Get the data

Download file into json var.

library(jsonlite)

url <- "http://fisherman.pt/uniplaces/cities.topo.json"
json <- fromJSON(url, simplifyDataFrame = FALSE)

Clean the data

Structured data

Non-structured to structured data.

jsonCities <- json$objects$cities$geometries

countCities <- length(jsonCities)
print(countCities)

## [1] 648

countFeatures <- length(jsonCities[[1]]$properties)
print(countFeatures)

## [1] 19

data <- data.frame()
row <- vector()
for (i in 1:countCities) {
    for (j in 1:countFeatures) {
          row[j] <- jsonCities[[i]]$properties[[j]]
    }
    data <- rbind(data, t(as.data.frame(row)))
}

Rename columns, rows

Rename columns, rows.

library(knitr)

names(data) <- names(jsonCities[[1]]$properties)
row.names(data) <- 1:length(jsonCities)

data$count <- as.numeric(as.character(data$count))
data$accom <- as.numeric(as.character(data$accom))
data$landl <- as.numeric(as.character(data$landl))
data$cost1 <- as.numeric(as.character(data$cost1))  
data$cost2 <- as.numeric(as.character(data$cost2))
data$cost3 <- as.numeric(as.character(data$cost3))  
data$cost4 <- as.numeric(as.character(data$cost4))  
data$cost5 <- as.numeric(as.character(data$cost5))  
data$nlife <- as.numeric(as.character(data$nlife))  
data$odoor <- as.numeric(as.character(data$odoor))  
data$qlife <- as.numeric(as.character(data$qlife))  
data$shope <- as.numeric(as.character(data$shope))  
data$unive <- as.numeric(as.character(data$unive))  
data$cultu <- as.numeric(as.character(data$cultu))  
data$clink <- as.character(data$clink)
data$cname <- as.character(data$cname)
data$votes <- as.numeric(as.character(data$votes))

Drop `img` and `clink`

We will drop the columns img and clink, as they hold no meaningful information.

library(dplyr)

data <- data %>%
            select(-img, -clink)

Let’s print our structured data now.

kable(head(data, n = 5), digits=2, caption="**Table 1: Sample data - Top 5 cities**")

**Table 1: Sample data - Top 5 cities**
ccode	count	accom	landl	cost1	cost2	cost3	cost4	cost5	nlife	odoor	qlife	shope	unive	cultu	cname	votes
2553	1116	73.57	76.82	4.39	33.15	28.49	30.73	3.23	33.78	5.82	34.77	5.02	2.24	18.37	Madrid	1396
2777	974	91.11	89.44	1.33	6.26	21.46	64.07	6.88	8.01	25.56	39.63	0.51	20.53	5.75	Oulu	1293
3602	593	77.30	79.71	11.64	44.18	28.50	15.18	0.51	22.60	14.17	34.06	4.38	3.04	21.75	Lisbon	755
3412	441	90.61	86.89	35.37	44.44	16.10	3.17	0.91	38.55	9.98	19.73	3.85	10.88	17.01	Vilnius	564
2691	337	81.54	81.99	22.85	48.66	20.47	7.42	0.59	16.91	7.12	47.48	4.45	2.97	21.07	Sevilla	544

Remove NAs

Inspect if NAs can be ommited from the analysis, or if they mean something.

sum(is.na(data$cost1))

## [1] 304

sum(is.na(data$cost1))/nrow(data)

## [1] 0.4691358

dataNa <- filter(data, is.na(cost1))
kable(head(dataNa), digits=2, caption="**Table 2: Sample data - NA values**")

**Table 2: Sample data - NA values**
ccode	cost1	cost2	cost3	cost4	cost5	nlife	odoor	qlife	shope	unive	cultu	cname	votes
3595	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Bragança	184
3595	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Covilhã	134
3595	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Malaga	132
3595	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Strasbourg	105
3595	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Rovaniemi	100
3595	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	Aveiro	95

We can safely remove NAs.

data <- filter(data, !is.na(cost1))

Prepare the data

Units

The scale appears to range between 0 and 100, for all features.

Overral

library(ggplot2)
summary(data$accom)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   40.00   80.00   80.00   79.92   82.46  100.00

qplot(data$accom, geom="histogram", xlab = "\nRating", ylab = "Count\n", main="Histogram of accom\n")

summary(data$landl)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   45.00   82.84   86.00   83.79   86.00  100.00

qplot(data$landl, geom="histogram", xlab = "\nRating", ylab = "Count\n", main="Histogram of landl\n")

Best known for

Should add up to 100.

dataBestFor <- mutate(data, total = nlife + odoor + qlife + shope + unive + cultu) 

kable(head(dataBestFor, n= 5), digits = 2, caption="**Table 3: Sample data - Best for**")

**Table 3: Sample data - Best for**
ccode	count	accom	landl	cost1	cost2	cost3	cost4	cost5	nlife	odoor	qlife	shope	unive	cultu	cname	votes	total
2553	1116	73.57	76.82	4.39	33.15	28.49	30.73	3.23	33.78	5.82	34.77	5.02	2.24	18.37	Madrid	1396	100
2777	974	91.11	89.44	1.33	6.26	21.46	64.07	6.88	8.01	25.56	39.63	0.51	20.53	5.75	Oulu	1293	100
3602	593	77.30	79.71	11.64	44.18	28.50	15.18	0.51	22.60	14.17	34.06	4.38	3.04	21.75	Lisbon	755	100
3412	441	90.61	86.89	35.37	44.44	16.10	3.17	0.91	38.55	9.98	19.73	3.85	10.88	17.01	Vilnius	564	100
2691	337	81.54	81.99	22.85	48.66	20.47	7.42	0.59	16.91	7.12	47.48	4.45	2.97	21.07	Sevilla	544	100

Check!

Cost

Compute total cost

We will use a weighted average.

data <- mutate(data, cost = 1 * cost1/100 + 2 * cost2/100 + 3 * cost3/100 + 4 * cost4/100 + 5 * cost5/100)

kable(head(data, n= 5), digits = 2, caption="**Table 4: Sample data - Total cost**")

**Table 4: Sample data - Total cost**
ccode	count	accom	landl	cost1	cost2	cost3	cost4	cost5	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2553	1116	73.57	76.82	4.39	33.15	28.49	30.73	3.23	33.78	5.82	34.77	5.02	2.24	18.37	Madrid	1396	2.95
2777	974	91.11	89.44	1.33	6.26	21.46	64.07	6.88	8.01	25.56	39.63	0.51	20.53	5.75	Oulu	1293	3.69
3602	593	77.30	79.71	11.64	44.18	28.50	15.18	0.51	22.60	14.17	34.06	4.38	3.04	21.75	Lisbon	755	2.49
3412	441	90.61	86.89	35.37	44.44	16.10	3.17	0.91	38.55	9.98	19.73	3.85	10.88	17.01	Vilnius	564	1.90
2691	337	81.54	81.99	22.85	48.66	20.47	7.42	0.59	16.91	7.12	47.48	4.45	2.97	21.07	Sevilla	544	2.14

