Resumen

En este informe se realizó un procesamiento extenso y resumen de los datos de datos de una empresa para sintetizar y presentar los aspectos más importantes de los mismos. Este análisis fue hecho en el programa RStudio Cloud en su totalidad, con el que se hizo la interpretación del nivel de satisfacción de 14999 trabajadores de la compañía. Donde por medio de métodos de gráficos y numéricos se logró construir un informe acerca de la información los trabajadores para que la empresa pueda tomar decisiones administrativas a partir de estos, los resultados de este informe fueron publicados RPubs.

Introducción

En nuestra vida cotidiana muchas veces nos encontramos con la necesidad de analizar de manera eficiente un conjunto de datos, este análisis se facilita cuando la cantidad de datos es baja y el análisis requerido es simple, ejemplo sumar los valores. Sin embargo, al aumentar de numero de dichos datos a nuestro cerebro se le dificulta manejar tanta información y terminamos optando por usar una calculadora o programa, esto mismo sucede cuando el análisis requerido de los datos es bastante profundo y completo, en este caso nos vemos en la necesidad de utilizar herramientas para la recolección, organización y compresión de los mismos. Como respuesta a la problemática anterior surge el análisis estadístico, el cual nos da todos los recursos para optimizar la compresión de grandes cantidades de datos de manera precisa y profunda.

Métodos

Para esta actividad se requirió de la aplicación del software RStudio Cloud. Donde en primer lugar se cargaron los datos proporcionados a dicho software y por medio de la función install.packages(“readr”) se procedió a descargar algunas librerías importantes para este informe, las cuales son: library(readr), library(readxl), library(knitr), library (RColorBrewer), library (“openxlsx”), library(ggplot2), library(questionr) y library(dplyr). Posteriormente se construyó una tabla de frecuencia simple con los datos del nivel de satisfacción del empleado, esta consiste en agrupar a todos los empleados que tuvieron el mismo nivel de satisfacción y contar los resultados, la tabla se muestran en 3.1. Para construir esta tabla se lee nuestro y se guarda la columna “satisfaction_level” como “NivelSat” para poder usar la función (questionr) que nos guardará nuestra información como “table”, para mostrar dicha tabla se utilizó (knitr).

También se desea conocer algunas medidas de tendencia central por lo cual se usaron las funciones simples de mean, median para conocer el promedio y mediana respectivamente. Para las otras medidas de tendencia central como lo son la moda y el rango medio se utilizaron formulas, para la moda se contó cual era el dato más repetido por medio del comando: f <- data.frame(table(NivelSat)) moda <- f[which.max(f$Freq),1], y para el rango medio se sumó el valor inicial con el final entre 2 por medio del comando: Medior= (max(NivelSat)+min(NivelSat))/2.

Respecto a los datos de colocación como lo son los cuartiles estos se obtuvieron simplemente con la función quantile especificando los intervalos requeridos. Por su parte, todas las medidas de dispersión de los datos, fueron calculadas por medio de funciones simples de modo que para la varianza se usó variance <- function (NivelSat) sum((NivelSat-mean(NivelSat))^2)/(length(NivelSat)-1) variance (NivelSat), para la desviación estándar la función sqrt(variance), el rango restando el valor mínimo al máximo y dividiendo entre 2 y el IQR o rango intercuartil por IQR(). Finalmente, el diagrama de caja y extensión se logró gracias a boxplot() especificando el color deseado como azul.

Resultados ## R Markdown

install.packages(“readr”)

Librerías:

library(readr)
library(readxl)
library(knitr) 
library (RColorBrewer)

