1 Load library

library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0     ✔ purrr   0.3.0
✔ tibble  2.0.1     ✔ dplyr   0.8.0
✔ tidyr   0.8.2     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.3.0
── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(janitor)

Hourly Weather data downloaded from http://www.frontierweather.com/historicaldatasubscribers_hourly.html

Daily Weather data downloaded from http://www.frontierweather.com/weatherdatastore.html

2 Daily weather data

2.1 Read the Houston daily weather data

houston_weather <- read_delim("weather_data/KIAH_daily.txt", delim = ",")
Parsed with column specification:
cols(
  Site4 = col_character(),
  Date = col_character(),
  Source = col_character(),
  `Max Temp` = col_double(),
  `Min Temp` = col_double(),
  `Avg Temp` = col_double(),
  HDDs = col_double(),
  CDDs = col_double(),
  `Precipitation Water Equiv` = col_double(),
  Snowfall = col_double(),
  `Snow/Ice Depth` = col_double()
)
  • Find out what these columns mean.
    • Site4 : Name of the site

    • Date : Date when the data was collected

    • Source : Name of their instrument

    • Max Temp : Maximum temperature of the day

    • Min Temp : Minimum temperature of the day

    • Avg Temp : Average temperature (Max + Min)/2

    • HDDs : Heating degree day (Temps below 65) https://w1.weather.gov/glossary/index.php?letter=h

    • CDDs : Cooling degree day (Temps above 65) - A form of degree day used to estimate energy requirements for air conditioning or refrigeration. For example, if a location experiences a mean temperature of 75°F on a certain day, there were 10 CDD (Cooling Degree Days) that day because 75 - 65 = 10.) https://w1.weather.gov/glossary/index.php?letter=c

    • Precipitation Water Equiv : Rain in inches

    • Snowfall : Amount of snowfall since 24 hours (https://www.weather.gov/gsp/snow)

    • Snow/Ice Depth : The depth of the new and old snow remaining on the ground at observation time (https://www.weather.gov/gsp/snow)

2.1.1 Find out more about this function

??read_delim

2.2 “Format” the data

houston_weather_df <- houston_weather %>%
  mutate(Date=as.Date(Date,format='%m/%d/%Y')) %>%
  separate(Date,c("year","month","day")) %>%
  janitor::clean_names()

2.3 Read Chicago daily weather data

chicago_weather <- read_csv("weather_data/KORD_daily.txt") 
Parsed with column specification:
cols(
  Site4 = col_character(),
  Date = col_character(),
  Source = col_character(),
  `Max Temp` = col_double(),
  `Min Temp` = col_double(),
  `Avg Temp` = col_double(),
  HDDs = col_double(),
  CDDs = col_double(),
  `Precipitation Water Equiv` = col_double(),
  Snowfall = col_double(),
  `Snow/Ice Depth` = col_double()
)

Notice we have used different function but without explicitly mentioning delimiter.

2.4 “Format” Chicago weather

chicago_weather_df <- chicago_weather %>%
  mutate(Date=as.Date(Date,format='%m/%d/%Y')) %>%
  separate(Date,c("year","month","day")) %>%
  janitor::clean_names()

2.5 Get Seattle weather data and format

seattle_weather_df <- read_csv("weather_data/KSEA_daily.txt")  %>%
  mutate(Date = as.Date(Date, format='%m/%d/%Y')) %>%
  separate(Date, c("year","month","day")) %>%
  janitor::clean_names()
Parsed with column specification:
cols(
  Site4 = col_character(),
  Date = col_character(),
  Source = col_character(),
  `Max Temp` = col_double(),
  `Min Temp` = col_double(),
  `Avg Temp` = col_double(),
  HDDs = col_double(),
  CDDs = col_double(),
  `Precipitation Water Equiv` = col_double(),
  Snowfall = col_double(),
  `Snow/Ice Depth` = col_double()
)

2.6 Comparing mean temperature between Houston and Chicago

houston_weather_df %>%
  bind_rows(., chicago_weather_df, seattle_weather_df) %>%
  group_by(site4, year) %>%
  summarise(mean_temp_year=mean(avg_temp)) %>%
  ungroup() %>%
  ggplot(.,aes(x = as.numeric(year), y = mean_temp_year, color = site4)) +
  geom_line(lwd = 2) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
  theme_bw()

Fix the x axis label and the legend??

