PARCIAL_1
JESUS Y ALEJANDRO
2024-08-22
library(readxl)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(conflicted)
conflicts_prefer(dplyr::filter)## [conflicted] Will prefer dplyr::filter over any other package.Baseeuropa <- read_excel("BASEDEDATOS.xlsx", sheet = "DATA(FM)")
dim(Baseeuropa) #dimensiones de la base ## [1] 655 21str(Baseeuropa) #tipo de variable (categóricas o numéricas)## tibble [655 × 21] (S3: tbl_df/tbl/data.frame)
## $ DATE : chr [1:655] "1970-01-31" "1970-02-28" "1970-03-31" "1970-04-30" ...
## $ TIME PERIOD : chr [1:655] "Jan 1970" "Feb 1970" "Mar 1970" "Apr 1970" ...
## $ OBS.VALUE : num [1:655] 7.75 7.9 8.11 8.07 8.24 ...
## $ OBS.STATUS : chr [1:655] "A" "A" "A" "A" ...
## $ OBS.COMMENT : logi [1:655] NA NA NA NA NA NA ...
## $ SERIES KEY : chr [1:655] "FM.M.U2.EUR.4F.BB.U2_10Y.YLD" "FM.M.U2.EUR.4F.BB.U2_10Y.YLD" "FM.M.U2.EUR.4F.BB.U2_10Y.YLD" "FM.M.U2.EUR.4F.BB.U2_10Y.YLD" ...
## $ TITLE : chr [1:655] "Euro Area 10 Years Government Benchmark Bond - Yield" "Euro Area 10 Years Government Benchmark Bond - Yield" "Euro Area 10 Years Government Benchmark Bond - Yield" "Euro Area 10 Years Government Benchmark Bond - Yield" ...
## $ FREQUENCY : chr [1:655] "M" "M" "M" "M" ...
## $ FREQUENCY (DESC.) : chr [1:655] "Monthly" "Monthly" "Monthly" "Monthly" ...
## $ REFERENCE AREA : chr [1:655] "U2" "U2" "U2" "U2" ...
## $ REFERENCE AREA (DESC.) : chr [1:655] "Euro area (changing composition) (U2)" "Euro area (changing composition) (U2)" "Euro area (changing composition) (U2)" "Euro area (changing composition) (U2)" ...
## $ CURRENCY : chr [1:655] "EUR" "EUR" "EUR" "EUR" ...
## $ CURRENCY (DESC.) : chr [1:655] "Euro (EUR)" "Euro (EUR)" "Euro (EUR)" "Euro (EUR)" ...
## $ FINANCIAL MARKET PROVIDER : chr [1:655] "4F" "4F" "4F" "4F" ...
## $ FINANCIAL MARKET PROVIDER (DESC.) : chr [1:655] "ECB (4F)" "ECB (4F)" "ECB (4F)" "ECB (4F)" ...
## $ FINANCIAL MARKET INSTRUMENT : chr [1:655] "BB" "BB" "BB" "BB" ...
## $ FINANCIAL MARKET INSTRUMENT (DESC.) : chr [1:655] "Benchmark bond (BB)" "Benchmark bond (BB)" "Benchmark bond (BB)" "Benchmark bond (BB)" ...
## $ FINANCIAL MARKET PROVIDER IDENTIFIER : chr [1:655] "U2_10Y" "U2_10Y" "U2_10Y" "U2_10Y" ...
## $ FINANCIAL MARKET PROVIDER IDENTIFIER (DESC.): chr [1:655] "Euro Area 10 Years Government Benchmark Bond (U2_10Y)" "Euro Area 10 Years Government Benchmark Bond (U2_10Y)" "Euro Area 10 Years Government Benchmark Bond (U2_10Y)" "Euro Area 10 Years Government Benchmark Bond (U2_10Y)" ...
## $ FINANCIAL MARKET DATA TYPE : chr [1:655] "YLD" "YLD" "YLD" "YLD" ...
## $ FINANCIAL MARKET DATA TYPE (DESC.) : chr [1:655] "Yield (YLD)" "Yield (YLD)" "Yield (YLD)" "Yield (YLD)" ...summary(Baseeuropa) #resumen de cada variable## DATE TIME PERIOD OBS.VALUE OBS.STATUS
## Length:655 Length:655 Min. :-0.0915 Length:655
## Class :character Class :character 1st Qu.: 3.6244 Class :character
## Mode :character Mode :character Median : 6.1800 Mode :character
## Mean : 6.5488
## 3rd Qu.: 9.6459
## Max. :15.4353
## OBS.COMMENT SERIES KEY TITLE FREQUENCY
## Mode:logical Length:655 Length:655 Length:655
## NA's:655 Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## FREQUENCY (DESC.) REFERENCE AREA REFERENCE AREA (DESC.)
