El primer paso para ejecutar anĆ”lisis de datos en R (o en cualquier otro entorno similar), es la importaciĆ³n de las tablas de datos que se va a utilizar.
Con esto en mente, se debe tener en cuenta que dichas tablas se pueden presentar en diversos formatos, por lo que a su vez se requieren de diferentes mĆ©todos de importaciĆ³n. Al respecto, los principales de estos formatos son:
Este tipo de archivos ademĆ”s, pueden estar ubicados en un repositorio local, en una herramienta de gestiĆ³n de datos o en una pĆ”gina web.
Para atender todas estas necesidades (y otras que ya veremos mĆ”s adelante), R posee una librerĆa especializada llamada tidyverse.
Ejecute el siguiente comando en su consola para instalar por primera vez el tidyverse:
install.packages("tidyverse")Espere mientras R Studio busca en los repositorios de internet la librerĆa y la instala.
Una vez instalada, una librerĆa puede ser cargada ejecutando el siguiente comando:
library(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
-- Attaching packages --------------------------------------- tidyverse 1.3.0 --
v ggplot2 3.3.3 v purrr 0.3.4
v tibble 3.1.1 v dplyr 1.0.5
v tidyr 1.1.3 v stringr 1.4.0
v readr 1.4.0 v forcats 0.5.1
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()Es importante seƱalar que una vez que se ha instalado una librerĆa, la misma queda ya guardada en R, y puede cargarse siempre en cualquier script.
Ahora bien, una vez cargada, puede verificar que la librerĆa tidyverse estĆ” activa en la sesiĆ³n ejecutando el siguiente comando que permite visualizar todas las librerĆas en uso:
(.packages()) [1] "forcats" "stringr" "dplyr" "purrr" "readr" "tidyr"
[7] "tibble" "ggplot2" "tidyverse" "stats" "graphics" "grDevices"
[13] "utils" "datasets" "methods" "base" Como segunda acciĆ³n importante antes de improtar datos, se debe establecer un directorio de trabajo, esto es, se debe indicar a R el lugar donde se encuentran localizados los archivos que queremos importar.
Para emprezar, verifiquemos el directorio de trabajo actual:
getwd()[1] "C:/Users/jsara/OneDrive/Documentos/UHemisferios/BI 2021-2/Scripts"Y a continuaciĆ³n definamos el directorio deseado (aquel donde estĆ”n los archivos a importar). Para esto, hay dos formas:
Esta forma requiere que se conozca la direcciĆ³n de la carpeta donde estĆ”n los archivos, tal que:
#Ingrese la direcciĆ³n que correponda a cada una de sus computadoras donde guardaron los archivos que desacargaron del Aula Virtual
direccion <- "C:/Users/jsara/OneDrive/Documentos/UHemisferios/BI 2021-2/Datasets/Importacion"
setwd(direccion)Una forma de obtener el texto con la direcciĆ³n es usando el explorador, dirigiĆ©ndose a la carpeta deseada, y dando click sobre la tab superior. Inmediatamente allĆ aparecerĆ” el texto, el cual se lo puede copiar y pegar en el script entre comillas y cambiando los caracteres ā\ā por ā/ā si corresponde.
Esta segunda forma, puede verse como una mĆ”s amigable y que requiere menor cantidad de cĆ³digo. FuncionarĆ” muy bien siempre que solamente se requiera un directorio de trabajo en un mismo script.
Para esto, seguir los siguientes pasos:
Si nos parece mĆ”s fĆ”cil, tambiĆ©n podemos usar el shortcut Ctrl+Shift+H para llegar al navegador y allĆ seleccionar la carpeta deseada.
Para verificar los archivos existentes en el directorio de trabajo es posible utilizar la siguiente funciĆ³n:
list.files() [1] "01_Objetos_Basicos.nb.html" "01_Objetos_Basicos.R"
[3] "01_Objetos_Basicos.Rmd" "02_Importacion.nb.html"
[5] "02_Importacion.Rmd" "ejemplo_csv.csv"
[7] "ejemplo_pyc.csv" "ejemplo_tsv.txt"
[9] "ejemplo_xls.xlsx" "rsconnect" Los archivos separados por comas (CSV) son una forma bastante frecuente de guardar tablas de datos orginadas en Excel dada su capacidad de compresiĆ³n y bajo uso de memoria de almacenamiento. La funciĆ³n read_csv de la librerĆa tidyverse permite leer y cargar archivos de este tipo, de la siguiente forma.
datos_csv <- read_csv(file = "ejemplo_csv.csv",
col_names = TRUE) #En caso de que estemos seguros que la tabla en el archivo tiene nombre de columnas
-- Column specification --------------------------------------------------------
cols(
date = col_character(),
cliente_id = col_double(),
prod_id = col_double(),
unidades = col_double(),
valor = col_double()
)Miremos en mƔs detalle el tipo de objeto creado:
class(datos_csv)[1] "spec_tbl_df" "tbl_df" "tbl" "data.frame" Notemos que el archivo es un data frame, pero tambiĆ©n es una ātibbleā que es tipo de tabla de datos mĆ”s eficiente computacionalmente y propio del tidyverse.
Estudiemos su estructura, pero ahora en vez de usar str, veamos la opciĆ³n de la funciĆ³n glimpse del tidyverse
glimpse(datos_csv)Rows: 438,692
Columns: 5
$ date <chr> "1/1/2015", "1/3/2015", "1/5/2015", "1/6/2015", "1/7/2015",~
$ cliente_id <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
$ prod_id <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
$ unidades <dbl> 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2,~
$ valor <dbl> 38.72, 38.72, 38.72, 19.36, 38.72, 38.72, 38.72, 38.72, 19.~Notemos que el resultado obtenido entre str y glimpse es el mismo, solamente hay una mejora a nivel de la presentaciĆ³n. En adelante, podemos usar cualquiera de estas sintaxis de forma indistinta.
Finalmente, demos una mirada a los datos con View (o su equivalente view en el tidyverse).
view(datos_csv)Concluyamos esta parte haciendo un resumen estadĆstico del data frame:
summary(datos_csv) date cliente_id prod_id unidades
Length:438692 Min. : 1.000 Min. : 1.0 Min. : 1.000
Class :character 1st Qu.: 3.000 1st Qu.:13.0 1st Qu.: 4.000
Mode :character Median : 6.000 Median :26.0 Median : 6.000
Mean : 5.501 Mean :25.5 Mean : 6.039
3rd Qu.: 8.000 3rd Qu.:38.0 3rd Qu.: 8.000
Max. :10.000 Max. :50.0 Max. :24.000
valor
Min. : 14.08
1st Qu.: 70.40
Median :110.00
Mean :123.59
3rd Qu.:158.40
Max. :528.00 Notemos que la importaciĆ³n funcionĆ³, pero con algunos pequeƱos contratiempos:
Para arreglar esto, vamos a volver a importar, pero con unas especificaciones adicionales en la funciĆ³n read_csv:
datos_csv <- read_csv(file = "ejemplo_csv.csv",
#Se especifican nombres personalizados a cada columna
col_names = c("fecha",
"cliente",
"producto",
"unidades",
"valor"),
#No se consideran los nombres originales
skip = 1,
#Se define la clase de cada columna
col_types = cols(fecha = col_date(format = "%m/%d/%Y"),
cliente = col_factor(),
producto = col_factor(),
unidades = col_integer(),
valor = col_double()))Veamos la estructura de nuestra tabla con estos ajustes:
glimpse(datos_csv)Rows: 438,692
Columns: 5
$ fecha <date> 2015-01-01, 2015-01-03, 2015-01-05, 2015-01-06, 2015-01-07, ~
$ cliente <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
$ producto <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
$ unidades <int> 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2~
$ valor <dbl> 38.72, 38.72, 38.72, 19.36, 38.72, 38.72, 38.72, 38.72, 19.36~Y el resumen estadĆstico:
summary(datos_csv) fecha cliente producto unidades
Min. :2015-01-01 5 : 44037 24 : 8876 Min. : 1.000
1st Qu.:2015-10-02 6 : 43938 3 : 8847 1st Qu.: 4.000
Median :2016-07-02 8 : 43900 16 : 8847 Median : 6.000
Mean :2016-07-01 1 : 43892 26 : 8831 Mean : 6.039
3rd Qu.:2017-04-02 10 : 43880 42 : 8824 3rd Qu.: 8.000
Max. :2017-12-31 4 : 43860 29 : 8816 Max. :24.000
(Other):175185 (Other):385651
valor
Min. : 14.08
1st Qu.: 70.40
Median :110.00
Mean :123.59
3rd Qu.:158.40
Max. :528.00
Se ha logrado una importaciĆ³n mucho mĆ”s limpia incluso considerando que estamos trabajando con cerca de medio millĆ³n de registros.
En muchas ocasiones, las tablas de datos se guardan de forma bƔsica y genƩrica en formatos texto (txt). Cuando los archivos estƔn en este formato, pueden compartirse entre distintos sistemas operativos (Windows, Linux, Mac), o cargarse de forma mƔs sencilla en pƔginas web.
Con R podemos cargar datos, siempre que sepamos que los separadores son tabulados, usando la funciĆ³n read_tsv del tidyverse.
datos_tsv <- read_tsv(file = "ejemplo_tsv.txt",
col_names = TRUE)
-- Column specification --------------------------------------------------------
cols(
.default = col_double()
)
i Use `spec()` for the full column specifications.Al igual que antes, veamos la estructura y un resumen estadĆstico de esta tabla importada.