Drop `cost1`, `cost2`, `cost3`, `cost4`, `cost5`

data <- data %>%
            select(-cost1, -cost2, -cost3, -cost4, -cost5)

Minimum number of votes

summary(data$count)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.00    5.00    5.00   43.74   36.00 1116.00

dataSelected <- filter(data, count >= 100)

Results

City level

Most voted

kable(head(arrange(dataSelected, desc(votes)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2553	1116	73.56631	76.81900	33.781362	5.824373	34.76703	5.0179211	2.240143	18.369176	Madrid	1396	2.952509
2777	974	91.10883	89.43532	8.008214	25.564682	39.63039	0.5133470	20.533881	5.749487	Oulu	1293	3.688912
3602	593	77.30186	79.71332	22.596965	14.165261	34.06408	4.3844857	3.035413	21.753794	Lisbon	755	2.487352
3412	441	90.61224	86.89342	38.548753	9.977324	19.72789	3.8548753	10.884354	17.006803	Vilnius	564	1.897959
2691	337	81.54303	81.98813	16.913947	7.121662	47.47774	4.4510386	2.967359	21.068249	Sevilla	544	2.142433
3608	383	80.83551	82.84595	25.848564	11.749347	33.68146	2.8720627	4.960835	20.887729	Porto	487	2.368146
2379	346	72.08092	75.52023	21.098266	10.404624	39.01734	3.7572254	2.312139	23.410405	Barcelona	447	3.225433
3279	314	80.89172	81.56051	66.242038	7.006369	12.10191	1.9108280	2.547771	10.191083	Budapest	421	1.885350
2090	317	84.73186	80.41009	42.902208	8.201893	21.76656	0.9463722	4.416404	21.766562	Prague	409	2.100946
2232	285	85.47368	85.96491	20.000000	10.175439	44.21053	1.7543860	12.631579	11.228070	Regensburg	406	2.947368

Accommodation

Hardest to find

kable(head(arrange(dataSelected, accom), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
3081	118	53.72881	66.18644	5.932203	5.084746	22.88136	0.8474576	7.627119	57.627119	Paris	152	4.381356
3325	185	60.75676	70.37838	39.459460	4.864865	24.86486	2.7027027	5.945946	22.162162	Bologna	267	3.486486
3382	170	65.88235	71.35294	10.000000	6.470588	15.88235	2.9411765	1.176471	63.529412	Rome	216	3.852941
3361	179	68.49162	74.58101	33.519553	2.793296	22.90503	16.2011173	6.703911	17.877095	Milan	220	4.055866
3743	128	72.03125	75.00000	12.500000	7.812500	8.59375	7.0312500	2.343750	61.718750	Istanbul	161	2.343750
2379	346	72.08092	75.52023	21.098266	10.404624	39.01734	3.7572254	2.312139	23.410405	Barcelona	447	3.225433
2553	1116	73.56631	76.81900	33.781362	5.824373	34.76703	5.0179211	2.240143	18.369176	Madrid	1396	2.952509
3385	146	75.06849	77.87671	3.424657	2.739726	42.46575	2.0547945	6.849315	42.465753	Siena	164	3.595890
2004	123	75.60976	78.04878	2.439024	5.691057	60.97561	0.8130081	3.252032	26.829268	Vienna	152	3.528455
2018	285	76.28070	79.01754	36.491228	4.561403	20.35088	0.0000000	35.438597	3.157895	Leuven	393	3.414035

Easiest to find

kable(head(arrange(dataSelected, desc(accom)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2069	162	93.82716	90.43210	45.061728	6.1728395	22.22222	3.7037037	14.814815	8.024691	Brno	221	1.858025
2782	121	91.90083	90.57851	13.223140	19.0082645	38.01653	1.6528926	23.966942	4.132231	Turku	146	3.851240
2777	974	91.10883	89.43532	8.008214	25.5646817	39.63039	0.5133470	20.533881	5.749487	Oulu	1293	3.688912
3628	105	90.66667	87.61905	55.238095	7.6190476	19.04762	0.9523810	5.714286	11.428571	Cluj-Napoca	144	1.809524
3412	441	90.61224	86.89342	38.548753	9.9773243	19.72789	3.8548753	10.884354	17.006803	Vilnius	564	1.897959
2731	104	89.42308	89.42308	32.692308	0.9615385	44.23077	3.8461538	12.500000	5.769231	Valladolid	126	2.067308
2338	120	89.00000	88.83333	25.833333	18.3333333	43.33333	0.8333333	2.500000	9.166667	Almeria	154	1.783333
3573	166	88.31325	84.93976	42.168675	6.0240964	30.12048	5.4216867	4.819277	11.445783	Wrocław	215	1.825301
2492	274	87.59124	86.42336	33.941606	4.7445255	30.65693	2.1897810	2.554744	25.912409	Granada	365	1.839416
3542	215	87.53488	84.04651	46.976744	8.3720930	26.04651	6.0465116	7.441860	5.116279	Poznań	269	1.725581

Landlord

Worst

kable(head(arrange(dataSelected, landl), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
3081	118	53.72881	66.18644	5.932203	5.084746	22.88136	0.8474576	7.627119	57.627119	Paris	152	4.381356
3325	185	60.75676	70.37838	39.459460	4.864865	24.86486	2.7027027	5.945946	22.162162	Bologna	267	3.486486
3382	170	65.88235	71.35294	10.000000	6.470588	15.88235	2.9411765	1.176471	63.529412	Rome	216	3.852941
3361	179	68.49162	74.58101	33.519553	2.793296	22.90503	16.2011173	6.703911	17.877095	Milan	220	4.055866
3743	128	72.03125	75.00000	12.500000	7.812500	8.59375	7.0312500	2.343750	61.718750	Istanbul	161	2.343750
2379	346	72.08092	75.52023	21.098266	10.404624	39.01734	3.7572254	2.312139	23.410405	Barcelona	447	3.225433
2553	1116	73.56631	76.81900	33.781362	5.824373	34.76703	5.0179211	2.240143	18.369176	Madrid	1396	2.952509
3385	146	75.06849	77.87671	3.424657	2.739726	42.46575	2.0547945	6.849315	42.465753	Siena	164	3.595890
2004	123	75.60976	78.04878	2.439024	5.691057	60.97561	0.8130081	3.252032	26.829268	Vienna	152	3.528455
2018	285	76.28070	79.01754	36.491228	4.561403	20.35088	0.0000000	35.438597	3.157895	Leuven	393	3.414035