## Length:655 Length:655 Length:655
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## CURRENCY CURRENCY (DESC.) FINANCIAL MARKET PROVIDER
## Length:655 Length:655 Length:655
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## FINANCIAL MARKET PROVIDER (DESC.) FINANCIAL MARKET INSTRUMENT
## Length:655 Length:655
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## FINANCIAL MARKET INSTRUMENT (DESC.) FINANCIAL MARKET PROVIDER IDENTIFIER
## Length:655 Length:655
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## FINANCIAL MARKET PROVIDER IDENTIFIER (DESC.) FINANCIAL MARKET DATA TYPE
## Length:655 Length:655
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## FINANCIAL MARKET DATA TYPE (DESC.)
## Length:655
## Class :character
## Mode :character
##
##
## names(Baseeuropa) #nombre de las variables en las columnas## [1] "DATE"
## [2] "TIME PERIOD"
## [3] "OBS.VALUE"
## [4] "OBS.STATUS"
## [5] "OBS.COMMENT"
## [6] "SERIES KEY"
## [7] "TITLE"
## [8] "FREQUENCY"
## [9] "FREQUENCY (DESC.)"
## [10] "REFERENCE AREA"
## [11] "REFERENCE AREA (DESC.)"
## [12] "CURRENCY"
## [13] "CURRENCY (DESC.)"
## [14] "FINANCIAL MARKET PROVIDER"
## [15] "FINANCIAL MARKET PROVIDER (DESC.)"
## [16] "FINANCIAL MARKET INSTRUMENT"
## [17] "FINANCIAL MARKET INSTRUMENT (DESC.)"
## [18] "FINANCIAL MARKET PROVIDER IDENTIFIER"
## [19] "FINANCIAL MARKET PROVIDER IDENTIFIER (DESC.)"
## [20] "FINANCIAL MARKET DATA TYPE"
## [21] "FINANCIAL MARKET DATA TYPE (DESC.)"head(Baseeuropa) #primeros 6 datos de la base## # A tibble: 6 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 1970-01-31 Jan 1970 7.75 A NA FM.M.U2.EUR.4… Euro…
## 2 1970-02-28 Feb 1970 7.90 A NA FM.M.U2.EUR.4… Euro…
## 3 1970-03-31 Mar 1970 8.11 A NA FM.M.U2.EUR.4… Euro…
## 4 1970-04-30 Apr 1970 8.07 A NA FM.M.U2.EUR.4… Euro…
## 5 1970-05-31 May 1970 8.24 A NA FM.M.U2.EUR.4… Euro…
## 6 1970-06-30 Jun 1970 8.45 A NA FM.M.U2.EUR.4… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …tail(Baseeuropa) #últimos 6 datos de la base## # A tibble: 6 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 2024-02-29 Feb 2024 3.02 A NA FM.M.U2.EUR.4… Euro…
## 2 2024-03-31 Mar 2024 2.95 A NA FM.M.U2.EUR.4… Euro…
## 3 2024-04-30 Apr 2024 3.07 A NA FM.M.U2.EUR.4… Euro…
## 4 2024-05-31 May 2024 3.12 A NA FM.M.U2.EUR.4… Euro…
## 5 2024-06-30 Jun 2024 3.17 A NA FM.M.U2.EUR.4… Euro…
## 6 2024-07-31 Jul 2024 3.11 A NA FM.M.U2.EUR.4… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …media <- mean(Baseeuropa$OBS.VALUE)
print(media)## [1] 6.548814mediana <- median(Baseeuropa$OBS.VALUE)
print(mediana)## [1] 6.18percentiles <- quantile(Baseeuropa$OBS.VALUE, probs = c(0.25, 0.5, 0.75))
