Ejercicio_1
Kevin Gargate Osorio
2024-03-29
Lectura de los datos
Pregunta 1,2,3,4
eggs <- read_ods("Eggsorg.ods", sheet = 1); eggs #Lectura de datos (ods)## # A tibble: 96 × 9
## `Name ` `Family ` ` Size ` ` Nesting ground ` ` Eggs ` ` Length ` ` Width` ` Mass ` ` Hatch`
## <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 Barn owl Owl 34 Hollow trees, barns 4-7 39.2 30.8 20.7 30
## 2 Black cap Warbler 14 Bushes, low trees 4-6 19.6 14.7 1.9 14-15
## 3 Black redstaft Chat 14 Hales, crevices 5-6 19.4 14.4 2 13
## 4 Blackbird Thrush 25 Trees, bushes 4-6 28.6 21 9 13-14
## 5 Blue tit Tit 11.5 Holes 10-13 15.4 11.9 1.3 13-15
## 6 Brambling Finch 14.5 Trees, bushes 5-6 19.5 14.6 2.2 12-13
## 7 Bullfinch Finch 15 Bushes, conifers 4-5 20.2 15.1 2.1 13
## 8 Canada goose Goose 95 Islets, marshes 5-6 85.7 58.2 228 28-30
## 9 Carrion crow Crow 47 Tall trees 4-6 43.5 30.1 19 17-19
## 10 Chaffinch Finch 15 Bushes, trees, hedges 4-6 19.3 14.6 2.1 12-13
## # ℹ 86 more rowsstr(eggs) # estructura data-frame## tibble [96 × 9] (S3: tbl_df/tbl/data.frame)
## $ Name : chr [1:96] "Barn owl" "Black cap" "Black redstaft" "Blackbird" ...
## $ Family : chr [1:96] "Owl" "Warbler" "Chat" "Thrush" ...
## $ Size : num [1:96] 34 14 14 25 11.5 14.5 15 95 47 15 ...
## $ Nesting ground : chr [1:96] "Hollow trees, barns" "Bushes, low trees" "Hales, crevices" "Trees, bushes" ...
## $ Eggs : chr [1:96] "4-7" "4-6" "5-6" "4-6" ...
## $ Length : num [1:96] 39.2 19.6 19.4 28.6 15.4 19.5 20.2 85.7 43.5 19.3 ...
## $ Width : num [1:96] 30.8 14.7 14.4 21 11.9 14.6 15.1 58.2 30.1 14.6 ...
## $ Mass : num [1:96] 20.7 1.9 2 9 1.3 2.2 2.1 228 19 2.1 ...
## $ Hatch : chr [1:96] "30" "14-15" "13" "13-14" ...eggs <- read_ods("Eggsorg.ods", sheet = 1, row_names = TRUE,as_tibble = FALSE); eggs #Lectura de datos de la hoja 1## Family Size Nesting ground Eggs Length Width Mass Hatch
## Barn owl Owl 34.0 Hollow trees, barns 4-7 39.2 30.8 20.7 30
## Black cap Warbler 14.0 Bushes, low trees 4-6 19.6 14.7 1.9 14-15
## Black redstaft Chat 14.0 Hales, crevices 5-6 19.4 14.4 2.0 13
## Blackbird Thrush 25.0 Trees, bushes 4-6 28.6 21.0 9.0 13-14
## Blue tit Tit 11.5 Holes 10-13 15.4 11.9 1.3 13-15
## Brambling Finch 14.5 Trees, bushes 5-6 19.5 14.6 2.2 12-13
## Bullfinch Finch 15.0 Bushes, conifers 4-5 20.2 15.1 2.1 13
## Canada goose Goose 95.0 Islets, marshes 5-6 85.7 58.2 228.0 28-30
## Carrion crow Crow 47.0 Tall trees 4-6 43.5 30.1 19.0 17-19
## Chaffinch Finch 15.0 Bushes, trees, hedges 4-6 19.3 14.6 2.1 12-13
## Chiff-chaff Warbler 11.0 Wood verges, hedgerows 5-6 15.5 12.0 1.3 13-14
## Chough Crow 39.0 Crevices, cliff, ledges 3-5 39.4 27.9 13.0 19-21
## Cirl bunting Bunting 16.0 