Best

kable(head(arrange(dataSelected, desc(landl)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2782	121	91.90083	90.57851	13.223140	19.0082645	38.01653	1.6528926	23.966942	4.132231	Turku	146	3.851240
2069	162	93.82716	90.43210	45.061728	6.1728395	22.22222	3.7037037	14.814815	8.024691	Brno	221	1.858025
2777	974	91.10883	89.43532	8.008214	25.5646817	39.63039	0.5133470	20.533881	5.749487	Oulu	1293	3.688912
2731	104	89.42308	89.42308	32.692308	0.9615385	44.23077	3.8461538	12.500000	5.769231	Valladolid	126	2.067308
2338	120	89.00000	88.83333	25.833333	18.3333333	43.33333	0.8333333	2.500000	9.166667	Almeria	154	1.783333
3628	105	90.66667	87.61905	55.238095	7.6190476	19.04762	0.9523810	5.714286	11.428571	Cluj-Napoca	144	1.809524
3412	441	90.61224	86.89342	38.548753	9.9773243	19.72789	3.8548753	10.884354	17.006803	Vilnius	564	1.897959
2492	274	87.59124	86.42336	33.941606	4.7445255	30.65693	2.1897810	2.554744	25.912409	Granada	365	1.839416
3680	131	86.56489	86.18321	13.740458	18.3206107	52.67176	10.6870229	1.526718	3.053435	Maribor	165	2.274809
2288	115	87.13043	86.17391	13.913044	4.3478261	59.13043	0.8695652	15.652174	6.086957	Odense	172	3.695652

Nightlife

Worst

kable(head(arrange(dataSelected, nlife), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2004	123	75.60976	78.04878	2.439024	5.691057	60.97561	0.8130081	3.252032	26.829268	Vienna	152	3.528455
3385	146	75.06849	77.87671	3.424657	2.739726	42.46575	2.0547945	6.849315	42.465753	Siena	164	3.595890
3081	118	53.72881	66.18644	5.932203	5.084746	22.88136	0.8474576	7.627119	57.627119	Paris	152	4.381356
2777	974	91.10883	89.43532	8.008214	25.564682	39.63039	0.5133470	20.533881	5.749487	Oulu	1293	3.688912
3382	170	65.88235	71.35294	10.000000	6.470588	15.88235	2.9411765	1.176471	63.529412	Rome	216	3.852941
3679	163	83.19018	84.60123	12.269939	16.564417	48.46626	10.4294479	7.975460	4.294479	Ljubljana	221	2.564417
3743	128	72.03125	75.00000	12.500000	7.812500	8.59375	7.0312500	2.343750	61.718750	Istanbul	161	2.343750
2782	121	91.90083	90.57851	13.223140	19.008264	38.01653	1.6528926	23.966942	4.132231	Turku	146	3.851240
3680	131	86.56489	86.18321	13.740458	18.320611	52.67176	10.6870229	1.526718	3.053435	Maribor	165	2.274809
2288	115	87.13043	86.17391	13.913044	4.347826	59.13043	0.8695652	15.652174	6.086957	Odense	172	3.695652

Best

kable(head(arrange(dataSelected, desc(nlife)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
3279	314	80.89172	81.56051	66.24204	7.006369	12.10191	1.9108280	2.547771	10.191083	Budapest	421	1.885350
3628	105	90.66667	87.61905	55.23810	7.619048	19.04762	0.9523810	5.714286	11.428571	Cluj-Napoca	144	1.809524
3946	221	85.97285	83.30317	53.39367	2.714932	22.17195	1.8099548	6.334842	13.574661	Salamanca	270	1.932127
3542	215	87.53488	84.04651	46.97674	8.372093	26.04651	6.0465116	7.441860	5.116279	Poznań	269	1.725581
3571	231	80.17316	81.99134	46.32035	5.627706	19.04762	4.3290043	7.792208	16.883117	Warszawa	298	1.822511
3589	224	86.87500	84.91071	45.98214	1.785714	28.12500	0.4464286	11.160714	12.500000	Coimbra	292	2.142857
2069	162	93.82716	90.43210	45.06173	6.172840	22.22222	3.7037037	14.814815	8.024691	Brno	221	1.858025
3517	241	82.40664	81.82573	43.15353	6.224066	21.57676	3.7344398	5.394191	19.917012	Kraków	316	1.792531
2090	317	84.73186	80.41009	42.90221	8.201893	21.76656	0.9463722	4.416404	21.766562	Prague	409	2.100946
3573	166	88.31325	84.93976	42.16867	6.024096	30.12048	5.4216867	4.819277	11.445783	Wrocław	215	1.825301

Outdoor

Worst

kable(head(arrange(dataSelected, odoor), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2731	104	89.42308	89.42308	32.692308	0.9615385	44.23077	3.8461538	12.500000	5.769231	Valladolid	126	2.067308
3589	224	86.87500	84.91071	45.982143	1.7857143	28.12500	0.4464286	11.160714	12.500000	Coimbra	292	2.142857
3946	221	85.97285	83.30317	53.393665	2.7149321	22.17195	1.8099548	6.334842	13.574661	Salamanca	270	1.932127
3385	146	75.06849	77.87671	3.424657	2.7397260	42.46575	2.0547945	6.849315	42.465753	Siena	164	3.595890
3361	179	68.49162	74.58101	33.519553	2.7932961	22.90503	16.2011173	6.703911	17.877095	Milan	220	4.055866
2288	115	87.13043	86.17391	13.913044	4.3478261	59.13043	0.8695652	15.652174	6.086957	Odense	172	3.695652
2018	285	76.28070	79.01754	36.491228	4.5614035	20.35088	0.0000000	35.438597	3.157895	Leuven	393	3.414035
2492	274	87.59124	86.42336	33.941606	4.7445255	30.65693	2.1897810	2.554744	25.912409	Granada	365	1.839416
2014	167	81.43713	82.45509	22.754491	4.7904192	44.31138	1.1976048	14.371258	12.574850	Ghent	209	3.586826
3325	185	60.75676	70.37838	39.459460	4.8648649	24.86486	2.7027027	5.945946	22.162162	Bologna	267	3.486486

Best

kable(head(arrange(dataSelected, desc(odoor)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2777	974	91.10883	89.43532	8.008214	25.56468	39.63039	0.5133470	20.533881	5.749487	Oulu	1293	3.688912
2782	121	91.90083	90.57851	13.223140	19.00826	38.01653	1.6528926	23.966942	4.132231	Turku	146	3.851240
2338	120	89.00000	88.83333	25.833333	18.33333	43.33333	0.8333333	2.500000	9.166667	Almeria	154	1.783333
3680	131	86.56489	86.18321	13.740458	18.32061	52.67176	10.6870229	1.526718	3.053435	Maribor	165	2.274809
2409	214	81.40187	81.86916	16.355140	16.82243	42.99065	1.8691589	3.271028	18.691589	Cadiz	275	2.163551
3679	163	83.19018	84.60123	12.269939	16.56442	48.46626	10.4294479	7.975460	4.294479	Ljubljana	221	2.564417
3602	593	77.30186	79.71332	22.596965	14.16526	34.06408	4.3844857	3.035413	21.753794	Lisbon	755	2.487352
2297	155	83.09677	85.03226	25.806452	13.54839	29.03226	3.2258065	12.258064	16.129032	Tallinn	195	2.451613
3608	383	80.83551	82.84595	25.848564	11.74935	33.68146	2.8720627	4.960835	20.887729	Porto	487	2.368146
2301	111	84.14414	83.51351	25.225225	11.71171	45.04505	3.6036036	6.306306	8.108108	A Coruña	139	2.387387