print(percentiles)## 25% 50% 75%
## 3.62445 6.18000 9.64595desviación <- sd(Baseeuropa$OBS.VALUE)
print(desviación)## [1] 3.840756coeficiente_variación <- (desviación / media) * 100
print(coeficiente_variación)## [1] 58.64812hist(Baseeuropa$OBS.VALUE, main="Histograma de distribución normal", xlab="Datos", ylab="Frecuencia", col="blue") #no existe distribución normal
summary(Baseeuropa$OBS.VALUE) #única variable numérica## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.0915 3.6244 6.1800 6.5488 9.6459 15.4353#construiremos un diagrama con nuestra variable numérica
round(Baseeuropa$OBS.VALUE)## [1] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [26] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9
## [51] 10 10 10 10 11 11 11 11 10 11 10 10 10 10 10 9 9 9 10 9 9 9 9 9 10
## [76] 10 10 10 10 10 10 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [101] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 12 12 13 13 12
## [126] 12 12 12 12 13 13 13 13 13 14 14 15 15 15 15 15 15 15 15 15 15 15 14 14 15
## [151] 15 14 14 14 14 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 12 12 13 12 12
## [176] 12 12 12 11 11 11 11 11 11 11 11 11 11 11 10 10 10 10 10 9 9 8 9 9 8
## [201] 8 8 9 8 8 9 8 8 9 9 9 10 10 10 9 9 9 9 9 9 9 9 9 9 9
## [226] 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11
## [251] 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [276] 10 10 9 9 9 9 9 8 8 8 7 7 7 7 7 7 7 8 8 8 9 9 9 9 9
## [301] 9 9 9 9 9 9 9 8 8 8 8 8 7 8 8 8 7 8 7 7 7 7 6 6 6
## [326] 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4
## [351] 4 4 4 5 5 5 5 5 5 5 6 6 5 5 6 5 5 5 5 5 5 5 5 5 5
## [376] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4
## [401] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3
## [426] 3 3 3 3 3 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5
## [451] 5 4 4 4 4 4 4 4 4 4 4 5 5 4 5 4 4 4 4 4 4 4 4 4 4
## [476] 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 4 4 4 4 4 5 4 4 5 4
## [501] 4 4 4 4 4 4 3 3 4 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3 3
## [526] 3 3 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 2 2 1 1 1
## [551] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [576] 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 0 0 0 0 0 0
## [601] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [626] 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 4 4 3 3 3 3
## [651] 3 3 3 3 3diagrama1 <- table(round(Baseeuropa$OBS.VALUE))
barplot(diagrama1) #podemos ver la frecuencua por intervalos
filtro1 <- filter(Baseeuropa, `TIME PERIOD` == "Jan 1970")
