Data Analytics and Computational Social Science: Ethan Campbell HW5

Ethan Campbell

Introduction/Importing data

Importing and viewing the data to determine major cleaning changes that need to be made. Here we notice that all the months are separated and that the PARAMETER column holds all of the unique values. I want to combine the months into one column and then spread the unique values. Once I complete this I can select the columns that I want and then remove the Na values. When I have this setup I will be able to run an analysis on the specific segment that I want.

Fort_Worth <- read.csv("Fort_Worth_climate.csv", skip = 18)

Fort_Worth %>%
  slice(1:12) %>%
  knitr::kable(caption = "Original Table", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 1: Original Table
PARAMETER	YEAR	JAN	FEB	MAR	APR	MAY	JUN	JUL	AUG	SEP	OCT	NOV	DEC	ANN
PS	1981	99.17	99.00	98.53	98.64	98.18	98.29	98.49	98.45	98.65	98.69	98.76	98.81	98.64
PS	1982	98.81	99.11	98.50	98.52	98.27	98.28	98.51	98.53	98.58	98.76	98.83	98.78	98.62
PS	1983	98.91	98.51	98.06	98.08	98.23	98.26	98.63	98.59	98.65	98.80	98.37	99.20	98.53
PS	1984	99.40	98.61	98.42	98.03	98.38	98.36	98.49	98.47	98.72	98.58	98.90	98.89	98.61
PS	1985	99.24	99.08	98.57	98.47	98.26	98.42	98.49	98.42	98.60	98.66	98.61	99.26	98.67
PS	1986	99.22	98.56	98.70	98.41	98.27	98.40	98.58	98.55	98.56	98.87	98.91	99.18	98.69
PS	1987	98.84	98.61	98.53	98.61	98.38	98.49	98.54	98.45	98.56	99.00	98.90	98.79	98.64
PS	1988	99.33	99.13	98.70	98.34	98.42	98.51	98.57	98.36	98.49	98.90	98.49	99.21	98.70
PS	1989	99.06	99.39	98.64	98.53	98.30	98.40	98.60	98.46	98.69	98.81	98.75	99.24	98.73
PS	1990	98.84	98.85	98.83	98.59	98.21	98.37	98.63	98.63	98.63	98.79	98.89	98.99	98.69
PS	1991	99.10	99.03	98.23	98.19	98.32	98.42	98.56	98.64	98.85	98.61	99.07	99.16	98.68
PS	1992	99.02	98.70	98.55	98.50	98.61	98.08	98.48	98.74	98.61	98.70	98.69	98.92	98.63

Month_combined <- Fort_Worth %>%
pivot_longer(
  cols = c(NOV, JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, DEC),
  names_to = "MONTH",
  values_to = "Month_AVG",
)

Month_combined %>%
  select("PARAMETER", "YEAR", "MONTH", "Month_AVG", "ANN") %>%
  slice(1:12) %>%
  knitr::kable(caption = "Month Combined", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 1: Month Combined
PARAMETER	YEAR	MONTH	Month_AVG	ANN
PS	1981	NOV	98.76	98.64
PS	1981	JAN	99.17	98.64
PS	1981	FEB	99.00	98.64
PS	1981	MAR	98.53	98.64
PS	1981	APR	98.64	98.64
PS	1981	MAY	98.18	98.64
PS	1981	JUN	98.29	98.64
PS	1981	JUL	98.49	98.64
PS	1981	AUG	98.45	98.64
PS	1981	SEP	98.65	98.64
PS	1981	OCT	98.69	98.64
PS	1981	DEC	98.81	98.64

Para_split <- Month_combined %>%
  pivot_wider(names_from = PARAMETER,
              values_from = Month_AVG,
              )


Para_split %>%
  slice(1:12) %>%
  knitr::kable(caption = "PARAMETER split", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 1: PARAMETER split
YEAR	ANN	MONTH	PS	TS	T2M	QV2M	RH2M	WD50M	WS10M	WS50M	PRECTOTCORR	PRECTOTCORR_SUM
1981	98.64	NOV	98.76	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	JAN	99.17	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	FEB	99.00	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	MAR	98.53	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	APR	98.64	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	MAY	98.18	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	JUN	98.29	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	JUL	98.49	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	AUG	98.45	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	SEP	98.65	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	OCT	98.69	NA	NA	NA	NA	NA	NA	NA	NA	NA
1981	98.64	DEC	98.81	NA	NA	NA	NA	NA	NA	NA	NA	NA

Cleaning the Temperature column

# Rename in the temperature column and then mutating to change from C to F and then rearranged in the order i want while removed the Na values.