Quality of life

Worst

kable(head(arrange(dataSelected, qlife), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
3743	128	72.03125	75.00000	12.50000	7.812500	8.59375	7.0312500	2.343750	61.718750	Istanbul	161	2.343750
3279	314	80.89172	81.56051	66.24204	7.006369	12.10191	1.9108280	2.547771	10.191083	Budapest	421	1.885350
3382	170	65.88235	71.35294	10.00000	6.470588	15.88235	2.9411765	1.176471	63.529412	Rome	216	3.852941
3571	231	80.17316	81.99134	46.32035	5.627706	19.04762	4.3290043	7.792208	16.883117	Warszawa	298	1.822511
3628	105	90.66667	87.61905	55.23810	7.619048	19.04762	0.9523810	5.714286	11.428571	Cluj-Napoca	144	1.809524
3412	441	90.61224	86.89342	38.54875	9.977324	19.72789	3.8548753	10.884354	17.006803	Vilnius	564	1.897959
2018	285	76.28070	79.01754	36.49123	4.561403	20.35088	0.0000000	35.438597	3.157895	Leuven	393	3.414035
3517	241	82.40664	81.82573	43.15353	6.224066	21.57676	3.7344398	5.394191	19.917012	Kraków	316	1.792531
2090	317	84.73186	80.41009	42.90221	8.201893	21.76656	0.9463722	4.416404	21.766562	Prague	409	2.100946
3946	221	85.97285	83.30317	53.39367	2.714932	22.17195	1.8099548	6.334842	13.574661	Salamanca	270	1.932127

Best

kable(head(arrange(dataSelected, desc(qlife)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2004	123	75.60976	78.04878	2.439024	5.6910569	60.97561	0.8130081	3.252032	26.829268	Vienna	152	3.528455
2288	115	87.13043	86.17391	13.913044	4.3478261	59.13043	0.8695652	15.652174	6.086957	Odense	172	3.695652
3680	131	86.56489	86.18321	13.740458	18.3206107	52.67176	10.6870229	1.526718	3.053435	Maribor	165	2.274809
2729	229	82.96943	84.36681	24.890830	8.7336245	49.34498	1.7467249	5.240175	10.043668	Valencia	304	2.222707
3679	163	83.19018	84.60123	12.269939	16.5644172	48.46626	10.4294479	7.975460	4.294479	Ljubljana	221	2.564417
2691	337	81.54303	81.98813	16.913947	7.1216617	47.47774	4.4510386	2.967359	21.068249	Sevilla	544	2.142433
2301	111	84.14414	83.51351	25.225225	11.7117117	45.04505	3.6036036	6.306306	8.108108	A Coruña	139	2.387387
2014	167	81.43713	82.45509	22.754491	4.7904192	44.31138	1.1976048	14.371258	12.574850	Ghent	209	3.586826
2731	104	89.42308	89.42308	32.692308	0.9615385	44.23077	3.8461538	12.500000	5.769231	Valladolid	126	2.067308
2232	285	85.47368	85.96491	20.000000	10.1754386	44.21053	1.7543860	12.631579	11.228070	Regensburg	406	2.947368

Shopping

Worst

kable(head(arrange(dataSelected, shope), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2018	285	76.28070	79.01754	36.491228	4.561403	20.35088	0.0000000	35.438597	3.157895	Leuven	393	3.414035
3589	224	86.87500	84.91071	45.982143	1.785714	28.12500	0.4464286	11.160714	12.500000	Coimbra	292	2.142857
2777	974	91.10883	89.43532	8.008214	25.564682	39.63039	0.5133470	20.533881	5.749487	Oulu	1293	3.688912
2004	123	75.60976	78.04878	2.439024	5.691057	60.97561	0.8130081	3.252032	26.829268	Vienna	152	3.528455
2338	120	89.00000	88.83333	25.833333	18.333333	43.33333	0.8333333	2.500000	9.166667	Almeria	154	1.783333
3081	118	53.72881	66.18644	5.932203	5.084746	22.88136	0.8474576	7.627119	57.627119	Paris	152	4.381356
2288	115	87.13043	86.17391	13.913044	4.347826	59.13043	0.8695652	15.652174	6.086957	Odense	172	3.695652
2090	317	84.73186	80.41009	42.902208	8.201893	21.76656	0.9463722	4.416404	21.766562	Prague	409	2.100946
3628	105	90.66667	87.61905	55.238095	7.619048	19.04762	0.9523810	5.714286	11.428571	Cluj-Napoca	144	1.809524
2014	167	81.43713	82.45509	22.754491	4.790419	44.31138	1.1976048	14.371258	12.574850	Ghent	209	3.586826

Best

kable(head(arrange(dataSelected, desc(shope)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
3361	179	68.49162	74.58101	33.51955	2.793296	22.90503	16.201117	6.703911	17.877095	Milan	220	4.055866
3680	131	86.56489	86.18321	13.74046	18.320611	52.67176	10.687023	1.526718	3.053435	Maribor	165	2.274809
3679	163	83.19018	84.60123	12.26994	16.564417	48.46626	10.429448	7.975460	4.294479	Ljubljana	221	2.564417
3259	105	81.52381	83.42857	38.09524	7.619048	22.85714	9.523810	3.809524	18.095238	Thessaloniki	126	2.542857
3743	128	72.03125	75.00000	12.50000	7.812500	8.59375	7.031250	2.343750	61.718750	Istanbul	161	2.343750
2589	128	85.00000	83.20312	36.71875	6.250000	39.06250	6.250000	6.250000	5.468750	Murcia	173	1.921875
3542	215	87.53488	84.04651	46.97674	8.372093	26.04651	6.046512	7.441860	5.116279	Poznań	269	1.725581
3573	166	88.31325	84.93976	42.16867	6.024096	30.12048	5.421687	4.819277	11.445783	Wrocław	215	1.825301
2553	1116	73.56631	76.81900	33.78136	5.824373	34.76703	5.017921	2.240143	18.369176	Madrid	1396	2.952509
2691	337	81.54303	81.98813	16.91395	7.121662	47.47774	4.451039	2.967359	21.068249	Sevilla	544	2.142433

