Ejercicios - Visualización de datos

Ejercicio 1

A partir del dataset df, el cual trata de los incendios forestales, realice las siguientes tareas:

Filtrar los registros para incluir únicamente los incendios ocurridos en el estado de Idaho.
Seleccionar únicamente las columnas YEAR_, CAUSE y TOTALACRES.
Renombrar estas columnas con nombres más claros y descriptivos.
Agrupar la información por CAUSE y YEAR_.
Resumir el total de acres quemados para cada combinación de causa y año.
Elaborar una visualización que muestre los resultados de manera clara.

df <- read_csv("~/Desktop/RDataSets/StudyArea.csv")

Desarrollo

1. Filtrar los registros para incluir únicamente los incendios ocurridos en el estado de Idaho.

df %>% 
  filter(STATE == 'Idaho') -> df1

df1

## # A tibble: 36,510 × 14
##      FID ORGANIZATI UNIT  SUBUNIT SUBUNIT2       FIRENAME CAUSE YEAR_ STARTDATED
##    <dbl> <chr>      <chr> <chr>   <chr>          <chr>    <chr> <dbl> <chr>     
##  1  3971 FWS        14613 USIDBLR Bear Lake Nat… Y ROAD   Human  1987 5/7/87 0:…
##  2  3972 FWS        14613 USIDBLR Bear Lake Nat… LIFTON   Natu…  1991 5/2/91 0:…
##  3  3973 FWS        14613 USIDBLR Bear Lake Nat… SPRING … Human  1991 5/20/91 0…
##  4  3974 FWS        14613 USIDBLR Bear Lake Nat… RAINBOW  Natu…  1990 6/9/90 0:…
##  5  3975 FWS        14613 USIDBLR Bear Lake Nat… RAINBOW… Human  1985 4/18/85 0…
##  6  3976 FWS        14613 USIDBLR Bear Lake Nat… HOAGESON Human  1988 10/24/88 …
##  7  3977 FWS        14613 USIDBLR Bear Lake Nat… MERKLEY  Natu…  1991 7/25/91 0…
##  8  3978 FWS        14613 USIDBLR Bear Lake Nat… MERKLEY  Human  1992 9/2/92 0:…
##  9  3979 FWS        14613 USIDBLR Bear Lake Nat… MERKLEY  Human  2002 3/29/02 0…
## 10  3980 FWS        14613 USIDBLR Bear Lake Nat… N MERKL… Human  1994 7/11/94 0…
## # ℹ 36,500 more rows
## # ℹ 5 more variables: CONTRDATED <chr>, OUTDATED <chr>, STATE <chr>,
## #   STATE_FIPS <dbl>, TOTALACRES <dbl>

2. Seleccionar únicamente las columnas YEAR_, CAUSE y TOTALACRES.

df1 %>% 
  select(YEAR_,CAUSE,TOTALACRES) -> df2

df2

## # A tibble: 36,510 × 3
##    YEAR_ CAUSE   TOTALACRES
##    <dbl> <chr>        <dbl>
##  1  1987 Human          5  
##  2  1991 Natural      150  
##  3  1991 Human        800  
##  4  1990 Natural        2  
##  5  1985 Human         38  
##  6  1988 Human          2  
##  7  1991 Natural        0.2
##  8  1992 Human        150  
##  9  2002 Human         15  
## 10  1994 Human         30  
## # ℹ 36,500 more rows

3. Renombrar estas columnas con nombres más claros y descriptivos.

df2 %>% 
  select(Año_del_Incendio =YEAR_, Causa_del_Incendio=CAUSE, Area_Total_Quemada=TOTALACRES) -> df3

df3

## # A tibble: 36,510 × 3
##    Año_del_Incendio Causa_del_Incendio Area_Total_Quemada
##               <dbl> <chr>                           <dbl>
##  1             1987 Human                             5  
##  2             1991 Natural                         150  
##  3             1991 Human                           800  
##  4             1990 Natural                           2  
##  5             1985 Human                            38  
##  6             1988 Human                             2  
##  7             1991 Natural                           0.2
##  8             1992 Human                           150  
##  9             2002 Human                            15  
## 10             1994 Human                            30  
## # ℹ 36,500 more rows

4. Agrupar la información por CAUSE y YEAR_.

df3 %>% 
  group_by(Causa_del_Incendio, Año_del_Incendio)  -> df4
  

df4

## # A tibble: 36,510 × 3
## # Groups:   Causa_del_Incendio, Año_del_Incendio [96]
##    Año_del_Incendio Causa_del_Incendio Area_Total_Quemada
##               <dbl> <chr>                           <dbl>
##  1             1987 Human                             5  
##  2             1991 Natural                         150  
##  3             1991 Human                           800  
##  4             1990 Natural                           2  
##  5             1985 Human                            38  
##  6             1988 Human                             2  
##  7             1991 Natural                           0.2
##  8             1992 Human                           150  
##  9             2002 Human                            15  
## 10             1994 Human                            30  
## # ℹ 36,500 more rows

#group_by prepara los grupos, summarise hace el cálculo sobre cada grupo.

5. Resumir el total de acres quemados para cada combinación de causa y año.

df4 <- df4 %>% filter(!is.na(Causa_del_Incendio))

df4 %>% 
  summarise(Acres_quemados = sum(Area_Total_Quemada))->df5

df5

## # A tibble: 79 × 3
## # Groups:   Causa_del_Incendio [3]
##    Causa_del_Incendio Año_del_Incendio Acres_quemados
##    <chr>                         <dbl>          <dbl>
##  1 Human                          1980         71975.
##  2 Human                          1981        219362.
##  3 Human                          1982         34016.
##  4 Human                          1983         48242.
##  5 Human                          1984         36838.
##  6 Human                          1985         68035.
##  7 Human                          1986         43181.
##  8 Human                          1987         35128.
##  9 Human                          1988        810403.
## 10 Human                          1989         28022.
## # ℹ 69 more rows

6. Elaborar una visualización que muestre los resultados de manera clara.

ggplot(df5, aes(x = Año_del_Incendio, y = Acres_quemados, color = Causa_del_Incendio)) +
  geom_line() +
  labs(
    title = "Acres quemados por causa a través del tiempo",
    x = "Año",
    y = "Acres quemados"
  )

Ejercicio 2

Trabajaremos con el conjunto de datos de 120 años de historia olímpica adquirido por Randi Griffin en Randi-Griffin y puesto a disposición en athlete_events.

Su tarea consiste en identificar los cinco deportes más importantes según el mayor número de medallas otorgadas en el año 2016, y luego realizar el siguiente análisis:

1.Genere una tabla que indique el número de medallas concedidas en cada uno de los cinco principales deportes en 2016.

2.Elabore una tabla que muestre la distribución de la edad de los ganadores de medallas en los cinco principales deportes en 2016.

3.Identifique qué equipos nacionales ganaron el mayor número de medallas en los cinco principales deportes en 2016.

4.Presente un resumen de la tendencia del peso de los atletas masculinos y femeninos ganadores en los cinco principales deportes en 2016.

datos <-read_csv("~/Downloads/athlete_events.csv")

Desarrollo

1. Genere una tabla que indique el número de medallas concedidas en cada uno de los cinco principales deportes en 2016.

datos %>% 
  filter(Year==2016, !is.na(Medal)) %>% 
  group_by(Sport) %>% 
  summarise(Total_medallas = n(), .groups = "drop") %>% 
  arrange(desc(Total_medallas)) %>%
  head(n=5) ->Top5_deportes

kable(Top5_deportes)

Sport	Total_medallas
Athletics	192
Swimming	191
Rowing	144
Football	106
Hockey	99

2. Elabore una tabla que muestre la distribución de la edad de los ganadores de medallas en los cinco principales deportes en 2016.

La distribución de la edad de los medallistas se describe inicialmente mediante estadísticos resumen por deporte.

datos %>% 
  filter(Year==2016, !is.na(Medal), Sport %in% Top5_deportes$Sport, !is.na(Age))->top


top %>% 
  group_by(Sport) %>% 
   summarise(media_edad = mean(Age), 
            ds_edad = sd(Age),
            mediana_edad = median(Age), 
            ri_edad = IQR(Age),
            min_edad = min(Age),
            max_edad = max(Age),
            q1_edad = quantile(Age)[2],
            q3_edad = quantile(Age)[4], .groups = "drop") ->tabla2

kable(tabla2)

Sport	media_edad	ds_edad	mediana_edad	ri_edad	min_edad	max_edad	q1_edad	q3_edad
Athletics	26.41146	4.131665	26	5.25	19	40	24	29.25
Football	24.08491	4.336156	23	6.00	17	34	21	27.00
Hockey	26.38384	4.072574	27	5.00	19	37	24	29.00
Rowing	28.12500	3.871855	28	6.00	20	40	25	31.00
Swimming	23.23037	4.013047	22	4.00	16	36	21	25.00

En los cinco principales deportes de 2016, la edad promedio de los medallistas varía según la disciplina. Swimming presenta el promedio más bajo de 23.23 años (desviación estándar de 4.01 años) y mediana de 22 años (RI = 4 años), donde el 50% se encuentra entre 21 y 25 años, indicando predominio de atletas jóvenes. En contraste, Rowing muestra la mayor edad promedio de 28.13 años (desviación estándar de 3.87 años), con una mediana de 28 años (RI = 6 años) y el 50% de los atletas entre 25 y 31 años, indicando una distribución centrada en edades más maduras sugiriendo que el rendimiento máximo se alcanza en estas edades. En Athletics la edad promedio de los medallistas es 26.4 años (desviación estándar de 4.1 años), con una mediana de 26 años (RI=5.25 años), el 50% de los atletas se concentra entre 24 y 29.3 años, siendo edades entre 19 y 40 algunos valores altos poco frecuentes. En Football los medallistas presentan una edad promedio de 24.1 años (desviación estándar de 4.3 años), mediana de 23 años (RI = 6 años) y el 50% de los atletas entre 21 y 27 años, lo que indica una concentración en atletas jóvenes. Finalmente, en Hockey a edad promedio es de 26.4 años (desviación estándar de 4.1 años), con mediana de 27 años (RI = 5 años), el 50% de las edades se ubica entre 24 y 29 años, lo que indica una dispersión moderada y una concentración alrededor del valor central.