Bushes, hedges 3-5 20.9 15.9 2.8 11-13
## Coal tit Tit 11.0 Holes, burrows 7-10 14.7 11.6 1.4 14-16
## Collared dove Pigeon 32.0 Trees, bushes 2 31.9 24.0 8.5 15-16
## Common sandpiper Sandpiper 20.0 Lake shores 4 36.3 36.0 12.0 21-22
## Corn bunting Bunting 18.0 Grassy hollows 5-6 23.8 17.7 3.7 12-13
## Crested tit Tit 11.5 Tree hales 5-7 16.0 12.4 1.3 15-18
## Crossbill Finch 16.0 Conifers 3-4 22.1 16.1 2.9 12-13
## Dotterel Plover 22.0 Moors, scree 2-4 41.1 28.9 11.0 27-28
## Dunlin Sandpiper 18.0 Moors, marshes 3-5 34.7 24.7 11.0 17
## Eider Duck 60.0 Near sea, islets 4-6 77.6 51.9 110.3 25-26
## Fieldfare Thrush 26.0 Woodland trees 5-6 28.8 20.9 7.0 13-14
## Firecrest Warbler 9.0 Conifers 7-12 13.5 10.3 0.7 14-15
## Gadwall Duck 51.0 Reedbeds 8-12 51.8 37.5 42.5 26
## Garden warbler Warbler 14.0 Shrubs, brambles 4-5 20.1 14.8 2.5 12
## Garganey Duck 39.0 Dry ground, vegetation 8-11 45.3 33.3 25.7 21-23
## Goldcrest Warbler 9.0 Tall conifers 8-11 13.6 10.3 0.7 16
## Golden Plover Plover 28.0 Boggy moors 4 51.8 35.9 34.0 27
## Goldeneye Duck 46.0 Tree holes, burrows 6-11 58.4 43.2 56.5 30
## Goldfinch Finch 12.0 Trees 5-6 17.0 12.8 1.4 12-13
## Goosander Duck 62.0 Tree holes 8-12 66.4 46.4 83.5 32
## Grasshopper warbler Warbler 13.0 Moors, marshes 5-6 18.1 13.8 1.7 13-15
## Great tit Tit 14.0 Tree holes 7-10 17.3 135.0 1.6 13-14
## Green sandpiper Sandpiper 23.0 Trees on marshy ground 4 39.1 28.0 16.0 20-22
## Greenfinch Finch 14.5 Bushes, hedges 5 20.2 14.5 2.0 13-14
## Grey wagtail Wagtail 18.0 Holes, ledges 5-6 19.0 14.5 1.9 12-14
## Greylag goose Goose 83.0 Reedbeds 5-8 85.3 58.0 176.0 28-29
## Howlinch Finch 18.0 Deciduous trees 5-6 24.5 17.5 3.4 14
## Hooded crow Crow 47.0 Trees, bushes 5-6 43.5 30.1 19.0 17-19
## House sparrow Sparrow 14.5 Holes, crevices 5-6 22.5 15.7 3.0 13
## Jackdaw Crow 34.0 Holes, crevices 5-6 33.7 25.2 12.0 17-18
## Jay Crow 34.0 Bushes, trees 5-6 31.6 23.0 8.0 16-17
## Lapwing Plover 30.0 Fields, moors 4 47.1 33.7 25.0 24-26
## Lesser whitethrwt Warbler 13.5 Thickets, Hedgerows 5-6 16.5 12.6 1.7 10-11
## Linnet Finch 13.0 Bushes, thickets 5-6 17.7 13.3 2.0 12-14
## Little owl Owl 22.0 Treeholes 3-5 33.6 28.1 15.5 28
## Little ringed plover Plover 15.0 Mud, gravelpits 4 29.8 22.1 7.0 23-26
## Long-eared owl Owl 36.0 Abondoned nests 4-6 40.9 32.7 23.0 27-28
## Long-tailed tit Tit 14.0 Trees, thickets 7-11 13.6 10.9 0.9 12-13
## Magpie Crow 46.0 Tall trees, hedges 5-7 34.1 24.2 9.0 17-18
## Mallard Duck 58.0 Ground cover 7-11 58.4 39.5 49.5 28
## Marsh tit Tit 11.5 Tree holes 7-9 16.1 12.2 1.0 14
## Mistle thrush Thrush 27.0 Tall trees 3-5 31.2 22.3 7.0 13-14
## Nightingale Chat 16.5 Ground