2.7 Plot Histogram

houston_weather_df %>%
  bind_rows(., chicago_weather_df, seattle_weather_df) %>%
  ggplot(., aes(x=avg_temp, fill=site4, color=site4)) +
  geom_histogram(position="identity",  alpha = 0.5)

  geom_histogram(position="identity")
geom_bar: na.rm = FALSE
stat_bin: binwidth = NULL, bins = NULL, na.rm = FALSE, pad = FALSE
position_identity 

2.8 Plot Empirical Cumulative Distribution Function

houston_weather_df %>%
  bind_rows(., chicago_weather_df, seattle_weather_df) %>%
  ggplot(.,aes(avg_temp, color = site4)) +
  stat_ecdf(geom = "point") 

A CDF is a function such as y=f(x) where y is the probability of the number x, or any lower number, being chosen at random from that distribution.

source: https://www.che.utah.edu/~tony/course/material/Statistics/18_rv_pdf_cdf.php

Here we are talking about ECDF “Empirical” CDF. This means this is not a representation of the entire set (or theoretical distribution) but what was observed and hence the word “empirical”.

2.9 Stem and leaf plot

stem(houston_weather_df$max_temp)

  The decimal point is 1 digit(s) to the right of the |

   2 | 788889
   3 | 0011112223333333344444444
   3 | 5555666666666677777777777777888888888888999999999999999
   4 | 00000000000000000001111111111111111111111222222222222222222222222222+74
   4 | 55555555555555555555555555555555555555555555555555555556666666666666+210
   5 | 00000000000000000000000000000000000000000000000000000000000000000000+395
   5 | 55555555555555555555555555555555555555555555555555555555555555555555+632
   6 | 00000000000000000000000000000000000000000000000000000000000000000000+850
   6 | 55555555555555555555555555555555555555555555555555555555555555555555+1193
   7 | 00000000000000000000000000000000000000000000000000000000000000000000+1615
   7 | 55555555555555555555555555555555555555555555555555555555555555555555+2158
   8 | 00000000000000000000000000000000000000000000000000000000000000000000+2383
   8 | 55555555555555555555555555555555555555555555555555555555555555555555+2507
   9 | 00000000000000000000000000000000000000000000000000000000000000000000+3016
   9 | 55555555555555555555555555555555555555555555555555555555555555555555+1743
  10 | 00000000000000000000000000000000000000000000000000000000000000000000+167
  10 | 55555566777777899

It can be informative but compared to histogram or ecdf plot, it can be hard to read.

2.10 See the spread of the temperature by month

houston_weather_df %>%
  bind_rows(chicago_weather_df, seattle_weather_df) %>%
  group_by(site4,month) %>%
  summarise(max = max(max_temp,na.rm = T), 
            min = min(min_temp, na.rm = T), 
            mean = mean(avg_temp, na.rm = T)) %>%
  ungroup() %>%
  ggplot(., aes(x = month, y = mean)) +
  geom_linerange(aes( ymin = min, ymax = max), lwd = 2, color = "#FF5400") +
  geom_point(size = 3, color = "#2142A6") +
  coord_flip() +
  theme_bw(base_size = 14) +
  geom_hline(yintercept = 65, linetype = "dashed") +
  xlab("Month") +
  ylab("Temperature in Farahenit") +
  facet_grid(.~site4)

3 Percent drop as compare to previous month.

houston_weather_df %>%
  bind_rows(chicago_weather_df) %>%
  bind_rows(seattle_weather_df) %>%
  group_by(site4, month) %>%
  summarise(mean_avg_temp = mean(avg_temp,na.rm = T)) %>%
  mutate(pct_change = (mean_avg_temp / lag(mean_avg_temp) - 1) * 100) %>%
  mutate(pct_change = case_when(
    is.na(pct_change) ~ 0,
    TRUE ~ as.numeric(pct_change)
  )) %>%
  arrange(pct_change) %>% 
  ggplot(.,aes(x = month, y = pct_change, color = site4)) +
  geom_line(aes(group = site4), size = 1)+
  geom_point() +
  theme_bw()

3.1 Where does it rain the most?

houston_weather_df %>%
  bind_rows(chicago_weather_df, seattle_weather_df) %>%
  group_by(site4,year) %>%
  summarise(sum = sum(precipitation_water_equiv, na.rm = T)) %>%
  ungroup() %>%
  ggplot(., aes(x = year, y = sum, color = site4) ) +
  geom_point() +
  geom_line(aes(group = site4), size = 1)+
  theme_bw(base_size = 14) +
  theme(axis.text.x = element_text(angle = 65, hjust = 1)) +
  ylab("Rainfall in inches") 

This graph shows total rainfall in inches over the years.