Para complementar el análisis, se presenta la distribución de frecuencias de la edad agrupada en intervalos.

top %>% 
  mutate(Rango_edad = ifelse(Age %in% 10:14, "10-14",
                      ifelse(Age %in% 15:19, "15-19",
                      ifelse(Age %in% 20:24, "20-24",
                      ifelse(Age %in% 25:29, "25-29",
                      ifelse(Age %in% 30:34, "30-34",
                      ifelse(Age %in% 35:39, "35-39", "40+"))))))) %>%
  group_by(Sport, Rango_edad) %>%
  summarise(Frecuencia = n(), .groups = "drop") -> tabla_final

kable(tabla_final)

Sport	Rango_edad	Frecuencia
Athletics	15-19	3
Athletics	20-24	67
Athletics	25-29	74
Athletics	30-34	43
Athletics	35-39	4
Athletics	40+	1
Football	15-19	11
Football	20-24	55
Football	25-29	24
Football	30-34	16
Hockey	15-19	4
Hockey	20-24	29
Hockey	25-29	45
Hockey	30-34	19
Hockey	35-39	2
Rowing	20-24	23
Rowing	25-29	74
Rowing	30-34	38
Rowing	35-39	7
Rowing	40+	2
Swimming	15-19	32
Swimming	20-24	98
Swimming	25-29	42
Swimming	30-34	16
Swimming	35-39	3

Lo cual confirma lo previamente dicho, la mayoría de los medallistas se concentra en intervalos de edad jóvenes-adultos, aunque se observan algunos casos de edades más altas.

3. Identifique qué equipos nacionales ganaron el mayor número de medallas en los cinco principales deportes en 2016.

De forma general, los equipos nacionales que ganaron el mayor número de medallas totales en los cinco principales deportes en 2016 sin especificar por deporte son los siguientes:

datos %>% 
  filter(Year==2016, !is.na(Medal), Sport %in% Top5_deportes$Sport, !is.na(Team)) ->top2

top2 %>% 
  group_by(Team) %>% 
  summarise(Frecuencia = n(), .groups = "drop") %>% 
  arrange(desc(Frecuencia))

## # A tibble: 54 × 2
##    Team          Frecuencia
##    <chr>              <int>
##  1 United States        127
##  2 Germany               88
##  3 Great Britain         69
##  4 Canada                45
##  5 Australia             43
##  6 Netherlands           34
##  7 Jamaica               30
##  8 Sweden                21
##  9 France                20
## 10 Brazil                19
## # ℹ 44 more rows

Sin embargo, si queremos identificarlos por deporte, sería de la siguiente manera:

datos %>% 
  filter(Year==2016, !is.na(Medal), Sport %in% Top5_deportes$Sport, !is.na(Team)) ->top2

top2 %>% 
  group_by(Sport, Team) %>% 
  summarise(Frecuencia = n(), .groups = "drop") %>% 
  group_by(Sport) %>% 
  slice_max(Frecuencia, n = 1, with_ties = FALSE) %>% 
  ungroup() %>% 
  arrange(desc(Frecuencia))

## # A tibble: 5 × 3
##   Sport     Team          Frecuencia
##   <chr>     <chr>              <int>
## 1 Swimming  United States         71
## 2 Athletics United States         46
## 3 Football  Germany               35
## 4 Hockey    Germany               33
## 5 Rowing    Great Britain         26

4. Presente un resumen de la tendencia del peso de los atletas masculinos y femeninos ganadores en los cinco principales deportes en 2016.

De manera similar al punto anterior, si queremos una resumen de la tendencia del peso de los atletas masculinos y femeninos ganadores en los cinco principales deportes en 2016 en total sin especificar los deportes sería el siguiente:

datos %>% 
  filter(Year==2016, !is.na(Medal), Sport %in% Top5_deportes$Sport, !is.na(Sex), !is.na(Weight)) ->top3

top3 %>% 
  group_by(Sex) %>% 
  summarise(
    media_peso = mean(Weight),
    sd_peso = sd(Weight),
    mediana_peso = median(Weight),
    ri_peso = IQR(Weight),
    min_peso = min(Weight),
    max_peso = max(Weight),
    q1_peso = quantile(Weight)[2],
    q3_peso = quantile(Weight)[4],
    .groups = "drop"
    
 )->tabla_peso
  
kable(tabla_peso)

Sex	media_peso	sd_peso	mediana_peso	ri_peso	min_peso	max_peso	q1_peso	q3_peso
F	65.24507	10.04168	65	12	40	136	59	71
M	81.80914	12.97826	80	17	47	134	73	90

En los cinco principales deportes de 2016, las atletas femeninas presentan un peso promedio de 65.25 kg (desviación estándar de 10.04 kg), donde el 50% de las medallistas pesa menos o igual a 65 kg, con un mínimo de 40 kg y un máximo de 136 kg. El 25% de las atletas pesa menos o igual a 59 kg y el 75% menos o igual a 71 kg. El valor máximo es considerablemente mayor que el tercer cuartil, lo que sugiere la presencia de valores atípicos.

Por su parte, los atletas masculinos presentan un peso promedio de 81.81 kg (desviación estándar de 12.98 kg), con una mediana de 80 kg, un mínimo de 47 kg y un máximo de 134 kg. El 25% pesa menos o igual a 73 kg y el 75% menos o igual a 90 kg, mostrando una mayor dispersión en comparación con las mujeres.

En general, se observa que los atletas masculinos tienen un peso promedio aproximadamente 16.5 kg mayor que las atletas femeninas, así como una mayor variabilidad en los valores registrados. Además, los valores máximos en ambos grupos superan considerablemente el tercer cuartil, sugiriendo posibles valores atípicos.

No obstante, si queremos especificar por deporte sería el siguiente resumen numérico:

datos %>% 
  filter(Year==2016, !is.na(Medal), Sport %in% Top5_deportes$Sport, !is.na(Sex), !is.na(Weight)) ->top3

top3 %>% 
  group_by(Sport, Sex) %>% 
  summarise(
    media_peso = mean(Weight),
    sd_peso = sd(Weight),
    mediana_peso = median(Weight),
    ri_peso = IQR(Weight),
    min_peso = min(Weight),
    max_peso = max(Weight),
    q1_peso = quantile(Weight)[2],
    q3_peso = quantile(Weight)[4],
    .groups = "drop"
    
 )->tabla_peso1
  
kable(tabla_peso1)

Sport	Sex	media_peso	sd_peso	mediana_peso	ri_peso	min_peso	max_peso	q1_peso	q3_peso
Athletics	F	62.57895	14.940719	59.0	12.50	40	136	55.5	68.00
Athletics	M	79.07447	17.764432	75.5	16.75	47	134	68.0	84.75
Football	F	62.96296	5.917556	63.0	7.75	52	75	59.0	66.75
Football	M	75.72549	8.251251	76.0	9.50	51	95	70.5	80.00
Hockey	F	63.06122	5.363488	63.0	9.00	54	74	58.0	67.00
Hockey	M	78.38000	6.249539	77.0	8.00	64	95	74.0	82.00
Rowing	F	71.76667	7.971063	73.5	8.00	50	86	68.0	76.00
Rowing	M	88.83333	12.520666	93.0	15.00	55	110	82.0	97.00
Swimming	F	66.19588	6.371550	66.0	9.00	52	85	61.0	70.00
Swimming	M	83.40860	8.877471	85.0	13.00	59	100	77.0	90.00

En los cinco deportes principales de 2016 se observa que, en todos los casos, los atletas masculinos presentan un peso promedio mayor que las atletas femeninas.

Por ejemplo, en Athletics, las mujeres tienen un peso promedio de 62.6 kg (desviación estándar de 14.94 kg), donde el 50% de las atletas pesa menos o igual a 59 kg, con un mínimo de 40 kg y un máximo de 136 kg. El 25% pesa menos o igual a 55.5 kg y el 75% menos o igual a 68 kg. Este valor máximo es considerablemente alto respecto al tercer cuartil, lo que sugiere la presencia de posibles valores atípicos. En contraste, los hombres en Athletics presentan un promedio de 79.07 kg (desviación estándar de 17.76 kg), con una mediana de 75.5 kg, un mínimo de 47 kg y un máximo de 134 kg. El 25% ésa menos o igual a 68 kg y el 75% menos o igual a 84.75, con un máximo que también indica la presencia de posibles valores atípicos.

De manera similar, en Football, los hombres tienen un peso promedio cercano a 75.7 kg, mientras que las mujeres presentan aproximadamente 63 kg. En Hockey, el promedio masculino es de 78.38 kg frente a 63.06 kg en mujeres. En Rowing, se presenta la mayor diferencia, con un promedio de 88.83 kg en hombres y 71.77 kg en mujeres. En Swimming, los hombres promedian 83.41 kg, mientras que las mujeres aproximadamente 66.20 kg.

Ejercicio 3

Considere el conjunto de datos us_state_population.tsv ubicado en la carpeta de datos de Python de github. Repita el procedimiento planteado en cada ítem de esta sección para obtener el nuevo dataframe con las nuevas columnas Year y Population. Realice unión y separación utilizando las columnas State y Code.

Desarrollo

Primero, cargamos el dataset a utilizar que en este caso es us_state_polutation.tsv:

df <- read_tsv("~/Downloads/us_state_population.tsv")
kable(df,
    caption = "Población de Estados Unidos",
    align = "c") %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = TRUE,
    position = "center"
  ) %>% 
  row_spec(0, bold = TRUE, background = "lightgray") %>% 
  scroll_box(height = "300px")

