CUNY - Bridge R HW 2

BONUS - place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.

library(tidyverse)

## -- Attaching packages -------------------------------------------------------- tidyverse 1.2.1 --

## v ggplot2 3.0.0     v purrr   0.2.5
## v tibble  1.4.2     v dplyr   0.7.6
## v tidyr   0.8.1     v stringr 1.3.1
## v readr   1.1.1     v forcats 0.3.0

## -- Conflicts ----------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

df <- read_csv('https://raw.githubusercontent.com/mandiemannz/MSDS_Bridge-2018/master/terrorism.csv')

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   X1 = col_integer(),
##   year = col_integer(),
##   methodology = col_character(),
##   method = col_character(),
##   incidents = col_integer(),
##   incidents.us = col_integer(),
##   suicide = col_integer(),
##   suicide.us = col_integer(),
##   nkill = col_integer(),
##   nkill.us = col_integer(),
##   nwound = col_integer(),
##   nwound.us = col_integer()
## )

## See spec(...) for full column specifications.

head(df)

## # A tibble: 6 x 26
##      X1  year methodology method incidents incidents.us suicide suicide.us
##   <int> <int> <chr>       <chr>      <int>        <int>   <int>      <int>
## 1     1  1970 PGIS        p            651          468       0          0
## 2     2  1971 PGIS        p            470          247       0          0
## 3     3  1972 PGIS        p            494           64       0          0
## 4     4  1973 PGIS        p            473           58       0          0
## 5     5  1974 PGIS        p            580           94       0          0
## 6     6  1975 PGIS        p            740          149       0          0
## # ... with 18 more variables: nkill <int>, nkill.us <int>, nwound <int>,
## #   nwound.us <int>, pNA.nkill <dbl>, pNA.nkill.us <dbl>,
## #   pNA.nwound <dbl>, pNA.nwound.us <dbl>, worldPopulation <dbl>,
## #   USpopulation <dbl>, worldDeathRate <dbl>, USdeathRate <dbl>,
## #   worldDeaths <dbl>, USdeaths <dbl>, kill.pmp <dbl>, kill.pmp.us <dbl>,
## #   pkill <dbl>, pkill.us <dbl>

summary(df)

##        X1             year      methodology           method         
##  Min.   : 1.00   Min.   :1970   Length:46          Length:46         
##  1st Qu.:12.25   1st Qu.:1981   Class :character   Class :character  
##  Median :23.50   Median :1992   Mode  :character   Mode  :character  
##  Mean   :23.50   Mean   :1992                                        
##  3rd Qu.:34.75   3rd Qu.:2004                                        
##  Max.   :46.00   Max.   :2015                                        
##                                                                      
##    incidents      incidents.us       suicide      suicide.us 
##  Min.   :  470   Min.   :  6.00   Min.   :  0   Min.   :0.0  
##  1st Qu.: 1348   1st Qu.: 27.00   1st Qu.:  1   1st Qu.:0.0  
##  Median : 2865   Median : 40.00   Median : 11   Median :0.0  
##  Mean   : 3516   Mean   : 59.84   Mean   :106   Mean   :0.2  
##  3rd Qu.: 4213   3rd Qu.: 64.00   3rd Qu.:119   3rd Qu.:0.0  
##  Max.   :16840   Max.   :468.00   Max.   :906   Max.   :4.0  
##                  NA's   :1        NA's   :1     NA's   :1    
##      nkill          nkill.us           nwound        nwound.us      
##  Min.   :  171   Min.   :   6.00   Min.   :   82   Min.   :   3.00  
##  1st Qu.: 3646   1st Qu.:  16.25   1st Qu.: 3423   1st Qu.:  28.25  
##  Median : 6715   Median :  25.50   Median : 6620   Median :  47.00  
##  Mean   : 7803   Mean   : 113.91   Mean   : 9842   Mean   : 117.63  
##  3rd Qu.: 9226   3rd Qu.:  59.75   3rd Qu.:12630   3rd Qu.:  81.75  
##  Max.   :43550   Max.   :2910.00   Max.   :43495   Max.   :1214.00  
##                                                                     
##    pNA.nkill         pNA.nkill.us        pNA.nwound      
##  Min.   :0.001544   Min.   :0.000000   Min.   :0.005145  
##  1st Qu.:0.012920   1st Qu.:0.009448   1st Qu.:0.036557  
##  Median :0.030534   Median :0.898408   Median :0.079877  
##  Mean   :0.072987   Mean   :0.552738   Mean   :0.141271  
##  3rd Qu.:0.103594   3rd Qu.:0.958357   3rd Qu.:0.173619  
##  Max.   :0.315914   Max.   :0.990177   Max.   :0.668016  
##  NA's   :1          NA's   :1          NA's   :1         
##  pNA.nwound.us     worldPopulation    USpopulation    worldDeathRate  
##  Min.   :0.00000   Min.   :3682488   Min.   :209486   Min.   : 7.748  
##  1st Qu.:0.01759   1st Qu.:4538702   1st Qu.:232313   1st Qu.: 8.322  
##  Median :0.90068   Median :5527580   Median :259218   Median : 9.090  
##  Mean   :0.55578   Mean   :5498924   Mean   :262592   Mean   : 9.321  
##  3rd Qu.:0.95807   3rd Qu.:6420073   3rd Qu.:292900   3rd Qu.:10.087  
##  Max.   :0.98986   Max.   :7349472   Max.   :321774   Max.   :11.966  
##  NA's   :1                                                            
##   USdeathRate     worldDeaths          USdeaths          kill.pmp      
##  Min.   :7.900   Min.   :44064648   Min.   :1892879   Min.   :0.04604  
##  1st Qu.:8.325   1st Qu.:45780625   1st Qu.:1980946   1st Qu.:0.60046  
##  Median :8.600   Median :50246087   Median :2222083   Median :1.14272  
##  Mean   :8.580   Mean   :49978598   Mean   :2224823   Mean   :1.28422  
##  3rd Qu.:8.800   3rd Qu.:53356899   3rd Qu.:2425626   3rd Qu.:1.53252  
##  Max.   :9.500   Max.   :57421426   Max.   :2665996   Max.   :5.99385  
##                                                                        
##   kill.pmp.us           pkill              pkill.us        
##  Min.   : 0.02715   Min.   :3.881e-06   Min.   :3.142e-06  
##  1st Qu.: 0.06778   1st Qu.:6.988e-05   1st Qu.:8.137e-06  
##  Median : 0.09195   Median :1.353e-04   Median :1.138e-05  
##  Mean   : 0.41003   Mean   :1.488e-04   Mean   :4.842e-05  
##  3rd Qu.: 0.23054   3rd Qu.:1.701e-04   3rd Qu.:2.692e-05  
##  Max.   :10.18208   Max.   :7.736e-04   Max.   :1.204e-03  
##

