example 1. loading csv into r

library(readr)
city_weekly_temperature_wide <-read_csv(file = '/Users/joycealdrich/Documents/SPS Data Science/Data 607/Project_2/city_week_temp.csv')

## Rows: 6 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): city
## dbl (7): sun, mon, tue, wed, thr, fri, sat
## 
## 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.

head(city_weekly_temperature_wide)

## # A tibble: 6 × 8
##   city       sun   mon   tue   wed   thr   fri   sat
##   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 houston     78    89    90    85    79    83    94
## 2 boston      88    82    93    87    79    82    89
## 3 seattle     64    67    69    61    70    73    65
## 4 new york    88    93    90    89    86    83    92
## 5 chicago     67    63    66    70    71    73    68
## 6 orlando     90    92    89    86    91    87    93

reshaping data to long format

library(tidyr)
city_weekly_temperature_long <- city_weekly_temperature_wide %>%
gather(day,temperature, -c(city))
head(city_weekly_temperature_long)

## # A tibble: 6 × 3
##   city     day   temperature
##   <chr>    <chr>       <dbl>
## 1 houston  sun            78
## 2 boston   sun            88
## 3 seattle  sun            64
## 4 new york sun            88
## 5 chicago  sun            67
## 6 orlando  sun            90

adding the average temperature for each city

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

city_weekly_temperature_long <- city_weekly_temperature_long %>%
  group_by(city) %>%
  mutate(average_temperature = mean(temperature),
         temperature_diff = temperature - mean(temperature)
         )
head(city_weekly_temperature_long)

## # A tibble: 6 × 5
## # Groups:   city [6]
##   city     day   temperature average_temperature temperature_diff
##   <chr>    <chr>       <dbl>               <dbl>            <dbl>
## 1 houston  sun            78                85.4           -7.43 
## 2 boston   sun            88                85.7            2.29 
## 3 seattle  sun            64                67             -3    
## 4 new york sun            88                88.7           -0.714
## 5 chicago  sun            67                68.3           -1.29 
## 6 orlando  sun            90                89.7            0.286

using ggplot geom_point to see each city’s weekly temperature

library(ggplot2)
ggplot(data= city_weekly_temperature_long ) +
  geom_point(mapping = aes(x=day, y=temperature, color=city)) +
  facet_wrap(~city, nrow=1)

compare 6 cities’ weekly temperature and place the overall mean in the graph

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble  3.1.8     ✔ stringr 1.4.1
## ✔ purrr   0.3.4     ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(ggplot2)

city_weekly_temperature_long %>%
  ggplot(aes(x=day, y=temperature,group=city,colour=city))+
  geom_point(size =3, alpha=0.3)+
  geom_line(size =0.5)+
  geom_hline(aes(yintercept=mean(temperature)))+
  theme_minimal()+
  labs(title ="Weekly Temperature of Cities")

example 2:

loading cvs file into r

library(readr)
exam_results <-read_csv(file = '/Users/joycealdrich/Documents/SPS Data Science/Data 607/Project_2/exam_result.csv')

## Rows: 6 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Student, Gender
## dbl (3): Test 1, Test 2, Test 3
## 
## 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.

head(exam_results)

## # A tibble: 6 × 5
##   Student Gender `Test 1` `Test 2` `Test 3`
##   <chr>   <chr>     <dbl>    <dbl>    <dbl>
## 1 Amanda  F            88       78       92
## 2 Brenda  F            67       97       85
## 3 Cindy   F            79       84       88
## 4 Daniel  M            95       84       82
## 5 Eric    M            64       NA       80
## 6 Frank   M            45       65       NA

reshaping to the long format

library(tidyr)
exam_results_long<- exam_results %>%
  gather(exam,score, -c(Student,Gender))
head(exam_results_long)

## # A tibble: 6 × 4
##   Student Gender exam   score
##   <chr>   <chr>  <chr>  <dbl>
## 1 Amanda  F      Test 1    88
## 2 Brenda  F      Test 1    67
## 3 Cindy   F      Test 1    79
## 4 Daniel  M      Test 1    95
## 5 Eric    M      Test 1    64
## 6 Frank   M      Test 1    45

adding score_average_by_student col & removing the na row

exam_results_long <- exam_results_long %>%
  group_by(Student) %>%
  filter(!is.na(score)) %>%
  mutate(Score_average_by_student = mean(score),
         )
head(exam_results_long)