Población de Estados Unidos
State	Code	2010	2011	2012	2013	2014	2015	2016	2017	2018
Alabama	AL	4785448	4798834	4815564	4830460	4842481	4853160	4864745	4875120	4887871
Alaska	AK	713906	722038	730399	737045	736307	737547	741504	739786	737438
Arizona	AZ	6407774	6473497	6556629	6634999	6733840	6833596	6945452	7048876	7171646
Arkansas	AR	2921978	2940407	2952109	2959549	2967726	2978407	2990410	3002997	3013825
California	CA	37320903	37641823	37960782	38280824	38625139	38953142	39209127	39399349	39557045
Colorado	CO	5048281	5121771	5193721	5270482	5351218	5452107	5540921	5615902	5695564
Connecticut	CT	3579125	3588023	3594395	3594915	3594783	3587509	3578674	3573880	3572665
Delaware	DE	899595	907316	915188	923638	932596	941413	949216	957078	967171
District of Columbia	DC	605085	619602	634725	650431	662513	675254	686575	695691	702455
Florida	FL	18845785	19093352	19326230	19563166	19860330	20224249	20629982	20976812	21299325
Georgia	GA	9711810	9801578	9901496	9973326	10069001	10181111	10304763	10413055	10519475
Hawaii	HI	1363963	1379252	1394905	1408453	1414862	1422484	1428105	1424203	1420491
Idaho	ID	1570773	1583828	1595441	1611530	1631479	1651523	1682930	1718904	1754208
Illinois	IL	12840762	12867291	12884119	12898269	12888962	12864342	12826895	12786196	12741080
Indiana	IN	6490436	6516045	6537640	6568367	6593533	6608296	6633344	6660082	6691878
Iowa	IA	3050767	3066054	3076097	3093078	3109504	3121460	3131785	3143637	3156145
Kansas	KS	2858213	2869035	2885361	2893510	2900896	2909502	2911263	2910689	2911505
Kentucky	KY	4348200	4369488	4386381	4404817	4414483	4425999	4438229	4453874	4468402
Louisiana	LA	4544532	4575184	4600814	4624577	4644204	4664851	4678215	4670818	4659978
Maine	ME	1327632	1328150	1327691	1328196	1330760	1328484	1331370	1335063	1338404
Maryland	MD	5788642	5838991	5887072	5923704	5958165	5986717	6004692	6024891	6042718
Massachusetts	MA	6566431	6613149	6663158	6713944	6763652	6795891	6826022	6863246	6902149
Michigan	MI	9877535	9881521	9896930	9913349	9930589	9932573	9951890	9976447	9995915
Minnesota	MN	5310843	5345668	5376550	5413693	5451522	5482503	5523409	5568155	5611179
Mississippi	MS	2970536	2978470	2983767	2988797	2990623	2988693	2988298	2989663	2986530
Missouri	MO	5995976	6009641	6024081	6040658	6056293	6071745	6087203	6108612	6126452
Montana	MT	990722	997221	1003754	1013564	1021891	1030503	1040863	1053090	1062305
Nebraska	NE	1829536	1840538	1853323	1865414	1879522	1891507	1905924	1917575	1929268
Nevada	NV	2702464	2712799	2744566	2776972	2819012	2868666	2919772	2972405	3034392
New Hampshire	NH	1316777	1319815	1323962	1326408	1333223	1336294	1342373	1349767	1356458
New Jersey	NJ	8799624	8827783	8845483	8858362	8866780	8870869	8874516	8888543	8908520
New Mexico	NM	2064588	2080395	2087549	2092792	2090342	2090211	2092789	2093395	2095428
New York	NY	19400080	19498514	19574549	19628043	19656330	19661411	19641589	19590719	19542209
North Carolina	NC	9574293	9656754	9749123	9843599	9933944	10033079	10156679	10270800	10383620
North Dakota	ND	674710	685136	701116	721999	737382	754022	754353	755176	760077
Ohio	OH	11539327	11543463	11548369	11576576	11602973	11617850	11635003	11664129	11689442
Oklahoma	OK	3759632	3787821	3818600	3853205	3878367	3909831	3926769	3932640	3943079
Oregon	OR	3837532	3871728	3899118	3922908	3964106	4016918	4091404	4146592	4190713
Pennsylvania	PA	12711158	12744583	12766827	12776621	12789101	12785759	12783538	12790447	12807060
Rhode Island	RI	1053938	1053536	1054601	1055122	1056017	1056173	1057063	1056486	1057315
South Carolina	SC	4635656	4671422	4717112	4764153	4823793	4892253	4958235	5021219	5084127
South Dakota	SD	816165	823484	833496	842270	849088	853933	862890	873286	882235
Tennessee	TN	6355301	6397410	6451281	6493432	6540826	6590808	6645011	6708794	6770010
Texas	TX	25242679	25646227	26089620	26489464	26977142	27486814	27937492	28322717	28701845
Utah	UT	2775334	2814216	2853467	2897927	2937399	2982497	3042613	3103118	3161105
Vermont	VT	625880	626979	626063	626212	625218	625197	623644	624525	626299
Virginia	VA	8023680	8100469	8185229	8253053	8312076	8362907	8410946	8465207	8517685
Washington	WA	6742902	6821655	6892876	6962906	7052439	7163543	7294680	7425432	7535591
West Virginia	WV	1854214	1856074	1856764	1853873	1849467	1841996	1830929	1817048	1805832
Wisconsin	WI	5690479	5704755	5719855	5736952	5751974	5761406	5772958	5792051	5813568
Wyoming	WY	564483	567224	576270	582123	582548	585668	584290	578934	577737

Utilizamos la función gather() para convertir las columnas 2010, 2011,…, 2018 en dos nuevas columnas: una llamada Year, que contendrá los años, y otra llamada Population, que almacenará los valores correspondientes a cada año.

df1 <- gather(df, 
              "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018",
              key = "Year",
              value = "Population")
kable(df1,
    align = "c") %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = TRUE,
    position = "center"
  ) %>% 
  row_spec(0, bold = TRUE, background = "lightgray") %>% 
  scroll_box(height = "300px")