4 Average rainfall with the circles showing the maximum recorded rainfall for single day

houston_weather_df %>%
  bind_rows(chicago_weather_df, seattle_weather_df) %>%
  group_by(site4,month) %>%
  summarise(max = max(precipitation_water_equiv,na.rm = T), 
            mean = mean(precipitation_water_equiv, na.rm = T)) %>%
  ungroup() %>%
  ggplot(. ) +
  geom_point(aes(x = month, y = mean,size = max), color = "#2142A6") +
  coord_flip() +
  theme_bw(base_size = 14) +
  xlab("Month") +
  ylab("Average rain fall in inches") +
  facet_wrap(.~site4)

5 Hourly weather data

5.1 Read Houston hourly weather data

houston_weather_hourly <- read_csv("weather_data/KIAH.txt") %>%
  mutate(date=as.Date(Date,format='%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  janitor::clean_names()
Parsed with column specification:
cols(
  Site = col_character(),
  Date = col_character(),
  Hour = col_double(),
  Temperature = col_double(),
  Dewpoint = col_double(),
  RH = col_double(),
  WindDir = col_logical(),
  Windspeed = col_double(),
  CldFrac = col_double(),
  MSLP = col_logical(),
  Weather = col_character(),
  Precip = col_logical(),
  Source = col_character()
)
435615 parsing failures.
 row    col           expected  actual                    file
4296 Precip 1/0/T/F/TRUE/FALSE 0.00    'weather_data/KIAH.txt'
7237 Precip 1/0/T/F/TRUE/FALSE 0.00    'weather_data/KIAH.txt'
8392 Precip 1/0/T/F/TRUE/FALSE 0.00    'weather_data/KIAH.txt'
8760 Precip 1/0/T/F/TRUE/FALSE 0.00    'weather_data/KIAH.txt'
8761 Precip 1/0/T/F/TRUE/FALSE 1.0e-04 'weather_data/KIAH.txt'
.... ...... .................. ....... .......................
See problems(...) for more details.

5.2 Read Chicago Hourly weather data

chicago_weather_hourly <- read_csv("weather_data/KORD.txt") %>%
  mutate(date = as.Date(Date, format = '%m/%d/%Y')) %>%
  separate(date, c("year","month","day")) %>%
  janitor::clean_names()
Parsed with column specification:
cols(
  Site = col_character(),
  Date = col_character(),
  Hour = col_double(),
  Temperature = col_double(),
  Dewpoint = col_double(),
  RH = col_double(),
  WindDir = col_logical(),
  Windspeed = col_double(),
  CldFrac = col_double(),
  MSLP = col_logical(),
  Weather = col_character(),
  Precip = col_logical(),
  Source = col_character()
)
435438 parsing failures.
 row    col           expected  actual                    file
5556 Precip 1/0/T/F/TRUE/FALSE 0.00    'weather_data/KORD.txt'
8760 Precip 1/0/T/F/TRUE/FALSE 0.00    'weather_data/KORD.txt'
8761 Precip 1/0/T/F/TRUE/FALSE 0.00    'weather_data/KORD.txt'
8762 Precip 1/0/T/F/TRUE/FALSE 0.00    'weather_data/KORD.txt'
8763 Precip 1/0/T/F/TRUE/FALSE 1.0e-04 'weather_data/KORD.txt'
.... ...... .................. ....... .......................
See problems(...) for more details.

5.3 Add Seattle hourly weather data

seattle_weather_hourly <- read_csv("weather_data/KSEA.txt") %>%
  mutate(date=as.Date(Date, format = '%m/%d/%Y')) %>%
  separate(date, c("year", "month", "day")) %>%
  janitor::clean_names()
Parsed with column specification:
cols(
  Site = col_character(),
  Date = col_character(),
  Hour = col_double(),
  Temperature = col_double(),
  Dewpoint = col_double(),
  RH = col_double(),
  WindDir = col_logical(),
  Windspeed = col_double(),
  CldFrac = col_double(),
  MSLP = col_logical(),
  Weather = col_character(),
  Precip = col_double(),
  Source = col_character()
)
261428 parsing failures.
  row     col           expected actual                    file
52584 WindDir 1/0/T/F/TRUE/FALSE  120   'weather_data/KSEA.txt'
52584 MSLP    1/0/T/F/TRUE/FALSE  29.74 'weather_data/KSEA.txt'
52585 WindDir 1/0/T/F/TRUE/FALSE  120   'weather_data/KSEA.txt'
52585 MSLP    1/0/T/F/TRUE/FALSE  29.73 'weather_data/KSEA.txt'
52586 WindDir 1/0/T/F/TRUE/FALSE  000   'weather_data/KSEA.txt'
..... ....... .................. ...... .......................
See problems(...) for more details.