University

Worst

kable(head(arrange(dataSelected, unive), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
3382	170	65.88235	71.35294	10.00000	6.470588	15.88235	2.9411765	1.176471	63.529412	Rome	216	3.852941
3680	131	86.56489	86.18321	13.74046	18.320611	52.67176	10.6870229	1.526718	3.053435	Maribor	165	2.274809
2553	1116	73.56631	76.81900	33.78136	5.824373	34.76703	5.0179211	2.240143	18.369176	Madrid	1396	2.952509
2379	346	72.08092	75.52023	21.09827	10.404624	39.01734	3.7572254	2.312139	23.410405	Barcelona	447	3.225433
3743	128	72.03125	75.00000	12.50000	7.812500	8.59375	7.0312500	2.343750	61.718750	Istanbul	161	2.343750
2338	120	89.00000	88.83333	25.83333	18.333333	43.33333	0.8333333	2.500000	9.166667	Almeria	154	1.783333
3279	314	80.89172	81.56051	66.24204	7.006369	12.10191	1.9108280	2.547771	10.191083	Budapest	421	1.885350
2492	274	87.59124	86.42336	33.94161	4.744525	30.65693	2.1897810	2.554744	25.912409	Granada	365	1.839416
2691	337	81.54303	81.98813	16.91395	7.121662	47.47774	4.4510386	2.967359	21.068249	Sevilla	544	2.142433
3602	593	77.30186	79.71332	22.59696	14.165261	34.06408	4.3844857	3.035413	21.753794	Lisbon	755	2.487352

Best

kable(head(arrange(dataSelected, desc(unive)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
2018	285	76.28070	79.01754	36.491228	4.5614035	20.35088	0.0000000	35.43860	3.157895	Leuven	393	3.414035
2782	121	91.90083	90.57851	13.223140	19.0082645	38.01653	1.6528926	23.96694	4.132231	Turku	146	3.851240
2777	974	91.10883	89.43532	8.008214	25.5646817	39.63039	0.5133470	20.53388	5.749487	Oulu	1293	3.688912
2288	115	87.13043	86.17391	13.913044	4.3478261	59.13043	0.8695652	15.65217	6.086957	Odense	172	3.695652
2069	162	93.82716	90.43210	45.061728	6.1728395	22.22222	3.7037037	14.81481	8.024691	Brno	221	1.858025
2014	167	81.43713	82.45509	22.754491	4.7904192	44.31138	1.1976048	14.37126	12.574850	Ghent	209	3.586826
2232	285	85.47368	85.96491	20.000000	10.1754386	44.21053	1.7543860	12.63158	11.228070	Regensburg	406	2.947368
2731	104	89.42308	89.42308	32.692308	0.9615385	44.23077	3.8461538	12.50000	5.769231	Valladolid	126	2.067308
2297	155	83.09677	85.03226	25.806452	13.5483871	29.03226	3.2258065	12.25806	16.129032	Tallinn	195	2.451613
3589	224	86.87500	84.91071	45.982143	1.7857143	28.12500	0.4464286	11.16071	12.500000	Coimbra	292	2.142857

Culture

Worst

kable(head(arrange(dataSelected, cultu), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
3680	131	86.56489	86.18321	13.740458	18.3206107	52.67176	10.6870229	1.526718	3.053435	Maribor	165	2.274809
2018	285	76.28070	79.01754	36.491228	4.5614035	20.35088	0.0000000	35.438597	3.157895	Leuven	393	3.414035
2782	121	91.90083	90.57851	13.223140	19.0082645	38.01653	1.6528926	23.966942	4.132231	Turku	146	3.851240
3679	163	83.19018	84.60123	12.269939	16.5644172	48.46626	10.4294479	7.975460	4.294479	Ljubljana	221	2.564417
3542	215	87.53488	84.04651	46.976744	8.3720930	26.04651	6.0465116	7.441860	5.116279	Poznań	269	1.725581
2589	128	85.00000	83.20312	36.718750	6.2500000	39.06250	6.2500000	6.250000	5.468750	Murcia	173	1.921875
2777	974	91.10883	89.43532	8.008214	25.5646817	39.63039	0.5133470	20.533881	5.749487	Oulu	1293	3.688912
2731	104	89.42308	89.42308	32.692308	0.9615385	44.23077	3.8461538	12.500000	5.769231	Valladolid	126	2.067308
2288	115	87.13043	86.17391	13.913044	4.3478261	59.13043	0.8695652	15.652174	6.086957	Odense	172	3.695652
2069	162	93.82716	90.43210	45.061728	6.1728395	22.22222	3.7037037	14.814815	8.024691	Brno	221	1.858025

Best

kable(head(arrange(dataSelected, desc(cultu)), n = 10))

ccode	count	accom	landl	nlife	odoor	qlife	shope	unive	cultu	cname	votes	cost
3382	170	65.88235	71.35294	10.000000	6.470588	15.88235	2.9411765	1.176471	63.52941	Rome	216	3.852941
3743	128	72.03125	75.00000	12.500000	7.812500	8.59375	7.0312500	2.343750	61.71875	Istanbul	161	2.343750
3081	118	53.72881	66.18644	5.932203	5.084746	22.88136	0.8474576	7.627119	57.62712	Paris	152	4.381356
3385	146	75.06849	77.87671	3.424657	2.739726	42.46575	2.0547945	6.849315	42.46575	Siena	164	3.595890
2004	123	75.60976	78.04878	2.439024	5.691057	60.97561	0.8130081	3.252032	26.82927	Vienna	152	3.528455
2492	274	87.59124	86.42336	33.941606	4.744525	30.65693	2.1897810	2.554744	25.91241	Granada	365	1.839416
2379	346	72.08092	75.52023	21.098266	10.404624	39.01734	3.7572254	2.312139	23.41040	Barcelona	447	3.225433
3325	185	60.75676	70.37838	39.459460	4.864865	24.86486	2.7027027	5.945946	22.16216	Bologna	267	3.486486
2090	317	84.73186	80.41009	42.902208	8.201893	21.76656	0.9463722	4.416404	21.76656	Prague	409	2.100946
3602	593	77.30186	79.71332	22.596965	14.165261	34.06408	4.3844857	3.035413	21.75379	Lisbon	755	2.487352

Country level

Add country level to table

country <- read.csv("cityToCountry.csv")
country <- country %>% select(country, cname)
dataCountry <- inner_join(data, country)
write.csv(dataCountry, "exportPerCity.csv")
dataCountry <- dataCountry%>%
                   group_by(country) %>%
                       summarize(
                             avgAccom = mean(accom),
                             avgLandl = mean(landl),
                             avgNlife = mean(nlife),
                             avgOdoor = mean(odoor),
                             avgQlife = mean(qlife),
                             avgShope = mean(shope),
                             avgUnive = mean(unive),
                             avgCultu = mean(cultu),
                             totalVotes = sum(votes)
                       )
write.csv(dataCountry, "exportPerCountry.csv")

dataCountry <- dataCountry %>% 
                   filter(totalVotes >= 100)