State	Code	Year	Population
Alabama	AL	2010	4785448
Alaska	AK	2010	713906
Arizona	AZ	2010	6407774
Arkansas	AR	2010	2921978
California	CA	2010	37320903
Colorado	CO	2010	5048281
Connecticut	CT	2010	3579125
Delaware	DE	2010	899595
District of Columbia	DC	2010	605085
Florida	FL	2010	18845785
Georgia	GA	2010	9711810
Hawaii	HI	2010	1363963
Idaho	ID	2010	1570773
Illinois	IL	2010	12840762
Indiana	IN	2010	6490436
Iowa	IA	2010	3050767
Kansas	KS	2010	2858213
Kentucky	KY	2010	4348200
Louisiana	LA	2010	4544532
Maine	ME	2010	1327632
Maryland	MD	2010	5788642
Massachusetts	MA	2010	6566431
Michigan	MI	2010	9877535
Minnesota	MN	2010	5310843
Mississippi	MS	2010	2970536
Missouri	MO	2010	5995976
Montana	MT	2010	990722
Nebraska	NE	2010	1829536
Nevada	NV	2010	2702464
New Hampshire	NH	2010	1316777
New Jersey	NJ	2010	8799624
New Mexico	NM	2010	2064588
New York	NY	2010	19400080
North Carolina	NC	2010	9574293
North Dakota	ND	2010	674710
Ohio	OH	2010	11539327
Oklahoma	OK	2010	3759632
Oregon	OR	2010	3837532
Pennsylvania	PA	2010	12711158
Rhode Island	RI	2010	1053938
South Carolina	SC	2010	4635656
South Dakota	SD	2010	816165
Tennessee	TN	2010	6355301
Texas	TX	2010	25242679
Utah	UT	2010	2775334
Vermont	VT	2010	625880
Virginia	VA	2010	8023680
Washington	WA	2010	6742902
West Virginia	WV	2010	1854214
Wisconsin	WI	2010	5690479
Wyoming	WY	2010	564483
Alabama	AL	2011	4798834
Alaska	AK	2011	722038
Arizona	AZ	2011	6473497
Arkansas	AR	2011	2940407
California	CA	2011	37641823
Colorado	CO	2011	5121771
Connecticut	CT	2011	3588023
Delaware	DE	2011	907316
District of Columbia	DC	2011	619602
Florida	FL	2011	19093352
Georgia	GA	2011	9801578
Hawaii	HI	2011	1379252
Idaho	ID	2011	1583828
Illinois	IL	2011	12867291
Indiana	IN	2011	6516045
Iowa	IA	2011	3066054
Kansas	KS	2011	2869035
Kentucky	KY	2011	4369488
Louisiana	LA	2011	4575184
Maine	ME	2011	1328150
Maryland	MD	2011	5838991
Massachusetts	MA	2011	6613149
Michigan	MI	2011	9881521
Minnesota	MN	2011	5345668
Mississippi	MS	2011	2978470
Missouri	MO	2011	6009641
Montana	MT	2011	997221
Nebraska	NE	2011	1840538
Nevada	NV	2011	2712799
New Hampshire	NH	2011	1319815
New Jersey	NJ	2011	8827783
New Mexico	NM	2011	2080395
New York	NY	2011	19498514
North Carolina	NC	2011	9656754
North Dakota	ND	2011	685136
Ohio	OH	2011	11543463
Oklahoma	OK	2011	3787821
Oregon	OR	2011	3871728
Pennsylvania	PA	2011	12744583
Rhode Island	RI	2011	1053536
South Carolina	SC	2011	4671422
South Dakota	SD	2011	823484
Tennessee	TN	2011	6397410
Texas	TX	2011	25646227
Utah	UT	2011	2814216
Vermont	VT	2011	626979
Virginia	VA	2011	8100469
Washington	WA	2011	6821655
West Virginia	WV	2011	1856074
Wisconsin	WI	2011	5704755
Wyoming	WY	2011	567224
Alabama	AL	2012	4815564
Alaska	AK	2012	730399
Arizona	AZ	2012	6556629
Arkansas	AR	2012	2952109
California	CA	2012	37960782
Colorado	CO	2012	5193721
Connecticut	CT	2012	3594395
Delaware	DE	2012	915188
District of Columbia	DC	2012	634725
Florida	FL	2012	19326230
Georgia	GA	2012	9901496
Hawaii	HI	2012	1394905
Idaho	ID	2012	1595441
Illinois	IL	2012	12884119
Indiana	IN	2012	6537640
Iowa	IA	2012	3076097
Kansas	KS	2012	2885361
Kentucky	KY	2012	4386381
Louisiana	LA	2012	4600814
Maine	ME	2012	1327691
Maryland	MD	2012	5887072
Massachusetts	MA	2012	6663158
Michigan	MI	2012	9896930
Minnesota	MN	2012	5376550
Mississippi	MS	2012	2983767
Missouri	MO	2012	6024081
Montana	MT	2012	1003754
Nebraska	NE	2012	1853323
Nevada	NV	2012	2744566
New Hampshire	NH	2012	1323962
New Jersey	NJ	2012	8845483
New Mexico	NM	2012	2087549
New York	NY	2012	19574549
North Carolina	NC	2012	9749123
North Dakota	ND	2012	701116
Ohio	OH	2012	11548369
Oklahoma	OK	2012	3818600
Oregon	OR	2012	3899118
Pennsylvania	PA	2012	12766827
Rhode Island	RI	2012	1054601
South Carolina	SC	2012	4717112
South Dakota	SD	2012	833496
Tennessee	TN	2012	6451281
Texas	TX	2012	26089620
Utah	UT	2012	2853467
Vermont	VT	2012	626063
Virginia	VA	2012	8185229
Washington	WA	2012	6892876
West Virginia	WV	2012	1856764
Wisconsin	WI	2012	5719855
Wyoming	WY	2012	576270
Alabama	AL	2013	4830460
Alaska	AK	2013	737045
Arizona	AZ	2013	6634999
Arkansas	AR	2013	2959549
California	CA	2013	38280824
Colorado	CO	2013	5270482
Connecticut	CT	2013	3594915
Delaware	DE	2013	923638
District of Columbia	DC	2013	650431
Florida	FL	2013	19563166
Georgia	GA	2013	9973326
Hawaii	HI	2013	1408453
Idaho	ID	2013	1611530
Illinois	IL	2013	12898269
Indiana	IN	2013	6568367
Iowa	IA	2013	3093078
Kansas	KS	2013	2893510
Kentucky	KY	2013	4404817
Louisiana	LA	2013	4624577
Maine	ME	2013	1328196
Maryland	MD	2013	5923704
Massachusetts	MA	2013	6713944
Michigan	MI	2013	9913349
Minnesota	MN	2013	5413693
Mississippi	MS	2013	2988797
Missouri	MO	2013	6040658
Montana	MT	2013	1013564
Nebraska	NE	2013	1865414
Nevada	NV	2013	2776972
New Hampshire	NH	2013	1326408
New Jersey	NJ	2013	8858362
New Mexico	NM	2013	2092792
New York	NY	2013	19628043
North Carolina	NC	2013	9843599
North Dakota	ND	2013	721999
Ohio	OH	2013	11576576
Oklahoma	OK	2013	3853205
Oregon	OR	2013	3922908
Pennsylvania	PA	2013	12776621
Rhode Island	RI	2013	1055122
South Carolina	SC	2013	4764153
South Dakota	SD	2013	842270
Tennessee	TN	2013	6493432
Texas	TX	2013	26489464
Utah	UT	2013	2897927
Vermont	VT	2013	626212
Virginia	VA	2013	8253053
Washington	WA	2013	6962906
West Virginia	WV	2013	1853873
Wisconsin	WI	2013	5736952
Wyoming	WY	2013	582123
Alabama	AL	2014	4842481
Alaska	AK	2014	736307
Arizona	AZ	2014	6733840
Arkansas	AR	2014	2967726
California	CA	2014	38625139
Colorado	CO	2014	5351218
Connecticut	CT	2014	3594783
Delaware	DE	2014	932596
District of Columbia	DC	2014	662513
Florida	FL	2014	19860330
Georgia	GA	2014	10069001
Hawaii	HI	2014	1414862
Idaho	ID	2014	1631479
Illinois	IL	2014	12888962
Indiana	IN	2014	6593533
Iowa	IA	2014	3109504
Kansas	KS	2014	2900896
Kentucky	KY	2014	4414483
Louisiana	LA	2014	4644204
Maine	ME	2014	1330760
Maryland	MD	2014	5958165
Massachusetts	MA	2014	6763652
Michigan	MI	2014	9930589
Minnesota	MN	2014	5451522
Mississippi	MS	2014	2990623
Missouri	MO	2014	6056293
Montana	MT	2014	1021891
Nebraska	NE	2014	1879522
Nevada	NV	2014	2819012
New Hampshire	NH	2014	1333223
New Jersey	NJ	2014	8866780
New Mexico	NM	2014	2090342
New York	NY	2014	19656330
North Carolina	NC	2014	9933944
North Dakota	ND	2014	737382
Ohio	OH	2014	11602973
Oklahoma	OK	2014	3878367
Oregon	OR	2014	3964106
Pennsylvania	PA	2014	12789101
Rhode Island	RI	2014	1056017
South Carolina	SC	2014	4823793
South Dakota	SD	2014	849088
Tennessee	TN	2014	6540826
Texas	TX	2014	26977142
Utah	UT	2014	2937399
Vermont	VT	2014	625218
Virginia	VA	2014	8312076
Washington	WA	2014	7052439
West Virginia	WV	2014	1849467
Wisconsin	WI	2014	5751974
Wyoming	WY	2014	582548
Alabama	AL	2015	4853160
Alaska	AK	2015	737547
Arizona	AZ	2015	6833596
Arkansas	AR	2015	2978407
California	CA	2015	38953142
Colorado	CO	2015	5452107
Connecticut	CT	2015	3587509
Delaware	DE	2015	941413
District of Columbia	DC	2015	675254
Florida	FL	2015	20224249
Georgia	GA	2015	10181111
Hawaii	HI	2015	1422484
Idaho	ID	2015	1651523
Illinois	IL	2015	12864342
Indiana	IN	2015	6608296
Iowa	IA	2015	3121460
Kansas	KS	2015	2909502
Kentucky	KY	2015	4425999
Louisiana	LA	2015	4664851
Maine	ME	2015	1328484
Maryland	MD	2015	5986717
Massachusetts	MA	2015	6795891
Michigan	MI	2015	9932573
Minnesota	MN	2015	5482503
Mississippi	MS	2015	2988693
Missouri	MO	2015	6071745
Montana	MT	2015	1030503
Nebraska	NE	2015	1891507
Nevada	NV	2015	2868666
New Hampshire	NH	2015	1336294
New Jersey	NJ	2015	8870869
New Mexico	NM	2015	2090211
New York	NY	2015	19661411
North Carolina	NC	2015	10033079
North Dakota	ND	2015	754022
Ohio	OH	2015	11617850
Oklahoma	OK	2015	3909831
Oregon	OR	2015	4016918
Pennsylvania	PA	2015	12785759
Rhode Island	RI	2015	1056173
South Carolina	SC	2015	4892253
South Dakota	SD	2015	853933
Tennessee	TN	2015	6590808
Texas	TX	2015	27486814
Utah	UT	2015	2982497
Vermont	VT	2015	625197
Virginia	VA	2015	8362907
Washington	WA	2015	7163543
West Virginia	WV	2015	1841996
Wisconsin	WI	2015	5761406
Wyoming	WY	2015	585668
Alabama	AL	2016	4864745
Alaska	AK	2016	741504
Arizona	AZ	2016	6945452
Arkansas	AR	2016	2990410
California	CA	2016	39209127
Colorado	CO	2016	5540921
Connecticut	CT	2016	3578674
Delaware	DE	2016	949216
District of Columbia	DC	2016	686575
Florida	FL	2016	20629982
Georgia	GA	2016	10304763
Hawaii	HI	2016	1428105
Idaho	ID	2016	1682930
Illinois	IL	2016	12826895
Indiana	IN	2016	6633344
Iowa	IA	2016	3131785
Kansas	KS	2016	2911263
Kentucky	KY	2016	4438229
Louisiana	LA	2016	4678215
Maine	ME	2016	1331370
Maryland	MD	2016	6004692
Massachusetts	MA	2016	6826022
Michigan	MI	2016	9951890
Minnesota	MN	2016	5523409
Mississippi	MS	2016	2988298
Missouri	MO	2016	6087203
Montana	MT	2016	1040863
Nebraska	NE	2016	1905924
Nevada	NV	2016	2919772
New Hampshire	NH	2016	1342373
New Jersey	NJ	2016	8874516
New Mexico	NM	2016	2092789
New York	NY	2016	19641589
North Carolina	NC	2016	10156679
North Dakota	ND	2016	754353
Ohio	OH	2016	11635003
Oklahoma	OK	2016	3926769
Oregon	OR	2016	4091404
Pennsylvania	PA	2016	12783538
Rhode Island	RI	2016	1057063
South Carolina	SC	2016	4958235
South Dakota	SD	2016	862890
Tennessee	TN	2016	6645011
Texas	TX	2016	27937492
Utah	UT	2016	3042613
Vermont	VT	2016	623644
Virginia	VA	2016	8410946
Washington	WA	2016	7294680
West Virginia	WV	2016	1830929
Wisconsin	WI	2016	5772958
Wyoming	WY	2016	584290
Alabama	AL	2017	4875120
Alaska	AK	2017	739786
Arizona	AZ	2017	7048876
Arkansas	AR	2017	3002997
California	CA	2017	39399349
Colorado	CO	2017	5615902
Connecticut	CT	2017	3573880
Delaware	DE	2017	957078
District of Columbia	DC	2017	695691
Florida	FL	2017	20976812
Georgia	GA	2017	10413055
Hawaii	HI	2017	1424203
Idaho	ID	2017	1718904
Illinois	IL	2017	12786196
Indiana	IN	2017	6660082
Iowa	IA	2017	3143637
Kansas	KS	2017	2910689
Kentucky	KY	2017	4453874
Louisiana	LA	2017	4670818
Maine	ME	2017	1335063
Maryland	MD	2017	6024891
Massachusetts	MA	2017	6863246
Michigan	MI	2017	9976447
Minnesota	MN	2017	5568155
Mississippi	MS	2017	2989663
Missouri	MO	2017	6108612
Montana	MT	2017	1053090
Nebraska	NE	2017	1917575
Nevada	NV	2017	2972405
New Hampshire	NH	2017	1349767
New Jersey	NJ	2017	8888543
New Mexico	NM	2017	2093395
New York	NY	2017	19590719
North Carolina	NC	2017	10270800
North Dakota	ND	2017	755176
Ohio	OH	2017	11664129
Oklahoma	OK	2017	3932640
Oregon	OR	2017	4146592
Pennsylvania	PA	2017	12790447
Rhode Island	RI	2017	1056486
South Carolina	SC	2017	5021219
South Dakota	SD	2017	873286
Tennessee	TN	2017	6708794
Texas	TX	2017	28322717
Utah	UT	2017	3103118
Vermont	VT	2017	624525
Virginia	VA	2017	8465207
Washington	WA	2017	7425432
West Virginia	WV	2017	1817048
Wisconsin	WI	2017	5792051
Wyoming	WY	2017	578934
Alabama	AL	2018	4887871
Alaska	AK	2018	737438
Arizona	AZ	2018	7171646
Arkansas	AR	2018	3013825
California	CA	2018	39557045
Colorado	CO	2018	5695564
Connecticut	CT	2018	3572665
Delaware	DE	2018	967171
District of Columbia	DC	2018	702455
Florida	FL	2018	21299325
Georgia	GA	2018	10519475
Hawaii	HI	2018	1420491
Idaho	ID	2018	1754208
Illinois	IL	2018	12741080
Indiana	IN	2018	6691878
Iowa	IA	2018	3156145
Kansas	KS	2018	2911505
Kentucky	KY	2018	4468402
Louisiana	LA	2018	4659978
Maine	ME	2018	1338404
Maryland	MD	2018	6042718
Massachusetts	MA	2018	6902149
Michigan	MI	2018	9995915
Minnesota	MN	2018	5611179
Mississippi	MS	2018	2986530
Missouri	MO	2018	6126452
Montana	MT	2018	1062305
Nebraska	NE	2018	1929268
Nevada	NV	2018	3034392
New Hampshire	NH	2018	1356458
New Jersey	NJ	2018	8908520
New Mexico	NM	2018	2095428
New York	NY	2018	19542209
North Carolina	NC	2018	10383620
North Dakota	ND	2018	760077
Ohio	OH	2018	11689442
Oklahoma	OK	2018	3943079
Oregon	OR	2018	4190713
Pennsylvania	PA	2018	12807060
Rhode Island	RI	2018	1057315
South Carolina	SC	2018	5084127
South Dakota	SD	2018	882235
Tennessee	TN	2018	6770010
Texas	TX	2018	28701845
Utah	UT	2018	3161105
Vermont	VT	2018	626299
Virginia	VA	2018	8517685
Washington	WA	2018	7535591
West Virginia	WV	2018	1805832
Wisconsin	WI	2018	5813568
Wyoming	WY	2018	577737