cover 4-6 21.0 15.6 2.6 13
## Pied wagtail Wagtail 18.0 Holes, niches 5-6 20.4 15.1 2.2 12-14
## Pintail Duck 58.0 Moors, marshes 7-11 51.8 37.0 47.0 22-23
## Pochard Duck 45.0 Reeds, banks 6-9 61.3 43.7 67.0 24-26
## Raven Crow 64.0 Ledges, crevices, trees 4-7 49.7 33.4 33.0 20-21
## Redpoll Finch 11.5 Scrub growth 5-6 16.9 12.6 1.3 10-11
## Redstart Chat 14.0 Tree holes 5-7 18.7 13.8 2.0 13-14
## Redwing Thrush 21.0 Bushes, trees 5 25.8 19.2 6.5 13
## Reed bunting Bunting 15.0 Reeds, marshes 5-6 19.3 14.3 2.1 13-14
## Reed warbler Warbler 12.5 Reeds, bushes 3-5 18.3 13.6 1.8 11-12
## Ring ouzel Thrush 24.0 Bushes, small trees 4-5 30.4 21.5 8.0 14
## Ringed plover Plover 19.0 Beaches, mud 3-5 35.7 25.9 10.0 24-27
## Robin Chat 14.0 Hollows, holes 5-7 19.4 14.8 2.0 13-14
## Rock dove Pigeon 33.0 Cliff holes 2 39.3 29.1 17.0 17
## Rook Crow 46.0 Tall trees 4-5 41.0 28.3 16.0 16-18
## Scaup Duck 47.0 Moors, islands 7-11 63.2 43.5 60.0 24-25
## Sedge warbler Warbler 13.0 Reeds, thickets 4-6 17.7 13.1 1.5 12-13
## Shelduck Duck 62.0 Sandy burrows 8-12 65.8 47.6 78.0 28-29
## Shoit-eared owl Owl 38.0 Moors, marshes 4-7 40.1 31.8 21.5 26-27
## Shoveler Duck 48.0 Moors, marshes 8-12 51.8 37.0 39.0 23-25
## Siskin Finch 12.0 Conifers 4-5 16.4 12.3 1.0 11-12
## Skylark Lark 18.0 Open ground 3-5 24.1 16.8 3.4 14
## Snow bunting Bunting 16.5 Stony ground 5-6 22.4 16.8 1.7 10-13
## Song thrush Thrush 23.0 Bushes, trees 4-6 27.3 20.4 6.5 12-13
## Stock dove Pigeon 33.0 Tree holes 2-3 37.9 29.0 16.0 16-17
## Stonechat Chat 12.5 Grassy hollows 5-6 18.9 14.4 2.0 14-15
## Towny owl Owl 38.0 Hollow trees 2-5 48.2 38.7 39.0 28-29
## Teal Duck 36.0 Dry ground, vegetotion 5 45.6 33.5 26.8 21-22
## Temminck's stint Sandpiper 14.0 Islets 4 28.0 20.4 6.0 13-14
## Tree sparrow Sparrow 14.0 Holes, crevices 5-6 19.3 14.0 2.0 13-14
## Turtle dove Pigeon 32.0 Trees, hedges 2-3 30.7 23.0 9.0 13-15
## Twite Finch 13.0 Ground cover 5-6 17.2 12.9 1.5 12-13
## Wheatear Chat 15.0 Holes, burrows 5-6 21.2 15.9 2.5 14
## Whinchat Chat 12.5 Grassy hollows 5-7 19.2 14.8 2.0 13-14
## Whitethroat Warbler 14.0 Scrub, bushes 4-5 18.1 13.8 2.0 11-13
## Wigeon Duck 48.0 Islets, ground cover 9 53.9 38.2 44.0 22-23
## Wood sandpiper Sandpiper 20.0 Boggy moors 4 38.3 27.0 13.0 21-24
## Wood warbler Warbler 13.0 Woodland ground 5-6 16.1 12.6 1.3 13
## Wood-pigeon Pigeon 41.0 Trees, bushes 2-3 40.1 28.7 18.5 15-17
## Woodlark Lork 15.0 Wood verges 3-4 21.6 16.3 2.7 13-15
## Yellow wagtail Wagtail 16.5 Grassy hollows 5-6 19.1 14.3 1.9 13-14
## Yellowhammer Bunting 16.5 Grassy hollows 3-5 21.2 15.9 2.7 12-14str(eggs) # estructura data-frame## 'data.frame': 96 obs. of 8 variables:
## $ Family : chr "Owl" "Warbler" "Chat" "Thrush" ...
## $ Size : num 34 14 14 25 11.5 14.5 15 95 47 15 ...
## $ Nesting ground : chr "Hollow trees, barns" "Bushes, low trees" "Hales, crevices" "Trees, bushes" ...
## $ Eggs : chr "4-7" "4-6" "5-6" "4-6" ...
## $ Length : num 39.2 19.6 19.4 28.6 15.4 19.5 20.2 85.7 43.5 19.3 ...
## $ Width : num 30.8 14.7 14.4 21 11.9 14.6 15.1 58.2 30.1 14.6 ...
## $ Mass : num 20.7 1.9 2 9 1.3 2.2 2.1 228 19 2.1 ...
## $ Hatch : chr "30" "14-15" "13" "13-14" ...write.csv(eggs, file = "Eggs.csv") #escritura en csvegg <- read_ods("Eggsorg.ods", sheet = 1) # Lectura de datos de la hoja 1
eggs <- egg[,2:9] # selección de las variables en posición 2 al 9
rownames(eggs) <- trimws(rownames(eggs), which = c("both")); eggs # eliminación de espacios en blanco (inicio y final)## Warning: Setting row names on a tibble is deprecated.## # A tibble: 96 × 8
## `Family ` ` Size ` ` Nesting ground ` ` Eggs ` ` Length ` ` Width` ` Mass ` ` Hatch`
## * <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 Owl 34 Hollow trees, barns 4-7 39.2 30.8 20.7 30
## 2 Warbler 14 Bushes, low trees 4-6 19.6 14.7 1.9 14-15
## 3 Chat 14 Hales, crevices 5-6 19.4 14.4 2 13
## 4 Thrush 25 Trees, bushes 4-6 28.6 21 9 13-14
## 5 Tit 11.5 Holes 10-13 15.4 11.9 1.3 13-15
## 6 Finch 14.5 Trees, bushes 5-6 19.5 14.6 2.2 12-13
## 7 Finch 15 Bushes, conifers 4-5 20.2 15.1 2.1 13
## 8 Goose 95 Islets, marshes 5-6 85.7 58.2 228 28-30
## 9 Crow 47 Tall trees 4-6 43.5 30.1 19 17-19
## 10 Finch 15 Bushes, trees, hedges 4-6 19.3 14.6 2.1 12-13
## # ℹ 86 more rowswrite.csv(eggs, file = "Eggs.csv") #escritura en csv
colnames(eggs) # etiquetas columnas ## [1] "Family " " Size " " Nesting ground " " Eggs " " Length " " Width" " Mass " " Hatch"colnames(eggs) <- c("Family","Size","Nesting ground" ,"Eggs","Length","Width","Mass","Hatch") # Etiquetas de columnas
write_ods(eggs, "Eggs.ods", sheet = "Data", row_names = TRUE, col_names = TRUE) #escritura en ods
write.csv(eggs, file = "Eggs.csv") #escritura en csvPregunta 5
SudAmerica
#########################################################################################
# Lectura de los datos
SudAmerica <- read.csv("SudAmerica.csv", row.names = 1)