3.1 Tabla de Frecuencia Simple

library ("openxlsx")
df <- read_xlsx("HR_Employee_Data.xlsx")
#tabla de frecuencia simple de nivel de satisfacción
NivelSat=df$satisfaction_level
clases= nclass.Sturges(NivelSat)
library(questionr)
table <- questionr::freq(NivelSat, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
0.1 358 2.4 2.4 2.4 2.4
0.11 335 2.2 2.2 4.6 4.6
0.74 257 1.7 1.7 6.3 6.3
0.77 252 1.7 1.7 8.0 8.0
0.84 247 1.6 1.6 9.7 9.7
0.73 246 1.6 1.6 11.3 11.3
0.37 241 1.6 1.6 12.9 12.9
0.78 241 1.6 1.6 14.5 14.5
0.82 241 1.6 1.6 16.1 16.1
0.89 237 1.6 1.6 17.7 17.7
0.76 234 1.6 1.6 19.3 19.3
0.83 234 1.6 1.6 20.8 20.8
0.72 230 1.5 1.5 22.4 22.4
0.5 229 1.5 1.5 23.9 23.9
0.66 228 1.5 1.5 25.4 25.4
0.75 226 1.5 1.5 26.9 26.9
0.87 225 1.5 1.5 28.4 28.4
0.43 224 1.5 1.5 29.9 29.9
0.91 224 1.5 1.5 31.4 31.4
0.8 222 1.5 1.5 32.9 32.9
0.81 220 1.5 1.5 34.3 34.3
0.9 220 1.5 1.5 35.8 35.8
0.59 219 1.5 1.5 37.3 37.3
0.79 217 1.4 1.4 38.7 38.7
0.44 211 1.4 1.4 40.1 40.1
0.57 210 1.4 1.4 41.5 41.5
0.4 209 1.4 1.4 42.9 42.9
0.49 209 1.4 1.4 44.3 44.3
0.63 209 1.4 1.4 45.7 45.7
0.69 209 1.4 1.4 47.1 47.1
0.61 208 1.4 1.4 48.5 48.5
0.85 207 1.4 1.4 49.9 49.9
0.7 205 1.4 1.4 51.2 51.2
0.45 203 1.4 1.4 52.6 52.6
0.96 203 1.4 1.4 53.9 53.9
0.86 200 1.3 1.3 55.3 55.3
0.65 199 1.3 1.3 56.6 56.6
0.92 198 1.3 1.3 57.9 57.9
0.52 196 1.3 1.3 59.2 59.2
0.09 195 1.3 1.3 60.5 60.5
0.6 193 1.3 1.3 61.8 61.8
0.38 189 1.3 1.3 63.1 63.1
0.62 188 1.3 1.3 64.3 64.3
0.51 187 1.2 1.2 65.6 65.6
0.56 187 1.2 1.2 66.8 66.8
0.64 187 1.2 1.2 68.1 68.1
0.88 187 1.2 1.2 69.3 69.3
0.54 185 1.2 1.2 70.5 70.5
0.98 183 1.2 1.2 71.8 71.8
0.58 182 1.2 1.2 73.0 73.0
0.95 181 1.2 1.2 74.2 74.2
0.53 179 1.2 1.2 75.4 75.4
0.55 179 1.2 1.2 76.6 76.6
0.67 177 1.2 1.2 77.8 77.8
0.97 176 1.2 1.2 78.9 78.9
0.39 175 1.2 1.2 80.1 80.1
0.99 172 1.1 1.1 81.2 81.2
0.41 171 1.1 1.1 82.4 82.4
0.71 171 1.1 1.1 83.5 83.5
0.93 169 1.1 1.1 84.6 84.6
0.94 167 1.1 1.1 85.8 85.8
0.68 162 1.1 1.1 86.8 86.8
0.42 155 1.0 1.0 87.9 87.9
0.48 149 1.0 1.0 88.9 88.9
0.36 139 0.9 0.9 89.8 89.8
1 111 0.7 0.7 90.5 90.5
0.46 95 0.6 0.6 91.2 91.2
0.24 80 0.5 0.5 91.7 91.7
0.16 79 0.5 0.5 92.2 92.2
0.15 76 0.5 0.5 92.7 92.7
0.19 74 0.5 0.5 93.2 93.2
0.14 73 0.5 0.5 93.7 93.7
0.17 72 0.5 0.5 94.2 94.2
0.2 69 0.5 0.5 94.7 94.7
0.21 67 0.4 0.4 95.1 95.1
0.18 63 0.4 0.4 95.5 95.5
0.22 60 0.4 0.4 95.9 95.9
0.31 59 0.4 0.4 96.3 96.3
0.13 54 0.4 0.4 96.7 96.7
0.23 54 0.4 0.4 97.0 97.0
0.32 50 0.3 0.3 97.4 97.4
0.34 48 0.3 0.3 97.7 97.7
0.47 42 0.3 0.3 98.0 98.0
0.3 39 0.3 0.3 98.2 98.2
0.29 38 0.3 0.3 98.5 98.5
0.35 37 0.2 0.2 98.7 98.7
0.33 36 0.2 0.2 99.0 99.0
0.25 34 0.2 0.2 99.2 99.2
0.28 31 0.2 0.2 99.4 99.4
0.12 30 0.2 0.2 99.6 99.6
0.26 30 0.2 0.2 99.8 99.8
0.27 30 0.2 0.2 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de última evaluación
LastE=df$last_evaluation
clases= nclass.Sturges(LastE)
library(questionr)
table <- questionr::freq(LastE, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
0.55 358 2.4 2.4 2.4 2.4
0.5 353 2.4 2.4 4.7 4.7
0.54 350 2.3 2.3 7.1 7.1
0.51 345 2.3 2.3 9.4 9.4
0.57 333 2.2 2.2 11.6 11.6
0.49 332 2.2 2.2 13.8 13.8
0.87 326 2.2 2.2 16.0 16.0
0.53 324 2.2 2.2 18.1 18.1
0.56 322 2.1 2.1 20.3 20.3
0.85 316 2.1 2.1 22.4 22.4
0.9 313 2.1 2.1 24.5 24.5
0.52 309 2.1 2.1 26.5 26.5
0.89 296 2.0 2.0 28.5 28.5
0.84 294 2.0 2.0 30.5 30.5
0.48 292 1.9 1.9 32.4 32.4
0.91 287 1.9 1.9 34.3 34.3
1 283 1.9 1.9 36.2 36.2
0.97 276 1.8 1.8 38.1 38.1
0.86 273 1.8 1.8 39.9 39.9
0.83 269 1.8 1.8 41.7 41.7
0.92 269 1.8 1.8 43.5 43.5
0.93 269 1.8 1.8 45.3 45.3
0.77 263 1.8 1.8 47.0 47.0
0.94 263 1.8 1.8 48.8 48.8
0.98 263 1.8 1.8 50.5 50.5
0.74 260 1.7 1.7 52.3 52.3
0.95 258 1.7 1.7 54.0 54.0
0.99 258 1.7 1.7 55.7 55.7
0.59 255 1.7 1.7 57.4 57.4
0.81 255 1.7 1.7 59.1 59.1
0.8 251 1.7 1.7 60.8 60.8
0.96 249 1.7 1.7 62.4 62.4
0.67 245 1.6 1.6 64.1 64.1
0.79 241 1.6 1.6 65.7 65.7
0.75 238 1.6 1.6 67.3 67.3
0.82 237 1.6 1.6 68.8 68.8
0.63 236 1.6 1.6 70.4 70.4
0.64 235 1.6 1.6 72.0 72.0
0.88 235 1.6 1.6 73.5 73.5
0.61 234 1.6 1.6 75.1 75.1
0.62 233 1.6 1.6 76.7 76.7
0.58 225 1.5 1.5 78.2 78.2
0.73 223 1.5 1.5 79.6 79.6
0.66 222 1.5 1.5 81.1 81.1
0.68 222 1.5 1.5 82.6 82.6
0.6 221 1.5 1.5 84.1 84.1
0.76 216 1.4 1.4 85.5 85.5
0.78 214 1.4 1.4 86.9 86.9
0.7 213 1.4 1.4 88.4 88.4
0.46 211 1.4 1.4 89.8 89.8