Ahora, haremos uso de la función union() para combinar las columnas State y Code en una sola. A la nueva columna la llamaremos State-Code.

df2 <- unite(df1, "State-Code", State, Code, sep = "-")
kable(df2,
    align = "c") %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = TRUE,
    position = "center"
  ) %>% 
  row_spec(0, bold = TRUE, background = "lightgray") %>% 
  scroll_box(height = "300px")

State-Code	Year	Population
Alabama-AL	2010	4785448
Alaska-AK	2010	713906
Arizona-AZ	2010	6407774
Arkansas-AR	2010	2921978
California-CA	2010	37320903
Colorado-CO	2010	5048281
Connecticut-CT	2010	3579125
Delaware-DE	2010	899595
District of Columbia-DC	2010	605085
Florida-FL	2010	18845785
Georgia-GA	2010	9711810
Hawaii-HI	2010	1363963
Idaho-ID	2010	1570773
Illinois-IL	2010	12840762
Indiana-IN	2010	6490436
Iowa-IA	2010	3050767
Kansas-KS	2010	2858213
Kentucky-KY	2010	4348200
Louisiana-LA	2010	4544532
Maine-ME	2010	1327632
Maryland-MD	2010	5788642
Massachusetts-MA	2010	6566431
Michigan-MI	2010	9877535
Minnesota-MN	2010	5310843
Mississippi-MS	2010	2970536
Missouri-MO	2010	5995976
Montana-MT	2010	990722
Nebraska-NE	2010	1829536
Nevada-NV	2010	2702464
New Hampshire-NH	2010	1316777
New Jersey-NJ	2010	8799624
New Mexico-NM	2010	2064588
New York-NY	2010	19400080
North Carolina-NC	2010	9574293
North Dakota-ND	2010	674710
Ohio-OH	2010	11539327
Oklahoma-OK	2010	3759632
Oregon-OR	2010	3837532
Pennsylvania-PA	2010	12711158
Rhode Island-RI	2010	1053938
South Carolina-SC	2010	4635656
South Dakota-SD	2010	816165
Tennessee-TN	2010	6355301
Texas-TX	2010	25242679
Utah-UT	2010	2775334
Vermont-VT	2010	625880
Virginia-VA	2010	8023680
Washington-WA	2010	6742902
West Virginia-WV	2010	1854214
Wisconsin-WI	2010	5690479
Wyoming-WY	2010	564483
Alabama-AL	2011	4798834
Alaska-AK	2011	722038
Arizona-AZ	2011	6473497
Arkansas-AR	2011	2940407
California-CA	2011	37641823
Colorado-CO	2011	5121771
Connecticut-CT	2011	3588023
Delaware-DE	2011	907316
District of Columbia-DC	2011	619602
Florida-FL	2011	19093352
Georgia-GA	2011	9801578
Hawaii-HI	2011	1379252
Idaho-ID	2011	1583828
Illinois-IL	2011	12867291
Indiana-IN	2011	6516045
Iowa-IA	2011	3066054
Kansas-KS	2011	2869035
Kentucky-KY	2011	4369488
Louisiana-LA	2011	4575184
Maine-ME	2011	1328150
Maryland-MD	2011	5838991
Massachusetts-MA	2011	6613149
Michigan-MI	2011	9881521
Minnesota-MN	2011	5345668
Mississippi-MS	2011	2978470
Missouri-MO	2011	6009641
Montana-MT	2011	997221
Nebraska-NE	2011	1840538
Nevada-NV	2011	2712799
New Hampshire-NH	2011	1319815
New Jersey-NJ	2011	8827783
New Mexico-NM	2011	2080395
New York-NY	2011	19498514
North Carolina-NC	2011	9656754
North Dakota-ND	2011	685136
Ohio-OH	2011	11543463
Oklahoma-OK	2011	3787821
Oregon-OR	2011	3871728
Pennsylvania-PA	2011	12744583
Rhode Island-RI	2011	1053536
South Carolina-SC	2011	4671422
South Dakota-SD	2011	823484
Tennessee-TN	2011	6397410
Texas-TX	2011	25646227
Utah-UT	2011	2814216
Vermont-VT	2011	626979
Virginia-VA	2011	8100469
Washington-WA	2011	6821655
West Virginia-WV	2011	1856074
Wisconsin-WI	2011	5704755
Wyoming-WY	2011	567224
Alabama-AL	2012	4815564
Alaska-AK	2012	730399
Arizona-AZ	2012	6556629
Arkansas-AR	2012	2952109
California-CA	2012	37960782
Colorado-CO	2012	5193721
Connecticut-CT	2012	3594395
Delaware-DE	2012	915188
District of Columbia-DC	2012	634725
Florida-FL	2012	19326230
Georgia-GA	2012	9901496
Hawaii-HI	2012	1394905
Idaho-ID	2012	1595441
Illinois-IL	2012	12884119
Indiana-IN	2012	6537640
Iowa-IA	2012	3076097
Kansas-KS	2012	2885361
Kentucky-KY	2012	4386381
Louisiana-LA	2012	4600814
Maine-ME	2012	1327691
Maryland-MD	2012	5887072
Massachusetts-MA	2012	6663158
Michigan-MI	2012	9896930
Minnesota-MN	2012	5376550
Mississippi-MS	2012	2983767
Missouri-MO	2012	6024081
Montana-MT	2012	1003754
Nebraska-NE	2012	1853323
Nevada-NV	2012	2744566
New Hampshire-NH	2012	1323962
New Jersey-NJ	2012	8845483
New Mexico-NM	2012	2087549
New York-NY	2012	19574549
North Carolina-NC	2012	9749123
North Dakota-ND	2012	701116
Ohio-OH	2012	11548369
Oklahoma-OK	2012	3818600
Oregon-OR	2012	3899118
Pennsylvania-PA	2012	12766827
Rhode Island-RI	2012	1054601
South Carolina-SC	2012	4717112
South Dakota-SD	2012	833496
Tennessee-TN	2012	6451281
Texas-TX	2012	26089620
Utah-UT	2012	2853467
Vermont-VT	2012	626063
Virginia-VA	2012	8185229
Washington-WA	2012	6892876
West Virginia-WV	2012	1856764
Wisconsin-WI	2012	5719855
Wyoming-WY	2012	576270
Alabama-AL	2013	4830460
Alaska-AK	2013	737045
Arizona-AZ	2013	6634999
Arkansas-AR	2013	2959549
California-CA	2013	38280824
Colorado-CO	2013	5270482
Connecticut-CT	2013	3594915
Delaware-DE	2013	923638
District of Columbia-DC	2013	650431
Florida-FL	2013	19563166
Georgia-GA	2013	9973326
Hawaii-HI	2013	1408453
Idaho-ID	2013	1611530
Illinois-IL	2013	12898269
Indiana-IN	2013	6568367
Iowa-IA	2013	3093078
Kansas-KS	2013	2893510
Kentucky-KY	2013	4404817
Louisiana-LA	2013	4624577
Maine-ME	2013	1328196
Maryland-MD	2013	5923704
Massachusetts-MA	2013	6713944
Michigan-MI	2013	9913349
Minnesota-MN	2013	5413693
Mississippi-MS	2013	2988797
Missouri-MO	2013	6040658
Montana-MT	2013	1013564
Nebraska-NE	2013	1865414
Nevada-NV	2013	2776972
New Hampshire-NH	2013	1326408
New Jersey-NJ	2013	8858362
New Mexico-NM	2013	2092792
New York-NY	2013	19628043
North Carolina-NC	2013	9843599
North Dakota-ND	2013	721999
Ohio-OH	2013	11576576
Oklahoma-OK	2013	3853205
Oregon-OR	2013	3922908
Pennsylvania-PA	2013	12776621
Rhode Island-RI	2013	1055122
South Carolina-SC	2013	4764153
South Dakota-SD	2013	842270
Tennessee-TN	2013	6493432
Texas-TX	2013	26489464
Utah-UT	2013	2897927
Vermont-VT	2013	626212
Virginia-VA	2013	8253053
Washington-WA	2013	6962906
West Virginia-WV	2013	1853873
Wisconsin-WI	2013	5736952
Wyoming-WY	2013	582123
Alabama-AL	2014	4842481
Alaska-AK	2014	736307
Arizona-AZ	2014	6733840
Arkansas-AR	2014	2967726
California-CA	2014	38625139
Colorado-CO	2014	5351218
Connecticut-CT	2014	3594783
Delaware-DE	2014	932596
District of Columbia-DC	2014	662513
Florida-FL	2014	19860330
Georgia-GA	2014	10069001
Hawaii-HI	2014	1414862
Idaho-ID	2014	1631479
Illinois-IL	2014	12888962
Indiana-IN	2014	6593533
Iowa-IA	2014	3109504
Kansas-KS	2014	2900896
Kentucky-KY	2014	4414483
Louisiana-LA	2014	4644204
Maine-ME	2014	1330760
Maryland-MD	2014	5958165
Massachusetts-MA	2014	6763652
Michigan-MI	2014	9930589
Minnesota-MN	2014	5451522
Mississippi-MS	2014	2990623
Missouri-MO	2014	6056293
Montana-MT	2014	1021891
Nebraska-NE	2014	1879522
Nevada-NV	2014	2819012
New Hampshire-NH	2014	1333223
New Jersey-NJ	2014	8866780
New Mexico-NM	2014	2090342
New York-NY	2014	19656330
North Carolina-NC	2014	9933944
North Dakota-ND	2014	737382
Ohio-OH	2014	11602973
Oklahoma-OK	2014	3878367
Oregon-OR	2014	3964106
Pennsylvania-PA	2014	12789101
Rhode Island-RI	2014	1056017
South Carolina-SC	2014	4823793
South Dakota-SD	2014	849088
Tennessee-TN	2014	6540826
Texas-TX	2014	26977142
Utah-UT	2014	2937399
Vermont-VT	2014	625218
Virginia-VA	2014	8312076
Washington-WA	2014	7052439
West Virginia-WV	2014	1849467
Wisconsin-WI	2014	5751974
Wyoming-WY	2014	582548
Alabama-AL	2015	4853160
Alaska-AK	2015	737547
Arizona-AZ	2015	6833596
Arkansas-AR	2015	2978407
California-CA	2015	38953142
Colorado-CO	2015	5452107
Connecticut-CT	2015	3587509
Delaware-DE	2015	941413
District of Columbia-DC	2015	675254
Florida-FL	2015	20224249
Georgia-GA	2015	10181111
Hawaii-HI	2015	1422484
Idaho-ID	2015	1651523
Illinois-IL	2015	12864342
Indiana-IN	2015	6608296
Iowa-IA	2015	3121460
Kansas-KS	2015	2909502
Kentucky-KY	2015	4425999
Louisiana-LA	2015	4664851
Maine-ME	2015	1328484
Maryland-MD	2015	5986717
Massachusetts-MA	2015	6795891
Michigan-MI	2015	9932573
Minnesota-MN	2015	5482503
Mississippi-MS	2015	2988693
Missouri-MO	2015	6071745
Montana-MT	2015	1030503
Nebraska-NE	2015	1891507
Nevada-NV	2015	2868666
New Hampshire-NH	2015	1336294
New Jersey-NJ	2015	8870869
New Mexico-NM	2015	2090211
New York-NY	2015	19661411
North Carolina-NC	2015	10033079
North Dakota-ND	2015	754022
Ohio-OH	2015	11617850
Oklahoma-OK	2015	3909831
Oregon-OR	2015	4016918
Pennsylvania-PA	2015	12785759
Rhode Island-RI	2015	1056173
South Carolina-SC	2015	4892253
South Dakota-SD	2015	853933
Tennessee-TN	2015	6590808
Texas-TX	2015	27486814
Utah-UT	2015	2982497
Vermont-VT	2015	625197
Virginia-VA	2015	8362907
Washington-WA	2015	7163543
West Virginia-WV	2015	1841996
Wisconsin-WI	2015	5761406
Wyoming-WY	2015	585668
Alabama-AL	2016	4864745
Alaska-AK	2016	741504
Arizona-AZ	2016	6945452
Arkansas-AR	2016	2990410
California-CA	2016	39209127
Colorado-CO	2016	5540921
Connecticut-CT	2016	3578674
Delaware-DE	2016	949216
District of Columbia-DC	2016	686575
Florida-FL	2016	20629982
Georgia-GA	2016	10304763
Hawaii-HI	2016	1428105
Idaho-ID	2016	1682930
Illinois-IL	2016	12826895
Indiana-IN	2016	6633344
Iowa-IA	2016	3131785
Kansas-KS	2016	2911263
Kentucky-KY	2016	4438229
Louisiana-LA	2016	4678215
Maine-ME	2016	1331370
Maryland-MD	2016	6004692
Massachusetts-MA	2016	6826022
Michigan-MI	2016	9951890
Minnesota-MN	2016	5523409
Mississippi-MS	2016	2988298
Missouri-MO	2016	6087203
Montana-MT	2016	1040863
Nebraska-NE	2016	1905924
Nevada-NV	2016	2919772
New Hampshire-NH	2016	1342373
New Jersey-NJ	2016	8874516
New Mexico-NM	2016	2092789
New York-NY	2016	19641589
North Carolina-NC	2016	10156679
North Dakota-ND	2016	754353
Ohio-OH	2016	11635003
Oklahoma-OK	2016	3926769
Oregon-OR	2016	4091404
Pennsylvania-PA	2016	12783538
Rhode Island-RI	2016	1057063
South Carolina-SC	2016	4958235
South Dakota-SD	2016	862890
Tennessee-TN	2016	6645011
Texas-TX	2016	27937492
Utah-UT	2016	3042613
Vermont-VT	2016	623644
Virginia-VA	2016	8410946
Washington-WA	2016	7294680
West Virginia-WV	2016	1830929
Wisconsin-WI	2016	5772958
Wyoming-WY	2016	584290
Alabama-AL	2017	4875120
Alaska-AK	2017	739786
Arizona-AZ	2017	7048876
Arkansas-AR	2017	3002997
California-CA	2017	39399349
Colorado-CO	2017	5615902
Connecticut-CT	2017	3573880
Delaware-DE	2017	957078
District of Columbia-DC	2017	695691
Florida-FL	2017	20976812
Georgia-GA	2017	10413055
Hawaii-HI	2017	1424203
Idaho-ID	2017	1718904
Illinois-IL	2017	12786196
Indiana-IN	2017	6660082
Iowa-IA	2017	3143637
Kansas-KS	2017	2910689
Kentucky-KY	2017	4453874
Louisiana-LA	2017	4670818
Maine-ME	2017	1335063
Maryland-MD	2017	6024891
Massachusetts-MA	2017	6863246
Michigan-MI	2017	9976447
Minnesota-MN	2017	5568155
Mississippi-MS	2017	2989663
Missouri-MO	2017	6108612
Montana-MT	2017	1053090
Nebraska-NE	2017	1917575
Nevada-NV	2017	2972405
New Hampshire-NH	2017	1349767
New Jersey-NJ	2017	8888543
New Mexico-NM	2017	2093395
New York-NY	2017	19590719
North Carolina-NC	2017	10270800
North Dakota-ND	2017	755176
Ohio-OH	2017	11664129
Oklahoma-OK	2017	3932640
Oregon-OR	2017	4146592
Pennsylvania-PA	2017	12790447
Rhode Island-RI	2017	1056486
South Carolina-SC	2017	5021219
South Dakota-SD	2017	873286
Tennessee-TN	2017	6708794
Texas-TX	2017	28322717
Utah-UT	2017	3103118
Vermont-VT	2017	624525
Virginia-VA	2017	8465207
Washington-WA	2017	7425432
West Virginia-WV	2017	1817048
Wisconsin-WI	2017	5792051
Wyoming-WY	2017	578934
Alabama-AL	2018	4887871
Alaska-AK	2018	737438
Arizona-AZ	2018	7171646
Arkansas-AR	2018	3013825
California-CA	2018	39557045
Colorado-CO	2018	5695564
Connecticut-CT	2018	3572665
Delaware-DE	2018	967171
District of Columbia-DC	2018	702455
Florida-FL	2018	21299325
Georgia-GA	2018	10519475
Hawaii-HI	2018	1420491
Idaho-ID	2018	1754208
Illinois-IL	2018	12741080
Indiana-IN	2018	6691878
Iowa-IA	2018	3156145
Kansas-KS	2018	2911505
Kentucky-KY	2018	4468402
Louisiana-LA	2018	4659978
Maine-ME	2018	1338404
Maryland-MD	2018	6042718
Massachusetts-MA	2018	6902149
Michigan-MI	2018	9995915
Minnesota-MN	2018	5611179
Mississippi-MS	2018	2986530
Missouri-MO	2018	6126452
Montana-MT	2018	1062305
Nebraska-NE	2018	1929268
Nevada-NV	2018	3034392
New Hampshire-NH	2018	1356458
New Jersey-NJ	2018	8908520
New Mexico-NM	2018	2095428
New York-NY	2018	19542209
North Carolina-NC	2018	10383620
North Dakota-ND	2018	760077
Ohio-OH	2018	11689442
Oklahoma-OK	2018	3943079
Oregon-OR	2018	4190713
Pennsylvania-PA	2018	12807060
Rhode Island-RI	2018	1057315
South Carolina-SC	2018	5084127
South Dakota-SD	2018	882235
Tennessee-TN	2018	6770010
Texas-TX	2018	28701845
Utah-UT	2018	3161105
Vermont-VT	2018	626299
Virginia-VA	2018	8517685
Washington-WA	2018	7535591
West Virginia-WV	2018	1805832
Wisconsin-WI	2018	5813568
Wyoming-WY	2018	577737