head(filtro1) ## # A tibble: 1 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 1970-01-31 Jan 1970 7.75 A NA FM.M.U2.EUR.4… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …filtro2 <- subset(Baseeuropa, DATE == "1974-08-31")
head(filtro2) ## # A tibble: 1 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 1974-08-31 Aug 1974 10.7 A NA FM.M.U2.EUR.4… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …filtro3 <- subset(Baseeuropa, TITLE == "Euro Area 10 Years Government Benchmark Bond - Yield")
head(filtro3) ## # A tibble: 6 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 1970-01-31 Jan 1970 7.75 A NA FM.M.U2.EUR.4… Euro…
## 2 1970-02-28 Feb 1970 7.90 A NA FM.M.U2.EUR.4… Euro…
## 3 1970-03-31 Mar 1970 8.11 A NA FM.M.U2.EUR.4… Euro…
## 4 1970-04-30 Apr 1970 8.07 A NA FM.M.U2.EUR.4… Euro…
## 5 1970-05-31 May 1970 8.24 A NA FM.M.U2.EUR.4… Euro…
## 6 1970-06-30 Jun 1970 8.45 A NA FM.M.U2.EUR.4… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …filtro4 <- subset(Baseeuropa, OBS.VALUE > 3)
head(filtro4)## # A tibble: 6 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 1970-01-31 Jan 1970 7.75 A NA FM.M.U2.EUR.4… Euro…
## 2 1970-02-28 Feb 1970 7.90 A NA FM.M.U2.EUR.4… Euro…
## 3 1970-03-31 Mar 1970 8.11 A NA FM.M.U2.EUR.4… Euro…
## 4 1970-04-30 Apr 1970 8.07 A NA FM.M.U2.EUR.4… Euro…
## 5 1970-05-31 May 1970 8.24 A NA FM.M.U2.EUR.4… Euro…
## 6 1970-06-30 Jun 1970 8.45 A NA FM.M.U2.EUR.4… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …filtro5 <- subset(Baseeuropa, OBS.VALUE > 5 )
head(filtro5)## # A tibble: 6 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 1970-01-31 Jan 1970 7.75 A NA FM.M.U2.EUR.4… Euro…
## 2 1970-02-28 Feb 1970 7.90 A NA FM.M.U2.EUR.4… Euro…
## 3 1970-03-31 Mar 1970 8.11 A NA FM.M.U2.EUR.4… Euro…
## 4 1970-04-30 Apr 1970 8.07 A NA FM.M.U2.EUR.4… Euro…
## 5 1970-05-31 May 1970 8.24 A NA FM.M.U2.EUR.4… Euro…
## 6 1970-06-30 Jun 1970 8.45 A NA FM.M.U2.EUR.4… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …tail(filtro5)## # A tibble: 6 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 2002-02-28 Feb 2002 5.07 A NA FM.M.U2.EUR.4… Euro…
## 2 2002-03-31 Mar 2002 5.32 A NA FM.M.U2.EUR.4… Euro…
## 3 2002-04-30 Apr 2002 5.30 A NA FM.M.U2.EUR.4… Euro…
## 4 2002-05-31 May 2002 5.30 A NA FM.M.U2.EUR.4… Euro…
## 5 2002-06-30 Jun 2002 5.16 A NA FM.M.U2.EUR.4… Euro…
## 6 2002-07-31 Jul 2002 5.03 A NA FM.M.U2.EUR.4… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …filtro6 <- subset(Baseeuropa, OBS.VALUE>14)
print(filtro6)## # A tibble: 16 × 21
