Reto 1

#Reto N°1: Series de tiempo y Graficas con datos categoricos. -Integrantes -Juan Cardozo -Wendy Ortega -Mateo fontalvo -Daniela Albor

#Series de tiempo.

library(readr)
## Attaching package: 'readr'
## The following object is masked from 'package:scales':
##
##     col_factor

library(knitr)
df <- read_csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/annual_csv.csv")

## Rows: 274 Columns: 3

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): Source
## dbl (2): Year, Mean

## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## -- Column specification --------------------------------------------------------
## cols(
##   Source = col_character(),
##   Year = col_double(),
##   Mean = col_double()
## )

knitr::kable(head(df))

Source	Year	Mean
GCAG	2016	0.9363
GISTEMP	2016	0.9900
GCAG	2015	0.8998
GISTEMP	2015	0.8700
GCAG	2014	0.7408
GISTEMP	2014	0.7400

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

ggplot(df, aes(x = Year, y = Mean)) +
  geom_line(color="#69b3a2", size = 1) +
  xlab("Year")

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