Por último, realizaremos la acción justamente inversa que se hace con la función separate(); esto es, separar dos variables que se colocaron en la misma columna.

df3 <- separate(df2, "State-Code", into = c("State", "Code"), sep = "-")
kable(df3,
    align = "c") %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = TRUE,
    position = "center"
  ) %>% 
  row_spec(0, bold = TRUE, background = "lightgray")%>% 
  scroll_box(height = "300px")

State	Code	Year	Population
Alabama	AL	2010	4785448
Alaska	AK	2010	713906
Arizona	AZ	2010	6407774
Arkansas	AR	2010	2921978
California	CA	2010	37320903
Colorado	CO	2010	5048281
Connecticut	CT	2010	3579125
Delaware	DE	2010	899595
District of Columbia	DC	2010	605085
Florida	FL	2010	18845785
Georgia	GA	2010	9711810
Hawaii	HI	2010	1363963
Idaho	ID	2010	1570773
Illinois	IL	2010	12840762
Indiana	IN	2010	6490436
Iowa	IA	2010	3050767
Kansas	KS	2010	2858213
Kentucky	KY	2010	4348200
Louisiana	LA	2010	4544532
Maine	ME	2010	1327632
Maryland	MD	2010	5788642
Massachusetts	MA	2010	6566431
Michigan	MI	2010	9877535
Minnesota	MN	2010	5310843
Mississippi	MS	2010	2970536
Missouri	MO	2010	5995976
Montana	MT	2010	990722
Nebraska	NE	2010	1829536
Nevada	NV	2010	2702464
New Hampshire	NH	2010	1316777
New Jersey	NJ	2010	8799624
New Mexico	NM	2010	2064588
New York	NY	2010	19400080
North Carolina	NC	2010	9574293
North Dakota	ND	2010	674710
Ohio	OH	2010	11539327
Oklahoma	OK	2010	3759632
Oregon	OR	2010	3837532
Pennsylvania	PA	2010	12711158
Rhode Island	RI	2010	1053938
South Carolina	SC	2010	4635656
South Dakota	SD	2010	816165
Tennessee	TN	2010	6355301
Texas	TX	2010	25242679
Utah	UT	2010	2775334
Vermont	VT	2010	625880
Virginia	VA	2010	8023680
Washington	WA	2010	6742902
West Virginia	WV	2010	1854214
Wisconsin	WI	2010	5690479
Wyoming	WY	2010	564483
Alabama	AL	2011	4798834
Alaska	AK	2011	722038
Arizona	AZ	2011	6473497
Arkansas	AR	2011	2940407
California	CA	2011	37641823
Colorado	CO	2011	5121771
Connecticut	CT	2011	3588023
Delaware	DE	2011	907316
District of Columbia	DC	2011	619602
Florida	FL	2011	19093352
Georgia	GA	2011	9801578
Hawaii	HI	2011	1379252
Idaho	ID	2011	1583828
Illinois	IL	2011	12867291
Indiana	IN	2011	6516045
Iowa	IA	2011	3066054
Kansas	KS	2011	2869035
Kentucky	KY	2011	4369488
Louisiana	LA	2011	4575184
Maine	ME	2011	1328150
Maryland	MD	2011	5838991
Massachusetts	MA	2011	6613149
Michigan	MI	2011	9881521
Minnesota	MN	2011	5345668
Mississippi	MS	2011	2978470
Missouri	MO	2011	6009641
Montana	MT	2011	997221
Nebraska	NE	2011	1840538
Nevada	NV	2011	2712799
New Hampshire	NH	2011	1319815
New Jersey	NJ	2011	8827783
New Mexico	NM	2011	2080395
New York	NY	2011	19498514
North Carolina	NC	2011	9656754
North Dakota	ND	2011	685136
Ohio	OH	2011	11543463
Oklahoma	OK	2011	3787821
Oregon	OR	2011	3871728
Pennsylvania	PA	2011	12744583
Rhode Island	RI	2011	1053536
South Carolina	SC	2011	4671422
South Dakota	SD	2011	823484
Tennessee	TN	2011	6397410
Texas	TX	2011	25646227
Utah	UT	2011	2814216
Vermont	VT	2011	626979
Virginia	VA	2011	8100469
Washington	WA	2011	6821655
West Virginia	WV	2011	1856074
Wisconsin	WI	2011	5704755
Wyoming	WY	2011	567224
Alabama	AL	2012	4815564
Alaska	AK	2012	730399
Arizona	AZ	2012	6556629
Arkansas	AR	2012	2952109
California	CA	2012	37960782
Colorado	CO	2012	5193721
Connecticut	CT	2012	3594395
Delaware	DE	2012	915188
District of Columbia	DC	2012	634725
Florida	FL	2012	19326230
Georgia	GA	2012	9901496
Hawaii	HI	2012	1394905
Idaho	ID	2012	1595441
Illinois	IL	2012	12884119
Indiana	IN	2012	6537640
Iowa	IA	2012	3076097
Kansas	KS	2012	2885361
Kentucky	KY	2012	4386381
Louisiana	LA	2012	4600814
Maine	ME	2012	1327691
Maryland	MD	2012	5887072
Massachusetts	MA	2012	6663158
Michigan	MI	2012	9896930
Minnesota	MN	2012	5376550
Mississippi	MS	2012	2983767
Missouri	MO	2012	6024081
Montana	MT	2012	1003754
Nebraska	NE	2012	1853323
Nevada	NV	2012	2744566
New Hampshire	NH	2012	1323962
New Jersey	NJ	2012	8845483
New Mexico	NM	2012	2087549
New York	NY	2012	19574549
North Carolina	NC	2012	9749123
North Dakota	ND	2012	701116
Ohio	OH	2012	11548369
Oklahoma	OK	2012	3818600
Oregon	OR	2012	3899118
Pennsylvania	PA	2012	12766827
Rhode Island	RI	2012	1054601
South Carolina	SC	2012	4717112
South Dakota	SD	2012	833496
Tennessee	TN	2012	6451281
Texas	TX	2012	26089620
Utah	UT	2012	2853467
Vermont	VT	2012	626063
Virginia	VA	2012	8185229
Washington	WA	2012	6892876
West Virginia	WV	2012	1856764
Wisconsin	WI	2012	5719855
Wyoming	WY	2012	576270
Alabama	AL	2013	4830460
Alaska	AK	2013	737045
Arizona	AZ	2013	6634999
Arkansas	AR	2013	2959549
California	CA	2013	38280824
Colorado	CO	2013	5270482
Connecticut	CT	2013	3594915
Delaware	DE	2013	923638
District of Columbia	DC	2013	650431
Florida	FL	2013	19563166
Georgia	GA	2013	9973326
Hawaii	HI	2013	1408453
Idaho	ID	2013	1611530
Illinois	IL	2013	12898269
Indiana	IN	2013	6568367
Iowa	IA	2013	3093078
Kansas	KS	2013	2893510
Kentucky	KY	2013	4404817
Louisiana	LA	2013	4624577
Maine	ME	2013	1328196
Maryland	MD	2013	5923704
Massachusetts	MA	2013	6713944
Michigan	MI	2013	9913349
Minnesota	MN	2013	5413693
Mississippi	MS	2013	2988797
Missouri	MO	2013	6040658
Montana	MT	2013	1013564
Nebraska	NE	2013	1865414
Nevada	NV	2013	2776972
New Hampshire	NH	2013	1326408
New Jersey	NJ	2013	8858362
New Mexico	NM	2013	2092792
New York	NY	2013	19628043
North Carolina	NC	2013	9843599
North Dakota	ND	2013	721999
Ohio	OH	2013	11576576
Oklahoma	OK	2013	3853205
Oregon	OR	2013	3922908
Pennsylvania	PA	2013	12776621
Rhode Island	RI	2013	1055122
South Carolina	SC	2013	4764153
South Dakota	SD	2013	842270
Tennessee	TN	2013	6493432
Texas	TX	2013	26489464
Utah	UT	2013	2897927
Vermont	VT	2013	626212
Virginia	VA	2013	8253053
Washington	WA	2013	6962906
West Virginia	WV	2013	1853873
Wisconsin	WI	2013	5736952
Wyoming	WY	2013	582123
Alabama	AL	2014	4842481
Alaska	AK	2014	736307
Arizona	AZ	2014	6733840
Arkansas	AR	2014	2967726
California	CA	2014	38625139
Colorado	CO	2014	5351218
Connecticut	CT	2014	3594783
Delaware	DE	2014	932596
District of Columbia	DC	2014	662513
Florida	FL	2014	19860330
Georgia	GA	2014	10069001
Hawaii	HI	2014	1414862
Idaho	ID	2014	1631479
Illinois	IL	2014	12888962
Indiana	IN	2014	6593533
Iowa	IA	2014	3109504
Kansas	KS	2014	2900896