Final_Temperature <- Para_split %>%
  rename(Temperature = T2M) %>%
  mutate(Temperature_F = Temperature * 9/5 + 32) %>%
  mutate(Annual_Temperature = ANN * 9/5 + 32) %>%
  select(YEAR, MONTH, Temperature_F, Annual_Temperature) %>%
  na.omit(Temperature_F)

Final_Temperature %>%
  slice(1:12) %>%
  knitr::kable(caption = "Temperature", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 2: Temperature
YEAR	MONTH	Temperature_F	Annual_Temperature
1981	NOV	53.24	64.45
1981	JAN	42.19	64.45
1981	FEB	47.93	64.45
1981	MAR	55.02	64.45
1981	APR	69.01	64.45
1981	MAY	70.27	64.45
1981	JUN	79.36	64.45
1981	JUL	86.67	64.45
1981	AUG	85.33	64.45
1981	SEP	76.17	64.45
1981	OCT	64.42	64.45
1981	DEC	42.94	64.45

# When was Temperature the highest (Jul of 2011)

Final_Temperature %>%
  select(YEAR, MONTH, Temperature_F, Annual_Temperature) %>%
  arrange(desc(Temperature_F)) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Highest Temerpature", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 2: Highest Temerpature
YEAR	MONTH	Temperature_F	Annual_Temperature
2011	JUL	94.23	67.51
2011	AUG	94.17	67.51
1998	JUL	93.24	67.17
1999	AUG	92.17	66.83
2000	AUG	91.87	65.71
1985	AUG	90.95	64.00
2006	AUG	90.34	67.37
2018	JUL	90.21	65.03
2001	JUL	90.05	64.78
1988	AUG	89.56	64.27
1993	AUG	89.56	63.05
2010	AUG	89.51	64.40

# When was Temperature the lowest (Dec of 1983)

Final_Temperature %>%
  select(YEAR, MONTH, Temperature_F, Annual_Temperature) %>%
  arrange(Temperature_F) %>%
  na.omit(Temperature_F) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Lowest Temperature", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 2: Lowest Temperature
YEAR	MONTH	Temperature_F	Annual_Temperature
1983	DEC	33.49	62.73
1985	JAN	34.29	64.00
1989	DEC	35.56	62.24
2000	DEC	35.69	65.71
1984	JAN	36.12	64.80
1988	JAN	37.26	64.27
2007	JAN	38.26	63.75
2009	DEC	38.68	64.51
1991	JAN	38.75	63.90
2010	FEB	39.04	64.40
2011	JAN	39.15	67.51
2001	JAN	39.22	64.78

Final_Temperature %>%
  select(YEAR, MONTH, Temperature_F, Annual_Temperature) %>%
  filter(YEAR < 1982) %>%
  mutate(Mean = mean(Temperature_F)) %>%
  mutate(Standard_Deviation = sd(Temperature_F)) %>%
  mutate(Median = median(Temperature_F)) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Temperature stats data", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 2: Temperature stats data
YEAR	MONTH	Temperature_F	Annual_Temperature	Mean	Standard_Deviation	Median
1981	NOV	53.24	64.45	64.38	15.92	66.71
1981	JAN	42.19	64.45	64.38	15.92	66.71
1981	FEB	47.93	64.45	64.38	15.92	66.71
1981	MAR	55.02	64.45	64.38	15.92	66.71
1981	APR	69.01	64.45	64.38	15.92	66.71
1981	MAY	70.27	64.45	64.38	15.92	66.71
1981	JUN	79.36	64.45	64.38	15.92	66.71
1981	JUL	86.67	64.45	64.38	15.92	66.71
1981	AUG	85.33	64.45	64.38	15.92	66.71
1981	SEP	76.17	64.45	64.38	15.92	66.71
1981	OCT	64.42	64.45	64.38	15.92	66.71
1981	DEC	42.94	64.45	64.38	15.92	66.71