Most voted

Accommodation

Hardest to find

kable(head(arrange(dataCountry, avgAccom), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
IE	63.31781	69.21773	35.902778	8.031046	18.61928	0.6535948	15.645425	21.147876	112
FR	73.89814	76.78320	12.782470	17.808194	37.24746	2.5549317	11.039060	18.567888	579
GR	76.01814	82.10658	30.952381	18.752834	13.40136	14.9659864	1.609977	20.317460	264
LV	76.32576	80.27273	10.795454	14.393939	32.27273	0.1893939	21.325758	21.022727	127
NL	76.76150	82.27853	2.540004	1.290569	56.47110	0.0517598	37.576777	2.069790	482
DE	77.09032	81.28841	9.868833	7.683723	50.01204	2.4025205	22.473879	7.559007	1162
IT	77.93540	83.50275	4.172640	1.368243	52.90207	1.1212325	31.807551	8.628268	1527
UK	78.36058	83.94827	6.909223	2.681993	49.87898	1.2588948	33.658848	5.612061	418
DK	78.97833	80.20713	7.014445	1.086957	73.17077	0.2173913	13.575872	4.934568	260
SE	79.78694	85.15585	2.162630	2.314316	59.43257	0.4524887	34.070765	1.567226	299

Easiest to find

kable(head(arrange(dataCountry, desc(avgAccom)), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
FI	90.44099	89.20359	7.932185	19.114774	43.57973	0.4332479	24.165757	4.7743087	1681
EE	89.88172	88.81821	24.361559	14.586694	29.62030	1.6129032	20.191532	9.6270161	335
SK	89.67710	87.76838	25.085524	12.749270	41.68461	0.4255319	19.204005	0.8510638	120
RO	87.53704	86.57143	48.346561	5.747355	21.01190	1.9973545	11.937831	10.9589947	253
PT	85.36973	86.79596	19.210137	6.709199	48.75145	1.4405613	10.600036	13.2886158	1716
LT	84.23356	86.36757	17.154195	5.328798	41.61224	2.9931973	27.843537	5.0680272	627
SI	83.54988	85.72389	6.502599	15.665701	50.00673	9.4457844	12.375544	6.0036451	418
CZ	82.54850	86.16369	11.162061	1.655199	51.18595	0.4675883	33.017786	2.5114151	908
HR	82.53003	84.81231	16.261261	18.596096	35.40541	1.3513514	3.986486	24.3993994	161
TR	81.45409	86.27549	1.750905	2.359134	50.34040	3.7461275	32.676352	9.1270774	263

Landlord

Worst

kable(head(arrange(dataCountry, avgLandl), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
IE	63.31781	69.21773	35.902778	8.031046	18.61928	0.6535948	15.645425	21.147876	112
FR	73.89814	76.78320	12.782470	17.808194	37.24746	2.5549317	11.039060	18.567888	579
DK	78.97833	80.20713	7.014445	1.086957	73.17077	0.2173913	13.575872	4.934568	260
LV	76.32576	80.27273	10.795454	14.393939	32.27273	0.1893939	21.325758	21.022727	127
DE	77.09032	81.28841	9.868833	7.683723	50.01204	2.4025205	22.473879	7.559007	1162
HU	80.99899	81.74824	45.472492	7.013819	18.65385	2.3913286	17.223526	9.244981	521
GR	76.01814	82.10658	30.952381	18.752834	13.40136	14.9659864	1.609977	20.317460	264
NL	76.76150	82.27853	2.540004	1.290569	56.47110	0.0517598	37.576777	2.069790	482
ES	81.38877	83.09755	25.111527	10.422382	37.09779	2.9823586	5.878184	18.507755	4844
BE	81.05056	83.20388	17.333983	3.851312	39.19469	0.6117038	33.004112	6.004195	930

Best

kable(head(arrange(dataCountry, desc(avgLandl)), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
FI	90.44099	89.20359	7.9321852	19.114774	43.57973	0.4332479	24.16576	4.7743087	1681
EE	89.88172	88.81821	24.3615591	14.586694	29.62030	1.6129032	20.19153	9.6270161	335
SK	89.67710	87.76838	25.0855236	12.749270	41.68461	0.4255319	19.20400	0.8510638	120
PT	85.36973	86.79596	19.2101374	6.709199	48.75145	1.4405613	10.60004	13.2886158	1716
RO	87.53704	86.57143	48.3465608	5.747355	21.01190	1.9973545	11.93783	10.9589947	253
LT	84.23356	86.36757	17.1541950	5.328798	41.61224	2.9931973	27.84354	5.0680272	627
TR	81.45409	86.27549	1.7509050	2.359134	50.34040	3.7461275	32.67635	9.1270774	263
CZ	82.54850	86.16369	11.1620611	1.655199	51.18595	0.4675883	33.01779	2.5114151	908
NO	81.45054	85.93181	0.1031992	13.175955	52.71561	0.0000000	33.72512	0.2801120	184
SI	83.54988	85.72389	6.5025992	15.665701	50.00673	9.4457844	12.37554	6.0036451	418

Nightlife

Worst

kable(head(arrange(dataCountry, avgNlife), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
NO	81.45054	85.93181	0.1031992	13.175955	52.71561	0.0000000	33.72512	0.280112	184
TR	81.45409	86.27549	1.7509050	2.359134	50.34040	3.7461275	32.67635	9.127077	263
SE	79.78694	85.15585	2.1626298	2.314316	59.43257	0.4524887	34.07076	1.567226	299
NL	76.76150	82.27853	2.5400039	1.290569	56.47110	0.0517598	37.57678	2.069790	482
AT	80.79314	84.65353	3.2470608	7.279465	53.83821	0.1921288	26.91141	8.531723	311
IT	77.93540	83.50275	4.1726396	1.368243	52.90207	1.1212325	31.80755	8.628268	1527
SI	83.54988	85.72389	6.5025992	15.665701	50.00673	9.4457844	12.37554	6.003645	418
UK	78.36058	83.94827	6.9092228	2.681993	49.87898	1.2588948	33.65885	5.612061	418
DK	78.97833	80.20713	7.0144451	1.086957	73.17077	0.2173913	13.57587	4.934568	260
FI	90.44099	89.20359	7.9321852	19.114774	43.57973	0.4332479	24.16576	4.774309	1681

Best

kable(head(arrange(dataCountry, desc(avgNlife)), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
RO	87.53704	86.57143	48.34656	5.747355	21.01190	1.9973545	11.937831	10.9589947	253
HU	80.99899	81.74824	45.47249	7.013819	18.65385	2.3913286	17.223526	9.2449808	521
IE	63.31781	69.21773	35.90278	8.031046	18.61928	0.6535948	15.645425	21.1478758	112
GR	76.01814	82.10658	30.95238	18.752834	13.40136	14.9659864	1.609977	20.3174603	264
ES	81.38877	83.09755	25.11153	10.422382	37.09779	2.9823586	5.878184	18.5077548	4844
SK	89.67710	87.76838	25.08552	12.749270	41.68461	0.4255319	19.204005	0.8510638	120
EE	89.88172	88.81821	24.36156	14.586694	29.62030	1.6129032	20.191532	9.6270161	335
PT	85.36973	86.79596	19.21014	6.709199	48.75145	1.4405613	10.600036	13.2886158	1716
BE	81.05056	83.20388	17.33398	3.851312	39.19469	0.6117038	33.004112	6.0041947	930
LT	84.23356	86.36757	17.15420	5.328798	41.61224	2.9931973	27.843537	5.0680272	627