## DATE `TIME PERIOD` OBS.VALUE OBS.STATUS OBS.COMMENT `SERIES KEY` TITLE
## <chr> <chr> <dbl> <chr> <lgl> <chr> <chr>
## 1 1981-05-31 May 1981 14.7 A NA FM.M.U2.EUR.… Euro…
## 2 1981-06-30 Jun 1981 14.9 A NA FM.M.U2.EUR.… Euro…
## 3 1981-07-31 Jul 1981 15.0 A NA FM.M.U2.EUR.… Euro…
## 4 1981-08-31 Aug 1981 15.2 A NA FM.M.U2.EUR.… Euro…
## 5 1981-09-30 Sep 1981 15.4 A NA FM.M.U2.EUR.… Euro…
## 6 1981-10-31 Oct 1981 15.2 A NA FM.M.U2.EUR.… Euro…
## 7 1981-11-30 Nov 1981 15.2 A NA FM.M.U2.EUR.… Euro…
## 8 1981-12-31 Dec 1981 14.9 A NA FM.M.U2.EUR.… Euro…
## 9 1982-01-31 Jan 1982 14.8 A NA FM.M.U2.EUR.… Euro…
## 10 1982-02-28 Feb 1982 14.9 A NA FM.M.U2.EUR.… Euro…
## 11 1982-03-31 Mar 1982 14.6 A NA FM.M.U2.EUR.… Euro…
## 12 1982-04-30 Apr 1982 14.5 A NA FM.M.U2.EUR.… Euro…
## 13 1982-05-31 May 1982 14.4 A NA FM.M.U2.EUR.… Euro…
## 14 1982-06-30 Jun 1982 14.6 A NA FM.M.U2.EUR.… Euro…
## 15 1982-07-31 Jul 1982 14.6 A NA FM.M.U2.EUR.… Euro…
## 16 1982-08-31 Aug 1982 14.2 A NA FM.M.U2.EUR.… Euro…
## # ℹ 14 more variables: FREQUENCY <chr>, `FREQUENCY (DESC.)` <chr>,
## # `REFERENCE AREA` <chr>, `REFERENCE AREA (DESC.)` <chr>, CURRENCY <chr>,
## # `CURRENCY (DESC.)` <chr>, `FINANCIAL MARKET PROVIDER` <chr>,
## # `FINANCIAL MARKET PROVIDER (DESC.)` <chr>,
## # `FINANCIAL MARKET INSTRUMENT` <chr>,
## # `FINANCIAL MARKET INSTRUMENT (DESC.)` <chr>,
## # `FINANCIAL MARKET PROVIDER IDENTIFIER` <chr>, …table(Baseeuropa$`TIME PERIOD`)##
## Apr 1970 Apr 1971 Apr 1972 Apr 1973 Apr 1974 Apr 1975 Apr 1976 Apr 1977
## 1 1 1 1 1 1 1 1
## Apr 1978 Apr 1979 Apr 1980 Apr 1981 Apr 1982 Apr 1983 Apr 1984 Apr 1985
## 1 1 1 1 1 1 1 1
## Apr 1986 Apr 1987 Apr 1988 Apr 1989 Apr 1990 Apr 1991 Apr 1992 Apr 1993
## 1 1 1 1 1 1 1 1
## Apr 1994 Apr 1995 Apr 1996 Apr 1997 Apr 1998 Apr 1999 Apr 2000 Apr 2001
## 1 1 1 1 1 1 1 1
## Apr 2002 Apr 2003 Apr 2004 Apr 2005 Apr 2006 Apr 2007 Apr 2008 Apr 2009
## 1 1 1 1 1 1 1 1
## Apr 2010 Apr 2011 Apr 2012 Apr 2013 Apr 2014 Apr 2015 Apr 2016 Apr 2017
## 1 1 1 1 1 1 1 1
## Apr 2018 Apr 2019 Apr 2020 Apr 2021 Apr 2022 Apr 2023 Apr 2024 Aug 1970
## 1 1 1 1 1 1 1 1
## Aug 1971 Aug 1972 Aug 1973 Aug 1974 Aug 1975 Aug 1976 Aug 1977 Aug 1978
## 1 1 1 1 1 1 1 1
## Aug 1979 Aug 1980 Aug 1981 Aug 1982 Aug 1983 Aug 1984 Aug 1985 Aug 1986
## 1 1 1 1 1 1 1 1
## Aug 1987 Aug 1988 Aug 1989 Aug 1990 Aug 1991 Aug 1992 Aug 1993 Aug 1994
## 1 1 1 1 1 1 1 1
## Aug 1995 Aug 1996 Aug 1997 Aug 1998 Aug 1999 Aug 2000 Aug 2001 Aug 2002
## 1 1 1 1 1 1 1 1
## Aug 2003 Aug 2004 Aug 2005 Aug 2006 Aug 2007 Aug 2008 Aug 2009 Aug 2010
## 1 1 1 1 1 1 1 1
## Aug 2011 Aug 2012 Aug 2013 Aug 2014 Aug 2015 Aug 2016 Aug 2017 Aug 2018
## 1 1 1 1 1 1 1 1
## Aug 2019 Aug 2020 Aug 2021 Aug 2022 Aug 2023 Dec 1970 Dec 1971 Dec 1972
## 1 1 1 1 1 1 1 1
## Dec 1973 Dec 1974 Dec 1975 Dec 1976 Dec 1977 Dec 1978 Dec 1979 Dec 1980
## 1 1 1 1 1 1 