0.72 211 1.4 1.4 91.2 91.2
0.65 201 1.3 1.3 92.5 92.5
0.71 196 1.3 1.3 93.8 93.8
0.69 193 1.3 1.3 95.1 95.1
0.47 173 1.2 1.2 96.3 96.3
0.45 115 0.8 0.8 97.0 97.0
0.41 59 0.4 0.4 97.4 97.4
0.4 57 0.4 0.4 97.8 97.8
0.42 56 0.4 0.4 98.2 98.2
0.37 55 0.4 0.4 98.5 98.5
0.39 52 0.3 0.3 98.9 98.9
0.38 50 0.3 0.3 99.2 99.2
0.43 50 0.3 0.3 99.6 99.6
0.44 44 0.3 0.3 99.9 99.9
0.36 22 0.1 0.1 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de múmero de proyectos
NumProj=df$number_project
clases= nclass.Sturges(NumProj)
library(questionr)
table <- questionr::freq(NumProj, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
4 4365 29.1 29.1 29.1 29.1
3 4055 27.0 27.0 56.1 56.1
5 2761 18.4 18.4 74.5 74.5
2 2388 15.9 15.9 90.5 90.5
6 1174 7.8 7.8 98.3 98.3
7 256 1.7 1.7 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de promedio de horas mensuales
Average=df$average_montly_hours
clases= nclass.Sturges(Average)
library(questionr)
table <- questionr::freq(Average, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
135 153 1.0 1.0 1.0 1.0
156 153 1.0 1.0 2.0 2.0
149 148 1.0 1.0 3.0 3.0
151 147 1.0 1.0 4.0 4.0
160 136 0.9 0.9 4.9 4.9
145 134 0.9 0.9 5.8 5.8
140 129 0.9 0.9 6.7 6.7
143 127 0.8 0.8 7.5 7.5
157 126 0.8 0.8 8.4 8.4
257 126 0.8 0.8 9.2 9.2
155 125 0.8 0.8 10.0 10.0
158 124 0.8 0.8 10.9 10.9
245 124 0.8 0.8 11.7 11.7
260 124 0.8 0.8 12.5 12.5
148 123 0.8 0.8 13.3 13.3
137 122 0.8 0.8 14.1 14.1
153 122 0.8 0.8 15.0 15.0
154 121 0.8 0.8 15.8 15.8
159 121 0.8 0.8 16.6 16.6
139 120 0.8 0.8 17.4 17.4
147 118 0.8 0.8 18.2 18.2
141 115 0.8 0.8 18.9 18.9
255 115 0.8 0.8 19.7 19.7
134 114 0.8 0.8 20.4 20.4
254 113 0.8 0.8 21.2 21.2
142 112 0.7 0.7 21.9 21.9
152 112 0.7 0.7 22.7 22.7
224 112 0.7 0.7 23.4 23.4
243 112 0.7 0.7 24.2 24.2
264 111 0.7 0.7 24.9 24.9
146 110 0.7 0.7 25.7 25.7
258 110 0.7 0.7 26.4 26.4
263 110 0.7 0.7 27.1 27.1
150 108 0.7 0.7 27.8 27.8
238 108 0.7 0.7 28.6 28.6
247 108 0.7 0.7 29.3 29.3
266 105 0.7 0.7 30.0 30.0
136 104 0.7 0.7 30.7 30.7
271 104 0.7 0.7 31.4 31.4
144 102 0.7 0.7 32.1 32.1
233 102 0.7 0.7 32.7 32.7
246 102 0.7 0.7 33.4 33.4
261 102 0.7 0.7 34.1 34.1
269 102 0.7 0.7 34.8 34.8
253 101 0.7 0.7 35.4 35.4
132 100 0.7 0.7 36.1 36.1
250 100 0.7 0.7 36.8 36.8
242 98 0.7 0.7 37.4 37.4
244 98 0.7 0.7 38.1 38.1
251 98 0.7 0.7 38.7 38.7
259 98 0.7 0.7 39.4 39.4
232 97 0.6 0.6 40.0 40.0
162 96 0.6 0.6 40.7 40.7
173 96 0.6 0.6 41.3 41.3
192 96 0.6 0.6 42.0 42.0
239 96 0.6 0.6 42.6 42.6
225 95 0.6 0.6 43.2 43.2
167 94 0.6 0.6 43.9 43.9
274 94 0.6 0.6 44.5 44.5
185 93 0.6 0.6 45.1 45.1
223 93 0.6 0.6 45.7 45.7
226 93 0.6 0.6 46.3 46.3
229 93 0.6 0.6 47.0 47.0
240 93 0.6 0.6 47.6 47.6
249 93 0.6 0.6 48.2 48.2
268 93 0.6 0.6 48.8 48.8
270 93 0.6 0.6 49.4 49.4
168 92 0.6 0.6 50.1 50.1
265 91 0.6 0.6 50.7 50.7
237 90 0.6 0.6 51.3 51.3
138 88 0.6 0.6 51.9 51.9
180 88 0.6 0.6 52.4 52.4
267 88 0.6 0.6 53.0 53.0
273 88 0.6 0.6 53.6 53.6
133 87 0.6 0.6 54.2 54.2
161 87 0.6 0.6 54.8 54.8
217 87 0.6 0.6 55.4 55.4
256 87 0.6 0.6 55.9 55.9
169 86 0.6 0.6 56.5 56.5
198 86 0.6 0.6 57.1 57.1
201 86 0.6 0.6 57.7 57.7
248 86 0.6 0.6 58.2 58.2
252 86 0.6 0.6 58.8 58.8
262 86 0.6 0.6 59.4 59.4
272 86 0.6 0.6 60.0 60.0
178 85 0.6 0.6 60.5 60.5
189 85 0.6 0.6 61.1 61.1
219 85 0.6 0.6 61.7 61.7
241 85 0.6 0.6 62.2 62.2
183 84 0.6 0.6 62.8 62.8
222 84 0.6 0.6 63.3 63.3
171 83 0.6 0.6 63.9 63.9
206 83 0.6 0.6 64.4 64.4
236 83 0.6 0.6 65.0 65.0
275 82 0.5 0.5 65.5 65.5
176 81 0.5 0.5 66.1 66.1
177 81 0.5 0.5 66.6 66.6
221 81 0.5 0.5 67.2 67.2
184 80 0.5 0.5 67.7 67.7
191 80 0.5 0.5 68.2 68.2
202 80 0.5 0.5 68.8 68.8
196 79 0.5 0.5 69.3 69.3
199 79 0.5 0.5 69.8 69.8
211 79 0.5 0.5 70.3 70.3
218 79 0.5 0.5 70.9 70.9
164 78 0.5 0.5 71.4 71.4
165 78 0.5 0.5 71.9 71.9
174 78 0.5 0.5 72.4 72.4
181 78 0.5 0.5 73.0 73.0
214 78 0.5 0.5 73.5 73.5
227 77 0.5 0.5 74.0 74.0
231 77 0.5 0.5 74.5 74.5
170 76 0.5 0.5 75.0 75.0
175 76 0.5 0.5 75.5 75.5
186 76 0.5 0.5 76.0 76.0
216 76 0.5 0.5 76.5 76.5
228 76 0.5 0.5 77.0 77.0
235 76 0.5 0.5 77.5 77.5
182 75 0.5 0.5 78.0 78.0
190 75 0.5 0.5 78.5 78.5
234 74 0.5 0.5 79.0 79.0
163 73 0.5 0.5 79.5 79.5
166 73 0.5 0.5 80.0 80.0
179 73 0.5 0.5 80.5 80.5
188 73 0.5 0.5 81.0 81.0
205 73 0.5 0.5 81.5 81.5
127 72 0.5 0.5 81.9 81.9
203 72 0.5 0.5 82.4 82.4
208 72 0.5 0.5 82.9 82.9