Outdoor

Worst

kable(head(arrange(dataCountry, avgOdoor), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
DK	78.97833	80.20713	7.014445	1.086957	73.17077	0.2173913	13.57587	4.934568	260
PL	81.12258	85.50752	10.979667	1.141216	51.35388	1.3217916	32.59980	2.603649	1417
NL	76.76150	82.27853	2.540004	1.290569	56.47110	0.0517598	37.57678	2.069790	482
IT	77.93540	83.50275	4.172640	1.368243	52.90207	1.1212325	31.80755	8.628268	1527
CZ	82.54850	86.16369	11.162061	1.655199	51.18595	0.4675883	33.01779	2.511415	908
SE	79.78694	85.15585	2.162630	2.314316	59.43257	0.4524887	34.07076	1.567226	299
TR	81.45409	86.27549	1.750905	2.359134	50.34040	3.7461275	32.67635	9.127077	263
UK	78.36058	83.94827	6.909223	2.681993	49.87898	1.2588948	33.65885	5.612061	418
BE	81.05056	83.20388	17.333983	3.851312	39.19469	0.6117038	33.00411	6.004195	930
LT	84.23356	86.36757	17.154195	5.328798	41.61224	2.9931973	27.84354	5.068027	627

Best

kable(head(arrange(dataCountry, desc(avgOdoor)), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
FI	90.44099	89.20359	7.9321852	19.11477	43.57973	0.4332479	24.165757	4.7743087	1681
GR	76.01814	82.10658	30.9523810	18.75283	13.40136	14.9659864	1.609977	20.3174603	264
HR	82.53003	84.81231	16.2612613	18.59610	35.40541	1.3513514	3.986486	24.3993994	161
FR	73.89814	76.78320	12.7824702	17.80819	37.24746	2.5549317	11.039060	18.5678875	579
SI	83.54988	85.72389	6.5025992	15.66570	50.00673	9.4457844	12.375544	6.0036451	418
EE	89.88172	88.81821	24.3615591	14.58669	29.62030	1.6129032	20.191532	9.6270161	335
LV	76.32576	80.27273	10.7954545	14.39394	32.27273	0.1893939	21.325758	21.0227273	127
NO	81.45054	85.93181	0.1031992	13.17595	52.71561	0.0000000	33.725122	0.2801120	184
SK	89.67710	87.76838	25.0855236	12.74927	41.68461	0.4255319	19.204005	0.8510638	120
ES	81.38877	83.09755	25.1115267	10.42238	37.09779	2.9823586	5.878184	18.5077548	4844

Quality of life

Worst

kable(head(arrange(dataCountry, avgQlife), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
GR	76.01814	82.10658	30.95238	18.752834	13.40136	14.9659864	1.609977	20.317460	264
IE	63.31781	69.21773	35.90278	8.031046	18.61928	0.6535948	15.645425	21.147876	112
HU	80.99899	81.74824	45.47249	7.013819	18.65385	2.3913286	17.223526	9.244981	521
RO	87.53704	86.57143	48.34656	5.747355	21.01190	1.9973545	11.937831	10.958995	253
EE	89.88172	88.81821	24.36156	14.586694	29.62030	1.6129032	20.191532	9.627016	335
LV	76.32576	80.27273	10.79545	14.393939	32.27273	0.1893939	21.325758	21.022727	127
HR	82.53003	84.81231	16.26126	18.596096	35.40541	1.3513514	3.986486	24.399399	161
ES	81.38877	83.09755	25.11153	10.422382	37.09779	2.9823586	5.878184	18.507755	4844
FR	73.89814	76.78320	12.78247	17.808194	37.24746	2.5549317	11.039060	18.567888	579
BE	81.05056	83.20388	17.33398	3.851312	39.19469	0.6117038	33.004112	6.004195	930

Best

kable(head(arrange(dataCountry, desc(avgQlife)), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
DK	78.97833	80.20713	7.0144451	1.086957	73.17077	0.2173913	13.57587	4.934568	260
SE	79.78694	85.15585	2.1626298	2.314316	59.43257	0.4524887	34.07076	1.567226	299
NL	76.76150	82.27853	2.5400039	1.290569	56.47110	0.0517598	37.57678	2.069790	482
AT	80.79314	84.65353	3.2470608	7.279465	53.83821	0.1921288	26.91141	8.531723	311
IT	77.93540	83.50275	4.1726396	1.368243	52.90207	1.1212325	31.80755	8.628268	1527
NO	81.45054	85.93181	0.1031992	13.175955	52.71561	0.0000000	33.72512	0.280112	184
PL	81.12258	85.50752	10.9796668	1.141216	51.35388	1.3217916	32.59980	2.603649	1417
CZ	82.54850	86.16369	11.1620611	1.655199	51.18595	0.4675883	33.01779	2.511415	908
TR	81.45409	86.27549	1.7509050	2.359134	50.34040	3.7461275	32.67635	9.127077	263
DE	77.09032	81.28841	9.8688330	7.683723	50.01204	2.4025205	22.47388	7.559007	1162

Shopping

Worst

kable(head(arrange(dataCountry, avgShope), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
NO	81.45054	85.93181	0.1031992	13.175955	52.71561	0.0000000	33.72512	0.2801120	184
NL	76.76150	82.27853	2.5400039	1.290569	56.47110	0.0517598	37.57678	2.0697901	482
LV	76.32576	80.27273	10.7954545	14.393939	32.27273	0.1893939	21.32576	21.0227273	127
AT	80.79314	84.65353	3.2470608	7.279465	53.83821	0.1921288	26.91141	8.5317232	311
DK	78.97833	80.20713	7.0144451	1.086957	73.17077	0.2173913	13.57587	4.9345681	260
SK	89.67710	87.76838	25.0855236	12.749270	41.68461	0.4255319	19.20400	0.8510638	120
FI	90.44099	89.20359	7.9321852	19.114774	43.57973	0.4332479	24.16576	4.7743087	1681
SE	79.78694	85.15585	2.1626298	2.314316	59.43257	0.4524887	34.07076	1.5672260	299
CZ	82.54850	86.16369	11.1620611	1.655199	51.18595	0.4675883	33.01779	2.5114151	908
BE	81.05056	83.20388	17.3339826	3.851312	39.19469	0.6117038	33.00411	6.0041947	930