glimpse(datos_tsv)Rows: 284,807
Columns: 31
$ Time <dbl> 0, 0, 1, 1, 2, 2, 4, 7, 7, 9, 10, 10, 10, 11, 12, 12, 12, 13, 1~
$ V1 <dbl> -1.3598071, 1.1918571, -1.3583541, -0.9662717, -1.1582331, -0.4~
$ V2 <dbl> -0.07278117, 0.26615071, -1.34016307, -0.18522601, 0.87773676, ~
$ V3 <dbl> 2.53634674, 0.16648011, 1.77320934, 1.79299334, 1.54871785, 1.1~
$ V4 <dbl> 1.37815522, 0.44815408, 0.37977959, -0.86329128, 0.40303393, -0~
$ V5 <dbl> -0.33832077, 0.06001765, -0.50319813, -0.01030888, -0.40719338,~
$ V6 <dbl> 0.46238778, -0.08236081, 1.80049938, 1.24720317, 0.09592146, -0~
$ V7 <dbl> 0.239598554, -0.078802983, 0.791460956, 0.237608940, 0.59294074~
$ V8 <dbl> 0.098697901, 0.085101655, 0.247675787, 0.377435875, -0.27053267~
$ V9 <dbl> 0.3637870, -0.2554251, -1.5146543, -1.3870241, 0.8177393, -0.56~
$ V10 <dbl> 0.09079417, -0.16697441, 0.20764287, -0.05495192, 0.75307443, -~
$ V11 <dbl> -0.55159953, 1.61272666, 0.62450146, -0.22648726, -0.82284288, ~
$ V12 <dbl> -0.61780086, 1.06523531, 0.06608369, 0.17822823, 0.53819555, 0.~
$ V13 <dbl> -0.99138985, 0.48909502, 0.71729273, 0.50775687, 1.34585159, -0~
$ V14 <dbl> -0.31116935, -0.14377230, -0.16594592, -0.28792375, -1.11966984~
$ V15 <dbl> 1.468176972, 0.635558093, 2.345864949, -0.631418118, 0.17512113~
$ V16 <dbl> -0.47040053, 0.46391704, -2.89008319, -1.05964725, -0.45144918,~
$ V17 <dbl> 0.207971242, -0.114804663, 1.109969379, -0.684092786, -0.237033~
$ V18 <dbl> 0.02579058, -0.18336127, -0.12135931, 1.96577500, -0.03819479, ~
$ V19 <dbl> 0.40399296, -0.14578304, -2.26185709, -1.23262197, 0.80348692, ~
$ V20 <dbl> 0.25141210, -0.06908314, 0.52497973, -0.20803778, 0.40854236, 0~
$ V21 <dbl> -0.018306778, -0.225775248, 0.247998153, -0.108300452, -0.00943~
$ V22 <dbl> 0.277837576, -0.638671953, 0.771679402, 0.005273597, 0.79827849~
$ V23 <dbl> -0.110473910, 0.101288021, 0.909412262, -0.190320519, -0.137458~
$ V24 <dbl> 0.06692808, -0.33984648, -0.68928096, -1.17557533, 0.14126698, ~
$ V25 <dbl> 0.12853936, 0.16717040, -0.32764183, 0.64737603, -0.20600959, -~
$ V26 <dbl> -0.18911484, 0.12589453, -0.13909657, -0.22192884, 0.50229222, ~
$ V27 <dbl> 0.133558377, -0.008983099, -0.055352794, 0.062722849, 0.2194222~
$ V28 <dbl> -0.021053053, 0.014724169, -0.059751841, 0.061457629, 0.2151531~
$ Amount <dbl> 149.62, 2.69, 378.66, 123.50, 69.99, 3.67, 4.99, 40.80, 93.20, ~
$ Class <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~summary(datos_tsv) Time V1 V2 V3
Min. : 0 Min. :-56.40751 Min. :-72.71573 Min. :-48.3256
1st Qu.: 54202 1st Qu.: -0.92037 1st Qu.: -0.59855 1st Qu.: -0.8904
Median : 84692 Median : 0.01811 Median : 0.06549 Median : 0.1799
Mean : 94814 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
3rd Qu.:139321 3rd Qu.: 1.31564 3rd Qu.: 0.80372 3rd Qu.: 1.0272
Max. :172792 Max. : 2.45493 Max. : 22.05773 Max. : 9.3826
V4 V5 V6 V7
Min. :-5.68317 Min. :-113.74331 Min. :-26.1605 Min. :-43.5572
1st Qu.:-0.84864 1st Qu.: -0.69160 1st Qu.: -0.7683 1st Qu.: -0.5541
Median :-0.01985 Median : -0.05434 Median : -0.2742 Median : 0.0401
Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
3rd Qu.: 0.74334 3rd Qu.: 0.61193 3rd Qu.: 0.3986 3rd Qu.: 0.5704
Max. :16.87534 Max. : 34.80167 Max. : 73.3016 Max. :120.5895
V8 V9 V10 V11
Min. :-73.21672 Min. :-13.43407 Min. :-24.58826 Min. :-4.79747
1st Qu.: -0.20863 1st Qu.: -0.64310 1st Qu.: -0.53543 1st Qu.:-0.76249
Median : 0.02236 Median : -0.05143 Median : -0.09292 Median :-0.03276
Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
3rd Qu.: 0.32735 3rd Qu.: 0.59714 3rd Qu.: 0.45392 3rd Qu.: 0.73959
Max. : 20.00721 Max. : 15.59500 Max. : 23.74514 Max. :12.01891
V12 V13 V14 V15
Min. :-18.6837 Min. :-5.79188 Min. :-19.2143 Min. :-4.49894
1st Qu.: -0.4056 1st Qu.:-0.64854 1st Qu.: -0.4256 1st Qu.:-0.58288
Median : 0.1400 Median :-0.01357 Median : 0.0506 Median : 0.04807
Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.00000
3rd Qu.: 0.6182 3rd Qu.: 0.66251 3rd Qu.: 0.4931 3rd Qu.: 0.64882
Max. : 7.8484 Max. : 7.12688 Max. : 10.5268 Max. : 8.87774
V16 V17 V18
Min. :-14.12985 Min. :-25.16280 Min. :-9.498746
1st Qu.: -0.46804 1st Qu.: -0.48375 1st Qu.:-0.498850
Median : 0.06641 Median : -0.06568 Median :-0.003636
Mean : 0.00000 Mean : 0.00000 Mean : 0.000000
3rd Qu.: 0.52330 3rd Qu.: 0.39968 3rd Qu.: 0.500807
Max. : 17.31511 Max. : 9.25353 Max. : 5.041069
V19 V20 V21
Min. :-7.213527 Min. :-54.49772 Min. :-34.83038
1st Qu.:-0.456299 1st Qu.: -0.21172 1st Qu.: -0.22839
Median : 0.003735 Median : -0.06248 Median : -0.02945
Mean : 0.000000 Mean : 0.00000 Mean : 0.00000
3rd Qu.: 0.458949 3rd Qu.: 0.13304 3rd Qu.: 0.18638
Max. : 5.591971 Max. : 39.42090 Max. : 27.20284
V22 V23 V24
Min. :-10.933144 Min. :-44.80774 Min. :-2.83663
1st Qu.: -0.542350 1st Qu.: -0.16185 1st Qu.:-0.35459
Median : 0.006782 Median : -0.01119 Median : 0.04098
Mean : 0.000000 Mean : 0.00000 Mean : 0.00000
3rd Qu.: 0.528554 3rd Qu.: 0.14764 3rd Qu.: 0.43953
Max. : 10.503090 Max. : 22.52841 Max. : 4.58455
V25 V26 V27
Min. :-10.29540 Min. :-2.60455 Min. :-22.565679
1st Qu.: -0.31715 1st Qu.:-0.32698 1st Qu.: -0.070840
Median : 0.01659 Median :-0.05214 Median : 0.001342
Mean : 0.00000 Mean : 0.00000 Mean : 0.000000
3rd Qu.: 0.35072 3rd Qu.: 0.24095 3rd Qu.: 0.091045
Max. : 7.51959 Max. : 3.51735 Max. : 31.612198
V28 Amount Class
Min. :-15.43008 Min. : 0.00 Min. :0.000000
1st Qu.: -0.05296 1st Qu.: 5.60 1st Qu.:0.000000
Median : 0.01124 Median : 22.00 Median :0.000000
Mean : 0.00000 Mean : 88.35 Mean :0.001728
3rd Qu.: 0.07828 3rd Qu.: 77.17 3rd Qu.:0.000000
Max. : 33.84781 Max. :25691.16 Max. :1.000000 Los archivos planos tambiĆ©n pueden estar separados por caracteres diferentes a comas (,) o tabulados. En estos casos podemos usar la funciĆ³n read_delim del tidyverse, como en el siguiente ejemplo en el que los datos estĆ”n separados por el signo de punto y coma (;)
datos_pyc <- read_delim(file = "ejemplo_pyc.txt",
delim = ";",
col_names = TRUE)
-- Column specification --------------------------------------------------------
cols(
.default = col_double(),
city = col_character(),
city_ascii = col_character(),
city_alt = col_character(),
state_id = col_character(),
state_name = col_character(),
county_name = col_character(),
county_fips_all = col_character(),
county_name_all = col_character(),
source = col_character(),
military = col_logical(),
incorporated = col_logical(),
township = col_logical(),
timezone = col_character(),
zips = col_character(),
`,` = col_character()
)
i Use `spec()` for the full column specifications.Notemos que entre los parƔmetros (o atributos) de la funcion utilizada se encuentra delim, donde se indica cual es el separador de los datos que queremos importar.
Igual que antes, veamos su estructura y un resumen estadĆstico
glimpse(datos_pyc)Rows: 107,546
Columns: 82
$ city <chr> "Rodena Beach", "South Creek", "Toroda", "~
$ city_ascii <chr> "Rodena Beach", "South Creek", "Toroda", "~
$ city_alt <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
$ state_id <chr> "WA", "WA", "WA", "WA", "WA", "WA", "WA", ~
$ state_name <chr> "Washington", "Washington", "Washington", ~
$ county_fips <dbl> 53029, 53053, 53019, 53053, 53037, 53005, ~
$ county_name <chr> "Island", "Pierce", "Ferry", "Pierce", "Ki~
$ county_fips_all <chr> "53029", "53053", "53019", "53053", "53037~
$ county_name_all <chr> "Island", "Pierce", "Ferry", "Pierce", "Ki~
$ lat <dbl> 48.2190, 46.9994, 48.9429, 47.1223, 47.250~
$ lng <dbl> -122.6310, -122.3921, -118.7620, -122.5354~
$ population <dbl> NA, 2500, NA, NA, 947, NA, 441, 9507, 3591~
