Importación de datos
Archivo de Excel:
library(readxl)
real_state_excel <- read_excel("C:/Users/MovilDell/Downloads/real_state_excel.xlsx",
col_types = c("numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"))
head(real_state_excel, n = 10)
## # A tibble: 10 × 6
## house_age dist_metro num_stores lati longi price_house
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 32 84.9 10 25.0 122. 37.9
## 2 19.5 307. 9 25.0 122. 42.2
## 3 13.3 562. 5 25.0 122. 47.3
## 4 13.3 562. 5 25.0 122. 54.8
## 5 5 391. 5 25.0 122. 43.1
## 6 7.1 2175. 3 25.0 122. 32.1
## 7 34.5 623. 7 25.0 122. 40.3
## 8 20.3 288. 6 25.0 122. 46.7
## 9 31.7 5512. 1 25.0 121. 18.8
## 10 17.9 1783. 3 25.0 122. 22.1
Archivo CSV
Usando r base
real_state <- read.csv("C:/Users/MovilDell/Downloads/real_state.csv")
head(real_state, n=5)
## house_age dist_metro num_stores lati longi price_house
## 1 32.0 84.87882 10 24.98298 121.5402 37.9
## 2 19.5 306.59470 9 24.98034 121.5395 42.2
## 3 13.3 561.98450 5 24.98746 121.5439 47.3
## 4 13.3 561.98450 5 24.98746 121.5439 54.8
## 5 5.0 390.56840 5 24.97937 121.5425 43.1
Usando Readr
library(readr)
real_state_readr <- read_csv("C:/Users/MovilDell/Downloads/real_state.csv",
col_types = cols(dist_metro = col_number()))
head(real_state_readr)
## # A tibble: 6 × 6
## house_age dist_metro num_stores lati longi price_house
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 32 84.9 10 25.0 122. 37.9
## 2 19.5 307. 9 25.0 122. 42.2
## 3 13.3 562. 5 25.0 122. 47.3
## 4 13.3 562. 5 25.0 122. 54.8
## 5 5 391. 5 25.0 122. 43.1
## 6 7.1 2175. 3 25.0 122. 32.1
Archivo SPSS (SAV)
library(haven)
empresas <- read_sav("C:/Users/MovilDell/Downloads/empresas.sav")
head(empresas)
## # A tibble: 6 × 10
## ID AGR MIN MAN CEN CON SER BAN SECSER TC
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Bélgica 3.3 0.9 27.6 0.9 8.2 19.1 6.2 26.6 7.2
## 2 Dinamarca 9.2 0.1 21.8 0.6 8.3 14.6 6.5 32.2 7.1
## 3 Francia 10.8 0.8 27.5 0.9 8.9 16.8 6 22.6 5.7
## 4 Alemania O 6.7 1.3 35.8 0.9 7.3 14.4 5 22.3 6.1
## 5 Irlanda 23.2 1 20.7 1.3 7.5 16.8 2.8 20.8 6.1
## 6 Italia 15.9 0.6 27.6 0.5 10 18.1 1.6 20.1 5.7
Archivo de Stata
library(haven)
pwt91_capital_detail <- read_dta("C:/Users/MovilDell/Downloads/pwt91_capital_detail.dta")
head(pwt91_capital_detail, n = 30)
## # A tibble: 30 × 34
## countrycode year Ic_Struc Ic_Mach Ic_TraEq Ic_Other Ip_Struc Ip_Mach
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ABW 1950 NA NA NA NA NA NA
## 2 ABW 1951 NA NA NA NA NA NA
## 3 ABW 1952 NA NA NA NA NA NA
## 4 ABW 1953 NA NA NA NA NA NA
## 5 ABW 1954 NA NA NA NA NA NA
## 6 ABW 1955 NA NA NA NA NA NA
## 7 ABW 1956 NA NA NA NA NA NA
## 8 ABW 1957 NA NA NA NA NA NA
## 9 ABW 1958 NA NA NA NA NA NA
## 10 ABW 1959 NA NA NA NA NA NA
## # ℹ 20 more rows
## # ℹ 26 more variables: Ip_TraEq <dbl>, Ip_Other <dbl>, Nc_Struc <dbl>,
## # Nc_Mach <dbl>, Nc_TraEq <dbl>, Nc_Other <dbl>, Np_Struc <dbl>,
## # Np_Mach <dbl>, Np_TraEq <dbl>, Np_Other <dbl>, Dc_Struc <dbl>,
## # Dc_Mach <dbl>, Dc_TraEq <dbl>, Dc_Other <dbl>, Kc_Struc <dbl>,
## # Kc_Mach <dbl>, Kc_TraEq <dbl>, Kc_Other <dbl>, Kp_Struc <dbl>,
## # Kp_Mach <dbl>, Kp_TraEq <dbl>, Kp_Other <dbl>, Ksh_Struc <dbl>, …