’Dell, C. David L. Orme, Kamran Safi, Wes Sechrest, Elizabeth H. Boakes, Chris Carbone, Christina Connolly, Michael J. Cutts, Janine K. Foster, Richard Grenyer, Michael Habib, Christopher A. Plaster, Samantha A. Price, Elizabeth A. Rigby, Janna Rist, Amber Teacher, Olaf R. P. Bininda-Emonds, John L. Gittleman, Georgina M. Mace, and Andy Purvis. 2009. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90:2648.

  • Wickham, Hadley, and Garrett Grolemund. 2016. R for data science: import, tidy, transform, visualize, and model data. website

  • Data import tutorial- DataCamp

Session information

## R version 4.2.2 Patched (2022-11-10 r83330)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=es_ES.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=es_CR.UTF-8        LC_COLLATE=es_ES.UTF-8    
##  [5] LC_MONETARY=es_CR.UTF-8    LC_MESSAGES=es_ES.UTF-8   
##  [7] LC_PAPER=es_CR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=es_CR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] dplyr_1.1.0        tidyr_1.3.0        readxl_1.4.1       kableExtra_1.3.4  
## [5] knitr_1.42         ggplot2_3.4.2      RColorBrewer_1.1-3
## 
## loaded via a namespace (and not attached):
##  [1] cellranger_1.1.0  bslib_0.4.2       compiler_4.2.2    pillar_1.9.0     
##  [5] jquerylib_0.1.4   tools_4.2.2       digest_0.6.31     viridisLite_0.4.2
##  [9] jsonlite_1.8.4    evaluate_0.21     lifecycle_1.0.3   tibble_3.2.1     
## [13] gtable_0.3.3      pkgconfig_2.0.3   rlang_1.1.1       cli_3.6.1        
## [17] rstudioapi_0.14   yaml_2.3.7        xfun_0.39         fastmap_1.1.1    
## [21] stringr_1.5.0     xml2_1.3.4        httr_1.4.6        withr_2.5.0      
## [25] systemfonts_1.0.4 generics_0.1.3    vctrs_0.6.2       sass_0.4.6       
## [29] webshot_0.5.4     grid_4.2.2        tidyselect_1.2.0  svglite_2.1.0    
## [33] glue_1.6.2        R6_2.5.1          fansi_1.0.4       rmarkdown_2.21   
## [37] purrr_1.0.1       magrittr_2.0.3    scales_1.2.1      htmltools_0.5.5  
## [41] rvest_1.0.3       colorspace_2.1-0  utf8_1.2.3        stringi_1.7.12   
## [45] munsell_0.5.0     cachem_1.0.8