Kentucky	KY	2014	4414483
Louisiana	LA	2014	4644204
Maine	ME	2014	1330760
Maryland	MD	2014	5958165
Massachusetts	MA	2014	6763652
Michigan	MI	2014	9930589
Minnesota	MN	2014	5451522
Mississippi	MS	2014	2990623
Missouri	MO	2014	6056293
Montana	MT	2014	1021891
Nebraska	NE	2014	1879522
Nevada	NV	2014	2819012
New Hampshire	NH	2014	1333223
New Jersey	NJ	2014	8866780
New Mexico	NM	2014	2090342
New York	NY	2014	19656330
North Carolina	NC	2014	9933944
North Dakota	ND	2014	737382
Ohio	OH	2014	11602973
Oklahoma	OK	2014	3878367
Oregon	OR	2014	3964106
Pennsylvania	PA	2014	12789101
Rhode Island	RI	2014	1056017
South Carolina	SC	2014	4823793
South Dakota	SD	2014	849088
Tennessee	TN	2014	6540826
Texas	TX	2014	26977142
Utah	UT	2014	2937399
Vermont	VT	2014	625218
Virginia	VA	2014	8312076
Washington	WA	2014	7052439
West Virginia	WV	2014	1849467
Wisconsin	WI	2014	5751974
Wyoming	WY	2014	582548
Alabama	AL	2015	4853160
Alaska	AK	2015	737547
Arizona	AZ	2015	6833596
Arkansas	AR	2015	2978407
California	CA	2015	38953142
Colorado	CO	2015	5452107
Connecticut	CT	2015	3587509
Delaware	DE	2015	941413
District of Columbia	DC	2015	675254
Florida	FL	2015	20224249
Georgia	GA	2015	10181111
Hawaii	HI	2015	1422484
Idaho	ID	2015	1651523
Illinois	IL	2015	12864342
Indiana	IN	2015	6608296
Iowa	IA	2015	3121460
Kansas	KS	2015	2909502
Kentucky	KY	2015	4425999
Louisiana	LA	2015	4664851
Maine	ME	2015	1328484
Maryland	MD	2015	5986717
Massachusetts	MA	2015	6795891
Michigan	MI	2015	9932573
Minnesota	MN	2015	5482503
Mississippi	MS	2015	2988693
Missouri	MO	2015	6071745
Montana	MT	2015	1030503
Nebraska	NE	2015	1891507
Nevada	NV	2015	2868666
New Hampshire	NH	2015	1336294
New Jersey	NJ	2015	8870869
New Mexico	NM	2015	2090211
New York	NY	2015	19661411
North Carolina	NC	2015	10033079
North Dakota	ND	2015	754022
Ohio	OH	2015	11617850
Oklahoma	OK	2015	3909831
Oregon	OR	2015	4016918
Pennsylvania	PA	2015	12785759
Rhode Island	RI	2015	1056173
South Carolina	SC	2015	4892253
South Dakota	SD	2015	853933
Tennessee	TN	2015	6590808
Texas	TX	2015	27486814
Utah	UT	2015	2982497
Vermont	VT	2015	625197
Virginia	VA	2015	8362907
Washington	WA	2015	7163543
West Virginia	WV	2015	1841996
Wisconsin	WI	2015	5761406
Wyoming	WY	2015	585668
Alabama	AL	2016	4864745
Alaska	AK	2016	741504
Arizona	AZ	2016	6945452
Arkansas	AR	2016	2990410
California	CA	2016	39209127
Colorado	CO	2016	5540921
Connecticut	CT	2016	3578674
Delaware	DE	2016	949216
District of Columbia	DC	2016	686575
Florida	FL	2016	20629982
Georgia	GA	2016	10304763
Hawaii	HI	2016	1428105
Idaho	ID	2016	1682930
Illinois	IL	2016	12826895
Indiana	IN	2016	6633344
Iowa	IA	2016	3131785
Kansas	KS	2016	2911263
Kentucky	KY	2016	4438229
Louisiana	LA	2016	4678215
Maine	ME	2016	1331370
Maryland	MD	2016	6004692
Massachusetts	MA	2016	6826022
Michigan	MI	2016	9951890
Minnesota	MN	2016	5523409
Mississippi	MS	2016	2988298
Missouri	MO	2016	6087203
Montana	MT	2016	1040863
Nebraska	NE	2016	1905924
Nevada	NV	2016	2919772
New Hampshire	NH	2016	1342373
New Jersey	NJ	2016	8874516
New Mexico	NM	2016	2092789
New York	NY	2016	19641589
North Carolina	NC	2016	10156679
North Dakota	ND	2016	754353
Ohio	OH	2016	11635003
Oklahoma	OK	2016	3926769
Oregon	OR	2016	4091404
Pennsylvania	PA	2016	12783538
Rhode Island	RI	2016	1057063
South Carolina	SC	2016	4958235
South Dakota	SD	2016	862890
Tennessee	TN	2016	6645011
Texas	TX	2016	27937492
Utah	UT	2016	3042613
Vermont	VT	2016	623644
Virginia	VA	2016	8410946
Washington	WA	2016	7294680
West Virginia	WV	2016	1830929
Wisconsin	WI	2016	5772958
Wyoming	WY	2016	584290
Alabama	AL	2017	4875120
Alaska	AK	2017	739786
Arizona	AZ	2017	7048876
Arkansas	AR	2017	3002997
California	CA	2017	39399349
Colorado	CO	2017	5615902
Connecticut	CT	2017	3573880
Delaware	DE	2017	957078
District of Columbia	DC	2017	695691
Florida	FL	2017	20976812
Georgia	GA	2017	10413055
Hawaii	HI	2017	1424203
Idaho	ID	2017	1718904
Illinois	IL	2017	12786196
Indiana	IN	2017	6660082
Iowa	IA	2017	3143637
Kansas	KS	2017	2910689
Kentucky	KY	2017	4453874
Louisiana	LA	2017	4670818
Maine	ME	2017	1335063
Maryland	MD	2017	6024891
Massachusetts	MA	2017	6863246
Michigan	MI	2017	9976447
Minnesota	MN	2017	5568155
Mississippi	MS	2017	2989663
Missouri	MO	2017	6108612
Montana	MT	2017	1053090
Nebraska	NE	2017	1917575
Nevada	NV	2017	2972405
New Hampshire	NH	2017	1349767
New Jersey	NJ	2017	8888543
New Mexico	NM	2017	2093395
New York	NY	2017	19590719
North Carolina	NC	2017	10270800
North Dakota	ND	2017	755176
Ohio	OH	2017	11664129
Oklahoma	OK	2017	3932640
Oregon	OR	2017	4146592
Pennsylvania	PA	2017	12790447
Rhode Island	RI	2017	1056486
South Carolina	SC	2017	5021219
South Dakota	SD	2017	873286
Tennessee	TN	2017	6708794
Texas	TX	2017	28322717
Utah	UT	2017	3103118
Vermont	VT	2017	624525
Virginia	VA	2017	8465207
Washington	WA	2017	7425432
West Virginia	WV	2017	1817048
Wisconsin	WI	2017	5792051
Wyoming	WY	2017	578934
Alabama	AL	2018	4887871
Alaska	AK	2018	737438
Arizona	AZ	2018	7171646
Arkansas	AR	2018	3013825
California	CA	2018	39557045
Colorado	CO	2018	5695564
Connecticut	CT	2018	3572665
Delaware	DE	2018	967171
District of Columbia	DC	2018	702455
Florida	FL	2018	21299325
Georgia	GA	2018	10519475
Hawaii	HI	2018	1420491
Idaho	ID	2018	1754208
Illinois	IL	2018	12741080
Indiana	IN	2018	6691878
Iowa	IA	2018	3156145
Kansas	KS	2018	2911505
Kentucky	KY	2018	4468402
Louisiana	LA	2018	4659978
Maine	ME	2018	1338404
Maryland	MD	2018	6042718
Massachusetts	MA	2018	6902149
Michigan	MI	2018	9995915
Minnesota	MN	2018	5611179
Mississippi	MS	2018	2986530
Missouri	MO	2018	6126452
Montana	MT	2018	1062305
Nebraska	NE	2018	1929268
Nevada	NV	2018	3034392
New Hampshire	NH	2018	1356458
New Jersey	NJ	2018	8908520
New Mexico	NM	2018	2095428
New York	NY	2018	19542209
North Carolina	NC	2018	10383620
North Dakota	ND	2018	760077
Ohio	OH	2018	11689442
Oklahoma	OK	2018	3943079
Oregon	OR	2018	4190713
Pennsylvania	PA	2018	12807060
Rhode Island	RI	2018	1057315
South Carolina	SC	2018	5084127
South Dakota	SD	2018	882235
Tennessee	TN	2018	6770010
Texas	TX	2018	28701845
Utah	UT	2018	3161105
Vermont	VT	2018	626299
Virginia	VA	2018	8517685
Washington	WA	2018	7535591
West Virginia	WV	2018	1805832
Wisconsin	WI	2018	5813568
Wyoming	WY	2018	577737