5.4 Is Seattle really cloudy as compare to Chicago and Houston?

houston_weather_hourly %>%
  bind_rows(., chicago_weather_hourly,seattle_weather_hourly ) %>%
  mutate(date=as.Date(date,format='%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  group_by(site, year) %>%
  summarise(mean_cloud_coverage = mean(`cld_frac`)) %>%
  ungroup() %>% 
  ggplot(.,aes(x = as.numeric(year), y = mean_cloud_coverage, color = site)) +
  geom_line() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) 

houston_weather_hourly %>%
  bind_rows(., chicago_weather_hourly,seattle_weather_hourly ) %>%
  mutate(date=as.Date(date,format='%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  group_by(site, month) %>%
  summarise(sum_cloud_coverage = sum(`cld_frac`),avg_cloud_coverage = mean(`cld_frac`)) %>%
  ungroup() %>% 
  ggplot(.,aes(x = as.numeric(month), y = sum_cloud_coverage, color = site)) +
  geom_line(linetype = "dotdash") +
  geom_point(aes(inherit.aes=TRUE, size = avg_cloud_coverage)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) 
Ignoring unknown aesthetics: inherit.aes

6 Days with less than 30% average cloud coverage

houston_weather_hourly %>%
  bind_rows(., chicago_weather_hourly,seattle_weather_hourly ) %>%
  mutate(date=as.Date(date,format='%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  group_by(site, year, month, day) %>%
  summarise(avg_cloud_coverage = mean(`cld_frac`)) %>%
  ungroup() %>% 
  group_by(site, year) %>%
  summarise(no_cloudy_days = sum(avg_cloud_coverage < .30)) %>%
  ggplot(.,aes(x = as.numeric(year), y = no_cloudy_days, color = site)) +
  geom_line(linetype=2) +
  geom_point(aes(inherit.aes=TRUE)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
  ylab("Number of days with average cloud cover < 30%") +
  xlab("Year")
Ignoring unknown aesthetics: inherit.aes

6.1 Is Chicago the windiest city in our set?

houston_weather_hourly %>%
  bind_rows(., chicago_weather_hourly,seattle_weather_hourly) %>%
  mutate(date=as.Date(date,format = '%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  group_by(site, year) %>%
  summarise(mean_windspeed = mean(`windspeed`)) %>%
  ungroup() %>% 
  ggplot(.,aes(x = as.numeric(year), y = mean_windspeed, color = site)) +
  geom_line(size = 2) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) 

---
title: "R Notebook - Analyze weather data"
output: 
  html_document:
    df_print: paged
    toc: yes
  html_notebook:
    highlight: haddock
    mathjax: null
    number_sections: yes
    theme: spacelab
    toc: yes
    toc_float: yes
editor_options: 
  chunk_output_type: inline
---

# Load library
```{r}
library(tidyverse)
library(janitor)
```

Hourly Weather data downloaded from http://www.frontierweather.com/historicaldatasubscribers_hourly.html

Daily Weather data downloaded from http://www.frontierweather.com/weatherdatastore.html

# Daily weather data

## Read the Houston daily weather data
```{r}
houston_weather <- read_delim("weather_data/KIAH_daily.txt", delim = ",")
```

* Find out what these columns mean. 
    + Site4 : Name of the site
    
    + Date : Date when the data was collected
    
    + Source : Name of their instrument
    
    + Max Temp : Maximum temperature of the day
    
    + Min Temp : Minimum temperature of the day
    
    + Avg Temp : Average temperature (Max + Min)/2
    
    + HDDs : Heating degree day (Temps below 65) https://w1.weather.gov/glossary/index.php?letter=h
    
    + CDDs : Cooling degree day (Temps above 65) - A form of degree day used to estimate energy requirements for air conditioning or refrigeration.  For example, if a location experiences a mean temperature of 75°F on a certain day, there were 10 CDD (Cooling Degree Days) that day because 75 - 65 = 10.) https://w1.weather.gov/glossary/index.php?letter=c
    
    + Precipitation Water Equiv : Rain in inches
    
    + Snowfall : Amount of snowfall since 24 hours (https://www.weather.gov/gsp/snow)
    
    + Snow/Ice Depth : The depth of the new and old snow remaining on the ground at observation time (https://www.weather.gov/gsp/snow)




### Find out more about this function
```{r}
??read_delim
```


## "Format" the data
```{r}
houston_weather_df <- houston_weather %>%
  mutate(Date=as.Date(Date,format='%m/%d/%Y')) %>%
  separate(Date,c("year","month","day")) %>%
  janitor::clean_names()
```




## Read Chicago daily weather data
```{r}
chicago_weather <- read_csv("weather_data/KORD_daily.txt") 
```

Notice we have used different function but without explicitly mentioning delimiter. 