209 72 0.5 0.5 83.4 83.4
210 72 0.5 0.5 83.9 83.9
212 72 0.5 0.5 84.3 84.3
194 71 0.5 0.5 84.8 84.8
207 71 0.5 0.5 85.3 85.3
213 71 0.5 0.5 85.8 85.8
172 70 0.5 0.5 86.2 86.2
197 70 0.5 0.5 86.7 86.7
131 69 0.5 0.5 87.2 87.2
187 68 0.5 0.5 87.6 87.6
204 68 0.5 0.5 88.1 88.1
215 68 0.5 0.5 88.5 88.5
193 67 0.4 0.4 89.0 89.0
195 67 0.4 0.4 89.4 89.4
128 65 0.4 0.4 89.8 89.8
220 64 0.4 0.4 90.3 90.3
129 63 0.4 0.4 90.7 90.7
130 59 0.4 0.4 91.1 91.1
230 59 0.4 0.4 91.5 91.5
200 58 0.4 0.4 91.9 91.9
286 50 0.3 0.3 92.2 92.2
282 36 0.2 0.2 92.4 92.4
278 35 0.2 0.2 92.7 92.7
281 34 0.2 0.2 92.9 92.9
285 33 0.2 0.2 93.1 93.1
279 32 0.2 0.2 93.3 93.3
276 30 0.2 0.2 93.5 93.5
287 30 0.2 0.2 93.7 93.7
113 29 0.2 0.2 93.9 93.9
280 29 0.2 0.2 94.1 94.1
104 28 0.2 0.2 94.3 94.3
111 26 0.2 0.2 94.5 94.5
126 25 0.2 0.2 94.6 94.6
283 25 0.2 0.2 94.8 94.8
121 24 0.2 0.2 95.0 95.0
284 24 0.2 0.2 95.1 95.1
301 24 0.2 0.2 95.3 95.3
98 23 0.2 0.2 95.4 95.4
277 21 0.1 0.1 95.6 95.6
296 21 0.1 0.1 95.7 95.7
123 20 0.1 0.1 95.9 95.9
308 20 0.1 0.1 96.0 96.0
100 19 0.1 0.1 96.1 96.1
106 19 0.1 0.1 96.2 96.2
125 19 0.1 0.1 96.4 96.4
289 19 0.1 0.1 96.5 96.5
108 18 0.1 0.1 96.6 96.6
109 18 0.1 0.1 96.7 96.7
117 18 0.1 0.1 96.9 96.9
305 18 0.1 0.1 97.0 97.0
306 18 0.1 0.1 97.1 97.1
310 18 0.1 0.1 97.2 97.2
102 17 0.1 0.1 97.3 97.3
103 17 0.1 0.1 97.4 97.4
105 17 0.1 0.1 97.6 97.6
291 17 0.1 0.1 97.7 97.7
304 17 0.1 0.1 97.8 97.8
101 16 0.1 0.1 97.9 97.9
294 16 0.1 0.1 98.0 98.0
309 16 0.1 0.1 98.1 98.1
114 15 0.1 0.1 98.2 98.2
290 15 0.1 0.1 98.3 98.3
292 15 0.1 0.1 98.4 98.4
97 14 0.1 0.1 98.5 98.5
115 14 0.1 0.1 98.6 98.6
307 14 0.1 0.1 98.7 98.7
124 13 0.1 0.1 98.8 98.8
293 13 0.1 0.1 98.9 98.9
298 13 0.1 0.1 98.9 98.9
110 12 0.1 0.1 99.0 99.0
118 12 0.1 0.1 99.1 99.1
295 12 0.1 0.1 99.2 99.2
99 11 0.1 0.1 99.3 99.3
122 11 0.1 0.1 99.3 99.3
300 11 0.1 0.1 99.4 99.4
107 10 0.1 0.1 99.5 99.5
112 10 0.1 0.1 99.5 99.5
116 10 0.1 0.1 99.6 99.6
119 10 0.1 0.1 99.7 99.7
120 10 0.1 0.1 99.7 99.7
302 8 0.1 0.1 99.8 99.8
297 7 0.0 0.0 99.8 99.8
96 6 0.0 0.0 99.9 99.9
288 6 0.0 0.0 99.9 99.9
299 6 0.0 0.0 100.0 100.0
303 6 0.0 0.0 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de tiempo en la compañía
timespend=df$time_spend_company
clases= nclass.Sturges(timespend)
library(questionr)
table <- questionr::freq(timespend, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
3 6443 43.0 43.0 43.0 43.0
2 3244 21.6 21.6 64.6 64.6
4 2557 17.0 17.0 81.6 81.6
5 1473 9.8 9.8 91.5 91.5
6 718 4.8 4.8 96.2 96.2
10 214 1.4 1.4 97.7 97.7
7 188 1.3 1.3 98.9 98.9
8 162 1.1 1.1 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de accidentes laborales
workAcc=df$Work_accident
clases= nclass.Sturges(workAcc)
library(questionr)
table <- questionr::freq(workAcc, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
0 12830 85.5 85.5 85.5 85.5
1 2169 14.5 14.5 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de left
left=df$left
clases= nclass.Sturges(left)
library(questionr)
table <- questionr::freq(left, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
0 11428 76.2 76.2 76.2 76.2
1 3571 23.8 23.8 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de promocion en los últimos 5 años
prom=df$promotion_last_5years
clases= nclass.Sturges(prom)
library(questionr)
table <- questionr::freq(prom, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
0 14680 97.9 97.9 97.9 97.9
1 319 2.1 2.1 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de departamento
dep=df$Department
clases= nclass.Sturges(dep)
library(questionr)
table <- questionr::freq(dep, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
sales 4140 27.6 27.6 27.6 27.6
technical 2720 18.1 18.1 45.7 45.7
support 2229 14.9 14.9 60.6 60.6
IT 1227 8.2 8.2 68.8 68.8
product_mng 902 6.0 6.0 74.8 74.8
marketing 858 5.7 5.7 80.5 80.5
RandD 787 5.2 5.2 85.8 85.8
accounting 767 5.1 5.1 90.9 90.9
hr 739 4.9 4.9 95.8 95.8
management 630 4.2 4.2 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0 
#tabla de frecuencia simple de salario
sal=df$salary
clases= nclass.Sturges(sal)
library(questionr)
table <- questionr::freq(sal, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)
n % val% %cum val%cum
low 7316 48.8 48.8 48.8 48.8
medium 6446 43.0 43.0 91.8 91.8
high 1237 8.2 8.2 100.0 100.0
Total 14999 100.0 100.0 100.0 100.0