Temperature_DEC <- Final_Temperature %>%
  select(YEAR, MONTH, Temperature_F, Annual_Temperature) %>%
  filter(MONTH == "DEC") 

Temperature_July <- Final_Temperature %>%
  select(YEAR, MONTH, Temperature_F, Annual_Temperature) %>%
  filter(MONTH == "JUL") 

 




Final_Precipitation <- Para_split %>%
  rename(Precipitation = PRECTOTCORR_SUM) %>%
  mutate(Precipitation_annual = ANN / 25.4) %>%
  mutate(Precipitation_Monthly = Precipitation / 25.4) %>%
  select(YEAR, MONTH, Precipitation_Monthly, Precipitation_annual) %>%
  na.omit(Precipitation_Monthly)

Combined_data <- merge(Final_Temperature, Final_Precipitation, by ="YEAR") 


Temp_combine <- Final_Temperature %>%
  select(Temperature_F, Annual_Temperature)

Combined_data <- cbind(Final_Precipitation, Temp_combine)

Combined_data %>%
  select(YEAR, MONTH, Temperature_F,Annual_Temperature, Precipitation_Monthly, Precipitation_annual) %>%
  filter(Temperature_F < 39, Precipitation_Monthly > .1) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Potential Months with Snow days", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 2: Potential Months with Snow days
YEAR	MONTH	Temperature_F	Annual_Temperature	Precipitation_Monthly	Precipitation_annual
1983	DEC	33.49	62.73	0.66	26.99
1984	JAN	36.12	64.80	0.90	32.34
1985	JAN	34.29	64.00	0.71	32.93
1988	JAN	37.26	64.27	0.51	25.78
1989	DEC	35.56	62.24	0.31	43.01
1991	JAN	38.75	63.90	2.88	47.55
2000	DEC	35.69	65.71	2.58	32.05
2007	JAN	38.26	63.75	2.81	44.77
2009	DEC	38.68	64.51	1.75	38.44

Graphing Temperature

Final_Temperature %>%
ggplot(aes(x = Annual_Temperature)) +
geom_density(aes(fill = "blue"),
show.legend = F,
alpha = .5) +
labs(title = "Density plot",
x = "Annual temperature [f]",
y = "Probability")

Final_Temperature %>%
filter(Annual_Temperature > 50) %>%
ggplot(aes(x= YEAR,
y = Annual_Temperature,
size = Annual_Temperature,
color = YEAR)) +
geom_point() +
  geom_smooth() +
labs(title = "Temperature change over 40 years",
x = "Year",
y = "Annual Temperature")

Temperature_DEC %>%
ggplot(aes(x= YEAR,
y = Temperature_F,
size = Temperature_F,
color = YEAR)) +
geom_point() +
  geom_smooth() +
labs(title = "December Temperature change over 40 years",
x = "Year",
y = "December Temperature")

Temperature_July %>%
ggplot(aes(x= YEAR,
y = Temperature_F,
size = Temperature_F,
color = YEAR)) +
geom_point() +
  geom_smooth() +
labs(title = "July Temperature change over 40 years",
x = "Year",
y = "July Temperature")

Final_Temperature %>%
drop_na(Temperature_F) %>%
filter(Temperature_F > 50) %>%
ggplot(aes(Temperature_F, fill = MONTH)) +
geom_density(alpha = 0.5) +
facet_wrap(~MONTH) +
labs(title = "Density plot of a temperature greater than 50",
subtitle = "Decemeber does not show due to not reaching 50",
x = "Temperature",
y = "Probability") +
theme(legend.position = "none")