$ population_proper <dbl> NA, 2500, NA, NA, 947, NA, 441, 9507, 3591~
$ density <dbl> 134.2, 125.0, 18.3, 968.8, 84.0, 126.8, 16~
$ source <chr> "point", "polygon", "point", "point", "pol~
$ military <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, ~
$ incorporated <lgl> FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, TR~
$ township <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, ~
$ timezone <chr> "America/Los_Angeles", "America/Los_Angele~
$ ranking <dbl> 4, 3, 4, 4, 3, 4, 3, 3, 3, 5, 4, 3, 4, 5, ~
$ zips <chr> "98239", "98580 98387 98338", "99118", "98~
$ id <dbl> 1840097246, 1840116412, 1840097767, 184009~
$ age_median <dbl> NA, 50.1, NA, NA, 41.4, NA, 53.0, 44.8, 41~
$ age_under_10 <dbl> NA, 5.0, NA, NA, 5.0, NA, 11.5, 11.3, 10.9~
$ age_10_to_19 <dbl> NA, 9.4, NA, NA, 21.8, NA, 9.8, 12.5, 14.1~
$ age_20s <dbl> NA, 9.8, NA, NA, 8.1, NA, 4.7, 7.5, 10.0, ~
$ age_30s <dbl> NA, 7.3, NA, NA, 12.8, NA, 11.7, 10.4, 13.~
$ age_40s <dbl> NA, 18.1, NA, NA, 16.6, NA, 7.4, 15.9, 14.~
$ age_50s <dbl> NA, 26.1, NA, NA, 8.4, NA, 15.0, 11.6, 16.~
$ age_60s <dbl> NA, 11.8, NA, NA, 11.6, NA, 19.8, 14.6, 12~
$ age_70s <dbl> NA, 8.1, NA, NA, 8.4, NA, 15.7, 8.1, 6.6, ~
$ age_over_80 <dbl> NA, 4.4, NA, NA, 7.2, NA, 4.3, 8.1, 1.3, N~
$ male <dbl> NA, 53.8, NA, NA, 48.9, NA, 50.6, 48.5, 54~
$ female <dbl> NA, 46.2, NA, NA, 51.1, NA, 49.4, 51.5, 45~
$ married <dbl> NA, 49.8, NA, NA, 43.1, NA, 55.2, 55.2, 58~
$ divorced <dbl> NA, 20.4, NA, NA, 19.7, NA, 20.2, 11.9, 15~
$ never_married <dbl> NA, 25.9, NA, NA, 27.5, NA, 18.2, 23.5, 22~
$ widowed <dbl> NA, 4.0, NA, NA, 9.7, NA, 6.4, 9.4, 3.8, N~
$ family_size <dbl> NA, 2.67, NA, NA, 2.81, NA, 2.78, 2.94, 3.~
$ family_dual_income <dbl> NA, 58.3, NA, NA, 46.9, NA, 23.9, 45.5, 61~
$ income_household_median <dbl> NA, 58364, NA, NA, 54853, NA, 30833, 74159~
$ income_household_under_5 <dbl> NA, 0.0, NA, NA, 1.7, NA, 0.0, 3.3, 4.3, N~
$ income_household_5_to_10 <dbl> NA, 2.0, NA, NA, 3.8, NA, 8.4, 2.1, 0.0, N~
$ income_household_10_to_15 <dbl> NA, 1.7, NA, NA, 4.5, NA, 7.9, 1.9, 0.7, N~
$ income_household_15_to_20 <dbl> NA, 1.6, NA, NA, 9.4, NA, 6.8, 3.3, 2.1, N~
$ income_household_20_to_25 <dbl> NA, 8.0, NA, NA, 1.0, NA, 9.5, 2.9, 2.7, N~
$ income_household_25_to_35 <dbl> NA, 12.5, NA, NA, 6.6, NA, 25.3, 8.8, 2.7,~
$ income_household_35_to_50 <dbl> NA, 21.7, NA, NA, 16.0, NA, 17.4, 10.7, 10~
$ income_household_50_to_75 <dbl> NA, 17.9, NA, NA, 19.1, NA, 11.6, 18.0, 16~
$ income_household_75_to_100 <dbl> NA, 15.0, NA, NA, 24.7, NA, 5.3, 14.0, 15.~
$ income_household_100_to_150 <dbl> NA, 10.2, NA, NA, 4.2, NA, 5.3, 17.6, 21.1~
$ income_household_150_over <dbl> NA, 9.5, NA, NA, 9.0, NA, 2.6, 17.5, 22.7,~
$ income_household_six_figure <dbl> NA, 19.7, NA, NA, 13.2, NA, 7.9, 35.1, 43.~
$ income_individual_median <dbl> NA, 29304, NA, NA, 28393, NA, 16042, 36947~
$ home_ownership <dbl> NA, 85.1, NA, NA, 67.0, NA, 85.3, 59.9, 91~
$ home_value <dbl> NA, NA, NA, NA, 193836, NA, 88075, 380936,~
$ rent_median <dbl> NA, 1092, NA, NA, 1101, NA, 550, 1507, 178~
$ rent_burden <dbl> NA, 22.7, NA, NA, 18.7, NA, 20.3, 18.9, 22~
$ education_less_highschool <dbl> NA, 8.0, NA, NA, 9.7, NA, 7.2, 3.6, 7.9, N~
$ education_highschool <dbl> NA, 36.3, NA, NA, 37.0, NA, 35.4, 12.6, 32~
$ education_some_college <dbl> NA, 41.4, NA, NA, 29.6, NA, 44.8, 32.8, 35~
$ education_bachelors <dbl> NA, 10.9, NA, NA, 13.7, NA, 7.5, 31.4, 17.~
$ education_graduate <dbl> NA, 3.4, NA, NA, 10.0, NA, 5.0, 19.5, 7.6,~
$ education_college_or_above <dbl> NA, 14.3, NA, NA, 23.7, NA, 12.5, 50.9, 24~
$ education_stem_degree <dbl> NA, 71.0, NA, NA, 67.7, NA, 12.5, 44.9, 47~
$ labor_force_participation <dbl> NA, 66.0, NA, NA, 57.4, NA, 42.2, 56.1, 61~
$ unemployment_rate <dbl> NA, 3.5, NA, NA, 1.6, NA, 11.8, 9.4, 4.7, ~
$ race_white <dbl> NA, 93.0, NA, NA, 98.8, NA, 88.1, 87.8, 88~
$ race_black <dbl> NA, 1.2, NA, NA, 0.0, NA, 0.0, 0.9, 0.0, N~
$ race_asian <dbl> NA, 0.0, NA, NA, 0.0, NA, 0.0, 5.5, 5.4, N~
$ race_native <dbl> NA, 1.9, NA, NA, 0.6, NA, 0.7, 1.4, 0.0, N~
$ race_pacific <dbl> NA, 0.0, NA, NA, 0.0, NA, 0.0, 0.1, 0.0, N~
$ race_other <dbl> NA, 0.0, NA, NA, 0.0, NA, 1.0, 1.2, 0.6, N~
$ race_multiple <dbl> NA, 3.8, NA, NA, 0.6, NA, 10.3, 3.0, 5.3, ~
$ hispanic <dbl> NA, 2.6, NA, NA, 0.6, NA, 6.7, 4.3, 7.1, N~
$ disabled <dbl> NA, 21.8, NA, NA, 17.3, NA, 31.7, 14.9, 12~
$ poverty <dbl> NA, 2.8, NA, NA, 13.3, NA, 18.3, 5.6, 6.1,~
$ limited_english <dbl> NA, 1.5, NA, NA, 0.0, NA, 0.0, 1.9, 0.0, N~
$ commute_time <dbl> NA, 43.3, NA, NA, 21.5, NA, 31.8, 23.8, 34~
$ health_uninsured <dbl> NA, 3.6, NA, NA, 11.9, NA, 7.2, 5.4, 7.8, ~
$ veteran <dbl> NA, 18.1, NA, NA, 10.8, NA, 18.3, 12.3, 12~
$ `,` <chr> ",", ",", ",", ",", ",", ",", ",", ",", ",~summary(datos_pyc) city city_ascii city_alt state_id
Length:107546 Length:107546 Length:107546 Length:107546
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
state_name county_fips county_name county_fips_all
Length:107546 Min. : 1001 Length:107546 Length:107546
Class :character 1st Qu.:18141 Class :character Class :character
Mode :character Median :31051 Mode :character Mode :character
Mean :30565
3rd Qu.:42091
Max. :78030
county_name_all lat lng population
Length:107546 Min. :17.69 Min. :-176.63 Min. : 0
Class :character 1st Qu.:35.24 1st Qu.: -94.88 1st Qu.: 347
Mode :character Median :38.91 Median : -85.79 Median : 1174
Mean :38.42 Mean : -89.33 Mean : 13484
3rd Qu.:41.66 3rd Qu.: -78.96 3rd Qu.: 4580
Max. :71.27 Max. : 178.88 Max. :19354922
NA's :76405
population_proper density source military
Min. : 0 Min. : 0.0 Length:107546 Mode :logical
1st Qu.: 347 1st Qu.: 11.5 Class :character FALSE:107459
Median : 1171 Median : 50.6 Mode :character TRUE :87
Mean : 8599 Mean : 286.2
3rd Qu.: 4415 3rd Qu.: 264.0
Max. :8622698 Max. :32085.0
NA's :76405 NA's :236
incorporated township timezone ranking
Mode :logical Mode :logical Length:107546 Min. :1.000
FALSE:86899 FALSE:105291 Class :character 1st Qu.:3.000
TRUE :20647 TRUE :2255 Mode :character Median :4.000
Mean :3.909
3rd Qu.:4.000
Max. :5.000
zips id age_median age_under_10
Length:107546 Min. :1.630e+09 Min. : 3.90 Min. : 0.00
Class :character 1st Qu.:1.840e+09 1st Qu.:35.70 1st Qu.: 8.60
Mode :character Median :1.840e+09 Median :41.00 Median :11.80
Mean :1.838e+09 Mean :41.79 Mean :11.83
3rd Qu.:1.840e+09 3rd Qu.:47.00 3rd Qu.:14.90
Max. :1.850e+09 Max. :91.40 Max. :71.40
NA's :76859 NA's :76642
age_10_to_19 age_20s age_30s age_40s
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 9.50 1st Qu.: 8.10 1st Qu.: 8.60 1st Qu.: 9.50
Median :12.50 Median : 11.40 Median : 11.50 Median : 12.00
Mean :12.56 Mean : 11.69 Mean : 11.37 Mean : 12.07
3rd Qu.:15.40 3rd Qu.: 14.50 3rd Qu.: 14.00 3rd Qu.: 14.50
Max. :79.70 Max. :100.00 Max. :100.00 Max. :100.00
NA's :76642 NA's :76642 NA's :76642 NA's :76642
age_50s age_60s age_70s age_over_80
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0
1st Qu.: 11.30 1st Qu.: 9.10 1st Qu.: 4.80 1st Qu.: 2.2
Median : 14.00 Median : 11.80 Median : 6.80 Median : 3.9
Mean : 14.74 Mean : 13.07 Mean : 7.96 Mean : 4.7
3rd Qu.: 17.00 3rd Qu.: 15.10 3rd Qu.: 9.40 3rd Qu.: 6.0
Max. :100.00 Max. :100.10 Max. :100.00 Max. :100.0
NA's :76642 NA's :76642 NA's :76642 NA's :76642
male female married divorced
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 46.80 1st Qu.: 48.30 1st Qu.: 43.20 1st Qu.: 10.00
Median : 49.10 Median : 50.90 Median : 51.10 Median : 13.60
Mean : 49.48 Mean : 50.52 Mean : 50.69 Mean : 14.25