3.2 Tabla de Frecuencia Agrupada

df <- read_xlsx("HR_Employee_Data.xlsx")
#Tabla de frecuencia agrupada de promedio de horas mensuales
Average = df$average_montly_hours
knitr::kable(head(Average))
x
157
262
272
223
159
153 
n_sturges = 1 + log(length(14999))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)

n_clases = 0
if (n_sturgesc%%2 == 0) {
  n_clases = n_sturgesf
} else {
  n_clases = n_sturgesc
}
R = max(Average) - min(Average)
w = ceiling(R/n_sturges)
bins <- seq(min(Average), min(Average) + w)
bins
##   [1]  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113
##  [19] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131
##  [37] 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149
##  [55] 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
##  [73] 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185
##  [91] 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203
## [109] 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221
## [127] 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239
## [145] 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257
## [163] 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275
## [181] 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293
## [199] 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310
Promediodehorasmensuales <- cut(Average, bins)
Freq_table <- transform(table(Promediodehorasmensuales), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)
Promediodehorasmensuales Freq Rel_Freq Cum_Freq
(96,97] 14 0.0009338 14
(97,98] 23 0.0015340 37
(98,99] 11 0.0007337 48
(99,100] 19 0.0012673 67
(100,101] 16 0.0010672 83
(101,102] 17 0.0011339 100
(102,103] 17 0.0011339 117
(103,104] 28 0.0018675 145
(104,105] 17 0.0011339 162
(105,106] 19 0.0012673 181
(106,107] 10 0.0006670 191
(107,108] 18 0.0012006 209
(108,109] 18 0.0012006 227
(109,110] 12 0.0008004 239
(110,111] 26 0.0017341 265
(111,112] 10 0.0006670 275
(112,113] 29 0.0019342 304
(113,114] 15 0.0010005 319
(114,115] 14 0.0009338 333
(115,116] 10 0.0006670 343
(116,117] 18 0.0012006 361
(117,118] 12 0.0008004 373
(118,119] 10 0.0006670 383
(119,120] 10 0.0006670 393
(120,121] 24 0.0016007 417
(121,122] 11 0.0007337 428
(122,123] 20 0.0013340 448
(123,124] 13 0.0008671 461
(124,125] 19 0.0012673 480
(125,126] 25 0.0016674 505
(126,127] 72 0.0048022 577
(127,128] 65 0.0043354 642
(128,129] 63 0.0042020 705
(129,130] 59 0.0039352 764
(130,131] 69 0.0046021 833
(131,132] 100 0.0066698 933
(132,133] 87 0.0058027 1020
(133,134] 114 0.0076035 1134
(134,135] 153 0.0102048 1287
(135,136] 104 0.0069366 1391
(136,137] 122 0.0081371 1513
(137,138] 88 0.0058694 1601
(138,139] 120 0.0080037 1721
(139,140] 129 0.0086040 1850
(140,141] 115 0.0076702 1965
(141,142] 112 0.0074702 2077
(142,143] 127 0.0084706 2204
(143,144] 102 0.0068032 2306
(144,145] 134 0.0089375 2440
(145,146] 110 0.0073368 2550
(146,147] 118 0.0078703 2668
(147,148] 123 0.0082038 2791
(148,149] 148 0.0098713 2939
(149,150] 108 0.0072034 3047
(150,151] 147 0.0098046 3194
(151,152] 112 0.0074702 3306
(152,153] 122 0.0081371 3428
(153,154] 121 0.0080704 3549
(154,155] 125 0.0083372 3674
(155,156] 153 0.0102048 3827
(156,157] 126 0.0084039 3953
(157,158] 124 0.0082705 4077
(158,159] 121 0.0080704 4198
(159,160] 136 0.0090709 4334
(160,161] 87 0.0058027 4421
(161,162] 96 0.0064030 4517
(162,163] 73 0.0048689 4590
(163,164] 78 0.0052024 4668
(164,165] 78 0.0052024 4746
(165,166] 73 0.0048689 4819
(166,167] 94 0.0062696 4913
(167,168] 92 0.0061362 5005
(168,169] 86 0.0057360 5091
(169,170] 76 0.0050690 5167
(170,171] 83 0.0055359 5250
(171,172] 70 0.0046688 5320
(172,173] 96 0.0064030 5416
(173,174] 78 0.0052024 5494
(174,175] 76 0.0050690 5570
(175,176] 81 0.0054025 5651
(176,177] 81 0.0054025 5732
(177,178] 85 0.0056693 5817
(178,179] 73 0.0048689 5890
(179,180] 88 0.0058694 5978
(180,181] 78 0.0052024 6056
(181,182] 75 0.0050023 6131
(182,183] 84 0.0056026 6215
(183,184] 80 0.0053358 6295
(184,185] 93 0.0062029 6388
(185,186] 76 0.0050690 6464
(186,187] 68 0.0045354 6532
(187,188] 73 0.0048689 6605
(188,189] 85 0.0056693 6690
(189,190] 75 0.0050023 6765
(190,191] 80 0.0053358 6845
(191,192] 96 0.0064030 6941
(192,193] 67 0.0044688 7008
(193,194] 71 0.0047355 7079
(194,195] 67 0.0044688 7146
(195,196] 79 0.0052691 7225
(196,197] 70 0.0046688 7295
(197,198] 86 0.0057360 7381
(198,199] 79 0.0052691 7460
(199,200] 58 0.0038685 7518
(200,201] 86 0.0057360 7604
(201,202] 80 0.0053358 7684
(202,203] 72 0.0048022 7756
(203,204] 68 0.0045354 7824
(204,205] 73 0.0048689 7897