Final_Temperature %>%
drop_na(Temperature_F) %>%
filter(Temperature_F < 49) %>%
ggplot(aes(Temperature_F, fill = MONTH)) +
geom_density(alpha = 0.5) +
facet_wrap(~MONTH) +
labs(title = "Density plot of a temperature less than 49",
subtitle = "Only these months are below 49",
x = "Temperature",
y = "Probability") +
theme(legend.position = "none")

Final_Temperature %>%
ggplot(mapping = aes(x = Temperature_F , y = MONTH, fill = ..x..)) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01,
alpha = 5) +
scale_fill_viridis(name = "Temp. [F]", option = "C") +
labs(title = 'Temperatures in Fort Worth') +
theme_bw() +
theme(legend.position="none",
  panel.spacing = unit(0.1, "lines"),
  strip.text.x = element_text(size = 8))

Insights

I will start with temperature and I will need the columns YEAR, MONTH, ANN, and T2m. This will show the year that it occurred and the month. The t2m will show the temperature at 2meters above the surface level and will display it per month. The Ann will show the average for the year and this is good to use when looking at each year to get a comparison. The temperature column has NA values and is in C format so we need to rename the column and remove the Na values while switching it to F format.

Confirming the annual column to ensure it is the mean. We notice a slight difference between the annual column and the mean. This number falls in between the mean and the median and the sd is 16.5 showing a high range of variance in the values. This is a large range however, I believe this is due to the fact that we are comparing separate months with increasing and decreasing values of temperature. Naturally, the SD would be wide to accompany the wide range of temperatures.

Cleaning Humidty

# renaming the columns
Final_Humidity <- Para_split %>%
  rename(Humidity = RH2M) %>%
  rename(Annual_Humidity_percent = ANN) %>%
  select(YEAR, MONTH, Humidity, Annual_Humidity_percent) %>%
  na.omit(Humidity)

Final_Humidity %>%
  slice(1:12) %>%
  knitr::kable(caption = "Humidity", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 3: Humidity
YEAR	MONTH	Humidity	Annual_Humidity_percent
1981	NOV	80.19	68.81
1981	JAN	69.38	68.81
1981	FEB	67.19	68.81
1981	MAR	67.38	68.81
1981	APR	66.19	68.81
1981	MAY	71.12	68.81
1981	JUN	75.50	68.81
1981	JUL	57.81	68.81
1981	AUG	50.06	68.81
1981	SEP	63.50	68.81
1981	OCT	79.62	68.81
1981	DEC	78.31	68.81

# When was humidity the highest (Jan of 1998)

Final_Humidity %>%
  select(YEAR, MONTH, Humidity, Annual_Humidity_percent) %>%
  arrange(desc(Humidity)) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Highest Humidty", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 3: Highest Humidty
YEAR	MONTH	Humidity	Annual_Humidity_percent
1998	JAN	86.56	66.31
1991	DEC	86.50	71.12
1994	DEC	85.69	70.50
1984	DEC	85.62	62.44
2015	MAY	84.88	71.38
1992	JAN	84.75	72.75
2018	OCT	84.69	68.62
1986	DEC	83.62	69.62
1993	JAN	83.56	69.56
2001	JAN	83.38	69.50
2001	FEB	83.38	69.50
1992	FEB	83.25	72.75

# When was humidity the lowest (Aug of 2000)

Final_Humidity %>%
  select(YEAR, MONTH, Humidity, Annual_Humidity_percent) %>%
  arrange(Humidity) %>%
  na.omit(Humidity) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Lowest Humidity", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 3: Lowest Humidity
YEAR	MONTH	Humidity	Annual_Humidity_percent
2000	AUG	34.88	63.00
1999	AUG	35.81	61.38
2011	JUL	36.19	56.44
2011	AUG	37.31	56.44
1985	AUG	37.62	68.12
2000	SEP	40.44	63.00
2011	SEP	41.06	56.44
1998	JUL	41.25	66.31
1984	JUL	41.31	62.44
1988	AUG	44.06	61.31
2011	JUN	44.81	56.44
1993	AUG	45.00	69.56