## # A tibble: 6 × 5
## # Groups:   Student [6]
##   Student Gender exam   score Score_average_by_student
##   <chr>   <chr>  <chr>  <dbl>                    <dbl>
## 1 Amanda  F      Test 1    88                     86  
## 2 Brenda  F      Test 1    67                     83  
## 3 Cindy   F      Test 1    79                     83.7
## 4 Daniel  M      Test 1    95                     87  
## 5 Eric    M      Test 1    64                     72  
## 6 Frank   M      Test 1    45                     55

adding max_socre, min_score ,core_average_by_exam col

exam_results_long <- exam_results_long %>%
  group_by(exam) %>%
  filter(!is.na(score)) %>%
  mutate(Max_score_by_exam = max(score),
         Min_score_by_exam = min(score),
         average_score_by_exam = mean (score)
         )
head(exam_results_long)

## # A tibble: 6 × 8
## # Groups:   exam [1]
##   Student Gender exam   score Score_average_by_student Max_sco…¹ Min_s…² avera…³
##   <chr>   <chr>  <chr>  <dbl>                    <dbl>     <dbl>   <dbl>   <dbl>
## 1 Amanda  F      Test 1    88                     86          95      45      73
## 2 Brenda  F      Test 1    67                     83          95      45      73
## 3 Cindy   F      Test 1    79                     83.7        95      45      73
## 4 Daniel  M      Test 1    95                     87          95      45      73
## 5 Eric    M      Test 1    64                     72          95      45      73
## 6 Frank   M      Test 1    45                     55          95      45      73
## # … with abbreviated variable names ¹Max_score_by_exam, ²Min_score_by_exam,
## #   ³average_score_by_exam

compare each exam score by each student and place an overall mean in the graph

library(tidyverse)
library(ggplot2)

exam_results_long %>%
  ggplot(aes(x=exam, y=score, group=Student, colour=Student))+
  geom_point(size =3, alpha=0.3)+
  geom_hline(aes(yintercept=mean(score)))+
  theme_minimal()+
  labs(title ="score of three exams")

example 3:

loading excel(format download) file into r

library(readxl)
gdp_by_county <- read_excel("gdp_by_county.xlsx")

## New names:
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`

head(gdp_by_county)

## # A tibble: 6 × 9
##   FIPS  Countyname Postal LineCode IndustryName        Gross…¹ ...7  ...8  ...9 
##   <chr> <chr>      <chr>     <dbl> <chr>               <chr>   <chr> <chr> <chr>
## 1 <NA>  <NA>       <NA>         NA <NA>                (thous… <NA>  <NA>  <NA> 
## 2 <NA>  <NA>       <NA>         NA <NA>                2012    2013  2014  2015 
## 3 01001 Autauga    AL            1 All Industries      1383941 1363… 1402… 1539…
## 4 01001 Autauga    AL            2 Private goods-prod… 286396  3104… 3235… 3463…
## 5 01001 Autauga    AL            3 Private services-p… 948490  9045… 9284… 1037…
## 6 01001 Autauga    AL            4 Government and gov… 149055  1483… 1504… 1557…
## # … with abbreviated variable name ¹`Gross domestic product (GDP) by county`

updating the col name and remove NA rows

names(gdp_by_county)[6]<-paste("2012")
names(gdp_by_county)[7]<-paste("2013")
names(gdp_by_county)[8]<-paste("2014")
names(gdp_by_county)[9]<-paste("2015")

gdp_by_county <- gdp_by_county[-c(1,2),]
head(gdp_by_county)

## # A tibble: 6 × 9
##   FIPS  Countyname Postal LineCode IndustryName      `2012` `2013` `2014` `2015`
##   <chr> <chr>      <chr>     <dbl> <chr>             <chr>  <chr>  <chr>  <chr> 
## 1 01001 Autauga    AL            1 All Industries    13839… 13633… 14025… 15394…
## 2 01001 Autauga    AL            2 Private goods-pr… 286396 310468 323582 346355
## 3 01001 Autauga    AL            3 Private services… 948490 904599 928438 10373…
## 4 01001 Autauga    AL            4 Government and g… 149055 148301 150496 155742
## 5 01003 Baldwin    AL            1 All Industries    55991… 63650… 65473… 64361…
## 6 01003 Baldwin    AL            2 Private goods-pr… 681871 698500 711443 735432

keeping consolidation GDP amount

gdp_by_county_wide<- gdp_by_county %>%
filter(LineCode == 1) 
head(gdp_by_county_wide)