3rd Qu.: 51.70 3rd Qu.: 53.20 3rd Qu.: 58.80 3rd Qu.: 17.60
Max. :100.00 Max. :100.00 Max. :100.00 Max. :100.00
NA's :76884 NA's :76884 NA's :76884 NA's :76884
never_married widowed family_size family_dual_income
Min. : 0.00 Min. : 0.00 Min. : 1.17 Min. : 0.00
1st Qu.: 21.30 1st Qu.: 4.50 1st Qu.: 2.81 1st Qu.: 39.70
Median : 26.70 Median : 6.60 Median : 3.05 Median : 50.00
Mean : 27.69 Mean : 7.37 Mean : 3.12 Mean : 48.68
3rd Qu.: 32.90 3rd Qu.: 9.20 3rd Qu.: 3.32 3rd Qu.: 58.80
Max. :100.00 Max. :100.00 Max. :19.96 Max. :100.00
NA's :76884 NA's :76884 NA's :76828 NA's :76828
income_household_median income_household_under_5 income_household_5_to_10
Min. : 2499 Min. : 0.00 Min. : 0.00
1st Qu.: 38125 1st Qu.: 0.50 1st Qu.: 0.60
Median : 49063 Median : 2.20 Median : 2.50
Mean : 55252 Mean : 3.39 Mean : 4.01
3rd Qu.: 64492 3rd Qu.: 4.40 3rd Qu.: 5.40
Max. :250001 Max. :100.00 Max. :100.00
NA's :78407 NA's :76661 NA's :76661
income_household_10_to_15 income_household_15_to_20 income_household_20_to_25
Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 2.00 1st Qu.: 2.30 1st Qu.: 2.50
Median : 4.70 Median : 4.90 Median : 5.00
Mean : 5.96 Mean : 5.92 Mean : 5.85
3rd Qu.: 8.20 3rd Qu.: 8.00 3rd Qu.: 7.80
Max. :100.00 Max. :100.00 Max. :100.00
NA's :76661 NA's :76661 NA's :76661
income_household_25_to_35 income_household_35_to_50 income_household_50_to_75
Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 6.50 1st Qu.: 9.70 1st Qu.: 13.60
Median : 10.30 Median : 13.70 Median : 18.00
Mean : 11.11 Mean : 14.32 Mean : 18.31
3rd Qu.: 14.10 3rd Qu.: 17.70 3rd Qu.: 22.30
Max. :100.00 Max. :100.00 Max. :100.00
NA's :76661 NA's :76661 NA's :76661
income_household_75_to_100 income_household_100_to_150
Min. : 0.00 Min. : 0.00
1st Qu.: 7.50 1st Qu.: 5.40
Median : 11.50 Median : 10.20
Mean : 11.78 Mean : 11.44
3rd Qu.: 15.20 3rd Qu.: 16.30
Max. :100.00 Max. :100.00
NA's :76661 NA's :76661
income_household_150_over income_household_six_figure income_individual_median
Min. : 0.0 Min. : 0.00 Min. : 2499
1st Qu.: 1.1 1st Qu.: 8.10 1st Qu.: 21213
Median : 4.0 Median : 14.70 Median : 26170
Mean : 7.9 Mean : 19.34 Mean : 28084
3rd Qu.: 9.5 3rd Qu.: 26.10 3rd Qu.: 32300
Max. :100.0 Max. :100.10 Max. :250001
NA's :76661 NA's :76661 NA's :77897
home_ownership home_value rent_median rent_burden
Min. : 0.00 Min. : 10817 Min. : 99.0 Min. : 1.70
1st Qu.: 61.70 1st Qu.: 82632 1st Qu.: 548.0 1st Qu.: 13.70
Median : 73.40 Median : 122913 Median : 760.0 Median : 16.60
Mean : 71.93 Mean : 179826 Mean : 920.5 Mean : 16.98
3rd Qu.: 84.10 3rd Qu.: 205909 3rd Qu.:1125.0 3rd Qu.: 19.70
Max. :100.00 Max. :2000001 Max. :4001.0 Max. :732.80
NA's :76661 NA's :81973 NA's :78526 NA's :79440
education_less_highschool education_highschool education_some_college
Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 5.80 1st Qu.: 26.20 1st Qu.: 24.40
Median : 10.50 Median : 34.60 Median : 30.20
Mean : 13.17 Mean : 34.44 Mean : 30.18
3rd Qu.: 17.50 3rd Qu.: 42.40 3rd Qu.: 35.70
Max. :100.00 Max. :100.00 Max. :100.00
NA's :76892 NA's :76892 NA's :76892
education_bachelors education_graduate education_college_or_above
Min. : 0.00 Min. : 0.0 Min. : 0.00
1st Qu.: 7.10 1st Qu.: 2.5 1st Qu.: 10.80
Median : 12.10 Median : 5.6 Median : 17.80
Mean : 14.11 Mean : 8.1 Mean : 22.21
3rd Qu.: 19.20 3rd Qu.: 10.5 3rd Qu.: 29.30
Max. :100.00 Max. :100.0 Max. :100.00
NA's :76892 NA's :76892 NA's :76892
education_stem_degree labor_force_participation unemployment_rate
Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 30.00 1st Qu.: 53.20 1st Qu.: 2.90
Median : 39.70 Median : 61.20 Median : 5.50
Mean : 39.07 Mean : 59.52 Mean : 6.97
3rd Qu.: 47.70 3rd Qu.: 67.50 3rd Qu.: 8.90
Max. :100.00 Max. :100.00 Max. :100.00
NA's :77966 NA's :76642 NA's :76804
race_white race_black race_asian race_native
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 80.30 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 92.90 Median : 0.60 Median : 0.00 Median : 0.00
Mean : 84.09 Mean : 6.77 Mean : 1.76 Mean : 2.56
3rd Qu.: 97.60 3rd Qu.: 4.40 3rd Qu.: 1.50 3rd Qu.: 0.50
Max. :100.00 Max. :100.00 Max. :100.00 Max. :100.00
NA's :76884 NA's :76884 NA's :76884 NA's :76884
race_pacific race_other race_multiple hispanic
Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.40
Median : 0.00 Median : 0.1 Median : 1.50 Median : 3.00
Mean : 0.13 Mean : 2.1 Mean : 2.57 Mean : 9.58
3rd Qu.: 0.00 3rd Qu.: 1.5 3rd Qu.: 3.30 3rd Qu.: 9.20
Max. :75.00 Max. :100.0 Max. :100.00 Max. :100.00
NA's :76884 NA's :76884 NA's :76884 NA's :76884
disabled poverty limited_english commute_time
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 2.80
1st Qu.: 10.60 1st Qu.: 6.60 1st Qu.: 0.00 1st Qu.: 20.20
Median : 14.70 Median : 12.60 Median : 0.00 Median : 24.60
Mean : 16.07 Mean : 15.45 Mean : 2.77 Mean : 25.14
3rd Qu.: 19.80 3rd Qu.: 21.10 3rd Qu.: 1.90 3rd Qu.: 29.40
Max. :100.00 Max. :100.00 Max. :100.00 Max. :110.60
NA's :76645 NA's :76893 NA's :76661 NA's :78764
health_uninsured veteran ,
Min. : 0.00 Min. : 0.00 Length:107546
1st Qu.: 4.50 1st Qu.: 6.00 Class :character
Median : 8.20 Median : 8.50 Mode :character
Mean : 10.42 Mean : 9.28
3rd Qu.: 13.70 3rd Qu.: 11.40
Max. :100.00 Max. :100.00
NA's :76645 NA's :76644 Si bien en el mundo del anƔlisis de datos no se suele tener como un formato preferente a las hojas de cƔlculo de Excel, no se puede negar que son una de las formas mƔs comunes de presentar tablas de datos en todo el mundo.
En este sentido, R cuenta con una librerĆa adicional que facilita su lectura y posterior importaciĆ³n: readxl. InstalĆ©mosla y activĆ©mosla en nuestra sesiĆ³n de R Studio.
install.packages("readxl")library(readxl)Para entender el uso de esta librerĆa, veamos primero la funciĆ³n excel_sheets que nos permite visualizar todas las hojas de cĆ”lculo activades en un archivo Excel.
excel_sheets(path = "ejemplo_xls.xlsx")[1] "vw_propiedades" "vw_calendario" "vw_especificaciones"Como se puede apreciar, el archivo excel tiene 3 hojas distintas. Queremos importar solamente la primera, para lo cual vamos en primera instancia a guardar las hojas en un vector llamado āhojas_excelā.
hojas_excel <- excel_sheets(path = "ejemplo_xls.xlsx")Una vez, que contamos con este objeto, vamos a importar la primera hoja, utilizando la funciĆ³n read_excel de readxl.
datos_xls <- read_excel(path = "ejemplo_xls.xlsx",
sheet = hojas_excel[1],
col_names = TRUE)Veamos su estructura y el resumen estadĆstico correspondiente.
glimpse(datos_xls)Rows: 99
Columns: 5
$ id <chr> "101", "102", "103", "104", "105", "106", "107", "10~
$ fecha_publicacion <chr> "2020-12-17T00:00:00Z", "2020-12-16T00:00:00Z", "202~
$ nombre <chr> "Edificio Alba", "Deko", "Auki", "Tribeca", "Mon Rev~
$ tipo <chr> "Departamentos", "Departamentos", "Departamentos", "~
$ vendedor <chr> "CEP_EdificioAlba", "CEP_Deko", "METROEJECIA.LTDA.*"~summary(datos_xls) id fecha_publicacion nombre tipo
Length:99 Length:99 Length:99 Length:99
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
vendedor
Length:99
Class :character
Mode :character No siempre se cuentan con archivos guardados de forma local en el contexto del anƔlisis de datos. En muchas ocasiones, las tablas que necesitamos estan disponibles en pƔginas web y puede resultar laborioso el proceso de descargar, guardar y luego importar.
Para optimizar esto, todas las funciones que hemos visto hasta ahora de las librerĆas tidyverse y readxl, pueden automĆ”ticamente importar directamente archivos desde pĆ”ginas web especificando la direcciĆ³n URL donde se encuentran.
