About the data

The data considered here is a dataset which has five colors cloths and their reaction to cooling and heating in every 10 minutes. Below are the variables description in the data

color - Cloth color
minute.0 - initial reaction
minute.10 - temp after 10th minute (in Farenheit)
minute.20 - temp after 20th minute (in Farenheit)
minute.30 - temp after 30th minute (in Farenheit)
minute.40 - temp after 40th minute (in Farenheit)
minute.50 - temp after 50th minute (in Farenheit)
minute.60 - temp after 60th minute (in Farenheit)
phase - heating / colling

Load the data

The dataset is placed in a csv file in the github repository and loaded here using read.csv function. kable library is used to display data tables.

# get the github URL from github
theURL <- "https://raw.githubusercontent.com/amit-kapoor/data607/master/week6/hcdata.csv"

# read the data from csv
tempreact_df <- read.csv(theURL)

# show intial rows through head
kable(head(tempreact_df), align = "l")

color	minute.0	minute.10	minute.20	minute.30	minute.40	minute.50	minute.60	phase
white	78	81	83	88	93	96	98	heating
red	78	82	90	93	98	106	109	heating
pink	78	82	84	90	96	99	102	heating
black	78	88	92	98	108	116	121	heating
green	78	81	85	91	95	102	105	heating
white	98	96	93	80	78	78	78	cooling

Tidying the data

Approach: My goal is to make data appear in a way which would be easy for further analysis. Here are the steps being performed.

Rename columns where column names are abbreviated. Here I renamed all the columns starts with minute to make it meningful.
Next is to filter out the records from original dataframe to separate records for cooling and heating. Here I created 2 dataframes respectively. I have used dplyr filter method to perform this operation.
Also used gather function from tidyr to make data from wide to long for both cooling and heating datasets

# rename columns
tempreact_df <- tempreact_df %>% 
  rename("temp_initial"=minute.0) %>% 
  rename("temp_after_10"=minute.10) %>% 
  rename("temp_after_20"=minute.20) %>% 
  rename("temp_after_30"=minute.30) %>% 
  rename("temp_after_40"=minute.40) %>% 
  rename("temp_after_50"=minute.50) %>% 
  rename("temp_after_60"=minute.60)


# show intial rows through head
kable(head(tempreact_df), align = "l")

color	temp_initial	temp_after_10	temp_after_20	temp_after_30	temp_after_40	temp_after_50	temp_after_60	phase
white	78	81	83	88	93	96	98	heating
red	78	82	90	93	98	106	109	heating
pink	78	82	84	90	96	99	102	heating
black	78	88	92	98	108	116	121	heating
green	78	81	85	91	95	102	105	heating
white	98	96	93	80	78	78	78	cooling

cooling_phasedf <- filter(tempreact_df, tempreact_df$phase == "cooling")
kable(cooling_phasedf)

color	temp_initial	temp_after_10	temp_after_20	temp_after_30	temp_after_40	temp_after_50	temp_after_60	phase
white	98	96	93	80	78	78	78	cooling
red	109	106	95	87	82	80	78	cooling
pink	102	96	90	83	80	78	78	cooling
black	121	108	98	90	84	79	78	cooling
green	105	94	90	82	80	78	78	cooling

#Change from Wide to long
cooling_long_df <- gather(cooling_phasedf, Temperature, Value, "temp_initial":"temp_after_60")
cooling_long_df

##    color   phase   Temperature Value
## 1  white cooling  temp_initial    98
## 2    red cooling  temp_initial   109
## 3   pink cooling  temp_initial   102
## 4  black cooling  temp_initial   121
## 5  green cooling  temp_initial   105
## 6  white cooling temp_after_10    96
## 7    red cooling temp_after_10   106
## 8   pink cooling temp_after_10    96
## 9  black cooling temp_after_10   108
## 10 green cooling temp_after_10    94
## 11 white cooling temp_after_20    93
## 12   red cooling temp_after_20    95
## 13  pink cooling temp_after_20    90
## 14 black cooling temp_after_20    98
## 15 green cooling temp_after_20    90
## 16 white cooling temp_after_30    80
## 17   red cooling temp_after_30    87
## 18  pink cooling temp_after_30    83
## 19 black cooling temp_after_30    90
## 20 green cooling temp_after_30    82
## 21 white cooling temp_after_40    78
## 22   red cooling temp_after_40    82
## 23  pink cooling temp_after_40    80
## 24 black cooling temp_after_40    84
## 25 green cooling temp_after_40    80
## 26 white cooling temp_after_50    78
## 27   red cooling temp_after_50    80
## 28  pink cooling temp_after_50    78
## 29 black cooling temp_after_50    79
## 30 green cooling temp_after_50    78
## 31 white cooling temp_after_60    78
## 32   red cooling temp_after_60    78
## 33  pink cooling temp_after_60    78
## 34 black cooling temp_after_60    78
## 35 green cooling temp_after_60    78

heating_phasedf <- filter(tempreact_df, tempreact_df$phase == "heating")
kable(heating_phasedf)

color	temp_initial	temp_after_10	temp_after_20	temp_after_30	temp_after_40	temp_after_50	temp_after_60	phase
white	78	81	83	88	93	96	98	heating
red	78	82	90	93	98	106	109	heating
pink	78	82	84	90	96	99	102	heating
black	78	88	92	98	108	116	121	heating
green	78	81	85	91	95	102	105	heating

#Change from Wide to long
heating_long_df <- gather(heating_phasedf, Temperature, Value, "temp_initial":"temp_after_60")
heating_long_df