Final_Humidity %>%
  select(YEAR, MONTH, Humidity, Annual_Humidity_percent) %>%
  filter(YEAR < 1983, YEAR > 1981) %>%
  mutate(Mean = mean(Humidity)) %>%
  mutate(Standard_Deviation = sd(Humidity)) %>%
  mutate(Median = median(Humidity)) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Humidty stats data", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 3: Humidty stats data
YEAR	MONTH	Humidity	Annual_Humidity_percent	Mean	Standard_Deviation	Median
1982	NOV	70.38	67.88	67.97	10.97	70.28
1982	JAN	70.19	67.88	67.97	10.97	70.28
1982	FEB	78.69	67.88	67.97	10.97	70.28
1982	MAR	73.94	67.88	67.97	10.97	70.28
1982	APR	66.81	67.88	67.97	10.97	70.28
1982	MAY	79.69	67.88	67.97	10.97	70.28
1982	JUN	77.81	67.88	67.97	10.97	70.28
1982	JUL	65.75	67.88	67.97	10.97	70.28
1982	AUG	49.00	67.88	67.97	10.97	70.28
1982	SEP	48.31	67.88	67.97	10.97	70.28
1982	OCT	58.19	67.88	67.97	10.97	70.28
1982	DEC	76.88	67.88	67.97	10.97	70.28

Graphing Humidity

# change in humidity over years by each month

ggplot(data = Final_Humidity, mapping = aes(x = YEAR, y = Humidity)) +
  geom_point() +
  geom_smooth(mapping = aes(color = MONTH), se = FALSE)

# Facet wrap of the previous graph to separate them

ggplot(data = Final_Humidity, mapping = aes(x = YEAR, y = Humidity)) + 
  geom_point() +
  geom_smooth(mapping = aes(color = MONTH), se = FALSE) +
  facet_wrap(~ MONTH, nrow = 5)

Final_Humidity %>%
filter(Annual_Humidity_percent > 60) %>%
ggplot(aes(x= YEAR,
y = Annual_Humidity_percent,
size = Annual_Humidity_percent,
color = YEAR)) +
geom_point() +
  geom_smooth() +
labs(title = "Humidity change over 40 years",
x = "Year",
y = "Annual Humidity")

Insights

Here we are doing the same thing that we did to the previous section of temperature. We renamed the RH2m to humidity and then changed the ANN to annual_humidity_percent. Next we removed the na values to focus in on the information that we needed and then graphed it to start drawing conclusions.

Once more testing the mean, median, and sd of one year. Once more we notice a slight difference between the mean and the annual however, the difference is .11 which is not major. The standard deviation is almost 11 which could once again be explained by the rainy seasons that come and the increase in humidity that follows them however, in this one we notice it is a much a smaller gap compared to the temperature.

Cleaning Precipitation

# changing the precipitation from mm to inches and then renaming.
Final_Precipitation <- Para_split %>%
  rename(Precipitation = PRECTOTCORR_SUM) %>%
  mutate(Precipitation_annual = ANN / 25.4) %>%
  mutate(Precipitation_Monthly = Precipitation / 25.4) %>%
  select(YEAR, MONTH, Precipitation_Monthly, Precipitation_annual) %>%
  na.omit(Precipitation_Monthly)

Final_Precipitation %>%
  slice(1:12) %>%
  knitr::kable(caption = "Precipitation (Inches)", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 4: Precipitation (Inches)
YEAR	MONTH	Precipitation_Monthly	Precipitation_annual
1981	NOV	1.53	42.61
1981	JAN	0.39	42.61
1981	FEB	1.82	42.61
1981	MAR	3.33	42.61
1981	APR	3.04	42.61
1981	MAY	6.08	42.61
1981	JUN	4.05	42.61
1981	JUL	1.42	42.61
1981	AUG	2.12	42.61
1981	SEP	2.98	42.61
1981	OCT	15.63	42.61
1981	DEC	0.21	42.61