Veamos unos cuantos de ejemplos:
datos_web1 <- read_delim(file = "https://raw.githubusercontent.com/jsaraujo5081/house_pricing/main/dataset.csv",
delim = ";",
col_names = TRUE)
-- Column specification --------------------------------------------------------
cols(
Comuna = col_character(),
Link = col_character(),
Tipo_Vivienda = col_character(),
N_Habitaciones = col_double(),
N_Banos = col_double(),
N_Estacionamientos = col_character(),
Total_Superficie_M2 = col_double(),
Superficie_Construida_M2 = col_character(),
` Valor_UF ` = col_double(),
Valor_CLP = col_double(),
Direccion = col_character(),
Quien_Vende = col_character(),
Corredor = col_character()
)Veamos su estructura:
glimpse(datos_web1)Rows: 1,139
Columns: 13
$ Comuna <chr> "Calera de Tango", "Calera de Tango", "Calera~
$ Link <chr> "https://chilepropiedades.cl/ver-publicacion/~
$ Tipo_Vivienda <chr> "Casa", "Casa", "Casa", "Casa", "Casa", "Casa~
$ N_Habitaciones <dbl> 5, 6, 3, 8, 3, 3, 3, 5, 5, 6, 7, 6, 3, 3, 3, ~
$ N_Banos <dbl> 6, 6, 3, 6, 2, 2, 2, 4, 3, 4, 5, 5, 1, 2, 2, ~
$ N_Estacionamientos <chr> "3", "6", "No", "No", "3", "No", "4", "No", "~
$ Total_Superficie_M2 <dbl> 5000, 5000, 2027, 5000, 5000, 5000, 2600, 500~
$ Superficie_Construida_M2 <chr> "440", "430", "140", "480", "196", "170", "14~
$ ` Valor_UF ` <dbl> 12.200, 13.000, 10.300, 21.500, 9.100, 11.800~
$ Valor_CLP <dbl> 351360000, 374400000, 296640000, 619200000, 2~
$ Direccion <chr> "Calera de Tango, Queilen", "Calera de Tango,~
$ Quien_Vende <chr> "Gabriela Mellado V.", "Gabriela Mellado V.",~
$ Corredor <chr> "Zenpro Propiedades", "Zenpro Propiedades", "~Otro ejemplo con datos del departamento de estadĆsticas de los Estados Unidos:
datos_web2 <- read_csv(file = "https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/totals/co-est2019-alldata.csv",
col_names = TRUE)
-- Column specification --------------------------------------------------------
cols(
.default = col_double(),
SUMLEV = col_character(),
STATE = col_character(),
COUNTY = col_character(),
STNAME = col_character(),
CTYNAME = col_character()
)
i Use `spec()` for the full column specifications.Veamos su estructura y un resumen estadĆstico
glimpse(datos_web2)Rows: 3,193
Columns: 164
$ SUMLEV <chr> "040", "050", "050", "050", "050", "050", "050",~
$ REGION <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ~
$ DIVISION <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, ~
$ STATE <chr> "01", "01", "01", "01", "01", "01", "01", "01", ~
$ COUNTY <chr> "000", "001", "003", "005", "007", "009", "011",~
$ STNAME <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Ala~
$ CTYNAME <chr> "Alabama", "Autauga County", "Baldwin County", "~
$ CENSUS2010POP <dbl> 4779736, 54571, 182265, 27457, 22915, 57322, 109~
$ ESTIMATESBASE2010 <dbl> 4780125, 54597, 182265, 27455, 22915, 57322, 109~
$ POPESTIMATE2010 <dbl> 4785437, 54773, 183112, 27327, 22870, 57376, 108~
$ POPESTIMATE2011 <dbl> 4799069, 55227, 186558, 27341, 22745, 57560, 106~
$ POPESTIMATE2012 <dbl> 4815588, 54954, 190145, 27169, 22667, 57580, 106~
$ POPESTIMATE2013 <dbl> 4830081, 54727, 194885, 26937, 22521, 57619, 105~
$ POPESTIMATE2014 <dbl> 4841799, 54893, 199183, 26755, 22553, 57526, 106~
$ POPESTIMATE2015 <dbl> 4852347, 54864, 202939, 26283, 22566, 57526, 104~
$ POPESTIMATE2016 <dbl> 4863525, 55243, 207601, 25806, 22586, 57494, 103~
$ POPESTIMATE2017 <dbl> 4874486, 55390, 212521, 25157, 22550, 57787, 101~
$ POPESTIMATE2018 <dbl> 4887681, 55533, 217855, 24872, 22367, 57771, 101~
$ POPESTIMATE2019 <dbl> 4903185, 55869, 223234, 24686, 22394, 57826, 101~
$ NPOPCHG_2010 <dbl> 5312, 176, 847, -128, -45, 54, -35, -8, -118, -4~
$ NPOPCHG_2011 <dbl> 13632, 454, 3446, 14, -125, 184, -201, -66, -664~
$ NPOPCHG_2012 <dbl> 16519, -273, 3587, -172, -78, 20, -69, -196, -55~
$ NPOPCHG_2013 <dbl> 14493, -227, 4740, -232, -146, 39, -57, -314, -7~
$ NPOPCHG_2014 <dbl> 11718, 166, 4298, -182, 32, -93, 114, -29, -554,~
$ NPOPCHG_2015 <dbl> 10548, -29, 3756, -472, 13, 0, -263, -165, -448,~
$ NPOPCHG_2016 <dbl> 11178, 379, 4662, -477, 20, -32, -11, -150, -496~
$ NPOPCHG_2017 <dbl> 10961, 147, 4920, -649, -36, 293, -213, -124, -2~
$ NPOPCHG_2018 <dbl> 13195, 143, 5334, -285, -183, -16, -2, -257, -37~
$ NPOPCHG_2019 <dbl> 15504, 336, 5379, -186, 27, 55, -73, -183, -726,~
$ BIRTHS2010 <dbl> 14226, 150, 516, 71, 44, 184, 39, 65, 318, 83, 5~
$ BIRTHS2011 <dbl> 59690, 638, 2189, 331, 264, 744, 169, 274, 1385,~
$ BIRTHS2012 <dbl> 59067, 615, 2093, 300, 246, 712, 122, 240, 1356,~
$ BIRTHS2013 <dbl> 57929, 571, 2160, 282, 258, 647, 130, 241, 1309,~
$ BIRTHS2014 <dbl> 58903, 640, 2212, 264, 253, 619, 124, 251, 1315,~
$ BIRTHS2015 <dbl> 59647, 651, 2257, 271, 251, 716, 125, 239, 1388,~
$ BIRTHS2016 <dbl> 59389, 666, 2300, 276, 276, 700, 143, 230, 1382,~
$ BIRTHS2017 <dbl> 58961, 676, 2300, 280, 291, 660, 136, 244, 1324,~
$ BIRTHS2018 <dbl> 58271, 631, 2310, 263, 232, 679, 104, 216, 1299,~
$ BIRTHS2019 <dbl> 57313, 624, 2304, 256, 240, 651, 109, 213, 1269,~
$ DEATHS2010 <dbl> 11075, 157, 533, 131, 32, 131, 53, 66, 311, 80, ~
$ DEATHS2011 <dbl> 48833, 514, 1829, 323, 276, 569, 133, 264, 1325,~
$ DEATHS2012 <dbl> 48366, 560, 1885, 286, 236, 587, 116, 274, 1359,~
$ DEATHS2013 <dbl> 50851, 582, 1900, 295, 275, 583, 120, 262, 1410,~
$ DEATHS2014 <dbl> 49712, 573, 1987, 308, 247, 587, 115, 287, 1395,~
$ DEATHS2015 <dbl> 51876, 584, 2098, 332, 265, 633, 133, 276, 1455,~
$ DEATHS2016 <dbl> 51710, 547, 2022, 280, 241, 650, 130, 253, 1475,~
$ DEATHS2017 <dbl> 53195, 573, 2099, 295, 252, 721, 144, 269, 1393,~
$ DEATHS2018 <dbl> 53665, 518, 2312, 329, 263, 688, 92, 274, 1616, ~
$ DEATHS2019 <dbl> 53879, 541, 2326, 312, 252, 657, 109, 272, 1532,~
$ NATURALINC2010 <dbl> 3151, -7, -17, -60, 12, 53, -14, -1, 7, 3, -56, ~
$ NATURALINC2011 <dbl> 10857, 124, 360, 8, -12, 175, 36, 10, 60, -41, -~
$ NATURALINC2012 <dbl> 10701, 55, 208, 14, 10, 125, 6, -34, -3, -82, -8~
$ NATURALINC2013 <dbl> 7078, -11, 260, -13, -17, 64, 10, -21, -101, -49~
$ NATURALINC2014 <dbl> 9191, 67, 225, -44, 6, 32, 9, -36, -80, -29, -95~
$ NATURALINC2015 <dbl> 7771, 67, 159, -61, -14, 83, -8, -37, -67, -24, ~
$ NATURALINC2016 <dbl> 7679, 119, 278, -4, 35, 50, 13, -23, -93, -53, -~
$ NATURALINC2017 <dbl> 5766, 103, 201, -15, 39, -61, -8, -25, -69, -107~
$ NATURALINC2018 <dbl> 4606, 113, -2, -66, -31, -9, 12, -58, -317, -55,~
$ NATURALINC2019 <dbl> 3434, 83, -22, -56, -12, -6, 0, -59, -263, -87, ~
$ INTERNATIONALMIG2010 <dbl> 924, 25, 36, -1, 0, -2, 1, 0, -4, 6, 1, -2, 1, 1~
$ INTERNATIONALMIG2011 <dbl> 4665, 4, 177, -5, 10, -16, 17, 1, 26, 27, -2, 28~
$ INTERNATIONALMIG2012 <dbl> 5817, -14, 239, -12, 19, 5, 7, 6, 67, 31, 7, -3,~
$ INTERNATIONALMIG2013 <dbl> 5046, 12, 204, -10, 20, 45, 18, 7, 45, 29, 9, 11~
$ INTERNATIONALMIG2014 <dbl> 3684, 7, 113, 4, 14, 40, 6, 20, 66, 29, -1, 17, ~
$ INTERNATIONALMIG2015 <dbl> 4580, 13, 131, 13, 13, 13, 0, 27, 66, 14, -2, 29~
$ INTERNATIONALMIG2016 <dbl> 5777, -3, 180, 17, 14, 22, 8, 28, 102, 13, -2, 6~
$ INTERNATIONALMIG2017 <dbl> 3011, -12, 86, 12, 10, -1, 8, 22, 69, 7, -3, 18,~
$ INTERNATIONALMIG2018 <dbl> 3379, -7, 97, 12, 10, 5, 13, 17, 103, 6, -3, 35,~
$ INTERNATIONALMIG2019 <dbl> 2772, -16, 80, 13, 10, 6, -1, 18, 14, 6, -3, 35,~
$ DOMESTICMIG2010 <dbl> 1244, 147, 782, -69, -59, 9, -24, -4, -113, -54,~
$ DOMESTICMIG2011 <dbl> -1893, 327, 2899, 13, -124, 28, -254, -77, -752,~
$ DOMESTICMIG2012 <dbl> -114, -329, 3055, -176, -105, -100, -81, -170, -~
$ DOMESTICMIG2013 <dbl> 2297, -226, 4176, -210, -151, -65, -85, -304, -6~