(205,206] 83 0.0055359 7980
(206,207] 71 0.0047355 8051
(207,208] 72 0.0048022 8123
(208,209] 72 0.0048022 8195
(209,210] 72 0.0048022 8267
(210,211] 79 0.0052691 8346
(211,212] 72 0.0048022 8418
(212,213] 71 0.0047355 8489
(213,214] 78 0.0052024 8567
(214,215] 68 0.0045354 8635
(215,216] 76 0.0050690 8711
(216,217] 87 0.0058027 8798
(217,218] 79 0.0052691 8877
(218,219] 85 0.0056693 8962
(219,220] 64 0.0042687 9026
(220,221] 81 0.0054025 9107
(221,222] 84 0.0056026 9191
(222,223] 93 0.0062029 9284
(223,224] 112 0.0074702 9396
(224,225] 95 0.0063363 9491
(225,226] 93 0.0062029 9584
(226,227] 77 0.0051357 9661
(227,228] 76 0.0050690 9737
(228,229] 93 0.0062029 9830
(229,230] 59 0.0039352 9889
(230,231] 77 0.0051357 9966
(231,232] 97 0.0064697 10063
(232,233] 102 0.0068032 10165
(233,234] 74 0.0049356 10239
(234,235] 76 0.0050690 10315
(235,236] 83 0.0055359 10398
(236,237] 90 0.0060028 10488
(237,238] 108 0.0072034 10596
(238,239] 96 0.0064030 10692
(239,240] 93 0.0062029 10785
(240,241] 85 0.0056693 10870
(241,242] 98 0.0065364 10968
(242,243] 112 0.0074702 11080
(243,244] 98 0.0065364 11178
(244,245] 124 0.0082705 11302
(245,246] 102 0.0068032 11404
(246,247] 108 0.0072034 11512
(247,248] 86 0.0057360 11598
(248,249] 93 0.0062029 11691
(249,250] 100 0.0066698 11791
(250,251] 98 0.0065364 11889
(251,252] 86 0.0057360 11975
(252,253] 101 0.0067365 12076
(253,254] 113 0.0075369 12189
(254,255] 115 0.0076702 12304
(255,256] 87 0.0058027 12391
(256,257] 126 0.0084039 12517
(257,258] 110 0.0073368 12627
(258,259] 98 0.0065364 12725
(259,260] 124 0.0082705 12849
(260,261] 102 0.0068032 12951
(261,262] 86 0.0057360 13037
(262,263] 110 0.0073368 13147
(263,264] 111 0.0074035 13258
(264,265] 91 0.0060695 13349
(265,266] 105 0.0070033 13454
(266,267] 88 0.0058694 13542
(267,268] 93 0.0062029 13635
(268,269] 102 0.0068032 13737
(269,270] 93 0.0062029 13830
(270,271] 104 0.0069366 13934
(271,272] 86 0.0057360 14020
(272,273] 88 0.0058694 14108
(273,274] 94 0.0062696 14202
(274,275] 82 0.0054692 14284
(275,276] 30 0.0020009 14314
(276,277] 21 0.0014007 14335
(277,278] 35 0.0023344 14370
(278,279] 32 0.0021343 14402
(279,280] 29 0.0019342 14431
(280,281] 34 0.0022677 14465
(281,282] 36 0.0024011 14501
(282,283] 25 0.0016674 14526
(283,284] 24 0.0016007 14550
(284,285] 33 0.0022010 14583
(285,286] 50 0.0033349 14633
(286,287] 30 0.0020009 14663
(287,288] 6 0.0004002 14669
(288,289] 19 0.0012673 14688
(289,290] 15 0.0010005 14703
(290,291] 17 0.0011339 14720
(291,292] 15 0.0010005 14735
(292,293] 13 0.0008671 14748
(293,294] 16 0.0010672 14764
(294,295] 12 0.0008004 14776
(295,296] 21 0.0014007 14797
(296,297] 7 0.0004669 14804
(297,298] 13 0.0008671 14817
(298,299] 6 0.0004002 14823
(299,300] 11 0.0007337 14834
(300,301] 24 0.0016007 14858
(301,302] 8 0.0005336 14866
(302,303] 6 0.0004002 14872
(303,304] 17 0.0011339 14889
(304,305] 18 0.0012006 14907
(305,306] 18 0.0012006 14925
(306,307] 14 0.0009338 14939
(307,308] 20 0.0013340 14959
(308,309] 16 0.0010672 14975
(309,310] 18 0.0012006 14993 
#Tabla de frecuencia agrupada de tiempo en la compañía
timespend = df$time_spend_company
knitr::kable(head(timespend))
x
3
6
4
5
3
3 
n_sturges = 1 + log(length(14999))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)

n_clases = 0
if (n_sturgesc%%2 == 0) {
  n_clases = n_sturgesf
} else {
  n_clases = n_sturgesc
}
R = max(timespend) - min(timespend)
w = ceiling(R/n_sturges)
bins <- seq(min(timespend), min(timespend) + w)
bins
## [1]  2  3  4  5  6  7  8  9 10
ts <- cut(timespend, bins)
Freq_table <- transform(table(ts), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)
ts Freq Rel_Freq Cum_Freq
(2,3] 6443 0.5481072 6443
(3,4] 2557 0.2175245 9000
(4,5] 1473 0.1253084 10473
(5,6] 718 0.0610804 11191
(6,7] 188 0.0159932 11379
(7,8] 162 0.0137814 11541
(8,9] 0 0.0000000 11541
(9,10] 214 0.0182050 11755 
#Tabla de frecuencia agrupada de número de proyectos
NumProj= df$number_project
knitr::kable(head(NumProj))
x
2
5
7
5
2
2 
n_sturges = 1 + log(length(14999))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)

n_clases = 0
if (n_sturgesc%%2 == 0) {
  n_clases = n_sturgesf
} else {
  n_clases = n_sturgesc
}
R = max(NumProj) - min(NumProj)
w = ceiling(R/n_sturges)
bins <- seq(min(NumProj), min(NumProj) + w)
bins
## [1] 2 3 4 5 6 7
np <- cut(NumProj, bins)
Freq_table <- transform(table(np), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)
np Freq Rel_Freq Cum_Freq
(2,3] 4055 0.3215447 4055
(3,4] 4365 0.3461264 8420
(4,5] 2761 0.2189358 11181
(5,6] 1174 0.0930933 12355
(6,7] 256 0.0202997 12611