# When was precipitation the highest (OCT of 1981)

Final_Precipitation %>%
  select(YEAR, MONTH, Precipitation_Monthly, Precipitation_annual) %>%
  arrange(desc(Precipitation_Monthly)) %>%
  na.omit(Precipitation_Monthly) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Highest Precipitation (Inches)", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 4: Highest Precipitation (Inches)
YEAR	MONTH	Precipitation_Monthly	Precipitation_annual
1981	OCT	15.63	42.61
2015	MAY	15.24	57.96
1982	MAY	11.28	38.48
1989	MAY	10.61	43.01
2018	OCT	10.43	40.07
2004	JUN	10.23	45.36
2007	JUN	10.17	44.77
1989	JUN	9.64	43.01
1990	APR	9.36	46.70
1991	DEC	8.75	47.55
1991	OCT	8.74	47.55
2009	OCT	8.69	38.44

# When was it the lowest (Jan of 1986)

Final_Precipitation %>%
  select(YEAR, MONTH, Precipitation_Monthly, Precipitation_annual) %>%
  arrange(Precipitation_Monthly) %>%
  na.omit(Precipitation_Monthly) %>%
  slice(1:12) %>%
  knitr::kable(caption = "Lowest Precipitation (Inches)", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 4: Lowest Precipitation (Inches)
YEAR	MONTH	Precipitation_Monthly	Precipitation_annual
1986	JAN	0.01	39.02
2011	JUL	0.01	22.53
2000	AUG	0.02	32.05
2011	MAR	0.08	22.53
1993	JUL	0.08	36.92
2012	NOV	0.12	28.73
2018	JAN	0.17	40.07
2014	JAN	0.18	23.94
2005	NOV	0.19	18.28
1981	DEC	0.21	42.61
2005	DEC	0.21	18.28
1996	FEB	0.21	33.81

Stats_precipitation <- Final_Precipitation %>%
  select(YEAR, MONTH, Precipitation_Monthly, Precipitation_annual) %>%
  filter(YEAR < 1982) %>%
  mutate(Mean = mean(Precipitation_Monthly)) %>%
  mutate(Standard_Deviation = sd(Precipitation_Monthly)) %>%
  mutate(Median = median(Precipitation_Monthly))

Stats_precipitation %>%
  slice(1:12) %>%
  knitr::kable(caption = "Precipitation stats data (Inches)", digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = "striped", full_width = TRUE)

Table 4: Precipitation stats data (Inches)
YEAR	MONTH	Precipitation_Monthly	Precipitation_annual	Mean	Standard_Deviation	Median
1981	NOV	1.53	42.61	3.55	4.13	2.55
1981	JAN	0.39	42.61	3.55	4.13	2.55
1981	FEB	1.82	42.61	3.55	4.13	2.55
1981	MAR	3.33	42.61	3.55	4.13	2.55
1981	APR	3.04	42.61	3.55	4.13	2.55
1981	MAY	6.08	42.61	3.55	4.13	2.55
1981	JUN	4.05	42.61	3.55	4.13	2.55
1981	JUL	1.42	42.61	3.55	4.13	2.55
1981	AUG	2.12	42.61	3.55	4.13	2.55
1981	SEP	2.98	42.61	3.55	4.13	2.55
1981	OCT	15.63	42.61	3.55	4.13	2.55
1981	DEC	0.21	42.61	3.55	4.13	2.55

Graphing Precipitation

Final_Precipitation %>%
filter(Precipitation_annual > 20) %>%
ggplot(aes(x= YEAR,
y = Precipitation_annual,
size = Precipitation_annual,
color = YEAR)) +
geom_point() +
  geom_smooth() +
labs(title = "Precipitation change over 40 years",
x = "Year",
y = "Annual Precipitation")

ggplot(data = Final_Precipitation, mapping = aes(x = YEAR, y = Precipitation_Monthly)) +
  geom_smooth(mapping = aes(color = MONTH), se = FALSE)

# Facet grid by month of the precipitation changes over the years

ggplot(data = Final_Precipitation, mapping = aes(x = YEAR, y = Precipitation_Monthly)) + 
  geom_point() +
  geom_smooth(mapping = aes(color = MONTH), se = FALSE) +
  facet_wrap(~ MONTH, nrow = 5) +
  labs(title = "Each month's change in precipitation over 40 years", x = "Year", y = "Precipitation per month in Inches")

Insights

Once more we engaged in a similar process of isolating the information to draw conclusions by renaming and then selecting that information that is important. However, on this one we needed to change the precipitation from mm to inches which required us to divide the ANN and the monthly column by 25.4. During this analysis we aimed towards finding when it was the highest and lowest and learned they were within 5 years of each other.