## # A tibble: 6 × 9
##   FIPS  Countyname Postal LineCode IndustryName   `2012`  `2013`  `2014`  `2015`
##   <chr> <chr>      <chr>     <dbl> <chr>          <chr>   <chr>   <chr>   <chr> 
## 1 01001 Autauga    AL            1 All Industries 1383941 1363368 1402516 15394…
## 2 01003 Baldwin    AL            1 All Industries 5599194 6365080 6547396 64361…
## 3 01005 Barbour    AL            1 All Industries 639833  701750  689212  743779
## 4 01007 Bibb       AL            1 All Industries 297560  325906  329087  322307
## 5 01009 Blount     AL            1 All Industries 632761  701145  688525  819608
## 6 01011 Bullock    AL            1 All Industries 191052  190103  178408  178902

reshaping to long data format

library(tidyr)
gdp_by_county_long <- gdp_by_county_wide %>%
  gather(year, amount, -c(1:5))
head(gdp_by_county_long)

## # A tibble: 6 × 7
##   FIPS  Countyname Postal LineCode IndustryName   year  amount 
##   <chr> <chr>      <chr>     <dbl> <chr>          <chr> <chr>  
## 1 01001 Autauga    AL            1 All Industries 2012  1383941
## 2 01003 Baldwin    AL            1 All Industries 2012  5599194
## 3 01005 Barbour    AL            1 All Industries 2012  639833 
## 4 01007 Bibb       AL            1 All Industries 2012  297560 
## 5 01009 Blount     AL            1 All Industries 2012  632761 
## 6 01011 Bullock    AL            1 All Industries 2012  191052

random select 6 sample counties in AL state

gdp_by_county_long_1 <- gdp_by_county_long %>%
  filter(Postal =="AL" & FIPS %in% c('01005','01011','01017','01019','01061','01063'))

change class for amount var

gdp_by_county_long_1 <-gdp_by_county_long_1 %>%
   mutate(amount=as.integer(amount))

insert the col for growth rate

library(dbplyr)

## 
## Attaching package: 'dbplyr'

## The following objects are masked from 'package:dplyr':
## 
##     ident, sql

gdp_by_county_long_1 <- gdp_by_county_long_1 %>%
  group_by(Countyname)%>%
  mutate(Growth=(amount-lag(amount))/lag(amount))
head(gdp_by_county_long_1)

## # A tibble: 6 × 8
## # Groups:   Countyname [6]
##   FIPS  Countyname Postal LineCode IndustryName   year  amount Growth
##   <chr> <chr>      <chr>     <dbl> <chr>          <chr>  <int>  <dbl>
## 1 01005 Barbour    AL            1 All Industries 2012  639833     NA
## 2 01011 Bullock    AL            1 All Industries 2012  191052     NA
## 3 01017 Chambers   AL            1 All Industries 2012  567650     NA
## 4 01019 Cherokee   AL            1 All Industries 2012  443582     NA
## 5 01061 Geneva     AL            1 All Industries 2012  406627     NA
## 6 01063 Greene     AL            1 All Industries 2012  246604     NA

creating ggplot

library(tidyverse)
library(ggplot2)

gdp_by_county_long_1 %>%
  ggplot(aes(x=year, y=amount,color=Countyname))+
  geom_point(size =5, alpha=1)+
  geom_hline(aes(yintercept=mean(amount)))+
  theme_minimal()+
  labs(title ="Alabama- 6 Counties - 2012-2015 GDP")

Project2

Joyce Aldrich

2022-10-09

example 1. loading csv into r

reshaping data to long format

adding the average temperature for each city

using ggplot geom_point to see each city’s weekly temperature

compare 6 cities’ weekly temperature and place the overall mean in the graph

example 2:

loading cvs file into r

reshaping to the long format

adding score_average_by_student col & removing the na row

adding max_socre, min_score ,core_average_by_exam col

compare each exam score by each student and place an overall mean in the graph

example 3:

loading excel(format download) file into r

updating the col name and remove NA rows

keeping consolidation GDP amount

reshaping to long data format

random select 6 sample counties in AL state

change class for amount var

insert the col for growth rate

creating ggplot