## "Format" Chicago weather
```{r}
chicago_weather_df <- chicago_weather %>%
  mutate(Date=as.Date(Date,format='%m/%d/%Y')) %>%
  separate(Date,c("year","month","day")) %>%
  janitor::clean_names()
```



## Get Seattle weather data and format
```{r}
seattle_weather_df <- read_csv("weather_data/KSEA_daily.txt")  %>%
  mutate(Date = as.Date(Date, format='%m/%d/%Y')) %>%
  separate(Date, c("year","month","day")) %>%
  janitor::clean_names()
```









## Comparing mean temperature between Houston and Chicago
```{r}
houston_weather_df %>%
  bind_rows(., chicago_weather_df, seattle_weather_df) %>%
  group_by(site4, year) %>%
  summarise(mean_temp_year=mean(avg_temp)) %>%
  ungroup() %>%
  ggplot(.,aes(x = as.numeric(year), y = mean_temp_year, color = site4)) +
  geom_line(lwd = 2) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
  theme_bw()
```

Fix the x axis label and the legend??




## Plot Histogram
```{r}
houston_weather_df %>%
  bind_rows(., chicago_weather_df, seattle_weather_df) %>%
  ggplot(., aes(x=avg_temp, fill=site4, color=site4)) +
  geom_histogram(position="identity",  alpha = 0.5)
  geom_histogram(position="identity")
```


## Plot Empirical Cumulative Distribution Function 
```{r}
houston_weather_df %>%
  bind_rows(., chicago_weather_df, seattle_weather_df) %>%
  ggplot(.,aes(avg_temp, color = site4)) +
  stat_ecdf(geom = "point") 
```


A CDF is a function such as y=f(x) where y is the probability of the number x, or any lower number, being chosen at random from that distribution. 

source: https://www.che.utah.edu/~tony/course/material/Statistics/18_rv_pdf_cdf.php

Here we are talking about ECDF “Empirical” CDF. This means this is not a representation of the entire set (or theoretical distribution) but what was observed and hence the word “empirical”. 



## Stem and leaf plot
```{r}
stem(houston_weather_df$max_temp)
```

It can be informative but compared to histogram or ecdf plot, it can be hard to read. 



## See the spread of the temperature by month
```{r}
houston_weather_df %>%
  bind_rows(chicago_weather_df, seattle_weather_df) %>%
  group_by(site4,month) %>%
  summarise(max = max(max_temp,na.rm = T), 
            min = min(min_temp, na.rm = T), 
            mean = mean(avg_temp, na.rm = T)) %>%
  ungroup() %>%
  ggplot(., aes(x = month, y = mean)) +
  geom_linerange(aes( ymin = min, ymax = max), lwd = 2, color = "#FF5400") +
  geom_point(size = 3, color = "#2142A6") +
  coord_flip() +
  theme_bw(base_size = 14) +
  geom_hline(yintercept = 65, linetype = "dashed") +
  xlab("Month") +
  ylab("Temperature in Farahenit") +
  facet_grid(.~site4)
```




# Percent drop as compare to previous month. 
```{r}
houston_weather_df %>%
  bind_rows(chicago_weather_df) %>%
  bind_rows(seattle_weather_df) %>%
  group_by(site4, month) %>%
  summarise(mean_avg_temp = mean(avg_temp,na.rm = T)) %>%
  mutate(pct_change = (mean_avg_temp / lag(mean_avg_temp) - 1) * 100) %>%
  mutate(pct_change = case_when(
    is.na(pct_change) ~ 0,
    TRUE ~ as.numeric(pct_change)
  )) %>%
  arrange(pct_change) %>% 
  ggplot(.,aes(x = month, y = pct_change, color = site4)) +
  geom_line(aes(group = site4), size = 1)+
  geom_point() +
  theme_bw()
```










## Where does it rain the most?
```{r, fig.width=10}
houston_weather_df %>%
  bind_rows(chicago_weather_df, seattle_weather_df) %>%
  group_by(site4,year) %>%
  summarise(sum = sum(precipitation_water_equiv, na.rm = T)) %>%
  ungroup() %>%
  ggplot(., aes(x = year, y = sum, color = site4) ) +
  geom_point() +
  geom_line(aes(group = site4), size = 1)+
  theme_bw(base_size = 14) +
  theme(axis.text.x = element_text(angle = 65, hjust = 1)) +
  ylab("Rainfall in inches") 
```
This graph shows total rainfall in inches over the years. 