1 1
## Dec 1981 Dec 1982 Dec 1983 Dec 1984 Dec 1985 Dec 1986 Dec 1987 Dec 1988
## 1 1 1 1 1 1 1 1
## Dec 1989 Dec 1990 Dec 1991 Dec 1992 Dec 1993 Dec 1994 Dec 1995 Dec 1996
## 1 1 1 1 1 1 1 1
## Dec 1997 Dec 1998 Dec 1999 Dec 2000 Dec 2001 Dec 2002 Dec 2003 Dec 2004
## 1 1 1 1 1 1 1 1
## Dec 2005 Dec 2006 Dec 2007 Dec 2008 Dec 2009 Dec 2010 Dec 2011 Dec 2012
## 1 1 1 1 1 1 1 1
## Dec 2013 Dec 2014 Dec 2015 Dec 2016 Dec 2017 Dec 2018 Dec 2019 Dec 2020
## 1 1 1 1 1 1 1 1
## Dec 2021 Dec 2022 Dec 2023 Feb 1970 Feb 1971 Feb 1972 Feb 1973 Feb 1974
## 1 1 1 1 1 1 1 1
## Feb 1975 Feb 1976 Feb 1977 Feb 1978 Feb 1979 Feb 1980 Feb 1981 Feb 1982
## 1 1 1 1 1 1 1 1
## Feb 1983 Feb 1984 Feb 1985 Feb 1986 Feb 1987 Feb 1988 Feb 1989 Feb 1990
## 1 1 1 1 1 1 1 1
## Feb 1991 Feb 1992 Feb 1993 Feb 1994 Feb 1995 Feb 1996 Feb 1997 Feb 1998
## 1 1 1 1 1 1 1 1
## Feb 1999 Feb 2000 Feb 2001 Feb 2002 Feb 2003 Feb 2004 Feb 2005 Feb 2006
## 1 1 1 1 1 1 1 1
## Feb 2007 Feb 2008 Feb 2009 Feb 2010 Feb 2011 Feb 2012 Feb 2013 Feb 2014
## 1 1 1 1 1 1 1 1
## Feb 2015 Feb 2016 Feb 2017 Feb 2018 Feb 2019 Feb 2020 Feb 2021 Feb 2022
## 1 1 1 1 1 1 1 1
## Feb 2023 Feb 2024 Jan 1970 Jan 1971 Jan 1972 Jan 1973 Jan 1974 Jan 1975
## 1 1 1 1 1 1 1 1
## Jan 1976 Jan 1977 Jan 1978 Jan 1979 Jan 1980 Jan 1981 Jan 1982 Jan 1983
## 1 1 1 1 1 1 1 1
## Jan 1984 Jan 1985 Jan 1986 Jan 1987 Jan 1988 Jan 1989 Jan 1990 Jan 1991
## 1 1 1 1 1 1 1 1
## Jan 1992 Jan 1993 Jan 1994 Jan 1995 Jan 1996 Jan 1997 Jan 1998 Jan 1999
## 1 1 1 1 1 1 1 1
## Jan 2000 Jan 2001 Jan 2002 Jan 2003 Jan 2004 Jan 2005 Jan 2006 Jan 2007
## 1 1 1 1 1 1 1 1
## Jan 2008 Jan 2009 Jan 2010 Jan 2011 Jan 2012 Jan 2013 Jan 2014 Jan 2015
## 1 1 1 1 1 1 1 1
## Jan 2016 Jan 2017 Jan 2018 Jan 2019 Jan 2020 Jan 2021 Jan 2022 Jan 2023
## 1 1 1 1 1 1 1 1
## Jan 2024 Jul 1970 Jul 1971 Jul 1972 Jul 1973 Jul 1974 Jul 1975 Jul 1976
## 1 1 1 1 1 1 1 1
## Jul 1977 Jul 1978 Jul 1979 Jul 1980 Jul 1981 Jul 1982 Jul 1983 Jul 1984
## 1 1 1 1 1 1 1 1
## Jul 1985 Jul 1986 Jul 1987 Jul 1988 Jul 1989 Jul 1990 Jul 1991 Jul 1992
## 1 1 1 1 1 1 1 1
## Jul 1993 Jul 1994 Jul 1995 Jul 1996 Jul 1997 Jul 1998 Jul 1999 Jul 2000
## 1 1 1 1 1 1 1 1
## Jul 2001 Jul 2002 Jul 2003 Jul 2004 Jul 2005 Jul 2006 Jul 2007 Jul 2008
## 1 1 1 1 1 1 1 1
## Jul 2009 Jul 2010 Jul 2011 Jul 2012 Jul 2013 Jul 2014 Jul 2015 Jul 2016
## 1 1 1 1 1 1 1 1
## Jul 2017 Jul 2018 Jul 2019 Jul 2020 Jul 2021 Jul 2022 Jul 2023 Jul 2024
## 1 1 1 1 1 1 1 1
## Jun 1970 Jun 1971 Jun 1972 Jun 1973 Jun 1974 Jun 1975 Jun 1976 Jun 1977
## 1 1 1 1 1 1 1 1
## Jun 1978 Jun 1979 Jun 1980 Jun 1981 Jun 1982 Jun 1983 Jun 1984 Jun 1985
## 1 1 1 1 1 1 1 1
## Jun 1986 Jun 1987 