3.4 Diagrama de barras

#Departamento vs Número de proyectos
dep= df$Department
NumProj=df$number_project
dt <- data.frame(Departamentos = 
                   c(dep), 
                 NumProy = 
                   c(NumProj))
library(ggplot2)
ggplot(data=dt, aes(x=dep, y= NumProy)) + geom_bar(stat="identity", color= "white", width = 0.8)

#Accidentes laborales vs tiempo en la compañía
workAcc= df$Work_accident
timespend=df$time_spend_company
dt <- data.frame(AccLab = 
                   c(workAcc), 
                 Tiempo = 
                   c(timespend))
library(ggplot2)
ggplot(data=dt, aes(x=AccLab, y= Tiempo)) + geom_bar(stat="identity", color= "white", width = 0.8)

3.9 Media

LastE = df$last_evaluation
NumProj = df$number_project
Average = df$average_montly_hours
timespend =df$time_spend_company
workAcc = df$Work_accident
left = df$left
prom = df$promotion_last_5years
dep = df$Department
sal = df$salary
#Media del nivel de satisfacción
mean(NivelSat)
## [1] 0.6128335
#Media última evaluación
mean(LastE)
## [1] 0.7161017
#Media de número de proyectos
mean(NumProj)
## [1] 3.803054
#Media de media de horas mensuales
mean(Average)
## [1] 201.0503
#Media de tiempo en la compañía
mean(timespend)
## [1] 3.498233
#Media de accidentes en el trabajo
mean(workAcc)
## [1] 0.1446096
#Media de left
mean(left)
## [1] 0.2380825
#Media de promociones en los últimos 5 años
mean(prom)
## [1] 0.02126808

3.10 Mediana

LastE = df$last_evaluation
NumProj = df$number_project
Average = df$average_montly_hours
timespend =df$time_spend_company
workAcc = df$Work_accident
left = df$left
prom = df$promotion_last_5years
dep = df$Department
sal = df$salary
#Mediana nivel de satisfacción
median(NivelSat)
## [1] 0.64
#Mediana última evaluación
median(LastE)
## [1] 0.72
#Mediana número de proyectos
median(NumProj)
## [1] 4
#Mediana de media de horas mensuales
median(Average)
## [1] 200
#Mediana de tiempo en la compañía
median(timespend)
## [1] 3
#Mediana de accidentes en el trabajo
median(workAcc)
## [1] 0
#Mediana de left
median(left)
## [1] 0
#Mediana de promociones en los últimos 5 años
median(prom)
## [1] 0
#Mediana de departamentos
median(dep)
## [1] "sales"
#Mediana de salarios
median(sal)
## [1] "low"

3.11 Moda

#Moda de nivel de satisfacción
f <- data.frame(table(NivelSat))
moda <- f[which.max(f$Freq),1]
moda
## [1] 0.1
## 92 Levels: 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 ... 1
#Moda de última evaluación
f <- data.frame(table(LastE))
moda <- f[which.max(f$Freq),1]
moda
## [1] 0.55
## 65 Levels: 0.36 0.37 0.38 0.39 0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 ... 1
#Moda de número de proyectos
f <- data.frame(table(NumProj))
moda <- f[which.max(f$Freq),1]
moda
## [1] 4
## Levels: 2 3 4 5 6 7
#Moda de promedio de horas mensuales
f <- data.frame(table(Average))
moda <- f[which.max(f$Freq),1]
moda
## [1] 135
## 215 Levels: 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 ... 310
#Moda de tiempo en la compañía
f <- data.frame(table(timespend))
moda <- f[which.max(f$Freq),1]
moda
## [1] 3
## Levels: 2 3 4 5 6 7 8 10
#Moda de accidentes laborales
f <- data.frame(table(workAcc))
moda <- f[which.max(f$Freq),1]
moda
## [1] 0
## Levels: 0 1
#Moda de left
f <- data.frame(table(left))
moda <- f[which.max(f$Freq),1]
moda
## [1] 0
## Levels: 0 1
#Moda de promociones en los últimos 5 años
f <- data.frame(table(prom))
moda <- f[which.max(f$Freq),1]
moda
## [1] 0
## Levels: 0 1
#Moda de departamentos
f <- data.frame(table(dep))
moda <- f[which.max(f$Freq),1]
moda
## [1] sales
## 10 Levels: accounting hr IT management marketing product_mng RandD ... technical
#Moda de salarios
f <- data.frame(table(sal))
moda <- f[which.max(f$Freq),1]
moda
## [1] low
## Levels: high low medium

3.12 Rango Medio

#Rango medio nivel de satisfacción 
Medior= (max(NivelSat)+min(NivelSat))/2
Medior
## [1] 0.545
#Rango medio última evaluación
Medior= (max(LastE)+min(LastE))/2
Medior
## [1] 0.68
#Rango medio número de proyectos 
Medior= (max(NumProj)+min(NumProj))/2
Medior
## [1] 4.5
#Rango medio promedio de horas mensuales
Medior= (max(Average)+min(Average))/2
Medior
## [1] 203
#Rango medio tiempo en la compañía 
Medior= (max(timespend)+min(timespend))/2
Medior
## [1] 6
#Rango medio accidentes laborales 
Medior= (max(workAcc)+min(workAcc))/2
Medior
## [1] 0.5
#Rango medio left 
Medior= (max(left)+min(left))/2
Medior
## [1] 0.5
#Rango medio promociones en los últimos 5 años
Medior= (max(prom)+min(prom))/2
Medior
## [1] 0.5