$ DOMESTICMIG2014 <dbl> -959, 101, 3864, -142, 16, -158, 96, -11, -534, ~
$ DOMESTICMIG2015 <dbl> -1544, -107, 3433, -430, 17, -90, -259, -153, -4~
$ DOMESTICMIG2016 <dbl> -2157, 266, 4188, -492, -30, -102, -31, -154, -5~
$ DOMESTICMIG2017 <dbl> 2298, 59, 4619, -649, -83, 358, -213, -121, -259~
$ DOMESTICMIG2018 <dbl> 5279, 37, 5224, -231, -164, -9, -28, -216, -159,~
$ DOMESTICMIG2019 <dbl> 9387, 270, 5297, -141, 31, 59, -72, -141, -475, ~
$ NETMIG2010 <dbl> 2168, 172, 818, -70, -59, 7, -23, -4, -117, -48,~
$ NETMIG2011 <dbl> 2772, 331, 3076, 8, -114, 12, -237, -76, -726, -~
$ NETMIG2012 <dbl> 5703, -343, 3294, -188, -86, -95, -74, -164, -53~
$ NETMIG2013 <dbl> 7343, -214, 4380, -220, -131, -20, -67, -297, -6~
$ NETMIG2014 <dbl> 2725, 108, 3977, -138, 30, -118, 102, 9, -468, -~
$ NETMIG2015 <dbl> 3036, -94, 3564, -417, 30, -77, -259, -126, -372~
$ NETMIG2016 <dbl> 3620, 263, 4368, -475, -16, -80, -23, -126, -400~
$ NETMIG2017 <dbl> 5309, 47, 4705, -637, -73, 357, -205, -99, -190,~
$ NETMIG2018 <dbl> 8658, 30, 5321, -219, -154, -4, -15, -199, -56, ~
$ NETMIG2019 <dbl> 12159, 254, 5377, -128, 41, 65, -73, -123, -461,~
$ RESIDUAL2010 <dbl> -7, 11, 46, 2, 2, -6, 2, -3, -8, -2, -2, -4, 0, ~
$ RESIDUAL2011 <dbl> 3, -1, 10, -2, 1, -3, 0, 0, 2, -1, -2, 0, 1, -1,~
$ RESIDUAL2012 <dbl> 115, 15, 85, 2, -2, -10, -1, 2, -12, 0, -3, -3, ~
$ RESIDUAL2013 <dbl> 72, -2, 100, 1, 2, -5, 0, 4, -4, -2, -2, -4, 0, ~
$ RESIDUAL2014 <dbl> -198, -9, 96, 0, -4, -7, 3, -2, -6, -4, -6, -11,~
$ RESIDUAL2015 <dbl> -259, -2, 33, 6, -3, -6, 4, -2, -9, -2, -3, -2, ~
$ RESIDUAL2016 <dbl> -121, -3, 16, 2, 1, -2, -1, -1, -3, -2, -2, -2, ~
$ RESIDUAL2017 <dbl> -114, -3, 14, 3, -2, -3, 0, 0, -4, -3, -1, -2, 0~
$ RESIDUAL2018 <dbl> -69, 0, 15, 0, 2, -3, 1, 0, -6, -1, 1, -2, -1, -~
$ RESIDUAL2019 <dbl> -89, -1, 24, -2, -2, -4, 0, -1, -2, 0, 0, -2, 0,~
$ GQESTIMATESBASE2010 <dbl> 116185, 455, 2307, 3193, 2224, 489, 1690, 333, 2~
$ GQESTIMATES2010 <dbl> 116246, 455, 2307, 3193, 2224, 489, 1690, 333, 2~
$ GQESTIMATES2011 <dbl> 115180, 455, 2263, 3380, 2224, 489, 1690, 333, 2~
$ GQESTIMATES2012 <dbl> 115793, 455, 2240, 3390, 2228, 489, 1779, 333, 2~
$ GQESTIMATES2013 <dbl> 116932, 455, 2296, 3388, 2224, 489, 1717, 333, 2~
$ GQESTIMATES2014 <dbl> 119032, 455, 2331, 3352, 2245, 489, 1755, 333, 2~
$ GQESTIMATES2015 <dbl> 119972, 455, 2337, 3193, 2255, 489, 1660, 333, 2~
$ GQESTIMATES2016 <dbl> 118619, 455, 2275, 2975, 2204, 489, 1728, 333, 2~
$ GQESTIMATES2017 <dbl> 117094, 455, 2193, 2817, 2150, 489, 1660, 333, 2~
$ GQESTIMATES2018 <dbl> 116576, 455, 2170, 2813, 2146, 489, 1663, 333, 2~
$ GQESTIMATES2019 <dbl> 116625, 455, 2170, 2812, 2148, 489, 1663, 333, 2~
$ RBIRTH2011 <dbl> 12.455519, 11.600000, 11.842995, 12.109461, 11.5~
$ RBIRTH2012 <dbl> 12.286866, 11.163449, 11.112202, 11.007155, 10.8~
$ RBIRTH2013 <dbl> 12.011401, 10.412013, 11.219905, 10.423983, 11.4~
$ RBIRTH2014 <dbl> 12.180259, 11.676701, 11.226489, 9.833867, 11.22~
$ RBIRTH2015 <dbl> 12.305777, 11.862569, 11.225449, 10.219088, 11.1~
$ RBIRTH2016 <dbl> 12.225151, 12.097324, 11.204755, 10.597247, 12.2~
$ RBIRTH2017 <dbl> 12.109454, 12.220585, 10.949200, 10.988364, 12.8~
$ RBIRTH2018 <dbl> 11.938128, 11.377262, 10.734799, 10.513902, 10.3~
$ RBIRTH2019 <dbl> 11.707442, 11.202671, 10.446871, 10.331329, 10.7~
$ RDEATH2011 <dbl> 10.189988, 9.345455, 9.895312, 11.816785, 12.101~
$ RDEATH2012 <dbl> 10.060889, 10.165092, 10.007884, 10.493487, 10.3~
$ RDEATH2013 <dbl> 10.543800, 10.612595, 9.869361, 10.904521, 12.17~
$ RDEATH2014 <dbl> 10.279697, 10.454297, 10.084554, 11.472845, 10.9~
$ RDEATH2015 <dbl> 10.70254, 10.64169, 10.43464, 12.51933, 11.74671~
$ RDEATH2016 <dbl> 10.644438, 9.935790, 9.850441, 10.750830, 10.675~
$ RDEATH2017 <dbl> 10.925229, 10.358573, 9.992336, 11.577026, 11.16~
$ RDEATH2018 <dbl> 10.994485, 9.339812, 10.744094, 13.152372, 11.71~
$ RDEATH2019 <dbl> 11.005972, 9.712572, 10.546624, 12.591307, 11.25~
$ RNATURALINC2011 <dbl> 2.26553147, 2.25454545, 1.94768307, 0.29267579, ~
$ RNATURALINC2012 <dbl> 2.22597644, 0.99835725, 1.10431826, 0.51366722, ~
$ RNATURALINC2013 <dbl> 1.4676017, -0.2005817, 1.3505441, -0.4805382, -0~
$ RNATURALINC2014 <dbl> 1.9005612, 1.2224047, 1.1419349, -1.6389779, 0.2~
$ RNATURALINC2015 <dbl> 1.6032356, 1.2208789, 0.7908048, -2.3002376, -0.~
$ RNATURALINC2016 <dbl> 1.5807125, 2.1615338, 1.3543138, -0.1535833, 1.5~
$ RNATURALINC2017 <dbl> 1.1842254, 1.8620122, 0.9568649, -0.5886624, 1.7~
$ RNATURALINC2018 <dbl> 0.943642943, 2.037449402, -0.009294199, -2.63846~
$ RNATURALINC2019 <dbl> 0.70147013, 1.49009892, -0.09975311, -2.25997821~
$ RINTERNATIONALMIG2011 <dbl> 0.97344610, 0.07272727, 0.95761084, -0.18292237,~
$ RINTERNATIONALMIG2012 <dbl> 1.21002757, -0.25412730, 1.26890415, -0.44028619~
$ RINTERNATIONALMIG2013 <dbl> 1.04627268, 0.21881639, 1.05965769, -0.36964477,~
$ RINTERNATIONALMIG2014 <dbl> 0.76179605, 0.12771392, 0.57350508, 0.14899799, ~
$ RINTERNATIONALMIG2015 <dbl> 0.94490015, 0.23688694, 0.65154356, 0.49021456, ~
$ RINTERNATIONALMIG2016 <dbl> 1.18918817, -0.05449245, 0.87689385, 0.65272898,~
$ RINTERNATIONALMIG2017 <dbl> 0.61840144, -0.21693346, 0.40940489, 0.47092989,~
$ RINTERNATIONALMIG2018 <dbl> 0.69226433, -0.12621368, 0.45076863, 0.47972176,~
$ RINTERNATIONALMIG2019 <dbl> 0.56624205, -0.28724798, 0.36273859, 0.52463780,~
$ RDOMESTICMIG2011 <dbl> -0.39501253, 5.94545455, 15.68425893, 0.47559816~
$ RDOMESTICMIG2012 <dbl> -0.02371379, -5.97199154, 16.21967439, -6.457530~
$ RDOMESTICMIG2013 <dbl> 0.47627593, -4.12104193, 21.69181622, -7.7625402~
$ RDOMESTICMIG2014 <dbl> -0.1983068, 1.8427294, 19.6108286, -5.2894286, 0~
$ RDOMESTICMIG2015 <dbl> -0.3185428, -1.9497617, 17.0744202, -16.2147894,~
$ RDOMESTICMIG2016 <dbl> -0.44401573, 4.83166375, 20.40239684, -18.890744~
$ RDOMESTICMIG2017 <dbl> 0.4719650, 1.0665895, 21.9888509, -25.4694582, -~
$ RDOMESTICMIG2018 <dbl> 1.0815222, 0.6671295, 24.2764466, -9.2346439, -7~
$ RDOMESTICMIG2019 <dbl> 1.9175015, 4.8473097, 24.0178286, -5.6903023, 1.~
$ RNETMIG2011 <dbl> 0.5784336, 6.0181818, 16.6418698, 0.2926758, -4.~
$ RNETMIG2012 <dbl> 1.1863138, -6.2261188, 17.4885785, -6.8978169, -~
$ RNETMIG2013 <dbl> 1.5225486, -3.9022255, 22.7514739, -8.1321850, -~
$ RNETMIG2014 <dbl> 0.5634892, 1.9704433, 20.1843337, -5.1404306, 1.~
$ RNETMIG2015 <dbl> 0.6263574, -1.7128748, 17.7259638, -15.7245748, ~
$ RNETMIG2016 <dbl> 0.7451724, 4.7771713, 21.2792907, -18.2380157, -~
$ RNETMIG2017 <dbl> 1.0903664, 0.8496561, 22.3982557, -24.9985283, -~
$ RNETMIG2018 <dbl> 1.77378650, 0.54091577, 24.72721527, -8.75492215~
$ RNETMIG2019 <dbl> 2.4837435, 4.5600618, 24.3805672, -5.1656645, 1.~summary(datos_web2) SUMLEV REGION DIVISION STATE
Length:3193 Min. :1.000 Min. :1.000 Length:3193
Class :character 1st Qu.:2.000 1st Qu.:4.000 Class :character
Mode :character Median :3.000 Median :5.000 Mode :character
Mean :2.669 Mean :5.191
3rd Qu.:3.000 3rd Qu.:7.000
Max. :4.000 Max. :9.000
COUNTY STNAME CTYNAME CENSUS2010POP
Length:3193 Length:3193 Length:3193 Min. : 82
Class :character Class :character Class :character 1st Qu.: 11299
Mode :character Mode :character Mode :character Median : 26424
Mean : 193387
3rd Qu.: 71404
Max. :37253956
ESTIMATESBASE2010 POPESTIMATE2010 POPESTIMATE2011 POPESTIMATE2012
Min. : 82 Min. : 84 Min. : 90 Min. : 86
1st Qu.: 11296 1st Qu.: 11275 1st Qu.: 11257 1st Qu.: 11200
Median : 26405 Median : 26464 Median : 26422 Median : 26373
Mean : 193397 Mean : 193750 Mean : 195150 Mean : 196574