# Average rainfall with the circles showing the maximum recorded rainfall for single day
```{r}
houston_weather_df %>%
  bind_rows(chicago_weather_df, seattle_weather_df) %>%
  group_by(site4,month) %>%
  summarise(max = max(precipitation_water_equiv,na.rm = T), 
            mean = mean(precipitation_water_equiv, na.rm = T)) %>%
  ungroup() %>%
  ggplot(. ) +
  geom_point(aes(x = month, y = mean,size = max), color = "#2142A6") +
  coord_flip() +
  theme_bw(base_size = 14) +
  xlab("Month") +
  ylab("Average rain fall in inches") +
  facet_wrap(.~site4)
```










# Hourly weather data

## Read Houston hourly weather data
```{r}
houston_weather_hourly <- read_csv("weather_data/KIAH.txt") %>%
  mutate(date=as.Date(Date,format='%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  janitor::clean_names()
```


## Read Chicago Hourly weather data
```{r}
chicago_weather_hourly <- read_csv("weather_data/KORD.txt") %>%
  mutate(date = as.Date(Date, format = '%m/%d/%Y')) %>%
  separate(date, c("year","month","day")) %>%
  janitor::clean_names()
```





## Add Seattle hourly weather data
```{r}
seattle_weather_hourly <- read_csv("weather_data/KSEA.txt") %>%
  mutate(date=as.Date(Date, format = '%m/%d/%Y')) %>%
  separate(date, c("year", "month", "day")) %>%
  janitor::clean_names()
```


## Is Seattle really cloudy as compare to Chicago and Houston?
```{r}
houston_weather_hourly %>%
  bind_rows(., chicago_weather_hourly,seattle_weather_hourly ) %>%
  mutate(date=as.Date(date,format='%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  group_by(site, year) %>%
  summarise(mean_cloud_coverage = mean(`cld_frac`)) %>%
  ungroup() %>% 
  ggplot(.,aes(x = as.numeric(year), y = mean_cloud_coverage, color = site)) +
  geom_line() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) 
```


```{r}
houston_weather_hourly %>%
  bind_rows(., chicago_weather_hourly,seattle_weather_hourly ) %>%
  mutate(date=as.Date(date,format='%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  group_by(site, month) %>%
  summarise(sum_cloud_coverage = sum(`cld_frac`),avg_cloud_coverage = mean(`cld_frac`)) %>%
  ungroup() %>% 
  ggplot(.,aes(x = as.numeric(month), y = sum_cloud_coverage, color = site)) +
  geom_line(linetype = "dotdash") +
  geom_point(aes(inherit.aes=TRUE, size = avg_cloud_coverage)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) 
```


#Days with less than 30% average cloud coverage
```{r}
houston_weather_hourly %>%
  bind_rows(., chicago_weather_hourly,seattle_weather_hourly ) %>%
  mutate(date=as.Date(date,format='%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  group_by(site, year, month, day) %>%
  summarise(avg_cloud_coverage = mean(`cld_frac`)) %>%
  ungroup() %>% 
  group_by(site, year) %>%
  summarise(no_cloudy_days = sum(avg_cloud_coverage < .30)) %>%
  ggplot(.,aes(x = as.numeric(year), y = no_cloudy_days, color = site)) +
  geom_line(linetype=2) +
  geom_point(aes(inherit.aes=TRUE)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
  ylab("Number of days with average cloud cover < 30%") +
  xlab("Year")
```









## Is Chicago the windiest city in our set?
```{r}
houston_weather_hourly %>%
  bind_rows(., chicago_weather_hourly,seattle_weather_hourly) %>%
  mutate(date=as.Date(date,format = '%m/%d/%Y')) %>%
  separate(date,c("year","month","day")) %>%
  group_by(site, year) %>%
  summarise(mean_windspeed = mean(`windspeed`)) %>%
  ungroup() %>% 
  ggplot(.,aes(x = as.numeric(year), y = mean_windspeed, color = site)) +
  geom_line(size = 2) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) 
```