Here we do not have a mean or average to compare it to since the annual is the combination of the monthly values to show the yearly rainfall however, when dividing that annual combination by 12 we reach the same values which means that number is accurate for annual rain fall. We also notice a much lower sd as the rain seems to be a bit more consistent than the other two values as it is much harder to have a wide range. Texas already does not receive a large amount of rain and that range from 0 to the amount of rain we do get is not very different resulting in this lower sd

Questions

Does precipitation increase or decrease over the years and when was it the highest and lowest.
Is there a change in humidity and does it correlate with the change in precipitation, wind, and temperature.
Does the temperature increase over the years or is it decreasing and does it correlate to the other variables?
When considering all variables is there a noticeable change in the climate?
What is missing (if anything) in your analysis process so far?
What conclusions can you make about your research questions at this point?
What do you think a naive reader would need to fully understand your graphs?
Is there anything you want to answer with your dataset, but can’t?

Answers

Precipitation has changed over the last 40 years however, it has dropped and then returned to the original amount. The highest was in OCT of 1981 and the lowest it hit was in Jan of 1986
Humidity has changed in the last 40 years and has followed a very similar graph compared to the precipitation. It started at roughly 67% and then took a dip towards the lower 60’s and now has climbed to 70%.There is a correlation between precipitation and humidity however, the temperature has increased since the come of the 21st century.
There is an increase of temperature over the past 40 years, We notice roughly an increase of about 1.5 degrees and a major spike in temperature change between the years of 1990-2000.
When considering all variables we notice a very slight change to the climate. We notice that the temperature and humidity within the area has increased however, the precipitation decreased for 10 years and then returned to the beginning value which lined up with the changes in humidity. However, humidity has surpassed the starting point reaching a new high in the past 40 years.
The thing that I find slightly missing is more statistical data however, I have already incorporated mean, median, and the standard deviation. I will need to research more into how they perform statistical analysis on weather data, possibly look into future weather prediction on whether or not it will continue the same or change.
By this point in my research I am able to see the changes in temperature, humidity, and precipitation over 40 years. I have noticed there are definitely some changes especially in the temperature data. Over the 40 years we have noticed an increase in 1.5 degrees [F]. Humidity and precipitation have followed a similar graphing as they do correlate with each other however, the humidity is slightly different. We notice that they both started at a high point and then dropped during the mid 90’s and then continues downward in the early 21st century. However, when the year 2010 begins we notice a major uptick in both humidity and and precipitation and they correlate to be at the same time.
For a naive reader to understand my graphs they would need to know what measurement is being utilized. Whether that is the inches being used in precipitation or the percentage used in humidity or the [f] used in temperature. The graphs could use more color difference to create a quicker understanding of the graph. I could also try and break down certain weather conditions based on monthly data to enhance the imagery being drawn. For example, when there is rain but the temperature is below 32 degrees it could be labeled into snow or sleet.
Currently, I would love to be able to show which climate it is by combining all the information together. I would love to break things down into subsection like fog, dew, rain, etc… however, to do so I need to combine them all together and then find the correct way to calculate that and currently I am unsure how to appraoch this process.

References/Acknowledgements

Center, NASA Langley Research. The POWER Project. 05 08 2021. https://power.larc.nasa.gov.

(“These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program.”)

Types of data

parameter = CHR
- Precipitation_Monthly = DBL
- Temperature_F = DBL
- Humidity = DBL
year = Int
Months = NUM
ANN = DBL

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Ethan Campbell HW5

Introduction/Importing data

Cleaning the Temperature column

Graphing Temperature

Insights

Cleaning Humidty

Graphing Humidity

Insights

Cleaning Precipitation

Graphing Precipitation

Insights

Questions

Answers

References/Acknowledgements

Types of data

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Citation