Finalmente, aplicamos spread() para completar el ejercicio. Esta función transformará los datos de nuevo al formato original con columnas separadas por año.

df4 = spread(df3, key = "Year", value = "Population")
kable(df4,
    align = "c") %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = TRUE,
    position = "center"
  ) %>% 
  row_spec(0, bold = TRUE, background = "lightgray") %>% 
  scroll_box(height = "300px")

State	Code	2010	2011	2012	2013	2014	2015	2016	2017	2018
Alabama	AL	4785448	4798834	4815564	4830460	4842481	4853160	4864745	4875120	4887871
Alaska	AK	713906	722038	730399	737045	736307	737547	741504	739786	737438
Arizona	AZ	6407774	6473497	6556629	6634999	6733840	6833596	6945452	7048876	7171646
Arkansas	AR	2921978	2940407	2952109	2959549	2967726	2978407	2990410	3002997	3013825
California	CA	37320903	37641823	37960782	38280824	38625139	38953142	39209127	39399349	39557045
Colorado	CO	5048281	5121771	5193721	5270482	5351218	5452107	5540921	5615902	5695564
Connecticut	CT	3579125	3588023	3594395	3594915	3594783	3587509	3578674	3573880	3572665
Delaware	DE	899595	907316	915188	923638	932596	941413	949216	957078	967171
District of Columbia	DC	605085	619602	634725	650431	662513	675254	686575	695691	702455
Florida	FL	18845785	19093352	19326230	19563166	19860330	20224249	20629982	20976812	21299325
Georgia	GA	9711810	9801578	9901496	9973326	10069001	10181111	10304763	10413055	10519475
Hawaii	HI	1363963	1379252	1394905	1408453	1414862	1422484	1428105	1424203	1420491
Idaho	ID	1570773	1583828	1595441	1611530	1631479	1651523	1682930	1718904	1754208
Illinois	IL	12840762	12867291	12884119	12898269	12888962	12864342	12826895	12786196	12741080
Indiana	IN	6490436	6516045	6537640	6568367	6593533	6608296	6633344	6660082	6691878
Iowa	IA	3050767	3066054	3076097	3093078	3109504	3121460	3131785	3143637	3156145
Kansas	KS	2858213	2869035	2885361	2893510	2900896	2909502	2911263	2910689	2911505
Kentucky	KY	4348200	4369488	4386381	4404817	4414483	4425999	4438229	4453874	4468402
Louisiana	LA	4544532	4575184	4600814	4624577	4644204	4664851	4678215	4670818	4659978
Maine	ME	1327632	1328150	1327691	1328196	1330760	1328484	1331370	1335063	1338404
Maryland	MD	5788642	5838991	5887072	5923704	5958165	5986717	6004692	6024891	6042718
Massachusetts	MA	6566431	6613149	6663158	6713944	6763652	6795891	6826022	6863246	6902149
Michigan	MI	9877535	9881521	9896930	9913349	9930589	9932573	9951890	9976447	9995915
Minnesota	MN	5310843	5345668	5376550	5413693	5451522	5482503	5523409	5568155	5611179
Mississippi	MS	2970536	2978470	2983767	2988797	2990623	2988693	2988298	2989663	2986530
Missouri	MO	5995976	6009641	6024081	6040658	6056293	6071745	6087203	6108612	6126452
Montana	MT	990722	997221	1003754	1013564	1021891	1030503	1040863	1053090	1062305
Nebraska	NE	1829536	1840538	1853323	1865414	1879522	1891507	1905924	1917575	1929268
Nevada	NV	2702464	2712799	2744566	2776972	2819012	2868666	2919772	2972405	3034392
New Hampshire	NH	1316777	1319815	1323962	1326408	1333223	1336294	1342373	1349767	1356458
New Jersey	NJ	8799624	8827783	8845483	8858362	8866780	8870869	8874516	8888543	8908520
New Mexico	NM	2064588	2080395	2087549	2092792	2090342	2090211	2092789	2093395	2095428
New York	NY	19400080	19498514	19574549	19628043	19656330	19661411	19641589	19590719	19542209
North Carolina	NC	9574293	9656754	9749123	9843599	9933944	10033079	10156679	10270800	10383620
North Dakota	ND	674710	685136	701116	721999	737382	754022	754353	755176	760077
Ohio	OH	11539327	11543463	11548369	11576576	11602973	11617850	11635003	11664129	11689442
Oklahoma	OK	3759632	3787821	3818600	3853205	3878367	3909831	3926769	3932640	3943079
Oregon	OR	3837532	3871728	3899118	3922908	3964106	4016918	4091404	4146592	4190713
Pennsylvania	PA	12711158	12744583	12766827	12776621	12789101	12785759	12783538	12790447	12807060
Rhode Island	RI	1053938	1053536	1054601	1055122	1056017	1056173	1057063	1056486	1057315
South Carolina	SC	4635656	4671422	4717112	4764153	4823793	4892253	4958235	5021219	5084127
South Dakota	SD	816165	823484	833496	842270	849088	853933	862890	873286	882235
Tennessee	TN	6355301	6397410	6451281	6493432	6540826	6590808	6645011	6708794	6770010
Texas	TX	25242679	25646227	26089620	26489464	26977142	27486814	27937492	28322717	28701845
Utah	UT	2775334	2814216	2853467	2897927	2937399	2982497	3042613	3103118	3161105
Vermont	VT	625880	626979	626063	626212	625218	625197	623644	624525	626299
Virginia	VA	8023680	8100469	8185229	8253053	8312076	8362907	8410946	8465207	8517685
Washington	WA	6742902	6821655	6892876	6962906	7052439	7163543	7294680	7425432	7535591
West Virginia	WV	1854214	1856074	1856764	1853873	1849467	1841996	1830929	1817048	1805832
Wisconsin	WI	5690479	5704755	5719855	5736952	5751974	5761406	5772958	5792051	5813568
Wyoming	WY	564483	567224	576270	582123	582548	585668	584290	578934	577737

Ejercicios - Visualización de datos

Alejandra Meneses y Mariangel Yepes

2026-02-07

Ejercicio 1

Desarrollo

Ejercicio 2

Desarrollo

Ejercicio 3

Desarrollo