3rd Qu.: 71485 3rd Qu.: 71673 3rd Qu.: 72332 3rd Qu.: 72235
Max. :37254519 Max. :37319502 Max. :37638369 Max. :37948800
POPESTIMATE2013 POPESTIMATE2014 POPESTIMATE2015 POPESTIMATE2016
Min. : 89 Min. : 89 Min. : 88 Min. : 88
1st Qu.: 11154 1st Qu.: 11095 1st Qu.: 11136 1st Qu.: 11167
Median : 26421 Median : 26430 Median : 26342 Median : 26345
Mean : 197929 Mean : 199374 Mean : 200836 Mean : 202281
3rd Qu.: 72322 3rd Qu.: 72458 3rd Qu.: 72413 3rd Qu.: 72053
Max. :38260787 Max. :38596972 Max. :38918045 Max. :39167117
POPESTIMATE2017 POPESTIMATE2018 POPESTIMATE2019 NPOPCHG_2010
Min. : 86 Min. : 86 Min. : 86 Min. :-6606
1st Qu.: 11121 1st Qu.: 11117 1st Qu.: 11128 1st Qu.: -17
Median : 26443 Median : 26491 Median : 26516 Median : 10
Mean : 203561 Mean : 204627 Mean : 205599 Mean : 353
3rd Qu.: 72490 3rd Qu.: 73127 3rd Qu.: 73309 3rd Qu.: 86
Max. :39358497 Max. :39461588 Max. :39512223 Max. :95880
NPOPCHG_2011 NPOPCHG_2012 NPOPCHG_2013 NPOPCHG_2014
Min. :-11892 Min. : -7260 Min. :-15704 Min. :-10636
1st Qu.: -93 1st Qu.: -122 1st Qu.: -111 1st Qu.: -103
Median : 1 Median : -13 Median : -7 Median : -4
Mean : 1400 Mean : 1424 Mean : 1355 Mean : 1445
3rd Qu.: 240 3rd Qu.: 212 3rd Qu.: 198 3rd Qu.: 217
Max. :403658 Max. :438852 Max. :395785 Max. :484067
NPOPCHG_2015 NPOPCHG_2016 NPOPCHG_2017 NPOPCHG_2018
Min. :-25580 Min. :-38386 Min. :-43856 Min. :-59221
1st Qu.: -110 1st Qu.: -104 1st Qu.: -99 1st Qu.: -88
Median : -5 Median : 2 Median : 15 Median : 12
Mean : 1462 Mean : 1444 Mean : 1280 Mean : 1066
3rd Qu.: 233 3rd Qu.: 264 3rd Qu.: 293 3rd Qu.: 301
Max. :505723 Max. :444354 Max. :380863 Max. :333393
NPOPCHG_2019 BIRTHS2010 BIRTHS2011 BIRTHS2012
Min. :-76790.0 Min. : 0.0 Min. : 0 Min. : 0
1st Qu.: -96.0 1st Qu.: 31.0 1st Qu.: 129 1st Qu.: 127
Median : 7.0 Median : 78.0 Median : 313 Median : 312
Mean : 972.1 Mean : 618.8 Mean : 2489 Mean : 2466
3rd Qu.: 256.0 3rd Qu.: 216.0 3rd Qu.: 857 3rd Qu.: 839
Max. :367215.0 Max. :123327.0 Max. :509771 Max. :497451
BIRTHS2013 BIRTHS2014 BIRTHS2015 BIRTHS2016
Min. : 0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 126 1st Qu.: 130 1st Qu.: 127 1st Qu.: 124
Median : 308 Median : 312 Median : 311 Median : 306
Mean : 2468 Mean : 2482 Mean : 2501 Mean : 2482
3rd Qu.: 838 3rd Qu.: 850 3rd Qu.: 843 3rd Qu.: 839
Max. :499629 Max. :498914 Max. :500380 Max. :490358
BIRTHS2017 BIRTHS2018 BIRTHS2019 DEATHS2010
Min. : 0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 123 1st Qu.: 120 1st Qu.: 119 1st Qu.: 26
Median : 301 Median : 297 Median : 292 Median : 68
Mean : 2444 Mean : 2396 Mean : 2375 Mean : 375
3rd Qu.: 834 3rd Qu.: 820 3rd Qu.: 807 3rd Qu.: 161
Max. :481943 Max. :465017 Max. :462617 Max. :57319
DEATHS2011 DEATHS2012 DEATHS2013 DEATHS2014
Min. : 0 Min. : 1 Min. : 0 Min. : 0
1st Qu.: 124 1st Qu.: 122 1st Qu.: 125 1st Qu.: 124
Median : 274 Median : 271 Median : 283 Median : 283
Mean : 1574 Mean : 1567 Mean : 1634 Mean : 1618
3rd Qu.: 675 3rd Qu.: 670 3rd Qu.: 702 3rd Qu.: 688
Max. :238388 Max. :239535 Max. :247704 Max. :244055
DEATHS2015 DEATHS2016 DEATHS2017 DEATHS2018
Min. : 1 Min. : 0 Min. : 2 Min. : 0
1st Qu.: 131 1st Qu.: 129 1st Qu.: 133 1st Qu.: 121
Median : 290 Median : 291 Median : 300 Median : 291
Mean : 1691 Mean : 1693 Mean : 1746 Mean : 1769
3rd Qu.: 713 3rd Qu.: 728 3rd Qu.: 739 3rd Qu.: 743
Max. :253798 Max. :260121 Max. :265241 Max. :277578
DEATHS2019 NATURALINC2010 NATURALINC2011 NATURALINC2012
Min. : 0 Min. : -760.0 Min. : -2891.0 Min. : -2744.0
1st Qu.: 122 1st Qu.: -7.0 1st Qu.: -15.0 1st Qu.: -14.0
Median : 289 Median : 13.0 Median : 23.0 Median : 24.0
Mean : 1776 Mean : 243.8 Mean : 915.1 Mean : 899.1
3rd Qu.: 744 3rd Qu.: 60.0 3rd Qu.: 191.0 3rd Qu.: 187.0
Max. :282520 Max. :66008.0 Max. :271383.0 Max. :257916.0
NATURALINC2013 NATURALINC2014 NATURALINC2015 NATURALINC2016
Min. : -3121.0 Min. : -2697.0 Min. : -3210.0 Min. : -3048.0
1st Qu.: -22.0 1st Qu.: -18.0 1st Qu.: -25.0 1st Qu.: -25.0
Median : 14.0 Median : 18.0 Median : 10.0 Median : 10.0
Mean : 834.7 Mean : 864.9 Mean : 809.6 Mean : 788.9
3rd Qu.: 162.0 3rd Qu.: 172.0 3rd Qu.: 150.0 3rd Qu.: 143.0
Max. :251925.0 Max. :254859.0 Max. :246582.0 Max. :230237.0
NATURALINC2017 NATURALINC2018 NATURALINC2019 INTERNATIONALMIG2010
Min. : -4272.0 Min. : -4571.0 Min. : -4729.0 Min. : -114.0
1st Qu.: -33.0 1st Qu.: -30.0 1st Qu.: -33.0 1st Qu.: 0.0
Median : 3.0 Median : 7.0 Median : 5.0 Median : 2.0
Mean : 697.7 Mean : 626.5 Mean : 599.2 Mean : 109.2
3rd Qu.: 115.0 3rd Qu.: 104.0 3rd Qu.: 103.0 3rd Qu.: 12.0
Max. :216702.0 Max. :187439.0 Max. :180097.0 Max. :22164.0
INTERNATIONALMIG2011 INTERNATIONALMIG2012 INTERNATIONALMIG2013
Min. : -361.0 Min. : -348.0 Min. : -126
1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0
Median : 8.0 Median : 8.0 Median : 9
Mean : 484.9 Mean : 525.3 Mean : 520
3rd Qu.: 49.0 3rd Qu.: 54.0 3rd Qu.: 61
Max. :100548.0 Max. :98777.0 Max. :116528
INTERNATIONALMIG2014 INTERNATIONALMIG2015 INTERNATIONALMIG2016
Min. : -262.0 Min. : -148.0 Min. : -165.0
1st Qu.: 0.0 1st Qu.: 1.0 1st Qu.: 1.0
Median : 9.0 Median : 11.0 Median : 12.0
Mean : 580.4 Mean : 652.4 Mean : 655.6
3rd Qu.: 64.0 3rd Qu.: 76.0 3rd Qu.: 77.0
Max. :131001.0 Max. :156870.0 Max. :155926.0
INTERNATIONALMIG2017 INTERNATIONALMIG2018 INTERNATIONALMIG2019
Min. : -209.0 Min. : -992.0 Min. : -970.0
1st Qu.: 1.0 1st Qu.: 0.0 1st Qu.: 0.0
Median : 9.0 Median : 6.0 Median : 5.0
Mean : 582.8 Mean : 439.6 Mean : 372.9
3rd Qu.: 58.0 3rd Qu.: 42.0 3rd Qu.: 36.0
Max. :160385.0 Max. :131600.0 Max. :88678.0
DOMESTICMIG2010 DOMESTICMIG2011 DOMESTICMIG2012 DOMESTICMIG2013
Min. :-22616 Min. :-80582 Min. :-108406 Min. :-112483
1st Qu.: -32 1st Qu.: -156 1st Qu.: -204 1st Qu.: -171
Median : -1 Median : -33 Median : -58 Median : -35
Mean : 0 Mean : 0 Mean : 0 Mean : 0
3rd Qu.: 27 3rd Qu.: 65 3rd Qu.: 46 3rd Qu.: 67
Max. : 28097 Max. :120672 Max. : 141740 Max. : 111585
DOMESTICMIG2014 DOMESTICMIG2015 DOMESTICMIG2016 DOMESTICMIG2017
Min. :-145432 Min. :-165857 Min. :-194013 Min. :-187910
1st Qu.: -170 1st Qu.: -170 1st Qu.: -172 1st Qu.: -147
Median : -35 Median : -37 Median : -28 Median : -8
Mean : 0 Mean : 0 Mean : 0 Mean : 0
3rd Qu.: 72 3rd Qu.: 76 3rd Qu.: 111 3rd Qu.: 180
Max. : 160260 Max. : 197401 Max. : 216749 Max. : 164257
DOMESTICMIG2018 DOMESTICMIG2019 NETMIG2010 NETMIG2011
Min. :-181262 Min. :-203414 Min. :-15150.0 Min. :-33876.0
1st Qu.: -129 1st Qu.: -132 1st Qu.: -24.0 1st Qu.: -126.0
Median : -9 Median : -14 Median : 2.0 Median : -16.0
Mean : 0 Mean : 0 Mean : 109.2 Mean : 484.9
3rd Qu.: 163 3rd Qu.: 155 3rd Qu.: 36.0 3rd Qu.: 106.0
Max. : 135530 Max. : 133910 Max. : 42957.0 Max. :189731.0
NETMIG2012 NETMIG2013 NETMIG2014 NETMIG2015
Min. :-43686.0 Min. :-40418 Min. :-65886.0 Min. :-82040.0
1st Qu.: -166.0 1st Qu.: -135 1st Qu.: -137.0 1st Qu.: -128.0
Median : -36.0 Median : -20 Median : -18.0 Median : -15.0
Mean : 525.3 Mean : 520 Mean : 580.4 Mean : 652.4
3rd Qu.: 87.0 3rd Qu.: 106 3rd Qu.: 109.0 3rd Qu.: 127.0
Max. :226699.0 Max. :215179 Max. :267305.0 Max. :330283.0
NETMIG2016 NETMIG2017 NETMIG2018
Min. :-105667.0 Min. :-120168.0 Min. :-125303.0
1st Qu.: -131.0 1st Qu.: -112.0 1st Qu.: -106.0
Median : -10.0 Median : 8.0 Median : 3.0
Mean : 655.6 Mean : 582.8 Mean : 439.6
3rd Qu.: 169.0 3rd Qu.: 239.0 3rd Qu.: 224.0
Max. : 372675.0 Max. : 324642.0 Max. : 267130.0
NETMIG2019 RESIDUAL2010 RESIDUAL2011 RESIDUAL2012
Min. :-134896.0 Min. :-1358 Min. :-369 Min. :-3987
1st Qu.: -113.0 1st Qu.: -3 1st Qu.: -1 1st Qu.: -4