##    color   phase   Temperature Value
## 1  white heating  temp_initial    78
## 2    red heating  temp_initial    78
## 3   pink heating  temp_initial    78
## 4  black heating  temp_initial    78
## 5  green heating  temp_initial    78
## 6  white heating temp_after_10    81
## 7    red heating temp_after_10    82
## 8   pink heating temp_after_10    82
## 9  black heating temp_after_10    88
## 10 green heating temp_after_10    81
## 11 white heating temp_after_20    83
## 12   red heating temp_after_20    90
## 13  pink heating temp_after_20    84
## 14 black heating temp_after_20    92
## 15 green heating temp_after_20    85
## 16 white heating temp_after_30    88
## 17   red heating temp_after_30    93
## 18  pink heating temp_after_30    90
## 19 black heating temp_after_30    98
## 20 green heating temp_after_30    91
## 21 white heating temp_after_40    93
## 22   red heating temp_after_40    98
## 23  pink heating temp_after_40    96
## 24 black heating temp_after_40   108
## 25 green heating temp_after_40    95
## 26 white heating temp_after_50    96
## 27   red heating temp_after_50   106
## 28  pink heating temp_after_50    99
## 29 black heating temp_after_50   116
## 30 green heating temp_after_50   102
## 31 white heating temp_after_60    98
## 32   red heating temp_after_60   109
## 33  pink heating temp_after_60   102
## 34 black heating temp_after_60   121
## 35 green heating temp_after_60   105

Data Analysis

For data analysis I used ggplot, geom_point, geom_line methods from ggplot2 library. I plotted the color on x-axis and temperature on y-axis. I created 2 graphs below to show the cooling and heating effects for different color in data.

cooling_phasedf %>% ggplot(aes(x=cooling_phasedf$color)) + scale_x_discrete(limits=cooling_phasedf$color) + 
  geom_point(aes(y=cooling_phasedf$temp_initial , group=1)) + geom_line(aes(y=cooling_phasedf$temp_initial, group=1, col="Intial Temp"), size=1) + 
  geom_point(aes(y=cooling_phasedf$temp_after_10, group=1)) + geom_line(aes(y=cooling_phasedf$temp_after_10, group=1, col="Temp after 10"), size=1) + 
    geom_point(aes(y=cooling_phasedf$temp_after_20 , group=1)) + geom_line(aes(y=cooling_phasedf$temp_after_20, group=1, col="Temp after 20"), size=1) + 
  geom_point(aes(y=cooling_phasedf$temp_after_30, group=1)) + geom_line(aes(y=cooling_phasedf$temp_after_30, group=1, col="Temp after 30"), size=1) + 
  geom_point(aes(y=cooling_phasedf$temp_after_40, group=1)) + geom_line(aes(y=cooling_phasedf$temp_after_40, group=1, col="Temp after 40"), size=1) +
  geom_point(aes(y=cooling_phasedf$temp_after_50, group=1)) + geom_line(aes(y=cooling_phasedf$temp_after_50, group=1, col="Temp after 50"), size=1) + 
  geom_point(aes(y=cooling_phasedf$temp_after_60, group=1)) + geom_line(aes(y=cooling_phasedf$temp_after_60, group=1, col="Temp after 60"), size=1) +
  labs(title="Cooling Temperature Versus Color", x="Color", y="Degrees in Farenheit", colour="") + 
  scale_colour_manual(values = c("orange", "grey" , "blue" , "green", "cyan" , "black" , "red"))

heating_phasedf %>% ggplot(aes(x=heating_phasedf$color)) + scale_x_discrete(limits=heating_phasedf$color) + 
  geom_point(aes(y=heating_phasedf$temp_initial , group=1)) + geom_line(aes(y=heating_phasedf$temp_initial, group=1, col="Intial Temp"), size=1) + 
  geom_point(aes(y=heating_phasedf$temp_after_10, group=1)) + geom_line(aes(y=heating_phasedf$temp_after_10, group=1, col="Temp after 10"), size=1) + 
    geom_point(aes(y=heating_phasedf$temp_after_20 , group=1)) + geom_line(aes(y=heating_phasedf$temp_after_20, group=1, col="Temp after 20"), size=1) + 
  geom_point(aes(y=heating_phasedf$temp_after_30, group=1)) + geom_line(aes(y=heating_phasedf$temp_after_30, group=1, col="Temp after 30"), size=1) + 
  geom_point(aes(y=heating_phasedf$temp_after_40, group=1)) + geom_line(aes(y=heating_phasedf$temp_after_40, group=1, col="Temp after 40"), size=1) +
  geom_point(aes(y=heating_phasedf$temp_after_50, group=1)) + geom_line(aes(y=heating_phasedf$temp_after_50, group=1, col="Temp after 50"), size=1) + 
  geom_point(aes(y=heating_phasedf$temp_after_60, group=1)) + geom_line(aes(y=heating_phasedf$temp_after_60, group=1, col="Temp after 60"), size=1) +
  labs(title="Heating Temperature Versus Color", x="Color", y="Degrees in Farenheit", colour="") + 
  scale_colour_manual(values = c("orange", "grey" , "blue" , "green", "cyan" , "black" , "red"))

Summary/Conclusion

This data used a small .csv file to demonstrate the color effect. I found above graphs interesting for both heating and cooling analysis. We can see above that black color cloth absorbs all colors and appears warmer than white color. More light a color absorbs, more warmer it would be.

Data607 - Project2C

Amit Kapoor

3/4/2020

About the data

Load the data

Tidying the data

Data Analysis

Summary/Conclusion