3.13 Cuartiles

#Cuartiles nivel de satisfacción
quantile(NivelSat, c(0.25, 0.50, 0.75))
##  25%  50%  75% 
## 0.44 0.64 0.82
#Cuartiles última evaluación
quantile(LastE, c(0.25, 0.50, 0.75))
##  25%  50%  75% 
## 0.56 0.72 0.87
#Cuartiles número de proyectos
quantile(NumProj, c(0.25, 0.50, 0.75))
## 25% 50% 75% 
##   3   4   5
#Cuartiles promedio de horas mensuales
quantile(Average, c(0.25, 0.50, 0.75))
## 25% 50% 75% 
## 156 200 245
#Cuartiles tiempo en la compañía
quantile(timespend, c(0.25, 0.50, 0.75))
## 25% 50% 75% 
##   3   3   4
#Cuartiles accidentes laborales
quantile(workAcc, c(0.25, 0.50, 0.75))
## 25% 50% 75% 
##   0   0   0
#Cuartiles left
quantile(left, c(0.25, 0.50, 0.75))
## 25% 50% 75% 
##   0   0   0
#Cuartiles promociones en los últimos 5 años
quantile(prom, c(0.25, 0.50, 0.75))
## 25% 50% 75% 
##   0   0   0

3.14 Varianza

#Varianza de nivel de satisfacción
variance <- function (NivelSat) sum((NivelSat-mean(NivelSat))^2)/(length(NivelSat)-1)
variance (NivelSat)
## [1] 0.0618172
#Varianza de última evaluación
variance <- function (LastE) sum((LastE-mean(LastE))^2)/(length(LastE)-1)
variance (LastE)
## [1] 0.02929886
#Varianza de número de proyectos
variance <- function (NumProj) sum((NumProj-mean(NumProj))^2)/(length(NumProj)-1)
variance (NumProj)
## [1] 1.519284
#Varianza de promedio de horas mensuales
variance <- function (Average) sum((Average-mean(Average))^2)/(length(Average)-1)
variance (Average)
## [1] 2494.313
#Varianza de tiempo en la compañía
variance <- function (timespend) sum((timespend-mean(timespend))^2)/(length(timespend)-1)
variance (timespend)
## [1] 2.131998
#Varianza de accidentes laborales
variance <- function (workAcc) sum((workAcc-mean(workAcc))^2)/(length(workAcc)-1)
variance (workAcc)
## [1] 0.1237059
#Varianza de left
variance <- function (left) sum((left-mean(left))^2)/(length(left)-1)
variance (left)
## [1] 0.1814113
#Varianza de promoción en los últimos 5 años
variance <- function (prom) sum((prom-mean(prom))^2)/(length(prom)-1)
variance (prom)
## [1] 0.02081714

3.15 Desviación estándar

#DS nivel de satisfacción
desvia = sqrt(variance(NivelSat))
desvia
## [1] 0.2486307
#DS última evaluación
desvia = sqrt(variance(LastE))
desvia
## [1] 0.1711691
#DS número de proyectos
desvia = sqrt(variance(NumProj))
desvia
## [1] 1.232592
#DS promedio de horas mensuales
desvia = sqrt(variance(Average))
desvia
## [1] 49.9431
#DS tiempo en la compañía
desvia = sqrt(variance(timespend))
desvia
## [1] 1.460136
#DS accidentes laborales
desvia = sqrt(variance(workAcc))
desvia
## [1] 0.3517186
#DS left
desvia = sqrt(variance(left))
desvia
## [1] 0.4259241
#DS promociones en los últimos 5 años
desvia = sqrt(variance(prom))
desvia
## [1] 0.1442815

3.16 Rango

#Rango nivel de satisfacción
rango = max(NivelSat)-min(NivelSat)
rango
## [1] 0.91
#Rango última evaluación
rango = max(LastE)-min(LastE)
rango
## [1] 0.64
#Rango número de proyectos
rango = max(NumProj)-min(NumProj)
rango
## [1] 5
#Rango promedio de horas mensuales
rango = max(Average)-min(Average)
rango
## [1] 214
#Rango tiempo en la compañía
rango = max(timespend)-min(timespend)
rango
## [1] 8
#Rango accidentes laborales
rango = max(workAcc)-min(workAcc)
rango
## [1] 1
#Rango left
rango = max(left)-min(left)
rango
## [1] 1
#Rango promociones en los últimos 5 años
rango = max(prom)-min(prom)
rango
## [1] 1

3.17 Rango Intercuartil

#IQR de nivel de satisfacción
IQR(NivelSat)
## [1] 0.38
#IQR de última evaluación
IQR(LastE)
## [1] 0.31
#IQR de número de proyectos
IQR(NumProj)
## [1] 2
#IQR de promedio de horas mensuales
IQR(Average)
## [1] 89
#IQR de tiempo en la compañía
IQR(timespend)
## [1] 1
#IQR de accidentes laborales
IQR(workAcc)
## [1] 0
#IQR de left
IQR(left)
## [1] 0
#IQR de promociones en los últimos 5 años
IQR(prom)
## [1] 0

3.18 Diagrama de caja y extensión

boxplot(NivelSat, horizontal= TRUE, main= "Diagrama de caja y extensión nivel de satisfacción", col="blue")

boxplot(LastE, horizontal= TRUE, main= "Diagrama de caja y extensión última evaluación", col="green")

boxplot(NumProj, horizontal= TRUE, main= "Diagrama de caja y extensión de número de proyectos", col="blue")

boxplot(Average, horizontal= TRUE, main= "Diagrama de caja y extensión de promedio de horas al mes", col="green")

boxplot(timespend, horizontal= TRUE, main= "Diagrama de caja y extensión de tiempo en la compañía", col="blue")

boxplot(workAcc, horizontal= TRUE, main= "Diagrama de caja y extensión de accidentes laborales", col="green")

boxplot(left, horizontal= TRUE, main= "Diagrama de caja y extensión de left", col="blue")

boxplot(prom, horizontal= TRUE, main= "Diagrama de caja y extensión de promociones en los últimos 5 años", col="green")

Conclusiones

Como resultados de aprendizaje queremos destacar al programa RStudio Cloud como una herramienta de gran utilidad a la hora del procesamiento y análisis de un gran número de datos. Debido a que nos ofrece una gran variedad de opciones a la hora de programar, lo que nos ayuda convertir esos datos en información útil de una manera más sencilla.