Jun 1988 Jun 1989 Jun 1990 Jun 1991 Jun 1992 Jun 1993
## 1 1 1 1 1 1 1 1
## Jun 1994 Jun 1995 Jun 1996 Jun 1997 Jun 1998 Jun 1999 Jun 2000 Jun 2001
## 1 1 1 1 1 1 1 1
## Jun 2002 Jun 2003 Jun 2004 Jun 2005 Jun 2006 Jun 2007 Jun 2008 Jun 2009
## 1 1 1 1 1 1 1 1
## Jun 2010 Jun 2011 Jun 2012 Jun 2013 Jun 2014 Jun 2015 Jun 2016 Jun 2017
## 1 1 1 1 1 1 1 1
## Jun 2018 Jun 2019 Jun 2020 Jun 2021 Jun 2022 Jun 2023 Jun 2024 Mar 1970
## 1 1 1 1 1 1 1 1
## Mar 1971 Mar 1972 Mar 1973 Mar 1974 Mar 1975 Mar 1976 Mar 1977 Mar 1978
## 1 1 1 1 1 1 1 1
## Mar 1979 Mar 1980 Mar 1981 Mar 1982 Mar 1983 Mar 1984 Mar 1985 Mar 1986
## 1 1 1 1 1 1 1 1
## Mar 1987 Mar 1988 Mar 1989 Mar 1990 Mar 1991 Mar 1992 Mar 1993 Mar 1994
## 1 1 1 1 1 1 1 1
## Mar 1995 Mar 1996 Mar 1997 Mar 1998 Mar 1999 Mar 2000 Mar 2001 Mar 2002
## 1 1 1 1 1 1 1 1
## Mar 2003 Mar 2004 Mar 2005 Mar 2006 Mar 2007 Mar 2008 Mar 2009 Mar 2010
## 1 1 1 1 1 1 1 1
## Mar 2011 Mar 2012 Mar 2013 Mar 2014 Mar 2015 Mar 2016 Mar 2017 Mar 2018
## 1 1 1 1 1 1 1 1
## Mar 2019 Mar 2020 Mar 2021 Mar 2022 Mar 2023 Mar 2024 May 1970 May 1971
## 1 1 1 1 1 1 1 1
## May 1972 May 1973 May 1974 May 1975 May 1976 May 1977 May 1978 May 1979
## 1 1 1 1 1 1 1 1
## May 1980 May 1981 May 1982 May 1983 May 1984 May 1985 May 1986 May 1987
## 1 1 1 1 1 1 1 1
## May 1988 May 1989 May 1990 May 1991 May 1992 May 1993 May 1994 May 1995
## 1 1 1 1 1 1 1 1
## May 1996 May 1997 May 1998 May 1999 May 2000 May 2001 May 2002 May 2003
## 1 1 1 1 1 1 1 1
## May 2004 May 2005 May 2006 May 2007 May 2008 May 2009 May 2010 May 2011
## 1 1 1 1 1 1 1 1
## May 2012 May 2013 May 2014 May 2015 May 2016 May 2017 May 2018 May 2019
## 1 1 1 1 1 1 1 1
## May 2020 May 2021 May 2022 May 2023 May 2024 Nov 1970 Nov 1971 Nov 1972
## 1 1 1 1 1 1 1 1
## Nov 1973 Nov 1974 Nov 1975 Nov 1976 Nov 1977 Nov 1978 Nov 1979 Nov 1980
## 1 1 1 1 1 1 1 1
## Nov 1981 Nov 1982 Nov 1983 Nov 1984 Nov 1985 Nov 1986 Nov 1987 Nov 1988
## 1 1 1 1 1 1 1 1
## Nov 1989 Nov 1990 Nov 1991 Nov 1992 Nov 1993 Nov 1994 Nov 1995 Nov 1996
## 1 1 1 1 1 1 1 1
## Nov 1997 Nov 1998 Nov 1999 Nov 2000 Nov 2001 Nov 2002 Nov 2003 Nov 2004
## 1 1 1 1 1 1 1 1
## Nov 2005 Nov 2006 Nov 2007 Nov 2008 Nov 2009 Nov 2010 Nov 2011 Nov 2012
## 1 1 1 1 1 1 1 1
## Nov 2013 Nov 2014 Nov 2015 Nov 2016 Nov 2017 Nov 2018 Nov 2019 Nov 2020
## 1 1 1 1 1 1 1 1
## Nov 2021 Nov 2022 Nov 2023 Oct 1970 Oct 1971 Oct 1972 Oct 1973 Oct 1974
## 1 1 1 1 1 1 1 1
## Oct 1975 Oct 1976 Oct 1977 Oct 1978 Oct 1979 Oct 1980 Oct 1981 Oct 1982
## 1 1 1 1 1 1 1 1
## Oct 1983 Oct 1984 Oct 1985 Oct 1986 Oct 1987 Oct 1988 Oct 1989 Oct 1990
## 1 1 1 1 1 1 1 1
## Oct 1991 Oct 1992 Oct 1993 Oct 1994 Oct 1995 