Best

kable(head(arrange(dataCountry, desc(avgShope)), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
GR	76.01814	82.10658	30.952381	18.752834	13.40136	14.965986	1.609977	20.317460	264
SI	83.54988	85.72389	6.502599	15.665701	50.00673	9.445784	12.375544	6.003645	418
TR	81.45409	86.27549	1.750905	2.359134	50.34040	3.746128	32.676352	9.127077	263
LT	84.23356	86.36757	17.154195	5.328798	41.61224	2.993197	27.843537	5.068027	627
ES	81.38877	83.09755	25.111527	10.422382	37.09779	2.982359	5.878184	18.507755	4844
FR	73.89814	76.78320	12.782470	17.808194	37.24746	2.554932	11.039060	18.567888	579
DE	77.09032	81.28841	9.868833	7.683723	50.01204	2.402521	22.473879	7.559007	1162
HU	80.99899	81.74824	45.472492	7.013819	18.65385	2.391329	17.223526	9.244981	521
RO	87.53704	86.57143	48.346561	5.747355	21.01190	1.997354	11.937831	10.958995	253
EE	89.88172	88.81821	24.361559	14.586694	29.62030	1.612903	20.191532	9.627016	335

University

Worst

kable(head(arrange(dataCountry, avgUnive), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
GR	76.01814	82.10658	30.952381	18.752834	13.40136	14.9659864	1.609977	20.317460	264
HR	82.53003	84.81231	16.261261	18.596096	35.40541	1.3513514	3.986486	24.399399	161
ES	81.38877	83.09755	25.111527	10.422382	37.09779	2.9823586	5.878184	18.507755	4844
PT	85.36973	86.79596	19.210137	6.709199	48.75145	1.4405613	10.600036	13.288616	1716
FR	73.89814	76.78320	12.782470	17.808194	37.24746	2.5549317	11.039060	18.567888	579
RO	87.53704	86.57143	48.346561	5.747355	21.01190	1.9973545	11.937831	10.958995	253
SI	83.54988	85.72389	6.502599	15.665701	50.00673	9.4457844	12.375544	6.003645	418
DK	78.97833	80.20713	7.014445	1.086957	73.17077	0.2173913	13.575872	4.934568	260
IE	63.31781	69.21773	35.902778	8.031046	18.61928	0.6535948	15.645425	21.147876	112
HU	80.99899	81.74824	45.472492	7.013819	18.65385	2.3913286	17.223526	9.244981	521

Best

kable(head(arrange(dataCountry, desc(avgUnive)), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
NL	76.76150	82.27853	2.5400039	1.290569	56.47110	0.0517598	37.57678	2.069790	482
SE	79.78694	85.15585	2.1626298	2.314316	59.43257	0.4524887	34.07076	1.567226	299
NO	81.45054	85.93181	0.1031992	13.175955	52.71561	0.0000000	33.72512	0.280112	184
UK	78.36058	83.94827	6.9092228	2.681993	49.87898	1.2588948	33.65885	5.612061	418
CZ	82.54850	86.16369	11.1620611	1.655199	51.18595	0.4675883	33.01779	2.511415	908
BE	81.05056	83.20388	17.3339826	3.851312	39.19469	0.6117038	33.00411	6.004195	930
TR	81.45409	86.27549	1.7509050	2.359134	50.34040	3.7461275	32.67635	9.127077	263
PL	81.12258	85.50752	10.9796668	1.141216	51.35388	1.3217916	32.59980	2.603649	1417
IT	77.93540	83.50275	4.1726396	1.368243	52.90207	1.1212325	31.80755	8.628268	1527
LT	84.23356	86.36757	17.1541950	5.328798	41.61224	2.9931973	27.84354	5.068027	627

Culture

Worst

kable(head(arrange(dataCountry, avgCultu), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
NO	81.45054	85.93181	0.1031992	13.175955	52.71561	0.0000000	33.72512	0.2801120	184
SK	89.67710	87.76838	25.0855236	12.749270	41.68461	0.4255319	19.20400	0.8510638	120
SE	79.78694	85.15585	2.1626298	2.314316	59.43257	0.4524887	34.07076	1.5672260	299
NL	76.76150	82.27853	2.5400039	1.290569	56.47110	0.0517598	37.57678	2.0697901	482
CZ	82.54850	86.16369	11.1620611	1.655199	51.18595	0.4675883	33.01779	2.5114151	908
PL	81.12258	85.50752	10.9796668	1.141216	51.35388	1.3217916	32.59980	2.6036492	1417
FI	90.44099	89.20359	7.9321852	19.114774	43.57973	0.4332479	24.16576	4.7743087	1681
DK	78.97833	80.20713	7.0144451	1.086957	73.17077	0.2173913	13.57587	4.9345681	260
LT	84.23356	86.36757	17.1541950	5.328798	41.61224	2.9931973	27.84354	5.0680272	627
UK	78.36058	83.94827	6.9092228	2.681993	49.87898	1.2588948	33.65885	5.6120607	418

Best

kable(head(arrange(dataCountry, desc(avgCultu)), n = 10))

country	avgAccom	avgLandl	avgNlife	avgOdoor	avgQlife	avgShope	avgUnive	avgCultu	totalVotes
HR	82.53003	84.81231	16.26126	18.596096	35.40541	1.3513514	3.986486	24.399399	161
IE	63.31781	69.21773	35.90278	8.031046	18.61928	0.6535948	15.645425	21.147876	112
LV	76.32576	80.27273	10.79545	14.393939	32.27273	0.1893939	21.325758	21.022727	127
GR	76.01814	82.10658	30.95238	18.752834	13.40136	14.9659864	1.609977	20.317460	264
FR	73.89814	76.78320	12.78247	17.808194	37.24746	2.5549317	11.039060	18.567888	579
ES	81.38877	83.09755	25.11153	10.422382	37.09779	2.9823586	5.878184	18.507755	4844
PT	85.36973	86.79596	19.21014	6.709199	48.75145	1.4405613	10.600036	13.288616	1716
RO	87.53704	86.57143	48.34656	5.747355	21.01190	1.9973545	11.937831	10.958995	253
EE	89.88172	88.81821	24.36156	14.586694	29.62030	1.6129032	20.191532	9.627016	335
HU	80.99899	81.74824	45.47249	7.013819	18.65385	2.3913286	17.223526	9.244981	521

Best Erasmus City

Uniplaces

5 April 2016

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Units

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Cost

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Minimum number of votes

Results

City level

Most voted

Accommodation

Hardest to find

Easiest to find

Landlord

Worst

Best

Nightlife

Worst

Best

Outdoor

Worst

Best

Quality of life

Worst

Best

Shopping

Worst

Best

University

Worst

Best

Culture

Worst

Best

Country level

Add country level to table

Most voted

Accommodation

Hardest to find

Easiest to find

Landlord

Worst

Best

Nightlife

Worst

Best

Outdoor

Worst

Best

Quality of life

Worst

Best

Shopping

Worst

Best

University

Worst

Best

Culture

Worst

Best

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