# Guardando el marco de datos SudAmerica en un archivo .ods.
# y se incluirán los nombres de fila y columna en el archivo.
write_ods(SudAmerica, "SudAmerica.ods", row_names = TRUE, col_names = TRUE)Baseball
# Lectura de los datos Baseball
Baseball <- read.csv("Baseball.csv", row.names = 1)
# Guardar a un archivo .ods para Baseball.csv
write_ods(Baseball, "Baseball.ods", row_names = TRUE, col_names = TRUE)Students
# Lectura de los datos Students
Students <- read.csv("Students.csv", row.names = 1)
# Seleccionar las variables midterm, final
Students <- Students[,1:2]
# Guardar a un archivo .ods para Baseball.csv
write_ods(Students, "Students.ods", row_names = TRUE, col_names = TRUE)Pregunta 6
SudAmerica
# Gráfico de Variables Tajo_Urbano y IDH del dataframe de Sudamerica
attach(SudAmerica) # archivo en uso
lab1 <- rownames(SudAmerica) # etiquetas de las unidades en lab1
plot(Tajo_urbano,IDH,xlab="Tajo_urbano",ylab="IDH") # diagrama de dispersión de dos carácteres
text(Tajo_urbano,IDH,lab1) # escritura de etiquetas sobre el gráfico
identify(Tajo_urbano,IDH,lab1) # escritura de etiquetas cliqueando cadauna
## integer(0)# Comentarios: En el gráfico de dispersión entre las variables Tajo Urbano y IDH, observamos una tendencia que sugiere que a mayores valores de Tajo Urbano podrían corresponder mayores valores de IDH. Esta tendencia indica una posible correlación positiva entre el desarrollo urbano y el índice de desarrollo humano en las ubicaciones estudiadas
#Algunos países, como Uruguay, Chile y Argentina, parecen tener tanto un "Tajo_urbano" alto como un IDH alto, lo que podría indicar un nivel de desarrollo urbano y humano más avanzado. Por otro lado, Bolivia se destaca con un valor más bajo en ambas variablesBaseball
# Gráfico de Variables BattingAvg y WinningPerc del dataframe de Baseball
attach(Baseball) # archivo en uso
lab2 <- rownames(Baseball) # etiquetas de las unidades en lab2
plot(BattingAvg,WinningPerc,xlab="BattingAvg",ylab="WinningPerc") # diagrama de dispersión de dos carácteres
text(BattingAvg,WinningPerc,lab2) # escritura de etiquetas sobre el gráfico
identify(BattingAvg,WinningPerc,lab2) # escritura de etiquetas cliqueando cadauna
## integer(0)# Comentarios: El gráfico de dispersión entre las variables BattingAvg y WinningPerc podemos obervar que hay una tendencia, donde a mayor valor de BattingAvg se obtendrá mayores valores de WinningPerc
# BattingAvg parecen variar entre aproximadamente 0.245 y 0.275, mientras que el WinningPerc varía entre 0.40 y 0.65.Students
# Gráfico de Variables midterm y final del dataframe de Students
attach(Students) # archivo en uso
lab3 <- rownames(Students) # etiquetas de las unidades en lab3
plot(midterm,final,xlab="midterm",ylab="final") # diagrama de dispersión de dos carácteres
text(midterm,final,final) # escritura de etiquetas sobre el gráfico
identify(midterm,final,lab3) # escritura de etiquetas cliqueando cadauna
## integer(0)# Comentarios: El gráfico de dispersión entre las variables midterm y final podemos obervar que hay una tendencia, donde a mayor valor de midterm se obtendrá mayores valores de midterm. Además que hay una mayor concentración de datos entre valores de 15 a 25 respecto a la variable midterm. Asimismo, si analizamos la variable final hay mayor cantidad de datos en el rango de 15 a 35Pregunta 7
SudAmerica
# regresión lm dibujo de datos con recta y predictos
attach(SudAmerica)## The following objects are masked from SudAmerica (pos = 5):
##
## IDH, Tajo_urbanoplot(Tajo_urbano,IDH,xlab="Tajo_urbano",ylab="IDH") # otro diagrama
identify(Tajo_urbano,IDH,lab1) # escritura de etiquetas cliqueando cadauna## integer(0)lmSA <- lm(IDH~Tajo_urbano,data=SudAmerica) # regresión
abline(lmSA,col="red") # se traza la recta estimada
points(Tajo_urbano,lmSA$fitted.values,col="red") # y los IDH estimados
Baseball
# regresión lm dibujo de datos con recta y predictos
# Baseball
attach(Baseball)## The following objects are masked from Baseball (pos = 5):
##
## BattingAvg, WinningPercplot(BattingAvg,WinningPerc,xlab="BattingAvg",ylab="WinningPerc") # otro diagrama
identify(BattingAvg,WinningPerc,lab2) # escritura de etiquetas cliqueando ## integer(0)lmSA2<-lm(WinningPerc~BattingAvg,data=Baseball) # regresión
abline(lmSA2,col="red") # se traza la recta estimada
points(BattingAvg,lmSA2$fitted.values,col="red") # y los WinningPerc estimados
Students
# regresión lm dibujo de datos con recta y predictos
# Baseball
attach(Students)## The following objects are masked from Students (pos = 5):
##
## final, midtermplot(midterm,final,xlab="midterm",ylab="final") # otro diagrama
identify(midterm,final,lab3) # escritura de etiquetas cliqueando ## integer(0)lmSA3<-lm(final~midterm,data=Students) # regresión
abline(lmSA3,col="red") # se traza la recta estimada
points(midterm,lmSA3$fitted.values,col="red") # y los final estimados