Median : -2.0 Median : -1 Median : 0 Median : 0
Mean : 372.9 Mean : 0 Mean : 0 Mean : 0
3rd Qu.: 186.0 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 4
Max. : 222588.0 Max. : 2014 Max. : 344 Max. : 3241
RESIDUAL2013 RESIDUAL2014 RESIDUAL2015 RESIDUAL2016 RESIDUAL2017
Min. :-2774 Min. :-3354 Min. :-2441 Min. :-688 Min. :-590
1st Qu.: -3 1st Qu.: -4 1st Qu.: -3 1st Qu.: -2 1st Qu.: -2
Median : 0 Median : 0 Median : -1 Median : 0 Median : 0
Mean : 0 Mean : 0 Mean : 0 Mean : 0 Mean : 0
3rd Qu.: 2 3rd Qu.: 2 3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 1
Max. : 2509 Max. : 3153 Max. : 2178 Max. : 780 Max. : 733
RESIDUAL2018 RESIDUAL2019 GQESTIMATESBASE2010 GQESTIMATES2010
Min. :-514 Min. :-628 Min. : 0 Min. : 0
1st Qu.: -2 1st Qu.: -2 1st Qu.: 183 1st Qu.: 183
Median : 0 Median : 0 Median : 636 Median : 635
Mean : 0 Mean : 0 Mean : 5010 Mean : 5032
3rd Qu.: 1 3rd Qu.: 1 3rd Qu.: 2408 3rd Qu.: 2419
Max. : 575 Max. : 829 Max. :820359 Max. :818897
GQESTIMATES2011 GQESTIMATES2012 GQESTIMATES2013 GQESTIMATES2014
Min. : 0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 183 1st Qu.: 184 1st Qu.: 180 1st Qu.: 181
Median : 637 Median : 645 Median : 640 Median : 642
Mean : 5050 Mean : 5063 Mean : 5052 Mean : 5076
3rd Qu.: 2426 3rd Qu.: 2422 3rd Qu.: 2414 3rd Qu.: 2455
Max. :816262 Max. :816845 Max. :807615 Max. :817753
GQESTIMATES2015 GQESTIMATES2016 GQESTIMATES2017 GQESTIMATES2018
Min. : 0 Min. : 0 Min. : 0 Min. : 0
1st Qu.: 181 1st Qu.: 178 1st Qu.: 177 1st Qu.: 178
Median : 637 Median : 637 Median : 639 Median : 640
Mean : 5077 Mean : 5066 Mean : 5068 Mean : 5066
3rd Qu.: 2441 3rd Qu.: 2428 3rd Qu.: 2426 3rd Qu.: 2421
Max. :820530 Max. :813594 Max. :819850 Max. :825817
GQESTIMATES2019 RBIRTH2011 RBIRTH2012 RBIRTH2013
Min. : 0 Min. : 0.00 Min. : 0.000 Min. : 0.00
1st Qu.: 179 1st Qu.:10.07 1st Qu.: 9.972 1st Qu.:10.04
Median : 637 Median :11.55 Median :11.457 Median :11.49
Mean : 5064 Mean :11.76 Mean :11.652 Mean :11.66
3rd Qu.: 2421 3rd Qu.:13.08 3rd Qu.:13.013 3rd Qu.:12.97
Max. :826521 Max. :32.28 Max. :31.208 Max. :29.34
RBIRTH2014 RBIRTH2015 RBIRTH2016 RBIRTH2017
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.000
1st Qu.:10.09 1st Qu.:10.18 1st Qu.:10.13 1st Qu.: 9.936
Median :11.54 Median :11.57 Median :11.50 Median :11.327
Mean :11.71 Mean :11.76 Mean :11.68 Mean :11.465
3rd Qu.:13.07 3rd Qu.:13.07 3rd Qu.:12.88 3rd Qu.:12.747
Max. :29.36 Max. :30.41 Max. :29.04 Max. :28.490
RBIRTH2018 RBIRTH2019 RDEATH2011 RDEATH2012
Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.7943
1st Qu.: 9.707 1st Qu.: 9.54 1st Qu.: 8.37 1st Qu.: 8.2859
Median :11.059 Median :10.91 Median :10.13 Median :10.0531
Mean :11.155 Mean :11.00 Mean :10.16 Mean :10.0873
3rd Qu.:12.366 3rd Qu.:12.16 3rd Qu.:11.87 3rd Qu.:11.8083
Max. :28.538 Max. :28.19 Max. :24.94 Max. :22.5724
RDEATH2013 RDEATH2014 RDEATH2015 RDEATH2016
Min. : 0.000 Min. : 0.000 Min. : 1.061 Min. : 0.000
1st Qu.: 8.631 1st Qu.: 8.497 1st Qu.: 8.897 1st Qu.: 8.856
Median :10.482 Median :10.348 Median :10.804 Median :10.804
Mean :10.476 Mean :10.356 Mean :10.765 Mean :10.728
3rd Qu.:12.278 3rd Qu.:12.204 3rd Qu.:12.588 3rd Qu.:12.584
Max. :25.282 Max. :24.837 Max. :25.403 Max. :34.359
RDEATH2017 RDEATH2018 RDEATH2019 RNATURALINC2011
Min. : 1.785 Min. : 0.000 Min. : 0.000 Min. :-15.642
1st Qu.: 9.074 1st Qu.: 8.723 1st Qu.: 8.721 1st Qu.: -1.203
Median :11.096 Median :10.496 Median :10.434 Median : 1.303
Mean :11.032 Mean :10.359 Mean :10.383 Mean : 1.599
3rd Qu.:12.871 3rd Qu.:12.194 3rd Qu.:12.175 3rd Qu.: 4.086
Max. :26.492 Max. :25.561 Max. :22.102 Max. : 24.214
RNATURALINC2012 RNATURALINC2013 RNATURALINC2014 RNATURALINC2015
Min. :-16.688 Min. :-18.4489 Min. :-17.962 Min. :-17.1404
1st Qu.: -1.122 1st Qu.: -1.5476 1st Qu.: -1.432 1st Qu.: -1.7674
Median : 1.292 Median : 0.8713 Median : 1.019 Median : 0.5786
Mean : 1.564 Mean : 1.1794 Mean : 1.357 Mean : 0.9956
3rd Qu.: 4.046 3rd Qu.: 3.6162 3rd Qu.: 3.737 3rd Qu.: 3.4345
Max. : 24.772 Max. : 26.0144 Max. : 25.826 Max. : 27.0658
RNATURALINC2016 RNATURALINC2017 RNATURALINC2018 RNATURALINC2019
Min. :-24.3379 Min. :-24.0000 Min. :-15.4302 Min. :-13.5405
1st Qu.: -1.7629 1st Qu.: -2.2918 1st Qu.: -1.7496 1st Qu.: -1.9098
Median : 0.5989 Median : 0.1995 Median : 0.4970 Median : 0.3771
Mean : 0.9475 Mean : 0.4331 Mean : 0.7966 Mean : 0.6143
3rd Qu.: 3.3812 3rd Qu.: 2.8911 3rd Qu.: 2.9400 3rd Qu.: 2.7642
Max. : 24.5579 Max. : 24.9930 Max. : 25.0029 Max. : 24.4291
RINTERNATIONALMIG2011 RINTERNATIONALMIG2012 RINTERNATIONALMIG2013
Min. :-2.0429 Min. :-1.8182 Min. :-1.5252
1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000
Median : 0.3434 Median : 0.3555 Median : 0.4132
Mean : 0.8781 Mean : 0.9978 Mean : 0.9856
3rd Qu.: 1.1252 3rd Qu.: 1.1987 3rd Qu.: 1.2709
Max. :25.8366 Max. :42.6835 Max. :28.2895
RINTERNATIONALMIG2014 RINTERNATIONALMIG2015 RINTERNATIONALMIG2016
Min. :-1.7911 Min. :-2.20204 Min. :-1.67785
1st Qu.: 0.0000 1st Qu.: 0.06631 1st Qu.: 0.08247
Median : 0.4383 Median : 0.53562 Median : 0.55443
Mean : 1.0528 Mean : 1.24337 Mean : 1.26020
3rd Qu.: 1.3238 3rd Qu.: 1.53425 3rd Qu.: 1.50681
Max. :25.1429 Max. :31.53457 Max. :43.48905
RINTERNATIONALMIG2017 RINTERNATIONALMIG2018 RINTERNATIONALMIG2019
Min. :-1.67177 Min. :-2.8471 Min. :-1.7356
1st Qu.: 0.02412 1st Qu.: 0.0000 1st Qu.: 0.0000
Median : 0.43145 Median : 0.2816 Median : 0.2300
Mean : 1.08652 Mean : 0.7577 Mean : 0.6493
3rd Qu.: 1.25000 3rd Qu.: 0.9165 3rd Qu.: 0.7889
Max. :34.61426 Max. :26.6688 Max. :26.0417
RDOMESTICMIG2011 RDOMESTICMIG2012 RDOMESTICMIG2013 RDOMESTICMIG2014
Min. :-124.542 Min. :-100.717 Min. :-110.730 Min. :-174.359
1st Qu.: -6.168 1st Qu.: -7.666 1st Qu.: -6.677 1st Qu.: -7.139
Median : -1.772 Median : -2.661 Median : -1.925 Median : -2.033
Mean : -1.691 Mean : -2.663 Mean : -1.656 Mean : -1.880
3rd Qu.: 2.650 3rd Qu.: 2.393 3rd Qu.: 3.153 3rd Qu.: 3.286
Max. : 122.905 Max. : 119.421 Max. : 208.333 Max. : 149.973
RDOMESTICMIG2015 RDOMESTICMIG2016 RDOMESTICMIG2017 RDOMESTICMIG2018
Min. :-99.217 Min. :-124.710 Min. :-432.8903 Min. :-73.67214
1st Qu.: -7.246 1st Qu.: -7.384 1st Qu.: -6.5629 1st Qu.: -6.24056
Median : -2.076 Median : -1.749 Median : -0.4755 Median : -0.53346
Mean : -1.683 Mean : -1.355 Mean : -0.2737 Mean : -0.04087
3rd Qu.: 3.790 3rd Qu.: 4.544 3rd Qu.: 6.6725 3rd Qu.: 6.06691
Max. :278.846 Max. : 208.819 Max. : 152.0000 Max. : 71.17438
RDOMESTICMIG2019 RNETMIG2011 RNETMIG2012 RNETMIG2013
Min. :-165.2510 Min. :-124.5421 Min. :-99.448 Min. :-99.6057
1st Qu.: -5.9771 1st Qu.: -5.6820 1st Qu.: -7.064 1st Qu.: -5.9886
Median : -0.7582 Median : -0.8585 Median : -1.777 Median : -1.0367
Mean : -0.3591 Mean : -0.8127 Mean : -1.665 Mean : -0.6705
3rd Qu.: 5.3982 3rd Qu.: 3.8429 3rd Qu.: 3.480 3rd Qu.: 4.2443
Max. : 126.1830 Max. : 122.9050 Max. :119.154 Max. :208.3333
RNETMIG2014 RNETMIG2015 RNETMIG2016
Min. :-174.3590 Min. :-76.5239 Min. :-110.29447
1st Qu.: -6.2206 1st Qu.: -6.0288 1st Qu.: -6.22554
Median : -1.1219 Median : -0.9382 Median : -0.65151
Mean : -0.8275 Mean : -0.4394 Mean : -0.09467
3rd Qu.: 4.3103 3rd Qu.: 5.0226 3rd Qu.: 5.95338
Max. : 149.8741 Max. :278.8462 Max. : 208.59713
RNETMIG2017 RNETMIG2018 RNETMIG2019
Min. :-422.6669 Min. :-74.1226 Min. :-165.3853
1st Qu.: -5.4549 1st Qu.: -5.5229 1st Qu.: -5.3541
Median : 0.5187 Median : 0.2595 Median : -0.1294
Mean : 0.8128 Mean : 0.7168 Mean : 0.2902
3rd Qu.: 7.5439 3rd Qu.: 6.5807 3rd Qu.: 5.9360
Max. : 152.0000 Max. : 71.1744 Max. : 126.1830