1. Use the summary function to gain an overview of the data set. Then display the mean and median for at least two attributes

mean(df$nkill)

## [1] 7802.543

median(df$nkill)

## [1] 6715

2. Create a new data frame with a subset of the columns and rows. Make sure to rename it.

mean(df$nkill.us)

## [1] 113.913

median(df$nkill.us)

## [1] 25.5

3. Create new column names for the new data frame.

data_terror <- df %>%
  select(year, nkill.us, nkill, USpopulation, USdeathRate) %>%
  rename(Year = year, NumkilledUS = nkill.us, Numkilled = nkill, USpop = USpopulation, USDeath = USdeathRate)

4. Use the summary function to create an overview of your new data frame. The print the mean and median for the same two attributes. Please compare$

summary(data_terror)

##       Year       NumkilledUS        Numkilled         USpop       
##  Min.   :1970   Min.   :   6.00   Min.   :  171   Min.   :209486  
##  1st Qu.:1981   1st Qu.:  16.25   1st Qu.: 3646   1st Qu.:232313  
##  Median :1992   Median :  25.50   Median : 6715   Median :259218  
##  Mean   :1992   Mean   : 113.91   Mean   : 7803   Mean   :262592  
##  3rd Qu.:2004   3rd Qu.:  59.75   3rd Qu.: 9226   3rd Qu.:292900  
##  Max.   :2015   Max.   :2910.00   Max.   :43550   Max.   :321774  
##     USDeath     
##  Min.   :7.900  
##  1st Qu.:8.325  
##  Median :8.600  
##  Mean   :8.580  
##  3rd Qu.:8.800  
##  Max.   :9.500

mean(data_terror$NumkilledUS)

## [1] 113.913

median(data_terror$NumkilledUS)

## [1] 25.5

mean(data_terror$Numkilled)

## [1] 7802.543

median(data_terror$Numkilled)

## [1] 6715

ggplot(data_terror, aes(Year, data_terror$Numkilled)) + 
  geom_point() +
  geom_smooth() +
  ggtitle("Number killed worldwide")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data_terror, aes(Year, data_terror$NumkilledUS)) + 
  geom_point() +
  ggtitle("Number killed in the US")

ggplot(data_terror, aes(Year)) +
  geom_line(aes(y = NumkilledUS), color="blue") +
  geom_line(aes(y = Numkilled), color="grey") +
  xlab("Year") +
  ylab("Number killed") +
  ggtitle("Number killed world wide vs. USA")

ggplot(data_terror, aes(x = cut(Year, breaks = 9), y = Numkilled)) + geom_boxplot()

5. For at least 3 values in a column please rename so that every value in that column is renamed. For example, suppose I have 20 values of the letter “e” in one column. Rename those values so that all 20 would show as“excellent”.

i <- 1
for (x in data_terror$Year){
  if (x == 1970){
    data_terror$Year[i] <- "Nineteen seventy"
  }else if (x == 1971){
    data_terror$Year[i] <- "Nineteen seventy one"
  }else if (x == 1977){
    data_terror$Year[i] <- "Nineteen seventy seven"
  }
  i <- i + 1
}
data_terror

## # A tibble: 46 x 5
##    Year                   NumkilledUS Numkilled   USpop USDeath
##    <chr>                        <int>     <int>   <dbl>   <dbl>
##  1 Nineteen seventy                28       171 209486.     9.5
##  2 Nineteen seventy one            15       173 211358.     9.3
##  3 1972                            12       566 213220.     9.4
##  4 1973                            73       370 215093.     9.3
##  5 1974                            17       542 217002.     9.1
##  6 1975                            22       617 218964.     8.8
##  7 1976                             6       672 220993.     8.8
##  8 Nineteen seventy seven           7       456 223091.     8.6
##  9 1978                            11      1459 225239.     8.7
## 10 1979                            16      2100 227412.     8.5
## # ... with 36 more rows

CUNY - Bridge R HW 2

Amanda Arce

July 23, 2018

BONUS - place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.

1. Use the summary function to gain an overview of the data set. Then display the mean and median for at least two attributes

2. Create a new data frame with a subset of the columns and rows. Make sure to rename it.

3. Create new column names for the new data frame.

4. Use the summary function to create an overview of your new data frame. The print the mean and median for the same two attributes. Please compare$

5. For at least 3 values in a column please rename so that every value in that column is renamed. For example, suppose I have 20 values of the letter “e” in one column. Rename those values so that all 20 would show as“excellent”.

6. Display enough rows to see examples of all of steps 1-5 above.