Oct 1996 Oct 1997 Oct 1998
## 1 1 1 1 1 1 1 1
## Oct 1999 Oct 2000 Oct 2001 Oct 2002 Oct 2003 Oct 2004 Oct 2005 Oct 2006
## 1 1 1 1 1 1 1 1
## Oct 2007 Oct 2008 Oct 2009 Oct 2010 Oct 2011 Oct 2012 Oct 2013 Oct 2014
## 1 1 1 1 1 1 1 1
## Oct 2015 Oct 2016 Oct 2017 Oct 2018 Oct 2019 Oct 2020 Oct 2021 Oct 2022
## 1 1 1 1 1 1 1 1
## Oct 2023 Sep 1970 Sep 1971 Sep 1972 Sep 1973 Sep 1974 Sep 1975 Sep 1976
## 1 1 1 1 1 1 1 1
## Sep 1977 Sep 1978 Sep 1979 Sep 1980 Sep 1981 Sep 1982 Sep 1983 Sep 1984
## 1 1 1 1 1 1 1 1
## Sep 1985 Sep 1986 Sep 1987 Sep 1988 Sep 1989 Sep 1990 Sep 1991 Sep 1992
## 1 1 1 1 1 1 1 1
## Sep 1993 Sep 1994 Sep 1995 Sep 1996 Sep 1997 Sep 1998 Sep 1999 Sep 2000
## 1 1 1 1 1 1 1 1
## Sep 2001 Sep 2002 Sep 2003 Sep 2004 Sep 2005 Sep 2006 Sep 2007 Sep 2008
## 1 1 1 1 1 1 1 1
## Sep 2009 Sep 2010 Sep 2011 Sep 2012 Sep 2013 Sep 2014 Sep 2015 Sep 2016
## 1 1 1 1 1 1 1 1
## Sep 2017 Sep 2018 Sep 2019 Sep 2020 Sep 2021 Sep 2022 Sep 2023
## 1 1 1 1 1 1 1table(Baseeuropa$OBS.STATUS)##
## A
## 655table(Baseeuropa$`REFERENCE AREA`)##
## U2
## 655table(Baseeuropa$OBS.VALUE[361:372]) #año 2000##
## 5.068 5.3357 5.3454 5.3982 5.4138 5.4163 5.4537 5.4672 5.4906 5.5155 5.6636
## 1 1 1 1 1 1 1 1 1 1 1
## 5.6985
## 1table(Baseeuropa$DATE[601:612]) #año 2020##
## 2020-01-31 2020-02-29 2020-03-31 2020-04-30 2020-05-31 2020-06-30 2020-07-31
## 1 1 1 1 1 1 1
## 2020-08-31 2020-09-30 2020-10-31 2020-11-30 2020-12-31
## 1 1 1 1 1table(Baseeuropa$OBS.VALUE > 6.5)##
## FALSE TRUE
## 333 322G1 <- Baseeuropa$OBS.VALUE[1:12]
hist(G1, main = "Histograma del año 1970", xlab = "Datos de interés", ylab = "Frecuencia", col = "lightgreen")
G2 <- Baseeuropa$OBS.VALUE[13:84]
barplot(G2, main = "1971-1976", ylab = "INTERÉS", col = "lightblue")
G3 <- Baseeuropa$OBS.VALUE[457:480]
barplot(G3, main = "2008-2009", ylab = "INTERÉS", col = "lightyellow")
G4 <- Baseeuropa$OBS.VALUE[601:612]
barplot(G4, main = "2020", ylab = "INTERÉS", col = "red")
G5 <- Baseeuropa$OBS.VALUE[601:655]
barplot(G5, main = "2020-PRESENTE", ylab = "INTERÉS", col = "black")
N_A <- table(is.na(Baseeuropa))
N_A #La cantidad de TRUE es la cantidad de NA's en la base de datos ##
## FALSE TRUE
## 13100 655#todos los NA's pertenecen a la variable "OBS.COMMENT"boxplot(Baseeuropa$OBS.VALUE, main= "Identificación de valores atípicos", ylab= "Valores de Interés", col = "red")
plot(Baseeuropa$OBS.VALUE, main= "Identificación de valores atípicos", xlab = "1970-2024", ylab= "Valores de Interés") 
#Posible datos atípicos
atípicos <- abs(Baseeuropa$OBS.VALUE) > media + desviación
print(table(atípicos))## atípicos
## FALSE TRUE
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